# Licensed under a 3-clause BSD style license - see LICENSE.rst

import types

import pytest
import numpy as np
from numpy.testing import assert_allclose
from numpy.random import RandomState

from ..core import Fittable1DModel
from ..parameters import Parameter
from .. import models
from .. import fitting

from .utils import ignore_non_integer_warning

try:
    from scipy import optimize
    HAS_SCIPY = True
except ImportError:
    HAS_SCIPY = False


class TestNonLinearConstraints:

    def setup_class(self):
        self.g1 = models.Gaussian1D(10, 14.9, stddev=.3)
        self.g2 = models.Gaussian1D(10, 13, stddev=.4)
        self.x = np.arange(10, 20, .1)
        self.y1 = self.g1(self.x)
        self.y2 = self.g2(self.x)
        rsn = RandomState(1234567890)
        self.n = rsn.randn(100)
        self.ny1 = self.y1 + 2 * self.n
        self.ny2 = self.y2 + 2 * self.n

    @pytest.mark.skipif('not HAS_SCIPY')
    def test_fixed_par(self):
        g1 = models.Gaussian1D(10, mean=14.9, stddev=.3,
                               fixed={'amplitude': True})
        fitter = fitting.LevMarLSQFitter()
        model = fitter(g1, self.x, self.ny1)
        assert model.amplitude.value == 10

    @pytest.mark.skipif('not HAS_SCIPY')
    def test_tied_par(self):

        def tied(model):
            mean = 50 * model.stddev
            return mean
        g1 = models.Gaussian1D(10, mean=14.9, stddev=.3, tied={'mean': tied})
        fitter = fitting.LevMarLSQFitter()
        model = fitter(g1, self.x, self.ny1)
        assert_allclose(model.mean.value, 50 * model.stddev,
                        rtol=10 ** (-5))

    @pytest.mark.skipif('not HAS_SCIPY')
    def test_joint_fitter(self):
        g1 = models.Gaussian1D(10, 14.9, stddev=.3)
        g2 = models.Gaussian1D(10, 13, stddev=.4)
        jf = fitting.JointFitter([g1, g2], {g1: ['amplitude'],
                                            g2: ['amplitude']}, [9.8])
        x = np.arange(10, 20, .1)
        y1 = g1(x)
        y2 = g2(x)
        n = np.random.randn(100)
        ny1 = y1 + 2 * n
        ny2 = y2 + 2 * n
        jf(x, ny1, x, ny2)
        p1 = [14.9, .3]
        p2 = [13, .4]
        A = 9.8
        p = np.r_[A, p1, p2]

        def compmodel(A, p, x):
            return A * np.exp(-0.5 / p[1] ** 2 * (x - p[0]) ** 2)

        def errf(p, x1, y1, x2, y2):
            return np.ravel(
                np.r_[compmodel(p[0], p[1:3], x1) - y1,
                      compmodel(p[0], p[3:], x2) - y2])

        fitparams, _ = optimize.leastsq(errf, p, args=(x, ny1, x, ny2))
        assert_allclose(jf.fitparams, fitparams, rtol=10 ** (-5))
        assert_allclose(g1.amplitude.value, g2.amplitude.value)

    @pytest.mark.skipif('not HAS_SCIPY')
    def test_no_constraints(self):
        g1 = models.Gaussian1D(9.9, 14.5, stddev=.3)

        def func(p, x):
            return p[0] * np.exp(-0.5 / p[2] ** 2 * (x - p[1]) ** 2)

        def errf(p, x, y):
            return func(p, x) - y

        p0 = [9.9, 14.5, 0.3]
        y = g1(self.x)
        n = np.random.randn(100)
        ny = y + n
        fitpar, s = optimize.leastsq(errf, p0, args=(self.x, ny))
        fitter = fitting.LevMarLSQFitter()
        model = fitter(g1, self.x, ny)
        assert_allclose(model.parameters, fitpar, rtol=5 * 10 ** (-3))


