/usr/share/pyshared/matplotlib/tests/test_simplification.py is in python-matplotlib 1.1.1~rc1+git20120423-0ubuntu1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | import numpy as np
import matplotlib
from matplotlib.testing.decorators import image_comparison, knownfailureif, cleanup
import matplotlib.pyplot as plt
from pylab import *
import numpy as np
from matplotlib import patches, path, transforms
from nose.tools import raises
import cStringIO
nan = np.nan
Path = path.Path
# NOTE: All of these tests assume that path.simplify is set to True
# (the default)
@image_comparison(baseline_images=['clipping'])
def test_clipping():
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2*pi*t)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(t, s, linewidth=1.0)
ax.set_ylim((-0.20, -0.28))
ax.set_xticks([])
ax.set_yticks([])
@image_comparison(baseline_images=['overflow'], tol=1e-2)
def test_overflow():
x = np.array([1.0,2.0,3.0,2.0e5])
y = np.arange(len(x))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y)
ax.set_xlim(xmin=2,xmax=6)
ax.set_xticks([])
ax.set_yticks([])
@image_comparison(baseline_images=['clipping_diamond'])
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 = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xlim(xmin=-0.6, xmax=0.6)
ax.set_ylim(ymin=-0.6, ymax=0.6)
ax.set_xticks([])
ax.set_yticks([])
@cleanup
def test_noise():
np.random.seed(0)
x = np.random.uniform(size=(5000,)) * 50
fig = plt.figure()
ax = fig.add_subplot(111)
p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)
ax.set_xticks([])
ax.set_yticks([])
path = p1[0].get_path()
transform = p1[0].get_transform()
path = transform.transform_path(path)
simplified = list(path.iter_segments(simplify=(800, 600)))
print len(simplified)
assert len(simplified) == 3884
@cleanup
def test_sine_plus_noise():
np.random.seed(0)
x = np.sin(np.linspace(0, np.pi * 2.0, 1000)) + np.random.uniform(size=(1000,)) * 0.01
fig = plt.figure()
ax = fig.add_subplot(111)
p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)
ax.set_xticks([])
ax.set_yticks([])
path = p1[0].get_path()
transform = p1[0].get_transform()
path = transform.transform_path(path)
simplified = list(path.iter_segments(simplify=(800, 600)))
print len(simplified)
assert len(simplified) == 876
@image_comparison(baseline_images=['simplify_curve'])
def test_simplify_curve():
pp1 = patches.PathPatch(
Path([(0, 0), (1, 0), (1, 1), (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 = plt.figure()
ax = fig.add_subplot(111)
ax.add_patch(pp1)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim((0, 2))
ax.set_ylim((0, 2))
@image_comparison(baseline_images=['hatch_simplify'])
def test_hatch():
fig = plt.figure()
ax = fig.add_subplot(111)
ax.add_patch(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'])
def test_fft_peaks():
fig = plt.figure()
t = arange(65536)
ax = fig.add_subplot(111)
p1 = ax.plot(abs(fft(sin(2*pi*.01*t)*blackman(len(t)))))
ax.set_xticks([])
ax.set_yticks([])
path = p1[0].get_path()
transform = p1[0].get_transform()
path = transform.transform_path(path)
simplified = list(path.iter_segments(simplify=(800, 600)))
print len(simplified)
assert len(simplified) == 20
@cleanup
def test_start_with_moveto():
# Should be entirely clipped away to a single MOVETO
data = """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"""
verts = np.fromstring(data.decode('base64'), 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
@cleanup
@raises(OverflowError)
def test_throw_rendering_complexity_exceeded():
rcParams['path.simplify'] = False
xx = np.arange(200000)
yy = np.random.rand(200000)
yy[1000] = np.nan
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(xx, yy)
try:
fig.savefig(cStringIO.StringIO())
except e:
raise e
else:
rcParams['path.simplify'] = True
@image_comparison(baseline_images=['clipper_edge'])
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.xaxis.set_major_formatter(plt.NullFormatter())
ax.yaxis.set_major_locator(plt.MultipleLocator(1))
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_xlim(5, 9)
@image_comparison(baseline_images=['para_equal_perp'])
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 = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x + 1, y + 1)
ax.plot(x + 1, y + 1, 'ro')
if __name__=='__main__':
import nose
nose.runmodule(argv=['-s','--with-doctest'], exit=False)
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