matplotlib · tacaswell · Oct 11, 2016 · Sep 9, 2016 · Sep 9, 2016 · Sep 9, 2016
diff --git a/examples/api/bbox_intersect.py b/examples/api/bbox_intersect.py
@@ -3,9 +3,13 @@
 from matplotlib.transforms import Bbox
 from matplotlib.path import Path
 
-rect = plt.Rectangle((-1, -1), 2, 2, facecolor="#aaaaaa")
-plt.gca().add_patch(rect)
-bbox = Bbox.from_bounds(-1, -1, 2, 2)
+left, bottom, width, height = (-1, -1, 2, 2)
+rect = plt.Rectangle((left, bottom), width, height, facecolor="#aaaaaa")
+
+fig, ax = plt.subplots()
+ax.add_patch(rect)
+
+bbox = Bbox.from_bounds(left, bottom, width, height)
 
 for i in range(12):
     vertices = (np.random.random((2, 2)) - 0.5) * 6.0
@@ -14,6 +18,6 @@
         color = 'r'
     else:
         color = 'b'
-    plt.plot(vertices[:, 0], vertices[:, 1], color=color)
+    ax.plot(vertices[:, 0], vertices[:, 1], color=color)
 
 plt.show()
diff --git a/examples/api/colorbar_basics.py b/examples/api/colorbar_basics.py
@@ -0,0 +1,28 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# setup some generic data
+N = 37
+x, y = np.mgrid[:N, :N]
+Z = (np.cos(x*0.2) + np.sin(y*0.3))
+
+# mask out the negative and positve values, respectively
+Zpos = np.ma.masked_less(Z, 0)
+Zneg = np.ma.masked_greater(Z, 0)
+
+fig, (ax1, ax2) = plt.subplots(figsize=(8, 3), ncols=2)
+
+# plot just the positive data and save the
+# color "mappable" object returned by ax1.imshow
+pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
+
+# add the colorbar using the figure's method,
+# telling which mappable we're talking about and
+# which axes object it should be near
+fig.colorbar(pos, ax=ax1)
+
+# repeat everything above for the the negative data
+neg = ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
+fig.colorbar(neg, ax=ax2)
+
+plt.show()
diff --git a/examples/api/legend_demo.py b/examples/api/legend_demo.py
@@ -7,11 +7,12 @@
 d = c[::-1]
 
 # Create plots with pre-defined labels.
-plt.plot(a, c, 'k--', label='Model length')
-plt.plot(a, d, 'k:', label='Data length')
-plt.plot(a, c + d, 'k', label='Total message length')
+fig, ax = plt.subplots()
+ax.plot(a, c, 'k--', label='Model length')
+ax.plot(a, d, 'k:', label='Data length')
+ax.plot(a, c + d, 'k', label='Total message length')
 
-legend = plt.legend(loc='upper center', shadow=True, fontsize='x-large')
+legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
 
 # Put a nicer background color on the legend.
 legend.get_frame().set_facecolor('#00FFCC')

diff --git a/examples/api/patch_collection.py b/examples/api/patch_collection.py
@@ -42,6 +42,6 @@
 p = PatchCollection(patches, alpha=0.4)
 p.set_array(np.array(colors))
 ax.add_collection(p)
-plt.colorbar(p)
+fig.colorbar(p, ax=ax)
 
 plt.show()
diff --git a/examples/api/power_norm_demo.py b/examples/api/power_norm_demo.py
@@ -3,24 +3,23 @@
 import numpy as np
 from numpy.random import multivariate_normal
 
-data = np.vstack([multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
-                  multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
-                  ])
+data = np.vstack([
+    multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
+    multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
+])
 
 gammas = [0.8, 0.5, 0.3]
-xgrid = np.floor((len(gammas) + 1.) / 2)
-ygrid = np.ceil((len(gammas) + 1.) / 2)
 
-plt.subplot(xgrid, ygrid, 1)
-plt.title('Linear normalization')
-plt.hist2d(data[:, 0], data[:, 1], bins=100)
+fig, axes = plt.subplots(nrows=2, ncols=2)
 
-for i, gamma in enumerate(gammas):
-    plt.subplot(xgrid, ygrid, i + 2)
-    plt.title('Power law\n$(\gamma=%1.1f)$' % gamma)
-    plt.hist2d(data[:, 0], data[:, 1],
-               bins=100, norm=mcolors.PowerNorm(gamma))
+axes[0, 0].set_title('Linear normalization')
+axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
 
-plt.tight_layout()
+for ax, gamma in zip(axes.flat[1:], gammas):
+    ax.set_title('Power law $(\gamma=%1.1f)$' % gamma)
+    ax.hist2d(data[:, 0], data[:, 1],
+              bins=100, norm=mcolors.PowerNorm(gamma))
+
+fig.tight_layout()
 
 plt.show()
diff --git a/examples/api/radar_chart.py b/examples/api/radar_chart.py
@@ -169,14 +169,14 @@ def example_data():
     data = example_data()
     spoke_labels = data.pop(0)
 
