[type]: [description]
"""
X = epochs.copy().crop(crop[0], crop[1]).get_data()
num = X.shape[0]
y = np.zeros(num, ) + label
print(f'Got paired X: {X.shape} and y: {y.shape}')
return X, y
# %%
for idx in range(1, 11):
# Loading data ------------------------------------------
running_name = f'MEG_S{idx:02d}'
band_name = 'U07'
worker = MEG_Worker(running_name=running_name)
worker.pipeline(band_name=band_name)
# MVPA ----------------------------------------------------------------
# Prepare classifiers
_svm = svm.SVC(gamma='scale', kernel='rbf', class_weight='balanced')
clf = make_pipeline(Vectorizer(), StandardScaler(), _svm)
# Prepare paired X and y
# Set crop
crops = dict(a=(0.2, 0.4),
b=(0.4, 0.6),
c=(0.6, 0.8),
d=(0.2, 0.8),
e=(0.0, 1.0))
np.save(os.path.join(TMP_DIR,
f'{compare_name}.npy'),
entropy_matrix)
print(f'Done {compare_name}')
# %%
for idx in [1]: # range(1, 11):
# Setting -------------------------------------------
running_name = f'MEG_S{idx:02d}'
plt.style.use('ggplot')
# Worker pipeline -----------------------------------
worker = MEG_Worker(running_name=running_name)
worker.pipeline(band_name=BAND_NAME)
# Compute segments ----------------------------------
# Init empty segments and segment_data
segments = dict()
segment_data = dict()
# Set values
for key in SEGMENT_RANGES:
crop = SEGMENT_RANGES[key]['crop']
segments[key] = worker.clean_epochs.copy().crop(crop[0], crop[1])
segment_data[key] = np.mean(segments[key].get_data(), axis=-1)
# ---------------------------------------------------------------------
# Set up number of sensors
# %% import os import sys # Local tools ----------------------------------- sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'tools')) # noqa from MEG_worker import MEG_Worker # %% worker = MEG_Worker(running_name='MEG_S01') worker.pipeline(band_name='U07')