File size: 10,742 Bytes
77fe831 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 |
import io
import numpy as np
from numpy.testing import assert_array_almost_equal
from PIL import Image, TiffTags
import pytest
from matplotlib import (
collections, patheffects, pyplot as plt, transforms as mtransforms,
rcParams, rc_context)
from matplotlib.backends.backend_agg import RendererAgg
from matplotlib.figure import Figure
from matplotlib.image import imread
from matplotlib.path import Path
from matplotlib.testing.decorators import image_comparison
from matplotlib.transforms import IdentityTransform
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])
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)
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([[-10, 5], [10, 5], [10, -5], [-10, -5], [-10, 5]])])
ax.add_artist(collection)
ax.set_xlim(10**-3, 1)
plt.savefig(buff)
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')
def test_long_path():
buff = io.BytesIO()
fig = Figure()
ax = fig.subplots()
points = np.ones(100_000)
points[::2] *= -1
ax.plot(points)
fig.savefig(buff, format='png')
@image_comparison(['agg_filter.png'], remove_text=True)
def test_agg_filter():
def smooth1d(x, window_len):
# copied from https://scipy-cookbook.readthedocs.io/
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
A = np.apply_along_axis(smooth1d, 0, A, window_len)
A = np.apply_along_axis(smooth1d, 1, A, window_len)
return A
class BaseFilter:
def get_pad(self, dpi):
return 0
def process_image(self, padded_src, dpi):
raise NotImplementedError("Should be overridden by subclasses")
def __call__(self, im, dpi):
pad = self.get_pad(dpi)
padded_src = np.pad(im, [(pad, pad), (pad, pad), (0, 0)],
"constant")
tgt_image = self.process_image(padded_src, dpi)
return tgt_image, -pad, -pad
class OffsetFilter(BaseFilter):
def __init__(self, offsets=(0, 0)):
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 Gaussian filter."""
def __init__(self, sigma, alpha=0.5, color=(0, 0, 0)):
self.sigma = sigma
self.alpha = alpha
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.empty_like(padded_src)
tgt_image[:, :, :3] = self.color
tgt_image[:, :, 3] = smooth2d(padded_src[:, :, 3] * self.alpha,
self.sigma / 72 * dpi)
return tgt_image
class DropShadowFilter(BaseFilter):
def __init__(self, sigma, alpha=0.3, color=(0, 0, 0), offsets=(0, 0)):
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
fig, ax = plt.subplots()
# draw lines
line1, = 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")
line2, = 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 line in [line1, line2]:
# draw shadows with same lines with slight offset.
xx = line.get_xdata()
yy = line.get_ydata()
shadow, = ax.plot(xx, yy)
shadow.update_from(line)
# offset transform
transform = mtransforms.offset_copy(line.get_transform(), ax.figure,
x=4.0, y=-6.0, units='points')
shadow.set_transform(transform)
# adjust zorder of the shadow lines so that it is drawn below the
# original lines
shadow.set_zorder(line.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)
def test_too_large_image():
fig = plt.figure(figsize=(300, 1000))
buff = io.BytesIO()
with pytest.raises(ValueError):
fig.savefig(buff)
def test_chunksize():
x = range(200)
# Test without chunksize
fig, ax = plt.subplots()
ax.plot(x, np.sin(x))
fig.canvas.draw()
# Test with chunksize
fig, ax = plt.subplots()
rcParams['agg.path.chunksize'] = 105
ax.plot(x, np.sin(x))
fig.canvas.draw()
@pytest.mark.backend('Agg')
def test_jpeg_dpi():
