import copy

import numpy as np
from numpy.testing import (assert_allclose, assert_almost_equal,
                           assert_array_equal, assert_array_almost_equal)
import pytest

from matplotlib import scale
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.transforms as mtransforms
from matplotlib.transforms import Affine2D, Bbox, TransformedBbox
from matplotlib.path import Path
from matplotlib.testing.decorators import image_comparison, check_figures_equal


def test_non_affine_caching():
    class AssertingNonAffineTransform(mtransforms.Transform):
        """
        This transform raises an assertion error when called when it
        shouldn't be and ``self.raise_on_transform`` is True.

        """
        input_dims = output_dims = 2
        is_affine = False

        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)
            self.raise_on_transform = False
            self.underlying_transform = mtransforms.Affine2D().scale(10, 10)

        def transform_path_non_affine(self, path):
            assert not self.raise_on_transform, \
                'Invalidated affine part of transform unnecessarily.'
            return self.underlying_transform.transform_path(path)
        transform_path = transform_path_non_affine

        def transform_non_affine(self, path):
            assert not self.raise_on_transform, \
                'Invalidated affine part of transform unnecessarily.'
            return self.underlying_transform.transform(path)
        transform = transform_non_affine

    my_trans = AssertingNonAffineTransform()
    ax = plt.axes()
    plt.plot(np.arange(10), transform=my_trans + ax.transData)
    plt.draw()
    # enable the transform to raise an exception if it's non-affine transform
    # method is triggered again.
    my_trans.raise_on_transform = True
    ax.transAxes.invalidate()
    plt.draw()


def test_external_transform_api():
    class ScaledBy:
        def __init__(self, scale_factor):
            self._scale_factor = scale_factor

        def _as_mpl_transform(self, axes):
            return (mtransforms.Affine2D().scale(self._scale_factor)
                    + axes.transData)

    ax = plt.axes()
    line, = plt.plot(np.arange(10), transform=ScaledBy(10))
    ax.set_xlim(0, 100)
    ax.set_ylim(0, 100)
    # assert that the top transform of the line is the scale transform.
    assert_allclose(line.get_transform()._a.get_matrix(),
                    mtransforms.Affine2D().scale(10).get_matrix())


@image_comparison(['pre_transform_data'], remove_text=True, style='mpl20',
                  tol=0.05)
def test_pre_transform_plotting():
    # a catch-all for as many as possible plot layouts which handle
    # pre-transforming the data NOTE: The axis range is important in this
    # plot. It should be x10 what the data suggests it should be

    ax = plt.axes()
    times10 = mtransforms.Affine2D().scale(10)

    ax.contourf(np.arange(48).reshape(6, 8), transform=times10 + ax.transData)

    ax.pcolormesh(np.linspace(0, 4, 7),
                  np.linspace(5.5, 8, 9),
                  np.arange(48).reshape(8, 6),
                  transform=times10 + ax.transData)

    ax.scatter(np.linspace(0, 10), np.linspace(10, 0),
               transform=times10 + ax.transData)

    x = np.linspace(8, 10, 20)
    y = np.linspace(1, 5, 20)
    u = 2*np.sin(x) + np.cos(y[:, np.newaxis])
    v = np.sin(x) - np.cos(y[:, np.newaxis])

    ax.streamplot(x, y, u, v, transform=times10 + ax.transData,
                  linewidth=np.hypot(u, v))

    # reduce the vector data down a bit for barb and quiver plotting
    x, y = x[::3], y[::3]
    u, v = u[::3, ::3], v[::3, ::3]

    ax.quiver(x, y + 5, u, v, transform=times10 + ax.transData)

    ax.barbs(x - 3, y + 5, u**2, v**2, transform=times10 + ax.transData)


def test_contour_pre_transform_limits():
    ax = plt.axes()
    xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
    ax.contourf(xs, ys, np.log(xs * ys),
                transform=mtransforms.Affine2D().scale(0.1) + ax.transData)

    expected = np.array([[1.5, 1.24],
                         [2., 1.25]])
    assert_almost_equal(expected, ax.dataLim.get_points())


def test_pcolor_pre_transform_limits():
    # Based on test_contour_pre_transform_limits()
    ax = plt.axes()
    xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
    ax.pcolor(xs, ys, np.log(xs * ys)[:-1, :-1],
              transform=mtransforms.Affine2D().scale(0.1) + ax.transData)

