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We describe a python package for nonlinear phase linking of full resolution SAR images using both distributed and persistent scatterers. In the workflow, the first step is to find for each pixel the set of self-similar pixels in order to identify persistent and distributed scatterers. Next the phase linking is performed using the full complex coherence matrix containing the wrapped phase values of each distributed scatterer. Our package uses a hybrid approach consisting of eigenvalue decomposition-based maximum likelihood phase linking and the classic eigenvalue decomposition method. The latter is used for pixels with a non-invertible covariance matrix. A sequential mode achieves computational efficiency. The next step is to unwrap the phase by selecting an optimum unwrapping network of interferograms and invert for the unwrapped phase time-series which is converted to the displacement time-series. We show how …