https://github.com/thorstenwagner/traj

Library for diffusion trajectory analysis
https://github.com/thorstenwagner/traj

diffusion diffusion-coefficient diffusion-trajectory monte-carlo-simulation simulation trajectory-analysis

Last synced: 3 months ago
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Library for diffusion trajectory analysis

• Host: GitHub
• URL: https://github.com/thorstenwagner/traj
• Owner: thorstenwagner
• License: mit
• Created: 2015-12-03T15:42:16.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2017-05-04T19:16:35.000Z (over 8 years ago)
• Last Synced: 2025-07-20T05:12:05.567Z (3 months ago)
• Topics: diffusion, diffusion-coefficient, diffusion-trajectory, monte-carlo-simulation, simulation, trajectory-analysis
• Language: Java
• Size: 323 KB
• Stars: 12
• Watchers: 4
• Forks: 4
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: license.txt

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README

          
[![DOI](https://zenodo.org/badge/18649/thorstenwagner/TraJ.svg)](https://zenodo.org/badge/latestdoi/18649/thorstenwagner/TraJ) [![Build Status](https://travis-ci.org/thorstenwagner/TraJ.svg?branch=master)](https://travis-ci.org/thorstenwagner/TraJ)



 



# TraJ

Java library for diffusion trajectory (2D) analysis. 

# Features

## Trajectory characterization

- Diffusion coefficient via covariance estimator [1]

- Diffusion coefficient via regression estimator 

- Hydrodynamic diameter by Stokes-Einstein converter

- Aspect ratio

- Asymmetry features [7][10]

- Center of gravity

- Efficency [6]

- Elongation

- Exponent in power law fit to MSD curve [4]

- Fractal path dimension [2]

- Gaussianity [9]

- Kurtosis [6]

- Maximum distance between two positions

- Maximum distance for given timelag

- Mean speed [11]

- Mean squared displacment curve curvature [3]

- Mean squared displacment

- Short-time long-time diffusion coefficent ratio 

- Skeweness [6]

- Spline curve analysis features according to [5]

![Spline fit](https://dl.dropboxusercontent.com/u/560426/traj/splinefit.png "Spline fit")

- Standard deviation in direction

- Trapped probability [7]

## Simulation

- Brownian motion (free diffusion)

- Active Transport

- Confined diffusion

- Anomalous diffusion with fixed obstacles (spheres)

- Anomalous diffusion by weierstrass-mandelbrot approach [8]

## Other

- Global linear drift calculator

- Static drift corrector

- Trajectories are combineable

  

#Maven artifacts

TraJ can be found on maven central:

```

    de.biomedical-imaging.TraJ

    traj

    MOST RECENT RELEASE

```

  

References:

[1] C. L. Vestergaard, P. C. Blainey, and H. Flyvbjerg, “Optimal estimation of diffusion coefficients from single-particle trajectories,” Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys., vol. 89, no. 2, p. 022726, Feb. 2014.

[2] M. J. Katz and E. B. George, “Fractals and the analysis of growth paths,” Bull. Math. Biol., vol. 47, no. 2, pp. 273–286, 1985.

[3] S. Huet, E. Karatekin, V. S. Tran, I. Fanget, S. Cribier, and J.-P. Henry, “Analysis of transient behavior in complex trajectories: application to secretory vesicle dynamics.,” Biophys. J., vol. 91, no. 9, pp. 3542–3559, 2006.

[4] D. Arcizet, B. Meier, E. Sackmann, J. O. Rädler, and D. Heinrich, “Temporal analysis of active and passive transport in living cells,” Phys. Rev. Lett., vol. 101, no. 24, p. 248103, Dec. 2008.

[5] Spatial structur analysis of diffusive dynamics according to: B. R. Long and T. Q. Vu, “Spatial structure and diffusive dynamics from single-particle trajectories using spline analysis,” Biophys. J., vol. 98, no. 8, pp. 1712–1721, 2010.

[6] Helmuth, J.A. et al., 2007. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells. Journal of structural biology, 159(3), pp.347–58.

[7] Saxton, M.J., 1993. Lateral diffusion in an archipelago. Single-particle diffusion. Biophysical Journal, 64(6), pp.1766–1780.

[8] Guigas, G. & Weiss, M., 2008. Sampling the Cell with Anomalous Diffusion—The Discovery of Slowness. Biophysical Journal, 94(1), pp.90–94.

[9] Ernst, D., Köhler, J. & Weiss, M., 2014. Probing the type of anomalous diffusion with single-particle tracking. Physical chemistry chemical physics : PCCP, 16(17), pp.7686–91.

[10] Helmuth, J.A. et al., 2007., A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells., Journal of structural biology, 159(3), pp.347–58.

[11] Meijering, Erik; Dzyubachyk, Oleh; Smal, Ihor (2012): „Methods for Cell and Particle Tracking“. In: Imaging and Spectroscopic Analysis of Living Cells - Optical and Spectroscopic Techniques., S. 183-200, DOI: 10.1016/b978-0-12-391857-4.00009-4.

To Do:

- Size distribution estimation for trajectory sets according to: J. G. Walker, “Improved nano-particle tracking analysis,” Meas. Sci. Technol., vol. 23, no. 6, p. 065605, Jun. 2012. (Already implemented in NanoTrackJ - I just have to port it)

- Simulation: Add anomalous diffusion with brownian motion obstacles and Ornstein-Uhlenbeck obstacles