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diff --git a/python-tsfel.spec b/python-tsfel.spec new file mode 100644 index 0000000..0d2b929 --- /dev/null +++ b/python-tsfel.spec @@ -0,0 +1,476 @@ +%global _empty_manifest_terminate_build 0 +Name: python-tsfel +Version: 0.1.5 +Release: 1 +Summary: Library for time series feature extraction +License: BSD License +URL: https://github.com/fraunhoferportugal/tsfel/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0b/9d/f1c21f65817d86b9e5b47288b94fa9cc62965a001d0c776810260899ee0f/tsfel-0.1.5.tar.gz +BuildArch: noarch + +Requires: python3-Sphinx +Requires: python3-gspread +Requires: python3-ipython +Requires: python3-numpy +Requires: python3-oauth2client +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-setuptools + +%description +[](https://tsfel.readthedocs.io/en/latest/?badge=latest) +[](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt) + + +[](https://pepy.tech/project/tsfel) +[](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/master/notebooks/TSFEL_HAR_Example.ipynb) + +# Time Series Feature Extraction Library +## Intuitive time series feature extraction +This repository hosts the **TSFEL - Time Series Feature Extraction Library** python package. TSFEL assists researchers on exploratory feature extraction tasks on time series without requiring significant programming effort. + +Users can interact with TSFEL using two methods: +##### Online +It does not requires installation as it relies on Google Colabs and a user interface provided by Google Sheets + +##### Offline +Advanced users can take full potential of TSFEL by installing as a python package +```python +pip install tsfel +``` + +## Includes a comprehensive number of features +TSFEL is optimized for time series and **automatically extracts over 60 different features on the statistical, temporal and spectral domains.** + +## Functionalities +* **Intuitive, fast deployment and reproducible**: interactive UI for feature selection and customization +* **Computational complexity evaluation**: estimate the computational effort before extracting features +* **Comprehensive documentation**: each feature extraction method has a detailed explanation +* **Unit tested**: we provide unit tests for each feature +* **Easily extended**: adding new features is easy and we encourage you to contribute with your custom features + +## Get started +The code below extracts all the available features on an example dataset file. + +```python +import tsfel +import pandas as pd + +# load dataset +df = pd.read_csv('Dataset.txt') + +# Retrieves a pre-defined feature configuration file to extract all available features +cfg = tsfel.get_features_by_domain() + +# Extract features +X = tsfel.time_series_features_extractor(cfg, df) +``` + +## Available features + +#### Statistical domain +| Features | Computational Cost | +|----------------------------|:------------------:| +| ECDF | 1 | +| ECDF Percentile | 1 | +| ECDF Percentile Count | 1 | +| Histogram | 1 | +| Interquartile range | 1 | +| Kurtosis | 1 | +| Max | 1 | +| Mean | 1 | +| Mean absolute deviation | 1 | +| Median | 1 | +| Median absolute deviation | 1 | +| Min | 1 | +| Root mean square | 1 | +| Skewness | 1 | +| Standard deviation | 1 | +| Variance | 1 | + + +#### Temporal domain +| Features | Computational Cost | +|----------------------------|:------------------:| +| Absolute energy | 1 | +| Area under the curve | 1 | +| Autocorrelation | 1 | +| Centroid | 1 | +| Entropy | 1 | +| Mean absolute diff | 1 | +| Mean diff | 1 | +| Median absolute diff | 1 | +| Median diff | 1 | +| Negative turning points | 1 | +| Peak to peak distance | 1 | +| Positive turning points | 1 | +| Signal distance | 1 | +| Slope | 1 | +| Sum absolute diff | 1 | +| Total energy | 1 | +| Zero crossing rate | 1 | +| Neighbourhood peaks | 1 | + + +#### Spectral domain +| Features | Computational Cost | +|-----------------------------------|:------------------:| +| FFT mean coefficient | 1 | +| Fundamental frequency | 1 | +| Human range energy | 2 | +| LPCC | 1 | +| MFCC | 1 | +| Max power spectrum | 1 | +| Maximum frequency | 1 | +| Median frequency | 1 | +| Power bandwidth | 1 | +| Spectral centroid | 2 | +| Spectral decrease | 1 | +| Spectral distance | 1 | +| Spectral entropy | 1 | +| Spectral kurtosis | 2 | +| Spectral positive turning points | 1 | +| Spectral roll-off | 1 | +| Spectral roll-on | 1 | +| Spectral skewness | 2 | +| Spectral slope | 1 | +| Spectral spread | 2 | +| Spectral variation | 1 | +| Wavelet absolute mean | 2 | +| Wavelet energy | 2 | +| Wavelet standard deviation | 2 | +| Wavelet entropy | 2 | +| Wavelet variance | 2 | + + +## Citing +When using TSFEL please cite the following publication: + +Barandas, Marília and Folgado, Duarte, et al. "*TSFEL: Time Series Feature Extraction Library.