| Summary: | Set of serialised Python datasets, whose analysis is presented in the linked TC submission, and the source files that led to their production. Python files: * model code, scattering2d.py; * script using it to produce the raw data, launcher.py; * tools to analyse the raw data, atools.py; * script running these tools, analyzer.py; * script running Kolmogorov-Smirnov analyses, ks_bootstrap.py. Serialised files (through the standard Python module pickle): * results.zip holds two pickled Pandas dataframes: * * results_mono_dataframe.pickle corresponds to the `dataframe` field of the `Analyzer` object implemented in atools.py; * * results_combining_dataframe.pickle corresponds to the `combiners` field of the `Analyzer` object implemented in atools.py. * processed_results.zip holds a pickled dictionary: * * key `histograms` is a Pandas dataframe with preprocessed histograms counts; * * key `lognormal` is a Pandas dataframe with preprocessed lognormal fits; * * key `rnd_idx` is a dictionary, indexed by the row index of either of the two aforementionned dataframes, linking to these results the raw legnths used to produce them (from results_mono_dataframe.pickle). * ks_statistics.zip holds a collection of dictionaries of KS distances as described in the paper.
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