    expected = np.array([[1.5, 1.24],
                         [2., 1.25]])
    assert_almost_equal(expected, ax.dataLim.get_points())


def test_pcolormesh_pre_transform_limits():
    # Based on test_contour_pre_transform_limits()
    ax = plt.axes()
    xs, ys = np.meshgrid(np.linspace(15, 20, 15), np.linspace(12.4, 12.5, 20))
    ax.pcolormesh(xs, ys, np.log(xs * ys)[:-1, :-1],
                  transform=mtransforms.Affine2D().scale(0.1) + ax.transData)

    expected = np.array([[1.5, 1.24],
                         [2., 1.25]])
    assert_almost_equal(expected, ax.dataLim.get_points())


def test_Affine2D_from_values():
    points = np.array([[0, 0],
                       [10, 20],
                       [-1, 0],
                       ])

    t = mtransforms.Affine2D.from_values(1, 0, 0, 0, 0, 0)
    actual = t.transform(points)
    expected = np.array([[0, 0], [10, 0], [-1, 0]])
    assert_almost_equal(actual, expected)

    t = mtransforms.Affine2D.from_values(0, 2, 0, 0, 0, 0)
    actual = t.transform(points)
    expected = np.array([[0, 0], [0, 20], [0, -2]])
    assert_almost_equal(actual, expected)

    t = mtransforms.Affine2D.from_values(0, 0, 3, 0, 0, 0)
    actual = t.transform(points)
    expected = np.array([[0, 0], [60, 0], [0, 0]])
    assert_almost_equal(actual, expected)

    t = mtransforms.Affine2D.from_values(0, 0, 0, 4, 0, 0)
    actual = t.transform(points)
    expected = np.array([[0, 0], [0, 80], [0, 0]])
    assert_almost_equal(actual, expected)

    t = mtransforms.Affine2D.from_values(0, 0, 0, 0, 5, 0)
    actual = t.transform(points)
    expected = np.array([[5, 0], [5, 0], [5, 0]])
    assert_almost_equal(actual, expected)

    t = mtransforms.Affine2D.from_values(0, 0, 0, 0, 0, 6)
    actual = t.transform(points)
    expected = np.array([[0, 6], [0, 6], [0, 6]])
    assert_almost_equal(actual, expected)


def test_affine_inverted_invalidated():
    # Ensure that the an affine transform is not declared valid on access
    point = [1.0, 1.0]
    t = mtransforms.Affine2D()

    assert_almost_equal(point, t.transform(t.inverted().transform(point)))
    # Change and access the transform
    t.translate(1.0, 1.0).get_matrix()
    assert_almost_equal(point, t.transform(t.inverted().transform(point)))


def test_clipping_of_log():
    # issue 804
    path = Path._create_closed([(0.2, -99), (0.4, -99), (0.4, 20), (0.2, 20)])
    # something like this happens in plotting logarithmic histograms
    trans = mtransforms.BlendedGenericTransform(
        mtransforms.Affine2D(), scale.LogTransform(10, 'clip'))
    tpath = trans.transform_path_non_affine(path)
    result = tpath.iter_segments(trans.get_affine(),
                                 clip=(0, 0, 100, 100),
                                 simplify=False)
    tpoints, tcodes = zip(*result)
    assert_allclose(tcodes, path.codes[:-1])  # No longer closed.


class NonAffineForTest(mtransforms.Transform):
    """
    A class which looks like a non affine transform, but does whatever
    the given transform does (even if it is affine). This is very useful
    for testing NonAffine behaviour with a simple Affine transform.

    """
    is_affine = False
    output_dims = 2
    input_dims = 2

    def __init__(self, real_trans, *args, **kwargs):
        self.real_trans = real_trans
        super().__init__(*args, **kwargs)

    def transform_non_affine(self, values):
        return self.real_trans.transform(values)

    def transform_path_non_affine(self, path):
        return self.real_trans.transform_path(path)


class TestBasicTransform:
    def setup_method(self):

        self.ta1 = mtransforms.Affine2D(shorthand_name='ta1').rotate(np.pi / 2)
        self.ta2 = mtransforms.Affine2D(shorthand_name='ta2').translate(10, 0)
        self.ta3 = mtransforms.Affine2D(shorthand_name='ta3').scale(1, 2)

        self.tn1 = NonAffineForTest(mtransforms.Affine2D().translate(1, 2),
                                    shorthand_name='tn1')
        self.tn2 = NonAffineForTest(mtransforms.Affine2D().translate(1, 2),
                                    shorthand_name='tn2')
        self.tn3 = NonAffineForTest(mtransforms.Affine2D().translate(1, 2),
                                    shorthand_name='tn3')

