Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/matplotlib/tests/test_agg.py

@@ -0,0 +1,246 @@
+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, path, pyplot as plt, transforms as mtransforms, rcParams)
+from matplotlib.image import imread
+from matplotlib.figure import Figure
+from matplotlib.testing.decorators import image_comparison
+
+
+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.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, ax = plt.subplots()
+    np.random.seed(0)
+    points = np.random.rand(70000)
+    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 http://www.scipy.org/Cookbook/SignalSmooth
+        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
+    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)
+
+
+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"