matplotlib · efiring · May 23, 2017 · May 4, 2017 · May 4, 2017 · May 19, 2017
diff --git a/doc/users/prev_whats_new/whats_new_0.98.4.rst b/doc/users/prev_whats_new/whats_new_0.98.4.rst
@@ -79,7 +79,7 @@ psd amplitude scaling
 
 Ryan May did a lot of work to rationalize the amplitude scaling of
 :func:`~matplotlib.pyplot.psd` and friends.  See
-:ref:`sphx_glr_gallery_pylab_examples_psd_demo2.py`. and :ref:`sphx_glr_gallery_pylab_examples_psd_demo3.py`.
+:ref:`sphx_glr_gallery_pylab_examples_psd_demo.py`.
 The changes should increase MATLAB
 compatibility and increase scaling options.
 

diff --git a/doc/users/prev_whats_new/whats_new_1.1.rst b/doc/users/prev_whats_new/whats_new_1.1.rst
@@ -119,8 +119,8 @@ been improved in the presence of NANs.
 See the :ref:`sphx_glr_tutorials_02_intermediate_legend_guide.py` for more detailed explanation and
 examples.
 
-.. figure:: ../../gallery/pylab_examples/images/sphx_glr_legend_demo4_001.png
-   :target: ../../gallery/pylab_examples/legend_demo4.html
+.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_demo_004.png
+   :target: ../../gallery/text_labels_and_annotations/legend_demo.html
    :align: center
    :scale: 50
 

diff --git a/doc/users/prev_whats_new/whats_new_1.2.rst b/doc/users/prev_whats_new/whats_new_1.2.rst
@@ -128,8 +128,8 @@ confidence intervals into the :meth:`~matplotlib.axes.boxplot` method. For
 every column of data passed to boxplot, the user can specify an accompanying
 median and confidence interval.
 
-.. figure:: ../../gallery/pylab_examples/images/sphx_glr_boxplot_demo3_001.png
-   :target: ../../gallery/pylab_examples/boxplot_demo3.html
+.. figure:: ../../gallery/statistics/images/sphx_glr_boxplot_demo_003.png
+   :target: ../../gallery/statistics/boxplot_demo.html
    :align: center
    :scale: 50
 

diff --git a/doc/users/prev_whats_new/whats_new_1.3.rst b/doc/users/prev_whats_new/whats_new_1.3.rst
@@ -154,8 +154,8 @@ Till Stensitzki added non-zero baselines to
 :func:`~matplotlib.pyplot.stackplot`.  They may be symmetric or
 weighted.
 
-.. figure:: ../../gallery/pylab_examples/images/sphx_glr_stackplot_demo2_001.png
-   :target: ../../gallery/pylab_examples/stackplot_demo2.html
+.. figure:: ../../gallery/pylab_examples/images/sphx_glr_stackplot_demo_001.png
+   :target: ../../gallery/pylab_examples/stackplot_demo.html
    :align: center
    :scale: 50
 

diff --git a/...les/pylab_examples/image_slices_viewer.py → examples/animation/image_slices_viewer.py b/...les/pylab_examples/image_slices_viewer.py → examples/animation/image_slices_viewer.py
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -1,17 +1,176 @@
 """
-===================
-Displaying an image
-===================
+==========
+Image Demo
+==========
+
+Many ways to plot images in Matplotlib.
+
+The most common way to plot images in Matplotlib is with
+imshow. The following examples demonstrate much of the
+functionality of imshow and the many images you can create.
 
-Simple demo of the imshow function.
 """
+from __future__ import print_function
+
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 import matplotlib.cbook as cbook
+from matplotlib.path import Path
+from matplotlib.patches import PathPatch
+
+###############################################################################
+# First we'll generate a simple bivariate normal distribution.
+
+delta = 0.025
+x = y = np.arange(-3.0, 3.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+Z = Z2 - Z1  # difference of Gaussians
+
+im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
+                origin='lower', extent=[-3, 3, -3, 3],
+                vmax=abs(Z).max(), vmin=-abs(Z).max())
+
+plt.show()
+
+
+###############################################################################
+# It is also possible to show images of pictures.
 
