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
def do_spot_scatter_plot(data, xval, c='k', width=0.2, text=True,
mean_adjust=True):
plt.hold(True)
dd = data[data != 0]
r = width / 2.
for d in data:
plt.scatter(xval + (np.random.rand(1) - 0.5) * r, d, c=c, marker='x')
if len(data) > 0:
# fishers z transform for mean and then transform back
if mean_adjust:
mn = average_corrs(data)
else:
mn = data.mean()
plt.plot([xval - r, xval + r], [mn, mn], 'k', lw=3)
plt.grid(True, axis='y')
if text:
plt.text(xval - r, 0.9, '#%d' % len(dd))
def do_point_line_plot(data, xvals, c=['r', 'b'], width=0.2,
mean_adjust=True, text=True, alpha=0.3):
plt.hold(True)
dd = data[data.sum(1) != 0]
r = width / 2.
ind = np.argsort(data[:, 0])
data = data[ind]
for d in data:
offset = (np.random.rand(1) - 0.5) * r
x = xvals + offset
if len(x) > 1:
plt.plot(x, d, '-', c='0.7', lw=0.5, alpha=alpha)
for i in range(len(x)):
plt.scatter(x[i], d[i], c=c[i], marker='x', alpha=alpha)
if len(data) > 0:
# fishers z transform for mean and then transform back
for i in range(len(xvals)):
if mean_adjust:
mn = average_corrs(data[:, i])
else:
mn = data[:, i].mean()
plt.plot([xvals[i] - r, xvals[i] + r], [mn, mn], c[i], lw=4)
plt.plot([xvals[i] - r, xvals[i] + r], [mn, mn], 'k', lw=1.5)
for j in range(data.shape[1]):
if data[:, j].sum() == 0:
continue
p_offset = -0.2
for ind in range(j + 1, data.shape[1]):
if data[:, ind].sum() == 0:
continue
if mean_adjust:
_, p = scipy.stats.ttest_ind(np.arctanh(data[:, j]),
np.arctanh(data[:, ind]))
else:
_, p = scipy.stats.ttest_ind(data[:, j], data[:, ind])
print 'p', p, j, ind
if p < 0.05:
plt.scatter(xvals[j] + p_offset,
0.95, c=c[ind],
edgecolor=c[ind],
marker='*')
p_offset *= -1
plt.grid(True, axis='y')
y = plt.ylim()[1]
plt.text(0.7, y * 0.9, 'Avg Act: %.2f' % avg_w)
hist_mx = np.maximum(cnts.max(), hist_mx)
plt.xticks(tks)
plt.title('Mean Activation Histogram')
adjust_spines(hst_ax2, ['bottom', 'left'])
hst_ax1.set_ylim(0, hist_mx)
hst_ax2.set_ylim(0, hist_mx)
ax = plt.subplot(425)
plt.hold(True)
crrs = []
for c, w in zip(dat_c.mean(2), dat_w.mean(2)):
crrs.append(do_thresh_corr(c, w))
cr = average_corrs(crrs)
vals.append(cr)
csv_vals[4] += crrs
plt.plot(mn_w, label='Whole', color=clr_w, linewidth=1.5)
#plt.fill_between(range(psth_w.shape[1]), mn_c, mn_w,
# facecolor=clr2)
plt.plot(mn_c, color=clr_c,
label='Center', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1), frameon=False, ncol=2)
plt.text(10, 0.9, 'corr: %.2f' % (cr))
adjust_spines(ax, ['bottom', 'left'])
ylim = plt.ylim(0, 1)
plt.ylim([-0.01, ylim[1]])
plt.title('Mean Comparison')
cnt_axs = []
crr_axs = []
cnt_lims = [99999, 0]
crr_lims = [99999, 0]
if exp_type not in shift_max_mn:
shift_max_mn[exp_type] = {}
for i, k in enumerate(sorted(shift_max.keys())):
if k not in shift_max_mn[exp_type]:
shift_max_mn[exp_type][k] = []
ax = plt.subplot(1, 2, i + 1)
plt.title(k)
for s, t in zip(shifts, xaxis_time):
vals = np.array(shift_max[k][s])
# fisher z transform
shift_max_mn[exp_type][k].append(average_corrs(vals))
do_spot_scatter_plot(vals, t, 'k', 25, True, mean_adjust=True)
#do_box_plot(crrs, shifts, 'k', np.ones_like(shifts) * 0.5)
if i == 0:
plt.ylabel('mean corr of responders')
plt.xlabel('Shift in Frames')
adjuster = np.array([-10, 10])
ax.set_ylim(-0.01, 1)
ax.set_xlim(np.array([xaxis_time.min(), xaxis_time.max()]) + adjuster)