@pytest.mark.skipif('not HAS_SCIPY')
class TestBounds:

    def setup_class(self):
        A = -2.0
        B = 0.5
        self.x = np.linspace(-1.0, 1.0, 100)
        self.y = A * self.x + B + np.random.normal(scale=0.1, size=100)
        data = np.array([505.0, 556.0, 630.0, 595.0, 561.0, 553.0, 543.0, 496.0, 460.0, 469.0,
                         426.0, 518.0, 684.0, 798.0, 830.0, 794.0, 649.0, 706.0, 671.0, 545.0,
                         479.0, 454.0, 505.0, 700.0, 1058.0, 1231.0, 1325.0, 997.0, 1036.0, 884.0,
                         610.0, 487.0, 453.0, 527.0, 780.0, 1094.0, 1983.0, 1993.0, 1809.0, 1525.0,
                         1056.0, 895.0, 604.0, 466.0, 510.0, 678.0, 1130.0, 1986.0, 2670.0, 2535.0,
                         1878.0, 1450.0, 1200.0, 663.0, 511.0, 474.0, 569.0, 848.0, 1670.0, 2611.0,
                         3129.0, 2507.0, 1782.0, 1211.0, 723.0, 541.0, 511.0, 518.0, 597.0, 1137.0,
                         1993.0, 2925.0, 2438.0, 1910.0, 1230.0, 738.0, 506.0, 461.0, 486.0, 597.0,
                         733.0, 1262.0, 1896.0, 2342.0, 1792.0, 1180.0, 667.0, 482.0, 454.0, 482.0,
                         504.0, 566.0, 789.0, 1194.0, 1545.0, 1361.0, 933.0, 562.0, 418.0, 463.0,
                         435.0, 466.0, 528.0, 487.0, 664.0, 799.0, 746.0, 550.0, 478.0, 535.0, 443.0,
                         416.0, 439.0, 472.0, 472.0, 492.0, 523.0, 569.0, 487.0, 441.0, 428.0])
        self.data = data.reshape(11, 11)

    def test_bounds_lsq(self):
        guess_slope = 1.1
        guess_intercept = 0.0
        bounds = {'slope': (-1.5, 5.0), 'intercept': (-1.0, 1.0)}
        line_model = models.Linear1D(guess_slope, guess_intercept,
                                     bounds=bounds)
        fitter = fitting.LevMarLSQFitter()
        model = fitter(line_model, self.x, self.y)
        slope = model.slope.value
        intercept = model.intercept.value
        assert slope + 10 ** -5 >= bounds['slope'][0]
        assert slope - 10 ** -5 <= bounds['slope'][1]
        assert intercept + 10 ** -5 >= bounds['intercept'][0]
        assert intercept - 10 ** -5 <= bounds['intercept'][1]

    def test_bounds_slsqp(self):
        guess_slope = 1.1
        guess_intercept = 0.0
        bounds = {'slope': (-1.5, 5.0), 'intercept': (-1.0, 1.0)}
        line_model = models.Linear1D(guess_slope, guess_intercept,
                                     bounds=bounds)
        fitter = fitting.SLSQPLSQFitter()

        with ignore_non_integer_warning():
            model = fitter(line_model, self.x, self.y)

        slope = model.slope.value
        intercept = model.intercept.value
        assert slope + 10 ** -5 >= bounds['slope'][0]
        assert slope - 10 ** -5 <= bounds['slope'][1]
        assert intercept + 10 ** -5 >= bounds['intercept'][0]
        assert intercept - 10 ** -5 <= bounds['intercept'][1]

