Did I find the right examples for you? yes no

transforms.affine_utils.to_matrix_vector

All Samples(8)  |  Call(6)  |  Derive(0)  |  Import(2)

```
# Local imports
from ..transforms.affine_utils import to_matrix_vector, \
from_matrix_vector, get_bounds
from ..transforms.affine_transform import AffineTransform
```
```        else:
transform_affine = np.dot(np.linalg.inv(self.affine), affine)
A, b = to_matrix_vector(transform_affine)
A_inv = np.linalg.inv(A)
# If A is diagonal, ndimage.affine_transform is clever-enough
```
```                data) is made.
"""
A, b = to_matrix_vector(self.affine.copy())
if not np.all((np.abs(A) > 0.001).sum(axis=0) == 1):
if not resample:
```
```            first_inversion = np.argmax(np.diff(axis_numbers)<0)
img = img._swapaxes(first_inversion+1, first_inversion)
A, b = to_matrix_vector(img.affine)
axis_numbers = np.argmax(np.abs(A), axis=0)

```

```
# Local imports
from ..transforms.affine_utils import to_matrix_vector, \
from_matrix_vector, get_bounds
from ..transforms.affine_transform import AffineTransform
```
```        else:
transform_affine = np.dot(np.linalg.inv(self.affine), affine)
A, b = to_matrix_vector(transform_affine)
A_inv = np.linalg.inv(A)
# If A is diagonal, ndimage.affine_transform is clever-enough
```
```                data) is made.
"""
A, b = to_matrix_vector(self.affine.copy())
if not np.all((np.abs(A) > 0.001).sum(axis=0) == 1):
if not resample:
```
```            first_inversion = np.argmax(np.diff(axis_numbers)<0)
img = img._swapaxes(first_inversion+1, first_inversion)
A, b = to_matrix_vector(img.affine)
axis_numbers = np.argmax(np.abs(A), axis=0)

```