Merge pull request matplotlib#25198 from rcomer/examples-constrained-kwarg

DOC: remove constrained_layout kwarg from examples

Author: jklymak

examples/axisartist/simple_axisartist1.py

@@ -18,7 +18,7 @@
 
 from mpl_toolkits import axisartist
 
-fig = plt.figure(figsize=(6, 3), constrained_layout=True)
+fig = plt.figure(figsize=(6, 3), layout="constrained")
 # To construct axes of two different classes, we need to use gridspec (or
 # MATLAB-style add_subplot calls).
 gs = fig.add_gridspec(1, 2)

examples/images_contours_and_fields/contourf_demo.py

@@ -40,7 +40,7 @@
 # a good idea, because they don't occur on nice boundaries, but we do it here
 # for purposes of illustration.
 
-fig1, ax2 = plt.subplots(constrained_layout=True)
+fig1, ax2 = plt.subplots(layout='constrained')
 CS = ax2.contourf(X, Y, Z, 10, cmap=plt.cm.bone, origin=origin)
 
 # Note that in the following, we explicitly pass in a subset of the contour
@@ -66,7 +66,7 @@
 # Now make a contour plot with the levels specified, and with the colormap
 # generated automatically from a list of colors.
 
-fig2, ax2 = plt.subplots(constrained_layout=True)
+fig2, ax2 = plt.subplots(layout='constrained')
 levels = [-1.5, -1, -0.5, 0, 0.5, 1]
 CS3 = ax2.contourf(X, Y, Z, levels,
                    colors=('r', 'g', 'b'),
@@ -101,7 +101,7 @@
 # no effect:
 # cmap.set_bad("red")
 
-fig, axs = plt.subplots(2, 2, constrained_layout=True)
+fig, axs = plt.subplots(2, 2, layout="constrained")
 
 for ax, extend in zip(axs.flat, extends):
     cs = ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend, origin=origin)

examples/images_contours_and_fields/image_antialiasing.py

@@ -66,7 +66,7 @@
 # original colormap, so it is no longer possible to invert individual
 # pixels back to their data value.
 
-fig, axs = plt.subplots(2, 2, figsize=(5, 6), constrained_layout=True)
+fig, axs = plt.subplots(2, 2, figsize=(5, 6), layout='constrained')
 axs[0, 0].imshow(a, interpolation='nearest', cmap='RdBu_r')
 axs[0, 0].set_xlim(100, 200)
 axs[0, 0].set_ylim(275, 175)
@@ -104,7 +104,7 @@
 # Apart from the default 'hanning' antialiasing, `~.Axes.imshow` supports a
 # number of different interpolation algorithms, which may work better or
 # worse depending on the pattern.
-fig, axs = plt.subplots(1, 2, figsize=(7, 4), constrained_layout=True)
+fig, axs = plt.subplots(1, 2, figsize=(7, 4), layout='constrained')
 for ax, interp in zip(axs, ['hanning', 'lanczos']):
     ax.imshow(a, interpolation=interp, cmap='gray')
     ax.set_title(f"interpolation='{interp}'")

examples/images_contours_and_fields/image_nonuniform.py

@@ -26,7 +26,7 @@
 
 z = np.sqrt(x[np.newaxis, :]**2 + y[:, np.newaxis]**2)
 
-fig, axs = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
+fig, axs = plt.subplots(nrows=2, ncols=2, layout='constrained')
 fig.suptitle('NonUniformImage class', fontsize='large')
 ax = axs[0, 0]
 im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),

examples/images_contours_and_fields/pcolormesh_grids.py

@@ -89,7 +89,7 @@ def _annotate(ax, x, y, title):
 # to use, in this case ``shading='auto'`` will decide whether to use 'flat' or
 # 'nearest' shading based on the shapes of *X*, *Y* and *Z*.
 
-fig, axs = plt.subplots(2, 1, constrained_layout=True)
+fig, axs = plt.subplots(2, 1, layout='constrained')
 ax = axs[0]
 x = np.arange(ncols)
 y = np.arange(nrows)
@@ -110,7 +110,7 @@ def _annotate(ax, x, y, title):
 # be specified, where the color in the quadrilaterals is linearly interpolated
 # between the grid points.  The shapes of *X*, *Y*, *Z* must be the same.
 
