How to use the pypesto.Objective function in pypesto

def gaussian_problem():
    def nllh(x):
        return - gaussian_llh(x)

    objective = pypesto.Objective(fun=nllh)
    problem = pypesto.Problem(objective=objective, lb=[-10], ub=[10])
    return problem

def rosenbrock_problem():
    """Problem based on rosenbrock objective.

    Features
    --------
    * 3-dim
    * has fixed parameters
    """
    objective = pypesto.Objective(fun=so.rosen)

    dim_full = 2
    lb = -5 * np.ones((dim_full, 1))
    ub = 5 * np.ones((dim_full, 1))

    problem = pypesto.Problem(
            objective=objective, lb=lb, ub=ub,
            x_fixed_indices=[1], x_fixed_vals=[2])
    return problem

pypesto.logging.log_to_console(logging.WARN)
    filename = ".test_logging.tmp"
    pypesto.logging.log_to_file(logging.DEBUG, filename)
    logger = logging.getLogger('pypesto')
    if os.path.exists(filename):
        os.remove(filename)
    fh = logging.FileHandler(filename)
    fh.setLevel(logging.DEBUG)
    logger.addHandler(fh)
    logger.info("start test")

    # problem definition
    def fun(_):
        raise Exception("This function cannot be called.")

    objective = pypesto.Objective(fun=fun)
    problem = pypesto.Problem(objective, -1, 1)

    optimizer = pypesto.optimize.ScipyOptimizer()
    options = {'allow_failed_starts': True}

    # optimization
    pypesto.optimize.minimize(problem, optimizer, 5, options=options)

    # assert logging worked
    assert os.path.exists(filename)
    f = open(filename, 'rb')
    content = str(f.read())
    f.close()

    # tidy up
    os.remove(filename)

def test_empty_prior():
    """Check that priors are zero when none are defined."""
    # define negative log posterior
    posterior_fun = pypesto.Objective(fun=negative_log_posterior)

    # define pypesto problem without prior object
    test_problem = pypesto.Problem(objective=posterior_fun, lb=-10, ub=10,
                                   x_names=['x'])

    sampler = sample.AdaptiveMetropolisSampler()

    result = sample.sample(test_problem, n_samples=50, sampler=sampler,
                           x0=np.array([0.]))

    # get log prior values of first chain
    logprior_trace = -result.sample_result.trace_neglogprior[0, :]

    # check that all entries are zero
    assert (logprior_trace == 0.).all()

def gaussian_mixture_separated_modes_problem():
    """Problem based on a mixture of gaussians with far/separated modes."""
    def nllh(x):
        return - gaussian_mixture_separated_modes_llh(x)

    objective = pypesto.Objective(fun=nllh)
    problem = pypesto.Problem(objective=objective, lb=[-100], ub=[200],
                              x_names=['x'])
    return problem

arg_hess = False
        else:
            def arg_fun(x):
                return fun(x)
            arg_grad = arg_hess = False
    else:  # integrated
        if max_sensi_order >= 2:
            arg_hess = hess
        else:
            arg_hess = None
        if max_sensi_order >= 1:
            arg_grad = grad
        else:
            arg_grad = None
        arg_fun = fun
    obj = pypesto.Objective(fun=arg_fun, grad=arg_grad, hess=arg_hess)
    return {'obj': obj,
            'max_sensi_order': max_sensi_order,
            'x': x,
            'fval': fun(x),
            'grad': grad(x),
            'hess': hess(x)}

def test_prior():
    """Check that priors are defined for sampling."""
    # define negative log posterior
    posterior_fun = pypesto.Objective(fun=negative_log_posterior)

    # define negative log prior
    prior_fun = pypesto.Objective(fun=negative_log_prior)

    # define pypesto prior object
    prior_object = pypesto.NegLogPriors(objectives=[prior_fun])

    # define pypesto problem using prior object
    test_problem = pypesto.Problem(objective=posterior_fun,
                                   x_priors_defs=prior_object,
                                   lb=-10, ub=10,
                                   x_names=['x'])

    sampler = sample.AdaptiveMetropolisSampler()

    result = sample.sample(test_problem, n_samples=1e4, sampler=sampler,

from test.test_sbml_conversion import load_model_objective
from pypesto.objective.util import sres_to_schi2, res_to_chi2
from pypesto.objective import CsvHistory, HistoryOptions, MemoryHistory
from pypesto.optimize.optimizer import read_result_from_file, OptimizerResult

from pypesto.objective.constants import (
    FVAL, GRAD, HESS, RES, SRES, CHI2, SCHI2
)

from typing import Sequence


class HistoryTest(unittest.TestCase):
    problem: pypesto.Problem = None
    optimizer: pypesto.Optimizer = None
    obj: pypesto.Objective = None
    history_options: HistoryOptions = None
    ub: np.ndarray = None
    lb: np.ndarray = None
    x_fixed_indices = None
    x_fixed_vals = None
    fix_pars = True

    def check_history(self):
        kwargs = {
            'objective': self.obj,
            'ub': self.ub,
            'lb': self.lb,
        }
        if self.fix_pars:
            kwargs = {**kwargs, **{
                'x_fixed_indices': self.x_fixed_indices,