elif mode == 'single':
if actions in ['track','both']:
print >>logfile, "Tracking %s #%i"%(mouse,n);logfile.flush()
mt = MouseTracker(mouse=mouse, n=n, data_dir=data_dir, diff_thresh=diff_thresh, resample=resample_t, point_mode=point_finding_mode)
mt.run(show=show, wait=ms_bt_frames, start_pos=start_position)
if actions in ['mark','both']:
print >>logfile, "Marking %s #%i"%(mouse,n);logfile.flush()
m = Marker(mouse=mouse, n=n, data_dir=data_dir, mark_mode=mark_mode)
mres = m.run(resample=resample_m, start_time=start_time)
if not mres[0]:
print >>logfile, "WARNING: Marking for %s #%i declared a warning: %s"%(mouse,n,mres[1]);logfile.flush()
if actions == 'play':
print >>logfile, "Playing %s #%i"%(mouse,n);logfile.flush()
pb = Playback(ymaze_path=mouse, ymaze_n=n, data_dir=data_dir)
pb.play(draw=show_tracking)
elif mode == 'collect':
mice = [m for m in os.listdir(data_dir) if exclude_word not in m.lower() and m[0]!='.' and 'summary' not in m]
rows = []
for mouse in mice:
print >>logfile, mouse;logfile.flush()
fh = FileHandler(data_dir, mouse, n=1)
for tr in xrange(fh.get_n_trials_wbehav()):
fhm = FileHandler(data_dir, mouse, n=tr+1)
data = np.load(fhm.make_path('behaviour.npz'))
dic = dict(mouse=mouse, n=tr+1, score=data['score'], time_to_correct=data['time_to_correct'], distance=data['distance'], start_time=data['start_time'])
rows.append(dic)
rows = np.array(rows)
point_mode=point_finding_mode)
mt.run(show=show, wait=ms_bt_frames, start_pos=start_position)
if actions in ['mark', 'both']:
print >> logfile, "Marking %s #%i" % (mouse, n)
logfile.flush()
m = Marker(mouse=mouse, n=n, data_dir=data_dir, mark_mode=mark_mode)
mres = m.run(resample=resample_m, start_time=start_time)
if not mres[0]:
print >> logfile, "WARNING: Marking for %s #%i declared a warning: %s" % (
mouse, n, mres[1])
logfile.flush()
if actions == 'play':
print >> logfile, "Playing %s #%i" % (mouse, n)
logfile.flush()
pb = Playback(ymaze_path=mouse, ymaze_n=n, data_dir=data_dir)
pb.play(draw=show_tracking)
elif mode == 'collect':
mice = [
m for m in os.listdir(data_dir)
if exclude_word not in m.lower() and m[0] != '.' and 'summary' not in m
]
rows = []
for mouse in mice:
print >> logfile, mouse
logfile.flush()
fh = FileHandler(data_dir, mouse, n=1)
for tr in xrange(fh.get_n_trials_wbehav()):
fhm = FileHandler(data_dir, mouse, n=tr + 1)
data = np.load(fhm.make_path('behaviour.npz'))
low_addr = int(min(df['Address']), base=16)
high_addr = int(max(df['Address']), base=16)
max_time = max(df['Timestamp(ms)'])
saddr = args.saddr if args.saddr and args.saddr >= low_addr else low_addr
eaddr = args.eaddr if args.eaddr and args.eaddr <= high_addr else high_addr
stime = args.stime if args.stime and args.stime >= 0 else 0 # Time always starts a 0
etime = args.etime if args.etime and args.etime <= max_time else max_time
dff = df[(df.Address >= hex(saddr)) & (df.Address <= hex(eaddr)) &
(df['Timestamp(ms)'] >= stime) &
(df['Timestamp(ms)'] <= etime)].reset_index(drop=True)
playback = Playback(dff, args.mapfile, saddr, eaddr, stime, etime)
playback.play()
# --------------------------------------------------------------------------- #
elif args.command == 'analyze':
mapcsv = read_csv(args.mapfile)
df = DataFrame(mapcsv)
high_addr = int(max(df['Address']), base=16)
if (args.blocksize < 1) or (args.blocksize > high_addr):
print('Please provide a valid block size: [1, {0}]'.format(high_addr +
1))
exit()
iblocks = args.iblocks if args.iblocks else None
