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src/n/i/nipy-0.3.0/nipy/algorithms/resample.py   nipy(Download)
from scipy.ndimage import affine_transform
 
from nibabel.affines import from_matvec, to_matvec
 
from .interpolation import ImageInterpolator
        TV2IV = compose(image.coordmap.inverse(), TV2IW)
        if isinstance(TV2IV, AffineTransform): # still affine
            A, b = to_matvec(TV2IV.affine)
            idata = affine_transform(image.get_data(), A,
                                     offset=b,

src/n/i/nipy-0.3.0/nipy/core/reference/coordinate_map.py   nipy(Download)
import numpy.linalg as npl
 
from nibabel.affines import to_matvec, from_matvec
from ...fixes.nibabel import io_orientation
 
            out_shape = x.shape[:-1] + out_shape
        in_vals = self.function_domain._checked_values(x)
        A, b = to_matvec(self.affine)
        out_vals = np.dot(in_vals, A.T) + b[np.newaxis,:]
        final_vals = self.function_range._checked_values(out_vals)
    elif isinstance(cmap, AffineTransform):
        affine_transform = cmap
        A, b = to_matvec(affine_transform.affine)
 
        def _function(x):
        affine_transform_inv = affine_transform.inverse(preserve_dtype=True)
        if affine_transform_inv:
            Ainv, binv = to_matvec(affine_transform_inv.affine)
            def _inverse_function(x):
                value = np.dot(x, Ainv.T)
 
    for l, affine in enumerate(affine_mappings):
        A, b = to_matvec(affine.affine)
        M[i:(i+ndimout[l]),j:(j+ndimin[l])] = A
        M[i:(i+ndimout[l]),-1] = b

src/n/i/nipy-0.3.0/nipy/io/nifti_ref.py   nipy(Download)
 
import nibabel as nib
from nibabel.affines import to_matvec, from_matvec
 
from ..core.reference.coordinate_system import CoordinateSystem as CS
    hdr.set_data_dtype(data_dtype)
    # Remaining axes orthogonal?
    rzs, trans = to_matvec(coordmap.affine)
    if (not np.allclose(rzs[3:, :3], 0) or
        not np.allclose(rzs[:3, 3:], 0)):
    if data is None:
        data = img.get_data()
    rzs, trans = to_matvec(img.coordmap.affine)
    ns_pixdims = list(np.sqrt(np.sum(rzs[3:, 3:] ** 2, axis=0)))
    in_ax, out_ax, tl_name = _find_time_like(coordmap, fix0)