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NelleVNelleV
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Merge pull request #8858 from dstansby/pylab-moves3
Pylab example moves 3
2 parents 87638e3 + d4021aa commit c0cd944

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‎doc/users/prev_whats_new/whats_new_1.0.rst‎

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@@ -58,8 +58,8 @@ Additionally, he has contributed a new module :mod:`~matplotlib.tri` and
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helper function :func:`~matplotlib.pyplot.triplot` for creating and
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plotting unstructured triangular grids.
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_triplot_demo_001.png
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:target: ../../gallery/pylab_examples/triplot_demo.html
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.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_triplot_demo_001.png
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:target: ../../gallery/images_contours_and_fields/triplot_demo.html
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:align: center
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:scale: 50
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‎doc/users/prev_whats_new/whats_new_1.2.rst‎

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@@ -200,8 +200,8 @@ Ian Thomas extended :meth:`~matplotlib.pyplot.tripcolor` to allow one color
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value to be specified for each triangular face rather than for each point in
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a triangulation.
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_tripcolor_demo_001.png
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:target: ../../gallery/pylab_examples/tripcolor_demo.html
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.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_tripcolor_demo_001.png
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:target: ../../gallery/images_contours_and_fields/tripcolor_demo.html
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:align: center
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:scale: 50
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@@ -214,8 +214,8 @@ Phil Elson added support for hatching to
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:func:`~matplotlib.pyplot.contourf`, together with the ability
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to use a legend to identify contoured ranges.
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_contourf_hatching_001.png
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:target: ../../gallery/pylab_examples/contourf_hatching.html
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.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_contourf_hatching_001.png
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:target: ../../gallery/images_contours_and_fields/contourf_hatching.html
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:align: center
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:scale: 50
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@@ -227,8 +227,3 @@ Known issues in the matplotlib 1.2 release
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- When using the Qt4Agg backend with IPython 0.11 or later, the save
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dialog will not display. This should be fixed in a future version
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of IPython.
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‎doc/users/prev_whats_new/whats_new_1.3.rst‎

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@@ -141,8 +141,8 @@ perform mesh refinement and smooth contouring was also added
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implementing some basic tools for triangular mesh improvement was
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added (:class:`~matplotlib.tri.TriAnalyzer`).
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_tricontour_smooth_user_001.png
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:target: ../../gallery/pylab_examples/tricontour_smooth_user.html
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.. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_tricontour_smooth_user_001.png
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:target: ../../gallery/images_contours_and_fields/tricontour_smooth_user.html
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:align: center
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:scale: 50
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@@ -400,9 +400,3 @@ matplotlib will now display a `RuntimeWarning` when too many figures
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have been opened at once. By default, this is displayed for 20 or
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more figures, but the exact number may be controlled using the
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``figure.max_open_warning`` rcParam.
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examples/pylab_examples/contour_demo.py renamed to examples/images_contours_and_fields/contour_demo.py

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examples/pylab_examples/contour_image.py renamed to examples/images_contours_and_fields/contour_image.py

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Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
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Z = (Z1 - Z2) * 10
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levels = np.arange(-2.0, 1.601, 0.4) # Boost the upper limit to avoid truncation errors.
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# Boost the upper limit to avoid truncation errors.
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levels = np.arange(-2.0, 1.601, 0.4)
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norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
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cmap = cm.PRGn
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plt.subplot(2, 2, 1)
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cset1 = plt.contourf(X, Y, Z, levels,
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cmap=cm.get_cmap(cmap, len(levels) - 1),
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norm=norm,
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)
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cmap=cm.get_cmap(cmap, len(levels) - 1), norm=norm)
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# It is not necessary, but for the colormap, we need only the
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# number of levels minus 1. To avoid discretization error, use
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# either this number or a large number such as the default (256).
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# This is intentional. The Z values are defined at the center of each
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# image pixel (each color block on the following subplot), so the
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# domain that is contoured does not extend beyond these pixel centers.
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im = plt.imshow(Z, interpolation='nearest', extent=extent, cmap=cmap, norm=norm)
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im = plt.imshow(Z, interpolation='nearest', extent=extent,
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cmap=cmap, norm=norm)
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v = plt.axis()
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plt.contour(Z, levels, colors='k', origin='image', extent=extent)
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plt.axis(v)

examples/pylab_examples/contour_label_demo.py renamed to examples/images_contours_and_fields/contour_label_demo.py

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@@ -48,6 +48,7 @@ def __repr__(self):
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else:
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return '%.1f' % self.__float__()
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# Recast levels to new class
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CS.levels = [nf(val) for val in CS.levels]
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examples/pylab_examples/contourf_demo.py renamed to examples/images_contours_and_fields/contourf_demo.py

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import matplotlib.pyplot as plt
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origin = 'lower'
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#origin = 'upper'
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delta = 0.025
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nr, nc = Z.shape
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# put NaNs in one corner:
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Z[-nr//6:, -nc//6:] = np.nan
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Z[-nr // 6:, -nc // 6:] = np.nan
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# contourf will convert these to masked
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Z = np.ma.array(Z)
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# mask another corner:
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Z[:nr//6, :nc//6] = np.ma.masked
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Z[:nr // 6, :nc // 6] = np.ma.masked
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# mask a circle in the middle:
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interior = np.sqrt((X**2) + (Y**2)) < 0.5
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# this is usually not such a good idea, because they don't
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# occur on nice boundaries, but we do it here for purposes
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# of illustration.
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CS = plt.contourf(X, Y, Z, 10,
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#[-1, -0.1, 0, 0.1],
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#alpha=0.5,
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cmap=plt.cm.bone,
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origin=origin)
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CS = plt.contourf(X, Y, Z, 10, cmap=plt.cm.bone, origin=origin)
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# Note that in the following, we explicitly pass in a subset of
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# the contour levels used for the filled contours. Alternatively,
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# We could pass in additional levels to provide extra resolution,
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# or leave out the levels kwarg to use all of the original levels.
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CS2 = plt.contour(CS, levels=CS.levels[::2],
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colors='r',
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origin=origin)
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CS2 = plt.contour(CS, levels=CS.levels[::2], colors='r', origin=origin)
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plt.title('Nonsense (3 masked regions)')
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plt.xlabel('word length anomaly')

examples/pylab_examples/contourf_hatching.py renamed to examples/images_contours_and_fields/contourf_hatching.py

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examples/pylab_examples/figimage_demo.py renamed to examples/images_contours_and_fields/figimage_demo.py

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examples/pylab_examples/griddata_demo.py renamed to examples/images_contours_and_fields/griddata_demo.py

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