%global _empty_manifest_terminate_build 0
Name:		python-stockwell
Version:	1.1
Release:	1
Summary:	Time-frequency analysis through Stockwell transform
License:	CeCILL Free Software License Agreement, Version 2.1
URL:		https://github.com/claudiodsf/stockwell
Source0:	https://mirrors.aliyun.com/pypi/web/packages/46/f7/0e71c285d44320445cc39e786ee61b4be1489a3ca7cf4246f82bb370ccb4/stockwell-1.1.tar.gz

Requires:	python3-numpy

%description
# Stockwell

Python package for time-frequency analysis through Stockwell transform.

Based on original code from [NIMH MEG Core Facility].

[![cf-badge]][cf-link]
[![PyPI-badge]][PyPI-link]
[![license-badge]][license-link]

## Installation

### Using Anaconda

If you use [Anaconda], the latest release of Stockwell is available via
[conda-forge][cf-link].

To install, simply run:

    conda install -c conda-forge stockwell

### Using pip and PyPI

The latest release of Stockwell is available on the
[Python Package Index][PyPI-link].

You can install it easily through `pip`:

    pip install stockwell

### Installation from source

If no precompiled package is available for you architecture on PyPI, or if you
want to work on the source code, you will need to compile this package from
source.

To obtain the source code, download the latest release from the
[releases page][releases-link], or clone the GitHub project.

#### C compiler

Part of Stockwell is written in C, so you will need a C compiler.

On Linux (Debian or Ubuntu), install the `build-essential` package:

    sudo apt install build-essential

On macOS, install the XCode Command Line Tools:

    xcode-select --install

On Windows, install the [Microsoft C++ Build Tools].

#### FFTW

To compile Stockwell, you will need to have [FFTW]
installed.

If you use [Anaconda] (Linux, macOS, Windows):

    conda install fftw

If you use Homebrew (macOS)

    brew install fftw

If you use `apt` (Debian or Ubuntu)

    sudo apt install libfftw3-dev

#### Install the Python package from source

Finally, install this Python package using pip:

    pip install .

Or, alternatively, in "editable" mode:

    pip install -e .

## Usage

Example usage:

```python
import numpy as np
from scipy.signal import chirp
import matplotlib.pyplot as plt
from stockwell import st

t = np.linspace(0, 10, 5001)
w = chirp(t, f0=12.5, f1=2.5, t1=10, method='linear')

fmin = 0  # Hz
fmax = 25  # Hz
df = 1./(t[-1]-t[0])  # sampling step in frequency domain (Hz)
fmin_samples = int(fmin/df)
fmax_samples = int(fmax/df)
stock = st.st(w, fmin_samples, fmax_samples)
extent = (t[0], t[-1], fmin, fmax)

fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(t, w)
ax[0].set(ylabel='amplitude')
ax[1].imshow(np.abs(stock), origin='lower', extent=extent)
ax[1].axis('tight')
ax[1].set(xlabel='time (s)', ylabel='frequency (Hz)')
plt.show()
```
You should get the following output:

![stockwell.png](https://cdn.jsdelivr.net/gh/claudiodsf/stockwell/stockwell.png)

You can also compute the inverse Stockwell transform, ex:

```python
inv_stock = st.ist(stock, fmin_samples, fmax_samples)
fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(t, w, label='original signal')
ax[0].plot(t, inv_stock, label='inverse Stockwell')
ax[0].set(ylabel='amplitude')
ax[0].legend(loc='upper right')
ax[1].plot(t, w - inv_stock)
ax[1].set_xlim(0, 10)
ax[1].set(xlabel='time (s)', ylabel='amplitude difference')
plt.show()
```
![inv_stockwell.png](https://cdn.jsdelivr.net/gh/claudiodsf/stockwell/inv_stockwell.png)

## References

Stockwell, R.G., Mansinha, L. & Lowe, R.P., 1996. Localization of the complex
spectrum: the S transform, IEEE Trans. Signal Process., 44(4), 998–1001,
doi:[10.1109/78.492555](https://doi.org/10.1109/78.492555)

[S transform on Wikipedia].

[NIMH MEG Core Facility]: https://kurage.nimh.nih.gov/meglab/Meg/Stockwell

[cf-badge]: http://img.shields.io/conda/vn/conda-forge/stockwell.svg
[cf-link]: https://anaconda.org/conda-forge/stockwell
[PyPI-badge]: http://img.shields.io/pypi/v/stockwell.svg
[PyPI-link]: https://pypi.python.org/pypi/stockwell
[license-badge]: https://img.shields.io/badge/license-GPLv3-green
[license-link]: https://www.gnu.org/licenses/gpl-3.0.html
[releases-link]: https://github.com/claudiodsf/stockwell/releases

[Anaconda]: https://www.anaconda.com/products/individual
[Microsoft C++ Build Tools]:
https://visualstudio.microsoft.com/visual-cpp-build-tools
[FFTW]: http://www.fftw.org
[S transform on Wikipedia]: https://en.wikipedia.org/wiki/S_transform


%package -n python3-stockwell
Summary:	Time-frequency analysis through Stockwell transform
Provides:	python-stockwell
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-stockwell
# Stockwell

Python package for time-frequency analysis through Stockwell transform.

