matplotlib · tacaswell · May 31, 2017 · May 28, 2017 · May 28, 2017 · May 28, 2017
diff --git a/examples/animation/image_slices_viewer.py b/examples/animation/image_slices_viewer.py
@@ -5,7 +5,8 @@
 
 """
 from __future__ import print_function
-import numpy
+
+import numpy as np
 import matplotlib.pyplot as plt
 
 
@@ -24,9 +25,9 @@ def __init__(self, ax, X):
     def onscroll(self, event):
         print("%s %s" % (event.button, event.step))
         if event.button == 'up':
-            self.ind = numpy.clip(self.ind + 1, 0, self.slices - 1)
+            self.ind = (self.ind + 1) % self.slices
         else:
-            self.ind = numpy.clip(self.ind - 1, 0, self.slices - 1)
+            self.ind = (self.ind - 1) % self.slices
         self.update()
 
     def update(self):
@@ -37,7 +38,7 @@ def update(self):
 
 fig, ax = plt.subplots(1, 1)
 
-X = numpy.random.rand(20, 20, 40)
+X = np.random.rand(20, 20, 40)
 
 tracker = IndexTracker(ax, X)
 

diff --git a/examples/event_handling/pick_event_demo2.py b/examples/event_handling/pick_event_demo2.py
@@ -7,13 +7,13 @@
 mean vs stddev.  When you click on one of the mu, sigma points, plot the raw
 data from the dataset that generated the mean and stddev.
 """
-import numpy
+import numpy as np
 import matplotlib.pyplot as plt
 
 
-X = numpy.random.rand(100, 1000)
-xs = numpy.mean(X, axis=1)
-ys = numpy.std(X, axis=1)
+X = np.random.rand(100, 1000)
+xs = np.mean(X, axis=1)
+ys = np.std(X, axis=1)
 
 fig, ax = plt.subplots()
 ax.set_title('click on point to plot time series')

diff --git a/examples/event_handling/zoom_window.py b/examples/event_handling/zoom_window.py
@@ -14,7 +14,7 @@
 points**2, so their size is independent of the zoom
 """
 from matplotlib.pyplot import figure, show
-import numpy
+import numpy as np
 figsrc = figure()
 figzoom = figure()
 
@@ -23,7 +23,7 @@
                              autoscale_on=False)
 axsrc.set_title('Click to zoom')
 axzoom.set_title('zoom window')
-x, y, s, c = numpy.random.rand(4, 200)
+x, y, s, c = np.random.rand(4, 200)
 s *= 200
 
 

diff --git a/examples/frontpage/membrane.py b/examples/frontpage/membrane.py
@@ -11,8 +11,8 @@
 import numpy as np
 
 
-datafile = cbook.get_sample_data('membrane.dat', asfileobj=False)
-x = np.fromfile(datafile, np.float32)
+with cbook.get_sample_data('membrane.dat') as datafile:
+    x = np.fromfile(datafile, np.float32)
 # 0.0005 is the sample interval
 
 fig, ax = plt.subplots()

diff --git a/examples/images_contours_and_fields/image_clip_path.py b/examples/images_contours_and_fields/image_clip_path.py
@@ -10,8 +10,8 @@
 import matplotlib.cbook as cbook
 
 
-image_file = cbook.get_sample_data('grace_hopper.png')
-image = plt.imread(image_file)
+with cbook.get_sample_data('grace_hopper.png') as image_file:
+    image = plt.imread(image_file)
 
 fig, ax = plt.subplots()
 im = ax.imshow(image)

diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -41,8 +41,8 @@
 # It is also possible to show images of pictures.
 
