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|
%global _empty_manifest_terminate_build 0
Name: python-scikit-dsp-comm
Version: 2.0.3
Release: 1
Summary: DSP and Comm package.
License: BSD
URL: https://github.com/mwickert/scikit-dsp-comm
Source0: https://mirrors.aliyun.com/pypi/web/packages/ec/37/ab91d4521f593b657826b79036a180e73885fea021573544849573d1c9a2/scikit-dsp-comm-2.0.3.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-matplotlib
Requires: python3-scipy
Requires: python3-colorama
Requires: python3-pyaudio
Requires: python3-ipywidgets
%description
# scikit-dsp-comm
[](https://pypi.python.org/pypi/scikit-dsp-comm)
[](https://anaconda.org/conda-forge/scikit-dsp-comm)
[](http://scikit-dsp-comm.readthedocs.io/en/latest/?badge=latest)
## Background
The origin of this package comes from the writing the book Signals and Systems for Dummies, published by Wiley in 2013. The original module for this book is named `ssd.py`. In `scikit-dsp-comm` this module is renamed to `sigsys.py` to better reflect the fact that signal processing and communications theory is founded in signals and systems, a traditional subject in electrical engineering curricula.
## Package High Level Overview
This package is a collection of functions and classes to support signal processing and communications theory teaching and research. The foundation for this package is `scipy.signal`. The code in particular currently requires Python `>=3.5x`.
**There are presently ten modules that make up scikit-dsp-comm:**
1. `sigsys.py` for basic signals and systems functions both continuous-time and discrete-time, including graphical display tools such as pole-zero plots, up-sampling and down-sampling.
2. `digitalcomm.py` for digital modulation theory components, including asynchronous resampling and variable time delay functions, both useful in advanced modem testing.
3. `synchronization.py` which contains phase-locked loop simulation functions and functions for carrier and phase synchronization of digital communications waveforms.
4. `fec_conv.py` for the generation rate one-half and one-third convolutional codes and soft decision Viterbi algorithm decoding, including soft and hard decisions, trellis and trellis-traceback display functions, and puncturing.
5. `fir_design_helper.py` which for easy design of lowpass, highpass, bandpass, and bandstop filters using the Kaiser window and equal-ripple designs, also includes a list plotting function for easily comparing magnitude, phase, and group delay frequency responses.
6. `iir_design_helper.py` which for easy design of lowpass, highpass, bandpass, and bandstop filters using scipy.signal Butterworth, Chebyshev I and II, and elliptical designs, including the use of the cascade of second-order sections (SOS) topology from scipy.signal, also includes a list plotting function for easily comparing of magnitude, phase, and group delay frequency responses.
7. `multirate.py` that encapsulate digital filters into objects for filtering, interpolation by an integer factor, and decimation by an integer factor.
8. `coeff2header.py` write `C/C++` header files for FIR and IIR filters implemented in `C/C++`, using the cascade of second-order section representation for the IIR case. This last module find use in real-time signal processing on embedded systems, but can be used for simulation models in `C/C++`.
Presently the collection of modules contains about 125 functions and classes. The authors/maintainers are working to get more detailed documentation in place.
## Documentation
Documentation is now housed on `readthedocs` which you can get to by clicking the docs badge near the top of this `README`. Example notebooks can be viewed on [GitHub pages](https://mwickert.github.io/scikit-dsp-comm/). In time more notebook postings will be extracted from [Dr. Wickert's Info Center](http://www.eas.uccs.edu/~mwickert/).
## Getting Set-up on Your System
The best way to use this package is to clone this repository and then install it.
```bash
git clone https://github.com/mwickert/scikit-dsp-comm.git
```
There are package dependencies for some modules that you may want to avoid. Specifically these are whenever hardware interfacing is involved. Specific hardware and software configuration details are discussed in [wiki pages](https://github.com/mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm/wiki).
For Windows users `pip` install takes care of almost everything. I assume below you have Python on your path, so for example with [Anaconda](https://www.anaconda.com/download/#macos), I suggest letting the installer set these paths up for you.
### Editable Install with Dependencies
With the terminal in the root directory of the cloned repo perform an editable `pip` install using
```bash
pip install -e .
```
### Why an Editable Install?
The advantage of the editable `pip` install is that it is very easy to keep `scikit-dsp-comm ` up to date. If you know that updates have been pushed to the master branch, you simply go to your local repo folder and
```bash
git pull origin master
```
This will update you local repo and automatically update the Python install without the need to run `pip` again. **Note**: If you have any Python kernels running, such as a Jupyter Notebook, you will need to restart the kernel to insure any module changes get reloaded.
%package -n python3-scikit-dsp-comm
Summary: DSP and Comm package.
Provides: python-scikit-dsp-comm
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-scikit-dsp-comm
# scikit-dsp-comm
[](https://pypi.python.org/pypi/scikit-dsp-comm)
[](https://anaconda.org/conda-forge/scikit-dsp-comm)
[](http://scikit-dsp-comm.readthedocs.io/en/latest/?badge=latest)
## Background
The origin of this package comes from the writing the book Signals and Systems for Dummies, published by Wiley in 2013. The original module for this book is named `ssd.py`. In `scikit-dsp-comm` this module is renamed to `sigsys.py` to better reflect the fact that signal processing and communications theory is founded in signals and systems, a traditional subject in electrical engineering curricula.
## Package High Level Overview
This package is a collection of functions and classes to support signal processing and communications theory teaching and research. The foundation for this package is `scipy.signal`. The code in particular currently requires Python `>=3.5x`.
**There are presently ten modules that make up scikit-dsp-comm:**
1. `sigsys.py` for basic signals and systems functions both continuous-time and discrete-time, including graphical display tools such as pole-zero plots, up-sampling and down-sampling.
2. `digitalcomm.py` for digital modulation theory components, including asynchronous resampling and variable time delay functions, both useful in advanced modem testing.
3. `synchronization.py` which contains phase-locked loop simulation functions and functions for carrier and phase synchronization of digital communications waveforms.
4. `fec_conv.py` for the generation rate one-half and one-third convolutional codes and soft decision Viterbi algorithm decoding, including soft and hard decisions, trellis and trellis-traceback display functions, and puncturing.
5. `fir_design_helper.py` which for easy design of lowpass, highpass, bandpass, and bandstop filters using the Kaiser window and equal-ripple designs, also includes a list plotting function for easily comparing magnitude, phase, and group delay frequency responses.
6. `iir_design_helper.py` which for easy design of lowpass, highpass, bandpass, and bandstop filters using scipy.signal Butterworth, Chebyshev I and II, and elliptical designs, including the use of the cascade of second-order sections (SOS) topology from scipy.signal, also includes a list plotting function for easily comparing of magnitude, phase, and group delay frequency responses.
7. `multirate.py` that encapsulate digital filters into objects for filtering, interpolation by an integer factor, and decimation by an integer factor.
8. `coeff2header.py` write `C/C++` header files for FIR and IIR filters implemented in `C/C++`, using the cascade of second-order section representation for the IIR case. This last module find use in real-time signal processing on embedded systems, but can be used for simulation models in `C/C++`.
Presently the collection of modules contains about 125 functions and classes. The authors/maintainers are working to get more detailed documentation in place.
## Documentation
Documentation is now housed on `readthedocs` which you can get to by clicking the docs badge near the top of this `README`. Example notebooks can be viewed on [GitHub pages](https://mwickert.github.io/scikit-dsp-comm/). In time more notebook postings will be extracted from [Dr. Wickert's Info Center](http://www.eas.uccs.edu/~mwickert/).
## Getting Set-up on Your System
The best way to use this package is to clone this repository and then install it.
```bash
git clone https://github.com/mwickert/scikit-dsp-comm.git
```
There are package dependencies for some modules that you may want to avoid. Specifically these are whenever hardware interfacing is involved. Specific hardware and software configuration details are discussed in [wiki pages](https://github.com/mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm/wiki).
For Windows users `pip` install takes care of almost everything. I assume below you have Python on your path, so for example with [Anaconda](https://www.anaconda.com/download/#macos), I suggest letting the installer set these paths up for you.
### Editable Install with Dependencies
With the terminal in the root directory of the cloned repo perform an editable `pip` install using
```bash
pip install -e .
```
### Why an Editable Install?
The advantage of the editable `pip` install is that it is very easy to keep `scikit-dsp-comm ` up to date. If you know that updates have been pushed to the master branch, you simply go to your local repo folder and
```bash
git pull origin master
```
This will update you local repo and automatically update the Python install without the need to run `pip` again. **Note**: If you have any Python kernels running, such as a Jupyter Notebook, you will need to restart the kernel to insure any module changes get reloaded.
