def rechoose_boundary(self):
meta = helpy.load_meta(self.prefix)
boundary, path = ring_motion.find_boundary(self.prefix)
meta.update(boundary = boundary)
meta['path_to_tiffs'] = path
helpy.save_meta(self.prefix, meta)
self.x0, self.y0, self.R = boundary
self.side_len = self.R * self.real_particle /4.0
self.boundary_shape = Point(self.y0, 1024 - self.x0).buffer(self.R)
return
elif args.plotmsd or args.rr:
if verbose: print "loading msd data from npz files"
datapath = absprefix+"_MSD.npz"
msdnpz = np.load(datapath)
msds = msdnpz['msds']
try: msdids = msdnpz['msdids']
except KeyError: msdids = None
try:
dt0 = np.asscalar(msdnpz['dt0'])
dtau = np.asscalar(msdnpz['dtau'])
except KeyError:
dt0 = 10 # here's assuming...
dtau = 10 # should be true for all from before dt* was saved
if args.save:
helpy.save_meta(absprefix, meta)
if __name__=='__main__':
if args.plotmsd:
if verbose: print 'plotting msd now!'
plot_msd(msds, msdids, dtau, dt0, data['f'].max()+1, tnormalize=False,
prefix=absprefix, show_tracks=args.showtracks, show=args.show,
singletracks=args.singletracks, fps=fps, S=S, save=args.save,
kill_flats=args.killflat, kill_jumps=args.killjump*S*S)
if args.plottracks:
if verbose: print 'plotting tracks now!'
bgimage = helpy.find_first_frame([locdir, prefix])
if args.singletracks:
mask = np.in1d(trackids, args.singletracks)
else:
mask = None
areas = point[:, 5].astype(int)
amin, amax = size['min_area'], size['max_area']
s = np.ceil(hist_bin_auto(areas))
bins = np.arange(amin, amax + s, s)
label = "{} area ({} - {})".format(dot, amin, amax)
aax.hist(areas, bins, alpha=0.5, color='g', label=label)
aax.axvline(size['min_area'], c='g', lw=2)
aax.set_xlim(0, bins[-1])
aax.legend(loc='best', fontsize='small')
if args.save:
print savenotice(dot, out, ext)
np.savetxt(out + ext,
point,
header=hfmt.format(**size),
delimiter=' ',
fmt=txtfmt)
helpy.txt_to_npz(out + ext, verbose=args.verbose, compress=args.gz)
if args.save:
from shutil import copy
copy(first, prefix + '_' + os.path.basename(first))
helpy.save_meta(prefix,
meta,
path_to_tiffs=os.path.abspath(filepattern),
first_frame=os.path.abspath(first),
detect_thresh=args.thresh,
detect_removed=args.remove)
if args.plot:
fig.savefig(prefix + '_SEGMENTSTATS.pdf')
elif args.plot:
plt.show()
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(
args, meta,
['side', 'fps', 'start', 'width', 'zoom'],
['sidelength', 'fps', 'start_frame', 'crystal_width', 'crystal_zoom'],
[1, 1, 0, None, 1])
M = 4 # number of neighbors
W = args.width
if W is None:
eax.set_xlim(0, 1)
eax.set_xticks(np.arange(0, 1.1, .1))
eax.set_xticklabels(map('.{:d}'.format, np.arange(10)) + ['1'])
eax.legend(loc='best', fontsize='small')
areas = point[:, 5].astype(int)
amin, amax = size['min_area'], size['max_area']
s = np.ceil(hist_bin_auto(areas))
bins = np.arange(amin, amax+s, s)
label = "{} area ({} - {})".format(dot, amin, amax)
aax.hist(areas, bins, alpha=0.5, color='g', label=label)
aax.axvline(size['min_area'], c='g', lw=2)
aax.set_xlim(0, bins[-1])
aax.legend(loc='best', fontsize='small')
if args.save:
print savenotice(dot, out, ext)
np.savetxt(out+ext, point, header=hfmt.format(**size),
delimiter=' ', fmt=txtfmt)
helpy.txt_to_npz(out+ext, verbose=args.verbose, compress=args.gz)
if args.save:
