def test_assignment_by_sum(self):
np_array_1 = _np.ones(3)
gs_array_1 = gs.ones_like(gs.array(np_array_1))
np_array_1[2] += 1.5
gs_result = gs.assignment_by_sum(gs_array_1, 1.5, 2)
self.assertAllCloseToNp(gs_result, np_array_1)
np_array_1_list = _np.ones(3)
gs_array_1_list = gs.ones_like(gs.array(np_array_1_list))
indices = [1, 2]
np_array_1_list[indices] += 1.5
gs_result = gs.assignment_by_sum(gs_array_1_list, 1.5, indices)
self.assertAllCloseToNp(gs_result, np_array_1)
np_array_2 = _np.zeros((3, 2))
gs_array_2 = gs.zeros_like(gs.array(np_array_2))
np_array_2[0, :] += 1
gs_result = gs.assignment_by_sum(gs_array_2, 1, 0, axis=0)
self.assertAllCloseToNp(gs_result, np_array_2)
np_array_3 = _np.zeros((3, 3))
gs_array_3 = gs.zeros_like(gs.array(np_array_3))
np_array_3[0, 1] += 1
gs_result = gs.assignment_by_sum(gs_array_3, 1, (0, 1))
self.assertAllCloseToNp(gs_result, np_array_3)
np_array_4 = _np.zeros((3, 3, 2))
gs_array_4 = gs.zeros_like(gs.array(np_array_4))
np_array_4[0, :, 1] += 1
gs_result = gs.assignment_by_sum(gs_array_4, 1, (0, 1), axis=1)
self.assertAllCloseToNp(gs_result, np_array_4)
np_array_4_list = _np.zeros((3, 3, 2))
gs_array_4_list = gs.zeros_like(gs.array(np_array_4_list))
np_array_4_list[(0, 1), :, (1, 1)] += 1
gs_result = gs.assignment_by_sum(
gs_array_4_list, 1, [(0, 1), (1, 1)], axis=1)
self.assertAllCloseToNp(gs_result, np_array_4)
def test_assignment_by_sum(self):
gs_array_1 = gs.ones(3)
self.assertRaises(ValueError, gs.assignment_by_sum, gs_array_1,
[.1, 2., 1.], [0, 1])
np_array_1 = _np.ones(3)
gs_array_1 = gs.ones_like(gs.array(np_array_1))
np_array_1[2] += 1.5
gs_result = gs.assignment_by_sum(gs_array_1, 1.5, 2)
self.assertAllCloseToNp(gs_result, np_array_1)
gs_result_list = gs.assignment_by_sum(gs_array_1, [2., 1.5], [0, 2])
np_array_1[0] += 2.
self.assertAllCloseToNp(gs_result_list, np_array_1)
np_array_1_list = _np.ones(3)
gs_array_1_list = gs.ones_like(gs.array(np_array_1_list))
indices = [1, 2]
np_array_1_list[indices] += 1.5
gs_result = gs.assignment_by_sum(gs_array_1_list, 1.5, indices)
self.assertAllCloseToNp(gs_result, np_array_1_list)
np_array_2 = _np.zeros((3, 2))
gs_array_2 = gs.zeros_like(gs.array(np_array_2))
np_array_2[0, :] += 1
gs_result = gs.assignment_by_sum(gs_array_2, 1, 0, axis=1)
self.assertAllCloseToNp(gs_result, np_array_2)
np_array_3 = _np.zeros((3, 3))
gs_array_3 = gs.zeros_like(gs.array(np_array_3))
np_array_3[0, 1] += 1
gs_result = gs.assignment_by_sum(gs_array_3, 1, (0, 1))
self.assertAllCloseToNp(gs_result, np_array_3)
np_array_4 = _np.zeros((3, 3, 2))
gs_array_4 = gs.zeros_like(gs.array(np_array_4))
np_array_4[0, :, 1] += 1
gs_result = gs.assignment_by_sum(gs_array_4, 1, (0, 1), axis=1)
self.assertAllCloseToNp(gs_result, np_array_4)
np_array_4_list = _np.zeros((3, 3, 2))
gs_array_4_list = gs.zeros_like(gs.array(np_array_4_list))
np_array_4_list[(0, 1), :, (1, 1)] += 1
gs_result = gs.assignment_by_sum(gs_array_4_list,
1, [(0, 1), (1, 1)],
axis=1)
self.assertAllCloseToNp(gs_result, np_array_4_list)
n_samples = 3
theta = _np.array([0.1, 0.2, 0.3, 0.4, 5.5])
phi = _np.array([0.11, 0.22, 0.33, 0.44, -.55])
np_array = _np.ones((n_samples, 5, 4))
gs_array = gs.array(np_array)
gs_array = gs.assignment_by_sum(gs_array,
gs.cos(theta) * gs.cos(phi), (0, 0),
axis=1)
gs_array = gs.assignment_by_sum(gs_array,
-gs.sin(theta) * gs.sin(phi), (0, 1),
axis=1)
np_array[0, :, 0] += _np.cos(theta) * _np.cos(phi)
np_array[0, :, 1] -= _np.sin(theta) * _np.sin(phi)
# TODO (ninamiolane): This test fails 15% of the time,
# when gs and _np computations are in the reverse order.
# We should investigate this.
self.assertAllCloseToNp(gs_array, np_array)
np_array = _np.array([[22., 55.], [33., 88.], [77., 99.]])
gs_array = gs.array([[22., 55.], [33., 88.], [77., 99.]])
np_mask = _np.array([[False, False], [False, True], [True, True]])
gs_mask = gs.array([[False, False], [False, True], [True, True]])
np_array[np_mask] += _np.zeros_like(np_array[np_mask])
np_array[~np_mask] += 4 * np_array[~np_mask]
np_result = np_array
values_mask = gs.zeros_like(gs_array[gs_mask])
gs_result = gs.assignment_by_sum(gs_array, values_mask, gs_mask)
gs_result = gs.assignment_by_sum(gs_result, 4 * gs_array[~gs_mask],
~gs_mask)
self.assertAllCloseToNp(gs_result, np_result)