-fig, ax = plt.subplots(constrained_layout=True)
+fig, ax = plt.subplots(layout='constrained')
 x = np.arange(ncols)
 y = np.arange(nrows)
 ax.pcolormesh(x, y, Z, shading='gouraud', vmin=Z.min(), vmax=Z.max())

examples/lines_bars_and_markers/barchart.py

@@ -23,7 +23,7 @@
 width = 0.25  # the width of the bars
 multiplier = 0
 
-fig, ax = plt.subplots(constrained_layout=True)
+fig, ax = plt.subplots(layout='constrained')
 
 for attribute, measurement in penguin_means.items():
     offset = width * multiplier

examples/lines_bars_and_markers/curve_error_band.py

@@ -63,8 +63,7 @@ def draw_error_band(ax, x, y, err, **kwargs):
     ax.add_patch(PathPatch(path, **kwargs))
 
 
-axs = (plt.figure(constrained_layout=True)
-       .subplots(1, 2, sharex=True, sharey=True))
+_, axs = plt.subplots(1, 2, layout='constrained', sharex=True, sharey=True)
 errs = [
     (axs[0], "constant error", 0.05),
     (axs[1], "variable error", 0.05 * np.sin(2 * t) ** 2 + 0.04),

examples/lines_bars_and_markers/markevery_demo.py

@@ -44,7 +44,7 @@
 # markevery with linear scales
 # ----------------------------
 
-fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
 for ax, markevery in zip(axs.flat, cases):
     ax.set_title(f'markevery={markevery}')
     ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
@@ -58,7 +58,7 @@
 # fraction of figure size creates even distributions, because it's based on
 # fractions of the Axes diagonal, not on data coordinates or data indices.
 
-fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
 for ax, markevery in zip(axs.flat, cases):
     ax.set_title(f'markevery={markevery}')
     ax.set_xscale('log')
@@ -75,7 +75,7 @@
 # diagonal, it changes the displayed data range, and more points will be
 # displayed when zooming.
 
-fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True)
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained')
 for ax, markevery in zip(axs.flat, cases):
     ax.set_title(f'markevery={markevery}')
     ax.plot(x, y, 'o', ls='-', ms=4, markevery=markevery)
@@ -89,7 +89,7 @@
 r = np.linspace(0, 3.0, 200)
 theta = 2 * np.pi * r
 
-fig, axs = plt.subplots(3, 3, figsize=(10, 6), constrained_layout=True,
+fig, axs = plt.subplots(3, 3, figsize=(10, 6), layout='constrained',
                         subplot_kw={'projection': 'polar'})
 for ax, markevery in zip(axs.flat, cases):
     ax.set_title(f'markevery={markevery}')

examples/lines_bars_and_markers/psd_demo.py

@@ -112,7 +112,7 @@
 xn = (A * np.sin(2 * np.pi * f * t)).sum(axis=0)
 xn += 5 * np.random.randn(*t.shape)
 
-fig, (ax0, ax1) = plt.subplots(ncols=2, constrained_layout=True)
+fig, (ax0, ax1) = plt.subplots(ncols=2, layout='constrained')
 
 yticks = np.arange(-50, 30, 10)
 yrange = (yticks[0], yticks[-1])
@@ -152,7 +152,7 @@
 f = np.array([150, 140]).reshape(-1, 1)
 xn = (A * np.exp(2j * np.pi * f * t)).sum(axis=0) + 5 * prng.randn(*t.shape)
 
-fig, (ax0, ax1) = plt.subplots(ncols=2, constrained_layout=True)
+fig, (ax0, ax1) = plt.subplots(ncols=2, layout='constrained')
 
 yticks = np.arange(-50, 30, 10)
 yrange = (yticks[0], yticks[-1])

examples/lines_bars_and_markers/scatter_hist.py

@@ -89,7 +89,7 @@ def scatter_hist(x, y, ax, ax_histx, ax_histy):
 # of the axes.
 
 # Create a Figure, which doesn't have to be square.
-fig = plt.figure(constrained_layout=True)
+fig = plt.figure(layout='constrained')
 # Create the main axes, leaving 25% of the figure space at the top and on the
 # right to position marginals.
 ax = fig.add_gridspec(top=0.75, right=0.75).subplots()