/usr/lib/python2.7/dist-packages/matplotlib/tests/test_agg.py is in python-matplotlib 2.0.0+dfsg1-2.
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unicode_literals)
import six
import io
import os
from distutils.version import LooseVersion as V
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from matplotlib.image import imread
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.testing.decorators import (
cleanup, image_comparison, knownfailureif)
from matplotlib import pyplot as plt
from matplotlib import collections
from matplotlib import path
from matplotlib import transforms as mtransforms
@cleanup
def test_repeated_save_with_alpha():
# We want an image which has a background color of bluish green, with an
# alpha of 0.25.
fig = Figure([1, 0.4])
canvas = FigureCanvas(fig)
fig.set_facecolor((0, 1, 0.4))
fig.patch.set_alpha(0.25)
# The target color is fig.patch.get_facecolor()
buf = io.BytesIO()
fig.savefig(buf,
facecolor=fig.get_facecolor(),
edgecolor='none')
# Save the figure again to check that the
# colors don't bleed from the previous renderer.
buf.seek(0)
fig.savefig(buf,
facecolor=fig.get_facecolor(),
edgecolor='none')
# Check the first pixel has the desired color & alpha
# (approx: 0, 1.0, 0.4, 0.25)
buf.seek(0)
assert_array_almost_equal(tuple(imread(buf)[0, 0]),
(0.0, 1.0, 0.4, 0.250),
decimal=3)
@cleanup
def test_large_single_path_collection():
buff = io.BytesIO()
# Generates a too-large single path in a path collection that
# would cause a segfault if the draw_markers optimization is
# applied.
f, ax = plt.subplots()
collection = collections.PathCollection(
[path.Path([[-10, 5], [10, 5], [10, -5], [-10, -5], [-10, 5]])])
ax.add_artist(collection)
ax.set_xlim(10**-3, 1)
plt.savefig(buff)
def report_memory(i):
pid = os.getpid()
a2 = os.popen('ps -p %d -o rss,sz' % pid).readlines()
print(i, ' ', a2[1], end=' ')
return int(a2[1].split()[0])
# This test is disabled -- it uses old API. -ADS 2009-09-07
## def test_memleak():
## """Test agg backend for memory leaks."""
## from matplotlib.ft2font import FT2Font
## from numpy.random import rand
## from matplotlib.backend_bases import GraphicsContextBase
## from matplotlib.backends._backend_agg import RendererAgg
## fontname = '/usr/local/share/matplotlib/Vera.ttf'
## N = 200
## for i in range( N ):
## gc = GraphicsContextBase()
## gc.set_clip_rectangle( [20, 20, 20, 20] )
## o = RendererAgg( 400, 400, 72 )
## for j in range( 50 ):
## xs = [ 400*int(rand()) for k in range(8) ]
## ys = [ 400*int(rand()) for k in range(8) ]
## rgb = (1, 0, 0)
## pnts = zip( xs, ys )
## o.draw_polygon( gc, rgb, pnts )
## o.draw_polygon( gc, None, pnts )
## for j in range( 50 ):
## x = [ 400*int(rand()) for k in range(4) ]
## y = [ 400*int(rand()) for k in range(4) ]
## o.draw_lines( gc, x, y )
## for j in range( 50 ):
## args = [ 400*int(rand()) for k in range(4) ]
## rgb = (1, 0, 0)
## o.draw_rectangle( gc, rgb, *args )
## if 1: # add text
## font = FT2Font( fontname )
## font.clear()
## font.set_text( 'hi mom', 60 )
## font.set_size( 12, 72 )
## o.draw_text_image( font.get_image(), 30, 40, gc )
## fname = "agg_memleak_%05d.png"
## o.write_png( fname % i )
## val = report_memory( i )
## if i==1: start = val
## end = val
## avgMem = (end - start) / float(N)
## print 'Average memory consumed per loop: %1.4f\n' % (avgMem)
## #TODO: Verify the expected mem usage and approximate tolerance that
## # should be used
## #self.checkClose( 0.32, avgMem, absTol = 0.1 )
## # w/o text and w/o write_png: Average memory consumed per loop: 0.02
## # w/o text and w/ write_png : Average memory consumed per loop: 0.3400
## # w/ text and w/ write_png : Average memory consumed per loop: 0.32
@cleanup
def test_marker_with_nan():
# This creates a marker with nans in it, which was segfaulting the
# Agg backend (see #3722)
fig, ax = plt.subplots(1)
steps = 1000
data = np.arange(steps)
