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The Python scripts herein provided contain the implementation of the PWC Estimator algorithm and two examples of its execution.

In particular:
- 'pwc.py' is the main script defining the PWCDIstribution class.

- 'helperfunctions.py' contains functions used in 'pwc.py'.

- 'sobol.py' implements the sobol random number generator. See http://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html.

In addition to the above files, we provide the following scripts:

- 'testDefinitions.py', 'transitionPDF.py' are required by the examples in 'Example.ipynb' (see below).

Finally,

- 'Example.ipynb' and 'Example.html' show two examples of the algorithm execution.

In order to open 'Example.ipynb' having Jupyter Notebook installed is required.
If this is not available, the user can still
rely on the relative 'Example.html' generated file.

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Main Reference:
https://www.researchgate.net/publication/336473464_Density_estimation_of_multivariate_samples_using_Wasserstein_distance/citations