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| author | CoprDistGit <infra@openeuler.org> | 2023-04-11 21:31:52 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-11 21:31:52 +0000 |
| commit | 168d3a4f530d5beb6a42914c341b0e34c697c688 (patch) | |
| tree | dbd22b42d2986249ef79f4febff4bf472e0a5da4 | |
| parent | 1b63d3875c6e130c104d402b28e967388cb21b39 (diff) | |
automatic import of python-pyts
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-pyts.spec | 636 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 638 insertions, 0 deletions
@@ -0,0 +1 @@ +/pyts-0.12.0.tar.gz diff --git a/python-pyts.spec b/python-pyts.spec new file mode 100644 index 0000000..785b7ca --- /dev/null +++ b/python-pyts.spec @@ -0,0 +1,636 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pyts +Version: 0.12.0 +Release: 1 +Summary: A python package for time series classification +License: new BSD +URL: https://github.com/johannfaouzi/pyts +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8e/88/929f87082e8700d51c663e613c52c3f47f84558c888af689c52500f3ae65/pyts-0.12.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-joblib +Requires: python3-numba +Requires: python3-docutils +Requires: python3-sphinx +Requires: python3-sphinx-gallery +Requires: python3-numpydoc +Requires: python3-matplotlib +Requires: python3-pytest +Requires: python3-pytest-cov + +%description +[](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main) +[](https://pyts.readthedocs.io/) +[](https://codecov.io/gh/johannfaouzi/pyts) +[](https://img.shields.io/pypi/pyversions/pyts.svg) +[](https://badge.fury.io/py/pyts) +[](https://anaconda.org/conda-forge/pyts) +[](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python) +[](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[](https://doi.org/10.5281/zenodo.1244152) + +## pyts: a Python package for time series classification + +pyts is a Python package for time series classification. It +aims to make time series classification easily accessible by providing +preprocessing and utility tools, and implementations of +state-of-the-art algorithms. Most of these algorithms transform time series, +thus pyts provides several tools to perform these transformations. + + +### Installation + +#### Dependencies + +pyts requires: + +- Python (>= 3.7) +- NumPy (>= 1.17.5) +- SciPy (>= 1.3.0) +- Scikit-Learn (>=0.22.1) +- Joblib (>=0.12) +- Numba (>=0.48.0) + +To run the examples Matplotlib (>=2.0.0) is required. + + +#### User installation + +If you already have a working installation of numpy, scipy, scikit-learn, +joblib and numba, you can easily install pyts using ``pip`` + + pip install pyts + +or ``conda`` via the ``conda-forge`` channel + + conda install -c conda-forge pyts + +You can also get the latest version of pyts by cloning the repository + + git clone https://github.com/johannfaouzi/pyts.git + cd pyts + pip install . + + +#### Testing + +After installation, you can launch the test suite from outside the source +directory using pytest: + + pytest pyts + + +### Changelog + +See the [changelog](https://pyts.readthedocs.io/en/stable/changelog.html) +for a history of notable changes to pyts. + +### Development + +The development of this package is in line with the one of the scikit-learn +community. Therefore, you can refer to their +[Development Guide](https://scikit-learn.org/stable/developers/). A slight +difference is the use of Numba instead of Cython for optimization. + +### Documentation + +The section below gives some information about the implemented algorithms in pyts. +For more information, please have a look at the +[HTML documentation available via ReadTheDocs](https://pyts.readthedocs.io/). + +### Citation + +If you use pyts in a scientific publication, we would appreciate +citations to the following [paper](http://www.jmlr.org/papers/v21/19-763.html): +``` +Johann Faouzi and Hicham Janati. pyts: A python package for time series classification. +Journal of Machine Learning Research, 21(46):1−6, 2020. +``` + +Bibtex entry: +``` +@article{JMLR:v21:19-763, + author = {Johann Faouzi and Hicham Janati}, + title = {pyts: A Python Package for Time Series Classification}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {46}, + pages = {1-6}, + url = {http://jmlr.org/papers/v21/19-763.html} +} +``` + +### Implemented features + +**Note: the content described in this section corresponds to the main branch, +not the latest released version. You may have to install the latest version +to use some of these features.** + +pyts consists of the following modules: + +- `approximation`: This module provides implementations of algorithms that +approximate time series. Implemented algorithms are +[Piecewise Aggregate Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html), +[Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html), +[Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html), +[Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html) and +[Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html). + +- `bag_of_words`: This module provide tools to transform time series into bags +of words. Implemented algorithms are +[WordExtractor](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.WordExtractor.html) and +[BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html). + + +- `classification`: This module provides implementations of algorithms that +can classify time series. Implemented algorithms are +[KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html), +[SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html), +[BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html), +[LearningShapelets](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.LearningShapelets.html), +[TimeSeriesForest](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TimeSeriesForest.html) and +[TSBF](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TSBF.html). + +- `datasets`: