split_mats[0].append(temp_mat)
temp_mat = mat.copy()
temp_mat[temp_mat > 0] = 0
split_mats[1].append(temp_mat)
for sm, axe, type_title in zip(split_mats, axes,
["Outflow", "Inflow"]):
total_flow = np.sum(sm[si])
if (sm[si] == 0).all():
for a in axe:
a.axis("off")
continue
mfig = mlab.figure(size=(1280, 1024))
brain = plot_directed_cnx(sm[si],
labels,
parc,
fig=mfig,
alpha_min=mat_min,
alpha_max=mat_max,
uniform_weight=True,
alpha=0.99)
mlab.view(*side_views["left_side"])
axe[0].imshow(mlab.screenshot())
axe[0].axis("off")
axe[0].set_title("{}\n\nLeft Saggital View".format(type_title))
mlab.view(*side_views["right_side"])
axe[1].imshow(mlab.screenshot())
axe[1].axis("off")
axe[1].set_title("Right Saggital View")
mlab.view(*side_views["top_side"])
axe[2].imshow(mlab.screenshot())
axe[2].axis("off")
axe[2].set_title("Horizontal View")
data = []
for sub_idx, sub in enumerate(subjs):
# we actually only need the dPTE to get the number of trials
data_temp = load_sparse("{}nc_{}_{}_dPTE_{}.sps".format(
proc_dir, sub, cond, band))
for epo_idx in range(data_temp.shape[0]):
data.append(data_temp[epo_idx, ])
data = np.array(data)
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 "
rests = []
for sub in subjs:
idx = 0
for k in eff_dict.keys():
dPTE = load_sparse("{}nc_{}_{}_dPTE_{}.sps".format(
proc_dir, sub, k, band))
dPTEs[idx].append(dPTE.mean(axis=0))
idx += 1
dPTE = load_sparse("{}nc_{}_rest_dPTE_{}.sps".format(proc_dir, sub, band))
rests.append(dPTE.mean(axis=0))
dPTEs = np.mean(dPTEs, axis=1)
rest = np.mean(rests, axis=0)
# mask by significant edges
contrasts = {
"audio": dPTEs[0] - rest,
"visselten": dPTEs[1] - rest,
"visual": dPTEs[2] - rest,
"zaehlen": dPTEs[3] - rest,
}
for k, v in contrasts.items():
temp_dpte = np.zeros(v.shape)
for edge in eff_dict[k]["clusters"][1][0]:
temp_dpte[edge[0], edge[1]] = v[edge[0], edge[1]]
contrasts[k] = temp_dpte
brains = []
for k, v in contrasts.items():
brains.append(
plot_directed_cnx(v, labels, parc, lup_title=k, top_cnx=top_cnx))
# threshold by statistically significant edges
for edge_idx in range(len(edges)):
thresh_pte[edges[edge_idx,0],edges[edge_idx,1]] = \
temp_pte[edges[edge_idx,0],edges[edge_idx,1]]
# threshold by top x connections
top_thresh = np.sort(np.abs(thresh_pte.flatten()))[-top_cnx]
thresh_pte[np.abs(thresh_pte) < top_thresh] = 0
dPTEs.append(thresh_pte)
con_pte = dPTEs[0] - dPTEs[1]
alpha_max = max([np.abs(d).max() for d in dPTEs])
alpha_min = min([np.abs(d[d != 0]).min() for d in dPTEs])
brain0 = plot_directed_cnx(dPTEs[0],
labels,
parc,
figsize=figsize,
ldown_title=band,
rup_title=conds[0],
alpha_min=alpha_min,
alpha_max=alpha_max)
brain1 = plot_directed_cnx(dPTEs[1],
labels,
parc,
figsize=figsize,
ldown_title=band,
rup_title=conds[1],
alpha_min=alpha_min,
alpha_max=alpha_max)
brain2 = plot_directed_cnx(con_pte,
labels,
parc,
figsize=figsize,
mask_mat[ROI_idx, :] = np.ones(cnx_params[stat_cond].shape[1])
cnx_params[stat_cond] *= mask_mat
all_params = np.abs(
np.array([cnx_params[stat_cond] for stat_cond in stat_conds]).flatten())
all_params.sort()
alpha_max, alpha_min = all_params[-1:], all_params[-top_cnx].min()
alpha_max, alpha_min = 0.015, 0.001
alpha_max, alpha_min = None, None
params_brains = {}
for stat_cond, cond in zip(stat_conds, conds):
params_brains[cond] = plot_directed_cnx(cnx_params[stat_cond],
labels,
parc,
alpha_min=alpha_min,
alpha_max=alpha_max,
ldown_title="",
top_cnx=top_cnx,
figsize=figsize,
background=background,
text_color=text_color)
params_brains["task"] = plot_directed_cnx(cnx_params["task"],
labels,
parc,
alpha_min=None,
alpha_max=None,
ldown_title="",
top_cnx=top_cnx,
figsize=figsize,
background=background,
text_color=text_color)
mask_mat[:, ROI_idx] = np.ones(cnx_params[stat_cond].shape[0])
mask_mat[ROI_idx, :] = np.ones(cnx_params[stat_cond].shape[1])
cnx_params[stat_cond] *= mask_mat
all_params = np.abs(
np.array([cnx_params[stat_cond] for stat_cond in stat_conds]).flatten())
all_params.sort()
alpha_max, alpha_min = all_params[-1:], all_params[-top_cnx].min()
alpha_max, alpha_min = 0.015, 0.001
alpha_max, alpha_min = None, None
params_brains = {}
for stat_cond, cond in zip(stat_conds, conds):
params_brains[cond] = plot_directed_cnx(cnx_params[stat_cond],
labels,
parc,
alpha_min=alpha_min,
alpha_max=alpha_max,
ldown_title="",
top_cnx=top_cnx,
figsize=figsize)
if write_images:
make_brain_figure(views, params_brains[-1])
params_brains["simple_RT"] = plot_directed_cnx(cnx_params["simple_RT"],
labels,
parc,
alpha_min=None,
alpha_max=None,
ldown_title="",
top_cnx=top_cnx,
figsize=figsize)
if write_images:
mask_mat[ROI_idx, :] = np.ones(cnx_params[stat_cond].shape[1])
cnx_params[stat_cond] *= mask_mat
all_params = np.abs(
np.array([cnx_params[stat_cond] for stat_cond in stat_conds]).flatten())
all_params.sort()
alpha_max, alpha_min = all_params[-1:], all_params[-top_cnx].min()
alpha_max, alpha_min = 0.015, 0.001
alpha_max, alpha_min = None, None
params_brains = []
for stat_cond, cond in zip(stat_conds, conds):
params_brains.append(
plot_directed_cnx(cnx_params[stat_cond],
labels,
parc,
alpha_min=alpha_min,
alpha_max=alpha_max,
ldown_title=cond,
top_cnx=top_cnx))
if write_images:
make_brain_figure(views, params_brains[-1])
params_brains.append(
plot_directed_cnx(cnx_params["simple_task"],
labels,
parc,
alpha_min=None,
alpha_max=None,
ldown_title="Simple (task)",
top_cnx=top_cnx))
if write_images: