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Post-processing and Plotting Results

After running the optimization, use the PostProcessor object to analyze and plot the empirical probabilities from the generated samples.

from PostProcessing import PostProcessor
postprocessor = PostProcessor(samples_filename)

Parameters:

  • samples_filename (str): The filename containing the generated samples data.

The PostProcessor object provides multiple methods. One is the plot_empirical_probabilities method, which generates a plot of the empirical probabilities for different tolerances.

postprocessor.plot_empirical_probabilities(dpi=10, layout="32", tols=[1,2,3,4,5,6], running=False)

Parameters:

  • dpi (int): Resolution of the plot, in dots per inch.
  • layout (str): Layout of the plot, specified as a string. Must be one of ["13", "23", "32", "22"] (default is "23").
  • tols (list): List of tolerances for computing empirical probabilities (default is [1,2,3,4,5,6]).
  • running (bool): Whether to display the plot with a running average or not (default is False).

Another method is compute_tables, which generates tables of empirical means and standard deviations.

postprocessor.compute_tables(measured=[K], dpi=100, mode="mean", running="True")

Parameters:

  • measured (list): List with iteration counts to measure the empirical probabilities.
  • dpi (int): Resolution of the plot, in dots per inch.
  • mode (str): Mode for computing the tables, specified as a string. Must be one of ["mean", "std", "best"] (default is "mean").
  • running (bool): Whether to display the results are computed with a running average or not (default is True).