Python的斯托克韦尔变换_Python_C_下载.zip

# 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:  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 [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-CeCILL--2.1-green [license-link]: http://www.cecill.info/licences.en.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