        # creates a transform stack which looks like ((A, (N, A)), A)
        self.stack1 = (self.ta1 + (self.tn1 + self.ta2)) + self.ta3
        # creates a transform stack which looks like (((A, N), A), A)
        self.stack2 = self.ta1 + self.tn1 + self.ta2 + self.ta3
        # creates a transform stack which is a subset of stack2
        self.stack2_subset = self.tn1 + self.ta2 + self.ta3

        # when in debug, the transform stacks can produce dot images:
#        self.stack1.write_graphviz(file('stack1.dot', 'w'))
#        self.stack2.write_graphviz(file('stack2.dot', 'w'))
#        self.stack2_subset.write_graphviz(file('stack2_subset.dot', 'w'))

    def test_transform_depth(self):
        assert self.stack1.depth == 4
        assert self.stack2.depth == 4
        assert self.stack2_subset.depth == 3

    def test_left_to_right_iteration(self):
        stack3 = (self.ta1 + (self.tn1 + (self.ta2 + self.tn2))) + self.ta3
#        stack3.write_graphviz(file('stack3.dot', 'w'))

        target_transforms = [stack3,
                             (self.tn1 + (self.ta2 + self.tn2)) + self.ta3,
                             (self.ta2 + self.tn2) + self.ta3,
                             self.tn2 + self.ta3,
                             self.ta3,
                             ]
        r = [rh for _, rh in stack3._iter_break_from_left_to_right()]
        assert len(r) == len(target_transforms)

        for target_stack, stack in zip(target_transforms, r):
            assert target_stack == stack

    def test_transform_shortcuts(self):
        assert self.stack1 - self.stack2_subset == self.ta1
        assert self.stack2 - self.stack2_subset == self.ta1

        assert self.stack2_subset - self.stack2 == self.ta1.inverted()
        assert (self.stack2_subset - self.stack2).depth == 1

        with pytest.raises(ValueError):
            self.stack1 - self.stack2

        aff1 = self.ta1 + (self.ta2 + self.ta3)
        aff2 = self.ta2 + self.ta3

        assert aff1 - aff2 == self.ta1
        assert aff1 - self.ta2 == aff1 + self.ta2.inverted()

        assert self.stack1 - self.ta3 == self.ta1 + (self.tn1 + self.ta2)
        assert self.stack2 - self.ta3 == self.ta1 + self.tn1 + self.ta2

        assert ((self.ta2 + self.ta3) - self.ta3 + self.ta3 ==
                self.ta2 + self.ta3)

    def test_contains_branch(self):
        r1 = (self.ta2 + self.ta1)
        r2 = (self.ta2 + self.ta1)
        assert r1 == r2
        assert r1 != self.ta1
        assert r1.contains_branch(r2)
        assert r1.contains_branch(self.ta1)
        assert not r1.contains_branch(self.ta2)
        assert not r1.contains_branch(self.ta2 + self.ta2)

        assert r1 == r2

        assert self.stack1.contains_branch(self.ta3)
        assert self.stack2.contains_branch(self.ta3)

        assert self.stack1.contains_branch(self.stack2_subset)
        assert self.stack2.contains_branch(self.stack2_subset)

        assert not self.stack2_subset.contains_branch(self.stack1)
        assert not self.stack2_subset.contains_branch(self.stack2)

        assert self.stack1.contains_branch(self.ta2 + self.ta3)
        assert self.stack2.contains_branch(self.ta2 + self.ta3)

        assert not self.stack1.contains_branch(self.tn1 + self.ta2)

    def test_affine_simplification(self):
        # tests that a transform stack only calls as much is absolutely
        # necessary "non-affine" allowing the best possible optimization with
        # complex transformation stacks.
        points = np.array([[0, 0], [10, 20], [np.nan, 1], [-1, 0]],
                          dtype=np.float64)
        na_pts = self.stack1.transform_non_affine(points)
        all_pts = self.stack1.transform(points)

        na_expected = np.array([[1., 2.], [-19., 12.],
                                [np.nan, np.nan], [1., 1.]], dtype=np.float64)
        all_expected = np.array([[11., 4.], [-9., 24.],
                                 [np.nan, np.nan], [11., 2.]],
                                dtype=np.float64)