plt.subplots_adjust(left=0.06,
bottom=0.05,
right=0.97,
top=0.95,
wspace=0.23,
hspace=0.1)
fig4.savefig(fig_path + '%s_%s_shift_max.eps' % (str(filt), exp_type))
clf_args=clf_args,
edges=[0, 0])
pred_time = pred_time.ravel()
y = y.ravel()
crr = do_thresh_corr(pred_time,
y) #, corr_type='pearsonr')
crrs.append(crr)
res = 'Predict: %s, Using: %s, Dimension: %d, Crr: %.2f' % (
d, m, dim, crr)
pred_time = (pred_time -
pred_time.mean()) / np.std(pred_time)
y = (y - y.mean()) / np.std(y)
# print res
if crr > 0.6:
plt.figure(figsize=(14, 8))
plt.hold(True)
plt.plot(pred_time, label='pred')
plt.plot(y, label='movie')
plt.legend()
plt.title(res)
plt.show()
crrs = np.array(crrs)
av_crrs = average_corrs(crrs)
print alpha, av_crrs
plt.hist(crrs, bins=30)
plt.title('%.3f %.3f' % (alpha, av_crrs))
plt.show()
# print pred.shape, con_mask.shape
continue
mean_corr_c = dat["mean_corr_c"]
mean_corr_w = dat["mean_corr_w"]
win = dat['win']
c_crr = []
w_crr = []
mn_c_crr = []
mn_w_crr = []
print 'try to predict correlation from movie features, try to predict one from another, try to predict movie features from population'
for i in range(mean_corr_c.shape[1]):
for j in range(mean_corr_c.shape[1]):
if i < j:
mn_c_crr.append(mean_corr_c[i, j, win / 2:-(win / 2)])
mn_w_crr.append(mean_corr_w[i, j, win / 2:-(win / 2)])
dat_c = average_corrs(np.array(mn_c_crr))
dat_w = average_corrs(np.array(mn_w_crr))
lum_mask, con_mask, flow_mask, four_mask, four_mask_shape,\
freq_mask, orient_mask,\
lum_surr, con_surr, flow_surr, four_surr, four_surr_shape,\
freq_surr, orient_surr,\
lum_whole, con_whole, flow_whole, four_whole, four_whole_shape,\
freq_whole, orient_whole = load_parsed_movie_dat(exp, 'POP', None)
all_dat = {
'Data': {
'Centre': dat_c,
'Whole': dat_w,
'Diff': dat_c - dat_w
},
trls = np.arange(X.shape[0])
pred_time, coefs = CV_time(clf, X, y, folds=folds, clf_args=clf_args,
edges=[0, 0])
pred_time = pred_time.ravel()
y = y.ravel()
crr = do_thresh_corr(pred_time, y)#, corr_type='pearsonr')
crrs.append(crr)
res= 'Predict: %s, Using: %s, Dimension: %d, Crr: %.2f' %(
d, m, dim, crr)
pred_time = (pred_time - pred_time.mean()) / np.std(pred_time)
y = (y - y.mean()) / np.std(y)
# print res
if crr > 0.6:
plt.figure(figsize=(14, 8))
plt.hold(True)
plt.plot(pred_time, label='pred')
plt.plot(y, label='movie')
plt.legend()
plt.title(res)
plt.show()
crrs = np.array(crrs)
av_crrs = average_corrs(crrs)
print alpha, av_crrs
plt.hist(crrs, bins=30)
plt.title('%.3f %.3f' % (alpha, av_crrs))
plt.show()
# print pred.shape, con_mask.shape
cnt_axs = []
crr_axs = []
cnt_lims = [99999, 0]
crr_lims = [99999, 0]
if exp_type not in shift_max_mn:
shift_max_mn[exp_type] = {}
for i, k in enumerate(sorted(shift_max.keys())):
if k not in shift_max_mn[exp_type]:
shift_max_mn[exp_type][k] = []
ax = plt.subplot(1, 2, i + 1)
plt.title(k)
for s, t in zip(shifts, xaxis_time):
vals = np.array(shift_max[k][s])
# fisher z transform
shift_max_mn[exp_type][k].append(average_corrs(vals))
do_spot_scatter_plot(vals, t, 'k', 25, True, mean_adjust=True)
#do_box_plot(crrs, shifts, 'k', np.ones_like(shifts) * 0.5)
if i == 0:
plt.ylabel('mean corr of responders')
plt.xlabel('Shift in Frames')
adjuster = np.array([-10, 10])
ax.set_ylim(-0.01, 1)
ax.set_xlim(np.array([xaxis_time.min(), xaxis_time.max()]) + adjuster)
plt.subplots_adjust(left=0.06, bottom=0.05, right=0.97, top=0.95,
