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@@ -0,0 +1 @@ +/flirt-0.0.2.tar.gz diff --git a/python-flirt.spec b/python-flirt.spec new file mode 100644 index 0000000..177f33c --- /dev/null +++ b/python-flirt.spec @@ -0,0 +1,332 @@ +%global _empty_manifest_terminate_build 0 +Name: python-flirt +Version: 0.0.2 +Release: 1 +Summary: Wearable Data Processing Toolkit +License: MIT +URL: https://github.com/im-ethz/flirt +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ba/d3/55e03fd5481c29e99c1c6bc711a3856bde563baf13f9b0a12722952d7abc/flirt-0.0.2.tar.gz +BuildArch: noarch + +Requires: python3-scipy +Requires: python3-cvxopt +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-joblib +Requires: python3-tqdm +Requires: python3-ishneholterlib +Requires: python3-numba +Requires: python3-astropy +Requires: python3-numpydoc +Requires: python3-m2r2 +Requires: python3-sphinx-rtd-theme +Requires: python3-sphinx +Requires: python3-numpydoc +Requires: python3-sphinx-rtd-theme +Requires: python3-sphinx +Requires: python3-m2r2 + +%description +# FLIRT +[](https://pypi.org/project/flirt/) +[](https://pypi.org/project/flirt/) +[](https://flirt.readthedocs.io/en/latest/?badge=latest) +[](https://mybinder.org/v2/gh/im-ethz/flirt/master) + + + +⭐️ **Simple. Robust. Powerful.** + +**FLIRT** is a **F**eature generation too**L**k**I**t for wea**R**able da**T**a such as that from your smartwatch or smart ring. With FLIRT you can +easily transform wearable data into meaningful features which can then be used for example in machine learning or AI models. + +In contrast to other existing toolkits, FLIRT (1) focuses on physiological data recorded with +(consumer) **wearables** and (2) calculates features based on a **sliding-window approach**. +FLIRT is an easy-to-use, robust and efficient feature generation toolkit for your wearable device! + + + +➡️ **Are you ready to FLIRT with your wearable data?** + +## Main Features +A few things that FLIRT can do: + - Loading data from common wearable device formats such as from the Empatica E4 or Holter ECGs + - Overlapping sliding-window approach for feature calculation + - Calculating [HRV](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.hrv) (heart-rate variability) features from NN intervals (aka inter-beat intervals) + - Deriving features for [EDA](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.eda) (electrodermal activity) + - Computing features for [ACC](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.acc) (accelerometer) + - Provide and prepare features in one comprehensive DataFrame, so that they can directly be used for further steps + (e.g. training machine learning models) + +😎 FLIRT provides **high-level** implementations for fast and easy utilization of feature generators +(see [flirt.simple](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.simple)). + +🤓 For advanced users, who wish to adapt algorithms and parameters do their needs, FLIRT also provides **low-level** +implementations. +They allow for extensive configuration possibilities in feature generation and the specification of which algorithms to +use for generating features. + + +## Installation +FLIRT is available from [PyPI](https://pypi.org/project/flirt/) and can be installed via pip: +``` +pip install flirt +``` + +Alternatively, you can checkout the source code from the [GitHub repository](https://github.com/im-ethz/flirt): +``` +git clone https://github.com/im-ethz/flirt +``` + + +# Quick example +Generate a comprehensive set of features for an Empatica E4 data archive with a single line of code 🚀 +``` +import flirt +features = flirt.with_.empatica('./1234567890_A12345.zip') +``` + +Check out the [documentation](https://flirt.readthedocs.io/) and exemplary [Jupyter notebooks](https://github.com/im-ethz/flirt/tree/master/notebooks/). + +# Roadmap +Things FLIRT will be able to do in the future: + - [ ] Use FLIRT with Oura's smart ring and other consumer-grade wearable devices + - [ ] Use FLIRT with Apple Health to derive meaningful features from long-term data recordings + - [ ] Feature generation for additional sensor modalities such as: blood oxygen saturation, blood volume changes, respiration rate, and step counts + +# Authors +Made with ❤️ at [ETH Zurich](https://im.ethz.ch). + +Check out all [authors](https://github.com/im-ethz/flirt/tree/master/docs/authors.rst). + +# FAQs +- **How does FLIRT distinguish from other physiological data processing packages such as neurokit?