matplotlib · jklymak · Feb 14, 2023 · Feb 13, 2023
diff --git a/examples/axisartist/simple_axisartist1.py b/examples/axisartist/simple_axisartist1.py
@@ -18,7 +18,7 @@
 import numpy as np
 
 
-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)

diff --git a/examples/images_contours_and_fields/contourf_demo.py b/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)

diff --git a/examples/images_contours_and_fields/image_antialiasing.py b/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}'")

diff --git a/examples/images_contours_and_fields/image_nonuniform.py b/examples/images_contours_and_fields/image_nonuniform.py
@@ -25,7 +25,7 @@
 
 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),

diff --git a/examples/images_contours_and_fields/pcolormesh_grids.py b/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())

diff --git a/examples/lines_bars_and_markers/barchart.py b/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

diff --git a/examples/lines_bars_and_markers/curve_error_band.py b/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),

diff --git a/examples/lines_bars_and_markers/markevery_demo.py b/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}')

diff --git a/examples/lines_bars_and_markers/psd_demo.py b/examples/lines_bars_and_markers/psd_demo.py
@@ -111,7 +111,7 @@
 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])
@@ -151,7 +151,7 @@
 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])

diff --git a/examples/lines_bars_and_markers/scatter_hist.py b/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()

diff --git a/examples/lines_bars_and_markers/timeline.py b/examples/lines_bars_and_markers/timeline.py
@@ -68,7 +68,7 @@
                  int(np.ceil(len(dates)/6)))[:len(dates)]
 
 # Create figure and plot a stem plot with the date
-fig, ax = plt.subplots(figsize=(8.8, 4), constrained_layout=True)
+fig, ax = plt.subplots(figsize=(8.8, 4), layout="constrained")
 ax.set(title="Matplotlib release dates")
 
 ax.vlines(dates, 0, levels, color="tab:red")  # The vertical stems.

diff --git a/examples/misc/rasterization_demo.py b/examples/misc/rasterization_demo.py
@@ -44,7 +44,7 @@
 xx = x*np.cos(theta) - y*np.sin(theta)  # rotate x by -theta
 yy = x*np.sin(theta) + y*np.cos(theta)  # rotate y by -theta
 
-fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, constrained_layout=True)
+fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, layout="constrained")
 
 # pcolormesh without rasterization
 ax1.set_aspect(1)

diff --git a/examples/scales/asinh_demo.py b/examples/scales/asinh_demo.py
@@ -65,7 +65,7 @@
 
 ###############################################################################
 # Compare "asinh" graphs with different scale parameter "linear_width":
-fig2 = plt.figure(constrained_layout=True)
+fig2 = plt.figure(layout='constrained')
 axs = fig2.subplots(1, 3, sharex=True)
 for ax, (a0, base) in zip(axs, ((0.2, 2), (1.0, 0), (5.0, 10))):
     ax.set_title(f'linear_width={a0:.3g}')

diff --git a/examples/scales/scales.py b/examples/scales/scales.py
@@ -23,8 +23,7 @@
 x = np.arange(len(y))
 
 # plot with various axes scales
-fig, axs = plt.subplots(3, 2, figsize=(6, 8),
-                        constrained_layout=True)
+fig, axs = plt.subplots(3, 2, figsize=(6, 8), layout='constrained')
 
 # linear
 ax = axs[0, 0]

diff --git a/examples/shapes_and_collections/hatch_style_reference.py b/examples/shapes_and_collections/hatch_style_reference.py
@@ -16,7 +16,7 @@
 import matplotlib.pyplot as plt
 from matplotlib.patches import Rectangle
 
-fig, axs = plt.subplots(2, 5, constrained_layout=True, figsize=(6.4, 3.2))
+fig, axs = plt.subplots(2, 5, layout='constrained', figsize=(6.4, 3.2))
 
 hatches = ['/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*']
 
@@ -33,7 +33,7 @@ def hatches_plot(ax, h):
 ###############################################################################
 # Hatching patterns can be repeated to increase the density.
 
