def test_decode_roi_with_priors():
"""Acceptance test of ROI-based decoding with topic priors.
"""
model_file = join(get_test_data_path(), 'gclda_model.pkl')
roi_file = join(get_test_data_path(), 'roi.nii.gz')
model = Model.load(model_file)
_, priors = decode_roi(model, roi_file)
decoded_df, _ = decode_roi(model, roi_file, topic_priors=priors)
assert decoded_df.shape[0] == model.n_word_labels
def test_decode_roi_from_file():
"""Acceptance test of ROI-based decoding with str input.
"""
model_file = join(get_test_data_path(), 'gclda_model.pkl')
roi_file = join(get_test_data_path(), 'roi.nii.gz')
model = Model.load(model_file)
decoded_df, _ = decode_roi(model, roi_file)
assert decoded_df.shape[0] == model.n_word_labels
###############################################################################
# Create region of interest (ROI) image
# --------------------------------------
coords = [[-40, -52, -20]]
radii = [6] * len(coords)
roi_img = create_sphere(coords, radius=radii, mask=model.dataset.mask_img)
fig = plotting.plot_roi(roi_img,
display_mode='ortho',
cut_coords=[-40, -52, -20],
draw_cross=False)
###############################################################################
# Decode ROI
# -----------
df, topic_weights = decode_roi(model, roi_img)
###############################################################################
# Get associated terms
# ---------------------
df = df.sort_values(by='Weight', ascending=False)
print(df.head(10))
###############################################################################
# Plot topic weights
# ------------------
fig2, ax2 = plt.subplots()
ax2.plot(topic_weights)
ax2.set_xlabel('Topic #')
ax2.set_ylabel('Weight')
fig2.show()
affine = model.dataset.mask_img.affine
mask = nib.Nifti1Image(mask_data, affine)
###############################################################################
# Temporoparietal seed
# --------------------------------------
coords = [[-52, -56, 18]]
radii = [6] * len(coords)
roi_img = create_sphere(coords, radius=radii, mask=mask)
fig = plotting.plot_roi(roi_img,
display_mode='ortho',
cut_coords=[-52, -56, 18],
draw_cross=False)
df, _ = decode_roi(model, roi_img)
df = df.sort_values(by='Weight', ascending=False)
print(df.head(10))
###############################################################################
# Temporoparietal, medial parietal, and dorsomedial prefrontal seeds
# ------------------------------------------------------------------
coords = [[-56, -52, 18], [0, -58, 38], [4, 54, 26]]
radii = [6] * len(coords)
roi_img = create_sphere(coords, radius=radii, mask=mask)
fig = plotting.plot_roi(roi_img,
display_mode='ortho',
cut_coords=[-52, -56, 18],
draw_cross=False)