From ec5e8863a6352da673b55cb971529f38278cf64e Mon Sep 17 00:00:00 2001
From: Paul Hobson <pmhobson@gmail.com>
Date: Thu, 22 Mar 2018 12:10:01 -0700
Subject: [PATCH] DOC: use OO-ish interface in img/contour examples

---
 .../images_contours_and_fields/barb_demo.py   | 24 +++----
 .../barcode_demo.py                           |  8 +--
 .../contour_corner_mask.py                    | 14 ++--
 .../contour_demo.py                           | 68 +++++++++----------
 .../contour_image.py                          | 58 ++++++----------
 .../contour_label_demo.py                     | 32 +++++----
 .../contourf_demo.py                          | 33 ++++-----
 .../contourf_hatching.py                      | 23 +++----
 .../demo_bboximage.py                         | 12 ++--
 .../figimage_demo.py                          |  2 +-
 .../images_contours_and_fields/image_demo.py  |  9 +--
 .../image_masked.py                           |  2 +-
 .../interpolation_methods.py                  |  8 +--
 .../quadmesh_demo.py                          |  3 +-
 .../images_contours_and_fields/quiver_demo.py | 30 ++++----
 .../shading_example.py                        | 20 +++---
 .../specgram_demo.py                          | 20 +++---
 .../tricontour_demo.py                        | 26 +++----
 .../tricontour_smooth_delaunay.py             | 16 ++---
 .../tricontour_smooth_user.py                 | 12 ++--
 .../triinterp_demo.py                         | 39 ++++++-----
 .../tripcolor_demo.py                         | 34 +++++-----
 .../triplot_demo.py                           | 20 +++---
 lib/matplotlib/axes/_base.py                  |  2 +-
 24 files changed, 242 insertions(+), 273 deletions(-)

diff --git a/examples/images_contours_and_fields/barb_demo.py b/examples/images_contours_and_fields/barb_demo.py
index 12ad4d727ab2..36aa86410696 100644
--- a/examples/images_contours_and_fields/barb_demo.py
+++ b/examples/images_contours_and_fields/barb_demo.py
@@ -22,26 +22,23 @@
 data = np.array(data, dtype=[('x', np.float32), ('y', np.float32),
                              ('u', np.float32), ('v', np.float32)])
 
+fig1, axs1 = plt.subplots(nrows=2, ncols=2)
 # Default parameters, uniform grid
-ax = plt.subplot(2, 2, 1)
-ax.barbs(X, Y, U, V)
+axs1[0, 0].barbs(X, Y, U, V)
 
 # Arbitrary set of vectors, make them longer and change the pivot point
 # (point around which they're rotated) to be the middle
-ax = plt.subplot(2, 2, 2)
-ax.barbs(data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
+axs1[0, 1].barbs(data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
 
 # Showing colormapping with uniform grid.  Fill the circle for an empty barb,
 # don't round the values, and change some of the size parameters
-ax = plt.subplot(2, 2, 3)
-ax.barbs(X, Y, U, V, np.sqrt(U * U + V * V), fill_empty=True, rounding=False,
-         sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
+axs1[1, 0].barbs(X, Y, U, V, np.sqrt(U * U + V * V), fill_empty=True, rounding=False,
+                 sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
 
 # Change colors as well as the increments for parts of the barbs
-ax = plt.subplot(2, 2, 4)
-ax.barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
-         barbcolor=['b', 'g'],
-         barb_increments=dict(half=10, full=20, flag=100), flip_barb=True)
+axs1[1, 1].barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
+                 barbcolor=['b', 'g'], flip_barb=True,
+                 barb_increments=dict(half=10, full=20, flag=100))
 
 # Masked arrays are also supported
 masked_u = np.ma.masked_array(data['u'])
@@ -50,8 +47,7 @@
 
 # Identical plot to panel 2 in the first figure, but with the point at
 # (0.5, 0.25) missing (masked)
-fig2 = plt.figure()
-ax = fig2.add_subplot(1, 1, 1)
-ax.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
+fig2, ax2 = plt.subplots()
+ax2.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
 
 plt.show()
diff --git a/examples/images_contours_and_fields/barcode_demo.py b/examples/images_contours_and_fields/barcode_demo.py
index afe3700b2bb5..75227d01f42e 100644
--- a/examples/images_contours_and_fields/barcode_demo.py
+++ b/examples/images_contours_and_fields/barcode_demo.py
@@ -20,12 +20,12 @@
 fig = plt.figure()
 
 # a vertical barcode -- this is broken at present
-ax = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
-ax.imshow(x.reshape((-1, 1)), **barprops)
+ax1 = fig.add_axes([0.1, 0.3, 0.1, 0.6], **axprops)
+ax1.imshow(x.reshape((-1, 1)), **barprops)
 
 # a horizontal barcode
-ax = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
-ax.imshow(x.reshape((1, -1)), **barprops)
+ax2 = fig.add_axes([0.3, 0.1, 0.6, 0.1], **axprops)
+ax2.imshow(x.reshape((1, -1)), **barprops)
 
 
 plt.show()
diff --git a/examples/images_contours_and_fields/contour_corner_mask.py b/examples/images_contours_and_fields/contour_corner_mask.py
index 1a2466bf899b..03d821959299 100644
--- a/examples/images_contours_and_fields/contour_corner_mask.py
+++ b/examples/images_contours_and_fields/contour_corner_mask.py
@@ -23,16 +23,16 @@
 z = np.ma.array(z, mask=mask)
 
 corner_masks = [False, True]
-for i, corner_mask in enumerate(corner_masks):
-    plt.subplot(1, 2, i + 1)
-    cs = plt.contourf(x, y, z, corner_mask=corner_mask)
-    plt.contour(cs, colors='k')
-    plt.title('corner_mask = {0}'.format(corner_mask))
+fig, axs = plt.subplots(ncols=2)
+for ax, corner_mask in zip(axs, corner_masks):
+    cs = ax.contourf(x, y, z, corner_mask=corner_mask)
+    ax.contour(cs, colors='k')
+    ax.set_title('corner_mask = {0}'.format(corner_mask))
 
