def test_grid_generation(self):
"""Test that ``generate_grid_points`` generates a uniform grid.
Test makes assumptions about the ordering of the output that may be invalidated by
changes to numpy.meshgrid.
"""
domain_bounds = ClosedInterval.build_closed_intervals_from_list([[0.0, 1.0], [-2.0, 3.0], [2.71, 3.14]])
points_per_dimension = [7, 11, 8]
# Test that all points are present
grid = generate_grid_points(points_per_dimension, domain_bounds)
per_axis_grid = [numpy.linspace(bounds.min, bounds.max, points_per_dimension[i])
for i, bounds in enumerate(domain_bounds)]
# Loop ordering assumes the output is ordered a certain way.
for i, y_coord in enumerate(per_axis_grid[1]):
for j, x_coord in enumerate(per_axis_grid[0]):
for k, z_coord in enumerate(per_axis_grid[2]):
truth = numpy.array([x_coord, y_coord, z_coord])
index = i * per_axis_grid[2].size * per_axis_grid[0].size + j * per_axis_grid[2].size + k
test = grid[index, ...]
self.assert_vector_within_relative(test, truth, 0.0)
# Also test that scalar points_per_dimension works
points_per_dimension = [5, 5, 5]
grid_truth = generate_grid_points(points_per_dimension, domain_bounds)
points_per_dimension = 5
grid_test = generate_grid_points(points_per_dimension, domain_bounds)
self.assert_vector_within_relative(grid_test, grid_truth, 0.0)
def test_grid_generation(self):
"""Test that ``generate_grid_points`` generates a uniform grid.
Test makes assumptions about the ordering of the output that may be invalidated by
changes to numpy.meshgrid.
"""
domain_bounds = ClosedInterval.build_closed_intervals_from_list([[0.0, 1.0], [-2.0, 3.0], [2.71, 3.14]])
points_per_dimension = [7, 11, 8]
# Test that all points are present
grid = generate_grid_points(points_per_dimension, domain_bounds)
per_axis_grid = [numpy.linspace(bounds.min, bounds.max, points_per_dimension[i])
for i, bounds in enumerate(domain_bounds)]
# Loop ordering assumes the output is ordered a certain way.
for i, y_coord in enumerate(per_axis_grid[1]):
for j, x_coord in enumerate(per_axis_grid[0]):
for k, z_coord in enumerate(per_axis_grid[2]):
truth = numpy.array([x_coord, y_coord, z_coord])
index = i * per_axis_grid[2].size * per_axis_grid[0].size + j * per_axis_grid[2].size + k
test = grid[index, ...]
self.assert_vector_within_relative(test, truth, 0.0)
# Also test that scalar points_per_dimension works
points_per_dimension = [5, 5, 5]
grid_truth = generate_grid_points(points_per_dimension, domain_bounds)
points_per_dimension = 5
grid_test = generate_grid_points(points_per_dimension, domain_bounds)
self.assert_vector_within_relative(grid_test, grid_truth, 0.0)
def generate_grid_points_in_domain(self, points_per_dimension, random_source=None):
"""Generate a grid of ``N_0 by N_1 by ... by N_{dim-1}`` points, with each dimension uniformly spaced along the domain boundary.
See python.geometry_utils.generate_grid_points for more details.
:param points_per_dimension: (n_1, n_2, ... n_{dim}) number of stencil points per spatial dimension.
If len(points_per_dimension) == 1, then n_i = len(points_per_dimension)
:type points_per_dimension: tuple or scalar
:param random_source: random source producing uniform random numbers (e.g., numpy.random.uniform) (UNUSED)
:type random_source: callable yielding uniform random numbers in [0,1]
:return: uniform random sampling of points from the domain
"""
# TODO(GH-56): Allow users to pass in a random source.
return generate_grid_points(points_per_dimension, self._domain_bounds)
