def qmc():
"""qMC implementation.
"""
instance = Instance({Operator.O_F: ['settings_out[]']})
while instance.reuse_instance():
# o_f
settings0 = Settings({'test2': 14.4})
assert instance.is_connected('settings_out')
assert instance.is_vector_port('settings_out')
assert not instance.is_resizable('settings_out')
length = instance.get_port_length('settings_out')
assert length == 10
for slot in range(length):
instance.send('settings_out',
Message(0.0, None, settings0), slot)
def qmc_driver() -> None:
"""A driver for quasi-Monte Carlo Uncertainty Quantification.
This component attaches to a collection of model instances, and
feeds in different parameter values generated using a Sobol
sequence.
"""
instance = Instance({
Operator.O_I: ['parameters_out[]'],
Operator.S: ['states_in[]']})
while instance.reuse_instance():
# F_INIT
# get and check parameter distributions
n_samples = instance.get_setting('n_samples', 'int')
d_min = instance.get_setting('d_min', 'float')
d_max = instance.get_setting('d_max', 'float')
k_min = instance.get_setting('k_min', 'float')
k_max = instance.get_setting('k_max', 'float')
if d_max < d_min:
instance.error_shutdown('Invalid settings: d_max < d_min')
exit(1)
if k_max < k_min:
instance.error_shutdown('Invalid settings: k_max < k_min')
exit(1)
# generate UQ parameter values
sobol_sqn = sobol_seq.i4_sobol_generate(2, n_samples)
ds = d_min + sobol_sqn[:, 0] * (d_max - d_min)
ks = k_min + sobol_sqn[:, 1] * (k_max - k_min)
# configure output port
if not instance.is_resizable('parameters_out'):
instance.error_shutdown('This component needs a resizable'
' parameters_out port, but it is connected to'
' something that cannot be resized. Maybe try'
' adding a load balancer.')
exit(1)
instance.set_port_length('parameters_out', n_samples)
# run ensemble
Us = None
# O_I
for sample in range(n_samples):
uq_parameters = Settings({
'd': ds[sample],
'k': ks[sample]})
msg = Message(0.0, None, uq_parameters)
instance.send('parameters_out', msg, sample)
# S
for sample in range(n_samples):
msg = instance.receive_with_settings('states_in', sample)
U = np.array(msg.data)
# accumulate
if Us is None:
Us = U
else:
Us = np.vstack((Us, U))
mean = np.mean(Us, axis=0)
# O_F
if 'DONTPLOT' not in os.environ:
from matplotlib import pyplot as plt
t_max = instance.get_setting('t_max', 'float')
dt = instance.get_setting('dt', 'float')
x_max = instance.get_setting('x_max', 'float')
dx = instance.get_setting('dx', 'float')
plt.figure()
plt.imshow(
np.log(Us + 1e-20),
origin='upper',
extent=[
-0.5*dx, x_max - 0.5*dx,
n_samples-0.5, -0.5],
interpolation='none',
aspect='auto'
)
cbar = plt.colorbar()
cbar.set_label('log(Concentration)', rotation=270, labelpad=20)
plt.xlabel('x')
plt.ylabel('Sample')
plt.title('Final states')
plt.show()
def qmc_driver() -> None:
"""A driver for quasi-Monte Carlo Uncertainty Quantification.
This component attaches to a collection of model instances, and
feeds in different parameter values generated using a Sobol
sequence.
"""
instance = Instance({
Operator.O_I: ['parameters_out[]'],
Operator.S: ['states_in[]']
})
while instance.reuse_instance():
# F_INIT
# get and check parameter distributions
n_samples = instance.get_setting('n_samples', 'int')
d_min = instance.get_setting('d_min', 'float')
d_max = instance.get_setting('d_max', 'float')
k_min = instance.get_setting('k_min', 'float')
k_max = instance.get_setting('k_max', 'float')
if d_max < d_min:
instance.error_shutdown('Invalid settings: d_max < d_min')
exit(1)
if k_max < k_min:
instance.error_shutdown('Invalid settings: k_max < k_min')
exit(1)
# generate UQ parameter values
sobol_sqn = sobol_seq.i4_sobol_generate(2, n_samples)
ds = d_min + sobol_sqn[:, 0] * (d_max - d_min)
ks = k_min + sobol_sqn[:, 1] * (k_max - k_min)
# configure output port
if not instance.is_resizable('parameters_out'):
instance.error_shutdown(
'This component needs a resizable'
' parameters_out port, but it is connected to'
' something that cannot be resized. Maybe try'
' adding a load balancer.')
exit(1)
instance.set_port_length('parameters_out', n_samples)
# run ensemble
Us = None
# O_I
for sample in range(n_samples):
uq_parameters = Settings({'d': ds[sample], 'k': ks[sample]})
msg = Message(0.0, None, uq_parameters)
instance.send('parameters_out', msg, sample)
# S
for sample in range(n_samples):
msg = instance.receive_with_settings('states_in', sample)
U = np.array(msg.data)
# accumulate
if Us is None:
Us = U
else:
Us = np.vstack((Us, U))
mean = np.mean(Us, axis=0)
plt.figure()
plt.imshow(np.log(Us + 1e-20))
plt.show()