.. _pylab_examples-image_interp: pylab_examples example code: image_interp.py ============================================ .. plot:: /home/mdboom/Work/builds/matplotlib/doc/mpl_examples/pylab_examples/image_interp.py :: #!/usr/bin/env python """ 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. Eg, 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. """ from pylab import * A = rand(5,5) figure(1) imshow(A, interpolation='nearest') grid(True) figure(2) imshow(A, interpolation='bilinear') grid(True) figure(3) imshow(A, interpolation='bicubic') grid(True) show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)