from shutil import copy
copy(first, prefix+'_'+os.path.basename(first))
helpy.save_meta(prefix, meta,
path_to_tiffs=os.path.abspath(filepattern),
first_frame=os.path.abspath(first),
detect_thresh=args.thresh, detect_removed=args.remove)
if args.plot:
fig.savefig(prefix+'_SEGMENTSTATS.pdf')
elif args.plot:
plt.show()
print "Creating new directory", outdir
makedirs(outdir)
if output.endswith('.gz'):
gz = 1
output = output[:-3]
if output.endswith('.txt'):
output = output[:-4]
if output.endswith(suffix):
prefix = output[:-len(suffix)]
else:
prefix = output
output += suffix
outs = output, prefix + '_CORNER' + suffix
helpy.save_log_entry(prefix, argv)
helpy.save_meta(prefix, path_to_tiffs=path.abspath(filepattern))
kern_area = np.pi*args.kern**2
if args.min == -1:
args.min = kern_area/2
if args.verbose: print "using min =", args.min
if args.max == -1:
args.max = 2*kern_area
if args.verbose: print "using max =", args.max
thresh = {'center': {'max_ecc' : args.ecc,
'min_area': args.min,
'max_area': args.max,
'kern' : args.kern}}
if args.ckern:
'center': {
'max_ecc': args.ecc,
'min_area': args.min or int(kern_area // 2),
'max_area': args.max or int(kern_area * 2 + 1),
'kern': float(args.kern),
'thresh': args.thresh,
'convex': args.convex
}
}
meta.update({k: v for k, v in size['center'].iteritems()})
#initialize boundary
boundary = meta.get('boundary')
if boundary is None or boundary == [0.0] * 3:
boundary = helpy.circle_click(first)
meta.update(boundary=boundary)
helpy.save_meta(prefix, meta)
x0, y0, R0 = boundary
#initialize model for machine leanring
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# load category network
net_cat = ClassNet()
net_cat.load_state_dict(torch.load(args.cpath))
net_cat.eval()
net_cat.to(device)
# load orientation network
net = ConvNet()
net.load_state_dict(torch.load(args.opath))
net.eval()
net.to(device)
def extract_image(xys, image, image_size=32):
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(
args, meta, ['side', 'fps', 'start', 'width', 'zoom'],
['sidelength', 'fps', 'start_frame', 'crystal_width', 'crystal_zoom'],
[1, 1, 0, None, 1])
M = 4 # number of neighbors
W = args.width
if W is None:
data = helpy.load_data(args.prefix)
'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:
fits = command_hist(args, meta, compile_args)
if __name__ == '__main__' and args.save:
savename = os.path.abspath(args.prefix.rstrip('/._?*'))
helpy.save_meta(savename, meta)
if 'hist' in args.command:
helpy.save_fits(savename, fits)
savename += '_v' + ('corr' if args.autocorr else 'hist')
if args.suffix:
savename += '_' + args.suffix.strip('_')
savename += '.pdf'
print 'Saving plot to {}'.format(savename)
plt.savefig(savename)
if __name__ == '__main__' and args.show:
plt.show()
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:
fits = command_hist(args, meta, compile_args)
if __name__ == '__main__' and args.save:
savename = os.path.abspath(args.prefix.rstrip('/._?*'))
helpy.save_meta(savename, meta)
if 'hist' in args.command:
helpy.save_fits(savename, fits)
savename += '_v' + ('corr' if args.autocorr else 'hist')
if args.suffix:
savename += '_' + args.suffix.strip('_')
savename += '.pdf'
print 'Saving plot to {}'.format(savename)
plt.savefig(savename)
if __name__ == '__main__' and args.show:
plt.show()
