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''' Testing for orientations module '''

import numpy as np

from nose.tools import assert_true, assert_equal, assert_raises

from numpy.testing import assert_array_equal, assert_array_almost_equal

from ..orientations import (io_orientation, inv_ornt_aff, flip_axis,
                            apply_orientation, OrientationError, ornt2axcodes,
                            aff2axcodes)

from ..affines import from_matvec, to_matvec


IN_ARRS = [np.eye(4),
           [[0,0,1,0],
            [0,1,0,0],
            [1,0,0,0],
            [0,0,0,1]],
           [[0,1,0,0],
            [0,0,1,0],
            [1,0,0,0],
            [0,0,0,1]],
           [[3,1,0,0],
            [1,3,0,0],
            [0,0,1,0],
            [0,0,0,1]],
           [[1,3,0,0],
            [3,1,0,0],
            [0,0,1,0],
            [0,0,0,1]],
           ]

OUT_ORNTS = [[[0,1],
              [1,1],
              [2,1]],
             [[2,1],
              [1,1],
              [0,1]],
             [[2,1],
              [0,1],
              [1,1]],
             [[0,1],
              [1,1],
              [2,1]],
             [[1,1],
              [0,1],
              [2,1]],
            ]

IN_ARRS = IN_ARRS + [[[np.cos(np.pi/6+i*np.pi/2),np.sin(np.pi/6+i*np.pi/2),0,0], 
                      [-np.sin(np.pi/6+i*np.pi/2),np.cos(np.pi/6+i*np.pi/2),0,0],
                      [0,0,1,0],
                      [0,0,0,1]] for i in range(4)]

OUT_ORNTS = OUT_ORNTS + [[[0,1],
                          [1,1],
                          [2,1]],
                         [[1,-1],
                          [0,1],
                          [2,1]],
                         [[0,-1],
                          [1,-1],
                          [2,1]],
                         [[1,1],
                          [0,-1],
                          [2,1]]
                         ]


IN_ARRS = [np.array(arr) for arr in IN_ARRS]
OUT_ORNTS = [np.array(ornt) for ornt in OUT_ORNTS]


def same_transform(taff, ornt, shape):
    # Applying transformations implied by `ornt` to a made-up array
    # ``arr`` of shape `shape`, results in ``t_arr``. When the point
    # indices from ``arr`` are transformed by (the inverse of) `taff`,
    # and we index into ``t_arr`` with these transformed points, then we
    # should get the same values as we would from indexing into arr with
    # the untransformed points. 
    shape = np.array(shape)
    size = np.prod(shape)
    arr = np.arange(size).reshape(shape)
    # apply ornt transformations
    t_arr = apply_orientation(arr, ornt)
    # get all point indices in arr
    i,j,k = shape
    arr_pts = np.mgrid[:i,:j,:k].reshape((3,-1))
    # inverse of taff takes us from point index in arr to point index in
    # t_arr
    itaff = np.linalg.inv(taff)
    # apply itaff so that points indexed in t_arr should correspond 
    o2t_pts = np.dot(itaff[:3,:3], arr_pts) + itaff[:3,3][:,None]
    assert np.allclose(np.round(o2t_pts), o2t_pts)
    # fancy index out the t_arr values
    vals = t_arr[list(o2t_pts.astype('i'))]
    return np.all(vals == arr.ravel())


def test_apply():
    # most tests are in ``same_transform`` above, via the
    # test_io_orientations
    a = np.arange(24).reshape((2,3,4))
    # Test 4D with an example orientation
    ornt = OUT_ORNTS[-1]
    t_arr = apply_orientation(a[:,:,:,None], ornt)
    assert_equal(t_arr.ndim, 4)
    # Orientation errors
    assert_raises(OrientationError,
                  apply_orientation,
                  a[:,:,1], ornt)
    assert_raises(OrientationError,
                  apply_orientation,
                  a,
                  [[0,1],[np.nan,np.nan],[2,1]])


