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341 lines
11 KiB
Python
341 lines
11 KiB
Python
import io
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from PIL import features, Image, TiffTags
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import pytest
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from matplotlib import (
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collections, patheffects, pyplot as plt, transforms as mtransforms,
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rcParams, rc_context)
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from matplotlib.backends.backend_agg import RendererAgg
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from matplotlib.figure import Figure
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from matplotlib.image import imread
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from matplotlib.path import Path
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from matplotlib.testing.decorators import image_comparison
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from matplotlib.transforms import IdentityTransform
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def test_repeated_save_with_alpha():
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# We want an image which has a background color of bluish green, with an
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# alpha of 0.25.
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fig = Figure([1, 0.4])
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fig.set_facecolor((0, 1, 0.4))
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fig.patch.set_alpha(0.25)
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# The target color is fig.patch.get_facecolor()
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buf = io.BytesIO()
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fig.savefig(buf,
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facecolor=fig.get_facecolor(),
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edgecolor='none')
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# Save the figure again to check that the
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# colors don't bleed from the previous renderer.
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buf.seek(0)
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fig.savefig(buf,
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facecolor=fig.get_facecolor(),
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edgecolor='none')
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# Check the first pixel has the desired color & alpha
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# (approx: 0, 1.0, 0.4, 0.25)
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buf.seek(0)
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assert_array_almost_equal(tuple(imread(buf)[0, 0]),
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(0.0, 1.0, 0.4, 0.250),
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decimal=3)
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def test_large_single_path_collection():
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buff = io.BytesIO()
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# Generates a too-large single path in a path collection that
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# would cause a segfault if the draw_markers optimization is
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# applied.
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f, ax = plt.subplots()
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collection = collections.PathCollection(
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[Path([[-10, 5], [10, 5], [10, -5], [-10, -5], [-10, 5]])])
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ax.add_artist(collection)
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ax.set_xlim(10**-3, 1)
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plt.savefig(buff)
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def test_marker_with_nan():
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# This creates a marker with nans in it, which was segfaulting the
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# Agg backend (see #3722)
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fig, ax = plt.subplots(1)
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steps = 1000
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data = np.arange(steps)
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ax.semilogx(data)
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ax.fill_between(data, data*0.8, data*1.2)
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buf = io.BytesIO()
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fig.savefig(buf, format='png')
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def test_long_path():
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buff = io.BytesIO()
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fig = Figure()
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ax = fig.subplots()
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points = np.ones(100_000)
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points[::2] *= -1
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ax.plot(points)
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fig.savefig(buff, format='png')
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@image_comparison(['agg_filter.png'], remove_text=True)
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def test_agg_filter():
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def smooth1d(x, window_len):
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# copied from https://scipy-cookbook.readthedocs.io/
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s = np.r_[
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2*x[0] - x[window_len:1:-1], x, 2*x[-1] - x[-1:-window_len:-1]]
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w = np.hanning(window_len)
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y = np.convolve(w/w.sum(), s, mode='same')
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return y[window_len-1:-window_len+1]
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def smooth2d(A, sigma=3):
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window_len = max(int(sigma), 3) * 2 + 1
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A = np.apply_along_axis(smooth1d, 0, A, window_len)
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A = np.apply_along_axis(smooth1d, 1, A, window_len)
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return A
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class BaseFilter:
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def get_pad(self, dpi):
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return 0
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def process_image(self, padded_src, dpi):
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raise NotImplementedError("Should be overridden by subclasses")
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def __call__(self, im, dpi):
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pad = self.get_pad(dpi)
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padded_src = np.pad(im, [(pad, pad), (pad, pad), (0, 0)],
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"constant")
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tgt_image = self.process_image(padded_src, dpi)
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return tgt_image, -pad, -pad
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class OffsetFilter(BaseFilter):
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def __init__(self, offsets=(0, 0)):
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self.offsets = offsets
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def get_pad(self, dpi):
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return int(max(self.offsets) / 72 * dpi)
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def process_image(self, padded_src, dpi):
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ox, oy = self.offsets
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a1 = np.roll(padded_src, int(ox / 72 * dpi), axis=1)
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a2 = np.roll(a1, -int(oy / 72 * dpi), axis=0)
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return a2
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class GaussianFilter(BaseFilter):
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"""Simple Gaussian filter."""
