trackpy-particle-tracking
Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.
npx skills add BioTender-max/awesome-bio-agent-skills --skill trackpy-particle-tracking --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# trackpy ## Overview trackpy is a Python library for single-particle tracking (SPT) in video microscopy. It implements the Crocker-Grier algorithm to locate bright spots in each frame with subpixel precision, then links those positions across frames into continuous trajectories. From trajectories, trackpy computes mean squared displacement (MSD), diffusion coefficients, and motion classifications (confined, normal, directed). It handles 2D fluorescence videos, 3D confocal z-stacks, and large image sequences via memory-efficient streaming through the pims image reader library. ## When to Use - You have a fluorescence microscopy video of labeled particles (quantum dots, fluorescent beads, vesicles, receptors) and need to extract individual trajectories and diffusion coefficients. - You want to measure particle mobility: compute MSD curves and distinguish Brownian diffusion, directed motion, or confined motion from single-particle tracks. - You are analyzing colloid dynamics, lipid membrane diffusion, intracellular cargo transport, or virus-cell interactions where you need per-particle trajectory data. - You need 3D tracking from confocal z-stack time series to capture out-of-plane m
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: tp.locate() — Single-Frame Particle Detection
- Module 2: tp.batch() — Multi-Frame Detection
- Module 3: tp.link() — Trajectory Linking
- Module 4: tp.filterstubs() — Short-Track Removal
- Module 5: MSD Analysis — tp.imsd() and tp.emsd()
- Module 6: Motion Analysis — Characterize and Drift Correction
- Common Workflows
- Workflow 1: Full 2D Tracking Pipeline with MSD and Diffusion Coefficient
- Workflow 2: 3D Particle Tracking from Confocal Z-Stacks
pip install trackpy pims pandas numpy matplotlib scipy For reading multi-channel or proprietary formats: pip install pims[bioformats] # Bioformats via JPype pip install aicsimageio # ND2, CZI, LIF via AICSImageIO
What does the trackpy-particle-tracking skill do?
Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill trackpy-particle-tracking --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.