# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
from numpy.testing import assert_allclose
from .. import Parameter, Parameters, optimize_sherpa

pytest.importorskip("sherpa")


def fcn(parameters):
    x = parameters["x"].value
    y = parameters["y"].value
    z = parameters["z"].value
    x_opt, y_opt, z_opt = 2, 3e5, 4e-5
    x_err, y_err, z_err = 0.2, 3e4, 4e-6
    return (
        ((x - x_opt) / x_err) ** 2
        + ((y - y_opt) / y_err) ** 2
        + ((z - z_opt) / z_err) ** 2
    )


@pytest.fixture()
def pars():
    x = Parameter("x", 2.1)
    y = Parameter("y", 3.1, scale=1e5)
    z = Parameter("z", 4.1, scale=1e-5)
    return Parameters([x, y, z])


# TODO: levmar doesn't work yet; needs array of statval as return in likelihood
# method='gridsearch' would require very low tolerance asserts, not added for now


@pytest.mark.parametrize("method", ["moncar", "simplex"])
def test_sherpa(method, pars):
    factors, info, _ = optimize_sherpa(function=fcn, parameters=pars, method=method)

    assert info["success"]
    assert_allclose(fcn(pars), 0, atol=1e-12)

    # Check the result in parameters is OK
    assert_allclose(pars["x"].value, 2)
    assert_allclose(pars["y"].value, 3e5)
    assert_allclose(pars["z"].value, 4e-5)

    # Check that sherpa sees the parameter factors correctly
    assert_allclose(factors, [2, 3, 4])


def test_sherpa_frozen(pars):
    pars["y"].frozen = True

    factors, info, _ = optimize_sherpa(function=fcn, parameters=pars)

    assert info["success"]
    assert_allclose(pars["x"].value, 2)
    assert_allclose(pars["y"].value, 3.1e5)
    assert_allclose(pars["z"].value, 4.0e-5)
    assert_allclose(fcn(pars), 0.11111111, rtol=1e-6)


def test_sherpa_limits(pars):
    pars["y"].min = 301000

    factors, info, _ = optimize_sherpa(function=fcn, parameters=pars)

    assert info["success"]

    # Check the result in parameters is OK
    assert_allclose(pars["x"].value, 2, rtol=1e-2)
    assert_allclose(pars["y"].value, 301000, rtol=1e-3)
