The software can save data in a number of formats, but we would like to draw particular attention to the pickle, which saves a Python dictionary containing the data as a binary file. While this is no longer human readable in its raw form (vs JSON or a CSV file), it is significantly more optimized for loading and analysis speed. For larger datasets of several hundred files this cuts the speed of analysis and plotting from hours to seconds. Care must be taken to only load pickle files from known sources -malicious executable code can be embedded in a pickle file. The major drawback of the pickle format is that it loads the entire file at once and cannot break up files into smaller chunks. As a result, data is saved during experiments as an SQlite database to avoid memory limits or time overhead associated with repeated save/load cycles. At large enough data sizes (i.e. when the size of individual files to analyze approaches the computer's memory), SQlite becomes the faster method and should also be used for analysis. We note that this situation is unlikely to arise in normal usage but could be possible for large scans with long measurement times and very high time resolution.