# plot stats
xx, yy = np.meshgrid(times, range(len(freqs)), copy=False, indexing='xy')
ax.contour(xx, yy, sig, colors='black', levels=[0],
linestyles='dotted')
# pretty plot
pretty_plot(ax)
ticks = [0]
for freq in np.arange(10, 71, 10):
ticks.append(np.where(np.round(freqs) >= freq)[0][0])
ticks.append(len(freqs))
ax.set_yticks(ticks)
ax.set_yticklabels([])
if ii in [0, (len(analyses)//2)]:
ax.set_yticklabels([int(freqs[0]), 10, '', 30, '',
'', '', '', int(freqs[-1] + .1)])
xticks = np.arange(-.200, 1.301, .100)
ax.set_xticks(xticks)
ax.set_xticklabels([])
if ii >= (len(analyses)//2):
ax.set_xticklabels([int(tim * 1e3) if tim in [.0, .800] else ''
for tim in xticks])
ax.axvline([0.], color='k')
ax.axvline([.800], color='k')
ax.set_title(analysis['title'])
pretty_colorbar(im, ax=ax, ticks=[analysis['chance'], np.max(scores)])
report.add_figs_to_section([fig], ['all freqs'], 'all')
report.save()
smax = '%.2f' % (vmax + analysis['chance'])
if vmax < 5e-3:
smin = '0' if vmin == 0 else '%.0e' % vmin
smax = '%.0e' % vmax
# Plot topo snapshots
evoked.crop(-.100, .600)
opts = dict(sensors=False, scale=1, contours=False, show=False,
times=np.linspace(0, .500, 6), average=.025, colorbar=True)
fig_grad = evoked.plot_topomap(cmap=analysis['cmap'], ch_type='grad',
vmin=vmin, vmax=vmax, **opts)
cax = fig_grad.get_children()[-1]
cax.set_yticks([vmin, vmax])
cax.set_yticklabels([smin, smax])
cax.set_title('')
report.add_figs_to_section(fig_grad, 'topo_grad', analysis['name'])
fig_mag = evoked.plot_topomap(ch_type='mag', vmin=vmin_mag, vmax=vmax,
**opts)
cax = fig_mag.get_children()[-1]
cax.set_yticks([vmin, vmax])
cax.set_yticklabels([smin_mag, '', smax])
cax.set_title('')
# fig_mag.tight_layout()
report.add_figs_to_section(fig_mag, 'topo_mag', analysis['name'])
# Plot butterfly
fig_butt_m, ax = plt.subplots(1, figsize=fig_grad.get_size_inches())
plot_butterfly(evoked, ax=ax, sig=sig, color=analysis['color'],
ch_type='mag')
# ax.axvline(800, color='k')
save(out, 'score', analysis=ana_name)
return out
# only recompute on the relevant analyses
analyses = [
ana for ana in analyses
if ana['name'] in ['target_present', 'target_circAngle']
]
for analysis in analyses:
out = _stats(analysis)
scores = out['scores']
if 'circAngle' in analysis['name']:
scores /= 2.
times = out['times']
alpha = .05
chance = analysis['chance']
p_values = stats(scores - chance)
clim = np.percentile(np.diag(np.mean(scores, axis=0)), 97)
clim = [chance - (clim - chance), clim]
fig, ax_gat = plt.subplots(1, figsize=[7, 5.5])
pretty_gat(np.mean(scores, axis=0),
times=times,
sig=p_values < alpha,
chance=chance,
ax=ax_gat,
clim=clim)
report.add_figs_to_section([fig], [analysis['name']], analysis['name'])
report.save()
if tilt == 1:
continue # do not plot absent case
plot_tuning(results['tuning'][:, ii, jj, :, tilt],
ax=axes[ii, jj], shift=np.pi, color=color)
plot_tuning(results['tuning'][:, ii, jj, :, tilt],
ax=axes[ii, jj], shift=np.pi, color='k', alpha=0.)
axes[ii, jj].axvline((jj * 2 - 1) * np.pi / 3, color=colors[0])
axes[ii, jj].axvline(-(jj * 2 - 1) * np.pi / 3, color=colors[2])
pretty_axes(axes, xticklabels=['$-\pi/2$', '', '$\pi/2$'],
xlabel='Angle Error',
ylim=[0.014, .08],
yticks=[0.014, 1./len(results['bins']), .08],
yticklabels=[1.4, '', 8.], ylabel='Probability')
fig.tight_layout()
report.add_figs_to_section(fig, 'target_probe', 'cross_generalization')
# Plot bias Temporal Generalization Matrices
fig, axes = plt.subplots(2, 2, figsize=[6.15, 5.6])
fig.subplots_adjust(right=0.85, hspace=0.05, wspace=0.05)
for ii in range(2):
for jj in range(2):
scores = -np.array(results['bias'][:, ii, jj, ...])