    def test_bounds_gauss2d_lsq(self):
        X, Y = np.meshgrid(np.arange(11), np.arange(11))
        bounds = {"x_mean": [0., 11.],
                  "y_mean": [0., 11.],
                  "x_stddev": [1., 4],
                  "y_stddev": [1., 4]}
        gauss = models.Gaussian2D(amplitude=10., x_mean=5., y_mean=5.,
                                  x_stddev=4., y_stddev=4., theta=0.5,
                                  bounds=bounds)
        gauss_fit = fitting.LevMarLSQFitter()
        model = gauss_fit(gauss, X, Y, self.data)
        x_mean = model.x_mean.value
        y_mean = model.y_mean.value
        x_stddev = model.x_stddev.value
        y_stddev = model.y_stddev.value
        assert x_mean + 10 ** -5 >= bounds['x_mean'][0]
        assert x_mean - 10 ** -5 <= bounds['x_mean'][1]
        assert y_mean + 10 ** -5 >= bounds['y_mean'][0]
        assert y_mean - 10 ** -5 <= bounds['y_mean'][1]
        assert x_stddev + 10 ** -5 >= bounds['x_stddev'][0]
        assert x_stddev - 10 ** -5 <= bounds['x_stddev'][1]
        assert y_stddev + 10 ** -5 >= bounds['y_stddev'][0]
        assert y_stddev - 10 ** -5 <= bounds['y_stddev'][1]

    def test_bounds_gauss2d_slsqp(self):
        X, Y = np.meshgrid(np.arange(11), np.arange(11))
        bounds = {"x_mean": [0., 11.],
                  "y_mean": [0., 11.],
                  "x_stddev": [1., 4],
                  "y_stddev": [1., 4]}
        gauss = models.Gaussian2D(amplitude=10., x_mean=5., y_mean=5.,
                                  x_stddev=4., y_stddev=4., theta=0.5,
                                  bounds=bounds)
        gauss_fit = fitting.SLSQPLSQFitter()
        with ignore_non_integer_warning():
            model = gauss_fit(gauss, X, Y, self.data)
        x_mean = model.x_mean.value
        y_mean = model.y_mean.value
        x_stddev = model.x_stddev.value
        y_stddev = model.y_stddev.value
        assert x_mean + 10 ** -5 >= bounds['x_mean'][0]
        assert x_mean - 10 ** -5 <= bounds['x_mean'][1]
        assert y_mean + 10 ** -5 >= bounds['y_mean'][0]
        assert y_mean - 10 ** -5 <= bounds['y_mean'][1]
        assert x_stddev + 10 ** -5 >= bounds['x_stddev'][0]
        assert x_stddev - 10 ** -5 <= bounds['x_stddev'][1]
        assert y_stddev + 10 ** -5 >= bounds['y_stddev'][0]
        assert y_stddev - 10 ** -5 <= bounds['y_stddev'][1]


class TestLinearConstraints:

    def setup_class(self):
        self.p1 = models.Polynomial1D(4)
        self.p1.c0 = 0
        self.p1.c1 = 0
        self.p1.window = [0., 9.]
        self.x = np.arange(10)
        self.y = self.p1(self.x)
        rsn = RandomState(1234567890)
        self.n = rsn.randn(10)
        self.ny = self.y + self.n

    def test(self):
        self.p1.c0.fixed = True
        self.p1.c1.fixed = True
        pfit = fitting.LinearLSQFitter()
        model = pfit(self.p1, self.x, self.y)
        assert_allclose(self.y, model(self.x))

# Test constraints as parameter properties


def test_set_fixed_1():
    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1)
    gauss.mean.fixed = True
    assert gauss.fixed == {'amplitude': False, 'mean': True, 'stddev': False}


def test_set_fixed_2():
    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1,
                              fixed={'mean': True})
    assert gauss.mean.fixed is True


def test_set_tied_1():
    def tie_amplitude(model):
        return 50 * model.stddev

    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1)
    gauss.amplitude.tied = tie_amplitude
    assert gauss.amplitude.tied is not False
    assert isinstance(gauss.tied['amplitude'], types.FunctionType)


def test_set_tied_2():
    def tie_amplitude(model):
        return 50 * model.stddev

    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1,
                              tied={'amplitude': tie_amplitude})
    assert gauss.amplitude.tied


def test_unset_fixed():
    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1,
                              fixed={'mean': True})
    gauss.mean.fixed = False
    assert gauss.fixed == {'amplitude': False, 'mean': False, 'stddev': False}


def test_unset_tied():
    def tie_amplitude(model):
        return 50 * model.stddev

