def test_precomputed_injection():
"""Test injection with PrecomputedSparseFunction which accepts
precomputed values for interpolation coefficients
"""
shape = (11, 11)
coords = [(.05, .95), (.45, .45)]
origin = (0, 0)
result = 0.25
# Constant for linear interpolation
# because we interpolate across 2 neighbouring points in each dimension
r = 2
m = unit_box(shape=shape)
m.data[:] = 0.
gridpoints, interpolation_coeffs = precompute_linear_interpolation(
coords, m.grid, origin)
sf = PrecomputedSparseFunction(name='s',
grid=m.grid,
r=r,
npoint=len(coords),
gridpoints=gridpoints,
interpolation_coeffs=interpolation_coeffs)
expr = sf.inject(m, Float(1.))
Operator(expr)()
indices = [slice(0, 2, 1), slice(9, 11, 1)]
assert np.allclose(m.data[indices], result, rtol=1.e-5)
indices = [slice(4, 6, 1) for _ in coords]
assert np.allclose(m.data[indices], result, rtol=1.e-5)
def test_precomputed_interpolation():
""" Test interpolation with PrecomputedSparseFunction which accepts
precomputed values for interpolation coefficients
"""
shape = (101, 101)
points = [(.05, .9), (.01, .8), (0.07, 0.84)]
origin = (0, 0)
grid = Grid(shape=shape, origin=origin)
r = 2 # Constant for linear interpolation
# because we interpolate across 2 neighbouring points in each dimension
def init(data):
for i in range(data.shape[0]):
for j in range(data.shape[1]):
data[i, j] = sin(grid.spacing[0]*i) + sin(grid.spacing[1]*j)
return data
m = Function(name='m', grid=grid, initializer=init, space_order=0)
gridpoints, interpolation_coeffs = precompute_linear_interpolation(points,
grid, origin)
sf = PrecomputedSparseFunction(name='s', grid=grid, r=r, npoint=len(points),
gridpoints=gridpoints,
interpolation_coeffs=interpolation_coeffs)
eqn = sf.interpolate(m)
op = Operator(eqn)
op()
expected_values = [sin(point[0]) + sin(point[1]) for point in points]
assert(all(np.isclose(sf.data, expected_values, rtol=1e-6)))