Drag Force Particle Tracking

Two-notebook pipeline for measuring drag force on brightfield particles in a viscous fluid.

1. Drag Force Particle Tracking Automatic.ipynb — Track particle centroids from TIFF image sequences
2. Drag_auto.ipynb — Load tracking output, define movement regions, and compute drag force vs. displacement

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Part 1 — Particle Tracking

Workflow

1. 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.

2. Set tracking parameters — Configure the number of particles, bounding radius, mask radius, and initial ring radius guess in the setup cell.

3. 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.

4. Test run — Tracks the first 10 frames per speed to verify settings before committing to a full run.

5. 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.

6. 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.

7. Save output — Choose an output folder using the widget. Saves one CSV and one JSON metadata file per speed.

Key Parameters

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)

Output Format

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.

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Part 2 — Drag Force Analysis

Workflow

1. Load CSVs — Reads all *.csv files from a folder (output from Part 1). Automatically detects the number of particles from the column count.

2. Set regions (slider cell) — For each speed, an interactive plot shows the x-position traces for all particles over time. Use the three sliders to define:

   • Stationary — frames where the stage is not moving (baseline)
   • Movement 1 — first direction of stage movement
   • Movement 2 — second direction of stage movement (return)

   Click Confirm All & Continue when all speeds are set.

3. Analyze — Computes the mean displacement of each particle relative to its stationary baseline and the expected Stokes drag force at each speed. Fits a linear spring constant k (pN/µm) per particle per direction through the origin.

Key Parameters

Parameter	Description
folder	Path to folder containing {speed} mms.csv files
PIXELS_PER_MICRON	Pixel-to-micron conversion factor
ETA	Fluid viscosity (Pa·s); default: 0.001 (water)
R	Particle radius (m); default: 1e-6 (1 µm)
TRIM	Frames trimmed from each edge of a region to remove transients

Output Plot

• Color = particle (each particle gets a unique color)
• Filled circles = Direction 1, open circles = Direction 2
• Solid fit line = Direction 1, dotted fit line = Direction 2
• Separate origin-constrained fit per particle per direction with 95% confidence interval shading
• Printed spring constant k ± σ (pN/µm) for each particle and direction

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Dependencies

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

Install missing packages with pip install <package>.