Use OO interface more in merged examples.

QuLogic · QuLogic · commit ac305a947dc0 · 2017-05-28T06:02:36.000-04:00
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -58,14 +58,15 @@
 A = np.fromstring(s, np.uint16).astype(float).reshape((w, h))
 A /= A.max()
 
+fig, ax = plt.subplots()
 extent = (0, 25, 0, 25)
-im = plt.imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent)
+im = ax.imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent)
 
 markers = [(15.9, 14.5), (16.8, 15)]
 x, y = zip(*markers)
-plt.plot(x, y, 'o')
+ax.plot(x, y, 'o')
 
-plt.title('CT density')
+ax.set_title('CT density')
 
 plt.show()
 
@@ -121,17 +122,12 @@
 # suggested.
 
 A = np.random.rand(5, 5)
-plt.figure(1)
-plt.imshow(A, interpolation='nearest')
-plt.grid(True)
-
-plt.figure(2)
-plt.imshow(A, interpolation='bilinear')
-plt.grid(True)
 
-plt.figure(3)
-plt.imshow(A, interpolation='bicubic')
-plt.grid(True)
+fig, axs = plt.subplots(1, 3, figsize=(10, 3))
+for ax, interp in zip(axs, ['nearest', 'bilinear', 'bicubic']):
+    ax.imshow(A, interpolation=interp)
+    ax.set_title(interp.capitalize())
+    ax.grid(True)
 
 plt.show()
 
@@ -166,11 +162,13 @@
 
 path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
 patch = PathPatch(path, facecolor='none')
-plt.gca().add_patch(patch)
 
-im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
-                origin='lower', extent=[-3, 3, -3, 3],
-                clip_path=patch, clip_on=True)
+fig, ax = plt.subplots()
+ax.add_patch(patch)
+
+im = ax.imshow(Z, interpolation='bilinear', cmap=cm.gray,
+               origin='lower', extent=[-3, 3, -3, 3],
+               clip_path=patch, clip_on=True)
 im.set_clip_path(patch)
 
 plt.show()
diff --git a/examples/pylab_examples/line_collection.py b/examples/pylab_examples/line_collection.py
@@ -30,7 +30,7 @@
 segs = np.ma.masked_where((segs > 50) & (segs < 60), segs)
 
 # We need to set the plot limits.
-ax = plt.axes()
+fig, ax = plt.subplots()
 ax.set_xlim(x.min(), x.max())
 ax.set_ylim(ys.min(), ys.max())
 
@@ -60,7 +60,7 @@
 ys = [x + i for i in x]
 
 # We need to set the plot limits, they will not autoscale
-ax = plt.axes()
+fig, ax = plt.subplots()
 ax.set_xlim(np.min(x), np.max(x))
 ax.set_ylim(np.min(ys), np.max(ys))
 
@@ -77,7 +77,6 @@
                                linestyles='solid')
 line_segments.set_array(x)
 ax.add_collection(line_segments)
-fig = plt.gcf()
 axcb = fig.colorbar(line_segments)
 axcb.set_label('Line Number')
 ax.set_title('Line Collection with mapped colors')
diff --git a/examples/pylab_examples/major_minor_demo.py b/examples/pylab_examples/major_minor_demo.py
@@ -47,7 +47,7 @@
 s = np.sin(0.1*np.pi*t)*np.exp(-t*0.01)
 
 fig, ax = plt.subplots()
-plt.plot(t, s)
+ax.plot(t, s)
 
 ax.xaxis.set_major_locator(majorLocator)
 ax.xaxis.set_major_formatter(majorFormatter)
@@ -76,12 +76,12 @@
 s = np.sin(2*np.pi*t)*np.exp(-t*0.01)
 
 fig, ax = plt.subplots()
-plt.plot(t, s)
+ax.plot(t, s)
 
 ax.xaxis.set_minor_locator(minorLocator)
 
-plt.tick_params(which='both', width=2)
-plt.tick_params(which='major', length=7)
-plt.tick_params(which='minor', length=4, color='r')
+ax.tick_params(which='both', width=2)
+ax.tick_params(which='major', length=7)
+ax.tick_params(which='minor', length=4, color='r')
 
 plt.show()
diff --git a/examples/pylab_examples/pcolor_demo.py b/examples/pylab_examples/pcolor_demo.py
@@ -20,13 +20,13 @@
 
 Z = np.random.rand(6, 10)
 
-plt.subplot(2, 1, 1)
-c = plt.pcolor(Z)
-plt.title('default: no edges')
+fig, (ax0, ax1) = plt.subplots(2, 1)
 
-plt.subplot(2, 1, 2)
-c = plt.pcolor(Z, edgecolors='k', linewidths=4)
-plt.title('thick edges')
+c = ax0.pcolor(Z)
+ax0.set_title('default: no edges')
+
+c = ax1.pcolor(Z, edgecolors='k', linewidths=4)
+ax1.set_title('thick edges')
 
 plt.show()
 
@@ -49,37 +49,35 @@
 z = z[:-1, :-1]
 z_min, z_max = -np.abs(z).max(), np.abs(z).max()
 
+fig, axs = plt.subplots(2, 2)
 