** \ + While FLIRT works with physiological data like other packages, it places special emphasis on the inherent challenges + of data processing obtained from (consumer) wearable devices such as smartwaches instead of professional, + medical-grade recording devices such as ECGs or EEGs. As an example, when processing data from smartwatches, one + could be confronted with inaccurate data, which needs artifact removal, or measurement gaps, which need to be + dealt with. + + + + +%package -n python3-flirt +Summary: Wearable Data Processing Toolkit +Provides: python-flirt +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-flirt +# FLIRT +[](https://pypi.org/project/flirt/) +[](https://pypi.org/project/flirt/) +[](https://flirt.readthedocs.io/en/latest/?badge=latest) +[](https://mybinder.org/v2/gh/im-ethz/flirt/master) + + + +⭐️ **Simple. Robust. Powerful.** + +**FLIRT** is a **F**eature generation too**L**k**I**t for wea**R**able da**T**a such as that from your smartwatch or smart ring. With FLIRT you can +easily transform wearable data into meaningful features which can then be used for example in machine learning or AI models. + +In contrast to other existing toolkits, FLIRT (1) focuses on physiological data recorded with +(consumer) **wearables** and (2) calculates features based on a **sliding-window approach**. +FLIRT is an easy-to-use, robust and efficient feature generation toolkit for your wearable device! + + + +➡️ **Are you ready to FLIRT with your wearable data?** + +## Main Features +A few things that FLIRT can do: + - Loading data from common wearable device formats such as from the Empatica E4 or Holter ECGs + - Overlapping sliding-window approach for feature calculation + - Calculating [HRV](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.hrv) (heart-rate variability) features from NN intervals (aka inter-beat intervals) + - Deriving features for [EDA](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.eda) (electrodermal activity) + - Computing features for [ACC](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.acc) (accelerometer) + - Provide and prepare features in one comprehensive DataFrame, so that they can directly be used for further steps + (e.g. training machine learning models) + +😎 FLIRT provides **high-level** implementations for fast and easy utilization of feature generators +(see [flirt.simple](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.simple)). + +🤓 For advanced users, who wish to adapt algorithms and parameters do their needs, FLIRT also provides **low-level** +implementations. +They allow for extensive configuration possibilities in feature generation and the specification of which algorithms to +use for generating features. + + +## Installation +FLIRT is available from [PyPI](https://pypi.org/project/flirt/) and can be installed via pip: +``` +pip install flirt +``` + +Alternatively, you can checkout the source code from the [GitHub repository](https://github.com/im-ethz/flirt): +``` +git clone https://github.com/im-ethz/flirt +``` + + +# Quick example +Generate a comprehensive set of features for an Empatica E4 data archive with a single line of code 🚀 +``` +import flirt +features = flirt.with_.empatica('./1234567890_A12345.zip') +``` + +Check out the [documentation](https://flirt.readthedocs.io/) and exemplary [Jupyter notebooks](https://github.com/im-ethz/flirt/tree/master/notebooks/). + +# Roadmap +Things FLIRT will be able to do in the future: + - [ ] Use FLIRT with Oura's smart ring and other consumer-grade wearable devices + - [ ] Use FLIRT with Apple Health to derive meaningful features from long-term data recordings + - [ ] Feature generation for additional sensor modalities such as: blood oxygen saturation, blood volume changes, respiration rate, and step counts + +# Authors +Made with ❤️ at [ETH Zurich](https://im.ethz.ch). + +Check out all [authors](https://github.com/im-ethz/flirt/tree/master/docs/authors.rst). + +# FAQs +- **How does FLIRT distinguish from other physiological data processing packages such as neurokit?