def load_and_process(self):
self.pdata = helpy.load_data(self.prefix, 'p')
self.x0, self.y0, self.R = ring_motion.boundary(self.prefix)
self.side_len = self.R * self.real_particle /4.0 ##4inch in experiment
self.max_dist = self.side_len/1.25
self.frames = self.pdata['f']
self.boundary_shape = Point(self.y0, 1024 - self.x0).buffer(self.R)
def load_and_process(self):
self.pdata, self.odata = helpy.load_data(self.prefix, 'p o')
self.x0, self.y0, self.R = ring_motion.boundary(self.prefix)
self.side_len = self.R * self.real_particle / 4.0
self.max_dist = self.side_len / 1.25
self.odata['orient'] = (self.odata['orient'] + np.pi) % (2 * np.pi)
self.frames = self.pdata['f']
def convol_load_and_process(self):
self.pdata, self.odata = helpy.load_data(self.prefix, 'p o')
self.x0, self.y0, self.R = ring_motion.boundary(self.prefix)
self.side_len = self.R * self.real_particle /4.0
self.max_dist = self.side_len/1.25
self.odata['orient'] = (self.odata['orient'] + np.pi)%(2 * np.pi)
self.frames = self.pdata['f']
self.boundary_shape = Point(self.y0, 1024 - self.x0).buffer(self.R)
def load_process(self):
self.x0, self.y0, self.R = ring_motion.boundary(self.prefix)
data = helpy.load_data(self.prefix)
data['o'] = (data['o'] + np.pi)%(2 * np.pi)
max_frame = data['f'].max()
tracksets = helpy.load_tracksets(data, run_track_orient=True, min_length = max_frame//2, run_repair = 'interp')
track_prefix = {self.prefix: tracksets}
v_data = velocity.compile_noise(track_prefix, width=(0.525,), cat = False, side = self.side, \
fps = self.fps, ring = True, x0= self.x0, y0 = self.y0, skip = 5, \
grad = True, start = 0)
self.v_data = v_data[self.prefix]
def find_data(args):
suf = '_TRACKS.npz'
if '*' in args.prefix or '?' in args.prefix:
fs = iglob(args.prefix + suf)
else:
dirname, prefix = os.path.split(args.prefix)
dirm = (dirname or '*') + (prefix + '*/')
basm = prefix.strip('/._')
fs = iglob(dirm + basm + '*' + suf)
prefixes = [s[:-len(suf)] for s in fs] or [args.prefix]
if args.verbose:
print 'prefixes:',
print '\n '.join(prefixes)
return {prefix: helpy.load_data(prefix, 'tracks') for prefix in prefixes}
def find_data(args):
suf = '_TRACKS.npz'
if '*' in args.prefix or '?' in args.prefix:
fs = iglob(args.prefix+suf)
else:
dirname, prefix = os.path.split(args.prefix)
dirm = (dirname or '*') + (prefix + '*/')
basm = prefix.strip('/._')
fs = iglob(dirm + basm + '*' + suf)
prefixes = [s[:-len(suf)] for s in fs] or [args.prefix]
if args.verbose:
print 'prefixes:',
print '\n '.join(prefixes)
return {prefix: helpy.load_data(prefix, 'tracks') for prefix in prefixes}
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)
meta = helpy.load_meta(absprefix)
if args.load:
datapath = absprefix+'_CORNER'*args.corner+'_POSITIONS.txt'
helpy.txt_to_npz(datapath, verbose=True, compress=True)
if args.orient or args.track:
print 'NOTICE: not tracking, only converting file from txt to npz'
print ' please run again without `-l` to track/orient'
sys.exit()
if args.track or args.orient:
from scipy.spatial import cKDTree as KDTree
if args.track != args.orient and helpy.bool_input("Would you like to "
"simultaneously track and find orientations? (It's faster)\n"):
args.track = args.orient = True
if args.orient:
pdata, cdata = helpy.load_data(absprefix, 'position corner')
else:
pdata = helpy.load_data(absprefix, 'position')
pfsets = helpy.splitter(pdata, ret_dict=True)
pftrees = { f: KDTree(np.column_stack([pfset['x'], pfset['y']]), leafsize=50)
for f, pfset in pfsets.iteritems() }
if args.track:
meta.update(track_sidelength=args.side, track_maxdist=args.maxdist,
track_maxtime=args.giveup, track_stub=args.stub,
track_cut=args.cut)
trackids = find_tracks(pdata, maxdist=args.maxdist, giveup=args.giveup,
n=args.number, cut=args.cut, stub=args.stub)
trackids = remove_duplicates(trackids, data=pdata)
else:
trackids = None
if args.orient:
arg('-v', '--verbose', action='count', help='Be verbose, may repeat: -vv')
args = parser.parse_args()
if not args.verbose:
from warnings import filterwarnings
filterwarnings('ignore', category=RuntimeWarning,
module='numpy|scipy|matplot')
if '*' in args.prefix or '?' in args.prefix:
prefix_pattern = args.prefix
args.prefix = helpy.replace_all(args.prefix, '*?', '') + '_MRG'
helpy.save_log_entry(args.prefix, 'argv')
prefixes = [p[:-9] for p in glob.iglob(
helpy.with_suffix(prefix_pattern, '_MELT.npz'))]
metas, mdatas = zip(*[(helpy.load_meta(prefix),
helpy.load_data(prefix, 'm'))
for prefix in prefixes])
for meta, mdata in zip(metas, mdatas):
mdata['f'] = mdata['f'].astype(int) - int(meta['start_frame'])
mdata = np.concatenate(mdatas)
meta = helpy.merge_meta(metas, excl={'start_frame'},
excl_start=('center', 'corner'))
if args.save:
np.savez_compressed(args.prefix+'_MELT', data=mdata)
helpy.save_meta(args.prefix, meta, merged=prefixes)
print 'merged sets', prefixes, 'saved to', args.prefix
else:
helpy.save_log_entry(args.prefix, 'argv')
meta = helpy.load_meta(args.prefix)
helpy.sync_args_meta(
if not args.verbose:
from warnings import filterwarnings
filterwarnings('ignore',
category=RuntimeWarning,
module='numpy|scipy|matplot')
if '*' in args.prefix or '?' in args.prefix:
prefix_pattern = args.prefix
args.prefix = helpy.replace_all(args.prefix, '*?', '') + '_MRG'
helpy.save_log_entry(args.prefix, 'argv')
prefixes = [
p[:-9]
for p in glob.iglob(helpy.with_suffix(prefix_pattern, '_MELT.npz'))
]
metas, mdatas = zip(*[(helpy.load_meta(prefix),
helpy.load_data(prefix, 'm'))
for prefix in prefixes])
for meta, mdata in zip(metas, mdatas):
mdata['f'] = mdata['f'].astype(int) - int(meta['start_frame'])
mdata = np.concatenate(mdatas)
meta = helpy.merge_meta(metas,
excl={'start_frame'},
excl_start=('center', 'corner'))
if args.save:
np.savez_compressed(args.prefix + '_MELT', data=mdata)
helpy.save_meta(args.prefix, meta, merged=prefixes)
print 'merged sets', prefixes, 'saved to', args.prefix
else:
helpy.save_log_entry(args.prefix, 'argv')
meta = helpy.load_meta(args.prefix)
try:
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)
try:
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:
