def test_fdr():
n = 100
x = np.linspace(.5 / n, 1. - .5 / n, n)
x[:10] = .0005
x = norm.isf(x)
np.random.shuffle(x)
assert_almost_equal(fdr_threshold(x, .1), norm.isf(.0005))
assert_true(fdr_threshold(x, .001) == np.infty)
def test_fdr():
n = 100
x = np.linspace(.5 / n, 1. - .5 / n, n)
x[:10] = .0005
x = norm.isf(x)
np.random.shuffle(x)
assert_almost_equal(fdr_threshold(x, .1), norm.isf(.0005))
assert_true(fdr_threshold(x, .001) == np.infty)
def test_fdr():
n = 100
x = np.linspace(.5 / n, 1. - .5 / n, n)
x[:10] = .0005
x = norm.isf(x)
np.random.shuffle(x)
assert_almost_equal(fdr_threshold(x, .1), norm.isf(.0005))
assert fdr_threshold(x, .001) == np.infty
with pytest.raises(ValueError):
fdr_threshold(x, -.1)
with pytest.raises(ValueError):
fdr_threshold(x, 1.5)
labels, res = run_glm(texture.T,
design_matrix.values[:texture.shape[1]])
#######################################################################
# contrast estimation
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
print(' Contrast % 2i out of %i: %s' %
(index + 1, len(contrasts), contrast_id))
if subject_idx == 0:
effects[contrast_id] = []
contrast_ = compute_contrast(labels, res, contrast_val)
z_map = contrast_.z_score()
effect = contrast_.effect
effects[contrast_id].append(effect)
# Create snapshots of the contrasts
threshold = fdr_threshold(z_map, alpha=.05)
out_file = os.path.join(
write_dir, '%s_%s_z_map.png' % (contrast_id, hemisphere))
plotting.plot_surf_stat_map(fsaverage['infl_%s' % hemisphere],
z_map,
hemi=hemisphere,
title=contrast_id,
colorbar=True,
output_file=out_file,
threshold=threshold,
bg_map=fsaverage['sulc_%s' %
hemisphere])
import pandas as pd
n_subjects = len(subjects)
group_design_matrix = pd.DataFrame([1] * n_subjects, columns=['intercept'])
def threshold_and_save(img, masker):
data = masker.transform(img)
thr = fdr_threshold(data, 0.05)
data[np.abs(data) <= thr] = 0
thr_img = masker.inverse_transform(data)
thr_img.to_filename(img.replace('.nii.gz', '_thr.nii.gz'))