-plt.subplot(2, 2, 1)
-plt.pcolor(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
-plt.title('pcolor')
+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
-plt.axis([x.min(), x.max(), y.min(), y.max()])
-plt.colorbar()
-
+ax.axis([x.min(), x.max(), y.min(), y.max()])
+fig.colorbar(c, ax=ax)
 
-plt.subplot(2, 2, 2)
-plt.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
-plt.title('pcolormesh')
+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
-plt.axis([x.min(), x.max(), y.min(), y.max()])
-plt.colorbar()
-
+ax.axis([x.min(), x.max(), y.min(), y.max()])
+fig.colorbar(c, ax=ax)
 
-plt.subplot(2, 2, 3)
-plt.imshow(z, cmap='RdBu', vmin=z_min, vmax=z_max,
-           extent=[x.min(), x.max(), y.min(), y.max()],
-           interpolation='nearest', origin='lower')
-plt.title('image (nearest)')
-plt.colorbar()
+ax = axs[1, 0]
+c = ax.imshow(z, cmap='RdBu', vmin=z_min, vmax=z_max,
+              extent=[x.min(), x.max(), y.min(), y.max()],
+              interpolation='nearest', origin='lower')
+ax.set_title('image (nearest)')
+fig.colorbar(c, ax=ax)
 
+ax = axs[1, 1]
+c = ax.pcolorfast(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
+ax.set_title('pcolorfast')
+fig.colorbar(c, ax=ax)
 
-ax = plt.subplot(2, 2, 4)
-ax.pcolorfast(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
-plt.title('pcolorfast')
-plt.colorbar()
-
-plt.subplots_adjust(wspace=0.5, hspace=0.5)
+fig.subplots_adjust(wspace=0.5, hspace=0.5)
 
 plt.show()
 
@@ -98,12 +96,13 @@
 # linear scale only shows the spike.
 Z1 = bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
 
-plt.subplot(2, 1, 1)
-plt.pcolor(X, Y, Z1, norm=LogNorm(vmin=Z1.min(), vmax=Z1.max()), cmap='PuBu_r')
-plt.colorbar()
+fig, (ax0, ax1) = plt.subplots(2, 1)
+
+c = ax0.pcolor(X, Y, Z1,
+               norm=LogNorm(vmin=Z1.min(), vmax=Z1.max()), cmap='PuBu_r')
+fig.colorbar(c, ax=ax0)
 
-plt.subplot(2, 1, 2)
-plt.pcolor(X, Y, Z1, cmap='PuBu_r')
-plt.colorbar()
+c = ax1.pcolor(X, Y, Z1, cmap='PuBu_r')
+fig.colorbar(c, ax=ax1)
 
 plt.show()
diff --git a/examples/pylab_examples/stackplot_demo.py b/examples/pylab_examples/stackplot_demo.py
@@ -55,6 +55,6 @@ def bump(a):
 
 d = layers(3, 100)
 
-plt.subplots()
-plt.stackplot(range(100), d.T, baseline='wiggle')
+fig, ax = plt.subplots()
+ax.stackplot(range(100), d.T, baseline='wiggle')
 plt.show()
diff --git a/examples/statistics/barchart_demo.py b/examples/statistics/barchart_demo.py
@@ -34,27 +34,24 @@
 opacity = 0.4
 error_config = {'ecolor': '0.3'}
 
-rects1 = plt.bar(index, means_men, bar_width,
-                 alpha=opacity,
-                 color='b',
-                 yerr=std_men,
-                 error_kw=error_config,
-                 label='Men')
-
-rects2 = plt.bar(index + bar_width, means_women, bar_width,
-                 alpha=opacity,
-                 color='r',
-                 yerr=std_women,
-                 error_kw=error_config,
-                 label='Women')
-
-plt.xlabel('Group')
-plt.ylabel('Scores')
-plt.title('Scores by group and gender')
-plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D', 'E'))
-plt.legend()
-
-plt.tight_layout()
+rects1 = ax.bar(index, means_men, bar_width,
+                alpha=opacity, color='b',
+                yerr=std_men, error_kw=error_config,
+                label='Men')
+
+rects2 = ax.bar(index + bar_width, means_women, bar_width,
+                alpha=opacity, color='r',
+                yerr=std_women, error_kw=error_config,
+                label='Women')
+
+ax.set_xlabel('Group')
+ax.set_ylabel('Scores')
+ax.set_title('Scores by group and gender')
+ax.set_xticks(index + bar_width / 2)
+ax.set_xticklabels(('A', 'B', 'C', 'D', 'E'))
+ax.legend()
+
+fig.tight_layout()
 plt.show()
 
 
diff --git a/examples/statistics/boxplot_demo.py b/examples/statistics/boxplot_demo.py
@@ -25,28 +25,34 @@
 flier_low = np.random.rand(10) * -100
 data = np.concatenate((spread, center, flier_high, flier_low), 0)
 
+fig, axs = plt.subplots(2, 3)
+
 # basic plot
-plt.boxplot(data)
+axs[0, 0].boxplot(data)
+axs[0, 0].set_title('basic plot')
 
