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src/n/i/nipy-0.3.0/nipy/io/nifti_ref.py   nipy(Download)
from ..core.reference.coordinate_system import CoordinateSystem as CS
from ..core.reference.coordinate_map import (AffineTransform as AT,
                                             product as cm_product)
    aff = from_matvec(np.diag(ns_zooms), ns_trans)
    ns_cmap = AT(input_cs, output_cs, aff)
    cmap = cm_product(cmap3, ns_cmap,

src/n/i/nipy-0.3.0/nipy/algorithms/utils/tests/test_pca_image.py   nipy(Download)
from ..pca import pca_image, pca as pca_array
from ....core.api import Image, AffineTransform, CoordinateSystem as CS
from ....core.reference.coordinate_map import (product as cm_product,
                                               drop_io_dim, AxisError)
from ....core.image.image import rollimg
    vcs = CS('v')
    xtra_cmap = AffineTransform(vcs, vcs, np.eye(2))
    cmap_5d = cm_product(img.coordmap, xtra_cmap)
    data_5d = data.reshape(data.shape + (1,))
    fived = Image(data_5d, cmap_5d)
    mask = data_dict['mask']
    mask_data = mask.get_data()
    mask_data = mask_data.reshape(mask_data.shape + (1,))
    cmap_4d = cm_product(mask.coordmap, xtra_cmap)
    gcs = CS(['group'])
    xtra_cmap = AffineTransform(gcs, gcs, np.eye(2))
    cmap_5d = cm_product(img.coordmap, xtra_cmap)
    fived = Image(data_5d, cmap_5d)
    # Give the mask a 't' dimension, but no group dimension
    # the affine
    xtra_cmap = AffineTransform(CS('t'), CS('t'), np.diag([2., 1]))
    cmap_4d = cm_product(mask.coordmap, xtra_cmap)
    mask4d = Image(mask_data, cmap_4d)
    nimages = data_dict['nimages']