romtools.workflows.dakota.dakota_coupler

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  4#                         ROM Tools and Workflows
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 45
 46'''
 47This module implements the class required to couple a model to Dakota.
 48To couple to Dakota, a user should
 491. Complete a Model class for their application of interest
 502. Complete a driver script that instantiates the model and calls this coupler for use as the Dakota analysis driver
 51'''
 52
 53import os
 54import sys
 55import time
 56import numpy as np
 57
 58from romtools.workflows.models import QoiModel
 59
 60
 61def _create_parameter_dict(parameter_names, parameter_values):
 62    return dict(zip(parameter_names, parameter_values))
 63
 64
 65def run_model_for_dakota(
 66    model: QoiModel,
 67    multifidelity_flag: bool = False,
 68    add_core_time_metadata: bool = False,
 69):
 70    '''
 71    This function should be used in a driver script that will be called with
 72        `python <driver.py> params.in results.out`
 73
 74    by Dakota. The parameter space is built by parsing params.in,
 75    while the output QoI will be saved to results.out
 76
 77    Args:
 78        model: rom-tools model instance (FOM or ROM)
 79        multifidelity_flag: True if being used with Dakota's multifidelity (MF) tools
 80        add_core_time_metadata: True if computational cost should be included in QoI file
 81                                Useful for MF UQ when model does not have a cost model.
 82
 83    '''
 84    # Read parameters from param.in file (1st command line argument)
 85    dtype = [("floats", float), ("strings", "U100")]
 86    data = np.genfromtxt(sys.argv[1], dtype=dtype, encoding=None, delimiter=" ")
 87
 88    data_floats = data["floats"]
 89    data_strings = data["strings"]
 90
 91    num_vars = int(data_floats[0])
 92    parameter_values = np.array(data_floats[1 : 1 + num_vars])
 93    parameter_names = data_strings[1 : 1 + num_vars]
 94
 95    # Run directory is current dir, results dir is from Dakota
 96    run_directory = os.getcwd()
 97    results_file = sys.argv[2]
 98
 99    # Initialize and run ROM
100    parameter_sample = _create_parameter_dict(parameter_names, parameter_values)
101    model.populate_run_directory(run_directory, parameter_sample)
102    t0 = time.time()
103    model.run_model(run_directory, parameter_sample)
104    t = time.time() - t0
105
106    # Compute model QoI and save it to file
107    qoi = model.compute_qoi(run_directory, parameter_sample)
108
109    if multifidelity_flag:
110        assert qoi.size == 1, "For MF UQ, a scalar QoI is required"
111
112    if add_core_time_metadata:
113        qoi = np.append(qoi, t)  # Cost metadata for Q
114
115    np.savetxt(results_file, qoi)