 # notched plot
-plt.figure()
-plt.boxplot(data, 1)
+axs[0, 1].boxplot(data, 1)
+axs[0, 1].set_title('notched plot')
 
 # change outlier point symbols
-plt.figure()
-plt.boxplot(data, 0, 'gD')
+axs[0, 2].boxplot(data, 0, 'gD')
+axs[0, 2].set_title('change outlier\npoint symbols')
 
 # don't show outlier points
-plt.figure()
-plt.boxplot(data, 0, '')
+axs[1, 0].boxplot(data, 0, '')
+axs[1, 0].set_title("don't show\noutlier points")
 
 # horizontal boxes
-plt.figure()
-plt.boxplot(data, 0, 'rs', 0)
+axs[1, 1].boxplot(data, 0, 'rs', 0)
+axs[1, 1].set_title('horizontal boxes')
 
 # change whisker length
-plt.figure()
-plt.boxplot(data, 0, 'rs', 0, 0.75)
+axs[1, 2].boxplot(data, 0, 'rs', 0, 0.75)
+axs[1, 2].set_title('change whisker length')
+
+fig.subplots_adjust(left=0.08, right=0.98, bottom=0.05, top=0.9,
+                    hspace=0.4, wspace=0.3)
 
 # fake up some more data
 spread = np.random.rand(50) * 100
@@ -56,15 +62,15 @@
 d2 = np.concatenate((spread, center, flier_high, flier_low), 0)
 data.shape = (-1, 1)
 d2.shape = (-1, 1)
-# data = concatenate( (data, d2), 1 )
 # Making a 2-D array only works if all the columns are the
 # same length.  If they are not, then use a list instead.
 # This is actually more efficient because boxplot converts
 # a 2-D array into a list of vectors internally anyway.
 data = [data, d2, d2[::2, 0]]
-# multiple box plots on one figure
-plt.figure()
-plt.boxplot(data)
+
+# Multiple box plots on one Axes
+fig, ax = plt.subplots()
+ax.boxplot(data)
 
 plt.show()
 
@@ -101,9 +107,9 @@
 
 fig, ax1 = plt.subplots(figsize=(10, 6))
 fig.canvas.set_window_title('A Boxplot Example')
-plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
+fig.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
 
-bp = plt.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
+bp = ax1.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
 plt.setp(bp['boxes'], color='black')
 plt.setp(bp['whiskers'], color='black')
 plt.setp(bp['fliers'], color='red', marker='+')
@@ -142,20 +148,20 @@
     for j in range(2):
         medianX.append(med.get_xdata()[j])
         medianY.append(med.get_ydata()[j])
-        plt.plot(medianX, medianY, 'k')
+        ax1.plot(medianX, medianY, 'k')
         medians[i] = medianY[0]
     # Finally, overplot the sample averages, with horizontal alignment
     # in the center of each box
-    plt.plot([np.average(med.get_xdata())], [np.average(data[i])],
+    ax1.plot([np.average(med.get_xdata())], [np.average(data[i])],
              color='w', marker='*', markeredgecolor='k')
 
 # Set the axes ranges and axes labels
 ax1.set_xlim(0.5, numBoxes + 0.5)
 top = 40
 bottom = -5
 ax1.set_ylim(bottom, top)
-xtickNames = plt.setp(ax1, xticklabels=np.repeat(randomDists, 2))
-plt.setp(xtickNames, rotation=45, fontsize=8)
+ax1.set_xticklabels(np.repeat(randomDists, 2),
+                    rotation=45, fontsize=8)
 
 # Due to the Y-axis scale being different across samples, it can be
 # hard to compare differences in medians across the samples. Add upper
@@ -171,16 +177,16 @@
              color=boxColors[k])
 
 # Finally, add a basic legend
-plt.figtext(0.80, 0.08, str(N) + ' Random Numbers',
-            backgroundcolor=boxColors[0], color='black', weight='roman',
-            size='x-small')
-plt.figtext(0.80, 0.045, 'IID Bootstrap Resample',
-            backgroundcolor=boxColors[1],
-            color='white', weight='roman', size='x-small')
-plt.figtext(0.80, 0.015, '*', color='white', backgroundcolor='silver',
-            weight='roman', size='medium')
-plt.figtext(0.815, 0.013, ' Average Value', color='black', weight='roman',
-            size='x-small')
+fig.text(0.80, 0.08, str(N) + ' Random Numbers',
+         backgroundcolor=boxColors[0], color='black', weight='roman',
+         size='x-small')
+fig.text(0.80, 0.045, 'IID Bootstrap Resample',
+         backgroundcolor=boxColors[1],
+         color='white', weight='roman', size='x-small')
+fig.text(0.80, 0.015, '*', color='white', backgroundcolor='silver',
+         weight='roman', size='medium')
+fig.text(0.815, 0.013, ' Average Value', color='black', weight='roman',
+         size='x-small')
 
 plt.show()
 
diff --git a/examples/statistics/hexbin_demo.py b/examples/statistics/hexbin_demo.py
diff --git a/examples/text_labels_and_annotations/legend_demo.py b/examples/text_labels_and_annotations/legend_demo.py
