w. Solvay Korea, Ewha Research Team
tsmoothie_denoising.py: Denoising ("smoothing") the battery retention curves using a LOWESS smoother (Python tsmoothie package)
- Input: Raw battery data containing columns ‘battery_file_id’, 'DischargeCapacityRetention', 'Cyc_’, etc.
- Output: Plot visualizations of discharge capacity retention for all battery ids and the csv files of the smoothing results. The original curves are in blue, and the smoothed curves are red.
- Output example
statistical_test.py: Performing Fischer’s exact test (or chi-squared test)
- Input: profiling data that was put together for descriptive analysis of formed clusters
- Output: Table images (values of significance highlighted in green)
- Output example
SMOTEN_oversampling.py: Oversampling the clustered data while accounting for component distributions (Python SMOTEN package)
- Input: Clustering result with meta data containing battery id, cluster, and components
- Output: 29 plots and 1 csv file
- Output example