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def __init__(self, sigma, alpha=0.5, color=(0, 0, 0)):
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self.sigma = sigma
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self.alpha = alpha
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self.color = color
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def get_pad(self, dpi):
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return int(self.sigma*3 / 72 * dpi)
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def process_image(self, padded_src, dpi):
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tgt_image = np.empty_like(padded_src)
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tgt_image[:, :, :3] = self.color
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tgt_image[:, :, 3] = smooth2d(padded_src[:, :, 3] * self.alpha,
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self.sigma / 72 * dpi)
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return tgt_image
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class DropShadowFilter(BaseFilter):
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def __init__(self, sigma, alpha=0.3, color=(0, 0, 0), offsets=(0, 0)):
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self.gauss_filter = GaussianFilter(sigma, alpha, color)
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self.offset_filter = OffsetFilter(offsets)
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def get_pad(self, dpi):
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return max(self.gauss_filter.get_pad(dpi),
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self.offset_filter.get_pad(dpi))
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def process_image(self, padded_src, dpi):
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t1 = self.gauss_filter.process_image(padded_src, dpi)
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t2 = self.offset_filter.process_image(t1, dpi)
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return t2
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fig, ax = plt.subplots()
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# draw lines
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line1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
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mec="b", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
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line2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-",
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mec="r", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
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gauss = DropShadowFilter(4)
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for line in [line1, line2]:
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# draw shadows with same lines with slight offset.
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xx = line.get_xdata()
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yy = line.get_ydata()
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shadow, = ax.plot(xx, yy)
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shadow.update_from(line)
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# offset transform
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transform = mtransforms.offset_copy(
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line.get_transform(), fig, x=4.0, y=-6.0, units='points')
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shadow.set_transform(transform)
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# adjust zorder of the shadow lines so that it is drawn below the
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# original lines
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shadow.set_zorder(line.get_zorder() - 0.5)
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shadow.set_agg_filter(gauss)
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shadow.set_rasterized(True) # to support mixed-mode renderers
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ax.set_xlim(0., 1.)
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ax.set_ylim(0., 1.)
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ax.xaxis.set_visible(False)
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ax.yaxis.set_visible(False)
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def test_too_large_image():
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fig = plt.figure(figsize=(300, 2**25))
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buff = io.BytesIO()
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with pytest.raises(ValueError):
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fig.savefig(buff)
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def test_chunksize():
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x = range(200)
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# Test without chunksize
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fig, ax = plt.subplots()
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ax.plot(x, np.sin(x))
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fig.canvas.draw()
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# Test with chunksize
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fig, ax = plt.subplots()
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rcParams['agg.path.chunksize'] = 105
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ax.plot(x, np.sin(x))
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fig.canvas.draw()
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@pytest.mark.backend('Agg')
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def test_jpeg_dpi():
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# Check that dpi is set correctly in jpg files.
