matplotlib
diff --git a/‎examples/misc/demo_agg_filter.py
Lines changed: 46 additions & 88 deletions b/‎examples/misc/demo_agg_filter.py
Lines changed: 46 additions & 88 deletions
diff --git a/‎lib/matplotlib/tests/test_agg.py
Lines changed: 29 additions & 41 deletions b/‎lib/matplotlib/tests/test_agg.py
Lines changed: 29 additions & 41 deletions
@@ -4,97 +4,82 @@
 ===============
 
 """
-import matplotlib.pyplot as plt
 
-import numpy as np
 import matplotlib.cm as cm
+import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 from matplotlib.colors import LightSource
 from matplotlib.artist import Artist
+import numpy as np
 
 
 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
-    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
+    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 prepare_image(self, src_image, dpi, pad):
-        ny, nx, depth = src_image.shape
-        # tgt_image = np.zeros([pad*2+ny, pad*2+nx, depth], dtype="d")
-        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 process_image(padded_src, dpi):
+        raise NotImplementedError("Should be overridden by subclasses")
+
     def __call__(self, im, dpi):
         pad = self.get_pad(dpi)
-        padded_src = self.prepare_image(im, dpi, pad)
+        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=None):
-        if offsets is None:
-            self.offsets = (0, 0)
-        else:
-            self.offsets = offsets
+
+    def __init__(self, offsets=(0, 0)):
+        self.offsets = offsets
 
     def get_pad(self, dpi):
-        return int(max(*self.offsets)/72.*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)
+        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"
+    """Simple Gaussian filter."""
 
-    def __init__(self, sigma, alpha=0.5, color=None):
+    def __init__(self, sigma, alpha=0.5, color=(0, 0, 0)):
         self.sigma = sigma
         self.alpha = alpha
-        if color is None:
-            self.color = (0, 0, 0)
-        else:
-            self.color = color
+        self.color = color
 
     def get_pad(self, dpi):
-        return int(self.sigma*3/72.*dpi)
+        return int(self.sigma*3 / 72 * dpi)
 
     def process_image(self, padded_src, dpi):
-        # offsetx, offsety = int(self.offsets[0]), int(self.offsets[1])
-        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
+        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=None, offsets=None):
+
+    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)
 
@@ -109,7 +94,6 @@ def process_image(self, padded_src, dpi):
 
 
 class LightFilter(BaseFilter):
-    "simple gauss filter"
 
     def __init__(self, sigma, fraction=0.5):
         self.gauss_filter = GaussianFilter(sigma, alpha=1)
@@ -123,47 +107,36 @@ def process_image(self, padded_src, dpi):
         t1 = self.gauss_filter.process_image(padded_src, dpi)
         elevation = t1[:, :, 3]
         rgb = padded_src[:, :, :3]
-
+        alpha = padded_src[:, :, 3:]
         rgb2 = self.light_source.shade_rgb(rgb, elevation,
                                            fraction=self.fraction)
-
-        tgt = np.empty_like(padded_src)
-        tgt[:, :, :3] = rgb2
-        tgt[:, :, 3] = padded_src[:, :, 3]
-
-        return tgt
+        return np.concatenate([rgb2, alpha], -1)
 
 
 class GrowFilter(BaseFilter):
-    "enlarge the area"
+    """Enlarge the area."""
 
-    def __init__(self, pixels, color=None):
+    def __init__(self, pixels, color=(1, 1, 1)):
         self.pixels = pixels
-        if color is None:
-            self.color = (1, 1, 1)
-        else:
-            self.color = color
+        self.color = color
 
     def __call__(self, im, dpi):
-        ny, nx, depth = im.shape
-        alpha = np.pad(im[..., -1], self.pixels, "constant")
-        alpha2 = np.clip(smooth2d(alpha, self.pixels/72.*dpi) * 5, 0, 1)
+        alpha = np.pad(im[..., 3], self.pixels, "constant")
+        alpha2 = np.clip(smooth2d(alpha, self.pixels / 72 * dpi) * 5, 0, 1)
         new_im = np.empty((*alpha2.shape, 4))
-        new_im[:, :, -1] = alpha2
-        new_im[:, :, :-1] = self.color
+        new_im[:, :, :3] = self.color
+        new_im[:, :, 3] = alpha2
         offsetx, offsety = -self.pixels, -self.pixels
         return new_im, offsetx, offsety
 
 
 class FilteredArtistList(Artist):
-    """
-    A simple container to draw filtered artist.
-    """
+    """A simple container to filter multiple artists at once."""
 
