...

When zooming into a relatively more stable part of the graph such as this one, the data generally seems to be hovering around 47.0 g with significant “noise” between the data points. Only two outliers were reported for this section, which could indicate that there may have been an equipment issue when dealing with the later cells.

Final Results

Before showing the last results, there were a few cells which had completely invalid data across all 5 categories. This is not to be confused with the cells that only had invalid weight data (those will be published at a later point). These cells were:

• 128

• 152

• 554

• 658

• 659

• 696

• 727

• 815

• 818

• 837

• 958

• 998

• 1006

• 1022

• 1027

• 1036

• 1059

The algorithm calculated scores for each cell that was an outlier for each category. Here are all the scores:

...

Furthermore, I would recommend tackling the identification of outliers on an interval-by-interval basis (as explained in the OCV test section). Running the algorithm for the entire dataset seems to have only extracted the truly extreme outliers, but a closer inspection of each 100-cell interval could reveal more, potentially unsuitable cells that aren’t as apparent. I can begin working on this soon. In the meantime, here are the codes (the first one is the algorithm for all the cells; the second gives data for whichever one cell you choose).

Note that to run these codes, certain lines have to be changed depending on where you keep the CSV files with the raw data. These lines have comments on them, but feel free to reach out to me if assistance is required.