Typically, .IMS or .TIF files are opened in ImageJ and tracks are marked by the user with the KymoClear plugin. This produces a /kymograph directory that contains subdirectories and files that can be a headache when working with lots of samples. Moreover, things get weird if one doesn't segregate their input files into separate directories to start with. Moreover, the resulting kymographs need to be repackaged for quick upload to KymoButler for processing. Finally, the output from KymoButler can be cumbersome to sort through to generate desired graphs of velocity, flux, and so forth.
This kbresults repo contains three quick functions to address these problems. filetiffs(path) simply takes every TIF in (experiment_directory) and moves it into its own directory with the same name. Then once the images have been marked with KymoClear, sortkymos(experiment_directory) finds all the kymographs in all the subdirectories of the experiment and wraps them into a folder named /4KymoButler; this folder can then be compressed into a ZIP file for upload to KymoButler. The kymographs will be named according to the original movie title, plus the numerical suffix applied to the kymograph by KymoClear.
KymoButler produces an archive of directories and CSV files, organized primarily by sample with no numbering other than the order in which they were processed, and then secondarily by type of data. The sortkbcsv(experiment_directory, kboutput_directory) function will take the (unzipped) archive kboutput_directory and repackage all the files into experiment_directory/Results/ subdirectories by data type (velocity, pause times, etc.) with the original names of the kymographs applied to each CSV. This only works if the 4KymoButler directory is present in the experiment directory and contains all the kymographs in the experiment and no other files.
For Python users who like to work with downstream data as a Pandas dataframe (or take one to export to a CSV file), the summaryframe(experiment_dir, culture='000000') function extracts two dataframes from the experiment directory: one with the velocities/intensities/etc. for each individual vesicle and a second that lists the positive and negative flux for each axon. This also numbers the axons from 1 to N per experimental condition, rather than starting over with "axon 1" each time the data come from a new field of view. The culture variable allows the user to specify an identifier for an experiment such that the dataframe can be concatenated with dataframes from other experiments.
This has been tested with Python 3.6, and requires pandas, os, sys, and numpy libraries. Currently there are no error handling measures. The workflow has been tested with a filenaming convention where each starting TIF has several features separated by the underscore character. The summaryframe function will use the first two features to populate 'var_A' and 'var_B' columns, the third feature to populate a column labeled 'experiment', the fourth feature into a 'field of view' column. See the Examples folder for a notebook demonstrating all the functions in detail.