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@@ -0,0 +1 @@ +/EMD-signal-1.4.0.tar.gz diff --git a/python-emd-signal.spec b/python-emd-signal.spec new file mode 100644 index 0000000..c6f1768 --- /dev/null +++ b/python-emd-signal.spec @@ -0,0 +1,761 @@ +%global _empty_manifest_terminate_build 0 +Name: python-EMD-signal +Version: 1.4.0 +Release: 1 +Summary: Implementation of the Empirical Mode Decomposition (EMD) and its variations +License: Apache-2.0 +URL: https://github.com/laszukdawid/PyEMD +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/26/1d/69f23b15c0ba6702dd417a694c485b5533258fdefca0bd3e8953cf3b7a0b/EMD-signal-1.4.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-pathos +Requires: python3-tqdm +Requires: python3-pycodestyle +Requires: python3-black +Requires: python3-isort +Requires: python3-click +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-numpydoc +Requires: python3-numba +Requires: python3-pytest +Requires: python3-codecov + +%description +[](https://codecov.io/gh/laszukdawid/PyEMD) +[](https://app.travis-ci.com/laszukdawid/PyEMD) +[](https://pyemd.readthedocs.io/) +[](https://www.codacy.com/gh/laszukdawid/PyEMD/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/PyEMD&utm_campaign=Badge_Grade) +[](https://zenodo.org/badge/latestdoi/65324353) + +# PyEMD + +## Links + +- Online documentation: <https://pyemd.readthedocs.org> +- Issue tracker: <https://github.com/laszukdawid/pyemd/issues> +- Source code repository: <https://github.com/laszukdawid/pyemd> + +## Introduction + +This is yet another Python implementation of Empirical Mode +Decomposition (EMD). The package contains multiple EMD variations and +intends to deliver more in time. + +### EMD variations + +- Ensemble EMD (EEMD), +- "Complete Ensemble EMD" (CEEMDAN) +- different settings and configurations of vanilla EMD. +- Image decomposition (EMD2D & BEMD) (experimental, no support) +- Just-in-time compiled EMD (JitEMD) + +*PyEMD* allows you to use different splines for envelopes, stopping criteria +and extrema interpolations. + +### Available splines + +- Natural cubic (**default**) +- Pointwise cubic +- Akima +- Linear + +### Available stopping criteria + +- Cauchy convergence (**default**) +- Fixed number of iterations +- Number of consecutive proto-imfs + +### Extrema detection + +- Discrete extrema (**default**) +- Parabolic interpolation + +## Installation + +### PyPi (recommended) + +The quickest way to install package is through `pip`. + +> \$ pip install EMD-signal + +### From source + +In case, if you only want to *use* EMD and its variations, the best way to install PyEMD is through `pip`. +However, if you want to modify the code, anyhow you might want to download the code and build package yourself. +The source is publicaly available and hosted on [GitHub](https://github.com/laszukdawid/PyEMD). +To download the code you can either go to the source code page and click `Code -> Download ZIP`, or use **git** command line + +> \$ git clone <https://github.com/laszukdawid/PyEMD> + +Installing package from source is done using command line: + +> \$ python setup.py install + +**Note**, however, that this will install it in your current environment. If you are working on many projects, or sharing reources with others, we suggest using [virtual environments](https://docs.python.org/3/library/venv.html). + +## Example + +More detailed examples are included in the +[documentation](https://pyemd.readthedocs.io/en/latest/examples.html) or +in the +[PyEMD/examples](https://github.com/laszukdawid/PyEMD/tree/master/example). + +### EMD + +In most cases default settings are enough. Simply import `EMD` and pass +your signal to instance or to `emd()` method. + +```python +from PyEMD import EMD +import numpy as np + +s = np.random.random(100) +emd = EMD() +IMFs = emd(s) +``` + +The Figure below was produced with input: +$S(t) = cos(22 \pi t^2) + 6t^2$ + + + +### EEMD + +Simplest case of using Ensemble EMD (EEMD) is by importing `EEMD` and +passing your signal to the instance or `eemd()` method. + +**Windows**: Please don't skip the `if __name__ == "__main__"` section. + +```python +from PyEMD import EEMD +import numpy as np + +if __name__ == "__main__": + s = np.random.random(100) + eemd = EEMD() + eIMFs = eemd(s) +``` + +### CEEMDAN + +As with previous methods, also there is a simple way to use `CEEMDAN`. + +**Windows**: Please don't skip the `if __name__ == "__main__"` section. + +```python +from PyEMD import