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|
%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
|