*" SoftwareX 11 (2020). [https://doi.org/10.1016/j.softx.2020.100456](https://doi.org/10.1016/j.softx.2020.100456) + +## Acknowledgements +We would like to acknowledge the financial support obtained from the project Total Integrated and Predictive Manufacturing System Platform for Industry 4.0, co-funded by Portugal 2020, framed under the COMPETE 2020 (Operational Programme Competitiveness and Internationalization) and European Regional Development Fund (ERDF) from European Union (EU), with operation code POCI-01-0247-FEDER-038436. + + +%package -n python3-tsfel +Summary: Library for time series feature extraction +Provides: python-tsfel +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-tsfel +[](https://tsfel.readthedocs.io/en/latest/?badge=latest) +[](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt) + + +[](https://pepy.tech/project/tsfel) +[](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/master/notebooks/TSFEL_HAR_Example.ipynb) + +# Time Series Feature Extraction Library +## Intuitive time series feature extraction +This repository hosts the **TSFEL - Time Series Feature Extraction Library** python package. TSFEL assists researchers on exploratory feature extraction tasks on time series without requiring significant programming effort. + +Users can interact with TSFEL using two methods: +##### Online +It does not requires installation as it relies on Google Colabs and a user interface provided by Google Sheets + +##### Offline +Advanced users can take full potential of TSFEL by installing as a python package +```python +pip install tsfel +``` + +## Includes a comprehensive number of features +TSFEL is optimized for time series and **automatically extracts over 60 different features on the statistical, temporal and spectral domains.** + +## Functionalities +* **Intuitive, fast deployment and reproducible**: interactive UI for feature selection and customization +* **Computational complexity evaluation**: estimate the computational effort before extracting features +* **Comprehensive documentation**: each feature extraction method has a detailed explanation +* **Unit tested**: we provide unit tests for each feature +* **Easily extended**: adding new features is easy and we encourage you to contribute with your custom features + +## Get started +The code below extracts all the available features on an example dataset file. + +```python +import tsfel +import pandas as pd + +# load dataset +df = pd.read_csv('Dataset.txt') + +# Retrieves a pre-defined feature configuration file to extract all available features +cfg = tsfel.get_features_by_domain() + +# Extract features +X = tsfel.time_series_features_extractor(cfg, df) +``` + +## Available features + +#### Statistical domain +| Features | Computational Cost | +|----------------------------|:------------------:| +| ECDF | 1 | +| ECDF Percentile | 1 | +| ECDF Percentile Count | 1 | +| Histogram | 1 | +| Interquartile range | 1 | +| Kurtosis | 1 | +| Max | 1 | +| Mean | 1 | +| Mean absolute deviation | 1 | +| Median | 1 | +| Median absolute deviation | 1 | +| Min | 1 | +| Root mean square | 1 | +| Skewness | 1 | +| Standard deviation | 1 | +| Variance | 1 | + + +#### Temporal domain +| Features | Computational Cost | +|----------------------------|:------------------:| +| Absolute energy | 1 | +| Area under the curve | 1 | +| Autocorrelation | 1 | +| Centroid | 1 | +| Entropy | 1 | +| Mean absolute diff | 1 | +| Mean diff | 1 | +| Median absolute diff | 1 | +| Median diff | 1 | +| Negative turning points | 1 | +| Peak to peak distance | 1 | +| Positive turning points | 1 | +| Signal distance | 1 | +| Slope | 1 | +| Sum absolute diff | 1 | +| Total energy | 1 | +| Zero crossing rate | 1 | +| Neighbourhood peaks | 1 | + + +#### Spectral domain +| Features | Computational Cost | +|-----------------------------------|:------------------:| +| FFT mean coefficient | 1 | +| Fundamental frequency | 1 | +| Human range energy | 2 | +| LPCC | 1 | +| MFCC | 1 | +| Max power spectrum | 1 | +| Maximum frequency | 1 | +| Median frequency | 1 | +| Power bandwidth | 1 | +| Spectral centroid | 2 | +| Spectral decrease | 1 | +| Spectral distance | 1 | +| Spectral entropy | 1 | +| Spectral kurtosis | 2 | +| Spectral positive turning points | 1 | +| Spectral roll-off | 1 | +| Spectral roll-on | 1 | +| Spectral skewness | 2 | +| Spectral slope | 1 | +| Spectral spread | 2 | +| Spectral variation | 1 | +| Wavelet absolute mean | 2 | +| Wavelet energy | 2 | +| Wavelet standard deviation | 2 | +| Wavelet entropy | 2 | +| Wavelet variance | 2 | + + +## Citing +When using TSFEL please cite the following publication: + +Barandas, Marília and Folgado, Duarte, et al. "*TSFEL: Time Series Feature Extraction Library.