ax.semilogx(data)
ax.fill_between(data, data*0.8, data*1.2)
buf = io.BytesIO()
fig.savefig(buf, format='png')
@cleanup
def test_long_path():
buff = io.BytesIO()
fig, ax = plt.subplots()
np.random.seed(0)
points = np.random.rand(70000)
ax.plot(points)
fig.savefig(buff, format='png')
@image_comparison(baseline_images=['agg_filter'],
extensions=['png'], remove_text=True)
def test_agg_filter():
def smooth1d(x, window_len):
s = np.r_[2*x[0] - x[window_len:1:-1],
x,
2*x[-1] - x[-1:-window_len:-1]]
w = np.hanning(window_len)
y = np.convolve(w/w.sum(), s, mode='same')
return y[window_len-1:-window_len+1]
def smooth2d(A, sigma=3):
window_len = max(int(sigma), 3)*2 + 1
A1 = np.array([smooth1d(x, window_len) for x in np.asarray(A)])
A2 = np.transpose(A1)
A3 = np.array([smooth1d(x, window_len) for x in A2])
A4 = np.transpose(A3)
return A4
class BaseFilter(object):
def prepare_image(self, src_image, dpi, pad):
ny, nx, depth = src_image.shape
padded_src = np.zeros([pad*2 + ny, pad*2 + nx, depth], dtype="d")
padded_src[pad:-pad, pad:-pad, :] = src_image[:, :, :]
return padded_src # , tgt_image
def get_pad(self, dpi):
return 0
def __call__(self, im, dpi):
pad = self.get_pad(dpi)
padded_src = self.prepare_image(im, dpi, pad)
tgt_image = self.process_image(padded_src, dpi)
return tgt_image, -pad, -pad
class OffsetFilter(BaseFilter):
def __init__(self, offsets=None):
if offsets is None:
self.offsets = (0, 0)
else:
self.offsets = offsets
def get_pad(self, dpi):
return int(max(*self.offsets)/72.*dpi)
def process_image(self, padded_src, dpi):
ox, oy = self.offsets
a1 = np.roll(padded_src, int(ox/72.*dpi), axis=1)
a2 = np.roll(a1, -int(oy/72.*dpi), axis=0)
return a2
class GaussianFilter(BaseFilter):
"simple gauss filter"
def __init__(self, sigma, alpha=0.5, color=None):
self.sigma = sigma
self.alpha = alpha
if color is None:
self.color = (0, 0, 0)
else:
self.color = color
def get_pad(self, dpi):
return int(self.sigma*3/72.*dpi)
def process_image(self, padded_src, dpi):
tgt_image = np.zeros_like(padded_src)
aa = smooth2d(padded_src[:, :, -1]*self.alpha,
self.sigma/72.*dpi)
tgt_image[:, :, -1] = aa
tgt_image[:, :, :-1] = self.color
return tgt_image
class DropShadowFilter(BaseFilter):
def __init__(self, sigma, alpha=0.3, color=None, offsets=None):
self.gauss_filter = GaussianFilter(sigma, alpha, color)
self.offset_filter = OffsetFilter(offsets)
def get_pad(self, dpi):
return max(self.gauss_filter.get_pad(dpi),
self.offset_filter.get_pad(dpi))
def process_image(self, padded_src, dpi):
t1 = self.gauss_filter.process_image(padded_src, dpi)
t2 = self.offset_filter.process_image(t1, dpi)
return t2
if V(np.__version__) < V('1.7.0'):
return
fig = plt.figure()
ax = fig.add_subplot(111)
# draw lines
l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
mec="b", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
l2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-",
mec="r", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
gauss = DropShadowFilter(4)
for l in [l1, l2]:
# draw shadows with same lines with slight offset.
xx = l.get_xdata()
yy = l.get_ydata()
shadow, = ax.plot(xx, yy)
shadow.update_from(l)
# offset transform
ot = mtransforms.offset_copy(l.get_transform(), ax.figure,
x=4.0, y=-6.0, units='points')
shadow.set_transform(ot)
# adjust zorder of the shadow lines so that it is drawn below the
# original lines
shadow.set_zorder(l.get_zorder() - 0.5)
shadow.set_agg_filter(gauss)
shadow.set_rasterized(True) # to support mixed-mode renderers
ax.set_xlim(0., 1.)
ax.set_ylim(0., 1.)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
@cleanup
def test_too_large_image():
fig = plt.figure(figsize=(300, 1000))
buff = io.BytesIO()
assert_raises(ValueError, fig.savefig, buff)
if __name__ == "__main__":
import nose
nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
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