+# A sample image
 image_file = cbook.get_sample_data('ada.png')
 image = plt.imread(image_file)
 
 fig, ax = plt.subplots()
 ax.imshow(image)
 ax.axis('off')  # clear x- and y-axes
+
+
+# And another image
+
+w, h = 512, 512
+
+datafile = cbook.get_sample_data('ct.raw.gz', asfileobj=True)
+s = datafile.read()
+A = np.fromstring(s, np.uint16).astype(float).reshape((w, h))
+A /= A.max()
+
+extent = (0, 25, 0, 25)
+im = plt.imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent)
+
+markers = [(15.9, 14.5), (16.8, 15)]
+x, y = zip(*markers)
+plt.plot(x, y, 'o')
+
+plt.title('CT density')
+
+plt.show()
+
+
+###############################################################################
+# Interpolating images
+# --------------------
+#
+# It is also possible to interpolate images before displaying them. Be careful,
+# as this may manipulate the way your data looks, but it can be helpful for
+# achieving the look you want. Below we'll display the same (small) array,
+# interpolated with three different interpolation methods.
+#
+# The center of the pixel at A[i,j] is plotted at i+0.5, i+0.5.  If you
+# are using interpolation='nearest', the region bounded by (i,j) and
+# (i+1,j+1) will have the same color.  If you are using interpolation,
+# the pixel center will have the same color as it does with nearest, but
+# other pixels will be interpolated between the neighboring pixels.
+#
+# Earlier versions of matplotlib (<0.63) tried to hide the edge effects
+# from you by setting the view limits so that they would not be visible.
+# A recent bugfix in antigrain, and a new implementation in the
+# matplotlib._image module which takes advantage of this fix, no longer
+# makes this necessary.  To prevent edge effects, when doing
+# interpolation, the matplotlib._image module now pads the input array
+# with identical pixels around the edge.  e.g., if you have a 5x5 array
+# with colors a-y as below::
+#
+#   a b c d e
+#   f g h i j
+#   k l m n o
+#   p q r s t
+#   u v w x y
+#
+# the _image module creates the padded array,::
+#
+#   a a b c d e e
+#   a a b c d e e
+#   f f g h i j j
+#   k k l m n o o
+#   p p q r s t t
+#   o u v w x y y
+#   o u v w x y y
+#
+# does the interpolation/resizing, and then extracts the central region.
+# This allows you to plot the full range of your array w/o edge effects,
+# and for example to layer multiple images of different sizes over one
+# another with different interpolation methods - see
+# examples/layer_images.py.  It also implies a performance hit, as this
+# new temporary, padded array must be created.  Sophisticated
+# interpolation also implies a performance hit, so if you need maximal
+# performance or have very large images, interpolation='nearest' is
+# suggested.
+
+A = np.random.rand(5, 5)
+plt.figure(1)
+plt.imshow(A, interpolation='nearest')
+plt.grid(True)
+
+plt.figure(2)
+plt.imshow(A, interpolation='bilinear')
+plt.grid(True)
+
+plt.figure(3)
+plt.imshow(A, interpolation='bicubic')
+plt.grid(True)
+
+plt.show()
+
+
+###############################################################################
+# 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 = np.arange(120).reshape((10, 12))
+
+interp = 'bilinear'
+fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(3, 5))
+axs[0].set_title('blue should be up')
+axs[0].imshow(x, origin='upper', interpolation=interp)
+
+axs[1].set_title('blue should be down')
+axs[1].imshow(x, origin='lower', interpolation=interp)
+plt.show()
+
+
+###############################################################################
+# Finally, we'll show an image using a clip path.
+
+delta = 0.025
+x = y = np.arange(-3.0, 3.0, delta)
+X, Y = np.meshgrid(x, y)
+Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+Z = Z2 - Z1  # difference of Gaussians
+
+path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
+patch = PathPatch(path, facecolor='none')
+plt.gca().add_patch(patch)
+
+im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
+                origin='lower', extent=[-3, 3, -3, 3],
+                clip_path=patch, clip_on=True)
+im.set_clip_path(patch)
+
 plt.show()
diff --git a/examples/pylab_examples/image_masked.py → ...mages_contours_and_fields/image_masked.py b/examples/pylab_examples/image_masked.py → ...mages_contours_and_fields/image_masked.py
diff --git a/examples/pylab_examples/image_nonuniform.py → ...s_contours_and_fields/image_nonuniform.py b/examples/pylab_examples/image_nonuniform.py → ...s_contours_and_fields/image_nonuniform.py
diff --git a/examples/pylab_examples/annotation_demo.py b/examples/pylab_examples/annotation_demo.py