Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up

Complex Continuous Wavelet Transform

License

NotificationsYou must be signed in to change notification settings

Lichtso/CCWT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

37 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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

spectrogram

Take theTutorial with lots of examples and all features being explained.

Features

Complexcontinuous wavelet transformation

  • with agabor wavelet
  • interfaces for C99, python2.7 and python3.5
  • usinglibFFTW for performance
  • andlibPNG 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

[8]ページ先頭

©2009-2025 Movatter.jp