def compile_noise(prefixes, vs, width=3, side=1, fps=1, cat=True,
do_orientation=True, do_translation=True, subtract=True,
minlen=10, torient=True, interp=True, dupes=False, **ignored):
if np.isscalar(prefixes):
prefixes = [prefixes]
for prefix in prefixes:
if args.verbose:
print "Loading data for", prefix
data = helpy.load_data(prefix, 'tracks')
if dupes:
data['t'] = tracks.remove_duplicates(data['t'], data)
tracksets = helpy.load_tracksets(data, min_length=minlen,
run_track_orient=torient, run_fill_gaps=interp)
for track in tracksets:
tdata = tracksets[track]
velocities = noise_derivatives(tdata, width=width,
side=side, fps=fps, do_orientation=do_orientation,
do_translation=do_translation, subtract=subtract)
for v in velocities:
vs[v].append(velocities[v])
if cat:
for v in vs:
vs[v] = np.concatenate(vs[v], -1)
return len(tracksets)
def remove_duplicates(trackids=None, data=None, tracksets=None,
target='', inplace=False, verbose=False):
if tracksets is None:
target = target or 'trackids'
tracksets = helpy.load_tracksets(data, trackids, min_length=0)
elif trackids is None:
target = target or 'tracksets'
else:
target = target or 'trackids'
rejects = defaultdict(dict)
for t, tset in tracksets.iteritems():
fs = tset['f']
count = np.bincount(fs)
dup_fs = np.where(count>1)[0]
if not len(dup_fs):
continue
ftsets = helpy.splitter(tset, fs, ret_dict=True)
for f in dup_fs:
prv = fs[np.searchsorted(fs, f, 'left') - 1] if f > fs[0] else None
nxt = fs[np.searchsorted(fs, f, 'right')] if f < fs[-1] else None
if nxt is not None and nxt in dup_fs:
nxt = fs[np.searchsorted(fs, nxt, 'right')] if nxt < fs[-1] else None
if nxt is not None and nxt in dup_fs:
nxt = None
assert prv is not None, ("Duplicate track particles in too many "
"frames in a row at frame {} for track {}".format(f, t))
seps = np.zeros(count[f])
for neigh in (prv, nxt):
if neigh is None: continue
if count[neigh] > 1 and neigh in rejects[t]:
isreject = np.in1d(ftsets[neigh]['id'], rejects[t][neigh], assume_unique=True)
ftsets[neigh] = ftsets[neigh][~isreject]
sepx = ftsets[f]['x'] - ftsets[neigh]['x']
sepy = ftsets[f]['y'] - ftsets[neigh]['y']
seps += sepx*sepx + sepy*sepy
rejects[t][f] = ftsets[f][seps > seps.min()]['id']
if not rejects:
return None if inplace else trackids if target=='trackids' else tracksets
if target=='tracksets':
if not inplace:
tracksets = tracksets.copy()
for t, tr in rejects.iteritems():
trs = np.concatenate(tr.values())
tr = np.in1d(tracksets[t]['id'], trs, True, True)
new = tracksets[t][tr]
if inplace:
tracksets[t] = new
return None if inplace else tracksets
elif target=='trackids':
if not inplace:
trackids = trackids.copy()
rejects = np.concatenate([tfr for tr in rejects.itervalues()
for tfr in tr.itervalues()])
if data is None:
data_from_tracksets = np.concatenate(tracksets.values())
if len(data_from_tracksets)!=len(trackids):
raise ValueError, "You must provide data to return/modify trackids"
ids = data_from_tracksets['id']
ids.sort()
else:
ids = data['id']
rejects = np.searchsorted(ids, rejects)
trackids[rejects] = -1
return None if inplace else trackids
msg = 'No tifs found at the following pattern, please fix it\n{}\n'
pattern = raw_input(msg.format(pattern))
imfiles = glob(pattern)
meta['path_to_tiffs'] = pattern