p_val = results['bias_pval'][ii, jj, :, :]
pretty_gat(scores.mean(0), times=times, ax=axes[ii, jj],
colorbar=False, clim=[-.1, .1], sig=p_val < .05)
axes[ii, jj].axvline(.800, color='k')
axes[ii, jj].axhline(.800, color='k')
pretty_axes(axes, ylabelpad=-15, xticks=np.linspace(-.100, 1.400, 13),
xticklabels=['', 0] + [''] * 13 + [1400, ''])
pretty_colorbar(cax=fig.add_axes([.88, .25, .02, .55]), ax=axes[0, 0])
zorder = float(vis == 'low')
y_shift = .1 * float(vis == 'high')
times = np.linspace(0, 2., len(diag.mean(0))) + .1 * int(vis == 'low')
axes['decod'].fill_between(
times, .5 + y_shift, diag.mean(0) + y_shift, color=colors[vis],
alpha=1., linewidth=0., zorder=zorder)
axes['decod'].plot(times, diag.mean(0) + y_shift, color='k',
zorder=zorder)
axes['decod'].set_xticks([])
axes['decod'].set_yticks([])
axes['decod'].set_aspect('equal')
for side in ('left', 'right', 'top', 'bottom'):
axes['decod'].spines[side].set_visible(False)
axes['decod'].set_ylim(-.1, 1.1)
# Temporal generalization scores
axes[vis].matshow(score.mean(0), origin='lower',
vmin=0, vmax=1., cmap='RdBu_r')
axes[vis].set_xticks([])
axes[vis].set_yticks([])
pretty_plot(axes[vis])
# TG subtraction: high vis - low vis
axes['vis'].matshow(np.mean(scores[:, 1] - scores[:, 0], axis=0),
origin='lower', vmin=-.25, vmax=.25, cmap='RdBu_r')
axes['vis'].set_xticks([])
axes['vis'].set_yticks([])
pretty_plot(axes['vis'])
report.add_figs_to_section([fig], [model], model)
report.save()
# Print some sig cluster details
sig = p_val < .05
diff = np.diff(1. * sig)
starts, stops = np.where(diff > 0)[0], np.where(diff < 0)[0]
for start, stop in zip(starts, stops):
score_toi = np.mean(score[:, start:stop], axis=1)
details_roi.append(dict(
roi=roi,
p_val=p_val[stop],
start=times[start],
stop=times[stop],
mean=np.mean(score_toi), # FIXME contrast is inverted
inv_mean=1.-np.mean(score_toi), # XXX FIXME
sem=np.std(score_toi) / np.sqrt(len(score))))
report.add_figs_to_section([fig], section, 'source')
report.add_htmls_to_section(DataFrame(details_roi).to_html(),
section, 'roi')
report.add_htmls_to_section(DataFrame(details_toi).to_html(),
section, 'toi')
# Print significant clusters on all regions
connectivity = sparse.csr_matrix(np.eye((data.shape[1])))
p_vals = stats(data.transpose(0, 2, 1) - chance, None, n_jobs=-1)
sig_labels = DataFrame(labels[np.where(np.sum(p_vals < .05, axis=0))[0]])
report.add_htmls_to_section(sig_labels.to_html(), section, 'sig_cluster')
sig_labels = DataFrame(labels[np.where(np.sum(p_vals < .05, axis=0))[0]])
report.add_htmls_to_section(sig_labels.to_html(), section, 'sig_cluster')
report.save()
scores_diag = np.array([np.diag(score) for score in scores])
diag_offdiag = scores - np.tile([np.diag(score) for score in scores],
[len(times), 1, 1]).transpose(1, 0, 2)
slices_tois = np.arange(.100, 1.100, .200)
# Temporal generalization matrices
clim = np.percentile(np.diag(np.mean(scores, axis=0)), 97)
clim = [chance-(clim-chance), clim]
fig_gat, ax_gat = plt.subplots(1, figsize=[7, 5.5])
pretty_gat(np.mean(scores, axis=0), times=times, sig=p_values < alpha,
chance=chance, ax=ax_gat, clim=clim)
ax_gat.axvline(.800, color='k')
ax_gat.axhline(.800, color='k')
ax_gat.set_xlabel('Test Times', labelpad=-10)
ax_gat.set_ylabel('Train Times', labelpad=-15)
report.add_figs_to_section(fig_gat, 'gat', analysis['name'])
fig_, ax = plt.subplots(1)
ax.matshow(np.mean(scores, axis=0), origin='lower',
extent=[np.min(times), np.max(times)] * 2)
ax.set_xticks(np.arange(0., 1.200, .01))
ax.set_yticks(np.arange(0., 1.200, .01))
figs.append(fig_)
# ------ Plot times slices score
fig_offdiag, axs = plt.subplots(len(slices_tois), 1, figsize=[5, 6])
pretty_slices(scores, times=times, chance=chance, axes=axs,
sig=p_values < alpha, sig_diagoff=p_values_off < alpha,
colors=[analysis['color'], 'b'], tois=slices_tois,
fill_color=analysis['color'])
for ax in axs:
ax=axes[ii, jj],
shift=np.pi,
color='k',
alpha=0.)