This module provides utilities to make or load toy datasets, +as well as fetching datasets from the +[UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com). + +- `decomposition`: This module provides implementations of algorithms that +decompose a time series into several time series. The only implemented +algorithm is +[Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html). + +- `image`: This module provides implementations of algorithms that transform +time series into images. Implemented algorithms are +[Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html), +[Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html) and +[Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html). + +- `metrics`: This module provides implementations of metrics that are specific +to time series. Implemented metrics are +[Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html) +with several variants and the +[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html) +metric. + +- `multivariate`: This modules provides utilities to deal with multivariate +time series. Available tools are +[MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and +[MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html) +to transform and classify multivariate time series using tools for univariate +time series respectively, as well as +[JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and +[WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html). + +- `preprocessing`: This module provides most of the scikit-learn preprocessing +tools but applied sample-wise (i.e. to each time series independently) instead +of feature-wise, as well as an +[imputer](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html) +of missing values using interpolation. More information is available at the +[pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing). + +- `transformation`: This module provides implementations of algorithms that +transform a data set of time series with shape `(n_samples, n_timestamps)` into +a data set with shape `(n_samples, n_extracted_features)`. Implemented algorithms are +[BagOfPatterns](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BagOfPatterns.html), +[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html), +[ShapeletTransform](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ShapeletTransform.html), +[WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html) and +[ROCKET](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ROCKET.html). + +- `utils`: a simple module with +[utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils). + + + + +%package -n python3-pyts +Summary: A python package for time series classification +Provides: python-pyts +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pyts +[](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main) +[](https://pyts.readthedocs.io/) +[](https://codecov.io/gh/johannfaouzi/pyts) +[](https://img.shields.io/pypi/pyversions/pyts.svg) +[](https://badge.fury.io/py/pyts) +[](https://anaconda.org/conda-forge/pyts) +[](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python) +[](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[](https://doi.org/10.5281/zenodo.1244152) + +## pyts: a Python package for time series classification + +pyts is a Python package for time series classification. It +aims to make time series classification easily accessible by providing +preprocessing and utility tools, and implementations of +state-of-the-art algorithms. Most of these algorithms transform time series, +thus pyts provides several tools to perform these transformations. + + +### Installation + +#### Dependencies + +pyts requires: + +- Python (>= 3.7) +- NumPy (>= 1.17.5) +- SciPy (>= 1.3.0) +- Scikit-Learn (>=0.22.1) +- Joblib (>=0.12) +- Numba (>=0.48.0) + +To run the examples Matplotlib (>=2.0.0) is required. + + +#### User installation + +If you already have a working installation of numpy, scipy, scikit-learn, +joblib and numba, you can easily install pyts using ``pip`` + + pip install pyts + +or ``conda`` via the ``conda-forge`` channel + + conda install -c conda-forge pyts + +You can also get the latest version of pyts by cloning the repository + + git clone https://github.com/johannfaouzi/pyts.git + cd pyts + pip install . + + +#### Testing + +After installation, you can launch the test suite from outside the source +directory using pytest: + + pytest pyts + + +### Changelog + +See the [changelog](https://pyts.readthedocs.io/en/stable/changelog.html) +for a history of notable changes to pyts. + +### Development + +The development of this package is in line with the one of the scikit-learn +community. Therefore, you can refer to their +[Development Guide](https://scikit-learn.org/stable/developers/). A slight +difference is the use of Numba instead of Cython for optimization. + +### Documentation + +The section below gives some information about the implemented algorithms in pyts. +For more information, please have a look at the +[HTML documentation available via ReadTheDocs](https://pyts.readthedocs.io/). + +### Citation + +If you use pyts in a scientific publication, we would appreciate +citations to the following [paper](http://www.jmlr.org/papers/v21/19-763.html): +``` +Johann Faouzi and Hicham Janati. pyts: A python package for time series classification. +Journal of Machine Learning Research, 21(46):1−6, 2020. +``` + +Bibtex entry: +``` +@article{JMLR:v21:19-763, + author = {Johann Faouzi and Hicham Janati}, + title = {pyts: A Python Package for Time Series Classification}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {46}, + pages = {1-6}, + url = {http://jmlr.org/papers/v21/19-763.html} +} +``` + +### Implemented features + +**Note: the content described in this section corresponds to the main branch, +not the latest released version. You may have to install the latest version +to use some of these features.