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Frederik Hennig
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import numpy as np
from itertools import product
import pystencils.boundaries.createindexlist as cil
import pytest

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@pytest.mark.parametrize('single_link', [False, True])
@pytest.mark.skipif(not cil.cython_funcs_available, reason='Cython functions are not available')
def test_equivalence_cython_python_version(single_link):
# D2Q9
stencil_2d = tuple((x, y) for x, y in product([-1, 0, 1], [-1, 0, 1]))

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# D3Q19
stencil_3d = tuple(
(x, y, z) for x, y, z in product([-1, 0, 1], [-1, 0, 1], [-1, 0, 1]) if abs(x) + abs(y) + abs(z) < 3)

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for dtype in [int, np.int16, np.uint32]:
fluid_mask = dtype(1)
mask = dtype(2)
flag_field_2d = np.ones([15, 16], dtype=dtype) * fluid_mask
flag_field_3d = np.ones([15, 16, 17], dtype=dtype) * fluid_mask
flag_field_2d[0, :] = mask
flag_field_2d[-1, :] = mask
flag_field_2d[7, 7] = mask
flag_field_3d[0, :, :] = mask
flag_field_3d[-1, :, :] = mask
flag_field_3d[7, 7, 7] = mask
result_python_2d = cil._create_index_list_python(flag_field_2d, mask, fluid_mask,
stencil_2d, single_link, True, 1)

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result_python_3d = cil._create_index_list_python(flag_field_3d, mask, fluid_mask,
stencil_3d, single_link, True, 1)

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result_cython_2d = cil.create_boundary_index_list(flag_field_2d, stencil_2d, mask,
fluid_mask, 1, True, single_link)
result_cython_3d = cil.create_boundary_index_list(flag_field_3d, stencil_3d, mask,
fluid_mask, 1, True, single_link)
np.testing.assert_equal(result_python_2d, result_cython_2d)
np.testing.assert_equal(result_python_3d, result_cython_3d)

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@pytest.mark.parametrize('single_link', [False, True])
@pytest.mark.skipif(not cil.cython_funcs_available, reason='Cython functions are not available')
def test_equivalence_cell_idx_list_cython_python_version(single_link):
# D2Q9
stencil_2d = tuple((x, y) for x, y in product([-1, 0, 1], [-1, 0, 1]))

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# D3Q19
stencil_3d = tuple(
(x, y, z) for x, y, z in product([-1, 0, 1], [-1, 0, 1], [-1, 0, 1]) if abs(x) + abs(y) + abs(z) < 3)

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for dtype in [int, np.int16, np.uint32]:
fluid_mask = dtype(1)
mask = dtype(2)
flag_field_2d = np.ones([15, 16], dtype=dtype) * fluid_mask
flag_field_3d = np.ones([15, 16, 17], dtype=dtype) * fluid_mask
flag_field_2d[0, :] = mask
flag_field_2d[-1, :] = mask
flag_field_2d[7, 7] = mask
flag_field_3d[0, :, :] = mask
flag_field_3d[-1, :, :] = mask
flag_field_3d[7, 7, 7] = mask
result_python_2d = cil._create_index_list_python(flag_field_2d, mask, fluid_mask,
stencil_2d, single_link, False)

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result_python_3d = cil._create_index_list_python(flag_field_3d, mask, fluid_mask,
stencil_3d, single_link, False)

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result_cython_2d = cil.create_boundary_index_list(flag_field_2d, stencil_2d, mask, fluid_mask, None,
False, single_link)
result_cython_3d = cil.create_boundary_index_list(flag_field_3d, stencil_3d, mask, fluid_mask, None,
False, single_link)
np.testing.assert_equal(result_python_2d, result_cython_2d)
np.testing.assert_equal(result_python_3d, result_cython_3d)
@pytest.mark.parametrize('inner_or_boundary', [False, True])
def test_normal_calculation(inner_or_boundary):
stencil = tuple((x, y) for x, y in product([-1, 0, 1], [-1, 0, 1]))
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domain_size = (32, 32)
dtype = np.uint32
fluid_mask = dtype(1)
mask = dtype(2)
flag_field = np.ones([domain_size[0], domain_size[1]], dtype=dtype) * fluid_mask
radius_inner = domain_size[0] // 4
radius_outer = domain_size[0] // 2
y_mid = domain_size[1] / 2
x_mid = domain_size[0] / 2
for x in range(0, domain_size[0]):
for y in range(0, domain_size[1]):
if (y - y_mid) ** 2 + (x - x_mid) ** 2 < radius_inner ** 2:
flag_field[x, y] = mask
if (x - x_mid) ** 2 + (y - y_mid) ** 2 > radius_outer ** 2:
flag_field[x, y] = mask
args_no_gl = (flag_field, mask, fluid_mask, np.array(stencil, dtype=np.int32), True)
index_list = cil._create_index_list_python(*args_no_gl, inner_or_boundary=inner_or_boundary, nr_of_ghost_layers=1)
checkmask = mask if inner_or_boundary else fluid_mask
for cell in index_list:
idx = cell[2]
cell = tuple((cell[0], cell[1]))
sum_cells = np.zeros(len(cell))
for dir_idx, direction in enumerate(stencil):
neighbor_cell = tuple([cell_i + dir_i for cell_i, dir_i in zip(cell, direction)])
if any(not 0 <= e < upper for e, upper in zip(neighbor_cell, flag_field.shape)):
continue
if flag_field[neighbor_cell] & checkmask:
sum_cells += np.array(direction)
assert np.argmax(np.inner(sum_cells, stencil)) == idx