        # check we have the expected results from doing the affine part only
        assert_array_almost_equal(na_pts, na_expected)
        # check we have the expected results from a full transformation
        assert_array_almost_equal(all_pts, all_expected)
        # check we have the expected results from doing the transformation in
        # two steps
        assert_array_almost_equal(self.stack1.transform_affine(na_pts),
                                  all_expected)
        # check that getting the affine transformation first, then fully
        # transforming using that yields the same result as before.
        assert_array_almost_equal(self.stack1.get_affine().transform(na_pts),
                                  all_expected)

        # check that the affine part of stack1 & stack2 are equivalent
        # (i.e. the optimization is working)
        expected_result = (self.ta2 + self.ta3).get_matrix()
        result = self.stack1.get_affine().get_matrix()
        assert_array_equal(expected_result, result)

        result = self.stack2.get_affine().get_matrix()
        assert_array_equal(expected_result, result)


class TestTransformPlotInterface:
    def test_line_extent_axes_coords(self):
        # a simple line in axes coordinates
        ax = plt.axes()
        ax.plot([0.1, 1.2, 0.8], [0.9, 0.5, 0.8], transform=ax.transAxes)
        assert_array_equal(ax.dataLim.get_points(),
                           np.array([[np.inf, np.inf],
                                     [-np.inf, -np.inf]]))

    def test_line_extent_data_coords(self):
        # a simple line in data coordinates
        ax = plt.axes()
        ax.plot([0.1, 1.2, 0.8], [0.9, 0.5, 0.8], transform=ax.transData)
        assert_array_equal(ax.dataLim.get_points(),
                           np.array([[0.1,  0.5], [1.2,  0.9]]))

    def test_line_extent_compound_coords1(self):
        # a simple line in data coordinates in the y component, and in axes
        # coordinates in the x
        ax = plt.axes()
        trans = mtransforms.blended_transform_factory(ax.transAxes,
                                                      ax.transData)
        ax.plot([0.1, 1.2, 0.8], [35, -5, 18], transform=trans)
        assert_array_equal(ax.dataLim.get_points(),
                           np.array([[np.inf, -5.],
                                     [-np.inf, 35.]]))

    def test_line_extent_predata_transform_coords(self):
        # a simple line in (offset + data) coordinates
        ax = plt.axes()
        trans = mtransforms.Affine2D().scale(10) + ax.transData
        ax.plot([0.1, 1.2, 0.8], [35, -5, 18], transform=trans)
        assert_array_equal(ax.dataLim.get_points(),
                           np.array([[1., -50.], [12., 350.]]))

    def test_line_extent_compound_coords2(self):
        # a simple line in (offset + data) coordinates in the y component, and
        # in axes coordinates in the x
        ax = plt.axes()
        trans = mtransforms.blended_transform_factory(
            ax.transAxes, mtransforms.Affine2D().scale(10) + ax.transData)
        ax.plot([0.1, 1.2, 0.8], [35, -5, 18], transform=trans)
        assert_array_equal(ax.dataLim.get_points(),
                           np.array([[np.inf, -50.], [-np.inf, 350.]]))

    def test_line_extents_affine(self):
        ax = plt.axes()
        offset = mtransforms.Affine2D().translate(10, 10)
        plt.plot(np.arange(10), transform=offset + ax.transData)
        expected_data_lim = np.array([[0., 0.], [9.,  9.]]) + 10
        assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)

    def test_line_extents_non_affine(self):
        ax = plt.axes()
        offset = mtransforms.Affine2D().translate(10, 10)
        na_offset = NonAffineForTest(mtransforms.Affine2D().translate(10, 10))
        plt.plot(np.arange(10), transform=offset + na_offset + ax.transData)
        expected_data_lim = np.array([[0., 0.], [9.,  9.]]) + 20
        assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)

    def test_pathc_extents_non_affine(self):
        ax = plt.axes()
        offset = mtransforms.Affine2D().translate(10, 10)
        na_offset = NonAffineForTest(mtransforms.Affine2D().translate(10, 10))
        pth = Path([[0, 0], [0, 10], [10, 10], [10, 0]])
        patch = mpatches.PathPatch(pth,
                                   transform=offset + na_offset + ax.transData)
        ax.add_patch(patch)
        expected_data_lim = np.array([[0., 0.], [10.,  10.]]) + 20
        assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)