** + +pyts consists of the following modules: + +- `approximation`: This module provides implementations of algorithms that +approximate time series. Implemented algorithms are +[Piecewise Aggregate Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html), +[Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html), +[Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html), +[Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html) and +[Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html). + +- `bag_of_words`: This module provide tools to transform time series into bags +of words. Implemented algorithms are +[WordExtractor](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.WordExtractor.html) and +[BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html). + + +- `classification`: This module provides implementations of algorithms that +can classify time series. Implemented algorithms are +[KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html), +[SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html), +[BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html), +[LearningShapelets](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.LearningShapelets.html), +[TimeSeriesForest](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TimeSeriesForest.html) and +[TSBF](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TSBF.html). + +- `datasets`: This module provides utilities to make or load toy datasets, +as well as fetching datasets from the +[UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com). + +- `decomposition`: This module provides implementations of algorithms that +decompose a time series into several time series. The only implemented +algorithm is +[Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html). + +- `image`: This module provides implementations of algorithms that transform +time series into images. Implemented algorithms are +[Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html), +[Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html) and +[Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html). + +- `metrics`: This module provides implementations of metrics that are specific +to time series. Implemented metrics are +[Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html) +with several variants and the +[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html) +metric. + +- `multivariate`: This modules provides utilities to deal with multivariate +time series. Available tools are +[MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and +[MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html) +to transform and classify multivariate time series using tools for univariate +time series respectively, as well as +[JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and +[WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html). + +- `preprocessing`: This module provides most of the scikit-learn preprocessing +tools but applied sample-wise (i.e. to each time series independently) instead +of feature-wise, as well as an +[imputer](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html) +of missing values using interpolation. More information is available at the +[pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing). + +- `transformation`: This module provides implementations of algorithms that +transform a data set of time series with shape `(n_samples, n_timestamps)` into +a data set with shape `(n_samples, n_extracted_features)`. Implemented algorithms are +[BagOfPatterns](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BagOfPatterns.html), +[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html), +[ShapeletTransform](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ShapeletTransform.html), +[WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html) and +[ROCKET](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ROCKET.html). + +- `utils`: a simple module with +[utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils). + + + + +%package help +Summary: Development documents and examples for pyts +Provides: python3-pyts-doc +%description help +[](https://dev.azure.com/johannfaouzi0034/johannfaouzi/_build/latest?definitionId=1&branchName=main) +[](https://pyts.readthedocs.io/) +[](https://codecov.io/gh/johannfaouzi/pyts) +[](https://img.shields.io/pypi/pyversions/pyts.svg) +[](https://badge.fury.io/py/pyts) +[](https://anaconda.org/conda-forge/pyts) +[](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python) +[](https://gitter.im/johann-faouzi/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[](https://doi.org/10.5281/zenodo.1244152) + +## pyts: a Python package for time series classification + +pyts is a Python package for time series classification. It +aims to make time series classification easily accessible by providing +preprocessing and utility tools, and implementations of +state-of-the-art algorithms. Most of these algorithms transform time series, +thus pyts provides several tools to perform these transformations. + + +### Installation + +#### Dependencies + +pyts requires: + +- Python (>= 3.7) +- NumPy (>= 1.17.5) +- SciPy (>= 1.3.0) +- Scikit-Learn (>=0.22.1) +- Joblib (>=0.12) +- Numba (>=0.48.0) + +To run the examples Matplotlib (>=2.0.0) is required. + + +#### User installation + +If you already have a working installation of numpy, scipy, scikit-learn, +joblib and numba, you can easily install pyts using ``pip`` + + pip install pyts + +or ``conda`` via the ``conda-forge`` channel + + conda install -c conda-forge pyts + +You can also get the latest version of pyts by cloning the repository + + git clone https://github.com/johannfaouzi/pyts.git + cd pyts + pip install . + + +#### Testing + +After installation, you can launch the test suite