# Check that dpi is set correctly in jpg files.
plt.plot([0, 1, 2], [0, 1, 0])
buf = io.BytesIO()
plt.savefig(buf, format="jpg", dpi=200)
im = Image.open(buf)
assert im.info['dpi'] == (200, 200)
def test_pil_kwargs_png():
from PIL.PngImagePlugin import PngInfo
buf = io.BytesIO()
pnginfo = PngInfo()
pnginfo.add_text("Software", "test")
plt.figure().savefig(buf, format="png", pil_kwargs={"pnginfo": pnginfo})
im = Image.open(buf)
assert im.info["Software"] == "test"
def test_pil_kwargs_tiff():
buf = io.BytesIO()
pil_kwargs = {"description": "test image"}
plt.figure().savefig(buf, format="tiff", pil_kwargs=pil_kwargs)
im = Image.open(buf)
tags = {TiffTags.TAGS_V2[k].name: v for k, v in im.tag_v2.items()}
assert tags["ImageDescription"] == "test image"
def test_pil_kwargs_webp():
plt.plot([0, 1, 2], [0, 1, 0])
buf_small = io.BytesIO()
pil_kwargs_low = {"quality": 1}
plt.savefig(buf_small, format="webp", pil_kwargs=pil_kwargs_low)
assert len(pil_kwargs_low) == 1
buf_large = io.BytesIO()
pil_kwargs_high = {"quality": 100}
plt.savefig(buf_large, format="webp", pil_kwargs=pil_kwargs_high)
assert len(pil_kwargs_high) == 1
assert buf_large.getbuffer().nbytes > buf_small.getbuffer().nbytes
def test_webp_alpha():
plt.plot([0, 1, 2], [0, 1, 0])
buf = io.BytesIO()
plt.savefig(buf, format="webp", transparent=True)
im = Image.open(buf)
assert im.mode == "RGBA"
def test_draw_path_collection_error_handling():
fig, ax = plt.subplots()
ax.scatter([1], [1]).set_paths(Path([(0, 1), (2, 3)]))
with pytest.raises(TypeError):
fig.canvas.draw()
def test_chunksize_fails():
# NOTE: This test covers multiple independent test scenarios in a single
# function, because each scenario uses ~2GB of memory and we don't
# want parallel test executors to accidentally run multiple of these
# at the same time.
N = 100_000
dpi = 500
w = 5*dpi
h = 6*dpi
# make a Path that spans the whole w-h rectangle
x = np.linspace(0, w, N)
y = np.ones(N) * h
y[::2] = 0
path = Path(np.vstack((x, y)).T)
# effectively disable path simplification (but leaving it "on")
path.simplify_threshold = 0
# setup the minimal GraphicsContext to draw a Path
ra = RendererAgg(w, h, dpi)
gc = ra.new_gc()
gc.set_linewidth(1)
gc.set_foreground('r')
gc.set_hatch('/')
with pytest.raises(OverflowError, match='can not split hatched path'):
ra.draw_path(gc, path, IdentityTransform())
gc.set_hatch(None)
with pytest.raises(OverflowError, match='can not split filled path'):
ra.draw_path(gc, path, IdentityTransform(), (1, 0, 0))
# Set to zero to disable, currently defaults to 0, but let's be sure.
with rc_context({'agg.path.chunksize': 0}):
with pytest.raises(OverflowError, match='Please set'):
ra.draw_path(gc, path, IdentityTransform())
# Set big enough that we do not try to chunk.
with rc_context({'agg.path.chunksize': 1_000_000}):
with pytest.raises(OverflowError, match='Please reduce'):
ra.draw_path(gc, path, IdentityTransform())
# Small enough we will try to chunk, but big enough we will fail to render.
with rc_context({'agg.path.chunksize': 90_000}):
with pytest.raises(OverflowError, match='Please reduce'):
ra.draw_path(gc, path, IdentityTransform())
path.should_simplify = False
with pytest.raises(OverflowError, match="should_simplify is False"):
ra.draw_path(gc, path, IdentityTransform())
def test_non_tuple_rgbaface():
# This passes rgbaFace as a ndarray to draw_path.
fig = plt.figure()
fig.add_subplot(projection="3d").scatter(
[0, 1, 2], [0, 1, 2], path_effects=[patheffects.Stroke(linewidth=4)])
fig.canvas.draw()
|