matplotlib
diff --git a/‎examples/subplots_axes_and_figures/colorbar_placement.py
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+54Lines changed: 54 additions & 0 deletions b/‎examples/subplots_axes_and_figures/colorbar_placement.py
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+54Lines changed: 54 additions & 0 deletions
diff --git a/‎tutorials/intermediate/constrainedlayout_guide.py
Copy file name to clipboardExpand all lines: tutorials/intermediate/constrainedlayout_guide.py
+2-2Lines changed: 2 additions & 2 deletions b/‎tutorials/intermediate/constrainedlayout_guide.py
Copy file name to clipboardExpand all lines: tutorials/intermediate/constrainedlayout_guide.py
+2-2Lines changed: 2 additions & 2 deletions
@@ -0,0 +1,54 @@
+"""
+=================
+Placing Colorbars
+=================
+
+Colorbars indicate the quantitative extent of image data.  Placing in
+a figure is non-trivial because room needs to be made for them.
+
+The simplest case is just attaching a colorbar to each axes:
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+fig, axs = plt.subplots(2, 2)
+cm = ['RdBu_r', 'viridis']
+for col in range(2):
+    for row in range(2):
+        ax = axs[row, col]
+        pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1),
+                            cmap=cm[col])
+        fig.colorbar(pcm, ax=ax)
+plt.show()
+
+######################################################################
+# The first column has the same type of data in both rows, so it may
+# be desirable to combine the colorbar which we do by calling
+# `.Figure.colorbar` with a list of axes instead of a single axes.
+
+fig, axs = plt.subplots(2, 2)
+cm = ['RdBu_r', 'viridis']
+for col in range(2):
+    for row in range(2):
+        ax = axs[row, col]
+        pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1),
+                            cmap=cm[col])
+    fig.colorbar(pcm, ax=axs[:, col], shrink=0.6)
+plt.show()
+
+######################################################################
+# Relatively complicated colorbar layouts are possible using this
+# paradigm.  Note that this example works far better with
+# ``constrained_layout=True``
+
+fig, axs = plt.subplots(3, 3, constrained_layout=True)
+for ax in axs.flat:
+    pcm = ax.pcolormesh(np.random.random((20, 20)))
+
+fig.colorbar(pcm, ax=axs[0, :2], shrink=0.6, location='bottom')
+fig.colorbar(pcm, ax=[axs[0, 2]], location='bottom')
+fig.colorbar(pcm, ax=axs[1:, :], location='right', shrink=0.6)
+fig.colorbar(pcm, ax=[axs[2, 1]], location='left')
+
+
+plt.show()
@@ -121,7 +121,7 @@ def example_plot(ax, fontsize=12, nodec=False):
 #   For the `~.axes.Axes.pcolormesh` kwargs (``pc_kwargs``) we use a
 #   dictionary. Below we will assign one colorbar to a number of axes each
 #   containing a `~.cm.ScalarMappable`; specifying the norm and colormap
-#   ensuresthe colorbar is accurate for all the axes.
+#   ensures the colorbar is accurate for all the axes.
 
 arr = np.arange(100).reshape((10, 10))
 norm = mcolors.Normalize(vmin=0., vmax=100.)
@@ -471,7 +471,7 @@ def docomplicated(suptitle=None):
 #
 # ``constrained_layout`` usually adjusts the axes positions on each draw
 # of the figure.  If you want to get the spacing provided by
-# ``constrained_layout`` but then not have it update, then do the initial
+# ``constrained_layout`` but not have it update, then do the initial
 # draw and then call ``fig.set_constrained_layout(False)``.
 # This is potentially useful for animations where the tick labels may
 # change length.