CEEMDAN +import numpy as np + +if __name__ == "__main__": + s = np.random.random(100) + ceemdan = CEEMDAN() + cIMFs = ceemdan(s) +``` + +### Visualisation + +The package contains a simple visualisation helper that can help, e.g., with time series and instantaneous frequencies. + +```python +import numpy as np +from PyEMD import EMD, Visualisation + +t = np.arange(0, 3, 0.01) +S = np.sin(13*t + 0.2*t**1.4) - np.cos(3*t) + +# Extract imfs and residue +# In case of EMD +emd = EMD() +emd.emd(S) +imfs, res = emd.get_imfs_and_residue() + +# In general: +#components = EEMD()(S) +#imfs, res = components[:-1], components[-1] + +vis = Visualisation() +vis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True) +vis.plot_instant_freq(t, imfs=imfs) +vis.show() +``` + +## Experimental + +### JitEMD + +Just-in-time (JIT) compiled EMD is a version of EMD which exceed on very large signals +or reusing the same instance multiple times. It's strongly sugested to be used in +Jupyter notebooks when experimenting by modifyig input rather than the method itself. + +The problem with JIT is that the compilation happens on the first execution and it can be +quite costly. With small signals, or performing decomposition just once, the extra time +for compilation will be significantly larger than the decomposition, making it less performant. + +Please see documentation for more information or [examples](./example/) for how to use the code. +This is experimental as it's value is still questionable, and the author (me) isn't proficient +in JIT optimization so mistakes could've been made. + +Any feedback is welcomed. Happy to improve if there's intrest. Please open tickets with questions +and suggestions. + +To enable JIT in your PyEMD, please install with `jit` option, i.e. + +> \$ pip install EMD-signal[jit] + +### EMD2D/BEMD + +*Unfortunately, this is Experimental and we can't guarantee that the output is meaningful.* +The simplest use is to pass image as monochromatic numpy 2D array. Sample as +with the other modules one can use the default setting of an instance or, more explicitly, +use the `emd2d()` method. + +```python +from PyEMD.EMD2d import EMD2D #, BEMD +import numpy as np + +x, y = np.arange(128), np.arange(128).reshape((-1,1)) +img = np.sin(0.1*x)*np.cos(0.2*y) +emd2d = EMD2D() # BEMD() also works +IMFs_2D = emd2d(img) +``` + +## F.A.Q + +### Why is EEMD/CEEMDAN so slow? +Unfortunately, that's their nature. They execute EMD multiple times every time with slightly modified version. Added noise can cause a creation of many extrema which will decrease performance of the natural cubic spline. For some tweaks on how to deal with that please see [Speedup tricks](https://pyemd.readthedocs.io/en/latest/speedup.html) in the documentation. + +## Contact + +Feel free to contact me with any questions, requests or simply to say *hi*. +It's always nice to know that I've helped someone or made their work easier. +Contributing to the project is also acceptable and warmly welcomed. + +### Citation + +If you found this package useful and would like to cite it in your work +please use the following structure: + +```latex +@misc{pyemd, + author = {Laszuk, Dawid}, + title = {Python implementation of Empirical Mode Decomposition algorithm}, + year = {2017}, + publisher = {GitHub}, + journal = {GitHub Repository}, + howpublished = {\url{https://github.com/laszukdawid/PyEMD}}, + doi = {10.5281/zenodo.5459184} +} +``` + + +%package -n python3-EMD-signal +Summary: Implementation of the Empirical Mode Decomposition (EMD) and its variations +Provides: python-EMD-signal +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-EMD-signal +[](https://codecov.io/gh/laszukdawid/PyEMD) +[](https://app.travis-ci.com/laszukdawid/PyEMD) +[](https://pyemd.readthedocs.io/) +[](https://www.codacy.com/gh/laszukdawid/PyEMD/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/PyEMD&utm_campaign=Badge_Grade) +[](https://zenodo.org/badge/latestdoi/65324353) + +# PyEMD + +## Links + +- Online documentation: <https://pyemd.readthedocs.org> +- Issue tracker: <https://github.com/laszukdawid/pyemd/issues> +- Source code repository: <https://github.com/laszukdawid/pyemd> + +## Introduction + +This is yet another Python implementation of Empirical Mode +Decomposition (EMD). The package contains multiple EMD variations and +intends to deliver more in time. + +### EMD variations + +- Ensemble EMD (EEMD), +- "Complete Ensemble EMD" (CEEMDAN) +- different settings and