8000 Fix some typos in merged examples. · matplotlib/matplotlib@426a712 · GitHub
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Fix some typos in merged examples.
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‎examples/images_contours_and_fields/image_demo.py

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@@ -138,9 +138,9 @@
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###############################################################################
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# You can specify whether images should be plotted with the array origin
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# x[0,0] in the upper left or upper right by using the origin parameter.
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# You can also control the default be setting image.origin in your
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# matplotlibrc file; see http://matplotlib.org/matplotlibrc
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# x[0,0] in the upper left or lower right by using the origin parameter.
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# You can also control the default setting image.origin in your
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# :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`
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x = np.arange(120).reshape((10, 12))
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‎examples/pylab_examples/fancybox_demo.py

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@@ -93,7 +93,7 @@ def test2(ax):
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#p_fancy.set_boxstyle("round", pad=0.1, rounding_size=0.2)
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ax.text(0.1, 0.8,
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' boxstyle="round,pad=0.1\n rounding\\_size=0.2"',
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' boxstyle="round,pad=0.1\n rounding_size=0.2"',
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size=10, transform=ax.transAxes)
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# draws control points for the fancy box.
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ax.add_patch(p_fancy)
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ax.text(0.1, 0.8,
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' boxstyle="round,pad=0.1"\n mutation\\_scale=2',
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' boxstyle="round,pad=0.1"\n mutation_scale=2',
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size=10, transform=ax.transAxes)
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# draws control points for the fancy box.
@@ -152,7 +152,7 @@ def test4(ax):
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ax.add_patch(p_fancy)
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ax.text(0.1, 0.8,
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' boxstyle="round,pad=0.3"\n mutation\\_aspect=.5',
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' boxstyle="round,pad=0.3"\n mutation_aspect=.5',
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size=10, transform=ax.transAxes)
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draw_bbox(ax, bb)

‎examples/pylab_examples/psd_demo.py

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@@ -5,7 +5,7 @@
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Plotting Power Spectral Density (PSD) in Matplotlib.
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The PSD is a common plot in the field of signal processing. Numpy has
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The PSD is a common plot in the field of signal processing. NumPy has
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many useful libraries for computing a PSD. Below we demo a few examples
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of how this can be accomplished and visualized with Matplotlib.
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"""

‎examples/user_interfaces/histogram_demo_canvasagg_sgskip.py

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@@ -44,13 +44,13 @@
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s = canvas.tostring_rgb() # save this and convert to bitmap as needed
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# get the figure dimensions for creating bitmaps or numpy arrays,
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# Get the figure dimensions for creating bitmaps or NumPy arrays,
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# etc.
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l, b, w, h = fig.bbox.bounds
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w, h = int(w), int(h)
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if 0:
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# convert to a numpy array
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# Convert to a NumPy array
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X = np.fromstring(s, np.uint8).reshape((h, w, 3))
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if 0:

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