    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1,
                              tied={'amplitude': tie_amplitude})
    gauss.amplitude.tied = False
    assert gauss.tied == {'amplitude': False, 'mean': False, 'stddev': False}


def test_set_bounds_1():
    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1,
                              bounds={'stddev': (0, None)})
    assert gauss.bounds == {'amplitude': (None, None),
                            'mean': (None, None),
                            'stddev': (0.0, None)}


def test_set_bounds_2():
    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1)
    gauss.stddev.min = 0.
    assert gauss.bounds == {'amplitude': (None, None),
                            'mean': (None, None),
                            'stddev': (0.0, None)}


def test_unset_bounds():
    gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1,
                              bounds={'stddev': (0, 2)})
    gauss.stddev.min = None
    gauss.stddev.max = None
    assert gauss.bounds == {'amplitude': (None, None),
                            'mean': (None, None),
                            'stddev': (None, None)}


def test_default_constraints():
    """Regression test for https://github.com/astropy/astropy/issues/2396

    Ensure that default constraints defined on parameters are carried through
    to instances of the models those parameters are defined for.
    """

    class MyModel(Fittable1DModel):
        a = Parameter(default=1)
        b = Parameter(default=0, min=0, fixed=True)

        @staticmethod
        def evaluate(x, a, b):
            return x * a + b

    assert MyModel.a.default == 1
    assert MyModel.b.default == 0
    assert MyModel.b.min == 0
    assert MyModel.b.bounds == (0, None)
    assert MyModel.b.fixed is True

    m = MyModel()
    assert m.a.value == 1
    assert m.b.value == 0
    assert m.b.min == 0
    assert m.b.bounds == (0, None)
    assert m.b.fixed is True
    assert m.bounds == {'a': (None, None), 'b': (0, None)}
    assert m.fixed == {'a': False, 'b': True}

    # Make a model instance that overrides the default constraints and values
    m = MyModel(3, 4, bounds={'a': (1, None), 'b': (2, None)},
                fixed={'a': True, 'b': False})
    assert m.a.value == 3
    assert m.b.value == 4
    assert m.a.min == 1
    assert m.b.min == 2
    assert m.a.bounds == (1, None)
    assert m.b.bounds == (2, None)
    assert m.a.fixed is True
    assert m.b.fixed is False
    assert m.bounds == {'a': (1, None), 'b': (2, None)}
    assert m.fixed == {'a': True, 'b': False}


@pytest.mark.skipif('not HAS_SCIPY')
def test_fit_with_fixed_and_bound_constraints():
    """
    Regression test for https://github.com/astropy/astropy/issues/2235

    Currently doesn't test that the fit is any *good*--just that parameters
    stay within their given constraints.
    """

    m = models.Gaussian1D(amplitude=3, mean=4, stddev=1,
                          bounds={'mean': (4, 5)},
                          fixed={'amplitude': True})
    x = np.linspace(0, 10, 10)
    y = np.exp(-x ** 2 / 2)

    f = fitting.LevMarLSQFitter()
    fitted_1 = f(m, x, y)
    assert fitted_1.mean >= 4
    assert fitted_1.mean <= 5
    assert fitted_1.amplitude == 3.0

    m.amplitude.fixed = False
    fitted_2 = f(m, x, y)
    # It doesn't matter anymore what the amplitude ends up as so long as the
    # bounds constraint was still obeyed
    assert fitted_1.mean >= 4
    assert fitted_1.mean <= 5


@pytest.mark.skipif('not HAS_SCIPY')
def test_fit_with_bound_constraints_estimate_jacobian():
    """
    Regression test for https://github.com/astropy/astropy/issues/2400

    Checks that bounds constraints are obeyed on a custom model that does not
    define fit_deriv (and thus its Jacobian must be estimated for non-linear
    fitting).
    """

    class MyModel(Fittable1DModel):
        a = Parameter(default=1)
        b = Parameter(default=2)