configurations of vanilla EMD. +- Image decomposition (EMD2D & BEMD) (experimental, no support) +- Just-in-time compiled EMD (JitEMD) + +*PyEMD* allows you to use different splines for envelopes, stopping criteria +and extrema interpolations. + +### Available splines + +- Natural cubic (**default**) +- Pointwise cubic +- Akima +- Linear + +### Available stopping criteria + +- Cauchy convergence (**default**) +- Fixed number of iterations +- Number of consecutive proto-imfs + +### Extrema detection + +- Discrete extrema (**default**) +- Parabolic interpolation + +## Installation + +### PyPi (recommended) + +The quickest way to install package is through `pip`. + +> \$ pip install EMD-signal + +### From source + +In case, if you only want to *use* EMD and its variations, the best way to install PyEMD is through `pip`. +However, if you want to modify the code, anyhow you might want to download the code and build package yourself. +The source is publicaly available and hosted on [GitHub](https://github.com/laszukdawid/PyEMD). +To download the code you can either go to the source code page and click `Code -> Download ZIP`, or use **git** command line + +> \$ git clone <https://github.com/laszukdawid/PyEMD> + +Installing package from source is done using command line: + +> \$ python setup.py install + +**Note**, however, that this will install it in your current environment. If you are working on many projects, or sharing reources with others, we suggest using [virtual environments](https://docs.python.org/3/library/venv.html). + +## Example + +More detailed examples are included in the +[documentation](https://pyemd.readthedocs.io/en/latest/examples.html) or +in the +[PyEMD/examples](https://github.com/laszukdawid/PyEMD/tree/master/example). + +### EMD + +In most cases default settings are enough. Simply import `EMD` and pass +your signal to instance or to `emd()` method. + +```python +from PyEMD import EMD +import numpy as np + +s = np.random.random(100) +emd = EMD() +IMFs = emd(s) +``` + +The Figure below was produced with input: +$S(t) = cos(22 \pi t^2) + 6t^2$ + + + +### EEMD + +Simplest case of using Ensemble EMD (EEMD) is by importing `EEMD` and +passing your signal to the instance or `eemd()` method. + +**Windows**: Please don't skip the `if __name__ == "__main__"` section. + +```python +from PyEMD import EEMD +import numpy as np + +if __name__ == "__main__": + s = np.random.random(100) + eemd = EEMD() + eIMFs = eemd(s) +``` + +### CEEMDAN + +As with previous methods, also there is a simple way to use `CEEMDAN`. + +**Windows**: Please don't skip the `if __name__ == "__main__"` section. + +```python +from PyEMD import CEEMDAN +import numpy as np + +if __name__ == "__main__": + s = np.random.random(100) + ceemdan = CEEMDAN() + cIMFs = ceemdan(s) +``` + +### Visualisation + +The package contains a simple visualisation helper that can help, e.g., with time series and instantaneous frequencies. + +```python +import numpy as np +from PyEMD import EMD, Visualisation + +t = np.arange(0, 3, 0.01) +S = np.sin(13*t + 0.2*t**1.4) - np.cos(3*t) + +# Extract imfs and residue +# In case of EMD +emd = EMD() +emd.emd(S) +imfs, res = emd.get_imfs_and_residue() + +# In general: +#components = EEMD()(S) +#imfs, res = components[:-1], components[-1] + +vis = Visualisation() +vis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True) +vis.plot_instant_freq(t, imfs=imfs) +vis.show() +``` + +## Experimental + +### JitEMD + +Just-in-time (JIT) compiled EMD is a version of EMD which exceed on very large signals +or reusing the same instance multiple times. It's strongly sugested to be used in +Jupyter notebooks when experimenting by modifyig input rather than the method itself. + +The problem with JIT is that the compilation happens on the first execution and it can be +quite costly. With small signals, or performing decomposition just once, the extra time +for compilation will be significantly larger than the decomposition, making it less performant. + +Please see documentation for more information or [examples](./example/) for how to use the code. +This is experimental as it's value is still questionable, and the author (me) isn't proficient +in JIT optimization so mistakes could've been made. + +Any feedback is welcomed. Happy to improve if there's intrest. Please open tickets with questions +and suggestions. + +To enable JIT in your PyEMD, please install with `jit` option, i.e. + +> \$ pip install EMD-signal[jit] + +### EMD2D/BEMD + +*Unfortunately, this is Experimental and we can't guarantee that the output is meaningful.