Drag Force Particle Tracking Automatic

Tracks brightfield particles across multiple stage speeds and outputs centroid data for drag force analysis.

Workflow Overview

Load images — Scans a main folder for speed subfolders (e.g. 0.1, 0.15, 0.2, 0.25). Each subfolder contains a TIFF image sequence for one stage speed. Images are converted to grayscale, cropped, and inverted.
Set tracking parameters — Configure the number of particles, bounding radius, mask radius, and initial ring radius guess in the setup cell.
Click particle centers — An interactive figure opens for each speed. Click the center of each particle in frame 0. Clicks are stored and used as the initial guess for tracking.
Test run — Tracks the first 10 frames per speed to verify settings before committing to a full run.
Verify — Overlays the fitted rings on frame 0 for each speed. If the circles look off, adjust click positions or tracking parameters and re-run the test.
Full tracking — Runs the centroid-fitting algorithm across all frames for every speed. Each frame uses the previous frame's result as the next initial guess.
Save output — Choose an output folder using the widget. Saves one CSV and one JSON metadata file per speed.

Parameter	Description
`MAIN_FOLDER`	Path to the folder containing speed subfolders
`TIFF_PATTERN`	Glob pattern for images (default: `*.tiff`)
`crop_dimensions`	Pixel crop region applied to every frame
`num_particles`	Number of particles to track per frame
`particle_r_bound`	Search radius (px) around each particle centroid
`mask_r`	Masks bright pixels near the particle center (px)
`r0`	Initial guess for the bright ring radius (px)

For each speed subfolder, two files are saved:

{speed} mms.csv — No header. Each row is one frame. Columns are cx0, cy0, r0, cx1, cy1, r1, cx2, cy2, r2, ... (3 columns per particle).
{speed} mms.json — Metadata: tracking parameters and initial click positions.

numpy, pandas, scipy, scikit-image, pims, trackpy, matplotlib, ipywidgets, ipympl

Install missing packages with pip install <package>.

Load the output CSVs into Drag_auto.ipynb to select stationary/movement regions interactively and compute drag force vs. displacement per particle.