Model code and simulation results for the investigation of a wave-generated floe size distribution
<div>Set of serialised Python datasets, whose analysis is presented in the linked TC submission, and the source files that led to their production.</div><div><br></div><div>Python files:</div><div>* model code, scattering2d.py;<br> * script using...
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| مؤلفون آخرون: | |
| منشور في: |
2021
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إضافة وسم
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| الملخص: | <div>Set of serialised Python datasets, whose analysis is presented in the linked TC submission, and the source files that led to their production.</div><div><br></div><div>Python files:</div><div>* model code, scattering2d.py;<br> * script using it to produce the raw data, launcher.py;<br> * tools to analyse the raw data, atools.py;<br> * script running these tools, analyzer.py;<br> * script running Kolmogorov-Smirnov analyses, ks_bootstrap.py.</div><div><br></div><div>Serialised files (through the standard Python module pickle):</div><div>* results.zip holds two pickled Pandas dataframes:</div><div>* * results_mono_dataframe.pickle corresponds to the `dataframe` field of the `Analyzer` object implemented in atools.py;</div><div>* * results_combining_dataframe.pickle corresponds to the `combiners` field of the `Analyzer` object implemented in atools.py.</div><div>* processed_results.zip holds a pickled dictionary:</div><div>* * key `histograms` is a Pandas dataframe with preprocessed histograms counts;</div><div>* * key `lognormal` is a Pandas dataframe with preprocessed lognormal fits;</div><div>* * 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).</div><div>* ks_statistics.zip holds a collection of dictionaries of KS distances as described in the paper.<br></div> |
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