def test_qmc_sample_generation():
"""Tests that all the anscillary code (apart from actually calculating
the qois) for qmc points happens in an expected way."""
k = 1.0
h_spec = (1.0,-1.5)
J = 100
nu = 16
M = 4
point_generation_method = 'qmc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
qois = ['testing']
num_spatial_cores = 1
output = gen_samples.generate_samples(k,h_spec,J,nu,M,
point_generation_method,
delta,lambda_mult,j_scaling,qois,
num_spatial_cores,dim=2)
for shift_no in range(nu):
assert np.allclose(output[1][shift_no][0],[float(ii) for ii in range(1,2**M + 1)])
def test_mc_sample_generation():
k = 1.0
h_spec = (1.0,-1.5)
J = 100
nu = 16
M = 4
point_generation_method = 'mc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
num_spatial_cores = 1
qois = ['testing']
output = gen_samples.generate_samples(k,h_spec,J,nu,M,
point_generation_method,
delta,lambda_mult,j_scaling,qois,num_spatial_cores,dim=2)
N = float(nu*2**M)
assert np.isclose(output[1],(N+1.0)/2.0)
assert np.isclose(output[2],np.sqrt((N+1.0)/12.0))
def test_every_point_a_preconditioner():
"""Tests for expected behaviour when every point is a preconditioner."""
k = 10.0
h_spec = (1.0, -1.5)
J = 4
nu = 1
M = 5
point_generation_method = 'qmc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
num_spatial_cores = 1
# This is just testing that we correctly handle multiple qois
qois = ['integral']
dim = 2
display_progress = True
physically_realistic = False
nearby_preconditioning = True
# This is a hack to ensure that every point is selected - if you put this as 1.0, then because of how we select the preconditioning points, not every point is selected.
nearby_preconditioning_proportion = 3.0
output = gen_samples.generate_samples(
k, h_spec, J, nu, M, point_generation_method, delta, lambda_mult,
j_scaling, qois, num_spatial_cores, dim, display_progress,
physically_realistic, nearby_preconditioning,
nearby_preconditioning_proportion)
assert (np.array(output[3]) == 1).all()
def test_one_point_a_preconditioner():
"""Tests for expected behaviour when only one point is a preconditioner."""
k = 10.0
h_spec = (1.0, -1.5)
J = 4
nu = 1
M = 5
point_generation_method = 'qmc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
num_spatial_cores = 1
# This is just testing that we correctly handle multiple qois
qois = ['integral']
dim = 2
display_progress = True
physically_realistic = False
nearby_preconditioning = True
nearby_preconditioning_proportion = 1.0 / float(2**M)
output = gen_samples.generate_samples(
k, h_spec, J, nu, M, point_generation_method, delta, lambda_mult,
j_scaling, qois, num_spatial_cores, dim, display_progress,
physically_realistic, nearby_preconditioning,
nearby_preconditioning_proportion)
assert np.array(np.array(output[3]) == 1).sum() == 1
def test_nbpc_qmc():
"""Checks that QMC + nearby preconditioning doesn't crash."""
k = 1.0
h_spec = (1.0, -1.5)
J = 2
nu = 1
M = 6
point_generation_method = 'qmc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
num_spatial_cores = 1
# This is just testing that we correctly handle multiple qois
qois = ['integral']
dim = 2
display_progress = True
physically_realistic = False
nearby_preconditioning = True
nearby_preconditioning_proportion = 0.1
output = gen_samples.generate_samples(
k, h_spec, J, nu, M, point_generation_method, delta, lambda_mult,
j_scaling, qois, num_spatial_cores, dim, display_progress,
physically_realistic, nearby_preconditioning,
nearby_preconditioning_proportion)
def qmc_test(num_spatial_cores,
nearby_preconditioning=False,
nearby_preconditioning_proportion=None):
"""The test used to check parallelism is working correctly."""
k = 5.0
h_spec = (1.0, -1.5)
J = 10
nu = 20
M = 5
delta = 1.0
lambda_mult = 1.0
j_scaling = 1.0
qois = ['integral', 'origin']
return gen_samples.generate_samples(
k,
h_spec,
J,
nu,
M,
'qmc',
delta,
lambda_mult,
j_scaling,
qois,
num_spatial_cores=num_spatial_cores,
dim=2,
display_progress=True,
physically_realistic=False,
nearby_preconditioning=nearby_preconditioning,
nearby_preconditioning_proportion=nearby_preconditioning_proportion)
def test_multiple_qois_mc():
"""Checks that multiple qois are calculated correctly for MC."""
k = 1.0
h_spec = (1.0,-1.5)
J = 100
nu = 16
M = 4
point_generation_method = 'mc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
# This is just testing that we correctly handle multiple qois
qois = ['testing','testing']
output = gen_samples.generate_samples(k,h_spec,J,nu,M,
point_generation_method,
delta,lambda_mult,j_scaling,qois,dim=2)
N = nu*(2**M)
# First qoi
assert np.isclose(output[1][0],(float(N)+1.0)/2.0)
assert np.isclose(output[2][0],np.sqrt((float(N)+1.0)/12.0))
# Second qoi
assert np.isclose(output[1][1],(float(N)+1.0)/2.0)
assert np.isclose(output[2][1],np.sqrt((float(N)+1.0)/12.0))
def test_multiple_qois_qmc():
"""Checks that anscillary code with multiple qois works for QMC."""
k = 1.0
h_spec = (1.0,-1.5)
J = 100
# nu = 16
nu=2
M = 4
point_generation_method = 'qmc'
delta = 2.0
lambda_mult = 1.0
j_scaling = 1.0
num_spatial_cores = 1
# This is just testing that we correctly handle multiple qois
qois = ['testing','testing']
output = gen_samples.generate_samples(k,h_spec,J,nu,M,
point_generation_method,
delta,lambda_mult,j_scaling,qois,
num_spatial_cores,dim=2)
# First qoi
for shift_no in range(nu):
assert np.allclose(output[1][shift_no][0],[float(ii) for ii in range(1,2**M + 1)])
# Second qoi
for shift_no in range(nu):
assert np.allclose(output[1][shift_no][1],[float(ii) for ii in range(1,2**M + 1)])
# Assumes number of cores is a power of 2
num_spatial_cores = int(
2**np.floor(np.log2(np.max((1, dofs // 50000))))
) # 50,000 is Firedrake's recommendend minimum number of DoFs per node to get good parallel scalability
qmc_out = gen.generate_samples(
k=k,
h_spec=h_spec,
J=quants['J'],
nu=quants['nu'],
M=quants['M_high'],
point_generation_method='qmc',
delta=quants['delta'],
lambda_mult=quants['lambda_mult'],
j_scaling=quants['j_scal'],
qois=quants['qois'],
num_spatial_cores=num_spatial_cores,
dim=dim,
display_progress=True,
physically_realistic=False,
nearby_preconditioning=quants['nbpc'],
nearby_preconditioning_proportion=quants['nbpc_prop'])
if fd.COMM_WORLD.rank == 0:
if h_refinement == 0:
mkdir(folder_name)
with open(
'./' + folder_name + 'output-h_magnitude-' + str(h_spec[0]) +