-fig, axs = plt.subplots(2, 5, constrained_layout=True, figsize=(6.4, 3.2))
+fig, axs = plt.subplots(2, 5, layout='constrained', figsize=(6.4, 3.2))
 
 hatches = ['//', '\\\\', '||', '--', '++', 'xx', 'oo', 'OO', '..', '**']
 
@@ -43,7 +43,7 @@ def hatches_plot(ax, h):
 ###############################################################################
 # Hatching patterns can be combined to create additional patterns.
 
-fig, axs = plt.subplots(2, 5, constrained_layout=True, figsize=(6.4, 3.2))
+fig, axs = plt.subplots(2, 5, layout='constrained', figsize=(6.4, 3.2))
 
 hatches = ['/o', '\\|', '|*', '-\\', '+o', 'x*', 'o-', 'O|', 'O.', '*-']
 

diff --git a/examples/spines/spines.py b/examples/spines/spines.py
@@ -23,7 +23,7 @@
 y = 2 * np.sin(x)
 
 # Constrained layout makes sure the labels don't overlap the axes.
-fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, constrained_layout=True)
+fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, layout='constrained')
 
 ax0.plot(x, y)
 ax0.set_title('normal spines')

diff --git a/examples/statistics/barchart_demo.py b/examples/statistics/barchart_demo.py
@@ -45,7 +45,7 @@ def format_score(score):
 
 
 def plot_student_results(student, scores_by_test, cohort_size):
-    fig, ax1 = plt.subplots(figsize=(9, 7), constrained_layout=True)
+    fig, ax1 = plt.subplots(figsize=(9, 7), layout='constrained')
     fig.canvas.manager.set_window_title('Eldorado K-8 Fitness Chart')
 
     ax1.set_title(student.name)

diff --git a/examples/statistics/time_series_histogram.py b/examples/statistics/time_series_histogram.py
@@ -31,7 +31,7 @@
 import matplotlib.pyplot as plt
 from matplotlib.colors import LogNorm
 
-fig, axes = plt.subplots(nrows=3, figsize=(6, 8), constrained_layout=True)
+fig, axes = plt.subplots(nrows=3, figsize=(6, 8), layout='constrained')
 
 # Make some data; a 1D random walk + small fraction of sine waves
 num_series = 1000

diff --git a/examples/style_sheets/style_sheets_reference.py b/examples/style_sheets/style_sheets_reference.py
@@ -116,7 +116,7 @@ def plot_figure(style_label=""):
     prng = np.random.RandomState(96917002)
 
     fig, axs = plt.subplots(ncols=6, nrows=1, num=style_label,
-                            figsize=(14.8, 2.8), constrained_layout=True)
+                            figsize=(14.8, 2.8), layout='constrained')
 
     # make a suptitle, in the same style for all subfigures,
     # except those with dark backgrounds, which get a lighter color:

diff --git a/examples/subplots_axes_and_figures/axes_box_aspect.py b/examples/subplots_axes_and_figures/axes_box_aspect.py
@@ -74,10 +74,10 @@
 # height. `~.Axes.set_box_aspect` provides an easy solution to that by allowing
 # to have the normal plot's axes use the images dimensions as box aspect.
 #
-# This example also shows that ``constrained_layout`` interplays nicely with
+# This example also shows that *constrained layout* interplays nicely with
 # a fixed box aspect.
 
-fig4, (ax, ax2) = plt.subplots(ncols=2, constrained_layout=True)
+fig4, (ax, ax2) = plt.subplots(ncols=2, layout="constrained")
 
 np.random.seed(19680801)  # Fixing random state for reproducibility
 im = np.random.rand(16, 27)
@@ -139,7 +139,7 @@
 # following creates a 2 by 3 subplot grid with all square Axes.
 
 fig7, axs = plt.subplots(2, 3, subplot_kw=dict(box_aspect=1),
-                         sharex=True, sharey=True, constrained_layout=True)
+                         sharex=True, sharey=True, layout="constrained")
 
 for i, ax in enumerate(axs.flat):
     ax.scatter(i % 3, -((i // 3) - 0.5)*200, c=[plt.cm.hsv(i / 6)], s=300)

diff --git a/examples/subplots_axes_and_figures/colorbar_placement.py b/examples/subplots_axes_and_figures/colorbar_placement.py
@@ -41,9 +41,9 @@
 ######################################################################
 # Relatively complicated colorbar layouts are possible using this
 # paradigm.  Note that this example works far better with
-# ``constrained_layout=True``
+# ``layout='constrained'``
 
-fig, axs = plt.subplots(3, 3, constrained_layout=True)
+fig, axs = plt.subplots(3, 3, layout='constrained')
 for ax in axs.flat:
     pcm = ax.pcolormesh(np.random.random((20, 20)))
 
@@ -59,7 +59,7 @@
 # Placing colorbars for axes with a fixed aspect ratio pose a particular
 # challenge as the parent axes changes size depending on the data view.
 