     def __init__(self, artist_list, filter):
+        super().__init__()
         self._artist_list = artist_list
         self._filter = filter
-        Artist.__init__(self)
 
     def draw(self, renderer):
         renderer.start_rasterizing()
@@ -188,15 +161,13 @@ def filtered_text(ax):
 
     # draw
     ax.imshow(Z, interpolation='bilinear', origin='lower',
-              cmap=cm.gray, extent=(-3, 3, -2, 2))
+              cmap=cm.gray, extent=(-3, 3, -2, 2), aspect='auto')
     levels = np.arange(-1.2, 1.6, 0.2)
     CS = ax.contour(Z, levels,
                     origin='lower',
                     linewidths=2,
                     extent=(-3, 3, -2, 2))
 
-    ax.set_aspect("auto")
-
     # contour label
     cl = ax.clabel(CS, levels[1::2],  # label every second level
                    inline=1,
@@ -223,17 +194,14 @@ def drop_shadow_line(ax):
     # copied from examples/misc/svg_filter_line.py
 
     # 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")
+    l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-")
+    l2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-")
 
     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)
@@ -288,7 +256,6 @@ def light_filter_pie(ax):
     fracs = [15, 30, 45, 10]
     explode = (0, 0.05, 0, 0)
     pies = ax.pie(fracs, explode=explode)
-    ax.patch.set_visible(True)
 
     light_filter = LightFilter(9)
     for p in pies[0]:
@@ -305,21 +272,12 @@ def light_filter_pie(ax):
 
 if __name__ == "__main__":
 
-    plt.figure(figsize=(6, 6))
-    plt.subplots_adjust(left=0.05, right=0.95)
-
-    ax = plt.subplot(221)
-    filtered_text(ax)
-
-    ax = plt.subplot(222)
-    drop_shadow_line(ax)
-
-    ax = plt.subplot(223)
-    drop_shadow_patches(ax)
+    fix, axs = plt.subplots(2, 2)
 
-    ax = plt.subplot(224)
-    ax.set_aspect(1)
-    light_filter_pie(ax)
-    ax.set_frame_on(True)
+    filtered_text(axs[0, 0])
+    drop_shadow_line(axs[0, 1])
+    drop_shadow_patches(axs[1, 0])
+    light_filter_pie(axs[1, 1])
+    axs[1, 1].set_frame_on(True)
 
     plt.show()
@@ -81,79 +81,69 @@ def test_long_path():
 @image_comparison(['agg_filter.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]]
+        # 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
-        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
+        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 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 process_image(padded_src, dpi):
+            raise NotImplementedError("Should be overridden by subclasses")
+
         def __call__(self, im, dpi):
             pad = self.get_pad(dpi)
-            padded_src = self.prepare_image(im, dpi, pad)
+            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=None):
-            if offsets is None:
-                self.offsets = (0, 0)
-            else:
-                self.offsets = offsets
+
+        def __init__(self, offsets=(0, 0)):
+            self.offsets = offsets
 
         def get_pad(self, dpi):
-            return int(max(*self.offsets)/72.*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)
+            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"
+        """Simple Gaussian filter."""
 
-        def __init__(self, sigma, alpha=0.5, color=None):
+        def __init__(self, sigma, alpha=0.5, color=(0, 0, 0)):
             self.sigma = sigma
             self.alpha = alpha
-            if color is None:
-                self.color = (0, 0, 0)
-            else:
-                self.color = color
+            self.color = color
 
         def get_pad(self, dpi):
-            return int(self.sigma*3/72.*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
+            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=None, offsets=None):
+
+        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)
 
@@ -166,8 +156,7 @@ def process_image(self, padded_src, dpi):
             t2 = self.offset_filter.process_image(t1, dpi)
             return t2
 
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
+    fig, ax = plt.subplots()
 
     # draw lines
     l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
@@ -180,7 +169,6 @@ def process_image(self, padded_src, dpi):
     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)