# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
import numpy as np
from numpy.testing import assert_allclose
from gammapy.modeling import Parameter, Parameters
from gammapy.modeling.scipy import (
    confidence_scipy,
    optimize_scipy,
    stat_profile_ul_scipy,
)


class MyDataset:
    def __init__(self, parameters):
        self.parameters = parameters

    def fcn(self):
        x, y, z = [p.value for p in self.parameters]
        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])


@pytest.mark.parametrize("method", ["Nelder-Mead", "L-BFGS-B", "Powell", "BFGS"])
def test_scipy_basic(pars, method):
    ds = MyDataset(pars)
    factors, info, optimizer = optimize_scipy(
        function=ds.fcn, parameters=pars, method=method
    )

    assert info["success"]
    assert_allclose(ds.fcn(), 0, atol=1e-5)

    # Check the result in parameters is OK
    assert_allclose(pars["x"].value, 2, rtol=1e-3)
    assert_allclose(pars["y"].value, 3e5, rtol=1e-3)
    assert_allclose(pars["z"].value, 4e-5, rtol=2e-2)


@pytest.mark.parametrize("method", ["Nelder-Mead", "L-BFGS-B", "Powell"])
def test_scipy_frozen(pars, method):
    ds = MyDataset(pars)
    pars["y"].frozen = True

    factors, info, optimizer = optimize_scipy(
        function=ds.fcn, parameters=pars, method=method
    )

    assert info["success"]

    assert_allclose(pars["x"].value, 2, rtol=1e-4)
    assert_allclose(pars["y"].value, 3.1e5)
    assert_allclose(pars["z"].value, 4.0e-5, rtol=1e-4)
    assert_allclose(ds.fcn(), 0.1111111, rtol=1e-5)


@pytest.mark.parametrize("method", ["L-BFGS-B"])
def test_scipy_limits(pars, method):
    ds = MyDataset(pars)
    pars["y"].min = 301000

    factors, info, minuit = optimize_scipy(
        function=ds.fcn, parameters=pars, method=method
    )

    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)


def test_scipy_confidence(pars):
    ds = MyDataset(pars)
    factors, info, _ = optimize_scipy(function=ds.fcn, parameters=pars)

    assert_allclose(ds.fcn(), 0, atol=1e-5)

    par = pars["x"]
    par.min, par.max = 0, 10

    result = confidence_scipy(function=ds.fcn, parameters=pars, parameter=par, sigma=1)

    assert result["success_errp"]
    assert result["success_errn"]

    assert_allclose(result["errp"], 0.2, rtol=1e-3)
    assert_allclose(result["errn"], 0.2, rtol=1e-3)


def test_stat_profile_ul_scipy():
    x = np.linspace(-5, 5, 7)
    y = x ** 2
    ul = stat_profile_ul_scipy(x, y)
    assert_allclose(ul, 2)

    ul = stat_profile_ul_scipy(x, x, interp_scale="lin")
    assert_allclose(ul, 4)
