%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 [![Documentation Status](https://readthedocs.org/projects/tsfel/badge/?version=latest)](https://tsfel.readthedocs.io/en/latest/?badge=latest) [![license](https://img.shields.io/badge/License-BSD%203-brightgreen)](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tsfel) ![PyPI](https://img.shields.io/pypi/v/tsfel) [![Downloads](https://pepy.tech/badge/tsfel)](https://pepy.tech/project/tsfel) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 [![Documentation Status](https://readthedocs.org/projects/tsfel/badge/?version=latest)](https://tsfel.readthedocs.io/en/latest/?badge=latest) [![license](https://img.shields.io/badge/License-BSD%203-brightgreen)](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tsfel) ![PyPI](https://img.shields.io/pypi/v/tsfel) [![Downloads](https://pepy.tech/badge/tsfel)](https://pepy.tech/project/tsfel) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 [![Documentation Status](https://readthedocs.org/projects/tsfel/badge/?version=latest)](https://tsfel.readthedocs.io/en/latest/?badge=latest) [![license](https://img.shields.io/badge/License-BSD%203-brightgreen)](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tsfel) ![PyPI](https://img.shields.io/pypi/v/tsfel) [![Downloads](https://pepy.tech/badge/tsfel)](https://pepy.tech/project/tsfel) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 - 0.1.5-1 - Package Spec generated