OwlCyberSecurity - MANAGER

Edit File: 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(baseline_images=['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(baseline_images=['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(baseline_images=['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) 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) 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(baseline_images=['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(baseline_images=['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(baseline_images=['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))))) 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""" ZwAAAAku+v9UAQAA+Tj6/z8CAADpQ/r/KAMAANlO+v8QBAAAyVn6//UEAAC6ZPr/2gUAAKpv+v+8 BgAAm3r6/50HAACLhfr/ewgAAHyQ+v9ZCQAAbZv6/zQKAABepvr/DgsAAE+x+v/lCwAAQLz6/7wM AAAxx/r/kA0AACPS+v9jDgAAFN36/zQPAAAF6Pr/AxAAAPfy+v/QEAAA6f36/5wRAADbCPv/ZhIA AMwT+/8uEwAAvh77//UTAACwKfv/uRQAAKM0+/98FQAAlT/7/z0WAACHSvv//RYAAHlV+/+7FwAA bGD7/3cYAABea/v/MRkAAFF2+//pGQAARIH7/6AaAAA3jPv/VRsAACmX+/8JHAAAHKL7/7ocAAAP rfv/ah0AAAO4+/8YHgAA9sL7/8QeAADpzfv/bx8AANzY+/8YIAAA0OP7/78gAADD7vv/ZCEAALf5 +/8IIgAAqwT8/6kiAACeD/z/SiMAAJIa/P/oIwAAhiX8/4QkAAB6MPz/HyUAAG47/P+4JQAAYkb8 /1AmAABWUfz/5SYAAEpc/P95JwAAPmf8/wsoAAAzcvz/nCgAACd9/P8qKQAAHIj8/7cpAAAQk/z/ QyoAAAWe/P/MKgAA+aj8/1QrAADus/z/2isAAOO+/P9eLAAA2Mn8/+AsAADM1Pz/YS0AAMHf/P/g LQAAtur8/10uAACr9fz/2C4AAKEA/f9SLwAAlgv9/8ovAACLFv3/QDAAAIAh/f+1MAAAdSz9/ycx AABrN/3/mDEAAGBC/f8IMgAAVk39/3UyAABLWP3/4TIAAEFj/f9LMwAANm79/7MzAAAsef3/GjQA ACKE/f9+NAAAF4/9/+E0AAANmv3/QzUAAAOl/f+iNQAA+a/9/wA2AADvuv3/XDYAAOXF/f+2NgAA 29D9/w83AADR2/3/ZjcAAMfm/f+7NwAAvfH9/w44AACz/P3/XzgAAKkH/v+vOAAAnxL+//04AACW Hf7/SjkAAIwo/v+UOQAAgjP+/905AAB5Pv7/JDoAAG9J/v9pOgAAZVT+/606AABcX/7/7zoAAFJq /v8vOwAASXX+/207AAA/gP7/qjsAADaL/v/lOwAALZb+/x48AAAjof7/VTwAABqs/v+LPAAAELf+ /788AAAHwv7/8TwAAP7M/v8hPQAA9df+/1A9AADr4v7/fT0AAOLt/v+oPQAA2fj+/9E9AADQA/// +T0AAMYO//8fPgAAvRn//0M+AAC0JP//ZT4AAKsv//+GPgAAojr//6U+AACZRf//wj4AAJBQ///d PgAAh1v///c+AAB+Zv//Dz8AAHRx//8lPwAAa3z//zk/AABih///TD8AAFmS//9dPwAAUJ3//2w/ AABHqP//ej8AAD6z//+FPwAANb7//48/AAAsyf//lz8AACPU//+ePwAAGt///6M/AAAR6v//pj8A AAj1//+nPwAA/////w==""" 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(baseline_images=['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(baseline_images=['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(baseline_images=['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)])