if not (args.rn or args.rr) and args.show: pl.show()
if __name__=='__main__' and args.rn:
# Calculate the <rn> correlation for all the tracks in a given dataset
# TODO: fix this to combine multiple datasets (more than one prefix)
if not args.nn:
# if args.nn, then these have been loaded already
data, trackids, odata, omask = helpy.load_data(prefix, True, False)
tracksets, otracksets = helpy.load_tracksets(data, trackids, odata, omask,
min_length=max(100, args.stub))
corr_args = {'side': 'both', 'ret_dx': True,
'cumulant': (True, False), 'norm': 0 }
xcoscorrs = [ corr.crosscorr(tracksets[t]['x']/S, np.cos(otracksets[t]),
**corr_args) for t in tracksets ]
ysincorrs = [ corr.crosscorr(tracksets[t]['y']/S, np.sin(otracksets[t]),
**corr_args) for t in tracksets ]
# Align and merge them
fmax = int(2*fps/(D_R if args.nn else 12))
fmin = -fmax
rncorrs = xcoscorrs + ysincorrs
# TODO: align these so that even if a track doesn't reach the fmin edge,
# that is, if f.min() > fmin for a track, then it still aligns at zero
rncorrs = helpy.pad_uneven([
rn[np.searchsorted(f, fmin):np.searchsorted(f, fmax)]
for f, rn in rncorrs if f.min() <= fmin ],
np.nan)
tcorr = np.arange(fmin, fmax)/fps
meancorr = np.nanmean(rncorrs, 0)
save = absprefix+'_nn-corr.pdf'
print 'saving <nn> correlation plot to', save
plt.savefig(save)
if not (args.rn or args.rr) and args.show: plt.show()
if __name__=='__main__' and args.rn:
# Calculate the <rn> correlation for all the tracks in a given dataset
# TODO: fix this to combine multiple datasets (more than one prefix)
if not args.nn:
D_R = 1/12
corr_args = {'side': 'both', 'ret_dx': True,
'cumulant': (True, False), 'norm': 0 }
xcoscorrs = [ corr.crosscorr(trackset['x']/S, np.cos(trackset['o']),
**corr_args) for trackset in tracksets.values() ]
ysincorrs = [ corr.crosscorr(trackset['y']/S, np.sin(trackset['o']),
**corr_args) for trackset in tracksets.values() ]
# Align and merge them
fmax = int(2*fps/D_R*args.zoom)
fmin = -fmax
rncorrs = xcoscorrs + ysincorrs
# TODO: align these so that even if a track doesn't reach the fmin edge,
# that is, if f.min() > fmin for a track, then it still aligns at zero
rncorrs = helpy.pad_uneven([
rn[np.searchsorted(f, fmin):np.searchsorted(f, fmax)]
for f, rn in rncorrs if f.min() <= fmin ],
np.nan)
tcorr = np.arange(fmin, fmax)/fps
meancorr = np.nanmean(rncorrs, 0)
print 'saving to', save
pl.savefig(save)
if __name__=='__main__' and args.rn:
# Calculate the <rn> correlation for all the tracks in a given dataset
# TODO: fix this to combine multiple datasets (more than one prefix)
if not args.nn:
# if args.nn, then these have been loaded already
data, trackids, odata, omask = helpy.load_data(prefix, True, False)
tracksets, otracksets = helpy.load_tracksets(data, trackids, odata, omask,
min_length=max(100, args.stub))
corr_args = {'side': 'both', 'ret_dx': True, 'cumulant': True}
xcoscorrs = [ corr.crosscorr(tracksets[t]['x']/S, np.cos(otracksets[t]),
**corr_args) for t in tracksets ]
ysincorrs = [ corr.crosscorr(tracksets[t]['y']/S, np.sin(otracksets[t]),
**corr_args) for t in tracksets ]
# Align and merge them
fmin, fmax = -20, 100
rncorrs = xcoscorrs + ysincorrs
rncorrs = helpy.pad_uneven([
rn[np.searchsorted(t, fmin):np.searchsorted(t, fmax)]
for t, rn in rncorrs ], np.nan)
tcorr = np.arange(fmin, fmax)/fps
meancorr = np.nanmean(rncorrs, 0)
errcorr = np.nanstd(rncorrs, 0)/sqrt(len(rncorrs))
if verbose:
print "Merged rn corrs"