     # Plot grid.
-    plt.grid(c='k', ls='-', alpha=0.3)
+    ax.grid(c='k', ls='-', alpha=0.3)
 
     # Indicate masked points with red circles.
-    plt.plot(np.ma.array(x, mask=~mask), y, 'ro')
+    ax.plot(np.ma.array(x, mask=~mask), y, 'ro')
 
 plt.show()
diff --git a/examples/images_contours_and_fields/contour_demo.py b/examples/images_contours_and_fields/contour_demo.py
index 4689d3507f64..a3366c88979a 100644
--- a/examples/images_contours_and_fields/contour_demo.py
+++ b/examples/images_contours_and_fields/contour_demo.py
@@ -30,10 +30,10 @@
 # over the line segments of the contour, removing the lines beneath
 # the label
 
-plt.figure()
-CS = plt.contour(X, Y, Z)
-plt.clabel(CS, inline=1, fontsize=10)
-plt.title('Simplest default with labels')
+fig, ax = plt.subplots()
+CS = ax.contour(X, Y, Z)
+ax.clabel(CS, inline=1, fontsize=10)
+ax.set_title('Simplest default with labels')
 
 
 ###############################################################################
@@ -41,85 +41,79 @@
 # (in data coordinate). See ginput_manual_clabel.py for interactive
 # placement.
 
-plt.figure()
-CS = plt.contour(X, Y, Z)
+fig, ax = plt.subplots()
+CS = ax.contour(X, Y, Z)
 manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]
-plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)
-plt.title('labels at selected locations')
+ax.clabel(CS, inline=1, fontsize=10, manual=manual_locations)
+ax.set_title('labels at selected locations')
 
 
 ###############################################################################
 # You can force all the contours to be the same color.
 
-plt.figure()
-CS = plt.contour(X, Y, Z, 6,
+fig, ax = plt.subplots()
+CS = ax.contour(X, Y, Z, 6,
                  colors='k',  # negative contours will be dashed by default
                  )
-plt.clabel(CS, fontsize=9, inline=1)
-plt.title('Single color - negative contours dashed')
+ax.clabel(CS, fontsize=9, inline=1)
+ax.set_title('Single color - negative contours dashed')
 
 ###############################################################################
 # You can set negative contours to be solid instead of dashed:
 
 matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
-plt.figure()
-CS = plt.contour(X, Y, Z, 6,
+fig, ax = plt.subplots()
+CS = ax.contour(X, Y, Z, 6,
                  colors='k',  # negative contours will be dashed by default
                  )
-plt.clabel(CS, fontsize=9, inline=1)
-plt.title('Single color - negative contours solid')
+ax.clabel(CS, fontsize=9, inline=1)
+ax.set_title('Single color - negative contours solid')
 
 
 ###############################################################################
 # And you can manually specify the colors of the contour
 
-plt.figure()
-CS = plt.contour(X, Y, Z, 6,
+fig, ax = plt.subplots()
+CS = ax.contour(X, Y, Z, 6,
                  linewidths=np.arange(.5, 4, .5),
                  colors=('r', 'green', 'blue', (1, 1, 0), '#afeeee', '0.5')
                  )
-plt.clabel(CS, fontsize=9, inline=1)
-plt.title('Crazy lines')
+ax.clabel(CS, fontsize=9, inline=1)
+ax.set_title('Crazy lines')
 
 
 ###############################################################################
 # Or you can use a colormap to specify the colors; the default
 # colormap will be used for the contour lines
 
-plt.figure()
-im = plt.imshow(Z, interpolation='bilinear', origin='lower',
+fig, ax = plt.subplots()
+im = ax.imshow(Z, interpolation='bilinear', origin='lower',
                 cmap=cm.gray, extent=(-3, 3, -2, 2))
 levels = np.arange(-1.2, 1.6, 0.2)
-CS = plt.contour(Z, levels,
-                 origin='lower',
-                 linewidths=2,
-                 extent=(-3, 3, -2, 2))
+CS = ax.contour(Z, levels, origin='lower', cmap='flag',
+                linewidths=2, extent=(-3, 3, -2, 2))
 
 # Thicken the zero contour.
 zc = CS.collections[6]
 plt.setp(zc, linewidth=4)
 
-plt.clabel(CS, levels[1::2],  # label every second level
-           inline=1,
-           fmt='%1.1f',
-           fontsize=14)
+ax.clabel(CS, levels[1::2],  # label every second level
+          inline=1, fmt='%1.1f',
+          cmap='flag', fontsize=14)
 
 # make a colorbar for the contour lines
-CB = plt.colorbar(CS, shrink=0.8, extend='both')
+CB = fig.colorbar(CS, shrink=0.8, extend='both')
 
-plt.title('Lines with colorbar')
-#plt.hot()  # Now change the colormap for the contour lines and colorbar
-plt.flag()
+ax.set_title('Lines with colorbar')
 
 # We can still add a colorbar for the image, too.
-CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)
+CBI = fig.colorbar(im, orientation='horizontal', shrink=0.8)
 
 # This makes the original colorbar look a bit out of place,
 # so let's improve its position.
 
-l, b, w, h = plt.gca().get_position().bounds
+l, b, w, h = ax.get_position().bounds
 ll, bb, ww, hh = CB.ax.get_position().bounds
 CB.ax.set_position([ll, b + 0.1*h, ww, h*0.8])
 
-
 plt.show()
diff --git a/examples/images_contours_and_fields/contour_image.py b/examples/images_contours_and_fields/contour_image.py
index 3f0f007dcca4..94000053e159 100644
--- a/examples/images_contours_and_fields/contour_image.py
+++ b/examples/images_contours_and_fields/contour_image.py
@@ -34,14 +34,12 @@
 norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
 cmap = cm.PRGn
 
-fig = plt.figure()
+fig, _axs = plt.subplots(nrows=2, ncols=2)
 fig.subplots_adjust(hspace=0.3)
+axs = _axs.flatten()
 
-
-plt.subplot(2, 2, 1)
-
-cset1 = plt.contourf(X, Y, Z, levels,
-                     cmap=cm.get_cmap(cmap, len(levels) - 1), norm=norm)
+cset1 = axs[0].contourf(X, Y, Z, levels, norm=norm,
+                     cmap=cm.get_cmap(cmap, len(levels) - 1))
 # It is not necessary, but for the colormap, we need only the
 # number of levels minus 1.  To avoid discretization error, use
 # either this number or a large number such as the default (256).
@@ -51,7 +49,7 @@
 # of the polygons in the collections returned by contourf.
 # Use levels output from previous call to guarantee they are the same.
 