Based on original code from [NIMH MEG Core Facility].

[![cf-badge]][cf-link]
[![PyPI-badge]][PyPI-link]
[![license-badge]][license-link]

## Installation

### Using Anaconda

If you use [Anaconda], the latest release of Stockwell is available via
[conda-forge][cf-link].

To install, simply run:

    conda install -c conda-forge stockwell

### Using pip and PyPI

The latest release of Stockwell is available on the
[Python Package Index][PyPI-link].

You can install it easily through `pip`:

    pip install stockwell

### Installation from source

If no precompiled package is available for you architecture on PyPI, or if you
want to work on the source code, you will need to compile this package from
source.

To obtain the source code, download the latest release from the
[releases page][releases-link], or clone the GitHub project.

#### C compiler

Part of Stockwell is written in C, so you will need a C compiler.

On Linux (Debian or Ubuntu), install the `build-essential` package:

    sudo apt install build-essential

On macOS, install the XCode Command Line Tools:

    xcode-select --install

On Windows, install the [Microsoft C++ Build Tools].

#### FFTW

To compile Stockwell, you will need to have [FFTW]
installed.

If you use [Anaconda] (Linux, macOS, Windows):

    conda install fftw

If you use Homebrew (macOS)

    brew install fftw

If you use `apt` (Debian or Ubuntu)

    sudo apt install libfftw3-dev

#### Install the Python package from source

Finally, install this Python package using pip:

    pip install .

Or, alternatively, in "editable" mode:

    pip install -e .

## Usage

Example usage:

```python
import numpy as np
from scipy.signal import chirp
import matplotlib.pyplot as plt
from stockwell import st

t = np.linspace(0, 10, 5001)
w = chirp(t, f0=12.5, f1=2.5, t1=10, method='linear')

fmin = 0  # Hz
fmax = 25  # Hz
df = 1./(t[-1]-t[0])  # sampling step in frequency domain (Hz)
fmin_samples = int(fmin/df)
fmax_samples = int(fmax/df)
stock = st.st(w, fmin_samples, fmax_samples)
extent = (t[0], t[-1], fmin, fmax)

fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(t, w)
ax[0].set(ylabel='amplitude')
ax[1].imshow(np.abs(stock), origin='lower', extent=extent)
ax[1].axis('tight')
ax[1].set(xlabel='time (s)', ylabel='frequency (Hz)')
plt.show()
```
You should get the following output:

![stockwell.png](https://cdn.jsdelivr.net/gh/claudiodsf/stockwell/stockwell.png)

You can also compute the inverse Stockwell transform, ex:

```python
inv_stock = st.ist(stock, fmin_samples, fmax_samples)
fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(t, w, label='original signal')
ax[0].plot(t, inv_stock, label='inverse Stockwell')
ax[0].set(ylabel='amplitude')
ax[0].legend(loc='upper right')
ax[1].plot(t, w - inv_stock)
ax[1].set_xlim(0, 10)
ax[1].set(xlabel='time (s)', ylabel='amplitude difference')
plt.show()
```
![inv_stockwell.png](https://cdn.jsdelivr.net/gh/claudiodsf/stockwell/inv_stockwell.png)

## References

Stockwell, R.G., Mansinha, L. & Lowe, R.P., 1996. Localization of the complex
spectrum: the S transform, IEEE Trans. Signal Process., 44(4), 998–1001,
doi:[10.1109/78.492555](https://doi.org/10.1109/78.492555)

[S transform on Wikipedia].

[NIMH MEG Core Facility]: https://kurage.nimh.nih.gov/meglab/Meg/Stockwell

[cf-badge]: http://img.shields.io/conda/vn/conda-forge/stockwell.svg
[cf-link]: https://anaconda.org/conda-forge/stockwell
[PyPI-badge]: http://img.shields.io/pypi/v/stockwell.svg
[PyPI-link]: https://pypi.python.org/pypi/stockwell
[license-badge]: https://img.shields.io/badge/license-GPLv3-green
[license-link]: https://www.gnu.org/licenses/gpl-3.0.html
[releases-link]: https://github.com/claudiodsf/stockwell/releases

[Anaconda]: https://www.anaconda.com/products/individual
[Microsoft C++ Build Tools]:
https://visualstudio.microsoft.com/visual-cpp-build-tools
[FFTW]: http://www.fftw.org
[S transform on Wikipedia]: https://en.wikipedia.org/wiki/S_transform


%package help
Summary:	Development documents and examples for stockwell
Provides:	python3-stockwell-doc
%description help
# Stockwell

Python package for time-frequency analysis through Stockwell transform.

