matplotlib · tacaswell · May 18, 2019 · May 12, 2019
diff --git a/examples/axes_grid1/inset_locator_demo.py b/examples/axes_grid1/inset_locator_demo.py
@@ -74,7 +74,7 @@
 
 # We set the axis limits to something other than the default, in order to not
 # distract from the fact that axes coordinates are used here.
-ax.axis([0, 10, 0, 10])
+ax.set(xlim=(0, 10), ylim=(0, 10))
 
 
 # Note how the two following insets are created at the same positions, one by
@@ -126,7 +126,7 @@
 
 ax2 = fig.add_subplot(133)
 ax2.set_xscale("log")
-ax2.axis([1e-6, 1e6, -2, 6])
+ax2.set(xlim=(1e-6, 1e6), ylim=(-2, 6))
 
 # Create inset in data coordinates using ax.transData as transform
 axins3 = inset_axes(ax2, width="100%", height="100%",

diff --git a/examples/event_handling/ginput_manual_clabel_sgskip.py b/examples/event_handling/ginput_manual_clabel_sgskip.py
@@ -28,7 +28,6 @@ def tellme(s):
 
 
 plt.clf()
-plt.axis([-1., 1., -1., 1.])
 plt.setp(plt.gca(), autoscale_on=False)
 
 tellme('You will define a triangle, click to begin')
@@ -84,13 +83,14 @@ def f(x, y, pts):
 
 while True:
     tellme('Select two corners of zoom, middle mouse button to finish')
-    pts = np.asarray(plt.ginput(2, timeout=-1))
-
+    pts = plt.ginput(2, timeout=-1)
     if len(pts) < 2:
         break
-
-    pts = np.sort(pts, axis=0)
-    plt.axis(pts.T.ravel())
+    (x0, y0), (x1, y1) = pts
+    xmin, xmax = sorted([x0, x1])
+    ymin, ymax = sorted([y0, y1])
+    plt.xlim(xmin, xmax)
+    plt.ylim(ymin, ymax)
 
 tellme('All Done!')
 plt.show()
diff --git a/examples/event_handling/pick_event_demo.py b/examples/event_handling/pick_event_demo.py
@@ -165,11 +165,11 @@ def onpick3(event):
 def pick_image():
     # picking images (matplotlib.image.AxesImage)
     fig, ax = plt.subplots()
-    im1 = ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
-    im2 = ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
-    im3 = ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
-    im4 = ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
-    ax.axis([0, 5, 0, 5])
+    ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
+    ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
+    ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
+    ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
+    ax.set(xlim=(0, 5), ylim=(0, 5))
 
     def onpick4(event):
         artist = event.artist

diff --git a/examples/images_contours_and_fields/irregulardatagrid.py b/examples/images_contours_and_fields/irregulardatagrid.py
@@ -62,7 +62,7 @@
 
 fig.colorbar(cntr1, ax=ax1)
 ax1.plot(x, y, 'ko', ms=3)
-ax1.axis((-2, 2, -2, 2))
+ax1.set(xlim=(-2, 2), ylim=(-2, 2))
 ax1.set_title('grid and contour (%d points, %d grid points)' %
               (npts, ngridx * ngridy))
 
@@ -78,7 +78,7 @@
 
 fig.colorbar(cntr2, ax=ax2)
 ax2.plot(x, y, 'ko', ms=3)
-ax2.axis((-2, 2, -2, 2))
+ax2.set(xlim=(-2, 2), ylim=(-2, 2))
 ax2.set_title('tricontour (%d points)' % npts)
 
 plt.subplots_adjust(hspace=0.5)

diff --git a/examples/images_contours_and_fields/pcolor_demo.py b/examples/images_contours_and_fields/pcolor_demo.py
@@ -54,15 +54,11 @@
 ax = axs[0, 0]
 c = ax.pcolor(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
 ax.set_title('pcolor')
-# set the limits of the plot to the limits of the data
-ax.axis([x.min(), x.max(), y.min(), y.max()])
 fig.colorbar(c, ax=ax)
 
 ax = axs[0, 1]
 c = ax.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
 ax.set_title('pcolormesh')
-# set the limits of the plot to the limits of the data
-ax.axis([x.min(), x.max(), y.min(), y.max()])
 fig.colorbar(c, ax=ax)
 
 ax = axs[1, 0]

diff --git a/examples/images_contours_and_fields/plot_streamplot.py b/examples/images_contours_and_fields/plot_streamplot.py
@@ -53,7 +53,7 @@
 
 # Displaying the starting points with blue symbols.
 ax3.plot(seed_points[0], seed_points[1], 'bo')
-ax3.axis((-w, w, -w, w))
+ax3.set(xlim=(-w, w), ylim=(-w, w))
 
 # Create a mask
 mask = np.zeros(U.shape, dtype=bool)

diff --git a/examples/misc/contour_manual.py b/examples/misc/contour_manual.py
@@ -25,33 +25,33 @@
 
 ###############################################################################
 
-plt.figure()
+fig, ax = plt.subplots()
 
