/usr/lib/python2.7/dist-packages/matplotlib/tests/test_quiver.py is in python-matplotlib 2.0.0+dfsg1-2.
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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 | from __future__ import print_function
import warnings
import numpy as np
from nose.tools import raises
import sys
from matplotlib import pyplot as plt
from matplotlib.testing.decorators import cleanup
from matplotlib.testing.decorators import image_comparison
def draw_quiver(ax, **kw):
X, Y = np.meshgrid(np.arange(0, 2 * np.pi, 1),
np.arange(0, 2 * np.pi, 1))
U = np.cos(X)
V = np.sin(Y)
Q = ax.quiver(U, V, **kw)
return Q
@cleanup
def test_quiver_memory_leak():
fig, ax = plt.subplots()
Q = draw_quiver(ax)
ttX = Q.X
Q.remove()
del Q
assert sys.getrefcount(ttX) == 2
@cleanup
def test_quiver_key_memory_leak():
fig, ax = plt.subplots()
Q = draw_quiver(ax)
qk = ax.quiverkey(Q, 0.5, 0.92, 2, r'$2 \frac{m}{s}$',
labelpos='W',
fontproperties={'weight': 'bold'})
assert sys.getrefcount(qk) == 3
qk.remove()
assert sys.getrefcount(qk) == 2
@cleanup
def test_no_warnings():
fig, ax = plt.subplots()
X, Y = np.meshgrid(np.arange(15), np.arange(10))
U = V = np.ones_like(X)
phi = (np.random.rand(15, 10) - .5) * 150
with warnings.catch_warnings(record=True) as w:
ax.quiver(X, Y, U, V, angles=phi)
fig.canvas.draw()
assert len(w) == 0
@cleanup
def test_zero_headlength():
# Based on report by Doug McNeil:
# http://matplotlib.1069221.n5.nabble.com/quiver-warnings-td28107.html
fig, ax = plt.subplots()
X, Y = np.meshgrid(np.arange(10), np.arange(10))
U, V = np.cos(X), np.sin(Y)
with warnings.catch_warnings(record=True) as w:
ax.quiver(U, V, headlength=0, headaxislength=0)
fig.canvas.draw()
assert len(w) == 0
@image_comparison(baseline_images=['quiver_animated_test_image'],
extensions=['png'])
def test_quiver_animate():
# Tests fix for #2616
fig, ax = plt.subplots()
Q = draw_quiver(ax, animated=True)
qk = ax.quiverkey(Q, 0.5, 0.92, 2, r'$2 \frac{m}{s}$',
labelpos='W',
fontproperties={'weight': 'bold'})
@image_comparison(baseline_images=['quiver_with_key_test_image'],
extensions=['png'])
def test_quiver_with_key():
fig, ax = plt.subplots()
ax.margins(0.1)
Q = draw_quiver(ax)
qk = ax.quiverkey(Q, 0.5, 0.95, 2,
r'$2\, \mathrm{m}\, \mathrm{s}^{-1}$',
coordinates='figure',
labelpos='W',
fontproperties={'weight': 'bold',
'size': 'large'})
@image_comparison(baseline_images=['quiver_single_test_image'],
extensions=['png'], remove_text=True)
def test_quiver_single():
fig, ax = plt.subplots()
ax.margins(0.1)
ax.quiver([1], [1], [2], [2])
@cleanup
def test_quiver_copy():
fig, ax = plt.subplots()
uv = dict(u=np.array([1.1]), v=np.array([2.0]))
q0 = ax.quiver([1], [1], uv['u'], uv['v'])
uv['v'][0] = 0
assert q0.V[0] == 2.0
@image_comparison(baseline_images=['quiver_key_pivot'],
extensions=['png'], remove_text=True)
def test_quiver_key_pivot():
fig, ax = plt.subplots()
u, v = np.mgrid[0:2*np.pi:10j, 0:2*np.pi:10j]
q = ax.quiver(np.sin(u), np.cos(v))
ax.set_xlim(-2, 11)
ax.set_ylim(-2, 11)
ax.quiverkey(q, 0.5, 1, 1, 'N', labelpos='N')
ax.quiverkey(q, 1, 0.5, 1, 'E', labelpos='E')
ax.quiverkey(q, 0.5, 0, 1, 'S', labelpos='S')
ax.quiverkey(q, 0, 0.5, 1, 'W', labelpos='W')
@image_comparison(baseline_images=['barbs_test_image'],
extensions=['png'], remove_text=True)
def test_barbs():
x = np.linspace(-5, 5, 5)
X, Y = np.meshgrid(x, x)
U, V = 12*X, 12*Y
fig, ax = plt.subplots()
ax.barbs(X, Y, U, V, np.sqrt(U*U + V*V), fill_empty=True, rounding=False,
sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3),
cmap='viridis')
@cleanup
@raises(ValueError)
def test_bad_masked_sizes():
'Test error handling when given differing sized masked arrays'
x = np.arange(3)
y = np.arange(3)
u = np.ma.array(15. * np.ones((4,)))
v = np.ma.array(15. * np.ones_like(u))
u[1] = np.ma.masked
v[1] = np.ma.masked
fig, ax = plt.subplots()
ax.barbs(x, y, u, v)
if __name__ == '__main__':
import nose
nose.runmodule()
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