matplotlib
diff --git a/‎examples/images_contours_and_fields/image_demo.py
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+3-3Lines changed: 3 additions & 3 deletions b/‎examples/images_contours_and_fields/image_demo.py
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+3-3Lines changed: 3 additions & 3 deletions
diff --git a/‎examples/pylab_examples/fancybox_demo.py
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diff --git a/‎examples/pylab_examples/psd_demo.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎examples/pylab_examples/psd_demo.py
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diff --git a/‎examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
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+2-2Lines changed: 2 additions & 2 deletions b/‎examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
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+2-2Lines changed: 2 additions & 2 deletions
@@ -138,9 +138,9 @@
 
 ###############################################################################
 # You can specify whether images should be plotted with the array origin
-# x[0,0] in the upper left or upper right by using the origin parameter.
-# You can also control the default be setting image.origin in your
-# matplotlibrc file; see http://matplotlib.org/matplotlibrc
+# x[0,0] in the upper left or lower right by using the origin parameter.
+# You can also control the default setting image.origin in your
+# :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`
 
 x = np.arange(120).reshape((10, 12))
 
 
@@ -93,7 +93,7 @@ def test2(ax):
     #p_fancy.set_boxstyle("round", pad=0.1, rounding_size=0.2)
 
     ax.text(0.1, 0.8,
-            ' boxstyle="round,pad=0.1\n rounding\\_size=0.2"',
+            ' boxstyle="round,pad=0.1\n rounding_size=0.2"',
             size=10, transform=ax.transAxes)
 
     # draws control points for the fancy box.
@@ -118,7 +118,7 @@ def test3(ax):
     ax.add_patch(p_fancy)
 
     ax.text(0.1, 0.8,
-            ' boxstyle="round,pad=0.1"\n mutation\\_scale=2',
+            ' boxstyle="round,pad=0.1"\n mutation_scale=2',
             size=10, transform=ax.transAxes)
 
     # draws control points for the fancy box.
@@ -152,7 +152,7 @@ def test4(ax):
     ax.add_patch(p_fancy)
 
     ax.text(0.1, 0.8,
-            ' boxstyle="round,pad=0.3"\n mutation\\_aspect=.5',
+            ' boxstyle="round,pad=0.3"\n mutation_aspect=.5',
             size=10, transform=ax.transAxes)
 
     draw_bbox(ax, bb)
 
@@ -5,7 +5,7 @@
 
 Plotting Power Spectral Density (PSD) in Matplotlib.
 
-The PSD is a common plot in the field of signal processing. Numpy has
+The PSD is a common plot in the field of signal processing. NumPy has
 many useful libraries for computing a PSD. Below we demo a few examples
 of how this can be accomplished and visualized with Matplotlib.
 """
 
@@ -44,13 +44,13 @@
 
 s = canvas.tostring_rgb()  # save this and convert to bitmap as needed
 
-# get the figure dimensions for creating bitmaps or numpy arrays,
+# Get the figure dimensions for creating bitmaps or NumPy arrays,
 # etc.
 l, b, w, h = fig.bbox.bounds
 w, h = int(w), int(h)
 
 if 0:
-    # convert to a numpy array
+    # Convert to a NumPy array
     X = np.fromstring(s, np.uint8).reshape((h, w, 3))
 
 if 0: