data = data.mean(axis=0)
fontsize = 110
top = 100
background = (1, 1, 1)
brain_a = plot_directed_cnx(data,
regions,
parc,
top_cnx=top,
centre=0.5,
background=background)
vmin, vmax = get_vminmax(data, top)
print("Top {}: {} - {}".format(top, vmin, vmax))
img = make_brain_image(views,
brain_a,
text="A",
text_loc="lup",
text_pan=0,
fontsize=fontsize)
fig1, ax1 = plt.subplots(1, 1, figsize=(38.4, 12.8))
ax1.imshow(img)
ax1.axis("off")
fig1.suptitle(
"{}, {} band, Strongest {} connections, dPTE magnitude range "
"{:.4f}-{:.4f}".format(cond_keys[cond], band_info[band]["name"],
top, vmax, vmin),
fontsize=48)
plt.tight_layout()
fig1.canvas.draw()
mat1 = np.frombuffer(fig1.canvas.tostring_rgb(), dtype=np.uint8)
mat1 = mat1.reshape(fig1.canvas.get_width_height()[::-1] + (3, ))
axes[pan].axis("off")
for pad in pads:
axes[pad].axis("off")
params = aic_comps["sig_params"].copy()
simp_params = np.expand_dims(aic_comps["simp_params"].copy()[:, 1], 1)
params = np.concatenate((params, simp_params), axis=1)
params_n = param_norm(params)
vmin = -abs(params).max()
vmax = abs(params).max()
param_n = params_n[:, -1]
rgba = params_to_rgba(param_n)
brains.append(
plot_rgba(rgba, labels, parc, figsize=figsize, background=background))
img = make_brain_image(views, brains[-1], text="A", text_loc="lup", text_pan=0)
ax = axes["A"]
ax.imshow(img)
# changes from rest for each individual task
# audio
param_n = params_n[:, 0]
rgba = params_to_rgba(param_n)
brains.append(
plot_rgba(rgba, labels, parc, figsize=figsize, background=background))
img = make_brain_image(views, brains[-1], text="B", text_loc="lup", text_pan=0)
ax = axes["B"]
ax.imshow(img)
# visual
param_n = params_n[:, 1]
if pad:
mos_str += pad + "\n"
fig, axes = plt.subplot_mosaic(mos_str, figsize=(64.8, 108))
for desc, pan in zip(descs, pans):
axes[pan].set_title("{}".format(desc), fontsize=fontsize)
axes[pan].axis("off")
for pad in pads:
axes[pad].axis("off")
legend_props = [["Audio", "r"], ["Visual", "g"], ["Aud. Distr.", "b"]]
for pan, desc, cond in zip(pans, descs, conds):
if cond == "rainbow":
img = make_brain_image(views,
params_brains[cond],
text=pan,
text_loc="lup",
text_pan=0,
legend=legend_props,
legend_pan=2)
else:
img = make_brain_image(views,
params_brains[cond],
text=pan,
text_loc="lup",
text_pan=0)
axes[pan].imshow(img)
plt.suptitle("Estimated connectivity change from resting state",
fontsize=fontsize)
plt.savefig("../images/cnx_byresp_figure.png")
alpha_max=None,
ldown_title="",
top_cnx=top_cnx,
figsize=figsize,
background=background,
text_color=text_color)
with open("{}{}/cnx_params_{}.pickle".format(lmm_dir, band, band), "wb") as f:
pickle.dump(cnx_params, f)
###### make figure for manuscripts
# rest cnx by brainview
brain_img = make_brain_image(views,
params_brains["rest"],
text="",
text_loc="lup",
text_pan=0,
orient="horizontal")
# cnx conditions by matrix
# rearrange by region, hemisphere, ant/pos
inds = []
reg_arranged = []
hemi_arranged = []
for lobe, lobe_regs in region_dict.items():
for hemi in ["lh", "rh"]:
these_label_names = ["{}-{}".format(lr, hemi) for lr in lobe_regs]
these_labels = [x for x in labels if x.name in these_label_names]
# order based on ant-pos location
these_ypos = np.array([x.pos[:, 1].mean() for x in these_labels])
stc = mne.read_source_estimate("{}/stcs/{}".format(proc_dir, filename))
stc = morphs[trial_info[0]].apply(stc)
for reg_idx, reg in enumerate(regions):
temp_data = mne.extract_label_time_course(stc,
reg,
fs_src,
mode="mean")
data[reg_idx].append(temp_data.mean())
data = np.array(data)
data = data.mean(axis=1)
np.save("{}rest_dics.npy".format(proc_dir), data)
else:
data = np.load("{}rest_dics.npy".format(proc_dir))
data_norm = (data) / (data.max())
rgbs = cm.get_cmap(cmap)(data_norm)
rgbs[:, -1] = data_norm
brain = plot_rgba(rgbs, regions, parc, background=(1, 1, 1))
img = make_brain_image(views,
brain,
cbar=cmap,
vmin=0,
vmax=data.max(),
orient="horizontal",
cbar_label="DICS power (NAI normalised)")
fig, ax = plt.subplots(1, 1, figsize=(38.4, 12.8))
ax.imshow(img)
ax.axis("off")
plt.tight_layout()
plt.savefig("../images/dics_rest.png")