    def test_pathc_extents_affine(self):
        ax = plt.axes()
        offset = mtransforms.Affine2D().translate(10, 10)
        pth = Path([[0, 0], [0, 10], [10, 10], [10, 0]])
        patch = mpatches.PathPatch(pth, transform=offset + ax.transData)
        ax.add_patch(patch)
        expected_data_lim = np.array([[0., 0.], [10.,  10.]]) + 10
        assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)

    def test_line_extents_for_non_affine_transData(self):
        ax = plt.axes(projection='polar')
        # add 10 to the radius of the data
        offset = mtransforms.Affine2D().translate(0, 10)

        plt.plot(np.arange(10), transform=offset + ax.transData)
        # the data lim of a polar plot is stored in coordinates
        # before a transData transformation, hence the data limits
        # are not what is being shown on the actual plot.
        expected_data_lim = np.array([[0., 0.], [9.,  9.]]) + [0, 10]
        assert_array_almost_equal(ax.dataLim.get_points(), expected_data_lim)


def assert_bbox_eq(bbox1, bbox2):
    assert_array_equal(bbox1.bounds, bbox2.bounds)


def test_bbox_frozen_copies_minpos():
    bbox = mtransforms.Bbox.from_extents(0.0, 0.0, 1.0, 1.0, minpos=1.0)
    frozen = bbox.frozen()
    assert_array_equal(frozen.minpos, bbox.minpos)


def test_bbox_intersection():
    bbox_from_ext = mtransforms.Bbox.from_extents
    inter = mtransforms.Bbox.intersection

    r1 = bbox_from_ext(0, 0, 1, 1)
    r2 = bbox_from_ext(0.5, 0.5, 1.5, 1.5)
    r3 = bbox_from_ext(0.5, 0, 0.75, 0.75)
    r4 = bbox_from_ext(0.5, 1.5, 1, 2.5)
    r5 = bbox_from_ext(1, 1, 2, 2)

    # self intersection -> no change
    assert_bbox_eq(inter(r1, r1), r1)
    # simple intersection
    assert_bbox_eq(inter(r1, r2), bbox_from_ext(0.5, 0.5, 1, 1))
    # r3 contains r2
    assert_bbox_eq(inter(r1, r3), r3)
    # no intersection
    assert inter(r1, r4) is None
    # single point
    assert_bbox_eq(inter(r1, r5), bbox_from_ext(1, 1, 1, 1))


def test_bbox_as_strings():
    b = mtransforms.Bbox([[.5, 0], [.75, .75]])
    assert_bbox_eq(b, eval(repr(b), {'Bbox': mtransforms.Bbox}))
    asdict = eval(str(b), {'Bbox': dict})
    for k, v in asdict.items():
        assert getattr(b, k) == v
    fmt = '.1f'
    asdict = eval(format(b, fmt), {'Bbox': dict})
    for k, v in asdict.items():
        assert eval(format(getattr(b, k), fmt)) == v


def test_str_transform():
    # The str here should not be considered as "absolutely stable", and may be
    # reformatted later; this is just a smoketest for __str__.
    assert str(plt.subplot(projection="polar").transData) == """\
CompositeGenericTransform(
    CompositeGenericTransform(
        CompositeGenericTransform(
            TransformWrapper(
                BlendedAffine2D(
                    IdentityTransform(),
                    IdentityTransform())),
            CompositeAffine2D(
                Affine2D().scale(1.0),
                Affine2D().scale(1.0))),
        PolarTransform(
            PolarAxes(0.125,0.1;0.775x0.8),
            use_rmin=True,
            _apply_theta_transforms=False)),
    CompositeGenericTransform(
        CompositeGenericTransform(
            PolarAffine(
                TransformWrapper(
                    BlendedAffine2D(
                        IdentityTransform(),
                        IdentityTransform())),
                LockableBbox(
                    Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
                    [[-- --]
                     [-- --]])),
            BboxTransformFrom(
                _WedgeBbox(
                    (0.5, 0.5),
                    TransformedBbox(
                        Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
                        CompositeAffine2D(
                            Affine2D().scale(1.0),
                            Affine2D().scale(1.0))),
                    LockableBbox(
                        Bbox(x0=0.0, y0=0.0, x1=6.283185307179586, y1=1.0),
                        [[-- --]
                         [-- --]])))),
        BboxTransformTo(
            TransformedBbox(
                Bbox(x0=0.125, y0=0.09999999999999998, x1=0.9, y1=0.9),
                BboxTransformTo(
                    TransformedBbox(
                        Bbox(x0=0.0, y0=0.0, x1=8.0, y1=6.0),
                        Affine2D().scale(80.0)))))))"""


