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 (RunEveryParameter.py) Convenience script for running RunEveryParameter.py in background and logging all output (RunSingleParametesBash.sh) Log file for output of sampling from the model for each parameter in a loop (logAll.txt) Jupyter notebooks of the analysis run on <em>epLsar</em> as an example (Notebook_SingleParameter.inpyb) and of a summary of the whole analysis (Notebook_Overview.ipynb), plus associated HTML output files (*.html) For each parameter: Full samples of parameter values (*.pkl) Energy plots of Hamiltonian Monte Carlo (*_Energy.pdf) Contrast plots between each treatment (BrushDirt = Is_Is, BrushNoDirt = Is_No, RubDirt = No_Is) and the control (No_No) (*_Contrasts.pdf) Trace plots for each parameter (*_Trace.pdf) Distribution of posteriors for each parameter (*_Posterior.pdf) Prior and posterior predictive distributions for each parameter (*_PriorPosterior.pdf) Instructions to download all files at once are given here: https://doi.org/10.5281/zenodo.4011952