def make_fig2(save=False):
data_params = {}
data_params[
'path'] = '/research/vibs/Pedro/datasets/motorimagery/BCI-competitions/BCI-IV/2a/'
data_params['session'] = 1
data_params['task'] = 1
data_params['tparams'] = [1.25, 3.75]
data_params['fparams'] = [8.0, 35.0]
data_params['subject'] = 5
X, yworst = get_data(data_params)
X = X[(yworst == 1) | (yworst == 2)]
yworst = yworst[(yworst == 1) | (yworst == 2)]
covs = Covariances().fit_transform(X)
uworst, lworst = get_diffusionEmbedding(points=covs,
distance=distance_riemann)
data_params['subject'] = 1
X, ybest = get_data(data_params)
X = X[(ybest == 1) | (ybest == 2)]
ybest = ybest[(ybest == 1) | (ybest == 2)]
covs = Covariances().fit_transform(X)
ubest, lbest = get_diffusionEmbedding(points=covs,
distance=distance_riemann)
fig = plt.figure(figsize=(10.5, 5))
plt.subplots_adjust(wspace=0.020, hspace=0.025)
plt.subplot(1, 2, 1)
colorst = [['b', 'r'][int(t)] for t in (yworst - 2)]
plt.scatter(uworst[:, 1], uworst[:, 2], color=colorst, s=44)
plt.xlabel(r'$\phi_1$', fontsize=26)
plt.ylabel(r'$\phi_2$', fontsize=26)
plt.xticks([])
plt.yticks([])
ttl = plt.title('Worst subject', fontsize=20)
ttl.set_position([.5, 1.025])
ax = plt.subplot(1, 2, 2)
colorst = [['b', 'r'][int(t)] for t in (ybest - 2)]
plt.scatter(ubest[:, 1], ubest[:, 2], color=colorst, s=44)
plt.xlabel(r'$\phi_1$', fontsize=26)
plt.ylabel(r'$\phi_2$', fontsize=26)
ax.yaxis.set_label_position("right")
plt.xticks([])
plt.yticks([])
ttl = plt.title('Best subject', fontsize=20)
ttl.set_position([.5, 1.025])
if save:
name = 'figure2'
savefigure(name)
return [uworst, lworst], [ubest, lbest]
def get_trajectory(subject):
print 'subject ' + str(subject)
print ''
data_params = {}
data_params[
'path'] = '/research/vibs/Pedro/datasets/motorimagery/Physionet/eegmmidb/'
data_params['session'] = 1
data_params['task'] = 4
data_params['fparams'] = [8.0, 35.0]
data_params['subject'] = subject
data_params['tparams'] = [-13.8, +13.]
X, y = get_data(data_params)
L = 160
nt, nc, ns = X.shape
covm = []
for w in tqdm(gen_windows(L, ns, step=20)):
xw = X[:, :, w]
covs = Covariances().fit_transform(xw)
covm.append(mean_riemann(covs))
print 'getting the diffusion embedding'
covm = np.stack(covm)
u, l = get_diffusionEmbedding(covm, distance_riemann, alpha=1.0, tdiff=0)
filepath = './results/Physionet/'
filepath = filepath + 'trajectory_subject' + str(subject) + '.pkl'
embedding = [u, l]
joblib.dump(embedding, filepath)
print ''
def get_twoclasses(subject):
print 'subject ' + str(subject)
print ''
data_params = {}
data_params[
'path'] = '/research/vibs/Pedro/datasets/motorimagery/Physionet/eegmmidb/'
data_params['session'] = 1
data_params['task'] = 4
data_params['tparams'] = [1.0, 2.0]
data_params['fparams'] = [8.0, 35.0]
data_params['subject'] = subject
X, y = get_data(data_params)
covs = Covariances().fit_transform(X)
u, l = get_diffusionEmbedding(points=covs, distance=distance_riemann)
filepath = './results/Physionet/'
filepath = filepath + 'twoclasses_subject' + str(subject) + '.pkl'
embedding = [u, l]
joblib.dump([embedding, y], filepath)
def get_twoclasses(subject):
print 'subject ' + str(subject)
print ''
data_params = {}
data_params['path'] = '/research/vibs/Pedro/datasets/motorimagery/BCI-competitions/BCI-IV/2a/'
data_params['session'] = 1
data_params['task'] = 1
data_params['tparams'] = [1.25, 3.75]
data_params['fparams'] = [8.0, 35.0]
data_params['subject'] = subject
X,y = get_data(data_params)
X = X[(y == 1) | (y == 2)]
y = y[(y == 1) | (y == 2)]
covs = Covariances().fit_transform(X)
u,l = get_diffusionEmbedding(points=covs, distance=distance_riemann)
filepath = './results/BCI-IV/'
filepath = filepath + 'twoclasses_subject' + str(subject) + '.pkl'
