Sources_For_Win/ParaView/Lib/site-packages/matplotlib/tests/test_simplification.py

import base64
import io

import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal

import pytest

from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt

from matplotlib import patches, transforms
from matplotlib.path import Path


# NOTE: All of these tests assume that path.simplify is set to True
# (the default)

@image_comparison(['clipping'], remove_text=True)
def test_clipping():
    t = np.arange(0.0, 2.0, 0.01)
    s = np.sin(2*np.pi*t)

    fig, ax = plt.subplots()
    ax.plot(t, s, linewidth=1.0)
    ax.set_ylim((-0.20, -0.28))


@image_comparison(['overflow'], remove_text=True)
def test_overflow():
    x = np.array([1.0, 2.0, 3.0, 2.0e5])
    y = np.arange(len(x))

    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_xlim(2, 6)


@image_comparison(['clipping_diamond'], remove_text=True)
def test_diamond():
    x = np.array([0.0, 1.0, 0.0, -1.0, 0.0])
    y = np.array([1.0, 0.0, -1.0, 0.0, 1.0])

    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_xlim(-0.6, 0.6)
    ax.set_ylim(-0.6, 0.6)


def test_noise():
    np.random.seed(0)
    x = np.random.uniform(size=50000) * 50

    fig, ax = plt.subplots()
    p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)

    # Ensure that the path's transform takes the new axes limits into account.
    fig.canvas.draw()
    path = p1[0].get_path()
    transform = p1[0].get_transform()
    path = transform.transform_path(path)
    simplified = path.cleaned(simplify=True)

    assert simplified.vertices.size == 25512


def test_antiparallel_simplification():
    def _get_simplified(x, y):
        fig, ax = plt.subplots()
        p1 = ax.plot(x, y)

        path = p1[0].get_path()
        transform = p1[0].get_transform()
        path = transform.transform_path(path)
        simplified = path.cleaned(simplify=True)
        simplified = transform.inverted().transform_path(simplified)

        return simplified

    # test ending on a maximum
    x = [0, 0, 0, 0, 0, 1]
    y = [.5, 1, -1, 1, 2, .5]

    simplified = _get_simplified(x, y)

    assert_array_almost_equal([[0., 0.5],
                               [0., -1.],
                               [0., 2.],
                               [1., 0.5]],
                              simplified.vertices[:-2, :])

    # test ending on a minimum
    x = [0, 0,  0, 0, 0, 1]
    y = [.5, 1, -1, 1, -2, .5]

    simplified = _get_simplified(x, y)

    assert_array_almost_equal([[0., 0.5],
                               [0., 1.],
                               [0., -2.],
                               [1., 0.5]],
                              simplified.vertices[:-2, :])

    # test ending in between
    x = [0, 0, 0, 0, 0, 1]
    y = [.5, 1, -1, 1, 0, .5]

    simplified = _get_simplified(x, y)

    assert_array_almost_equal([[0., 0.5],
                               [0., 1.],
                               [0., -1.],
                               [0., 0.],
                               [1., 0.5]],
                              simplified.vertices[:-2, :])

    # test no anti-parallel ending at max
    x = [0, 0, 0, 0, 0, 1]
    y = [.5, 1, 2, 1, 3, .5]

    simplified = _get_simplified(x, y)

    assert_array_almost_equal([[0., 0.5],
                               [0., 3.],
                               [1., 0.5]],
                              simplified.vertices[:-2, :])

    # test no anti-parallel ending in middle
    x = [0, 0, 0, 0, 0, 1]
    y = [.5, 1, 2, 1, 1, .5]

    simplified = _get_simplified(x, y)

    assert_array_almost_equal([[0., 0.5],
                               [0., 2.],
                               [0., 1.],
                               [1., 0.5]],
                              simplified.vertices[:-2, :])


# Only consider angles in 0 <= angle <= pi/2, otherwise
# using min/max will get the expected results out of order:
# min/max for simplification code depends on original vector,
# and if angle is outside above range then simplification
# min/max will be opposite from actual min/max.
@pytest.mark.parametrize('angle', [0, np.pi/4, np.pi/3, np.pi/2])
@pytest.mark.parametrize('offset', [0, .5])
def test_angled_antiparallel(angle, offset):
    scale = 5
    np.random.seed(19680801)
    # get 15 random offsets
    # TODO: guarantee offset > 0 results in some offsets < 0
    vert_offsets = (np.random.rand(15) - offset) * scale
    # always start at 0 so rotation makes sense
    vert_offsets[0] = 0
    # always take the first step the same direction
    vert_offsets[1] = 1
    # compute points along a diagonal line
    x = np.sin(angle) * vert_offsets
    y = np.cos(angle) * vert_offsets