 # Filled contours using filled=True.
-cs = ContourSet(plt.gca(), [0, 1, 2], [filled01, filled12], filled=True, cmap=cm.bone)
-cbar = plt.colorbar(cs)
+cs = ContourSet(ax, [0, 1, 2], [filled01, filled12], filled=True, cmap=cm.bone)
+cbar = fig.colorbar(cs)
 
 # Contour lines (non-filled).
-lines = ContourSet(plt.gca(), [0, 1, 2], [lines0, lines1, lines2], cmap=cm.cool,
-                   linewidths=3)
+lines = ContourSet(
+    ax, [0, 1, 2], [lines0, lines1, lines2], cmap=cm.cool, linewidths=3)
 cbar.add_lines(lines)
 
-plt.axis([-0.5, 3.5, -0.5, 4.5])
-plt.title('User-specified contours')
+ax.set(xlim=(-0.5, 3.5), ylim=(-0.5, 4.5),
+       title='User-specified contours')
 
 ###############################################################################
 # Multiple filled contour lines can be specified in a single list of polygon
 # vertices along with a list of vertex kinds (code types) as described in the
 # Path class.  This is particularly useful for polygons with holes.
 # Here a code type of 1 is a MOVETO, and 2 is a LINETO.
 
-plt.figure()
+fig, ax = plt.subplots()
 filled01 = [[[0, 0], [3, 0], [3, 3], [0, 3], [1, 1], [1, 2], [2, 2], [2, 1]]]
 kinds01 = [[1, 2, 2, 2, 1, 2, 2, 2]]
-cs = ContourSet(plt.gca(), [0, 1], [filled01], [kinds01], filled=True)
-cbar = plt.colorbar(cs)
+cs = ContourSet(ax, [0, 1], [filled01], [kinds01], filled=True)
+cbar = fig.colorbar(cs)
 
-plt.axis([-0.5, 3.5, -0.5, 3.5])
-plt.title('User specified filled contours with holes')
+ax.set(xlim=(-0.5, 3.5), ylim=(-0.5, 3.5),
+       title='User specified filled contours with holes')
 
 plt.show()
diff --git a/examples/pyplots/pyplot_formatstr.py b/examples/pyplots/pyplot_formatstr.py
@@ -1,14 +1,14 @@
 """
-================
-Pyplot Formatstr
-================
+====================
+plot() format string
+====================
 
-Use a format string to colorize a `~matplotlib.axes.Axes.plot` and set its
-markers.
+Use a format string (here, 'ro') to set the color and markers of a
+`~matplotlib.axes.Axes.plot`.
 """
+
 import matplotlib.pyplot as plt
-plt.plot([1,2,3,4], [1,4,9,16], 'ro')
-plt.axis([0, 6, 0, 20])
+plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')
 plt.show()
 
 #############################################################################

diff --git a/examples/pyplots/pyplot_text.py b/examples/pyplots/pyplot_text.py
@@ -21,7 +21,8 @@
 plt.ylabel('Probability')
 plt.title('Histogram of IQ')
 plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
-plt.axis([40, 160, 0, 0.03])
+plt.xlim(40, 160)
+plt.ylim(0, 0.03)
 plt.grid(True)
 plt.show()
 

diff --git a/examples/pyplots/text_commands.py b/examples/pyplots/text_commands.py
@@ -35,7 +35,7 @@
 ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
             arrowprops=dict(facecolor='black', shrink=0.05))
 
-ax.axis([0, 10, 0, 10])
+ax.set(xlim=(0, 10), ylim=(0, 10))
 
 plt.show()
 

diff --git a/examples/specialty_plots/anscombe.py b/examples/specialty_plots/anscombe.py
@@ -8,52 +8,37 @@
 Edward Tufte uses this example from Anscombe to show 4 datasets of x
 and y that have the same mean, standard deviation, and regression
 line, but which are qualitatively different.
-
-matplotlib fun for a rainy day
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
 
-x = np.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
-y1 = np.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
-y2 = np.array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])
-y3 = np.array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])
-x4 = np.array([8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8])
-y4 = np.array([6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89])
+x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]
+y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]
+y2 = [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]
+y3 = [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]
+x4 = [8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8]
+y4 = [6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89]
 
 
 def fit(x):
     return 3 + 0.5 * x
 
 
-xfit = np.array([np.min(x), np.max(x)])
-
-plt.subplot(221)
-plt.plot(x, y1, 'ks', xfit, fit(xfit), 'r-', lw=2)
-plt.axis([2, 20, 2, 14])
-plt.setp(plt.gca(), xticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))
-plt.text(3, 12, 'I', fontsize=20)
-
-plt.subplot(222)
-plt.plot(x, y2, 'ks', xfit, fit(xfit), 'r-', lw=2)
-plt.axis([2, 20, 2, 14])
-plt.setp(plt.gca(), xticks=(0, 10, 20), xticklabels=[],
-         yticks=(4, 8, 12), yticklabels=[], )
-plt.text(3, 12, 'II', fontsize=20)
+fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
+axs[0, 0].set(xlim=(0, 20), ylim=(2, 14))
+axs[0, 0].set(xticks=(0, 10, 20), yticks=(4, 8, 12))
 