-cset2 = plt.contour(X, Y, Z, cset1.levels, colors='k')
+cset2 = axs[0].contour(X, Y, Z, cset1.levels, colors='k')
 
 # We don't really need dashed contour lines to indicate negative
 # regions, so let's turn them off.
@@ -64,28 +62,18 @@
 # We are making a thick green line as a zero contour.
 # Specify the zero level as a tuple with only 0 in it.
 
-cset3 = plt.contour(X, Y, Z, (0,), colors='g', linewidths=2)
-plt.title('Filled contours')
-plt.colorbar(cset1)
-
+cset3 = axs[0].contour(X, Y, Z, (0,), colors='g', linewidths=2)
+axs[0].set_title('Filled contours')
+fig.colorbar(cset1, ax=axs[0])
 
-plt.subplot(2, 2, 2)
 
-plt.imshow(Z, extent=extent, cmap=cmap, norm=norm)
-v = plt.axis()
-plt.contour(Z, levels, colors='k', origin='upper', extent=extent)
-plt.axis(v)
-plt.title("Image, origin 'upper'")
+axs[1].imshow(Z, extent=extent, cmap=cmap, norm=norm)
+axs[1].contour(Z, levels, colors='k', origin='upper', extent=extent)
+axs[1].set_title("Image, origin 'upper'")
 
-plt.subplot(2, 2, 3)
-
-plt.imshow(Z, origin='lower', extent=extent, cmap=cmap, norm=norm)
-v = plt.axis()
-plt.contour(Z, levels, colors='k', origin='lower', extent=extent)
-plt.axis(v)
-plt.title("Image, origin 'lower'")
-
-plt.subplot(2, 2, 4)
+axs[2].imshow(Z, origin='lower', extent=extent, cmap=cmap, norm=norm)
+axs[2].contour(Z, levels, colors='k', origin='lower', extent=extent)
+axs[2].set_title("Image, origin 'lower'")
 
 # We will use the interpolation "nearest" here to show the actual
 # image pixels.
@@ -93,15 +81,13 @@
 # This is intentional. The Z values are defined at the center of each
 # image pixel (each color block on the following subplot), so the
 # domain that is contoured does not extend beyond these pixel centers.
-im = plt.imshow(Z, interpolation='nearest', extent=extent,
+im = axs[3].imshow(Z, interpolation='nearest', extent=extent,
                 cmap=cmap, norm=norm)
-v = plt.axis()
-plt.contour(Z, levels, colors='k', origin='image', extent=extent)
-plt.axis(v)
-ylim = plt.get(plt.gca(), 'ylim')
-plt.setp(plt.gca(), ylim=ylim[::-1])
-plt.title("Origin from rc, reversed y-axis")
-plt.colorbar(im)
-
-plt.tight_layout()
+axs[3].contour(Z, levels, colors='k', origin='image', extent=extent)
+ylim = axs[3].get_ylim()
+axs[3].set_ylim(ylim[::-1])
+axs[3].set_title("Origin from rc, reversed y-axis")
+fig.colorbar(im, ax=axs[3])
+
+fig.tight_layout()
 plt.show()
diff --git a/examples/images_contours_and_fields/contour_label_demo.py b/examples/images_contours_and_fields/contour_label_demo.py
index 7140b9b13a44..ba5e2301b20f 100644
--- a/examples/images_contours_and_fields/contour_label_demo.py
+++ b/examples/images_contours_and_fields/contour_label_demo.py
@@ -8,6 +8,7 @@
 
 See also contour_demo.py.
 """
+
 import matplotlib
 import numpy as np
 import matplotlib.cm as cm
@@ -32,14 +33,10 @@
 # Make contour labels using creative float classes
 # Follows suggestion of Manuel Metz
 
-plt.figure()
-
-# Basic contour plot
-CS = plt.contour(X, Y, Z)
-
-
 # Define a class that forces representation of float to look a certain way
 # This remove trailing zero so '1.0' becomes '1'
+
+
 class nf(float):
     def __repr__(self):
         str = '%.1f' % (self.__float__(),)
@@ -49,6 +46,10 @@ def __repr__(self):
             return '%.1f' % self.__float__()
 
 
+# Basic contour plot
+fig, ax = plt.subplots()
+CS = ax.contour(X, Y, Z)
+
 # Recast levels to new class
 CS.levels = [nf(val) for val in CS.levels]
 
@@ -57,33 +58,34 @@ def __repr__(self):
     fmt = r'%r \%%'
 else:
     fmt = '%r %%'
-plt.clabel(CS, CS.levels, inline=True, fmt=fmt, fontsize=10)
+
+ax.clabel(CS, CS.levels, inline=True, fmt=fmt, fontsize=10)
 
 ###############################################################################
 # Label contours with arbitrary strings using a dictionary
 
-plt.figure()
+fig1, ax1 = plt.subplots()
 
 # Basic contour plot
-CS = plt.contour(X, Y, Z)
+CS1 = ax1.contour(X, Y, Z)
 
 fmt = {}
 strs = ['first', 'second', 'third', 'fourth', 'fifth', 'sixth', 'seventh']
-for l, s in zip(CS.levels, strs):
+for l, s in zip(CS1.levels, strs):
     fmt[l] = s
 
 # Label every other level using strings
-plt.clabel(CS, CS.levels[::2], inline=True, fmt=fmt, fontsize=10)
+ax1.clabel(CS1, CS1.levels[::2], inline=True, fmt=fmt, fontsize=10)
 
