title: Python algorithms in particle tracking microrheology.
abstract: BACKGROUND
  Particle tracking passive microrheology relates recorded trajectories of microbeads, embedded in soft samples, to the local mechanical properties of the sample. The method requires intensive numerical data processing and tools allowing control of the calculation errors.
  
  
  RESULTS
  We report the development of a software package collecting functions and scripts written in Python for automated and manual data processing, to extract viscoelastic information about the sample using recorded particle trajectories. The resulting program package analyzes the fundamental diffusion characteristics of particle trajectories and calculates the frequency dependent complex shear modulus using methods published in the literature. In order to increase conversion accuracy, segmentwise, double step, range-adaptive fitting and dynamic sampling algorithms are introduced to interpolate the data in a splinelike manner.
  
  
  CONCLUSIONS
  The presented set of algorithms allows for flexible data processing for particle tracking microrheology. The package presents improved algorithms for mean square displacement estimation, controlling effects of frame loss during recording, and a novel numerical conversion method using segmentwise interpolation, decreasing the conversion error from about 100% to the order of 1%.
authors.affiliation: Max-Planck Institute for Intelligent Systems, Advanced Materials and Biosystems, Heisenberg str, 3, 70569 Stuttgart, Germany. tamas.haraszti@uni-heidelberg.de.
authors.forename: Timo
authors.initials: T
authors.lastname: Maier
authors.forename: Tamás
authors.initials: T
authors.lastname: Haraszti