def vv_autocorr(vs, normalize=False):
normalize = normalize and 1
fields = helpy.vel_dtype.names
vvs = [corr.autocorr(helpy.consecutive_fields_view(tv, fields),
norm=normalize, cumulant=True)
for pvs in vs.itervalues() for tv in pvs.itervalues()]
vvs, vv, dvv = helpy.avg_uneven(vvs, weight=True)
return [np.array(a, order='C').astype('f4').view(helpy.vel_dtype).squeeze()
for a in vvs, vv, dvv]
def vv_autocorr(vs, normalize=False):
normalize = normalize and 1
fields = helpy.vel_dtype.names
vvs = [
corr.autocorr(helpy.consecutive_fields_view(tv, fields),
norm=normalize,
cumulant=True) for pvs in vs.itervalues()
for tv in pvs.itervalues()
]
vvs, vv, dvv = helpy.avg_uneven(vvs, weight=True)
return [
np.array(a, order='C').astype('f4').view(helpy.vel_dtype).squeeze()
for a in vvs, vv, dvv
]
def vv_autocorr(prefixes, corrlen=0.5, **compile_args):
vs = defaultdict(list)
compile_noise(prefixes, vs, cat=False, **compile_args)
vvs = {}
for v, tvs in vs.iteritems():
vcorrlen = int(corrlen*max(map(len, tvs))) if corrlen < 1 else corrlen
vv = np.full((len(tvs), vcorrlen), np.nan, float)
for i, tv in enumerate(tvs):
ac = corr.autocorr(tv, norm=1, cumulant=False)
vv[i, :len(ac)] = ac[:corrlen]
vvcount = np.isfinite(vv).sum(0)
vv = vv[:, vvcount > 0]
vv = np.nanmean(vv, 0)
dvv = np.nanstd(vv, 0)/np.sqrt(vvcount)
vvs[v] = vv, dvv
return vvs
# Calculate the <nn> correlation for all the tracks in a given dataset
# TODO: fix this to combine multiple datasets (more than one prefix)
if args.verbose:
print 'calculating <nn> correlations for track'
coscorrs = []
sincorrs = []
for t, trackset in tracksets.iteritems():
print t,
o = trackset['o']
if args.verbose > 1:
print o.shape, o.dtype
sys.stdout.flush()
cos = np.cos(o)
sin = np.sin(o)
coscorr = corr.autocorr(cos, cumulant=False, norm=False)
sincorr = corr.autocorr(sin, cumulant=False, norm=False)
coscorrs.append(coscorr)
sincorrs.append(sincorr)
else:
coscorrs = [ corr.autocorr(np.cos(trackset['o']), cumulant=False, norm=False)
for trackset in tracksets.values() ]
sincorrs = [ corr.autocorr(np.sin(trackset['o']), cumulant=False, norm=False)
for trackset in tracksets.values() ]
# Gather all the track correlations and average them
allcorr = coscorrs + sincorrs
allcorr = helpy.pad_uneven(allcorr, np.nan)
tcorr = np.arange(allcorr.shape[1])/fps
meancorr = np.nanmean(allcorr, 0)
added = np.sum(np.isfinite(allcorr), 0)
if args.singletracks:
mask = np.in1d(trackids, args.singletracks)
else:
mask = None
plot_tracks(data, trackids, bgimage, mask=mask,
save=args.save, show=args.show)
if __name__=='__main__' and args.nn:
# Calculate the <nn> correlation for all the tracks in a given dataset
# TODO: fix this to combine multiple datasets (more than one prefix)
data, trackids, odata, omask = helpy.load_data(prefix, True, False)
tracksets, otracksets = helpy.load_tracksets(data, trackids, odata, omask,
min_length=args.stub)
coscorrs = [ corr.autocorr(np.cos(otrackset), cumulant=False, norm=False)
for otrackset in otracksets.values() ]
sincorrs = [ corr.autocorr(np.sin(otrackset), cumulant=False, norm=False)
for otrackset in otracksets.values() ]
# Gather all the track correlations and average them
allcorr = coscorrs + sincorrs
allcorr = helpy.pad_uneven(allcorr, np.nan)
tcorr = np.arange(allcorr.shape[1])/fps
meancorr = np.nanmean(allcorr, 0)
added = np.sum(np.isfinite(allcorr), 0)
errcorr = np.nanstd(allcorr, 0)/np.sqrt(added - 1)
sigma = errcorr + 1e-5*errcorr.std() # add something small to prevent 0
if verbose:
print "Merged nn corrs"
bgimage = Im.open(locdir+prefix+'_001.tif')
except IOError:
bgimage = None
if singletracks:
mask = np.in1d(trackids, singletracks)
plot_tracks(data, trackids, bgimage, mask=mask)
if __name__=='__main__' and args.nn:
# Calculate the <nn> correlation for all the tracks in a given dataset
# TODO: fix this to combine multiple datasets (more than one prefix)
data, trackids, odata, omask = helpy.load_data(prefix, True, False)
tracksets, otracksets = helpy.load_tracksets(data, trackids, odata, omask,
min_length=args.stub)
coscorrs = [ corr.autocorr(np.cos(otrackset), cumulant=False, norm=False)
for otrackset in otracksets.values() ]
sincorrs = [ corr.autocorr(np.sin(otrackset), cumulant=False, norm=False)
for otrackset in otracksets.values() ]
# Gather all the track correlations and average them
allcorr = coscorrs + sincorrs
allcorr = helpy.pad_uneven(allcorr, np.nan)
tcorr = np.arange(allcorr.shape[1])/fps
meancorr = np.nanmean(allcorr, 0)
errcorr = np.nanstd(allcorr, 0)/sqrt(len(allcorr))
if verbose:
print "Merged nn corrs"
# Fit to exponential decay
tmax = 50