 ###############################################################################
 # Use a Formatter
 
-plt.figure()
+fig2, ax2 = plt.subplots()
 
-CS = plt.contour(X, Y, 100**Z, locator=plt.LogLocator())
+CS2 = ax2.contour(X, Y, 100**Z, locator=plt.LogLocator())
 fmt = ticker.LogFormatterMathtext()
 fmt.create_dummy_axis()
-plt.clabel(CS, CS.levels, fmt=fmt)
-plt.title("$100^Z$")
+ax2.clabel(CS2, CS2.levels, fmt=fmt)
+ax2.set_title("$100^Z$")
 
 plt.show()
diff --git a/examples/images_contours_and_fields/contourf_demo.py b/examples/images_contours_and_fields/contourf_demo.py
index 41523cfdf7c2..d4becd6d8ef1 100644
--- a/examples/images_contours_and_fields/contourf_demo.py
+++ b/examples/images_contours_and_fields/contourf_demo.py
@@ -37,32 +37,33 @@
 # this is usually not such a good idea, because they don't
 # occur on nice boundaries, but we do it here for purposes
 # of illustration.
-CS = plt.contourf(X, Y, Z, 10, cmap=plt.cm.bone, origin=origin)
+
+fig1, ax2 = plt.subplots()
+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 levels used for the filled contours.  Alternatively,
 # We could pass in additional levels to provide extra resolution,
 # or leave out the levels kwarg to use all of the original levels.
 
-CS2 = plt.contour(CS, levels=CS.levels[::2], colors='r', origin=origin)
+CS2 = ax2.contour(CS, levels=CS.levels[::2], colors='r', origin=origin)
 
-plt.title('Nonsense (3 masked regions)')
-plt.xlabel('word length anomaly')
-plt.ylabel('sentence length anomaly')
+ax2.set_title('Nonsense (3 masked regions)')
+ax2.set_xlabel('word length anomaly')
+ax2.set_ylabel('sentence length anomaly')
 
 # Make a colorbar for the ContourSet returned by the contourf call.
-cbar = plt.colorbar(CS)
+cbar = fig1.colorbar(CS)
 cbar.ax.set_ylabel('verbosity coefficient')
 # Add the contour line levels to the colorbar
 cbar.add_lines(CS2)
 
-plt.figure()
-
+fig2, ax2 = plt.subplots()
 # Now make a contour plot with the levels specified,
 # and with the colormap generated automatically from a list
 # of colors.
 levels = [-1.5, -1, -0.5, 0, 0.5, 1]
-CS3 = plt.contourf(X, Y, Z, levels,
+CS3 = ax2.contourf(X, Y, Z, levels,
                    colors=('r', 'g', 'b'),
                    origin=origin,
                    extend='both')
@@ -72,16 +73,16 @@
 CS3.cmap.set_under('yellow')
 CS3.cmap.set_over('cyan')
 
-CS4 = plt.contour(X, Y, Z, levels,
+CS4 = ax2.contour(X, Y, Z, levels,
                   colors=('k',),
                   linewidths=(3,),
                   origin=origin)
-plt.title('Listed colors (3 masked regions)')
-plt.clabel(CS4, fmt='%2.1f', colors='w', fontsize=14)
+ax2.set_title('Listed colors (3 masked regions)')
+ax2.clabel(CS4, fmt='%2.1f', colors='w', fontsize=14)
 
 # Notice that the colorbar command gets all the information it
 # needs from the ContourSet object, CS3.
-plt.colorbar(CS3)
+fig2.colorbar(CS3)
 
 # Illustrate all 4 possible "extend" settings:
 extends = ["neither", "both", "min", "max"]
@@ -94,12 +95,12 @@
 # no effect:
 # cmap.set_bad("red")
 
-fig, axs = plt.subplots(2, 2)
-fig.subplots_adjust(hspace=0.3)
+fig3, axs = plt.subplots(2, 2)
+fig3.subplots_adjust(hspace=0.3)
 
 for ax, extend in zip(axs.ravel(), extends):
     cs = ax.contourf(X, Y, Z, levels, cmap=cmap, extend=extend, origin=origin)
-    fig.colorbar(cs, ax=ax, shrink=0.9)
+    fig3.colorbar(cs, ax=ax, shrink=0.9)
     ax.set_title("extend = %s" % extend)
     ax.locator_params(nbins=4)
 
diff --git a/examples/images_contours_and_fields/contourf_hatching.py b/examples/images_contours_and_fields/contourf_hatching.py
index f13c477a9613..698f69b4ce24 100644
--- a/examples/images_contours_and_fields/contourf_hatching.py
+++ b/examples/images_contours_and_fields/contourf_hatching.py
@@ -20,27 +20,22 @@
 ###############################################################################
 # Plot 1: the simplest hatched plot with a colorbar
 
-fig = plt.figure()
-cs = plt.contourf(x, y, z, hatches=['-', '/', '\\', '//'],
-                  cmap=plt.get_cmap('gray'),
-                  extend='both', alpha=0.5
-                  )
-plt.colorbar()
+fig1, ax1 = plt.subplots()
+cs = ax1.contourf(x, y, z, hatches=['-', '/', '\\', '//'],
+                  cmap='gray', extend='both', alpha=0.5)
+fig1.colorbar(cs)
 
 ###############################################################################
 # Plot 2: a plot of hatches without color with a legend
 
-plt.figure()
+fig2, ax2 = plt.subplots()
 n_levels = 6
-plt.contour(x, y, z, n_levels, colors='black', linestyles='-')
-cs = plt.contourf(x, y, z, n_levels, colors='none',
+ax2.contour(x, y, z, n_levels, colors='black', linestyles='-')
+cs = ax2.contourf(x, y, z, n_levels, colors='none',
                   hatches=['.', '/', '\\', None, '\\\\', '*'],
-                  extend='lower'
-                  )
+                  extend='lower')
 
