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src/n/i/nipy-0.3.0/nipy/algorithms/registration/histogram_registration.py   nipy(Download)
from .optimizer import configure_optimizer
from .affine import inverse_affine, subgrid_affine, affine_transforms
from .chain_transform import ChainTransform
from .similarity_measures import similarity_measures as _sms
from ._registration import _joint_histogram
            Transform object implementing ``apply`` method
        Tv = ChainTransform(T, pre=self._from_affine, post=self._to_inv_affine)
        return self._eval(Tv)
        # Create transform chain object with T generating params
        Tv = ChainTransform(T, pre=self._from_affine, post=self._to_inv_affine)
        tc0 = Tv.param
        params = np.zeros([nparams, ntrials])
        Tv = ChainTransform(T0, pre=self._from_affine,
        param0 = Tv.param

src/n/i/nipy-0.3.0/nipy/algorithms/registration/tests/test_chain_transforms.py   nipy(Download)
from nibabel.affines import apply_affine
from ..chain_transform import ChainTransform
from ..affine import Affine
    # Reset the aff2 object
    aff2_obj = Affine(AFF2.copy())
    ct = ChainTransform(aff2_obj)
    # Check apply gives expected result
    aff2_obj = Affine(AFF2.copy())
    # Check apply gives the expected results
    ct = ChainTransform(aff2_obj, pre=AFF1_OBJ)
    assert_array_almost_equal(AFF1_OBJ.as_affine(), AFF1)
    assert_array_almost_equal(aff2_obj.as_affine(), AFF2)
    # Reset the aff2 object
    aff2_obj = Affine(AFF2.copy())
    ct = ChainTransform(aff2_obj, pre=AFF1_OBJ, post=AFF3_OBJ)
                       apply_affine(np.dot(AFF3, np.dot(AFF2, AFF1)), POINTS))