Based on original code from [NIMH MEG Core Facility].

[![cf-badge]][cf-link]
[![PyPI-badge]][PyPI-link]
[![license-badge]][license-link]

## Installation

### Using Anaconda

If you use [Anaconda], the latest release of Stockwell is available via
[conda-forge][cf-link].

To install, simply run:

    conda install -c conda-forge stockwell

### Using pip and PyPI

The latest release of Stockwell is available on the
[Python Package Index][PyPI-link].

You can install it easily through `pip`:

    pip install stockwell

### Installation from source

If no precompiled package is available for you architecture on PyPI, or if you
want to work on the source code, you will need to compile this package from
source.

To obtain the source code, download the latest release from the
[releases page][releases-link], or clone the GitHub project.

#### C compiler

Part of Stockwell is written in C, so you will need a C compiler.

On Linux (Debian or Ubuntu), install the `build-essential` package:

    sudo apt install build-essential

On macOS, install the XCode Command Line Tools:

    xcode-select --install

On Windows, install the [Microsoft C++ Build Tools].

#### FFTW

To compile Stockwell, you will need to have [FFTW]
installed.

If you use [Anaconda] (Linux, macOS, Windows):

    conda install fftw

If you use Homebrew (macOS)

    brew install fftw

If you use `apt` (Debian or Ubuntu)

    sudo apt install libfftw3-dev

#### Install the Python package from source

Finally, install this Python package using pip:

    pip install .

Or, alternatively, in "editable" mode:

    pip install -e .

## Usage

Example usage:

```python
import numpy as np
from scipy.signal import chirp
import matplotlib.pyplot as plt
from stockwell import st

t = np.linspace(0, 10, 5001)
w = chirp(t, f0=12.5, f1=2.5, t1=10, method='linear')

fmin = 0  # Hz
fmax = 25  # Hz
df = 1./(t[-1]-t[0])  # sampling step in frequency domain (Hz)
fmin_samples = int(fmin/df)
fmax_samples = int(fmax/df)
stock = st.st(w, fmin_samples, fmax_samples)
extent = (t[0], t[-1], fmin, fmax)

fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(t, w)
ax[0].set(ylabel='amplitude')
ax[1].imshow(np.abs(stock), origin='lower', extent=extent)
ax[1].axis('tight')
ax[1].set(xlabel='time (s)', ylabel='frequency (Hz)')
plt.show()
```
You should get the following output:

![stockwell.png](https://cdn.jsdelivr.net/gh/claudiodsf/stockwell/stockwell.png)

You can also compute the inverse Stockwell transform, ex:

```python
inv_stock = st.ist(stock, fmin_samples, fmax_samples)
fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(t, w, label='original signal')
ax[0].plot(t, inv_stock, label='inverse Stockwell')
ax[0].set(ylabel='amplitude')
ax[0].legend(loc='upper right')
ax[1].plot(t, w - inv_stock)
ax[1].set_xlim(0, 10)
ax[1].set(xlabel='time (s)', ylabel='amplitude difference')
plt.show()
```
![inv_stockwell.png](https://cdn.jsdelivr.net/gh/claudiodsf/stockwell/inv_stockwell.png)

## References

Stockwell, R.G., Mansinha, L. & Lowe, R.P., 1996. Localization of the complex
spectrum: the S transform, IEEE Trans. Signal Process., 44(4), 998–1001,
doi:[10.1109/78.492555](https://doi.org/10.1109/78.492555)

[S transform on Wikipedia].

[NIMH MEG Core Facility]: https://kurage.nimh.nih.gov/meglab/Meg/Stockwell

[cf-badge]: http://img.shields.io/conda/vn/conda-forge/stockwell.svg
[cf-link]: https://anaconda.org/conda-forge/stockwell
[PyPI-badge]: http://img.shields.io/pypi/v/stockwell.svg
[PyPI-link]: https://pypi.python.org/pypi/stockwell
[license-badge]: https://img.shields.io/badge/license-GPLv3-green
[license-link]: https://www.gnu.org/licenses/gpl-3.0.html
[releases-link]: https://github.com/claudiodsf/stockwell/releases

[Anaconda]: https://www.anaconda.com/products/individual
[Microsoft C++ Build Tools]:
https://visualstudio.microsoft.com/visual-cpp-build-tools
[FFTW]: http://www.fftw.org
[S transform on Wikipedia]: https://en.wikipedia.org/wiki/S_transform


%prep
%autosetup -n stockwell-1.1

%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-stockwell -f filelist.lst
%dir %{python3_sitearch}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1-1
- Package Spec generated