wspace=0.23, hspace=0.1)
fig4.savefig(fig_path + '%s_%s_shift_max.eps' % (str(filt), exp_type))
fig4.savefig(fig_path + '%s_%s_shift_max.png' % (str(filt), exp_type))
plt.close(fig4)
ps = []
y = plt.ylim()[1]
plt.text(0.7, y * 0.9, 'Avg Act: %.2f' % avg_w)
hist_mx = np.maximum(cnts.max(), hist_mx)
plt.xticks(tks)
plt.title('Mean Activation Histogram')
adjust_spines(hst_ax2, ['bottom', 'left'])
hst_ax1.set_ylim(0, hist_mx)
hst_ax2.set_ylim(0, hist_mx)
ax = plt.subplot(425)
plt.hold(True)
crrs = []
for c, w in zip(dat_c.mean(2), dat_w.mean(2)):
crrs.append(do_thresh_corr(c, w))
cr = average_corrs(crrs)
vals.append(cr)
csv_vals[4] += crrs
plt.plot(mn_w, label='Whole', color=clr_w, linewidth=1.5)
#plt.fill_between(range(psth_w.shape[1]), mn_c, mn_w,
# facecolor=clr2)
plt.plot(mn_c, color=clr_c, label='Center', linewidth=2)
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1),
frameon=False,
ncol=2)
plt.text(10, 0.9, 'corr: %.2f' % (cr))
adjust_spines(ax, ['bottom', 'left'])
ylim = plt.ylim(0, 1)
plt.ylim([-0.01, ylim[1]])
plt.title('Mean Comparison')
def plot_corrs(c_vals, w_vals, mn_c_crr, mn_w_crr, n_cells, header, fname):
fig = plt.figure(figsize=(14, 8))
fig.set_facecolor('white')
c_vals = average_corrs(c_vals)
w_vals = average_corrs(w_vals)
c_vals_mn = average_corrs(c_vals)
w_vals_mn = average_corrs(w_vals)
mn_c_vals_mn = average_corrs(mn_c_crr)
mn_w_vals_mn = average_corrs(mn_w_crr)
print mn_c_crr.min(), mn_c_crr.max()
# ax = plt.subplot(411)
# plt.hold(True)
# plt.plot(c_vals.T, '0.8')
# plt.plot(c_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Masked')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(412)
# plt.hold(True)
# plt.plot(w_vals.T, '0.8')
# plt.plot(w_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, w_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Whole Field')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(413)
# plt.hold(True)
# plt.plot(w_vals_mn, 'g', linewidth=2, label='Whole')
# plt.plot(c_vals_mn, 'k', linewidth=2, label='Centre')
# crr = do_thresh_corr(w_vals_mn, c_vals_mn)
# leg = plt.legend(ncol=2)
# leg.draw_frame(False)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.ylabel('Mean R')
# plt.xlabel('Sample')
# plt.title('Whole vs Masked: Crr: {0:.2f}'.format(crr))
ax = plt.subplot(211)
plt.hold(True)
plt.plot(mn_w_vals_mn, 'g', linewidth=2, label='Whole')
plt.plot(mn_c_vals_mn, 'k', linewidth=2, label='Centre')
crr = do_thresh_corr(mn_w_vals_mn, mn_c_vals_mn)
leg = plt.legend(ncol=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Mean Whole vs Masked: R^2: {0:.2f}'.format(crr))
plt.ylabel('Mean R')
plt.xlabel('Sample')
ax = plt.subplot(212)
plt.hold(True)
plt.plot(mn_c_vals_mn - mn_w_vals_mn, '0.3', linewidth=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Difference - Mean Whole vs Masked')
plt.ylabel('Mean R')
plt.xlabel('Sample')
plt.suptitle('%s - Intercell R^2 over Trials - #Cells: %d' %
(header, n_cells))
plt.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.9,
wspace=0.2,
hspace=0.25)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
plt.close(fig)
print fname
print n_cells
print '\tMean\tMax'
print 'Centre:\t%.3f\t%.3f' % (c_vals_mn.mean(), c_vals.max())
print 'Whole:\t%.3f\t%.3f' % (w_vals.mean(), w_vals.max())
print
def plot_corrs(c_vals, w_vals, mn_c_crr, mn_w_crr, n_cells, header, fname):