* +The simplest use is to pass image as monochromatic numpy 2D array. Sample as +with the other modules one can use the default setting of an instance or, more explicitly, +use the `emd2d()` method. + +```python +from PyEMD.EMD2d import EMD2D #, BEMD +import numpy as np + +x, y = np.arange(128), np.arange(128).reshape((-1,1)) +img = np.sin(0.1*x)*np.cos(0.2*y) +emd2d = EMD2D() # BEMD() also works +IMFs_2D = emd2d(img) +``` + +## F.A.Q + +### Why is EEMD/CEEMDAN so slow? +Unfortunately, that's their nature. They execute EMD multiple times every time with slightly modified version. Added noise can cause a creation of many extrema which will decrease performance of the natural cubic spline. For some tweaks on how to deal with that please see [Speedup tricks](https://pyemd.readthedocs.io/en/latest/speedup.html) in the documentation. + +## Contact + +Feel free to contact me with any questions, requests or simply to say *hi*. +It's always nice to know that I've helped someone or made their work easier. +Contributing to the project is also acceptable and warmly welcomed. + +### Citation + +If you found this package useful and would like to cite it in your work +please use the following structure: + +```latex +@misc{pyemd, + author = {Laszuk, Dawid}, + title = {Python implementation of Empirical Mode Decomposition algorithm}, + year = {2017}, + publisher = {GitHub}, + journal = {GitHub Repository}, + howpublished = {\url{https://github.com/laszukdawid/PyEMD}}, + doi = {10.5281/zenodo.5459184} +} +``` + + +%package help +Summary: Development documents and examples for EMD-signal +Provides: python3-EMD-signal-doc +%description help +[](https://codecov.io/gh/laszukdawid/PyEMD) +[](https://app.travis-ci.com/laszukdawid/PyEMD) +[](https://pyemd.readthedocs.io/) +[](https://www.codacy.com/gh/laszukdawid/PyEMD/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/PyEMD&utm_campaign=Badge_Grade) +[](https://zenodo.org/badge/latestdoi/65324353) + +# PyEMD + +## Links + +- Online documentation: <https://pyemd.readthedocs.org> +- Issue tracker: <https://github.com/laszukdawid/pyemd/issues> +- Source code repository: <https://github.com/laszukdawid/pyemd> + +## Introduction + +This is yet another Python implementation of Empirical Mode +Decomposition (EMD). The package contains multiple EMD variations and +intends to deliver more in time. + +### EMD variations + +- Ensemble EMD (EEMD), +- "Complete Ensemble EMD" (CEEMDAN) +- different settings and configurations of vanilla EMD. +- Image decomposition (EMD2D & BEMD) (experimental, no support) +- Just-in-time compiled EMD (JitEMD) + +*PyEMD* allows you to use different splines for envelopes, stopping criteria +and extrema interpolations. + +### Available splines + +- Natural cubic (**default**) +- Pointwise cubic +- Akima +- Linear + +### Available stopping criteria + +- Cauchy convergence (**default**) +- Fixed number of iterations +- Number of consecutive proto-imfs + +### Extrema detection + +- Discrete extrema (**default**) +- Parabolic interpolation + +## Installation + +### PyPi (recommended) + +The quickest way to install package is through `pip`. + +> \$ pip install EMD-signal + +### From source + +In case, if you only want to *use* EMD and its variations, the best way to install PyEMD is through `pip`. +However, if you want to modify the code, anyhow you might want to download the code and build package yourself. +The source is publicaly available and hosted on [GitHub](https://github.com/laszukdawid/PyEMD). +To download the code you can either go to the source code page and click `Code -> Download ZIP`, or use **git** command line + +> \$ git clone <https://github.com/laszukdawid/PyEMD> + +Installing package from source is done using command line: + +> \$ python setup.py install + +**Note**, however, that this will install it in your current environment. If you are working on many projects, or sharing reources with others, we suggest using [virtual environments](https://docs.python.org/3/library/venv.html). + +## Example + +More detailed examples are included in the +[documentation](https://pyemd.readthedocs.io/en/latest/examples.html) or +in the +[PyEMD/examples](https://github.com/laszukdawid/PyEMD/tree/master/example). + +### EMD + +In most cases default settings are enough. Simply import `EMD` and pass +your signal to instance or to `emd()` method. + +```python +from PyEMD import EMD +import numpy as np + +s = np.random.random(100) +emd = EMD() +IMFs = emd(s) +``` + +The Figure below was produced with input: +$S(t) = cos(22 \pi t^2) + 6t^2$ + + + +### EEMD + +Simplest case of using Ensemble EMD (EEMD) is by importing `EEMD` and +passing your signal to the instance or `eemd()` method. + +**Windows**: Please don't skip the `if __name__ == "__main__"` section. + +```python +from PyEMD import EEMD +import numpy as np + +if __name__ == "__main__": + s = np.random.random(100) + eemd = EEMD() + eIMFs = eemd(s) +``` + +### CEEMDAN + +As with previous methods, also there is a simple way to use `CEEMDAN`. + +**Windows**: Please don't skip the `if __name__ == "__main__"` section. + +```python +from PyEMD import CEEMDAN +import numpy as np + +if __name__ == "__main__": + s = np.random.random(100) + ceemdan = CEEMDAN() + cIMFs = ceemdan(s) +``` + +### Visualisation + +The package contains a simple visualisation helper that can help, e.g., with time series and instantaneous frequencies. + +```python +import numpy as np +from PyEMD import EMD, Visualisation + +t = np.arange(0, 3, 0.01) +S = np.sin(13*t + 0.2*t**1.4) - np.cos(3*t) + +# Extract imfs and residue +# In case of EMD +emd = EMD() +emd.emd(S) +imfs, res = emd.get_imfs_and_residue() + +# In general: +#components = EEMD()(S) +#imfs, res = components[:-1], components[-1] + +vis = Visualisation() +vis.plot_imfs(imfs=imfs, residue=res, t=t, include_residue=True) +vis.plot_instant_freq(t, imfs=imfs) +vis.show() +``` + +## Experimental + +### JitEMD + +Just-in-time (JIT) compiled EMD is a version of EMD which exceed on very large signals +or reusing the same instance multiple times. It's strongly sugested to be used in +Jupyter notebooks when experimenting by modifyig input rather than the method itself. + +The problem with JIT is that the compilation happens on the first execution and it can be +quite costly. With small signals, or performing decomposition just once, the extra time +for compilation will be significantly larger than the decomposition, making it less performant. + +Please see documentation for more information or [examples](./example/) for how to use the code. +This is experimental as it's value is still questionable, and the author (me) isn't proficient +in JIT optimization so mistakes could've been made. + +Any feedback is welcomed. Happy to improve if there's intrest. Please open tickets with questions +and suggestions. + +To enable JIT in your PyEMD, please install with `jit` option, i.e. + +> \$ pip install EMD-signal[jit] + +### EMD2D/BEMD + +*Unfortunately, this is Experimental and we can't guarantee that the output is meaningful.* +The simplest use is to pass image as monochromatic numpy 2D array. Sample as +with the other modules one can use the default setting of an instance or, more explicitly, +use the `emd2d()` method. + +```python +from PyEMD.EMD2d import EMD2D #, BEMD +import numpy as np + +x, y = np.arange(128), np.arange(128).reshape((-1,1)) +img = np.sin(0.1*x)*np.cos(0.2*y) +emd2d = EMD2D() # BEMD() also works +IMFs_2D = emd2d(img) +``` + +## F.A.Q + +### Why is EEMD/CEEMDAN so slow? +Unfortunately, that's their nature. They execute EMD multiple times every time with slightly modified version. Added noise can cause a creation of many extrema which will decrease performance of the natural cubic spline. For some tweaks on how to deal with that please see [Speedup tricks](https://pyemd.readthedocs.io/en/latest/speedup.html) in the documentation. + +## Contact + +Feel free to contact me with any questions, requests or simply to say *hi*. +It's always nice to know that I've helped someone or made their work easier. +Contributing to the project is also acceptable and warmly welcomed. + +### Citation + +If you found this package useful and would like to cite it in your work +please use the following structure: + +```latex +@misc{pyemd, + author = {Laszuk, Dawid}, + title = {Python implementation of Empirical Mode Decomposition algorithm}, + year = {2017}, + publisher = {GitHub}, + journal = {GitHub Repository}, + howpublished = {\url{https://github.com/laszukdawid/PyEMD}}, + doi = {10.5281/zenodo.5459184} +} +``` + + +%prep +%autosetup -n EMD-signal-1.4.0 + +%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-EMD-signal -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.0-1 +- Package Spec generated @@ -0,0 +1 @@ +9dade2aa07d241199393223a210f2ca6 EMD-signal-1.4.0.tar.gz |