-plt.subplot(223)
-plt.plot(x, y3, 'ks', xfit, fit(xfit), 'r-', lw=2)
-plt.axis([2, 20, 2, 14])
-plt.text(3, 12, 'III', fontsize=20)
-plt.setp(plt.gca(), yticks=(4, 8, 12), xticks=(0, 10, 20))
-
-plt.subplot(224)
+xfit = np.array([np.min(x), np.max(x)])
+axs[0, 0].plot(x, y1, 'ks', xfit, fit(xfit), 'r-', lw=2)
+axs[0, 1].plot(x, y2, 'ks', xfit, fit(xfit), 'r-', lw=2)
+axs[1, 0].plot(x, y3, 'ks', xfit, fit(xfit), 'r-', lw=2)
 xfit = np.array([np.min(x4), np.max(x4)])
-plt.plot(x4, y4, 'ks', xfit, fit(xfit), 'r-', lw=2)
-plt.axis([2, 20, 2, 14])
-plt.setp(plt.gca(), yticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))
-plt.text(3, 12, 'IV', fontsize=20)
+axs[1, 1].plot(x4, y4, 'ks', xfit, fit(xfit), 'r-', lw=2)
+
+for ax, label in zip(axs.flat, ['I', 'II', 'III', 'IV']):
+    ax.label_outer()
+    ax.text(3, 12, label, fontsize=20)
 
 # verify the stats
 pairs = (x, y1), (x, y2), (x, y3), (x4, y4)

diff --git a/examples/statistics/hexbin_demo.py b/examples/statistics/hexbin_demo.py
@@ -29,14 +29,14 @@
 fig.subplots_adjust(hspace=0.5, left=0.07, right=0.93)
 ax = axs[0]
 hb = ax.hexbin(x, y, gridsize=50, cmap='inferno')
-ax.axis([xmin, xmax, ymin, ymax])
+ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax))
 ax.set_title("Hexagon binning")
 cb = fig.colorbar(hb, ax=ax)
 cb.set_label('counts')
 
 ax = axs[1]
 hb = ax.hexbin(x, y, gridsize=50, bins='log', cmap='inferno')
-ax.axis([xmin, xmax, ymin, ymax])
+ax.set(xlim=(xmin, xmax), ylim=(ymin, ymax))
 ax.set_title("With a log color scale")
 cb = fig.colorbar(hb, ax=ax)
 cb.set_label('log10(N)')

diff --git a/examples/subplots_axes_and_figures/axes_demo.py b/examples/subplots_axes_and_figures/axes_demo.py
@@ -21,7 +21,8 @@
 
 # the main axes is subplot(111) by default
 plt.plot(t, s)
-plt.axis([0, 1, 1.1 * np.min(s), 2 * np.max(s)])
+plt.xlim(0, 1)
+plt.ylim(1.1 * np.min(s), 2 * np.max(s))
 plt.xlabel('time (s)')
 plt.ylabel('current (nA)')
 plt.title('Gaussian colored noise')

diff --git a/examples/subplots_axes_and_figures/axhspan_demo.py b/examples/subplots_axes_and_figures/axhspan_demo.py
@@ -32,6 +32,4 @@
 
 plt.axvspan(1.25, 1.55, facecolor='#2ca02c', alpha=0.5)
 
-plt.axis([-1, 2, -1, 2])
-
 plt.show()
diff --git a/examples/subplots_axes_and_figures/axis_equal_demo.py b/examples/subplots_axes_and_figures/axis_equal_demo.py
@@ -23,7 +23,7 @@
 
 axs[1, 0].plot(3 * np.cos(an), 3 * np.sin(an))
 axs[1, 0].axis('equal')
-axs[1, 0].axis([-3, 3, -3, 3])
+axs[1, 0].set(xlim=(-3, 3), ylim=(-3, 3))
 axs[1, 0].set_title('still a circle, even after changing limits', fontsize=10)
 
 axs[1, 1].plot(3 * np.cos(an), 3 * np.sin(an))

diff --git a/examples/text_labels_and_annotations/demo_text_rotation_mode.py b/examples/text_labels_and_annotations/demo_text_rotation_mode.py
@@ -49,7 +49,6 @@ def test_rotation_mode(fig, mode, subplot_location):
         # prepare axes layout
         for axis in ax.axis.values():
             axis.toggle(ticks=False, ticklabels=False)
-        ax.axis([0, 1, 0, 1])
         ax.axvline(0.5, color="skyblue", zorder=0)
         ax.axhline(0.5, color="skyblue", zorder=0)
         ax.plot(0.5, 0.5, color="C0", marker="o", zorder=1)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -32,6 +32,4 @@

		plt.axvspan(1.25, 1.55, facecolor='#2ca02c', alpha=0.5)

		plt.axis([-1, 2, -1, 2])

		plt.show()