** \ + While FLIRT works with physiological data like other packages, it places special emphasis on the inherent challenges + of data processing obtained from (consumer) wearable devices such as smartwaches instead of professional, + medical-grade recording devices such as ECGs or EEGs. As an example, when processing data from smartwatches, one + could be confronted with inaccurate data, which needs artifact removal, or measurement gaps, which need to be + dealt with. + + + + +%package help +Summary: Development documents and examples for flirt +Provides: python3-flirt-doc +%description help +# FLIRT +[](https://pypi.org/project/flirt/) +[](https://pypi.org/project/flirt/) +[](https://flirt.readthedocs.io/en/latest/?badge=latest) +[](https://mybinder.org/v2/gh/im-ethz/flirt/master) + + + +⭐️ **Simple. Robust. Powerful.** + +**FLIRT** is a **F**eature generation too**L**k**I**t for wea**R**able da**T**a such as that from your smartwatch or smart ring. With FLIRT you can +easily transform wearable data into meaningful features which can then be used for example in machine learning or AI models. + +In contrast to other existing toolkits, FLIRT (1) focuses on physiological data recorded with +(consumer) **wearables** and (2) calculates features based on a **sliding-window approach**. +FLIRT is an easy-to-use, robust and efficient feature generation toolkit for your wearable device! + + + +➡️ **Are you ready to FLIRT with your wearable data?** + +## Main Features +A few things that FLIRT can do: + - Loading data from common wearable device formats such as from the Empatica E4 or Holter ECGs + - Overlapping sliding-window approach for feature calculation + - Calculating [HRV](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.hrv) (heart-rate variability) features from NN intervals (aka inter-beat intervals) + - Deriving features for [EDA](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.eda) (electrodermal activity) + - Computing features for [ACC](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.acc) (accelerometer) + - Provide and prepare features in one comprehensive DataFrame, so that they can directly be used for further steps + (e.g. training machine learning models) + +😎 FLIRT provides **high-level** implementations for fast and easy utilization of feature generators +(see [flirt.simple](https://flirt.readthedocs.io/en/latest/api.html#module-flirt.simple)). + +🤓 For advanced users, who wish to adapt algorithms and parameters do their needs, FLIRT also provides **low-level** +implementations. +They allow for extensive configuration possibilities in feature generation and the specification of which algorithms to +use for generating features. + + +## Installation +FLIRT is available from [PyPI](https://pypi.org/project/flirt/) and can be installed via pip: +``` +pip install flirt +``` + +Alternatively, you can checkout the source code from the [GitHub repository](https://github.com/im-ethz/flirt): +``` +git clone https://github.com/im-ethz/flirt +``` + + +# Quick example +Generate a comprehensive set of features for an Empatica E4 data archive with a single line of code 🚀 +``` +import flirt +features = flirt.with_.empatica('./1234567890_A12345.zip') +``` + +Check out the [documentation](https://flirt.readthedocs.io/) and exemplary [Jupyter notebooks](https://github.com/im-ethz/flirt/tree/master/notebooks/). + +# Roadmap +Things FLIRT will be able to do in the future: + - [ ] Use FLIRT with Oura's smart ring and other consumer-grade wearable devices + - [ ] Use FLIRT with Apple Health to derive meaningful features from long-term data recordings + - [ ] Feature generation for additional sensor modalities such as: blood oxygen saturation, blood volume changes, respiration rate, and step counts + +# Authors +Made with ❤️ at [ETH Zurich](https://im.ethz.ch). + +Check out all [authors](https://github.com/im-ethz/flirt/tree/master/docs/authors.rst). + +# FAQs +- **How does FLIRT distinguish from other physiological data processing packages such as neurokit?** \ + While FLIRT works with physiological data like other packages, it places special emphasis on the inherent challenges + of data processing obtained from (consumer) wearable devices such as smartwaches instead of professional, + medical-grade recording devices such as ECGs or EEGs. As an example, when processing data from smartwatches, one + could be confronted with inaccurate data, which needs artifact removal, or measurement gaps, which need to be + dealt with. + + + + +%prep +%autosetup -n flirt-0.0.2 + +%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-flirt -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.2-1 +- Package Spec generated @@ -0,0 +1 @@ +a416ceb7ce99c0f9c905e9a1a81e0788 flirt-0.0.2.tar.gz |