Evaluating the microscopic effect of brushing stone tools as a cleaning procedure [Python analysis]

This upload includes the following files related to the Python analysis: - Raw data as a XLSX table (brushing_v2.xlsx), i.e. results from R Script #1 (see https://doi.org/10.5281/zenodo.3632517) - Python script of the whole analysis (BrushingDirt_Analysis.py) - Jupyter notebook files of the analysis run on epLsar as an example (NotebookBrushingDirt_4Level.inpyb) and of a summary of the whole analysis (NotebookBrushingDirt_Overview_4LevelPlots.ipynb), and associated HTML output files (*.html). - Full samples of parameter values for each parameter (*.pkl) - Energy plots of Hamiltonian Monte Carlo for each parameter, as PDF files (*_Energy.pdf) - Contrast plots between each treatment (No_Is, Is_No, Is_Is) and the control (No_No) for each parameter (*_Contrasts.pdf) - Trace plots for each parameter (*_Trace.pdf) - Distribution of posteriors for each parameter (*_Posterior.pdf)