def responsive_shuffle_xcorr(cell, centre, whole):
thresh = 2
rounds = 50
samples = len(centre)
xcorr_c = corr_trial_to_trial(centre.T, 0)
xcorr_w = corr_trial_to_trial(whole.T, 0)
shuffles = []
for i in xrange(rounds):
shift = np.random.randint(-samples, samples, 1)
shift_xcorr_c = corr_trial_to_trial(centre.T, shift)
shift_xcorr_w = corr_trial_to_trial(whole.T, shift)
shuffles.append([shift_xcorr_c, shift_xcorr_w])
shuffles = np.array(shuffles)
shift_xcorr_c = average_corrs(shuffles[:, 0])
shift_xcorr_w = average_corrs(shuffles[:, 1])
active = (xcorr_c > (shift_xcorr_c * thresh) or xcorr_w >
(shift_xcorr_w * thresh))
vals = [cell, active, xcorr_c, xcorr_w, shift_xcorr_c, shift_xcorr_w]
return vals
def responsive_shuffle_xcorr(cell, centre, whole):
thresh = 2
rounds = 50
samples = len(centre)
xcorr_c = corr_trial_to_trial(centre.T, 0)
xcorr_w = corr_trial_to_trial(whole.T, 0)
shuffles = []
for i in xrange(rounds):
shift = np.random.randint(-samples, samples, 1)
shift_xcorr_c = corr_trial_to_trial(centre.T, shift)
shift_xcorr_w = corr_trial_to_trial(whole.T, shift)
shuffles.append([shift_xcorr_c, shift_xcorr_w])
shuffles = np.array(shuffles)
shift_xcorr_c = average_corrs(shuffles[:, 0])
shift_xcorr_w = average_corrs(shuffles[:, 1])
active = (xcorr_c > (shift_xcorr_c * thresh)
or xcorr_w > (shift_xcorr_w * thresh))
vals = [cell, active, xcorr_c, xcorr_w, shift_xcorr_c, shift_xcorr_w]
return vals
for cell in cell_idx:
if cell in d:
print 'active'
else:
continue
print cell
cell_res = []
for i, src in enumerate(['dat_raw_c', 'dat_raw_w']):
dt = dat[src]
if i == 0 and crr_c is None:
try:
crr_c = np.load('%s%s_%s.npy' % (target, exp, src))
except:
crr_c = []
for c in cell_idx:
crr_c.append(corr_trial_to_trial(dt[c].T))
crr_c = np.array(crr_c)
np.save('%s%s_%s' % (target, exp, src), crr_c)
elif i == 1 and crr_w is None:
try:
crr_w = np.load('%s%s_%s.npy' % (target, exp, src))
except:
crr_w = []
for c in cell_idx:
crr_w.append(corr_trial_to_trial(dt[c].T))
crr_w = np.array(crr_w)
np.save('%s%s_%s' % (target, exp, src), crr_w)
src_res = []
trials = np.arange(dt.shape[2])
ys = []
preds = []
for cell in cell_idx:
if cell in d:
print 'active'
else:
continue
print cell
cell_res = []
for i, src in enumerate(['dat_raw_c', 'dat_raw_w']):
dt = dat[src]
if i == 0 and crr_c is None:
try:
crr_c = np.load('%s%s_%s.npy' % (target, exp, src))
except:
crr_c = []
for c in cell_idx:
crr_c.append(corr_trial_to_trial(dt[c].T))
crr_c = np.array(crr_c)
np.save('%s%s_%s' % (target, exp, src), crr_c)
elif i == 1 and crr_w is None:
try:
crr_w = np.load('%s%s_%s.npy' % (target, exp, src))
except:
crr_w = []
for c in cell_idx:
crr_w.append(corr_trial_to_trial(dt[c].T))
crr_w = np.array(crr_w)
np.save('%s%s_%s' % (target, exp, src), crr_w)
src_res = []
trials = np.arange(dt.shape[2])
ys = []
preds = []
