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Commit cb89c38

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tacaswelltacaswell
authored
Merge pull request #7067 from phobson/oo-interface-api-examples
DOC: OO interface in api and other examples
2 parents 1787fff + 32301f0 commit cb89c38
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-52
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‎examples/api/bbox_intersect.py

Copy file name to clipboardExpand all lines: examples/api/bbox_intersect.py
+8-4Lines changed: 8 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -3,9 +3,13 @@
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from matplotlib.transforms import Bbox
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from matplotlib.path import Path
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6-
rect = plt.Rectangle((-1, -1), 2, 2, facecolor="#aaaaaa")
7-
plt.gca().add_patch(rect)
8-
bbox = Bbox.from_bounds(-1, -1, 2, 2)
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left, bottom, width, height = (-1, -1, 2, 2)
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rect = plt.Rectangle((left, bottom), width, height, facecolor="#aaaaaa")
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fig, ax = plt.subplots()
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ax.add_patch(rect)
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bbox = Bbox.from_bounds(left, bottom, width, height)
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for i in range(12):
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vertices = (np.random.random((2, 2)) - 0.5) * 6.0
@@ -14,6 +18,6 @@
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color = 'r'
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else:
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color = 'b'
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plt.plot(vertices[:, 0], vertices[:, 1], color=color)
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ax.plot(vertices[:, 0], vertices[:, 1], color=color)
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plt.show()

‎examples/api/colorbar_basics.py

Copy file name to clipboard
+28Lines changed: 28 additions & 0 deletions
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@@ -0,0 +1,28 @@
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import numpy as np
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import matplotlib.pyplot as plt
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# setup some generic data
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N = 37
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x, y = np.mgrid[:N, :N]
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Z = (np.cos(x*0.2) + np.sin(y*0.3))
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# mask out the negative and positve values, respectively
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Zpos = np.ma.masked_less(Z, 0)
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Zneg = np.ma.masked_greater(Z, 0)
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fig, (ax1, ax2) = plt.subplots(figsize=(8, 3), ncols=2)
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# plot just the positive data and save the
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# color "mappable" object returned by ax1.imshow
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pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
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# add the colorbar using the figure's method,
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# telling which mappable we're talking about and
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# which axes object it should be near
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fig.colorbar(pos, ax=ax1)
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# repeat everything above for the the negative data
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neg = ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
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fig.colorbar(neg, ax=ax2)
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plt.show()

‎examples/api/legend_demo.py

Copy file name to clipboardExpand all lines: examples/api/legend_demo.py
+5-4Lines changed: 5 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -7,11 +7,12 @@
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d = c[::-1]
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# Create plots with pre-defined labels.
10-
plt.plot(a, c, 'k--', label='Model length')
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plt.plot(a, d, 'k:', label='Data length')
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plt.plot(a, c + d, 'k', label='Total message length')
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fig, ax = plt.subplots()
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ax.plot(a, c, 'k--', label='Model length')
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ax.plot(a, d, 'k:', label='Data length')
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ax.plot(a, c + d, 'k', label='Total message length')
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legend = plt.legend(loc='upper center', shadow=True, fontsize='x-large')
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legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
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# Put a nicer background color on the legend.
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legend.get_frame().set_facecolor('#00FFCC')

‎examples/api/patch_collection.py

Copy file name to clipboardExpand all lines: examples/api/patch_collection.py
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -42,6 +42,6 @@
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p = PatchCollection(patches, alpha=0.4)
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p.set_array(np.array(colors))
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ax.add_collection(p)
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plt.colorbar(p)
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fig.colorbar(p, ax=ax)
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plt.show()

‎examples/api/power_norm_demo.py

Copy file name to clipboardExpand all lines: examples/api/power_norm_demo.py
+13-14Lines changed: 13 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -3,24 +3,23 @@
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import numpy as np
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from numpy.random import multivariate_normal
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6-
data = np.vstack([multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
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multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
8-
])
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data = np.vstack([
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multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
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multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
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])
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gammas = [0.8, 0.5, 0.3]
11-
xgrid = np.floor((len(gammas) + 1.) / 2)
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ygrid = np.ceil((len(gammas) + 1.) / 2)
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plt.subplot(xgrid, ygrid, 1)
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plt.title('Linear normalization')
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plt.hist2d(data[:, 0], data[:, 1], bins=100)
13+
fig, axes = plt.subplots(nrows=2, ncols=2)
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for i, gamma in enumerate(gammas):
19-
plt.subplot(xgrid, ygrid, i + 2)
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plt.title('Power law\n$(\gamma=%1.1f)$' % gamma)
21-
plt.hist2d(data[:, 0], data[:, 1],
22-
bins=100, norm=mcolors.PowerNorm(gamma))
15+
axes[0, 0].set_title('Linear normalization')
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axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
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24-
plt.tight_layout()
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for ax, gamma in zip(axes.flat[1:], gammas):
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ax.set_title('Power law $(\gamma=%1.1f)$' % gamma)
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ax.hist2d(data[:, 0], data[:, 1],
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bins=100, norm=mcolors.PowerNorm(gamma))
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fig.tight_layout()
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plt.show()