-fig, axs = plt.subplots(2, 2,  constrained_layout=True)
+fig, axs = plt.subplots(2, 2,  layout='constrained')
 cmaps = ['RdBu_r', 'viridis']
 for col in range(2):
     for row in range(2):
@@ -78,7 +78,7 @@
 # axes in axes coordinates.  Note that if you zoom in on the axes, and
 # change the shape of the axes, the colorbar will also change position.
 
-fig, axs = plt.subplots(2, 2, constrained_layout=True)
+fig, axs = plt.subplots(2, 2, layout='constrained')
 cmaps = ['RdBu_r', 'viridis']
 for col in range(2):
     for row in range(2):

diff --git a/examples/subplots_axes_and_figures/demo_constrained_layout.py b/examples/subplots_axes_and_figures/demo_constrained_layout.py
@@ -3,7 +3,7 @@
 Resizing axes with constrained layout
 =====================================
 
-Constrained layout attempts to resize subplots in
+*Constrained layout* attempts to resize subplots in
 a figure so that there are no overlaps between axes objects and labels
 on the axes.
 
@@ -23,25 +23,25 @@ def example_plot(ax):
 
 
 ###############################################################################
-# If we don't use constrained_layout, then labels overlap the axes
+# If we don't use *constrained layout*, then labels overlap the axes
 
-fig, axs = plt.subplots(nrows=2, ncols=2, constrained_layout=False)
+fig, axs = plt.subplots(nrows=2, ncols=2, layout=None)
 
 for ax in axs.flat:
     example_plot(ax)
 
 ###############################################################################
-# adding ``constrained_layout=True`` automatically adjusts.
+# adding ``layout='constrained'`` automatically adjusts.
 
-fig, axs = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
+fig, axs = plt.subplots(nrows=2, ncols=2, layout='constrained')
 
 for ax in axs.flat:
     example_plot(ax)
 
 ###############################################################################
 # Below is a more complicated example using nested gridspecs.
 
-fig = plt.figure(constrained_layout=True)
+fig = plt.figure(layout='constrained')
 
 import matplotlib.gridspec as gridspec
 

diff --git a/examples/subplots_axes_and_figures/figure_title.py b/examples/subplots_axes_and_figures/figure_title.py
@@ -18,7 +18,7 @@
 
 x = np.linspace(0.0, 5.0, 501)
 
-fig, (ax1, ax2) = plt.subplots(1, 2, constrained_layout=True, sharey=True)
+fig, (ax1, ax2) = plt.subplots(1, 2, layout='constrained', sharey=True)
 ax1.plot(x, np.cos(6*x) * np.exp(-x))
 ax1.set_title('damped')
 ax1.set_xlabel('time (s)')
@@ -34,7 +34,7 @@
 # A global x- or y-label can be set using the `.FigureBase.supxlabel` and
 # `.FigureBase.supylabel` methods.
 
-fig, axs = plt.subplots(3, 5, figsize=(8, 5), constrained_layout=True,
+fig, axs = plt.subplots(3, 5, figsize=(8, 5), layout='constrained',
                         sharex=True, sharey=True)
 
 fname = get_sample_data('percent_bachelors_degrees_women_usa.csv',

diff --git a/examples/subplots_axes_and_figures/gridspec_multicolumn.py b/examples/subplots_axes_and_figures/gridspec_multicolumn.py
@@ -17,7 +17,7 @@ def format_axes(fig):
         ax.text(0.5, 0.5, "ax%d" % (i+1), va="center", ha="center")
         ax.tick_params(labelbottom=False, labelleft=False)
 
-fig = plt.figure(constrained_layout=True)
+fig = plt.figure(layout="constrained")
 
 gs = GridSpec(3, 3, figure=fig)
 ax1 = fig.add_subplot(gs[0, :])