def test_transform_single_point():
    t = mtransforms.Affine2D()
    r = t.transform_affine((1, 1))
    assert r.shape == (2,)


def test_log_transform():
    # Tests that the last line runs without exception (previously the
    # transform would fail if one of the axes was logarithmic).
    fig, ax = plt.subplots()
    ax.set_yscale('log')
    ax.transData.transform((1, 1))


def test_nan_overlap():
    a = mtransforms.Bbox([[0, 0], [1, 1]])
    b = mtransforms.Bbox([[0, 0], [1, np.nan]])
    assert not a.overlaps(b)


def test_transform_angles():
    t = mtransforms.Affine2D()  # Identity transform
    angles = np.array([20, 45, 60])
    points = np.array([[0, 0], [1, 1], [2, 2]])

    # Identity transform does not change angles
    new_angles = t.transform_angles(angles, points)
    assert_array_almost_equal(angles, new_angles)

    # points missing a 2nd dimension
    with pytest.raises(ValueError):
        t.transform_angles(angles, points[0:2, 0:1])

    # Number of angles != Number of points
    with pytest.raises(ValueError):
        t.transform_angles(angles, points[0:2, :])


def test_nonsingular():
    # test for zero-expansion type cases; other cases may be added later
    zero_expansion = np.array([-0.001, 0.001])
    cases = [(0, np.nan), (0, 0), (0, 7.9e-317)]
    for args in cases:
        out = np.array(mtransforms.nonsingular(*args))
        assert_array_equal(out, zero_expansion)


def test_invalid_arguments():
    t = mtransforms.Affine2D()
    # There are two different exceptions, since the wrong number of
    # dimensions is caught when constructing an array_view, and that
    # raises a ValueError, and a wrong shape with a possible number
    # of dimensions is caught by our CALL_CPP macro, which always
    # raises the less precise RuntimeError.
    with pytest.raises(ValueError):
        t.transform(1)
    with pytest.raises(ValueError):
        t.transform([[[1]]])
    with pytest.raises(RuntimeError):
        t.transform([])
    with pytest.raises(RuntimeError):
        t.transform([1])
    with pytest.raises(RuntimeError):
        t.transform([[1]])
    with pytest.raises(RuntimeError):
        t.transform([[1, 2, 3]])


def test_transformed_path():
    points = [(0, 0), (1, 0), (1, 1), (0, 1)]
    path = Path(points, closed=True)

    trans = mtransforms.Affine2D()
    trans_path = mtransforms.TransformedPath(path, trans)
    assert_allclose(trans_path.get_fully_transformed_path().vertices, points)

    # Changing the transform should change the result.
    r2 = 1 / np.sqrt(2)
    trans.rotate(np.pi / 4)
    assert_allclose(trans_path.get_fully_transformed_path().vertices,
                    [(0, 0), (r2, r2), (0, 2 * r2), (-r2, r2)],
                    atol=1e-15)

    # Changing the path does not change the result (it's cached).
    path.points = [(0, 0)] * 4
    assert_allclose(trans_path.get_fully_transformed_path().vertices,
                    [(0, 0), (r2, r2), (0, 2 * r2), (-r2, r2)],
                    atol=1e-15)


def test_transformed_patch_path():
    trans = mtransforms.Affine2D()
    patch = mpatches.Wedge((0, 0), 1, 45, 135, transform=trans)

    tpatch = mtransforms.TransformedPatchPath(patch)
    points = tpatch.get_fully_transformed_path().vertices

    # Changing the transform should change the result.
    trans.scale(2)
    assert_allclose(tpatch.get_fully_transformed_path().vertices, points * 2)

    # Changing the path should change the result (and cancel out the scaling
    # from the transform).
    patch.set_radius(0.5)
    assert_allclose(tpatch.get_fully_transformed_path().vertices, points)


@pytest.mark.parametrize('locked_element', ['x0', 'y0', 'x1', 'y1'])
def test_lockable_bbox(locked_element):
    other_elements = ['x0', 'y0', 'x1', 'y1']
    other_elements.remove(locked_element)

    orig = mtransforms.Bbox.unit()
    locked = mtransforms.LockableBbox(orig, **{locked_element: 2})

