https://github.com/claudiodsf/stockwell
Stockwell transform for Python
https://github.com/claudiodsf/stockwell
processing signal time-frequency-analysis transform
Last synced: about 1 year ago
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Stockwell transform for Python
- Host: GitHub
- URL: https://github.com/claudiodsf/stockwell
- Owner: claudiodsf
- License: gpl-3.0
- Created: 2018-03-12T17:15:02.000Z (about 8 years ago)
- Default Branch: main
- Last Pushed: 2025-01-08T09:20:25.000Z (over 1 year ago)
- Last Synced: 2025-04-13T00:41:52.153Z (about 1 year ago)
- Topics: processing, signal, time-frequency-analysis, transform
- Language: Python
- Homepage:
- Size: 306 KB
- Stars: 93
- Watchers: 7
- Forks: 29
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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README
# Stockwell
Python package for time-frequency analysis through Stockwell transform.
Based on original code from [NIMH MEG Core Facility].
[![changelog-badge]][changelog-link]
[![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.
On Linux and macOS, you can download and compile FFTW from source using
the script `get_fftw3.sh` provided in the `scripts` directory:
./scripts/get_fftw3.sh
Alternatively, you can install FFTW using your package manager:
- 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:
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()
```
## 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
[changelog-badge]: https://img.shields.io/badge/Changelog-136CB6.svg
[changelog-link]: https://github.com/claudiodsf/stockwell/blob/main/CHANGELOG.md
[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