Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
path : str, optional
Optional path for writing files
Returns
-------
ReducedModel
Return reduced model and associated metadata
"""
solution = ct.Solution(model_file, phase_name)
assert species_targets, 'Need to specify at least one target species.'
# first, sample thermochemical data and generate metrics for measuring error
# (e.g, ignition delays). Also produce adjacency matrices for graphs, which
# will be used to produce graphs for any threshold value.
sampled_metrics, sampled_data = sample(
model_file, ignition_conditions, phase_name=phase_name, num_threads=num_threads, path=path
)
matrices = []
for state in sampled_data:
matrices.append(create_drgep_matrix((state[0], state[1], state[2:]), solution))
# For DRGEP, find the overall interaction coefficients for all species
# using the maximum over all the sampled states
importance_coeffs = get_importance_coeffs(
solution.species_names, species_targets, matrices
)
# begin reduction iterations
logging.info('Beginning DRGEP reduction loop')
logging.info(45 * '-')
Optional path for writing files
Returns
-------
ReducedModel
Return reduced model and associated metadata
"""
solution = ct.Solution(model_file)
assert species_targets, 'Need to specify at least one target species.'
# first, sample thermochemical data and generate metrics for measuring error
# (e.g, ignition delays). Also produce adjacency matrices for graphs, which
# will be used to produce graphs for any threshold value.
sampled_metrics, sampled_data = sample(
model_file, ignition_conditions, num_threads=num_threads, path=path
)
matrices = []
for state in sampled_data:
matrices.append(create_pfa_matrix((state[0], state[1], state[2:]), solution))
# begin reduction iterations
logging.info('Beginning PFA reduction loop')
logging.info(45 * '-')
logging.info('Threshold | Number of species | Max error (%)')
# start with detailed (starting) model
previous_model = ReducedModel(model=solution, filename=model_file, error=0.0)
first = True
Optional path for writing files
Returns
-------
ReducedModel
Return reduced model and associated metadata
"""
solution = ct.Solution(model_file)
assert species_targets, 'Need to specify at least one target species.'
# first, sample thermochemical data and generate metrics for measuring error
# (e.g, ignition delays). Also produce adjacency matrices for graphs, which
# will be used to produce graphs for any threshold value.
sampled_metrics, sampled_data = sample(
model_file, ignition_conditions, num_threads=num_threads, path=path
)
matrices = []
for state in sampled_data:
matrices.append(create_drg_matrix((state[0], state[1], state[2:]), solution))
# begin reduction iterations
logging.info('Beginning DRG reduction loop')
logging.info(45 * '-')
logging.info('Threshold | Number of species | Max error (%)')
# start with detailed (starting) model
previous_model = ReducedModel(model=solution, filename=model_file, error=0.0)
first = True