 # create a legend for the contour set
 artists, labels = cs.legend_elements()
-plt.legend(artists, labels, handleheight=2)
-
-
+ax2.legend(artists, labels, handleheight=2)
 plt.show()
diff --git a/examples/images_contours_and_fields/demo_bboximage.py b/examples/images_contours_and_fields/demo_bboximage.py
index d714a6a06b43..2e774337163d 100644
--- a/examples/images_contours_and_fields/demo_bboximage.py
+++ b/examples/images_contours_and_fields/demo_bboximage.py
@@ -11,10 +11,9 @@
 
 if __name__ == "__main__":
 
-    fig = plt.figure(1)
-    ax = plt.subplot(121)
+    fig, (ax1, ax2) = plt.subplots(ncols=2)
 
-    txt = ax.text(0.5, 0.5, "test", size=30, ha="center", color="w")
+    txt = ax1.text(0.5, 0.5, "test", size=30, ha="center", color="w")
     kwargs = dict()
 
     bbox_image = BboxImage(txt.get_window_extent,
@@ -25,9 +24,8 @@
                            )
     a = np.arange(256).reshape(1, 256)/256.
     bbox_image.set_data(a)
-    ax.add_artist(bbox_image)
+    ax1.add_artist(bbox_image)
 
-    ax = plt.subplot(122)
     a = np.linspace(0, 1, 256).reshape(1, -1)
     a = np.vstack((a, a))
 
@@ -51,7 +49,7 @@
         bbox0 = Bbox.from_bounds(ix*dx*(1 + xpad_fraction),
                                  1. - iy*dy*(1 + ypad_fraction) - dy,
                                  dx, dy)
-        bbox = TransformedBbox(bbox0, ax.transAxes)
+        bbox = TransformedBbox(bbox0, ax2.transAxes)
 
         bbox_image = BboxImage(bbox,
                                cmap=plt.get_cmap(m),
@@ -61,7 +59,7 @@
                                )
 
         bbox_image.set_data(a)
-        ax.add_artist(bbox_image)
+        ax2.add_artist(bbox_image)
 
     plt.draw()
     plt.show()
diff --git a/examples/images_contours_and_fields/figimage_demo.py b/examples/images_contours_and_fields/figimage_demo.py
index 126c3de56e34..1c4ef70a2939 100644
--- a/examples/images_contours_and_fields/figimage_demo.py
+++ b/examples/images_contours_and_fields/figimage_demo.py
@@ -3,7 +3,7 @@
 Figimage Demo
 =============
 
-This illustrates placing images directly in the figure, with no axes.
+This illustrates placing images directly in the figure, with no Axes objects.
 
 """
 import numpy as np
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
index 820114cc2be0..a527446d5958 100644
--- a/examples/images_contours_and_fields/image_demo.py
+++ b/examples/images_contours_and_fields/image_demo.py
@@ -28,9 +28,10 @@
 Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
 Z = (Z1 - Z2) * 2
 
-im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
-                origin='lower', extent=[-3, 3, -3, 3],
-                vmax=abs(Z).max(), vmin=-abs(Z).max())
+fig, ax = plt.subplots()
+im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
+               origin='lower', extent=[-3, 3, -3, 3],
+               vmax=abs(Z).max(), vmin=-abs(Z).max())
 
 plt.show()
 
@@ -44,7 +45,7 @@
 
 fig, ax = plt.subplots()
 ax.imshow(image)
-ax.axis('off')  # clear x- and y-axes
+ax.axis('off')  # clear x-axis and y-axis
 
 
 # And another image
diff --git a/examples/images_contours_and_fields/image_masked.py b/examples/images_contours_and_fields/image_masked.py
index 029201ef1a1b..cadba0fe9e49 100644
--- a/examples/images_contours_and_fields/image_masked.py
+++ b/examples/images_contours_and_fields/image_masked.py
@@ -42,7 +42,7 @@
 # range to which the regular palette color scale is applied.
 # Anything above that range is colored based on palette.set_over, etc.
 
-# set up the axes
+# set up the Axes objets
 fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))
 
 # plot using 'continuous' color map
diff --git a/examples/images_contours_and_fields/interpolation_methods.py b/examples/images_contours_and_fields/interpolation_methods.py
index 0e613572089b..2108b79dbdba 100644
--- a/examples/images_contours_and_fields/interpolation_methods.py
+++ b/examples/images_contours_and_fields/interpolation_methods.py
@@ -27,13 +27,13 @@
 
 grid = np.random.rand(4, 4)
 
-fig, axes = plt.subplots(3, 6, figsize=(12, 6),
-                         subplot_kw={'xticks': [], 'yticks': []})
+fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(12, 6),
+                        subplot_kw={'xticks': [], 'yticks': []})
 
 fig.subplots_adjust(hspace=0.3, wspace=0.05)
 
-for ax, interp_method in zip(axes.flat, methods):
+for ax, interp_method in zip(axs.flat, methods):
     ax.imshow(grid, interpolation=interp_method, cmap='viridis')
-    ax.set_title(interp_method)
+    ax.set_title(str(interp_method))
 
 plt.show()
diff --git a/examples/images_contours_and_fields/quadmesh_demo.py b/examples/images_contours_and_fields/quadmesh_demo.py
index 33f2b78ca4c3..0c133e34200d 100644
--- a/examples/images_contours_and_fields/quadmesh_demo.py
+++ b/examples/images_contours_and_fields/quadmesh_demo.py
@@ -26,7 +26,7 @@
 # The color array can include masked values.
 Zm = np.ma.masked_where(np.abs(Qz) < 0.5 * np.max(Qz), Z)
 
-fig, axs = plt.subplots(1, 3)
+fig, axs = plt.subplots(nrows=1, ncols=3)
 axs[0].pcolormesh(Qx, Qz, Z, shading='gouraud')
 axs[0].set_title('Without masked values')
 
