def trackmsd(track, dt0, dtau):
""" trackmsd(track, dt0, dtau)
finds the track msd, as function of tau,
averaged over t0, for one track (worldline)
"""
trackdots = data[trackids==track]
if dt0 == dtau == 1:
if verbose: print "Using correlation"
xy = np.column_stack([trackdots['x'], trackdots['y']])
return corr.msd(xy, ret_taus=True)
trackbegin, trackend = trackdots['f'][[0,-1]]
tracklen = trackend - trackbegin + 1
if verbose:
print "tracklen =",tracklen
print "\t from %d to %d"%(trackbegin, trackend)
if isinstance(dtau, float):
taus = helpy.farange(dt0, tracklen, dtau)
elif isinstance(dtau, int):
taus = xrange(dtau, tracklen, dtau)
tmsd = []
for tau in taus: # for tau in T, by factor dtau
#print "tau =", tau
avg = t0avg(trackdots, tracklen, tau)
#print "avg =", avg
if avg > 0 and not np.isnan(avg):
tmsd.append([tau,avg[0]])
if verbose:
print "\t...actually", len(tmsd)
return tmsd
def trackmsd(track, dt0, dtau):
""" finds the mean squared displacement as a function of tau,
averaged over t0, for one track (particle)
parameters
----------
track : a single integer giving the track id to be calculated
dt0 : spacing stepsize for values of t0, gives the number of starting
points averaged over in `t0avg`
dtau : spacing stepsize for values of tau, gives the spacing of the
points in time at which the msd is evaluated
For dt0, dtau: Small values will take longer to calculate without
adding to the statistics. Large values calculate faster but give
worse statistics. For the special case dt0 = dtau = 1, a
correlation is used for a signicant speedup
returns
-------
a list of tuples (tau, msd(tau)) the value of tau and the mean squared
displacement for a single track at that value of tau
"""
trackdots = data[trackids==track]
if dt0 == dtau == 1:
if verbose: print "Using correlation"
xy = np.column_stack([trackdots['x'], trackdots['y']])
return corr.msd(xy, ret_taus=True)
trackbegin, trackend = trackdots['f'][[0,-1]]
tracklen = trackend - trackbegin + 1
if verbose:
print "tracklen =",tracklen
print "\t from %d to %d"%(trackbegin, trackend)
if isinstance(dtau, float):
taus = helpy.farange(dt0, tracklen, dtau)
elif isinstance(dtau, int):
taus = xrange(dtau, tracklen, dtau)
tmsd = []
for tau in taus: # for tau in T, by factor dtau
#print "tau =", tau
avg = t0avg(trackdots, tracklen, tau)
#print "avg =", avg
if avg > 0 and not np.isnan(avg):
tmsd.append([tau,avg[0]])
if verbose:
print "\t...actually", len(tmsd)
return tmsd
def trackmsd(trackset, dt0, dtau):
""" finds the mean squared displacement as a function of tau,
averaged over t0, for one track (particle)
parameters
----------
trackset : a subset of the data for a given track
dt0 : spacing stepsize for values of t0, gives the number of starting
points averaged over in `t0avg`
dtau : spacing stepsize for values of tau, gives the spacing of the
points in time at which the msd is evaluated
For dt0, dtau: Small values will take longer to calculate without
adding to the statistics. Large values calculate faster but give
worse statistics. For the special case dt0 = dtau = 1, a
correlation is used for a signicant speedup
returns
-------
a list of tuples (tau, msd(tau)) the value of tau and the mean squared
displacement for a single track at that value of tau
"""
if dt0 == dtau == 1:
xy = helpy.consecutive_fields_view(trackset, 'xy')
return corr.msd(xy, ret_taus=True)
trackbegin, trackend = trackset['f'][[0,-1]]
tracklen = trackend - trackbegin + 1
if verbose:
print "length {} from {} to {}".format(tracklen, trackbegin, trackend)
if isinstance(dtau, float):
taus = helpy.farange(dt0, tracklen, dtau)
elif isinstance(dtau, int):
taus = xrange(dtau, tracklen, dtau)
tmsd = []
for tau in taus:
avg = t0avg(trackset, tracklen, tau)
if avg > 0 and not np.isnan(avg):
tmsd.append([tau,avg[0]])
if verbose:
print "\t...actually", len(tmsd)
return tmsd
