trackpy.is_typical¶

trackpy.is_typical(msds, frame, lower=0.1, upper=0.9)¶

Identify which paritcles’ MSDs are in the central quantile.

Parameters:

Parameters:	msds : DataFrame This should be organized like the output of imsd(). Columns correspond to particles, indexed by lagtime in frames. frame : integer Compare MSDs at this lag interval. lower : float between 0 and 1, default 0.1 Probes with MSD up to this quantile are deemed outliers. upper : float between 0 and 1, default 0.9 Probes with MSD above this quantile are deemed outliers.
Returns:	Series of boolean values, indexed by particle number True = typical particle, False = outlier particle

msds : DataFrame

This should be organized like the output of imsd(). Columns correspond to particles, indexed by lagtime in frames.

frame : integer

Compare MSDs at this lag interval.

lower : float between 0 and 1, default 0.1

Probes with MSD up to this quantile are deemed outliers.

upper : float between 0 and 1, default 0.9

Probes with MSD above this quantile are deemed outliers.

Returns:

Series of boolean values, indexed by particle number

True = typical particle, False = outlier particle

Examples

>>> m = tp.imsd(traj, MPP, FPS)
>>> # Index by particle ID, slice using boolean output from is_typical(), and then
>>> # restore the original index, frame number.
>>> typical_traj = traj.set_index('particle').ix[is_typical(m)]    .reset_index().set_index('frame', drop=False)