-    fig = plt.figure(figsize=(9, 9))
+    fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
+                             subplot_kw=dict(projection='radar'))
     fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
 
     colors = ['b', 'r', 'g', 'm', 'y']
     # Plot the four cases from the example data on separate axes
-    for n, (title, case_data) in enumerate(data):
-        ax = fig.add_subplot(2, 2, n + 1, projection='radar')
-        plt.rgrids([0.2, 0.4, 0.6, 0.8])
+    for ax, (title, case_data) in zip(axes.flatten(), data):
+        ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
         ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
                      horizontalalignment='center', verticalalignment='center')
         for d, color in zip(case_data, colors):
@@ -185,11 +185,12 @@ def example_data():
         ax.set_varlabels(spoke_labels)
 
     # add legend relative to top-left plot
-    plt.subplot(2, 2, 1)
+    ax = axes[0, 0]
     labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
-    legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
-    plt.setp(legend.get_texts(), fontsize='small')
+    legend = ax.legend(labels, loc=(0.9, .95), labelspacing=0.1, fontsize='small')
+
+    fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
+             horizontalalignment='center', color='black', weight='bold',
+             size='large')
 
-    plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
-                ha='center', color='black', weight='bold', size='large')
     plt.show()
diff --git a/examples/color/color_cycle_demo.py b/examples/color/color_cycle_demo.py
@@ -31,5 +31,5 @@
 ax1.set_title('Set axes color cycle to cmyk')
 
 # Tweak spacing between subplots to prevent labels from overlapping
-plt.subplots_adjust(hspace=0.3)
+fig.subplots_adjust(hspace=0.3)
 plt.show()
diff --git a/examples/images_contours_and_fields/contourf_log.py b/examples/images_contours_and_fields/contourf_log.py
@@ -30,7 +30,8 @@
 
 # Automatic selection of levels works; setting the
 # log locator tells contourf to use a log scale:
-cs = plt.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
+fig, ax = plt.subplots()
+cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
 
 # Alternatively, you can manually set the levels
 # and the norm:
@@ -41,6 +42,6 @@
 
 # The 'extend' kwarg does not work yet with a log scale.
 
-cbar = plt.colorbar()
+cbar = fig.colorbar(cs)
 
 plt.show()
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -7,6 +7,7 @@
 image_file = cbook.get_sample_data('ada.png')
 image = plt.imread(image_file)
 
-plt.imshow(image)
-plt.axis('off')  # clear x- and y-axes
+fig, ax = plt.subplots()
+ax.imshow(image)
+ax.axis('off')  # clear x- and y-axes
 plt.show()
diff --git a/examples/images_contours_and_fields/image_demo_clip_path.py b/examples/images_contours_and_fields/image_demo_clip_path.py
@@ -14,5 +14,5 @@
 patch = patches.Circle((260, 200), radius=200, transform=ax.transData)
 im.set_clip_path(patch)
 
-plt.axis('off')
+ax.axis('off')
 plt.show()
diff --git a/examples/images_contours_and_fields/interpolation_none_vs_nearest.py b/examples/images_contours_and_fields/interpolation_none_vs_nearest.py
@@ -23,16 +23,13 @@
                      [0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
 
 # Create a 2x2 table of plots
-fig = plt.figure(figsize=[8.0, 7.5])
-ax = plt.subplot(2, 2, 1)
-ax.imshow(big_im, interpolation='none')
-ax = plt.subplot(2, 2, 2)
-ax.imshow(big_im, interpolation='nearest')
-ax = plt.subplot(2, 2, 3)
-ax.imshow(small_im, interpolation='none')
-ax = plt.subplot(2, 2, 4)
-ax.imshow(small_im, interpolation='nearest')
-plt.subplots_adjust(left=0.24, wspace=0.2, hspace=0.1,
+fig, axes = plt.subplots(figsize=[8.0, 7.5], ncols=2, nrows=2)
+
+axes[0, 0].imshow(big_im, interpolation='none')
+axes[0, 1].imshow(big_im, interpolation='nearest')
+axes[1, 0].imshow(small_im, interpolation='none')
+axes[1, 1].imshow(small_im, interpolation='nearest')
+fig.subplots_adjust(left=0.24, wspace=0.2, hspace=0.1,
                     bottom=0.05, top=0.86)
 
 # Label the rows and columns of the table
@@ -60,6 +57,6 @@
 pdf_im_path = cbook.get_sample_data('None_vs_nearest-pdf.png')
 pdf_im = plt.imread(pdf_im_path)
 fig2 = plt.figure(figsize=[8.0, 7.5])
-plt.figimage(pdf_im)
+fig2.figimage(pdf_im)
 
 plt.show()
diff --git a/examples/images_contours_and_fields/streamplot_demo_masking.py b/examples/images_contours_and_fields/streamplot_demo_masking.py
@@ -18,9 +18,10 @@
 U = np.ma.array(U, mask=mask)
 U[:20, :20] = np.nan
 
-plt.streamplot(X, Y, U, V, color='r')
+fig, ax = plt.subplots()
+ax.streamplot(X, Y, U, V, color='r')
 
-plt.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
-           interpolation='nearest', cmap=plt.cm.gray)
+ax.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
+          interpolation='nearest', cmap=plt.cm.gray)
 
 plt.show()