def plot_cluster_error(ax):
res_ftemp = "spatial_analysis/{}_{}_ifs.pkz"
for exp in ["dots", "sticks"]:
subjects = get_subject_order(exp)
color = get_colormap(exp, as_cmap=False)[20]
errs = []
for subj in subjects:
res = moss.load_pkl(res_ftemp.format(subj, exp))
x = res.steps
norm = res.null.mean()
errs.append(res.real / norm)
errs = np.vstack(errs)
mean = errs.mean(axis=0)
ax.plot(x, mean, color=color, lw=2)
sem = stats.sem(errs, axis=0)
ax.fill_between(x, mean - sem, mean + sem, alpha=.2, color=color)
ax.axhline(y=1,
lw=1,
dashes=[5, 2],
color=".5",
zorder=0,
xmin=.02,
xmax=.98)
ax.set(xlim=(0, 42),
ylim=(.55, 1.45),
yticks=[.6, .8, 1, 1.2, 1.4],
xticks=[0, 10, 20, 30, 40],
xlabel="Neighborhood radius (mm)",
ylabel="Normalized error")
sns.despine(ax=ax, trim=True)
mec=".2",
ms=3)
ax.set(xticks=x, xlim=(.6, 4.4), ylim=(0, .07))
sns.despine(ax=ax, trim=True)
plt.setp(axes[1:], yticklabels=[])
axes[0].set_ylabel("Correlation (r)")
if __name__ == "__main__":
set_style()
f, axes = setup_figure()
subjects = get_subject_order("sticks")
plot_time_corrs(subjects, axes)
axes[0].text(2.5, .041, "Same context", color=".2", size=7, ha="center")
axes[0].text(2.9,
.014,
"Different\ncontext",
color=".5",
size=7,
ha="center")
f.text(.55, .03, "Resting scan number", size=10, ha="center")
f.tight_layout()
f.subplots_adjust(bottom=.2)
ylabel = "Subnetwork strength\n($r_{\mathrm{same}} - r_{\mathrm{diff}}$)"
plt.setp(axes[1:7], yticklabels=[])
axes[0].set_ylabel(ylabel)
if exp == "dots":
plt.setp(axes[8:], yticklabels=[])
plt.setp(axes[:7], xticklabels=[])
axes[7].set_ylabel(ylabel)
if __name__ == "__main__":
set_style()
f, dots_axes, sticks_axes, rest_axes = setup_figure()
dots_subjects = get_subject_order("dots")
sticks_subjects = get_subject_order("sticks")
plot_distance_corrs(dots_subjects, dots_axes, "dots")
plot_distance_corrs(sticks_subjects, sticks_axes, "sticks")
plot_distance_corrs(sticks_subjects, rest_axes, "rest")
f.text(.525, .01, "Distance threshold (mm)", size=10, ha="center")
f.text(.525, .95, "Experiment 1 (residuals)", size=11, ha="center")
f.text(.525, .50, "Experiment 2 (residuals)", size=11, ha="center")
f.text(.525, .25, "Experiment 2 (resting)", size=11, ha="center")
dots_axes[0].text(10, .038, "2D Distance", color=".2", size=7)
dots_axes[0].text(10, .005, "3D Distance", color=".5", size=7)
savefig(f, __file__)
return f, brain_axes, hist_axes, cbar_ax
def plot_colorbar(f, ax):
cmap = get_colormap("dots")
xx = np.arange(200).reshape(1, 200)
ax.imshow(xx, rasterized=True, aspect="auto", cmap=cmap)
kws = dict(size=7, ha="center")
f.text(.35, .04, "Motion", **kws)
f.text(.65, .04, "Color", **kws)
ax.set(xticks=[], yticks=[])
if __name__ == "__main__":
set_style()
f, brain_axes, hist_axes, cbar_ax = setup_figure()
subjects = get_subject_order("dots")
plot_brains(subjects, brain_axes)
plot_hists(subjects, hist_axes, 2, 380)
plot_colorbar(f, cbar_ax)
savefig(f, __file__)