""" Tests for spaces module
"""

import numpy as np
import numpy.linalg as npl

from ..spaces import vox2out_vox, slice2volume
from ..affines import apply_affine, from_matvec
from ..nifti1 import Nifti1Image
from ..eulerangles import euler2mat


from numpy.testing import (assert_almost_equal,
                           assert_array_equal)

from nose.tools import (assert_true, assert_false, assert_raises,
                        assert_equal, assert_not_equal)



def assert_all_in(in_shape, in_affine, out_shape, out_affine):
    slices = tuple(slice(N) for N in in_shape)
    n_axes = len(in_shape)
    in_grid = np.mgrid[slices]
    in_grid = np.rollaxis(in_grid, 0, n_axes + 1)
    v2v = npl.inv(out_affine).dot(in_affine)
    if n_axes < 3: # reduced dimensions case
        new_v2v = np.eye(n_axes + 1)
        new_v2v[:n_axes, :n_axes] = v2v[:n_axes, :n_axes]
        new_v2v[:n_axes, -1] = v2v[:n_axes, -1]
        v2v = new_v2v
    out_grid = apply_affine(v2v, in_grid)
    TINY = 1e-12
    assert_true(np.all(out_grid > -TINY))
    assert_true(np.all(out_grid < np.array(out_shape) + TINY))


def test_vox2out_vox():
    # Test world space bounding box
    # Test basic case, identity, no voxel sizes passed
    shape, aff = vox2out_vox(((2, 3, 4), np.eye(4)))
    assert_array_equal(shape, (2, 3, 4))
    assert_array_equal(aff, np.eye(4))
    # Some affines as input to the tests
    trans_123 = [[1, 0, 0, 1], [0, 1, 0, 2], [0, 0, 1, 3], [0, 0, 0, 1]]
    trans_m123 = [[1, 0, 0, -1], [0, 1, 0, -2], [0, 0, 1, -3], [0, 0, 0, 1]]
    rot_3 = from_matvec(euler2mat(np.pi / 4), [0, 0, 0])
    for in_shape, in_aff, vox, out_shape, out_aff in (
        # Identity
        ((2, 3, 4), np.eye(4), None, (2, 3, 4), np.eye(4)),
        # Flip first axis
        ((2, 3, 4), np.diag([-1, 1, 1, 1]), None,
         (2, 3, 4), [[1, 0, 0, -1], # axis reversed -> -ve offset
                     [0, 1, 0, 0],
                     [0, 0, 1, 0],
                     [0, 0, 0, 1]]),
        # zooms for affine > 1 -> larger grid with default 1mm output voxels
        ((2, 3, 4), np.diag([4, 5, 6, 1]), None,
         (5, 11, 19), np.eye(4)),
        # set output voxels to be same size as input. back to original shape
        ((2, 3, 4), np.diag([4, 5, 6, 1]), (4, 5, 6),
         (2, 3, 4), np.diag([4, 5, 6, 1])),
        # Translation preserved in output
        ((2, 3, 4), trans_123, None,
         (2, 3, 4), trans_123),
        ((2, 3, 4), trans_m123, None,
         (2, 3, 4), trans_m123),
        # rotation around 3rd axis
        ((2, 3, 4), rot_3, None,
         # x diff, y diff now 3 cos pi / 4 == 2.12, ceil to 3, add 1
         # most negative x now 2 cos pi / 4
         (4, 4, 4), [[1, 0, 0, -2 * np.cos(np.pi / 4)],
                     [0, 1, 0, 0],
                     [0, 0, 1, 0],
                     [0, 0, 0, 1]]),
        # Less than 3 axes
        ((2, 3), np.eye(4), None,
         (2, 3),  np.eye(4)),
        ((2,), np.eye(4), None,
         (2,),  np.eye(4)),
        # Number of voxel sizes matches length
        ((2, 3), np.diag([4, 5, 6, 1]), (4, 5),
         (2, 3), np.diag([4, 5, 1, 1])),
    ):
        img = Nifti1Image(np.ones(in_shape), in_aff)
        for input in ((in_shape, in_aff), img):
            shape, aff = vox2out_vox(input, vox)
            assert_all_in(in_shape, in_aff, shape, aff)
            assert_equal(shape, out_shape)
            assert_almost_equal(aff, out_aff)
            assert_true(isinstance(shape, tuple))
            assert_true(isinstance(shape[0], int))
    # Enforce number of axes
    assert_raises(ValueError, vox2out_vox, ((2, 3, 4, 5), np.eye(4)))
    assert_raises(ValueError, vox2out_vox, ((2, 3, 4, 5, 6), np.eye(4)))
    # Voxel sizes must be positive
    assert_raises(ValueError, vox2out_vox, ((2, 3, 4), np.eye(4), [-1, 1, 1]))
    assert_raises(ValueError, vox2out_vox, ((2, 3, 4), np.eye(4), [1, 0, 1]))


def test_slice2volume():
    # Get affine expressing selection of single slice from volume
    for axis, def_aff in zip((0, 1, 2), (
        [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]],
        [[1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 1]],
        [[1, 0, 0], [0, 1, 0], [0, 0, 0], [0, 0, 1]])):
        for val in (0, 5, 10):
            exp_aff = np.array(def_aff)
            exp_aff[axis, -1] = val
            assert_array_equal(slice2volume(val, axis), exp_aff)
    assert_raises(ValueError, slice2volume, -1, 0)
    assert_raises(ValueError, slice2volume, 0, -1)
    assert_raises(ValueError, slice2volume, 0, 3)