*" SoftwareX 11 (2020). [https://doi.org/10.1016/j.softx.2020.100456](https://doi.org/10.1016/j.softx.2020.100456) + +## Acknowledgements +We would like to acknowledge the financial support obtained from the project Total Integrated and Predictive Manufacturing System Platform for Industry 4.0, co-funded by Portugal 2020, framed under the COMPETE 2020 (Operational Programme Competitiveness and Internationalization) and European Regional Development Fund (ERDF) from European Union (EU), with operation code POCI-01-0247-FEDER-038436. + + +%package help +Summary: Development documents and examples for tsfel +Provides: python3-tsfel-doc +%description help +[](https://tsfel.readthedocs.io/en/latest/?badge=latest) +[](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt) + + +[](https://pepy.tech/project/tsfel) +[](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/master/notebooks/TSFEL_HAR_Example.ipynb) + +# Time Series Feature Extraction Library +## Intuitive time series feature extraction +This repository hosts the **TSFEL - Time Series Feature Extraction Library** python package. TSFEL assists researchers on exploratory feature extraction tasks on time series without requiring significant programming effort. + +Users can interact with TSFEL using two methods: +##### Online +It does not requires installation as it relies on Google Colabs and a user interface provided by Google Sheets + +##### Offline +Advanced users can take full potential of TSFEL by installing as a python package +```python +pip install tsfel +``` + +## Includes a comprehensive number of features +TSFEL is optimized for time series and **automatically extracts over 60 different features on the statistical, temporal and spectral domains.** + +## Functionalities +* **Intuitive, fast deployment and reproducible**: interactive UI for feature selection and customization +* **Computational complexity evaluation**: estimate the computational effort before extracting features +* **Comprehensive documentation**: each feature extraction method has a detailed explanation +* **Unit tested**: we provide unit tests for each feature +* **Easily extended**: adding new features is easy and we encourage you to contribute with your custom features + +## Get started +The code below extracts all the available features on an example dataset file. + +```python +import tsfel +import pandas as pd + +# load dataset +df = pd.read_csv('Dataset.txt') + +# Retrieves a pre-defined feature configuration file to extract all available features +cfg = tsfel.get_features_by_domain() + +# Extract features +X = tsfel.time_series_features_extractor(cfg, df) +``` + +## Available features + +#### Statistical domain +| Features | Computational Cost | +|----------------------------|:------------------:| +| ECDF | 1 | +| ECDF Percentile | 1 | +| ECDF Percentile Count | 1 | +| Histogram | 1 | +| Interquartile range | 1 | +| Kurtosis | 1 | +| Max | 1 | +| Mean | 1 | +| Mean absolute deviation | 1 | +| Median | 1 | +| Median absolute deviation | 1 | +| Min | 1 | +| Root mean square | 1 | +| Skewness | 1 | +| Standard deviation | 1 | +| Variance | 1 | + + +#### Temporal domain +| Features | Computational Cost | +|----------------------------|:------------------:| +| Absolute energy | 1 | +| Area under the curve | 1 | +| Autocorrelation | 1 | +| Centroid | 1 | +| Entropy | 1 | +| Mean absolute diff | 1 | +| Mean diff | 1 | +| Median absolute diff | 1 | +| Median diff | 1 | +| Negative turning points | 1 | +| Peak to peak distance | 1 | +| Positive turning points | 1 | +| Signal distance | 1 | +| Slope | 1 | +| Sum absolute diff | 1 | +| Total energy | 1 | +| Zero crossing rate | 1 | +| Neighbourhood peaks | 1 | + + +#### Spectral domain +| Features | Computational Cost | +|-----------------------------------|:------------------:| +| FFT mean coefficient | 1 | +| Fundamental frequency | 1 | +| Human range energy | 2 | +| LPCC | 1 | +| MFCC | 1 | +| Max power spectrum | 1 | +| Maximum frequency | 1 | +| Median frequency | 1 | +| Power bandwidth | 1 | +| Spectral centroid | 2 | +| Spectral decrease | 1 | +| Spectral distance | 1 | +| Spectral entropy | 1 | +| Spectral kurtosis | 2 | +| Spectral positive turning points | 1 | +| Spectral roll-off | 1 | +| Spectral roll-on | 1 | +| Spectral skewness | 2 | +| Spectral slope | 1 | +| Spectral spread | 2 | +| Spectral variation | 1 | +| Wavelet absolute mean | 2 | +| Wavelet energy | 2 | +| Wavelet standard deviation | 2 | +| Wavelet entropy | 2 | +| Wavelet variance | 2 | + + +## Citing +When using TSFEL please cite the following publication: + +Barandas, Marília and Folgado, Duarte, et al. "*TSFEL: Time Series Feature Extraction Library.*" SoftwareX 11 (2020). [https://doi.org/10.1016/j.softx.2020.100456](https://doi.org/10.1016/j.softx.2020.100456) + +## Acknowledgements +We would like to acknowledge the financial support obtained from the project Total Integrated and Predictive Manufacturing System Platform for Industry 4.0, co-funded by Portugal 2020, framed under the COMPETE 2020 (Operational Programme Competitiveness and Internationalization) and European Regional Development Fund (ERDF) from European Union (EU), with operation code POCI-01-0247-FEDER-038436. + + +%prep +%autosetup -n tsfel-0.1.5 + +%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-tsfel -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.5-1 +- Package Spec generated |