Evaluating the microscopic effect of brushing stone tools as a cleaning procedure [Python analysis]
Creators
- 1. TraCEr, MONREPOS, RGZM
- 2. Scientific Computing and Bioinformatics, Institute of Computer Science, Johannes Gutenberg University, Mainz
Description
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)
Files
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