This repository contains Python libraries in the 'bin' folder, as well as documentation for some of the libraries.
Checking the validity and other properties of sessions uses the 'clean_data' module. Documentation is here.
The defaults for cleaning have been chosen through analysis of the source of problematic data. If we resolve some of the issues in data, default values should be changed.
Doing meta-analysis on sessions to identify problems, causes, and trends can be done with the 'directory_cleaner' module, which can do analysis on all or some charging sessions in a directory. Charging sessions can be in '.txt' or '.pkl' file formats.
An Excel spreadsheet is provided to see a selection of pre-made charts and PivotTables for your data. Each sheet in the template has multiple charts. Zoom out or scroll right and down to see more charts. PivotTables and PivotCharts can be easily changed to visualize or find anything.
The results from 'directory_cleaner' can be exported to an Excel file. Then, copy and paste the output of 'directory_cleaner' into the 'Data' tab of the pre-made Excel sheet. Go to a PivotTable and select "Refresh All" in the PivotTable>Analyze>Data section.
Caltech data from 2017 to March 2018 is currently inserted.
The 'visualize_profiles' module can plot and capture one or many '.pkl' profiles in a directory using matplotlib.