def test_flip_axis():
    a = np.arange(24).reshape((2,3,4))
    assert_array_equal(
        flip_axis(a),
        np.flipud(a))
    assert_array_equal(
        flip_axis(a, axis=0),
        np.flipud(a))
    assert_array_equal(
        flip_axis(a, axis=1),
        np.fliplr(a))
    # check accepts array-like
    assert_array_equal(
        flip_axis(a.tolist(), axis=0),
        np.flipud(a))
    # third dimension
    b = a.transpose()
    b = np.flipud(b)
    b = b.transpose()
    assert_array_equal(flip_axis(a, axis=2), b)


def test_io_orientation():
    for shape in ((2,3,4), (20, 15, 7)):
        for in_arr, out_ornt in zip(IN_ARRS, OUT_ORNTS):
            ornt = io_orientation(in_arr)
            assert_array_equal(ornt, out_ornt)
            taff = inv_ornt_aff(ornt, shape)
            assert_true(same_transform(taff, ornt, shape))
            for axno in range(3):
                arr = in_arr.copy()
                ex_ornt = out_ornt.copy()
                # flip the input axis in affine
                arr[:,axno] *= -1
                # check that result shows flip
                ex_ornt[axno, 1] *= -1
                ornt = io_orientation(arr)
                assert_array_equal(ornt, ex_ornt)
                taff = inv_ornt_aff(ornt, shape)
                assert_true(same_transform(taff, ornt, shape))
    # Test nasty hang for zero columns
    rzs = np.c_[np.diag([2, 3, 4, 5]), np.zeros((4,3))]
    arr = from_matvec(rzs, [15,16,17,18])
    ornt = io_orientation(arr)
    assert_array_equal(ornt, [[0, 1],
                              [1, 1],
                              [2, 1],
                              [3, 1],
                              [np.nan, np.nan],
                              [np.nan, np.nan],
                              [np.nan, np.nan]])


def test_ornt2axcodes():
    # Recoding orientation to axis codes
    labels = (('left', 'right'),('back', 'front'), ('down', 'up'))
    assert_equal(ornt2axcodes([[0,1],
                               [1,1],
                               [2,1]], labels), ('right', 'front', 'up'))
    assert_equal(ornt2axcodes([[0,-1],
                               [1,-1],
                               [2,-1]], labels), ('left', 'back', 'down'))
    assert_equal(ornt2axcodes([[2,-1],
                               [1,-1],
                               [0,-1]], labels), ('down', 'back', 'left'))
    assert_equal(ornt2axcodes([[1,1],
                               [2,-1],
                               [0,1]], labels), ('front', 'down', 'right'))
    # default is RAS output directions
    assert_equal(ornt2axcodes([[0,1],
                               [1,1],
                               [2,1]]), ('R', 'A', 'S'))
    # dropped axes produce None
    assert_equal(ornt2axcodes([[0,1],
                               [np.nan,np.nan],
                               [2,1]]), ('R', None, 'S'))
    # Non integer axes raises error
    assert_raises(ValueError, ornt2axcodes, [[0.1,1]])
    # As do directions not in range
    assert_raises(ValueError, ornt2axcodes, [[0,0]])


def test_aff2axcodes():
    labels = (('left', 'right'),('back', 'front'), ('down', 'up'))
    assert_equal(aff2axcodes(np.eye(4)), tuple('RAS'))
    aff = [[0,1,0,10],[-1,0,0,20],[0,0,1,30],[0,0,0,1]]
    assert_equal(aff2axcodes(aff, (('L','R'),('B','F'),('D','U'))),
                 ('B', 'R', 'U'))
    assert_equal(aff2axcodes(aff, (('L','R'),('B','F'),('D','U'))),
                 ('B', 'R', 'U'))


def test_inv_ornt_aff():
    # Extra tests for inv_ornt_aff routines (also tested in
    # io_orientations test)
    assert_raises(OrientationError, inv_ornt_aff,
                  [[0, 1], [1, -1], [np.nan, np.nan]], (3, 4, 5))
