Lichtso/CCWT：复连续小波变换

Lichtso

171

18 1

MIT License

项目介绍

Lichtso/CCWT 是一个托管在 GitHub 上的开源项目，主要使用 C 编写，采用 MIT License 许可证，目前获得 171 个 Star、18 次 Fork。

仓库全名: Lichtso/CCWT
开源许可证: MIT License
Star 数: 171
Fork 数: 18
未关闭 Issue: 1

README 摘要

CCWT library for C and Python Did you ever wanted to easily generate a spectrogram like this one? Take the Tutorial with lots of examples and all features being explained. Features Complex continuous wavelet transformation with a gabor wavelet interfaces for C99, python2.7 and python3.5 using libFFTW for performance and libPNG as possible output 4 synchrosqueeze modes 6 color schemes parallelization / multithreading…

完整内容见 GitHub 原始 README 或本页。

常见问题

CCWT 用什么语言开发？

主要使用 C 编写，采用 MIT License 许可证，目前有 171 个 Star。

CCWT 可以免费商用吗？

如何获取 CCWT 的源码？

可以直接克隆仓库：git clone https://github.com/Lichtso/CCWT.git，默认分支为 master；安装与运行步骤见仓库 README。

CCWT 最近还在维护吗？

仓库最近一次更新为 2024年4月12日，当前有 1 个未关闭 Issue；Star 数为 171，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

Complex Continuous Wavelet Transform

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

211

价值分

108

活跃状态

活跃

c99 fft png python-2 python-3 python-bindings signal-processing wavelet

GitHub

语言占比

Makefile

Python

贡献者

Lichtso

README

CCWT library for C and Python

Did you ever wanted to easily generate a spectrogram like this one?

spectrogram

Take the Tutorial with lots of examples and all features being explained.

Features

Complex continuous wavelet transformation

with a gabor wavelet
interfaces for C99, python2.7 and python3.5
using libFFTW for performance
and libPNG as possible output
4 synchrosqueeze modes
6 color schemes
parallelization / multithreading support
customizable frequency bands
helper method for linear and exponential frequency bands

Dependencies

Ubuntu

sudo apt-get install libfftw3-dev libpng-dev

Arch Linux

sudo pacman -S fftw libpng

Mac OS

brew install fftw libpng

Installation

[sudo] pip[3] install ccwt

Documentation

ccwt.fft()

input_signal: Numpy 1D float32, float64, complex64 or complex128 array
padding: Zero samples to be virtually added at each end of the input signal, default is 0
thread_count: Default is 1 (no multi threading)

ccwt.frequency_band()

height: Height of the resulting image in pixels and number of frequencies to analyze
frequency_range: Difference between the highest and the lowest frequency to analyze, default is height/2
frequency_offset: Lowest frequency to analyze, default is 0.0
frequency_basis: Values > 0.0 switch from a linear to an exponential frequency scale using this as basis, default is 0.0 / linear mode
deviation: Values near 0.0 have better frequency resolution, values towards infinity have better time resolution, default is 1.0

ccwt.numeric_output()

fourier_transformed_signal: Numpy 1D complex128 array generated by ccwt.fft()
frequency_band: Numpy 2D float64 array generated by ccwt.frequency_band()
width: Width of the resulting image in pixels, can be the length of the input signal or less for downsampling
padding: Same value as passed to ccwt.fft()
thread_count: Default is 1 (no multi threading)

ccwt.render_png()

Same as ccwt.numeric_output() but with these additionally at the beginning:

file: File object to store the resulting PNG image
render_mode: indicating the color scheme for rendering, see include/render_mode.h for possible values
logarithmic_basis: Values > 0.0 switch from a linear to a logarithmic intensity rendering using this as basis

CCWT