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

src/p/y/pypreprocess-HEAD/pypreprocess/external/nipy_labs/datasets/volumes/volume_img.py   pypreprocess(Download)
 
# 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)
 

src/n/i/nipy-0.3.0/nipy/labs/datasets/volumes/volume_img.py   nipy(Download)
 
# 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)