fig = plt.figure(figsize=(14, 8))
fig.set_facecolor('white')
c_vals = average_corrs(c_vals)
w_vals = average_corrs(w_vals)
c_vals_mn = average_corrs(c_vals)
w_vals_mn = average_corrs(w_vals)
mn_c_vals_mn = average_corrs(mn_c_crr)
mn_w_vals_mn = average_corrs(mn_w_crr)
print mn_c_crr.min(), mn_c_crr.max()
# ax = plt.subplot(411)
# plt.hold(True)
# plt.plot(c_vals.T, '0.8')
# plt.plot(c_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Masked')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(412)
# plt.hold(True)
# plt.plot(w_vals.T, '0.8')
# plt.plot(w_vals_mn, '0.4', linewidth=2)
# plt.xlim(0, w_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.title('Whole Field')
# plt.ylabel('Mean R')
#
# ax = plt.subplot(413)
# plt.hold(True)
# plt.plot(w_vals_mn, 'g', linewidth=2, label='Whole')
# plt.plot(c_vals_mn, 'k', linewidth=2, label='Centre')
# crr = do_thresh_corr(w_vals_mn, c_vals_mn)
# leg = plt.legend(ncol=2)
# leg.draw_frame(False)
# plt.xlim(0, c_vals.shape[1])
# adjust_spines(ax, ['bottom', 'left'])
# plt.ylabel('Mean R')
# plt.xlabel('Sample')
# plt.title('Whole vs Masked: Crr: {0:.2f}'.format(crr))
ax = plt.subplot(211)
plt.hold(True)
plt.plot(mn_w_vals_mn, 'g', linewidth=2, label='Whole')
plt.plot(mn_c_vals_mn, 'k', linewidth=2, label='Centre')
crr = do_thresh_corr(mn_w_vals_mn, mn_c_vals_mn)
leg = plt.legend(ncol=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Mean Whole vs Masked: R^2: {0:.2f}'.format(crr))
plt.ylabel('Mean R')
plt.xlabel('Sample')
ax = plt.subplot(212)
plt.hold(True)
plt.plot(mn_c_vals_mn - mn_w_vals_mn, '0.3', linewidth=2)
leg.draw_frame(False)
plt.xlim(0, c_vals.shape[1])
adjust_spines(ax, ['bottom', 'left'])
plt.title('Difference - Mean Whole vs Masked')
plt.ylabel('Mean R')
plt.xlabel('Sample')
plt.suptitle('%s - Intercell R^2 over Trials - #Cells: %d' %
(header, n_cells))
plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.9,
wspace=0.2, hspace=0.25)
fig.savefig(fname + '.eps')
fig.savefig(fname + '.png')
#plt.show()
plt.close(fig)
print fname
print n_cells
print '\tMean\tMax'
print 'Centre:\t%.3f\t%.3f' % (c_vals_mn.mean(), c_vals.max())
print 'Whole:\t%.3f\t%.3f' % (w_vals.mean(), w_vals.max())
print
continue
mean_corr_c = dat["mean_corr_c"]
mean_corr_w = dat["mean_corr_w"]
win = dat['win']
c_crr = []
w_crr = []
mn_c_crr = []
mn_w_crr = []
print 'try to predict correlation from movie features, try to predict one from another, try to predict movie features from population'
for i in range(mean_corr_c.shape[1]):
for j in range(mean_corr_c.shape[1]):
if i < j:
mn_c_crr.append(mean_corr_c[i, j, win / 2: -(win / 2)])
mn_w_crr.append(mean_corr_w[i, j, win / 2: -(win / 2)])
dat_c = average_corrs(np.array(mn_c_crr))
dat_w = average_corrs(np.array(mn_w_crr))
lum_mask, con_mask, flow_mask, four_mask, four_mask_shape,\
freq_mask, orient_mask,\
lum_surr, con_surr, flow_surr, four_surr, four_surr_shape,\
freq_surr, orient_surr,\
lum_whole, con_whole, flow_whole, four_whole, four_whole_shape,\
freq_whole, orient_whole = load_parsed_movie_dat(exp, 'POP', None)
all_dat = {'Data': {'Centre': dat_c, 'Whole': dat_w , 'Diff': dat_c - dat_w}, 'Movie': {}}
all_dat['Movie']['Contrast'] = con_mask
all_dat['Movie']['Luminence'] = lum_mask
all_dat['Movie']['Fourier'] = np.append(four_mask.real, four_mask.imag,
axis=2).astype(np.float)
all_dat['Movie']['Frequency'] = freq_mask