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plt.plot([0, 1, 2], [0, 1, 0])
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buf = io.BytesIO()
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plt.savefig(buf, format="jpg", dpi=200)
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im = Image.open(buf)
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assert im.info['dpi'] == (200, 200)
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def test_pil_kwargs_png():
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from PIL.PngImagePlugin import PngInfo
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buf = io.BytesIO()
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pnginfo = PngInfo()
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pnginfo.add_text("Software", "test")
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plt.figure().savefig(buf, format="png", pil_kwargs={"pnginfo": pnginfo})
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im = Image.open(buf)
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assert im.info["Software"] == "test"
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def test_pil_kwargs_tiff():
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buf = io.BytesIO()
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pil_kwargs = {"description": "test image"}
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plt.figure().savefig(buf, format="tiff", pil_kwargs=pil_kwargs)
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im = Image.open(buf)
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tags = {TiffTags.TAGS_V2[k].name: v for k, v in im.tag_v2.items()}
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assert tags["ImageDescription"] == "test image"
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@pytest.mark.skipif(not features.check("webp"), reason="WebP support not available")
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def test_pil_kwargs_webp():
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plt.plot([0, 1, 2], [0, 1, 0])
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buf_small = io.BytesIO()
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pil_kwargs_low = {"quality": 1}
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plt.savefig(buf_small, format="webp", pil_kwargs=pil_kwargs_low)
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assert len(pil_kwargs_low) == 1
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buf_large = io.BytesIO()
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pil_kwargs_high = {"quality": 100}
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plt.savefig(buf_large, format="webp", pil_kwargs=pil_kwargs_high)
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assert len(pil_kwargs_high) == 1
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assert buf_large.getbuffer().nbytes > buf_small.getbuffer().nbytes
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@pytest.mark.skipif(not features.check("webp"), reason="WebP support not available")
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def test_webp_alpha():
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plt.plot([0, 1, 2], [0, 1, 0])
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buf = io.BytesIO()
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plt.savefig(buf, format="webp", transparent=True)
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im = Image.open(buf)
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assert im.mode == "RGBA"
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def test_draw_path_collection_error_handling():
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fig, ax = plt.subplots()
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ax.scatter([1], [1]).set_paths(Path([(0, 1), (2, 3)]))
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with pytest.raises(TypeError):
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fig.canvas.draw()
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def test_chunksize_fails():
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# NOTE: This test covers multiple independent test scenarios in a single
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# function, because each scenario uses ~2GB of memory and we don't
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# want parallel test executors to accidentally run multiple of these
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# at the same time.
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N = 100_000
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dpi = 500
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w = 5*dpi
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h = 6*dpi
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# make a Path that spans the whole w-h rectangle
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x = np.linspace(0, w, N)
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y = np.ones(N) * h
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y[::2] = 0
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path = Path(np.vstack((x, y)).T)
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# effectively disable path simplification (but leaving it "on")
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path.simplify_threshold = 0
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# setup the minimal GraphicsContext to draw a Path
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ra = RendererAgg(w, h, dpi)
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gc = ra.new_gc()
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gc.set_linewidth(1)
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gc.set_foreground('r')
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gc.set_hatch('/')
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with pytest.raises(OverflowError, match='cannot split hatched path'):
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ra.draw_path(gc, path, IdentityTransform())
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gc.set_hatch(None)
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with pytest.raises(OverflowError, match='cannot split filled path'):
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ra.draw_path(gc, path, IdentityTransform(), (1, 0, 0))
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# Set to zero to disable, currently defaults to 0, but let's be sure.
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with rc_context({'agg.path.chunksize': 0}):
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with pytest.raises(OverflowError, match='Please set'):
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ra.draw_path(gc, path, IdentityTransform())
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# Set big enough that we do not try to chunk.
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with rc_context({'agg.path.chunksize': 1_000_000}):
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with pytest.raises(OverflowError, match='Please reduce'):
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ra.draw_path(gc, path, IdentityTransform())
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# Small enough we will try to chunk, but big enough we will fail to render.
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with rc_context({'agg.path.chunksize': 90_000}):
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with pytest.raises(OverflowError, match='Please reduce'):
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ra.draw_path(gc, path, IdentityTransform())
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path.should_simplify = False
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with pytest.raises(OverflowError, match="should_simplify is False"):
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ra.draw_path(gc, path, IdentityTransform())
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def test_non_tuple_rgbaface():
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# This passes rgbaFace as a ndarray to draw_path.
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fig = plt.figure()
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fig.add_subplot(projection="3d").scatter(
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[0, 1, 2], [0, 1, 2], path_effects=[patheffects.Stroke(linewidth=4)])
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fig.canvas.draw()
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