def interp_corr(locs,
corrs,
width=10,
vox_size=10,
outfile=None,
save_nii=None):
nii = se.load('std', vox_size=vox_size)
full_locs = nii.get_locs().values
W = np.exp(_log_rbf(full_locs, locs, width=width))
interp_corrs = np.dot(corrs, W.T)
bo_nii = se.Brain(data=interp_corrs, locs=full_locs)
nii_bo = _brain_to_nifti(bo_nii, nii)
ni_plt.plot_glass_brain(nii_bo,
colorbar=True,
threshold=None,
vmax=1,
vmin=0)
#ni_plt.plot_glass_brain(nii_bo, colorbar=True, threshold=None, vmax=1, vmin=0, display_mode='lyrz')
if save_nii:
nii_bo.save(save_nii)
if not outfile is None:
plt.savefig(outfile)
else:
plt.show()
def test_nii_bo_nii():
bo_nii = se.Brain(_gray(20))
nii = _brain_to_nifti(bo_nii, _gray(20))
nii_0 = _gray(20).get_data().flatten()
nii_0[np.isnan(nii_0)] = 0
assert np.allclose(nii_0, nii.get_data().flatten())
def test_bo_nii_bo():
nii = _brain_to_nifti(bo, _gray(20))
b_d, b_l, b_h = _nifti_to_brain(nii)
assert np.allclose(bo.get_locs(), b_l)
def test_brain_to_nifti():
nii = _brain_to_nifti(bo, _gray(20))
assert isinstance(nii, se.Nifti)
sub_locs = bo_full.locs.iloc[6:]
sub_data = bo_full.data.iloc[:, sub_locs.index]
bo = se.Brain(data=sub_data.as_matrix(),
sessions=bo_full.sessions,
locs=sub_locs,
sample_rate=10,
meta={'brain object locs sampled': 2})
# simulate correlation matrix
data = [
se.simulate_model_bos(n_samples=10, locs=locs, sample_locs=n_elecs)
for x in range(n_subs)
]
# test model to compare
test_model = se.Model(data=data, locs=locs, rbf_width=100)
bo_nii = se.Brain(_gray(20))
nii = _brain_to_nifti(bo_nii, _gray(20))
a = np.array([[1, 2, 3], [4, 5, 6], [
7,
8,
9,
]])
b = np.array([[-1, 2, 2], [-4, 5, 5], [
-7,
8,
8,
]])
c = np.array([[0, 4, 5], [0, 10, 11], [0, 16, 17]])
add_log = _to_log_complex(a)
a_log = _to_log_complex(a)
b_log = _to_log_complex(b)
def test_bo_nii_bo():
nii = _brain_to_nifti(bo, _gray(20))
print(type(str(nii.header['descrip'])))
b_d, b_l, b_h, affine = _nifti_to_brain(nii)
assert np.allclose(bo.get_locs(), b_l)