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

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

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
• 6 render modes
• parallelization / multithreading support
• customizable frequency bands
• helper method for linear and exponential frequency bands

sudo apt-get install libfftw3-dev libpng-dev

sudo pacman -S fftw libpng

brew install fftw libpng

[sudo] pip[3] install ccwt

• 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)

• 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

• 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)

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

• path: Filename of 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