matplotlib · jklymak · Dec 5, 2024 · Sep 3, 2024 · Dec 4, 2024 · timhoffm
diff --git a/galleries/examples/images_contours_and_fields/image_exact_placement.py b/galleries/examples/images_contours_and_fields/image_exact_placement.py
@@ -0,0 +1,165 @@
+"""
+=========================================
+Placing images, preserving relative sizes
+=========================================
+
+By default Matplotlib resamples images created with `~.Axes.imshow` to
+fit inside the parent `~.axes.Axes`.  This can mean that images that have very
+different original sizes can end up appearing similar in size.
-fit inside the parent `~.axes.Axes`.  This can mean that images that have very
-different original sizes can end up appearing similar in size.
+fit inside the parent `~.axes.Axes`.  As a result, images with very different
+original sizes may end up appearing similar in size.
-fit inside the parent `~.axes.Axes`.  This can mean that images that have very
-different original sizes can end up appearing similar in size.
+fit inside the parent `~.axes.Axes`.  As a result, images with very different
+original sizes may end up appearing similar in size.
+
+This example shows how to keep the images the same relative size, or
+how to make the images keep exactly the same pixels as the original data.
+
+Preserving relative sizes
+=========================
+
+By default the two images are made a similar size, despite one being 1.5 times the width
+of the other:
+"""
+
+# sphinx_gallery_thumbnail_number = -1
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import matplotlib.patches as mpatches
+
+# make the data:
+N = 450
+x = np.arange(N) / N
+y = np.arange(N) / N
+
+X, Y = np.meshgrid(x, y)
+R = np.sqrt(X**2 + Y**2)
+f0 = 5
+k = 100
+a = np.sin(np.pi * 2 * (f0 * R + k * R**2 / 2))
+A = a[:100, :300]
+B = A[:40, :200]
+
+# default layout: both axes have the same size
+fig, axs = plt.subplots(1, 2, facecolor='aliceblue')
+
+axs[0].imshow(A, vmin=-1, vmax=1)
+axs[1].imshow(B, vmin=-1, vmax=1)
+
+
+def annotate_rect(ax):
+    # add a rectangle that is the size of the B matrix
+    rect = mpatches.Rectangle((0, 0), 200, 40, linewidth=1,
+                              edgecolor='r', facecolor='none')
+    ax.add_patch(rect)
+    return rect
+
+annotate_rect(axs[0])
+
+# %%
+# Note that both images have an aspect ratio of 1 (i.e. pixels are square), but
+# pixels sizes differ because the images are scaled to the same width.
+#
+# If the size of the images are amenable, we can preserve the relative sizes of two
+# images by using either the *width_ratio* or *height_ratio* of the subplots.  Which
+# one you use depends on the shape of the image and the size of the figure.
+# We can control the relative sizes using the *width_ratios* argument *if* the images
+# are wider than they are tall and shown side by side, as is the case here.
+#
+# While we are making changes, let us also make the aspect ratio of the figure closer
+# to the aspect ratio of the axes using *figsize* so that the figure does not have so
+# much white space. Note that you could alternatively trim extra blank space when
+# saving a figure by passing ``bbox_inches="tight"`` to `~.Figure.savefig`.
+
+fig, axs = plt.subplots(1, 2, width_ratios=[300/200, 1],
+                        figsize=(6.4, 2), facecolor='aliceblue')
+
+axs[0].imshow(A, vmin=-1, vmax=1)
+annotate_rect(axs[0])
+
+axs[1].imshow(B, vmin=-1, vmax=1)
+# %%
+# Given that the data subsample is in the upper left of the larger image,
+# it might make sense if the top of the smaller Axes aligned with the top of the larger.
+# This can be done manually by using `~.Axes.set_anchor`, and using "NW" (for
+# northwest).
+
+fig, axs = plt.subplots(1, 2, width_ratios=[300/200, 1],
+                        figsize=(6.4, 2), facecolor='aliceblue')
+
+axs[0].imshow(A, vmin=-1, vmax=1)
+annotate_rect(axs[0])
+
+axs[0].set_anchor('NW')
+axs[1].imshow(B, vmin=-1, vmax=1)
+axs[1].set_anchor('NW')
+
+# %%
+# Explicit placement
+# ==================
+# The above approach with adjusting ``figsize`` and ``width_ratios`` does
+# not generalize well, because it needs manual parameter tuning, and
+# possibly even code changes to using ``height_ratios`` instead of
+# ``width_ratios`` depending on the aspects and layout of the images.
+#
+# We can alternative calculate positions explicitly and place Axes at absolute
+# coordinates using `~.Figure.add_axes`. This takes the position in the form
+# ``[left bottom width height]`` and is in
+# :ref:`figure coordinates <transforms_tutorial>`. In the following, we
+# determine figure size and Axes positions so that one image data point
+# is rendered exactly to one figure pixel.
+
+dpi = 100  # 100 pixels is one inch
+
+# All variables from here are in pixels:
+buffer = 0.35 * dpi  # pixels
+
+# Get the position of A axes
+left = buffer
+bottom = buffer
+ny, nx = np.shape(A)
+posA = [left, bottom, nx, ny]
+# we know this is tallest, so we can already get the fig height (in pixels)
+fig_height = bottom + ny + buffer
+
+# place the B axes to the right of the A axes
+left = left + nx + buffer
+
+ny, nx = np.shape(B)
+# align the bottom so that the top lines up with the top of the A axes:
+bottom = fig_height - buffer - ny
+posB = [left, bottom, nx, ny]
+
+# now we can get the fig width (in pixels)
+fig_width = left + nx + buffer
+
+# figsize must be in inches:
+fig = plt.figure(figsize=(fig_width / dpi, fig_height / dpi), facecolor='aliceblue')
+
+# the position posA must be normalized by the figure width and height:
+ax = fig.add_axes([posA[0] / fig_width, posA[1] / fig_height,
+                   posA[2] / fig_width, posA[3] / fig_height])
+ax.imshow(A, vmin=-1, vmax=1)
+annotate_rect(ax)
+
+ax = fig.add_axes([posB[0] / fig_width, posB[1] / fig_height,
+                   posB[2] / fig_width, posB[3] / fig_height])
+ax.imshow(B, vmin=-1, vmax=1)
+plt.show()
+# %%
+# Inspection of the image will show that it is exactly 3* 35 + 300 + 200 = 605
+# pixels wide, and 2 * 35 + 100 = 170 pixels high (or twice that if the 2x
+# version is used by the browser instead).  The images should be rendered with
+# exactly 1 pixel per data point (or four, if 2x).
+#
+# .. admonition:: References
+#
+#    The use of the following functions, methods, classes and modules is shown
+#    in this example:
+#
+#    - `matplotlib.axes.Axes.imshow`
+#    - `matplotlib.figure.Figure.add_axes`
+#
+# .. tags::
+#
+#    component: figure
+#    component: axes
+#    styling: position
+#    plot-type: image