Metadata-Version: 2.1
Name: stockwell
Version: 1.0.3
Summary: Time-frequency analysis through Stockwell transform
Home-page: http://www.ipgp.fr/~satriano
Author: Claudio Satriano
Author-email: satriano@ipgp.fr
License: CeCILL Free Software License Agreement, Version 2.1
Description: # Stockwell
        
        Python package for time-frequency analysis through Stockwell transform.
        
        Based on original code from [NIMH MEG Core Facility].
        
        [NIMH MEG Core Facility]: https://kurage.nimh.nih.gov/meglab/Meg/Stockwell.
        
        
        ## Installation
        
        ### Using pip and PyPI (preferred method)
        
        The latest release of Stockwell is available on the
        [Python Package Index](https://pypi.org/project/stockwell/).
        
        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](https://github.com/claudiodsf/stockwell/releases), 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].
        
        [Microsoft C++ Build Tools]:
        https://visualstudio.microsoft.com/visual-cpp-build-tools
        
        #### FFTW
        
        To compile Stockwell, you will need to have [FFTW](http://www.fftw.org)
        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](https://en.wikipedia.org/wiki/S_transform).
        
Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: CEA CNRS Inria Logiciel Libre License, version 2.1 (CeCILL-2.1)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Description-Content-Type: text/markdown
