.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/images_contours_and_fields/image_demo.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_images_contours_and_fields_image_demo.py: ========== Image Demo ========== Many ways to plot images in Matplotlib. The most common way to plot images in Matplotlib is with `~.axes.Axes.imshow`. The following examples demonstrate much of the functionality of imshow and the many images you can create. .. GENERATED FROM PYTHON SOURCE LINES 12-24 .. code-block:: default import numpy as np import matplotlib.cm as cm import matplotlib.pyplot as plt import matplotlib.cbook as cbook from matplotlib.path import Path from matplotlib.patches import PathPatch # Fixing random state for reproducibility np.random.seed(19680801) .. GENERATED FROM PYTHON SOURCE LINES 25-26 First we'll generate a simple bivariate normal distribution. .. GENERATED FROM PYTHON SOURCE LINES 26-42 .. code-block:: default delta = 0.025 x = y = np.arange(-3.0, 3.0, delta) X, Y = np.meshgrid(x, y) Z1 = np.exp(-X**2 - Y**2) Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2) Z = (Z1 - Z2) * 2 fig, ax = plt.subplots() im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn, origin='lower', extent=[-3, 3, -3, 3], vmax=abs(Z).max(), vmin=-abs(Z).max()) plt.show() .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_001.png :alt: image demo :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 43-44 It is also possible to show images of pictures. .. GENERATED FROM PYTHON SOURCE LINES 44-76 .. code-block:: default # A sample image with cbook.get_sample_data('ada.png') as image_file: image = plt.imread(image_file) fig, ax = plt.subplots() ax.imshow(image) ax.axis('off') # clear x-axis and y-axis # And another image w, h = 512, 512 with cbook.get_sample_data('ct.raw.gz') as datafile: s = datafile.read() A = np.frombuffer(s, np.uint16).astype(float).reshape((w, h)) A /= A.max() fig, ax = plt.subplots() extent = (0, 25, 0, 25) im = ax.imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent) markers = [(15.9, 14.5), (16.8, 15)] x, y = zip(*markers) ax.plot(x, y, 'o') ax.set_title('CT density') plt.show() .. rst-class:: sphx-glr-horizontal * .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_002.png :alt: image demo :class: sphx-glr-multi-img * .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_003.png :alt: CT density :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 77-122 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. To prevent edge effects when doing interpolation, Matplotlib pads the input array with identical pixels around the edge: 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 Matplotlib computes the interpolation and resizing on 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 and then extracts the central region of the result. (Extremely old versions of Matplotlib (<0.63) did not pad the array, but instead adjusted the view limits to hide the affected edge areas.) This approach allows plotting the full extent of an array without edge effects, and for example to layer multiple images of different sizes over one another with different interpolation methods -- see :doc:`/gallery/images_contours_and_fields/layer_images`. It also implies a performance hit, as this new temporary, padded array must be created. Sophisticated interpolation also implies a performance hit; for maximal performance or very large images, interpolation='nearest' is suggested. .. GENERATED FROM PYTHON SOURCE LINES 122-134 .. code-block:: default A = np.random.rand(5, 5) fig, axs = plt.subplots(1, 3, figsize=(10, 3)) for ax, interp in zip(axs, ['nearest', 'bilinear', 'bicubic']): ax.imshow(A, interpolation=interp) ax.set_title(interp.capitalize()) ax.grid(True) plt.show() .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_004.png :alt: Nearest, Bilinear, Bicubic :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 135-141 You can specify whether images should be plotted with the array origin x[0, 0] in the upper left or lower right by using the origin parameter. You can also control the default setting image.origin in your :ref:`matplotlibrc file `. For more on this topic see the :doc:`complete guide on origin and extent `. .. GENERATED FROM PYTHON SOURCE LINES 141-154 .. code-block:: default 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() .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_005.png :alt: blue should be up, blue should be down :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 155-156 Finally, we'll show an image using a clip path. .. GENERATED FROM PYTHON SOURCE LINES 156-177 .. code-block:: default delta = 0.025 x = y = np.arange(-3.0, 3.0, delta) X, Y = np.meshgrid(x, y) Z1 = np.exp(-X**2 - Y**2) Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2) Z = (Z1 - Z2) * 2 path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]]) patch = PathPatch(path, facecolor='none') fig, ax = plt.subplots() ax.add_patch(patch) im = ax.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() .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_006.png :alt: image demo :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 178-185 ------------ References """""""""" The use of the following functions and methods is shown in this example: .. GENERATED FROM PYTHON SOURCE LINES 186-192 .. code-block:: default import matplotlib matplotlib.axes.Axes.imshow matplotlib.pyplot.imshow matplotlib.artist.Artist.set_clip_path matplotlib.patches.PathPatch .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.671 seconds) .. _sphx_glr_download_gallery_images_contours_and_fields_image_demo.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: image_demo.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: image_demo.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature Keywords: matplotlib code example, codex, python plot, pyplot `Gallery generated by Sphinx-Gallery `_