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Commit 8ae0dbf

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efiringefiring
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Merge pull request #8266 from choldgraf/consolidate_annotation
Consolidate and move examples out of pylab
2 parents 459f185 + 95be2dd commit 8ae0dbf
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‎doc/users/prev_whats_new/whats_new_0.98.4.rst

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Ryan May did a lot of work to rationalize the amplitude scaling of
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:func:`~matplotlib.pyplot.psd` and friends. See
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:ref:`sphx_glr_gallery_pylab_examples_psd_demo2.py`. and :ref:`sphx_glr_gallery_pylab_examples_psd_demo3.py`.
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:ref:`sphx_glr_gallery_pylab_examples_psd_demo.py`.
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The changes should increase MATLAB
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compatibility and increase scaling options.
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‎doc/users/prev_whats_new/whats_new_1.1.rst

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See the :ref:`sphx_glr_tutorials_02_intermediate_legend_guide.py` for more detailed explanation and
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examples.
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_legend_demo4_001.png
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:target: ../../gallery/pylab_examples/legend_demo4.html
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.. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_demo_004.png
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:target: ../../gallery/text_labels_and_annotations/legend_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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every column of data passed to boxplot, the user can specify an accompanying
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median and confidence interval.
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_boxplot_demo3_001.png
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:target: ../../gallery/pylab_examples/boxplot_demo3.html
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.. figure:: ../../gallery/statistics/images/sphx_glr_boxplot_demo_003.png
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:target: ../../gallery/statistics/boxplot_demo.html
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:align: center
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‎doc/users/prev_whats_new/whats_new_1.3.rst

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:func:`~matplotlib.pyplot.stackplot`. They may be symmetric or
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weighted.
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_stackplot_demo2_001.png
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.. figure:: ../../gallery/pylab_examples/images/sphx_glr_stackplot_demo_001.png
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:target: ../../gallery/pylab_examples/stackplot_demo.html
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:align: center
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‎examples/pylab_examples/image_slices_viewer.py renamed to ‎examples/animation/image_slices_viewer.py

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‎examples/images_contours_and_fields/image_demo.py

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

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

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

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‎examples/pylab_examples/annotation_demo.py

Copy file name to clipboardExpand all lines: examples/pylab_examples/annotation_demo.py
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