axes[ii, jj].axvline((jj * 2 - 1) * np.pi / 3, color=colors[0])
axes[ii, jj].axvline(-(jj * 2 - 1) * np.pi / 3, color=colors[2])
pretty_axes(axes,
xticklabels=['$-\pi/2$', '', '$\pi/2$'],
xlabel='Angle Error',
ylim=[0.014, .08],
yticks=[0.014, 1. / len(results['bins']), .08],
yticklabels=[1.4, '', 8.],
ylabel='Probability')
fig.tight_layout()
report.add_figs_to_section(fig, 'target_probe', 'cross_generalization')
# Plot bias Temporal Generalization Matrices
fig, axes = plt.subplots(2, 2, figsize=[6.15, 5.6])
fig.subplots_adjust(right=0.85, hspace=0.05, wspace=0.05)
for ii in range(2):
for jj in range(2):
scores = -np.array(results['bias'][:, ii, jj, ...])
p_val = results['bias_pval'][ii, jj, :, :]
pretty_gat(scores.mean(0),
times=times,
ax=axes[ii, jj],
colorbar=False,
clim=[-.1, .1],
sig=p_val < .05)
axes[ii, jj].axvline(.800, color='k')
# Main plotting
colors = dict(visibility=plt.get_cmap('bwr')(np.linspace(0, 1, 4.)),
contrast=plt.get_cmap('hot_r')([.5, .75, 1.]))
# Loop across visibility and orientation analyses
for analysis in analyses:
# Plot correlation of decoding score with visibility and contrast
scores, R, times = _analyze_continuous(analysis)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=[20, 10])
sig = stats(R['visibility']) < .05
pretty_decod(-R['visibility'], times=times, sig=sig, ax=ax1,
color='purple', fill=True)
sig = stats(R['contrast']) < .05
pretty_decod(-R['contrast'], times=times, sig=sig, ax=ax2,
color='orange', fill=True)
report.add_figs_to_section([fig], ['continuous regress'], analysis['name'])
# Plot decoding score for each visibility level
all_scores, score_pvals, times = _subscore_pipeline(analysis)
if 'circAngle' in analysis['name']:
all_scores /= 2. # from circle to half circle
figs, axes = list(), list()
for vis in range(4):
fig, ax = plt.subplots(1, figsize=[14, 11])
scores = all_scores[:, vis, ...]
p_val = score_pvals[vis]
pretty_gat(np.nanmean(scores, axis=0), times=times,
chance=analysis['chance'],
ax=ax, colorbar=False)
xx, yy = np.meshgrid(times, times, copy=False, indexing='xy')
ax.contour(xx, yy, p_val < .05, colors='black', levels=[0],
scale=1,
contours=False,
show=False,
times=np.linspace(0, .500, 6),
average=.025,
colorbar=True)
fig_grad = evoked.plot_topomap(cmap=analysis['cmap'],
ch_type='grad',
vmin=vmin,
vmax=vmax,
**opts)
cax = fig_grad.get_children()[-1]
cax.set_yticks([vmin, vmax])
cax.set_yticklabels([smin, smax])
cax.set_title('')
report.add_figs_to_section(fig_grad, 'topo_grad', analysis['name'])
fig_mag = evoked.plot_topomap(ch_type='mag',
vmin=vmin_mag,
vmax=vmax,
**opts)
cax = fig_mag.get_children()[-1]
cax.set_yticks([vmin, vmax])
cax.set_yticklabels([smin_mag, '', smax])
cax.set_title('')
# fig_mag.tight_layout()
report.add_figs_to_section(fig_mag, 'topo_mag', analysis['name'])
# Plot butterfly
fig_butt_m, ax = plt.subplots(1, figsize=fig_grad.get_size_inches())
plot_butterfly(evoked,
ax.set_xlabel('Visibility Rating')
ax.set_ylabel('Contrast')
ax.zaxis.set_rotate_label(False)
ax.set_zlabel('Response %', rotation=90)
ax.set_xticks(np.linspace(0, 1, 2))
ax.set_yticks([])
ax.set_zticks(np.linspace(0, .7, 2))
ax.set_ylim(.25, 1.)