    # will check these later
    x_max = x[1:].max()
    x_min = x[1:].min()

    y_max = y[1:].max()
    y_min = y[1:].min()

    if offset > 0:
        p_expected = Path([[0, 0],
                           [x_max, y_max],
                           [x_min, y_min],
                           [x[-1], y[-1]],
                           [0, 0]],
                          codes=[1, 2, 2, 2, 0])

    else:
        p_expected = Path([[0, 0],
                           [x_max, y_max],
                           [x[-1], y[-1]],
                           [0, 0]],
                          codes=[1, 2, 2, 0])

    p = Path(np.vstack([x, y]).T)
    p2 = p.cleaned(simplify=True)

    assert_array_almost_equal(p_expected.vertices,
                              p2.vertices)
    assert_array_equal(p_expected.codes, p2.codes)


def test_sine_plus_noise():
    np.random.seed(0)
    x = (np.sin(np.linspace(0, np.pi * 2.0, 50000)) +
         np.random.uniform(size=50000) * 0.01)

    fig, ax = plt.subplots()
    p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)

    # Ensure that the path's transform takes the new axes limits into account.
    fig.canvas.draw()
    path = p1[0].get_path()
    transform = p1[0].get_transform()
    path = transform.transform_path(path)
    simplified = path.cleaned(simplify=True)

    assert simplified.vertices.size == 25240


@image_comparison(['simplify_curve'], remove_text=True)
def test_simplify_curve():
    pp1 = patches.PathPatch(
        Path([(0, 0), (1, 0), (1, 1), (np.nan, 1), (0, 0), (2, 0), (2, 2),
              (0, 0)],
             [Path.MOVETO, Path.CURVE3, Path.CURVE3, Path.CURVE3, Path.CURVE3,
              Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY]),
        fc="none")

    fig, ax = plt.subplots()
    ax.add_patch(pp1)
    ax.set_xlim((0, 2))
    ax.set_ylim((0, 2))


@image_comparison(['hatch_simplify'], remove_text=True)
def test_hatch():
    fig, ax = plt.subplots()
    ax.add_patch(plt.Rectangle((0, 0), 1, 1, fill=False, hatch="/"))
    ax.set_xlim((0.45, 0.55))
    ax.set_ylim((0.45, 0.55))


@image_comparison(['fft_peaks'], remove_text=True)
def test_fft_peaks():
    fig, ax = plt.subplots()
    t = np.arange(65536)
    p1 = ax.plot(abs(np.fft.fft(np.sin(2*np.pi*.01*t)*np.blackman(len(t)))))

    # Ensure that the path's transform takes the new axes limits into account.
    fig.canvas.draw()
    path = p1[0].get_path()
    transform = p1[0].get_transform()
    path = transform.transform_path(path)
    simplified = path.cleaned(simplify=True)

    assert simplified.vertices.size == 36


def test_start_with_moveto():
    # Should be entirely clipped away to a single MOVETO
    data = b"""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"""

    verts = np.frombuffer(base64.decodebytes(data), dtype='<i4')
    verts = verts.reshape((len(verts) // 2, 2))
    path = Path(verts)
    segs = path.iter_segments(transforms.IdentityTransform(),
                              clip=(0.0, 0.0, 100.0, 100.0))
    segs = list(segs)
    assert len(segs) == 1
    assert segs[0][1] == Path.MOVETO


def test_throw_rendering_complexity_exceeded():
    plt.rcParams['path.simplify'] = False
    xx = np.arange(200000)
    yy = np.random.rand(200000)
    yy[1000] = np.nan

    fig, ax = plt.subplots()
    ax.plot(xx, yy)
    with pytest.raises(OverflowError):
        fig.savefig(io.BytesIO())


@image_comparison(['clipper_edge'], remove_text=True)
def test_clipper():
    dat = (0, 1, 0, 2, 0, 3, 0, 4, 0, 5)
    fig = plt.figure(figsize=(2, 1))
    fig.subplots_adjust(left=0, bottom=0, wspace=0, hspace=0)

    ax = fig.add_axes((0, 0, 1.0, 1.0), ylim=(0, 5), autoscale_on=False)
    ax.plot(dat)
    ax.xaxis.set_major_locator(plt.MultipleLocator(1))
    ax.yaxis.set_major_locator(plt.MultipleLocator(1))
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')

    ax.set_xlim(5, 9)


@image_comparison(['para_equal_perp'], remove_text=True)
def test_para_equal_perp():
    x = np.array([0, 1, 2, 1, 0, -1, 0, 1] + [1] * 128)
    y = np.array([1, 1, 2, 1, 0, -1, 0, 0] + [0] * 128)

    fig, ax = plt.subplots()
    ax.plot(x + 1, y + 1)
    ax.plot(x + 1, y + 1, 'ro')


@image_comparison(['clipping_with_nans'])
def test_clipping_with_nans():
    x = np.linspace(0, 3.14 * 2, 3000)
    y = np.sin(x)
    x[::100] = np.nan

    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_ylim(-0.25, 0.25)


def test_clipping_full():
    p = Path([[1e30, 1e30]] * 5)
    simplified = list(p.iter_segments(clip=[0, 0, 100, 100]))
    assert simplified == []

    p = Path([[50, 40], [75, 65]], [1, 2])
    simplified = list(p.iter_segments(clip=[0, 0, 100, 100]))
    assert ([(list(x), y) for x, y in simplified] ==
            [([50, 40], 1), ([75, 65], 2)])

    p = Path([[50, 40]], [1])
    simplified = list(p.iter_segments(clip=[0, 0, 100, 100]))
    assert ([(list(x), y) for x, y in simplified] ==
            [([50, 40], 1)])