‎examples/api/radar_chart.py

Copy file name to clipboardExpand all lines: examples/api/radar_chart.py
+10-9Lines changed: 10 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -169,14 +169,14 @@ def example_data():
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data = example_data()
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spoke_labels = data.pop(0)
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172-
fig = plt.figure(figsize=(9, 9))
172+
fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
173+
subplot_kw=dict(projection='radar'))
173174
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
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colors = ['b', 'r', 'g', 'm', 'y']
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# Plot the four cases from the example data on separate axes
177-
for n, (title, case_data) in enumerate(data):
178-
ax = fig.add_subplot(2, 2, n + 1, projection='radar')
179-
plt.rgrids([0.2, 0.4, 0.6, 0.8])
178+
for ax, (title, case_data) in zip(axes.flatten(), data):
179+
ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
180180
ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
181181
horizontalalignment='center', verticalalignment='center')
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for d, color in zip(case_data, colors):
@@ -185,11 +185,12 @@ def example_data():
185185
ax.set_varlabels(spoke_labels)
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# add legend relative to top-left plot
188-
plt.subplot(2, 2, 1)
188+
ax = axes[0, 0]
189189
labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
190-
legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
191-
plt.setp(legend.get_texts(), fontsize='small')
190+
legend = ax.legend(labels, loc=(0.9, .95), labelspacing=0.1, fontsize='small')
191+
192+
fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
193+
horizontalalignment='center', color='black', weight='bold',
194+
size='large')
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193-
plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
194-
ha='center', color='black', weight='bold', size='large')
195196
plt.show()

‎examples/color/color_cycle_demo.py

Copy file name to clipboardExpand all lines: examples/color/color_cycle_demo.py
+1-1Lines changed: 1 addition & 1 deletion
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@@ -31,5 +31,5 @@
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ax1.set_title('Set axes color cycle to cmyk')
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# Tweak spacing between subplots to prevent labels from overlapping
34-
plt.subplots_adjust(hspace=0.3)
34+
fig.subplots_adjust(hspace=0.3)
3535
plt.show()

‎examples/images_contours_and_fields/contourf_log.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/contourf_log.py
+3-2Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -30,7 +30,8 @@
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# Automatic selection of levels works; setting the
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# log locator tells contourf to use a log scale:
33-
cs = plt.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
33+
fig, ax = plt.subplots()
34+
cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
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# Alternatively, you can manually set the levels
3637
# and the norm:
@@ -41,6 +42,6 @@
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# The 'extend' kwarg does not work yet with a log scale.
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44-
cbar = plt.colorbar()
45+
cbar = fig.colorbar(cs)
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4647
plt.show()

‎examples/images_contours_and_fields/image_demo.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/image_demo.py
+3-2Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -7,6 +7,7 @@
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image_file = cbook.get_sample_data('ada.png')
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image = plt.imread(image_file)
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10-
plt.imshow(image)
11-
plt.axis('off') # clear x- and y-axes
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fig, ax = plt.subplots()
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ax.imshow(image)
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ax.axis('off') # clear x- and y-axes
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plt.show()

‎examples/images_contours_and_fields/image_demo_clip_path.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/image_demo_clip_path.py
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -14,5 +14,5 @@
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patch = patches.Circle((260, 200), radius=200, transform=ax.transData)
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im.set_clip_path(patch)
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17-
plt.axis('off')
17+
ax.axis('off')
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plt.show()

‎examples/images_contours_and_fields/interpolation_none_vs_nearest.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/interpolation_none_vs_nearest.py
+8-11Lines changed: 8 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -23,16 +23,13 @@
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[0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
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# Create a 2x2 table of plots
26-
fig = plt.figure(figsize=[8.0, 7.5])
27-
ax = plt.subplot(2, 2, 1)
28-
ax.imshow(big_im, interpolation='none')
29-
ax = plt.subplot(2, 2, 2)
30-
ax.imshow(big_im, interpolation='nearest')
31-
ax = plt.subplot(2, 2, 3)
32-
ax.imshow(small_im, interpolation='none')
33-
ax = plt.subplot(2, 2, 4)
34-
ax.imshow(small_im, interpolation='nearest')
35-
plt.subplots_adjust(left=0.24, wspace=0.2, hspace=0.1,
26+
fig, axes = plt.subplots(figsize=[8.0, 7.5], ncols=2, nrows=2)
27+
28+
axes[0, 0].imshow(big_im, interpolation='none')
29+
axes[0, 1].imshow(big_im, interpolation='nearest')
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axes[1, 0].imshow(small_im, interpolation='none')
31+
axes[1, 1].imshow(small_im, interpolation='nearest')
32+
fig.subplots_adjust(left=0.24, wspace=0.2, hspace=0.1,
3633
bottom=0.05, top=0.86)
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# Label the rows and columns of the table
@@ -60,6 +57,6 @@
6057
pdf_im_path = cbook.get_sample_data('None_vs_nearest-pdf.png')
6158
pdf_im = plt.imread(pdf_im_path)
6259
fig2 = plt.figure(figsize=[8.0, 7.5])
63-
plt.figimage(pdf_im)
60+
fig2.figimage(pdf_im)
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6562
plt.show()

‎examples/images_contours_and_fields/streamplot_demo_masking.py

Copy file name to clipboardExpand all lines: examples/images_contours_and_fields/streamplot_demo_masking.py
+4-3Lines changed: 4 additions & 3 deletions
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@@ -18,9 +18,10 @@
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U = np.ma.array(U, mask=mask)
1919
U[:20, :20] = np.nan
2020

21-
plt.streamplot(X, Y, U, V, color='r')
21+
fig, ax = plt.subplots()
22+
ax.streamplot(X, Y, U, V, color='r')
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23-
plt.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
24-
interpolation='nearest', cmap=plt.cm.gray)
24+
ax.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
25+
interpolation='nearest', cmap=plt.cm.gray)
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plt.show()

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