    # LockableBbox should keep its locked element as specified in __init__.
    assert getattr(locked, locked_element) == 2
    assert getattr(locked, 'locked_' + locked_element) == 2
    for elem in other_elements:
        assert getattr(locked, elem) == getattr(orig, elem)

    # Changing underlying Bbox should update everything but locked element.
    orig.set_points(orig.get_points() + 10)
    assert getattr(locked, locked_element) == 2
    assert getattr(locked, 'locked_' + locked_element) == 2
    for elem in other_elements:
        assert getattr(locked, elem) == getattr(orig, elem)

    # Unlocking element should revert values back to the underlying Bbox.
    setattr(locked, 'locked_' + locked_element, None)
    assert getattr(locked, 'locked_' + locked_element) is None
    assert np.all(orig.get_points() == locked.get_points())

    # Relocking an element should change its value, but not others.
    setattr(locked, 'locked_' + locked_element, 3)
    assert getattr(locked, locked_element) == 3
    assert getattr(locked, 'locked_' + locked_element) == 3
    for elem in other_elements:
        assert getattr(locked, elem) == getattr(orig, elem)


def test_copy():
    a = mtransforms.Affine2D()
    b = mtransforms.Affine2D()
    s = a + b
    # Updating a dependee should invalidate a copy of the dependent.
    s.get_matrix()  # resolve it.
    s1 = copy.copy(s)
    assert not s._invalid and not s1._invalid
    a.translate(1, 2)
    assert s._invalid and s1._invalid
    assert (s1.get_matrix() == a.get_matrix()).all()
    # Updating a copy of a dependee shouldn't invalidate a dependent.
    s.get_matrix()  # resolve it.
    b1 = copy.copy(b)
    b1.translate(3, 4)
    assert not s._invalid
    assert (s.get_matrix() == a.get_matrix()).all()


def test_deepcopy():
    a = mtransforms.Affine2D()
    b = mtransforms.Affine2D()
    s = a + b
    # Updating a dependee shouldn't invalidate a deepcopy of the dependent.
    s.get_matrix()  # resolve it.
    s1 = copy.deepcopy(s)
    assert not s._invalid and not s1._invalid
    a.translate(1, 2)
    assert s._invalid and not s1._invalid
    assert (s1.get_matrix() == mtransforms.Affine2D().get_matrix()).all()
    # Updating a deepcopy of a dependee shouldn't invalidate a dependent.
    s.get_matrix()  # resolve it.
    b1 = copy.deepcopy(b)
    b1.translate(3, 4)
    assert not s._invalid
    assert (s.get_matrix() == a.get_matrix()).all()


def test_transformwrapper():
    t = mtransforms.TransformWrapper(mtransforms.Affine2D())
    with pytest.raises(ValueError, match=(
            r"The input and output dims of the new child \(1, 1\) "
            r"do not match those of current child \(2, 2\)")):
        t.set(scale.LogTransform(10))


@check_figures_equal(extensions=["png"])
def test_scale_swapping(fig_test, fig_ref):
    np.random.seed(19680801)
    samples = np.random.normal(size=10)
    x = np.linspace(-5, 5, 10)

    for fig, log_state in zip([fig_test, fig_ref], [True, False]):
        ax = fig.subplots()
        ax.hist(samples, log=log_state, density=True)
        ax.plot(x, np.exp(-(x**2) / 2) / np.sqrt(2 * np.pi))
        fig.canvas.draw()
        ax.set_yscale('linear')


def test_offset_copy_errors():
    with pytest.raises(ValueError,
                       match="'fontsize' is not a valid value for units;"
                             " supported values are 'dots', 'points', 'inches'"):
        mtransforms.offset_copy(None, units='fontsize')

    with pytest.raises(ValueError,
                       match='For units of inches or points a fig kwarg is needed'):
        mtransforms.offset_copy(None, units='inches')


def test_transformedbbox_contains():
    bb = TransformedBbox(Bbox.unit(), Affine2D().rotate_deg(30))
    assert bb.contains(.8, .5)
    assert bb.contains(-.4, .85)
    assert not bb.contains(.9, .5)
    bb = TransformedBbox(Bbox.unit(), Affine2D().translate(.25, .5))
    assert bb.contains(1.25, 1.5)
    assert not bb.fully_contains(1.25, 1.5)
    assert not bb.fully_contains(.1, .1)
