def load_tracksets(data, trackids=None, min_length=10, verbose=False,
run_remove_dupes=False, run_fill_gaps=False, run_track_orient=False):
""" Returns a dict of slices into data based on trackid
"""
if trackids is None:
# copy actually speeds it up by a factor of two
trackids = data['t'].copy()
elif not trackids.flags.owndata:
# copy in case called as ...(data, data['t'])
trackids = trackids.copy()
lengths = np.bincount(trackids+1)[1:]
if min_length > 1:
lengths = lengths >= min_length
longtracks = np.where(lengths)[0]
tracksets = {track: data[trackids==track] for track in longtracks}
if run_remove_dupes:
from tracks import remove_duplicates
remove_duplicates(tracksets=tracksets, inplace=True, verbose=verbose)
if run_fill_gaps:
from tracks import fill_gaps
fill_gaps(tracksets=tracksets, inplace=True, verbose=verbose)
if run_track_orient:
from orientation import track_orient
for track in tracksets:
tracksets[track]['o'] = track_orient(tracksets[track]['o'])
return tracksets
def load_tracksets(data, trackids, odata=None, omask=True, min_length=10):
""" Returns a dict of slices into data based on trackid
"""
if omask is not True:
trackids = trackids[omask]
longtracks = np.where(np.bincount(trackids + 1)[1:] >= min_length)[0]
tracksets = {track: data[(data["lab"] == track) & omask] for track in longtracks}
if odata is not None:
otracksets = {
track: orient.track_orient(odata[(data["lab"] == track) & omask]["orient"], onetrack=True)
for track in longtracks
}
return tracksets, otracksets
else:
return tracksets
def load_tracksets(data, trackids, odata=None, omask=True, min_length=10):
""" Returns a dict of slices into data based on trackid
"""
if omask is not True:
trackids = trackids[omask]
longtracks = np.where(np.bincount(trackids + 1)[1:] >= min_length)[0]
tracksets = {
track: data[(data['lab'] == track) & omask]
for track in longtracks
}
if odata is not None:
otracksets = {
track: orient.track_orient(odata[(data['lab'] == track)
& omask]['orient'],
onetrack=True)
for track in longtracks
}
return tracksets, otracksets
else:
return tracksets
def trackmsd(track, dt0, dtau, data, trackids, odata, omask, mod_2pi=False):
""" trackmsd(track, dt0, dtau, data, trackids, odata, omask)
finds the track msd, as function of tau, averaged over t0, for one track (worldline)
"""
tmask = (trackids==track) & omask
trackdots = data[tmask]
if mod_2pi:
trackodata = odata[tmask]['orient']
else:
from orientation import track_orient
trackodata = track_orient(odata, track, trackids, omask)
if dt0 == dtau == 1:
if verbose: print "Using correlation"
from correlation import msd as corrmsd
return corrmsd(trackodata, 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, trackodata, dt0, mod_2pi=mod_2pi)
#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, data, trackids, odata, omask, mod_2pi=False):
""" trackmsd(track, dt0, dtau, data, trackids, odata, omask)
finds the track msd, as function of tau, averaged over t0, for one track (worldline)
"""
tmask = (trackids == track) & omask
trackdots = data[tmask]
if mod_2pi:
trackodata = odata[tmask]['orient']
else:
from orientation import track_orient
trackodata = track_orient(odata, track, trackids, omask)
if dt0 == dtau == 1:
if verbose: print "Using correlation"
from correlation import msd as corrmsd
return corrmsd(trackodata, 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, trackodata, dt0, mod_2pi=mod_2pi)
#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