embedding = [u,l]
joblib.dump([embedding, y], filepath)
nsubjects = 48
nt = []
for subject in range(1, nsubjects + 1):
print 'subject ' + str(subject)
paramspath = './parameters/braininvaders.yaml'
pipesdir = './pipelines/braininvaders/'
resultsdir = './results/braininvaders/'
yml = load_yml(paramspath)
data_params, analysis_params = parse_yml_params(yml)
data_params['subject'] = subject
X, y = get_data(data_params)
select = y == 1
X1, y1 = X[select], y[select]
nt1 = X1.shape[0]
select = np.random.choice(nt1, size=150, replace=False)
X1, y1 = X1[select], y1[select]
select = y == 2
X2, y2 = X[select], y[select]
nt2 = X2.shape[0]
select = np.random.choice(nt2, size=30, replace=False)
X2, y2 = X2[select], y2[select]
select = np.random.choice(180, 180, replace=False)
X = np.concatenate((X1, X2))[select]
def make_fig3(save=False):
data_params = {}
data_params[
'path'] = '/research/vibs/Pedro/datasets/motorimagery/BCI-competitions/BCI-IV/2a/'
data_params['task'] = 1
data_params['tparams'] = [1.25, 3.75]
data_params['fparams'] = [8.0, 35.0]
data_params['subject'] = 1
data_params['session'] = 1
X1, y1 = get_data(data_params)
X1 = X1[(y1 == 1) | (y1 == 2)]
y1 = y1[(y1 == 1) | (y1 == 2)]
data_params['session'] = 2
X2, y2 = get_data(data_params)
X2 = X2[(y2 == 1) | (y2 == 2)]
y2 = y2[(y2 == 1) | (y2 == 2)]
X2 = np.delete(X2, (37), axis=0) # delete bad trial
y2 = np.delete(y2, (37), axis=0) # delete bad trial
y = np.concatenate((y1, y2), axis=0)
covs = Covariances().fit_transform(X1)
u1, l1 = get_diffusionEmbedding(points=covs, distance=distance_riemann)
covs = Covariances().fit_transform(X2)
u2, l2 = get_diffusionEmbedding(points=covs, distance=distance_riemann)
covs = Covariances().fit_transform(np.concatenate((X1, X2), axis=0))
u, l = get_diffusionEmbedding(points=covs, distance=distance_riemann)
fig1 = plt.figure(figsize=(15.5, 5))
plt.subplots_adjust(wspace=0.020, hspace=0.025)
ax1 = plt.subplot(1, 3, 1)
colorst = [['b', 'r'][int(t)] for t in (y1 - 2)]
for i, ui in enumerate(u1):
plt.scatter(ui[1], ui[2], color=colorst[i], s=44)
ax1.set_xticks([])
ax1.set_yticks([])
plt.xlabel(r'$\phi_1$', fontsize=26)
plt.ylabel(r'$\phi_2$', fontsize=26)
ttl = plt.title('Session 1', fontsize=24)
ttl.set_position([.5, 1.025])
ax2 = plt.subplot(1, 3, 2)
colorst = [['b', 'r'][int(t)] for t in (y2 - 2)]
for i, ui in enumerate(u2):
plt.scatter(ui[1], ui[2], color=colorst[i], s=44)
ax2.set_xticks([])
ax2.set_yticks([])
plt.xlabel(r'$\phi_1$', fontsize=26)
ttl = plt.title('Session 2', fontsize=24)
ttl.set_position([.5, 1.025])
ax3 = plt.subplot(1, 3, 3)
colorst = [['b', 'r'][int(t)] for t in (y - 2)]
markerst = ['o' for _ in range(len(y1))] + ['*' for _ in range(len(y2))]
for i, ui in enumerate(u):
plt.scatter(ui[1], ui[2], color=colorst[i], s=44, marker=markerst[i])
ax3.set_xticks([])
ax3.set_yticks([])
plt.xlabel(r'$\phi_1$', fontsize=26)
plt.ylabel(r'$\phi_2$', fontsize=26)
ax3.yaxis.set_label_position("right")
ttl = plt.title('Both sessions', fontsize=24)
ttl.set_position([.5, 1.025])
if save:
name = 'figure3a'
savefigure(name)
fig2 = plt.figure(figsize=(9, 8))
ax = fig2.add_subplot(111, projection='3d')
colorst = [['b', 'r'][int(t)] for t in (y - 2)]
markerst = ['o' for _ in range(len(y1))] + ['*' for _ in range(len(y2))]
for i, ui in enumerate(u):
ax.scatter(ui[1],
ui[2],
ui[3],
color=colorst[i],
s=44,
marker=markerst[i])
ax.set_xlabel(r'$\phi_1$', fontsize=26)
ax.set_ylabel(r'$\phi_2$', fontsize=26)
ax.set_zlabel(r'$\phi_3$', fontsize=26)
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
ttl = plt.title('Both sessions', fontsize=24)
ttl.set_position([0.50, 0.975])
if save:
name = 'figure3b'
savefigure(name)