        @staticmethod
        def evaluate(x, a, b):
            return a * x + b

    m_real = MyModel(a=1.5, b=-3)
    x = np.arange(100)
    y = m_real(x)

    m = MyModel()
    f = fitting.LevMarLSQFitter()
    fitted_1 = f(m, x, y)

    # This fit should be trivial so even without constraints on the bounds it
    # should be right
    assert np.allclose(fitted_1.a, 1.5)
    assert np.allclose(fitted_1.b, -3)

    m2 = MyModel()
    m2.a.bounds = (-2, 2)
    f2 = fitting.LevMarLSQFitter()
    fitted_2 = f2(m2, x, y)
    assert np.allclose(fitted_1.a, 1.5)
    assert np.allclose(fitted_1.b, -3)

    # Check that the estimated Jacobian was computed (it doesn't matter what
    # the values are so long as they're not all zero.
    assert np.any(f2.fit_info['fjac'] != 0)


# https://github.com/astropy/astropy/issues/6014
@pytest.mark.skipif('not HAS_SCIPY')
def test_gaussian2d_positive_stddev():
    # This is 2D Gaussian with noise to be fitted, as provided by @ysBach
    test = [
        [-54.33, 13.81, -34.55, 8.95, -143.71, -0.81, 59.25, -14.78, -204.9,
         -30.87, -124.39, 123.53, 70.81, -109.48, -106.77, 35.64, 18.29],
        [-126.19, -89.13, 63.13, 50.74, 61.83, 19.06, 65.7, 77.94, 117.14,
         139.37, 52.57, 236.04, 100.56, 242.28, -180.62, 154.02, -8.03],
        [91.43, 96.45, -118.59, -174.58, -116.49, 80.11, -86.81, 14.62, 79.26,
         7.56, 54.99, 260.13, -136.42, -20.77, -77.55, 174.52, 134.41],
        [33.88, 7.63, 43.54, 70.99, 69.87, 33.97, 273.75, 176.66, 201.94,
         336.34, 340.54, 163.77, -156.22, 21.49, -148.41, 94.88, 42.55],
        [82.28, 177.67, 26.81, 17.66, 47.81, -31.18, 353.23, 589.11, 553.27,
         242.35, 444.12, 186.02, 140.73, 75.2, -87.98, -18.23, 166.74],
        [113.09, -37.01, 134.23, 71.89, 107.88, 198.69, 273.88, 626.63, 551.8,
         547.61, 580.35, 337.8, 139.8, 157.64, -1.67, -26.99, 37.35],
        [106.47, 31.97, 84.99, -125.79, 195.0, 493.65, 861.89, 908.31, 803.9,
         781.01, 532.59, 404.67, 115.18, 111.11, 28.08, 122.05, -58.36],
        [183.62, 45.22, 40.89, 111.58, 425.81, 321.53, 545.09, 866.02, 784.78,
         731.35, 609.01, 405.41, -19.65, 71.2, -140.5, 144.07, 25.24],
        [137.13, -86.95, 15.39, 180.14, 353.23, 699.01, 1033.8, 1014.49,
         814.11, 647.68, 461.03, 249.76, 94.8, 41.17, -1.16, 183.76, 188.19],
        [35.39, 26.92, 198.53, -37.78, 638.93, 624.41, 816.04, 867.28, 697.0,
         491.56, 378.21, -18.46, -65.76, 98.1, 12.41, -102.18, 119.05],
        [190.73, 125.82, 311.45, 369.34, 554.39, 454.37, 755.7, 736.61, 542.43,
         188.24, 214.86, 217.91, 7.91, 27.46, -172.14, -82.36, -80.31],
        [-55.39, 80.18, 267.19, 274.2, 169.53, 327.04, 488.15, 437.53, 225.38,
         220.94, 4.01, -92.07, 39.68, 57.22, 144.66, 100.06, 34.96],
        [130.47, -4.23, 46.3, 101.49, 115.01, 217.38, 249.83, 115.9, 87.36,
         105.81, -47.86, -9.94, -82.28, 144.45, 83.44, 23.49, 183.9],
        [-110.38, -115.98, 245.46, 103.51, 255.43, 163.47, 56.52, 33.82,
         -33.26, -111.29, 88.08, 193.2, -100.68, 15.44, 86.32, -26.44, -194.1],
        [109.36, 96.01, -124.89, -16.4, 84.37, 114.87, -65.65, -58.52, -23.22,
         42.61, 144.91, -209.84, 110.29, 66.37, -117.85, -147.73, -122.51],
        [10.94, 45.98, 118.12, -46.53, -72.14, -74.22, 21.22, 0.39, 86.03,
         23.97, -45.42, 12.05, -168.61, 27.79, 61.81, 84.07, 28.79],
        [46.61, -104.11, 56.71, -90.85, -16.51, -66.45, -141.34, 0.96, 58.08,
         285.29, -61.41, -9.01, -323.38, 58.35, 80.14, -101.22, 145.65]]
    g_init = models.Gaussian2D(x_mean=8, y_mean=8)
    fitter = fitting.LevMarLSQFitter()
    y, x = np.mgrid[:17, :17]
    g_fit = fitter(g_init, x, y, test)

    # Compare with @ysBach original result:
    # - x_stddev was negative, so its abs value is used for comparison here.
    # - theta is beyond (-90, 90) deg, which doesn't make sense, so ignored.
    assert_allclose([g_fit.amplitude.value, g_fit.y_stddev.value],
                    [984.7694929790363, 3.1840618351417307], rtol=1.5e-6)
    assert_allclose(g_fit.x_mean.value, 7.198391516587464)
    assert_allclose(g_fit.y_mean.value, 7.49720660088511, rtol=5e-7)
    assert_allclose(g_fit.x_stddev.value, 1.9840185107597297, rtol=2e-6)


# Issue #6403
@pytest.mark.skipif('not HAS_SCIPY')
def test_2d_model():
    # 2D model with LevMarLSQFitter
    gauss2d = models.Gaussian2D(10.2, 4.3, 5, 2, 1.2, 1.4)
    fitter = fitting.LevMarLSQFitter()
    X = np.linspace(-1, 7, 200)
    Y = np.linspace(-1, 7, 200)
    x, y = np.meshgrid(X, Y)
    z = gauss2d(x, y)
    w = np.ones(x.size)
    w.shape = x.shape
    from ...utils import NumpyRNGContext

    with NumpyRNGContext(1234567890):

        n = np.random.randn(x.size)
        n.shape = x.shape
        m = fitter(gauss2d, x, y, z + 2 * n, weights=w)
        assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)
        m = fitter(gauss2d, x, y, z + 2 * n, weights=None)
        assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)

        # 2D model with LevMarLSQFitter, fixed constraint
        gauss2d.x_stddev.fixed = True
        m = fitter(gauss2d, x, y, z + 2 * n, weights=w)
        assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)
        m = fitter(gauss2d, x, y, z + 2 * n, weights=None)
        assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)

        # Polynomial2D, col_fit_deriv=False
        p2 = models.Polynomial2D(1, c0_0=1, c1_0=1.2, c0_1=3.2)
        z = p2(x, y)
        m = fitter(p2, x, y, z + 2 * n, weights=None)
        assert_allclose(m.parameters, p2.parameters, rtol=0.05)
        m = fitter(p2, x, y, z + 2 * n, weights=w)
        assert_allclose(m.parameters, p2.parameters, rtol=0.05)

        # Polynomial2D, col_fit_deriv=False, fixed constraint
        p2.c1_0.fixed = True
        m = fitter(p2, x, y, z + 2 * n, weights=w)
        assert_allclose(m.parameters, p2.parameters, rtol=0.05)
        m = fitter(p2, x, y, z + 2 * n, weights=None)
        assert_allclose(m.parameters, p2.parameters, rtol=0.05)
