def plot_dynamics(dynamics, ax=None, start=2,
n_columns=None, n_regions=None, clim=[-1, 1],
region=None, node=None, column=None, cmap=None):
if ax is None:
ax = plt.gca()
if (column is not None) or (region is not None) or (node is not None):
n_nodes = (len(dynamics.T) // n_columns - 1) // n_regions
sel = select_nodes(n_columns=n_columns, n_regions=n_regions,
n_nodes=n_nodes, node=node, region=region,
column=column)
dynamics = dynamics[:, sel]
if cmap is None:
cmap = nonlinear_cmap('RdBu_r', 0, clim)
clim = [0., 1.]
ax.matshow(dynamics.T, vmin=clim[0], vmax=clim[1], cmap=cmap,
origin='lower')
if (isinstance(column, list) and len(column) == 1):
for ii in range(n_columns):
ax.axhline(ii * (len(dynamics.T) // n_columns) - .5,
color='gray', linestyle=':')
ax.set_ylabel('Modules')
ax.set_xlabel('Times')
ax.set_aspect('auto')
if not isinstance(start, (list, np.ndarray)):
start = [start]
for start_ in start:
ax.set_xticks([start_ - .5])
ax.set_xticklabels([start_ - start[0]])
for start_ in start:
ax.axvline(start_ - .5, color='k')
pretty_plot(ax)
def plot_dynamics(dynamics,
ax=None,
start=2,
n_columns=None,
n_regions=None,
clim=[-1, 1],
region=None,
node=None,
column=None,
cmap=None):
if ax is None:
ax = plt.gca()
if (column is not None) or (region is not None) or (node is not None):
n_nodes = (len(dynamics.T) // n_columns - 1) // n_regions
sel = select_nodes(n_columns=n_columns,
n_regions=n_regions,
n_nodes=n_nodes,
node=node,
region=region,
column=column)
dynamics = dynamics[:, sel]
if cmap is None:
cmap = nonlinear_cmap('RdBu_r', 0, clim)
clim = [0., 1.]
ax.matshow(dynamics.T,
vmin=clim[0],
vmax=clim[1],
cmap=cmap,
origin='lower')
if (isinstance(column, list) and len(column) == 1):
for ii in range(n_columns):
ax.axhline(ii * (len(dynamics.T) // n_columns) - .5,
color='gray',
linestyle=':')
ax.set_ylabel('Modules')
ax.set_xlabel('Time')
ax.set_aspect('auto')
if not isinstance(start, (list, np.ndarray)):
start = [start]
for start_ in start:
ax.set_xticks([start_ - .5])
ax.set_xticklabels([start_ - start[0]])
for start_ in start:
ax.axvline(start_ - .5, color='k')
pretty_plot(ax)
def plot_connectivity(connectivity, ax=None, dual=False):
if ax is None:
ax = plt.gca()
ax.matshow(connectivity, cmap='RdBu_r', vmin=-1, vmax=1,
origin='lower')
ax.set_xticks([])
ax.set_yticks([])
if dual:
n_nodes = connectivity.shape[0]
ax.set_xticks([n_nodes / 4.-.5, n_nodes / 2.-.5,
.75 * n_nodes-.5, n_nodes-.5])
ax.set_xticklabels(['feedforward', '', 'feedback', ''])
ax.set_yticks([n_nodes / 4.-.5, n_nodes / 2.-.5,
.75 * n_nodes-.5, n_nodes-.5])
ax.set_yticklabels(['feedforward', '', 'feedback', ''])
ax.axhline(n_nodes/2.-.5, color='gray', linestyle=':')
ax.axvline(n_nodes/2.-.5, color='gray', linestyle=':')
ax.set_xlabel('neurons')
# ax.set_ylabel('neurons')
pretty_plot(ax)
def plot_connectivity(connectivity, ax=None, dual=False):
if ax is None:
ax = plt.gca()
ax.matshow(connectivity, cmap='RdBu_r', vmin=-1, vmax=1, origin='lower')
ax.set_xticks([])
ax.set_yticks([])
if dual:
n_nodes = connectivity.shape[0]
ax.set_xticks([
n_nodes / 4. - .5, n_nodes / 2. - .5, .75 * n_nodes - .5,
n_nodes - .5
])
ax.set_xticklabels(['feedforward', '', 'feedback', ''])
ax.set_yticks([
n_nodes / 4. - .5, n_nodes / 2. - .5, .75 * n_nodes - .5,
n_nodes - .5
])
ax.set_yticklabels(['feedforward', '', 'feedback', ''])
ax.axhline(n_nodes / 2. - .5, color='gray', linestyle=':')
ax.axvline(n_nodes / 2. - .5, color='gray', linestyle=':')
ax.set_xlabel('neurons')
# ax.set_ylabel('neurons')
pretty_plot(ax)
def make_3d_plot(ax, xs, y, zs, colors):
# make areas
verts = []
verts_sem = []
for ii in range(y.shape[2]):
ys = np.mean(y[:, :, ii], axis=0)
verts.append(list(zip(np.hstack((0., xs, 1.)),
np.hstack((0., ys, 0.)))))
ys += np.std(y[:, :, ii], axis=0) / np.sqrt(len(subjects))
verts_sem.append(list(zip(np.hstack((0., xs, 1.)),
np.hstack((0., ys, 0.)))))
poly = PolyCollection(verts, facecolors=colors, edgecolor='k',
linewidth=1)
poly.set_alpha(.75)
ax.add_collection3d(poly, zs=zs, zdir='y')
poly = PolyCollection(verts_sem, facecolors=colors, edgecolor='none')
poly.set_alpha(0.5)
ax.add_collection3d(poly, zs=zs, zdir='y')
return pretty_plot(ax)
sig = p_val < .05
# plot effect size
scores = np.mean(scores, axis=0)
im = ax.matshow(scores, aspect='auto', origin='lower',
extent=[times[0], times[-1], 0, len(freqs)],
vmin=analysis['chance'], vmax=np.max(scores),
cmap=analysis['cmap'])
# 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])
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()
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.),
ha='left', va='bottom')
ax.text(1., np.mean(x_vis[:, -1, -1], axis=0) + .05, 'Present',
color=cmap(1.), ha='right', va='bottom')
ax.set_xlabel('Visibility Rating')
ax.set_ylabel('Response %')
ax.set_xticks(np.linspace(0, 1, 2))
ax.set_yticks(np.linspace(0, .7, 2))
ax.set_ylim(0, .75)
ax = pretty_plot(ax)
report.add_figs_to_section(fig, 'visibility 2d', 'visibility')
# #############################################################################
# 2.0 Overall discrimination performance
x = dict(Accuracy=list(), Dprime=list())
for subject in subjects:
query = 'subject==%s and target_present==True' % subject
x['Accuracy'].append(np.nanmean(data.query(query)['discrim_correct']))
def count(tilt, acc):
query2 = ' and probe_tilt==%s and discrim_correct==%s' % (tilt, acc)
return len(data.query(query + query2))
hits = count(1, True)