from outside the source +directory using pytest: + + pytest pyts + + +### Changelog + +See the [changelog](https://pyts.readthedocs.io/en/stable/changelog.html) +for a history of notable changes to pyts. + +### Development + +The development of this package is in line with the one of the scikit-learn +community. Therefore, you can refer to their +[Development Guide](https://scikit-learn.org/stable/developers/). A slight +difference is the use of Numba instead of Cython for optimization. + +### Documentation + +The section below gives some information about the implemented algorithms in pyts. +For more information, please have a look at the +[HTML documentation available via ReadTheDocs](https://pyts.readthedocs.io/). + +### Citation + +If you use pyts in a scientific publication, we would appreciate +citations to the following [paper](http://www.jmlr.org/papers/v21/19-763.html): +``` +Johann Faouzi and Hicham Janati. pyts: A python package for time series classification. +Journal of Machine Learning Research, 21(46):1−6, 2020. +``` + +Bibtex entry: +``` +@article{JMLR:v21:19-763, + author = {Johann Faouzi and Hicham Janati}, + title = {pyts: A Python Package for Time Series Classification}, + journal = {Journal of Machine Learning Research}, + year = {2020}, + volume = {21}, + number = {46}, + pages = {1-6}, + url = {http://jmlr.org/papers/v21/19-763.html} +} +``` + +### Implemented features + +**Note: the content described in this section corresponds to the main branch, +not the latest released version. You may have to install the latest version +to use some of these features.** + +pyts consists of the following modules: + +- `approximation`: This module provides implementations of algorithms that +approximate time series. Implemented algorithms are +[Piecewise Aggregate Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html), +[Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html), +[Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html), +[Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html) and +[Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html). + +- `bag_of_words`: This module provide tools to transform time series into bags +of words. Implemented algorithms are +[WordExtractor](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.WordExtractor.html) and +[BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html). + + +- `classification`: This module provides implementations of algorithms that +can classify time series. Implemented algorithms are +[KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html), +[SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html), +[BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html), +[LearningShapelets](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.LearningShapelets.html), +[TimeSeriesForest](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TimeSeriesForest.html) and +[TSBF](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.TSBF.html). + +- `datasets`: This module provides utilities to make or load toy datasets, +as well as fetching datasets from the +[UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com). + +- `decomposition`: This module provides implementations of algorithms that +decompose a time series into several time series. The only implemented +algorithm is +[Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html). + +- `image`: This module provides implementations of algorithms that transform +time series into images. Implemented algorithms are +[Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html), +[Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html) and +[Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html). + +- `metrics`: This module provides implementations of metrics that are specific +to time series. Implemented metrics are +[Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html) +with several variants and the +[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html) +metric. + +- `multivariate`: This modules provides utilities to deal with multivariate +time series. Available tools are +[MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and +[MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html) +to transform and classify multivariate time series using tools for univariate +time series respectively, as well as +[JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and +[WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html). + +- `preprocessing`: This module provides most of the scikit-learn preprocessing +tools but applied sample-wise (i.e. to each time series independently) instead +of feature-wise, as well as an +[imputer](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html) +of missing values using interpolation. More information is available at the +[pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing). + +- `transformation`: This module provides implementations of algorithms that +transform a data set of time series with shape `(n_samples, n_timestamps)` into +a data set with shape `(n_samples, n_extracted_features)`. Implemented algorithms are +[BagOfPatterns](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BagOfPatterns.html), +[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html), +[ShapeletTransform](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ShapeletTransform.html), +[WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html) and +[ROCKET](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.ROCKET.html). + +- `utils`: a simple module with +[utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils). + + + + +%prep +%autosetup -n pyts-0.12.0 + +%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-pyts -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.12.0-1 +- Package Spec generated @@ -0,0 +1 @@ +697e8815fa739e3b4f8b2ab510477444 pyts-0.12.0.tar.gz |