@@ -42,5 +42,4 @@
 axs[2].set_title('With masked values')
 
 fig.tight_layout()
-
 plt.show()
diff --git a/examples/images_contours_and_fields/quiver_demo.py b/examples/images_contours_and_fields/quiver_demo.py
index bb92e10b5bc1..636a046ee0d5 100644
--- a/examples/images_contours_and_fields/quiver_demo.py
+++ b/examples/images_contours_and_fields/quiver_demo.py
@@ -6,7 +6,7 @@
 Known problem: the plot autoscaling does not take into account
 the arrows, so those on the boundaries are often out of the picture.
 This is *not* an easy problem to solve in a perfectly general way.
-The workaround is to manually expand the axes.
+The workaround is to manually expand the Axes objects.
 """
 import matplotlib.pyplot as plt
 import numpy as np
@@ -18,31 +18,31 @@
 
 ###############################################################################
 
-plt.figure()
-plt.title('Arrows scale with plot width, not view')
-Q = plt.quiver(X, Y, U, V, units='width')
-qk = plt.quiverkey(Q, 0.9, 0.9, 2, r'$2 \frac{m}{s}$', labelpos='E',
+fig1, ax1 = plt.subplots()
+ax1.set_title('Arrows scale with plot width, not view')
+Q = ax1.quiver(X, Y, U, V, units='width')
+qk = ax1.quiverkey(Q, 0.9, 0.9, 2, r'$2 \frac{m}{s}$', labelpos='E',
                    coordinates='figure')
 
 ###############################################################################
 
-plt.figure()
-plt.title("pivot='mid'; every third arrow; units='inches'")
-Q = plt.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],
+fig2, ax2 = plt.subplots()
+ax2.set_title("pivot='mid'; every third arrow; units='inches'")
+Q = ax2.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],
                pivot='mid', units='inches')
-qk = plt.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E',
+qk = ax2.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E',
                    coordinates='figure')
-plt.scatter(X[::3, ::3], Y[::3, ::3], color='r', s=5)
+ax2.scatter(X[::3, ::3], Y[::3, ::3], color='r', s=5)
 
 ###############################################################################
 
-plt.figure()
-plt.title("pivot='tip'; scales with x view")
+fig3, ax3 = plt.subplots()
+ax3.set_title("pivot='tip'; scales with x view")
 M = np.hypot(U, V)
-Q = plt.quiver(X, Y, U, V, M, units='x', pivot='tip', width=0.022,
+Q = ax3.quiver(X, Y, U, V, M, units='x', pivot='tip', width=0.022,
                scale=1 / 0.15)
-qk = plt.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E',
+qk = ax3.quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E',
                    coordinates='figure')
-plt.scatter(X, Y, color='k', s=5)
+ax3.scatter(X, Y, color='k', s=5)
 
 plt.show()
diff --git a/examples/images_contours_and_fields/shading_example.py b/examples/images_contours_and_fields/shading_example.py
index 795d5e3a6776..ef8d171a300e 100644
--- a/examples/images_contours_and_fields/shading_example.py
+++ b/examples/images_contours_and_fields/shading_example.py
@@ -35,26 +35,26 @@ def main():
 
 def compare(z, cmap, ve=1):
     # Create subplots and hide ticks
-    fig, axes = plt.subplots(ncols=2, nrows=2)
-    for ax in axes.flat:
+    fig, axs = plt.subplots(ncols=2, nrows=2)
+    for ax in axs.flat:
         ax.set(xticks=[], yticks=[])
 
     # Illuminate the scene from the northwest
     ls = LightSource(azdeg=315, altdeg=45)
 
-    axes[0, 0].imshow(z, cmap=cmap)
-    axes[0, 0].set(xlabel='Colormapped Data')
+    axs[0, 0].imshow(z, cmap=cmap)
+    axs[0, 0].set(xlabel='Colormapped Data')
 
-    axes[0, 1].imshow(ls.hillshade(z, vert_exag=ve), cmap='gray')
-    axes[0, 1].set(xlabel='Illumination Intensity')
+    axs[0, 1].imshow(ls.hillshade(z, vert_exag=ve), cmap='gray')
+    axs[0, 1].set(xlabel='Illumination Intensity')
 
     rgb = ls.shade(z, cmap=cmap, vert_exag=ve, blend_mode='hsv')
-    axes[1, 0].imshow(rgb)
-    axes[1, 0].set(xlabel='Blend Mode: "hsv" (default)')
+    axs[1, 0].imshow(rgb)
+    axs[1, 0].set(xlabel='Blend Mode: "hsv" (default)')
 
     rgb = ls.shade(z, cmap=cmap, vert_exag=ve, blend_mode='overlay')
-    axes[1, 1].imshow(rgb)
-    axes[1, 1].set(xlabel='Blend Mode: "overlay"')
+    axs[1, 1].imshow(rgb)
+    axs[1, 1].set(xlabel='Blend Mode: "overlay"')
 
     return fig
 
diff --git a/examples/images_contours_and_fields/specgram_demo.py b/examples/images_contours_and_fields/specgram_demo.py
index 15d1479826f2..681ac2ba36e6 100644
--- a/examples/images_contours_and_fields/specgram_demo.py
+++ b/examples/images_contours_and_fields/specgram_demo.py
@@ -11,7 +11,6 @@
 # Fixing random state for reproducibility
 np.random.seed(19680801)
 
-
 dt = 0.0005
 t = np.arange(0.0, 20.0, dt)
 s1 = np.sin(2 * np.pi * 100 * t)
@@ -25,16 +24,15 @@
 nse = 0.01 * np.random.random(size=len(t))
 
 x = s1 + s2 + nse  # the signal
-NFFT = 1024       # the length of the windowing segments
+NFFT = 1024  # the length of the windowing segments
 Fs = int(1.0 / dt)  # the sampling frequency
 