frange = raw_input("Number or range (as slice: 'start:end') of frames to view? "
"({} available) ".format(len(imfiles)))
fslice = slice(*[int(s) if s else None for s in frange.split(':')])
imstack = map(plt.imread, sorted(imfiles)[fslice])
datas = helpy.load_data(absprefix, 't c o')
fsets = map(lambda d: helpy.splitter(d, datas[0]['f']), datas)
animate_detection(imstack, *fsets, rc=args.rcorner, side=args.side,
verbose=args.verbose)
if args.msd or args.nn or args.rn:
tracksets = helpy.load_tracksets(data, min_length=args.stub,
run_fill_gaps=True, verbose=args.verbose)
if args.msd:
msds, msdids = find_msds(tracksets, dt0, dtau, min_length=args.stub)
if args.save:
save = absprefix+"_MSD.npz"
print "saving msd data to", save
np.savez(save,
msds = np.asarray(msds),
msdids = np.asarray(msdids),
dt0 = np.asarray(dt0),
dtau = np.asarray(dtau))
elif args.plotmsd or args.rr:
if verbose: print "loading msd data from npz files"
datapath = absprefix+"_MSD.npz"
msdnpz = np.load(datapath)
if W is None:
data = helpy.load_data(args.prefix)
N, W = square_size(helpy.mode(data['f'][data['t'] >= 0], count=True))
meta['crystal_width'] = W
N = W*W
nshells = (W+1)//2
print args.prefix
print "Crystal size {W}x{W} = {N} ({s} shells)".format(W=W, N=N, s=nshells)
if args.save:
helpy.save_meta(args.prefix, meta)
if args.melt:
print 'calculating'
data = helpy.load_data(args.prefix)
tsets = helpy.load_tracksets(data, run_repair='interp',
run_track_orient=True)
# to get the benefits of tracksets (interpolation, stub filtering):
data = np.concatenate(tsets.values())
data.sort(order=['f', 't'])
if not args.start:
args.start = find_start_frame(data, plot=args.plot)
mdata = melt_analysis(data)
if args.save:
np.savez_compressed(args.prefix + '_MELT.npz', data=mdata)
helpy.save_meta(args.prefix, meta, start_frame=args.start)
else:
mdata = np.load(args.prefix + '_MELT.npz')['data']
if args.plot:
stats = ['dens', 'psi', 'phi']
plot_args = make_plot_args(nshells, args)
bgimage = pl.imread(locdir+prefix+'_001.tif')
except IOError:
bgimage = None
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:
bgimage = Im.open(extdir+prefix+'_0001.tif')
except IOError:
try:
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"
['side', 'fps'], ['sidelength', 'fps'], [1, 1])
compile_args = dict(args.__dict__)
if args.prefix == 'simulate':
import simulate as sim
spar = {'DR': 1/21, 'v0': 0.3678, 'DT': 0.01,
'fps': args.fps, 'side': args.side, 'size': 1000}
print spar
sdata = [sim.SimTrack(num=i, **spar)
for i in xrange(1, 1001)]
data = np.concatenate([sdatum.track for sdatum in sdata])
data['id'] = np.arange(len(data))
data = {'simulate': data}
else:
data = find_data(args)
tsets = {prefix: helpy.load_tracksets(
data[prefix], min_length=args.stub, verbose=args.verbose,
run_remove_dupes=args.dupes, run_repair=args.gaps,
run_track_orient=args.torient)
for prefix in data}
label = {'o': r'$\xi$', 'par': r'$v_\parallel$', 'perp': r'$v_\perp$',
'etapar': r'$\eta_\parallel$', 'x': '$v_x$', 'y': '$v_y$'}
ls = {'o': '-', 'x': '-.', 'y': ':',
'par': '-.', 'perp': ':', 'etapar': '--'}
cs = {'mean': 'r', 'var': 'g', 'std': 'b'}
if __name__ == '__main__':
if 'widths' in args.command:
command_widths(tsets, compile_args, args)
if 'autocorr' in args.command:
command_autocorr(tsets, args)
if 'hist' in args.command:
