WARNING

This codebase is still under active development and, as such, is not stable. Expect some functionality not to be complete, and interfaces to be changed without warning.

PySTACHIO SMT, to a first approximation, a Python port of Adam Wollman's Single Molecule Tools[1]. PySTACHIO provides a utility for tracking and analysing localisation microscopy data, providing features for spot tracking and spot intensity & trajectory analysis.

$ pystachio <task> <seed name> [<parameter>=<value>...]

For example:

$ pystachio track data/exp_data
$ pystachio simulate,view data/sim_data num_spots=10
$ pystachio app data/exp_data

(Note: the app task is not yet fully functional)

PySTACHIO SMT can be used in two ways: A Web GUI for interactive use, or a command UI for terminal-based & scripted use. Both interfaces provide the following functionality:

This allows input image data (either from experiment or simulation) to be imported into the program and spots tracked across multiple frames.

This allows spot physical parameters such as ISingle values and diffusions coefficients to be extracted from tracked spots' trajectories & intensities.

This allows pseudo-microscopy data to be simulated based on a number of tunable parameters, such as the number of spots, number of frames, bleaching time, Signal-Noise ratio etc...

PySTACHIO SMT uses a few fairly common libraries, listed in requirements.txt In short, these are:

• Numpy for array handling
• OpenCV for image processing
• Dash/Plotly for a web interface.

Installation of these can be performed as such:

$ pip install -r requirements.txt

After that, just make sure pystachio is in your $PATH somewhere, or just refer to it directly using /path/to/pystachio ....

For the terminal UI, the command can be run as follows:

$ pystachio <tasks> <seedname> [parameters]

Here, <seedname> is used for telling the program which file(s) to read from and write to; and <tasks> is a comma-separated list of the tasks you want to be performed. For example, if you want to track spots in a dataset and then view the result, this can be done with pystachio track,view ....

For a given <seedname>, the files currently recognised are:

• seedname.tif: An input dataset saved as a multi-frame TIFF,
• seedname_trajectories.tsv: A tab-separated value list containing predicted trajectories for seedname.tif
• seedname_simulated_trajectories.tsv: A tab-separated value list containing simulated trajectories for seedname.tif.

The complete list of tasks currently implemented is:

• simulate: Create a simulated psuedo-dataset based upon a set of parameters
• track: Track the spots in a given dataset and produce predicted trajectories for that dataset.
• postprocess: Perform postprocessing on tracked trajectories
• view: View the dataset using the Matplotlib library.
• app: Launch the web application at http://localhost:8050

Information on how to contribute to the PySTACHIO SMT project is available in contributing.md. In short, the process is:

• Clone the repository,
• Create a branch for your new changes (functionality, bug fixes, etc...),
• Develop your changes in your new branch, and
• Create a pull-request to merge the branch back into master.

This code has been developed by Edward Higgins and Jack Shepherd in collaboration with the Physics of Life group at the University of York. For more information, contact Edward at [email protected].

Copyright © 2021, Edward Higgins. Released under the MIT License.

[1] Single Molecule Tools