-# Pxx is the segments x freqs array of instantaneous power, freqs is
-# the frequency vector, bins are the centers of the time bins in which
-# the power is computed, and im is the matplotlib.image.AxesImage
-# instance
-
-ax1 = plt.subplot(211)
-plt.plot(t, x)
-plt.subplot(212, sharex=ax1)
-Pxx, freqs, bins, im = plt.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)
+fig, (ax1, ax2) = plt.subplots(nrows=2)
+ax1.plot(t, x)
+Pxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)
+# The `specgram` method returns 4 objects. They are:
+# - Pxx: the periodogram
+# - freqs: the frequency vector
+# - bins: the centers of the time bins
+# - im: the matplotlib.image.AxesImage instance representing the data in the plot
 plt.show()
diff --git a/examples/images_contours_and_fields/tricontour_demo.py b/examples/images_contours_and_fields/tricontour_demo.py
index 47f8f74411ed..130ddfb02bcb 100644
--- a/examples/images_contours_and_fields/tricontour_demo.py
+++ b/examples/images_contours_and_fields/tricontour_demo.py
@@ -38,12 +38,12 @@
 ###############################################################################
 # pcolor plot.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.tricontourf(triang, z)
-plt.colorbar()
-plt.tricontour(triang, z, colors='k')
-plt.title('Contour plot of Delaunay triangulation')
+fig1, ax1 = plt.subplots()
+ax1.set_aspect('equal')
+tcf = ax1.tricontourf(triang, z)
+fig1.colorbar(tcf)
+ax1.tricontour(triang, z, colors='k')
+ax1.set_title('Contour plot of Delaunay triangulation')
 
 ###############################################################################
 # You can specify your own triangulation rather than perform a Delaunay
@@ -101,12 +101,12 @@
 # object if the same triangulation was to be used more than once to save
 # duplicated calculations.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.tricontourf(x, y, triangles, z)
-plt.colorbar()
-plt.title('Contour plot of user-specified triangulation')
-plt.xlabel('Longitude (degrees)')
-plt.ylabel('Latitude (degrees)')
+fig2, ax2 = plt.subplots()
+ax2.set_aspect('equal')
+tcf = ax2.tricontourf(x, y, triangles, z)
+fig2.colorbar(tcf)
+ax2.set_title('Contour plot of user-specified triangulation')
+ax2.set_xlabel('Longitude (degrees)')
+ax2.set_ylabel('Latitude (degrees)')
 
 plt.show()
diff --git a/examples/images_contours_and_fields/tricontour_smooth_delaunay.py b/examples/images_contours_and_fields/tricontour_smooth_delaunay.py
index 5aa3560b9548..0af199b2fc68 100644
--- a/examples/images_contours_and_fields/tricontour_smooth_delaunay.py
+++ b/examples/images_contours_and_fields/tricontour_smooth_delaunay.py
@@ -114,26 +114,26 @@ def experiment_res(x, y):
 levels = np.arange(0., 1., 0.025)
 cmap = cm.get_cmap(name='Blues', lut=None)
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.title("Filtering a Delaunay mesh\n" +
+fig, ax = plt.subplots()
+ax.set_aspect('equal')
+ax.set_title("Filtering a Delaunay mesh\n" +
           "(application to high-resolution tricontouring)")
 
 # 1) plot of the refined (computed) data contours:
-plt.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
+ax.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
                linewidths=[2.0, 0.5, 1.0, 0.5])
 # 2) plot of the expected (analytical) data contours (dashed):
 if plot_expected:
-    plt.tricontour(tri_refi, z_expected, levels=levels, cmap=cmap,
+    ax.tricontour(tri_refi, z_expected, levels=levels, cmap=cmap,
                    linestyles='--')
 # 3) plot of the fine mesh on which interpolation was done:
 if plot_refi_tri:
-    plt.triplot(tri_refi, color='0.97')
+    ax.triplot(tri_refi, color='0.97')
 # 4) plot of the initial 'coarse' mesh:
 if plot_tri:
-    plt.triplot(tri, color='0.7')
+    ax.triplot(tri, color='0.7')
 # 4) plot of the unvalidated triangles from naive Delaunay Triangulation:
 if plot_masked_tri:
-    plt.triplot(flat_tri, color='red')
+    ax.triplot(flat_tri, color='red')
 
 plt.show()
diff --git a/examples/images_contours_and_fields/tricontour_smooth_user.py b/examples/images_contours_and_fields/tricontour_smooth_user.py
index b51bcb0596cd..42b235d5543a 100644
--- a/examples/images_contours_and_fields/tricontour_smooth_user.py
+++ b/examples/images_contours_and_fields/tricontour_smooth_user.py
@@ -62,17 +62,17 @@ def function_z(x, y):
 #-----------------------------------------------------------------------------
 # Plot the triangulation and the high-res iso-contours
 #-----------------------------------------------------------------------------
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.triplot(triang, lw=0.5, color='white')
+fig, ax = plt.subplots()
+ax.set_aspect('equal')
+ax.triplot(triang, lw=0.5, color='white')
 
 levels = np.arange(0., 1., 0.025)
 cmap = cm.get_cmap(name='terrain', lut=None)
-plt.tricontourf(tri_refi, z_test_refi, levels=levels, cmap=cmap)
-plt.tricontour(tri_refi, z_test_refi, levels=levels,
+ax.tricontourf(tri_refi, z_test_refi, levels=levels, cmap=cmap)
+ax.tricontour(tri_refi, z_test_refi, levels=levels,
                colors=['0.25', '0.5', '0.5', '0.5', '0.5'],
                linewidths=[1.0, 0.5, 0.5, 0.5, 0.5])
 
-plt.title("High-resolution tricontouring")
+ax.set_title("High-resolution tricontouring")
 
 plt.show()
diff --git a/examples/images_contours_and_fields/triinterp_demo.py b/examples/images_contours_and_fields/triinterp_demo.py
index 9f8dacf0520f..650a4b621fe1 100644
--- a/examples/images_contours_and_fields/triinterp_demo.py
+++ b/examples/images_contours_and_fields/triinterp_demo.py
@@ -29,33 +29,32 @@
 interp_cubic_min_E = mtri.CubicTriInterpolator(triang, z, kind='min_E')
 zi_cubic_min_E = interp_cubic_min_E(xi, yi)
 
+# Set up the figure
+fig, axs = plt.subplots(nrows=2, ncols=2)
+axs = axs.flatten()
 
 # Plot the triangulation.
-plt.subplot(221)
-plt.tricontourf(triang, z)
-plt.triplot(triang, 'ko-')
-plt.title('Triangular grid')
+axs[0].tricontourf(triang, z)
+axs[0].triplot(triang, 'ko-')
+axs[0].set_title('Triangular grid')
 
 # Plot linear interpolation to quad grid.
-plt.subplot(222)
-plt.contourf(xi, yi, zi_lin)
-plt.plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
-plt.plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
-plt.title("Linear interpolation")
+axs[1].contourf(xi, yi, zi_lin)
+axs[1].plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
+axs[1].plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
+axs[1].set_title("Linear interpolation")
 
 # Plot cubic interpolation to quad grid, kind=geom
-plt.subplot(223)
-plt.contourf(xi, yi, zi_cubic_geom)
-plt.plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
-plt.plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
-plt.title("Cubic interpolation,\nkind='geom'")
+axs[2].contourf(xi, yi, zi_cubic_geom)
+axs[2].plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
+axs[2].plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
+axs[2].set_title("Cubic interpolation,\nkind='geom'")
 
 # Plot cubic interpolation to quad grid, kind=min_E
-plt.subplot(224)
-plt.contourf(xi, yi, zi_cubic_min_E)
-plt.plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
-plt.plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
-plt.title("Cubic interpolation,\nkind='min_E'")
+axs[3].contourf(xi, yi, zi_cubic_min_E)
+axs[3].plot(xi, yi, 'k-', lw=0.5, alpha=0.5)
+axs[3].plot(xi.T, yi.T, 'k-', lw=0.5, alpha=0.5)
+axs[3].set_title("Cubic interpolation,\nkind='min_E'")
 
-plt.tight_layout()
+fig.tight_layout()
 plt.show()
diff --git a/examples/images_contours_and_fields/tripcolor_demo.py b/examples/images_contours_and_fields/tripcolor_demo.py
index 8c885744895d..877d434ae1e1 100644
--- a/examples/images_contours_and_fields/tripcolor_demo.py
+++ b/examples/images_contours_and_fields/tripcolor_demo.py
@@ -38,20 +38,20 @@
 ###############################################################################
 # tripcolor plot.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.tripcolor(triang, z, shading='flat')
-plt.colorbar()
-plt.title('tripcolor of Delaunay triangulation, flat shading')
+fig1, ax1 = plt.subplots()
+ax1.set_aspect('equal')
+tpc = ax1.tripcolor(triang, z, shading='flat')
+fig1.colorbar(tpc)
+ax1.set_title('tripcolor of Delaunay triangulation, flat shading')
 
 ###############################################################################
 # Illustrate Gouraud shading.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.tripcolor(triang, z, shading='gouraud')
-plt.colorbar()
-plt.title('tripcolor of Delaunay triangulation, gouraud shading')
+fig2, ax2 = plt.subplots()
+ax2.set_aspect('equal')
+tpc = ax2.tripcolor(triang, z, shading='gouraud')
+fig2.colorbar(tpc)
+ax2.set_title('tripcolor of Delaunay triangulation, gouraud shading')
 
 
 ###############################################################################
@@ -115,12 +115,12 @@
 # Can specify one color value per face rather than one per point by using the
 # facecolors kwarg.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.tripcolor(x, y, triangles, facecolors=zfaces, edgecolors='k')
-plt.colorbar()
-plt.title('tripcolor of user-specified triangulation')
-plt.xlabel('Longitude (degrees)')
-plt.ylabel('Latitude (degrees)')
+fig3, ax3 = plt.subplots()
+ax3.set_aspect('equal')
+tpc = ax3.tripcolor(x, y, triangles, facecolors=zfaces, edgecolors='k')
+fig3.colorbar(tpc)
+ax3.set_title('tripcolor of user-specified triangulation')
+ax3.set_xlabel('Longitude (degrees)')
+ax3.set_ylabel('Latitude (degrees)')
 
 plt.show()
diff --git a/examples/images_contours_and_fields/triplot_demo.py b/examples/images_contours_and_fields/triplot_demo.py
index d3a65762d021..0efc7011b4a8 100644
--- a/examples/images_contours_and_fields/triplot_demo.py
+++ b/examples/images_contours_and_fields/triplot_demo.py
@@ -37,10 +37,10 @@
 ###############################################################################
 # Plot the triangulation.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.triplot(triang, 'bo-', lw=1)
-plt.title('triplot of Delaunay triangulation')
+fig1, ax1 = plt.subplots()
+ax1.set_aspect('equal')
+ax1.triplot(triang, 'bo-', lw=1)
+ax1.set_title('triplot of Delaunay triangulation')
 
 
 ###############################################################################
@@ -96,11 +96,11 @@
 # if the same triangulation was to be used more than once to save duplicated
 # calculations.
 
-plt.figure()
-plt.gca().set_aspect('equal')
-plt.triplot(x, y, triangles, 'go-', lw=1.0)
-plt.title('triplot of user-specified triangulation')
-plt.xlabel('Longitude (degrees)')
-plt.ylabel('Latitude (degrees)')
+fig2, ax2 = plt.subplots()
+ax2.set_aspect('equal')
+ax2.triplot(x, y, triangles, 'go-', lw=1.0)
+ax2.set_title('triplot of user-specified triangulation')
+ax2.set_xlabel('Longitude (degrees)')
+ax2.set_ylabel('Latitude (degrees)')
 
 plt.show()
diff --git a/lib/matplotlib/axes/_base.py b/lib/matplotlib/axes/_base.py
index 9979bc543036..7a27b77f7b15 100644
--- a/lib/matplotlib/axes/_base.py
+++ b/lib/matplotlib/axes/_base.py
@@ -53,7 +53,7 @@ def _process_plot_format(fmt):
     .. seealso::
 
         :func:`~matplotlib.Line2D.lineStyles` and
-        :func:`~matplotlib.pyplot.colors`
+        :attr:`~matplotlib.colors.cnames`
             for all possible styles and color format string.
     """