plt.ylim(*ylim)
if show_legend: plt.legend(loc='best')
if save is True:
save = prefix + "_MS{}D.pdf".format('A' if ang else '')
if save:
print "saving to", save
plt.savefig(save)
if show: plt.show()
return [fig] + fig.get_axes() + [taus] + [msd, msd_err] if errorbars else [msd]
if __name__=='__main__':
helpy.save_log_entry(absprefix, 'argv')
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)
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()
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()
pts, labels, convolved = find_particles(image,
keep=True,
**size['center'])
filebase = os.path.splitext(os.path.basename(file_name))[0]
imbase = os.path.join(imdir, filebase)
imprefix = imbase
plot_positions(pts, labels, convolved, centers, **size['center'])
savename = file_name.split('.')[0]
savename += '.png'
savename = os.path.join(imdir, savename)
plot_check(image, centers, savename)
if args.save:
from shutil import copy
copy(first, prefix + '_' + os.path.basename(first))
savenotice = "Saving {} positions to {}{{{},.npz}}".format
hfmt = (
'Kern {kern:.2f}, Min area {min_area:d}, '
'Max area {max_area:d}, Max eccen {max_ecc:.2f}\n'
'Frame X Y Label Eccen Area Orientation'
)
txtfmt = ['%6d', '%7.3f', '%7.3f', '%4d', '%1.3f', '%5d', '%1.3f']
ext = '.txt' + '.gz'
print(savenotice('center', output, ext))
np.savetxt(output + ext,
ret_points,
header=hfmt.format(**size['center']),
delimiter=' ',
fmt=txtfmt)
helpy.txt_to_npz(output + ext, verbose=False, compress=True)
