v0.11.3b1

This release fixes a minor issue which could cause an exception when attempting ridge extraction.

v0.11.2b1

This release fixes an exception raised when using constant padding with the wavelet transform.

v0.11.1b1

This release adds the Matlab implementation of the Bayesian inference algorithm, which is now the default.

v0.11.0b1

This release adds the experimental Bayesian inference algorithm.

v0.10.2b1

The wavelet transform now accepts the parameter wavelet="Morse-a".

v0.10.1b1

Wavelet transform

The wavelet transform can now be run in parallel by passing parallel=True. This can provide a modest performance improvement of around 25%, along with an increase in memory usage.

Shared memory is used to reduce the memory usage, so parallelization only works on Python 3.8 and higher. (Parallelization is automatically disabled on Python 3.7 and below, even if parallel==True.)

Plotting

The performance of pymodalib.contourf() has been greatly improved by automatically subsampling the data. The subsampling can be disabled by passing subsample=False, and the resolution can be changed using the subsample_width parameter.

Additionally, log=True can now be passed to pymodalib.contourf() to apply a logarithmic scale to the y-axis.

v0.10.0b1

This release greatly improves the reliability of the wavelet transform. The Python implementation of the wavelet transform is now the default.

v0.9.0b1

This release adds two new functions:

• pymodalib.contourf() allows easy plotting of data, such as wavelet transforms, in PyMODA style.
• pymodalib.colormap() loads the PyMODA colormap, which can be passed to matplotlib functions.

v0.8.1b1

This release fixes an issue with the Morlet wavelet in the wavelet transform.

v0.8.0b1

Changes in this release:

• Significant improvements to the reliability and performance of the wavelet transform.
• New function, generate_times(), allows you to easily create the array of time values associated with a signal.