Source code for pycif.plugins.simulators.dummy.simul

import numpy as np

from logging import info




def simul(self, chi, grad=True, run_id=-1, **kwargs):
    r"""Evaluate the analytical quadratic-bowl cost function and its gradient.

    Computes

    .. math::

        J(\boldsymbol{\chi}) = \sum_{i=0}^{n-1}(\chi_i - i)^2,
        \qquad
        \nabla J(\boldsymbol{\chi}) = 2\,(\boldsymbol{\chi} - \mathbf{i})

    with the global minimum at :math:`\chi^*_i = i`.  No obs-operator or
    observation vector is required.

    Args:
        self (Plugin): dummy simulator plugin instance.
        chi (np.ndarray): current iterate :math:`\boldsymbol{\chi}`,
            shape ``(n,)``.
        grad (bool): if ``True`` (default), return both cost and gradient.
        run_id (int): unused; accepted for interface consistency.
        **kwargs: unused; accepted for interface consistency.

    Returns:
        tuple or float: ``(zcost, zgrad)`` when ``grad=True``;
        ``zcost`` when ``grad=False``.
    """

    # Various variables
    datei = self.datei
    datef = self.datef
    workdir = self.workdir

    # # Get the observation operator from extra arguments
    # if not hasattr(self, "obsoperator"):
    #     raise Exception(
    #         "Observation operator is missing to compute the "
    #         "simulator. Please check your setup files"
    #     )

    # obsoper = self.obsoperator
    controlvect = self.controlvect

    # Saving chi to the control vector for later
    controlvect.chi = chi

    zcost = np.sum((chi - np.arange(len(chi))) ** 2)
    zgrad = 2 * (chi - np.arange(len(chi)))

    # Verbose the norms
    znorm_grad_b = np.dot(zgrad, zgrad) ** 0.5
    info(
        "In Simulator:\n"
        "    grad(Jb) = {}\n"
        "         Jb  = {}\n".format(znorm_grad_b, zcost)
    )

    return zcost, zgrad