def get_nifti(self, topo_view, base_nifti=None, **kwargs):
"""
Process the nifti
Parameters
----------
topo_view: array-like
Topological view to create nifti. 3D.
Returns
-------
image: nipy image
Nifti image from topological view.
"""
if base_nifti is None:
assert self.base_nifti is not None, ("`base.nii` not in dataset "
"directory. You may need to "
"reprocess.")
base_nifti = self.base_nifti
image = Image.from_image(base_nifti, data=topo_view)
else:
if isinstance(base_nifti, str):
base_nifti = load_image(base_nifti)
base2new_affine = np.linalg.inv(base_nifti.affine).dot(
self.base_nifti.affine)
cmap = AffineTransform("kji", "zxy", base2new_affine)
image = Image.from_image(base_nifti, data=topo_view, coordmap=cmap)
return image
def get_nifti(self, topo_view, base_nifti=None, **kwargs):
"""
Process the nifti
Parameters
----------
topo_view: array-like
Topological view to create nifti. 3D.
Returns
-------
image: nipy image
Nifti image from topological view.
"""
if base_nifti is None:
assert self.base_nifti is not None, ("`base.nii` not in dataset "
"directory. You may need to "
"reprocess.")
base_nifti = self.base_nifti
image = Image.from_image(base_nifti, data=topo_view)
else:
if isinstance(base_nifti, str):
base_nifti = load_image(base_nifti)
base2new_affine = np.linalg.inv(
base_nifti.affine).dot(self.base_nifti.affine)
cmap = AffineTransform("kji", "zxy", base2new_affine)
image = Image.from_image(base_nifti, data=topo_view, coordmap=cmap)
return image
def sources_to_nifti(CHECKPOINT, MASKMAT, BASENIFTI, ONAME, savepath, voxels, win):
bnifti = load_image(BASENIFTI)
mask = loadmat(MASKMAT)['mask']
model = np.load(CHECKPOINT) # Numpy array of sources from Infomax ICA
for i in range(len(model)): # Goes component by component
W = model[i,:].reshape([voxels,win])
f = zeros(len(mask))
idx = where(mask==1)
data = zeros((bnifti.shape[0],bnifti.shape[1],bnifti.shape[2],W.shape[1]))
f[idx[0].tolist()] = detrend(W)/std(W)
for j in range(0,W.shape[1]):
data[:,:,:,j] = reshape(f,(bnifti.shape[0],bnifti.shape[1],bnifti.shape[2] ), order='F')
img = Image.from_image(bnifti,data=data)
os.chdir(savepath)
fn = ONAME + "%s.nii" % (str(i)) # Where result should be saved and under what name
save_image(img,fn)
def make_image(self, X, base_nifti):
'''Create a nitfi image from array.
Args:
X (numpy.array): array from which to make nifti image.
base_nifti (nipy.core.api.Image): nifti image template.
Returns:
nipy.core.api.Image
'''
image = Image.from_image(base_nifti, data=X)
return image
def make_image(self, X, base_nifti):
'''Create a nitfi image from array.
Args:
X (numpy.array): array from which to make nifti image.
base_nifti (nipy.core.api.Image): nifti image template.
Returns:
nipy.core.api.Image
'''
image = Image.from_image(base_nifti, data=X)
return image
def save_npy_to_nifti(npy_data, filename, base_nifti_filename):
"""
Saves numpy to nifti.
Arguments:
npy_data: numpy array
filename: filename to save
base_nifti_filename: base nifti filename
"""
bnifti = load_image(base_nifti_filename)
img = Image.from_image(bnifti, data=npy_data.astype('uint8'))
save_image(img, filename)
print('Saved {}..'.format(filename))
def get_nifti(self, W, base_nifti=None):
"""
Function to make a nifti file from weights.
Parameters
----------
W: array-like
Weights.
"""
m, r, c, d = W.shape
if basenifti is None:
base_nifti = self.base_nifti
else:
base2new_affine = np.linalg.inv(
base_nifti.get_affine()).dot(self.base_nifti.get_affine())
data = np.zeros([r, c, d, m], dtype=W.dtype)
for i in range(m):
data[:, :, :, i] = W[i]
image = Image.from_image(base_nifti, data=data)
return image
def get_nifti(self, W, base_nifti=None):
"""
Function to make a nifti file from weights.
Parameters
----------
W: array-like
Weights.
"""
m, r, c, d = W.shape
if basenifti is None:
base_nifti = self.base_nifti
else:
base2new_affine = np.linalg.inv(base_nifti.get_affine()).dot(
self.base_nifti.get_affine())
data = np.zeros([r, c, d, m], dtype=W.dtype)
for i in range(m):
data[:, :, :, i] = W[i]
image = Image.from_image(base_nifti, data=data)
return image
def convert_npy_to_nii(npy_file, base_nifti_filename):
npy_data = np.load(npy_file).astype('uint8')
bnifti = load_image(base_nifti_filename)
img = Image.from_image(bnifti, data=npy_data)
print (img.get_data().shape, img.get_data().max(), img.get_data().min(), img.get_data().dtype)
save_image(img, npy_file[:-4] + '.nii.gz')
def make_image(self, X, base_nifti, do_pca=True):
if self.pca is not None and do_pca and self.pca_components:
X = self.pca.inverse_transform(X)
image = Image.from_image(base_nifti, data=X)
return image