ax.set_zlim(0, .75)
tmp_planes = ax.zaxis._PLANES
ax.zaxis.grid('off')
ax.zaxis._PLANES = (tmp_planes[2], tmp_planes[3],
tmp_planes[0], tmp_planes[1],
tmp_planes[4], tmp_planes[5])
# fig.show()
report.add_figs_to_section(fig, 'visibility 3d', 'visibility')
# 2D
fig, ax = plt.subplots(1, figsize=[6, 4])
abs_m = x_vis[:, 0, :].mean(0)
abs_sem = abs_m + x_vis[:, 0, :].mean(0).std(0) / x_vis.shape[0]
pst_m = x_vis[:, 1:, :].mean(1).mean(0)
pst_sem = pst_m + x_vis[:, 1:, :].mean(1).std(0).mean(0) / x_vis.shape[0]
fill = lambda z, c: ax.fill_between(np.linspace(0, 1., 4.), z, alpha=.5,
color=cmap(c), edgecolor='none')
fill(abs_sem, 0.)
fill(pst_sem, 1.)
fill(abs_m, 0.)
fill(pst_m, 1.)
ax.text(0.1, np.mean(x_vis[:, 0, 0], axis=0) - .05, 'Absent', color=cmap(0.),
[len(times), 1, 1]).transpose(1, 0, 2)
p_values_off = stats(diag_offdiag)
# Save stats results
out = dict(scores=scores, p_values=p_values, p_values_off=p_values_off,
times=times, analysis=analysis)
save(out, 'score', analysis=ana_name)
return out
# only recompute on the relevant analyses
analyses = [ana for ana in analyses if ana['name'] in
['target_present', 'target_circAngle']]
for analysis in analyses:
out = _stats(analysis)
scores = out['scores']
if 'circAngle' in analysis['name']:
scores /= 2.
times = out['times']
alpha = .05
chance = analysis['chance']
p_values = stats(scores - chance)
clim = np.percentile(np.diag(np.mean(scores, axis=0)), 97)
clim = [chance-(clim-chance), clim]
fig, ax_gat = plt.subplots(1, figsize=[7, 5.5])
pretty_gat(np.mean(scores, axis=0), times=times, sig=p_values < alpha,
chance=chance, ax=ax_gat, clim=clim)
report.add_figs_to_section([fig], [analysis['name']], analysis['name'])
report.save()
# Temporal generalization matrices
clim = np.percentile(np.diag(np.mean(scores, axis=0)), 97)
clim = [chance - (clim - chance), clim]
fig_gat, ax_gat = plt.subplots(1, figsize=[7, 5.5])
pretty_gat(np.mean(scores, axis=0),
times=times,
sig=p_values < alpha,
chance=chance,
ax=ax_gat,
clim=clim)
ax_gat.axvline(.800, color='k')
ax_gat.axhline(.800, color='k')
ax_gat.set_xlabel('Test Times', labelpad=-10)
ax_gat.set_ylabel('Train Times', labelpad=-15)
report.add_figs_to_section(fig_gat, 'gat', analysis['name'])
fig_, ax = plt.subplots(1)
ax.matshow(np.mean(scores, axis=0),
origin='lower',
extent=[np.min(times), np.max(times)] * 2)
ax.set_xticks(np.arange(0., 1.200, .01))
ax.set_yticks(np.arange(0., 1.200, .01))
figs.append(fig_)
# ------ Plot times slices score
fig_offdiag, axs = plt.subplots(len(slices_tois), 1, figsize=[5, 6])
pretty_slices(scores,
times=times,
chance=chance,
axes=axs,
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=[20, 10])
sig = stats(R['visibility']) < .05
pretty_decod(-R['visibility'],
times=times,
sig=sig,
ax=ax1,
color='purple',
fill=True)
sig = stats(R['contrast']) < .05
pretty_decod(-R['contrast'],
times=times,
sig=sig,
ax=ax2,
color='orange',
fill=True)
report.add_figs_to_section([fig], ['continuous regress'], analysis['name'])
# Plot decoding score for each visibility level
all_scores, score_pvals, times = _subscore_pipeline(analysis)
if 'circAngle' in analysis['name']:
all_scores /= 2. # from circle to half circle
figs, axes = list(), list()
for vis in range(4):
fig, ax = plt.subplots(1, figsize=[14, 11])
scores = all_scores[:, vis, ...]
p_val = score_pvals[vis]
pretty_gat(np.nanmean(scores, axis=0),
times=times,
chance=analysis['chance'],
ax=ax,
colorbar=False)