%package help
Summary: Development documents and examples for scikit-dsp-comm
Provides: python3-scikit-dsp-comm-doc
%description help
# scikit-dsp-comm
[](https://pypi.python.org/pypi/scikit-dsp-comm)
[](https://anaconda.org/conda-forge/scikit-dsp-comm)
[](http://scikit-dsp-comm.readthedocs.io/en/latest/?badge=latest)
## Background
The origin of this package comes from the writing the book Signals and Systems for Dummies, published by Wiley in 2013. The original module for this book is named `ssd.py`. In `scikit-dsp-comm` this module is renamed to `sigsys.py` to better reflect the fact that signal processing and communications theory is founded in signals and systems, a traditional subject in electrical engineering curricula.
## Package High Level Overview
This package is a collection of functions and classes to support signal processing and communications theory teaching and research. The foundation for this package is `scipy.signal`. The code in particular currently requires Python `>=3.5x`.
**There are presently ten modules that make up scikit-dsp-comm:**
1. `sigsys.py` for basic signals and systems functions both continuous-time and discrete-time, including graphical display tools such as pole-zero plots, up-sampling and down-sampling.
2. `digitalcomm.py` for digital modulation theory components, including asynchronous resampling and variable time delay functions, both useful in advanced modem testing.
3. `synchronization.py` which contains phase-locked loop simulation functions and functions for carrier and phase synchronization of digital communications waveforms.
4. `fec_conv.py` for the generation rate one-half and one-third convolutional codes and soft decision Viterbi algorithm decoding, including soft and hard decisions, trellis and trellis-traceback display functions, and puncturing.
5. `fir_design_helper.py` which for easy design of lowpass, highpass, bandpass, and bandstop filters using the Kaiser window and equal-ripple designs, also includes a list plotting function for easily comparing magnitude, phase, and group delay frequency responses.
6. `iir_design_helper.py` which for easy design of lowpass, highpass, bandpass, and bandstop filters using scipy.signal Butterworth, Chebyshev I and II, and elliptical designs, including the use of the cascade of second-order sections (SOS) topology from scipy.signal, also includes a list plotting function for easily comparing of magnitude, phase, and group delay frequency responses.
7. `multirate.py` that encapsulate digital filters into objects for filtering, interpolation by an integer factor, and decimation by an integer factor.
8. `coeff2header.py` write `C/C++` header files for FIR and IIR filters implemented in `C/C++`, using the cascade of second-order section representation for the IIR case. This last module find use in real-time signal processing on embedded systems, but can be used for simulation models in `C/C++`.
Presently the collection of modules contains about 125 functions and classes. The authors/maintainers are working to get more detailed documentation in place.
## Documentation
Documentation is now housed on `readthedocs` which you can get to by clicking the docs badge near the top of this `README`. Example notebooks can be viewed on [GitHub pages](https://mwickert.github.io/scikit-dsp-comm/). In time more notebook postings will be extracted from [Dr. Wickert's Info Center](http://www.eas.uccs.edu/~mwickert/).
## Getting Set-up on Your System
The best way to use this package is to clone this repository and then install it.
```bash
git clone https://github.com/mwickert/scikit-dsp-comm.git
```
There are package dependencies for some modules that you may want to avoid. Specifically these are whenever hardware interfacing is involved. Specific hardware and software configuration details are discussed in [wiki pages](https://github.com/mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm/wiki).
For Windows users `pip` install takes care of almost everything. I assume below you have Python on your path, so for example with [Anaconda](https://www.anaconda.com/download/#macos), I suggest letting the installer set these paths up for you.
### Editable Install with Dependencies
With the terminal in the root directory of the cloned repo perform an editable `pip` install using
```bash
pip install -e .
```
### Why an Editable Install?
The advantage of the editable `pip` install is that it is very easy to keep `scikit-dsp-comm ` up to date. If you know that updates have been pushed to the master branch, you simply go to your local repo folder and
```bash
git pull origin master
```
This will update you local repo and automatically update the Python install without the need to run `pip` again. **Note**: If you have any Python kernels running, such as a Jupyter Notebook, you will need to restart the kernel to insure any module changes get reloaded.
%prep
%autosetup -n scikit-dsp-comm-2.0.3
%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-scikit-dsp-comm -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.3-1
- Package Spec generated
|
