Source code for romtools.workflows.dakota.dakota_coupler

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'''
This module implements the class required to couple a model to Dakota.
To couple to Dakota, a user should
1. Complete a Model class for their application of interest
2. Complete a driver script that instantiates the model and calls this coupler for use as the Dakota analysis driver
'''

import os
import sys
import time
import numpy as np

from romtools.workflows.models import QoiModel


def _create_parameter_dict(parameter_names, parameter_values):
    return dict(zip(parameter_names, parameter_values))




def run_model_for_dakota(
    model: QoiModel,
    multifidelity_flag: bool = False,
    add_core_time_metadata: bool = False,
):
    '''
    This function should be used in a driver script that will be called with
        `python <driver.py> params.in results.out`

    by Dakota. The parameter space is built by parsing params.in,
    while the output QoI will be saved to results.out

    Args:
        model: rom-tools model instance (FOM or ROM)
        multifidelity_flag: True if being used with Dakota's multifidelity (MF) tools
        add_core_time_metadata: True if computational cost should be included in QoI file
                                Useful for MF UQ when model does not have a cost model.

    '''
    # Read parameters from param.in file (1st command line argument)
    dtype = [("floats", float), ("strings", "U100")]
    data = np.genfromtxt(sys.argv[1], dtype=dtype, encoding=None, delimiter=" ")

    data_floats = data["floats"]
    data_strings = data["strings"]

    num_vars = int(data_floats[0])
    parameter_values = np.array(data_floats[1 : 1 + num_vars])
    parameter_names = data_strings[1 : 1 + num_vars]

    # Run directory is current dir, results dir is from Dakota
    run_directory = os.getcwd()
    results_file = sys.argv[2]

    # Initialize and run ROM
    parameter_sample = _create_parameter_dict(parameter_names, parameter_values)
    model.populate_run_directory(run_directory, parameter_sample)
    t0 = time.time()
    model.run_model(run_directory, parameter_sample)
    t = time.time() - t0

    # Compute model QoI and save it to file
    qoi = model.compute_qoi(run_directory, parameter_sample)

    if multifidelity_flag:
        assert qoi.size == 1, "For MF UQ, a scalar QoI is required"

    if add_core_time_metadata:
        qoi = np.append(qoi, t)  # Cost metadata for Q

    np.savetxt(results_file, qoi)