 # A sample image
-image_file = cbook.get_sample_data('ada.png')
-image = plt.imread(image_file)
+with cbook.get_sample_data('ada.png') as image_file:
+    image = plt.imread(image_file)
 
 fig, ax = plt.subplots()
 ax.imshow(image)
@@ -53,19 +53,20 @@
 
 w, h = 512, 512
 
-datafile = cbook.get_sample_data('ct.raw.gz', asfileobj=True)
-s = datafile.read()
+with cbook.get_sample_data('ct.raw.gz', asfileobj=True) as datafile:
+    s = datafile.read()
 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()
 
@@ -121,26 +122,21 @@
 # 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()
 
 
 ###############################################################################
 # You can specify whether images should be plotted with the array origin
-# x[0,0] in the upper left or upper right by using the origin parameter.
-# You can also control the default be setting image.origin in your
-# matplotlibrc file; see http://matplotlib.org/matplotlibrc
+# x[0,0] in the upper left or lower right by using the origin parameter.
+# You can also control the default setting image.origin in your
+# :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`
 
 x = np.arange(120).reshape((10, 12))
 
@@ -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/ellipse_demo.py b/examples/pylab_examples/ellipse_demo.py
@@ -5,20 +5,22 @@
 
 """
 import matplotlib.pyplot as plt
-import numpy.random as rnd
+import numpy as np
 from matplotlib.patches import Ellipse
 
 NUM = 250
 
-ells = [Ellipse(xy=rnd.rand(2)*10, width=rnd.rand(), height=rnd.rand(), angle=rnd.rand()*360)
+ells = [Ellipse(xy=np.random.rand(2)*10,
+                width=np.random.rand(), height=np.random.rand(),
+                angle=np.random.rand()*360)
         for i in range(NUM)]
 
 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})
 for e in ells:
     ax.add_artist(e)
     e.set_clip_box(ax.bbox)
-    e.set_alpha(rnd.rand())
-    e.set_facecolor(rnd.rand(3))
+    e.set_alpha(np.random.rand())
+    e.set_facecolor(np.random.rand(3))
 
 ax.set_xlim(0, 10)
 ax.set_ylim(0, 10)

diff --git a/examples/pylab_examples/fancybox_demo.py b/examples/pylab_examples/fancybox_demo.py
@@ -93,7 +93,7 @@ def test2(ax):
     #p_fancy.set_boxstyle("round", pad=0.1, rounding_size=0.2)
 
     ax.text(0.1, 0.8,
-            ' boxstyle="round,pad=0.1\n rounding\\_size=0.2"',
+            ' boxstyle="round,pad=0.1\n rounding_size=0.2"',
             size=10, transform=ax.transAxes)
 
     # draws control points for the fancy box.
@@ -118,7 +118,7 @@ def test3(ax):
     ax.add_patch(p_fancy)
 
     ax.text(0.1, 0.8,
-            ' boxstyle="round,pad=0.1"\n mutation\\_scale=2',
+            ' boxstyle="round,pad=0.1"\n mutation_scale=2',
             size=10, transform=ax.transAxes)
 
     # draws control points for the fancy box.
@@ -152,7 +152,7 @@ def test4(ax):
     ax.add_patch(p_fancy)
 
     ax.text(0.1, 0.8,
-            ' boxstyle="round,pad=0.3"\n mutation\\_aspect=.5',
+            ' boxstyle="round,pad=0.3"\n mutation_aspect=.5',
             size=10, transform=ax.transAxes)
 
     draw_bbox(ax, bb)

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/mri_demo.py b/examples/pylab_examples/mri_demo.py
@@ -12,13 +12,12 @@
 import matplotlib.cm as cm
 import numpy as np
 
-fig, ax = plt.subplots(num="MRI_demo")
 
 # Data are 256x256 16 bit integers
-dfile = cbook.get_sample_data('s1045.ima.gz')
-im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
-dfile.close()
+with cbook.get_sample_data('s1045.ima.gz') as dfile:
+    im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
 
+fig, ax = plt.subplots(num="MRI_demo")
 ax.imshow(im, cmap=cm.gray)
 ax.axis('off')
 

diff --git a/examples/pylab_examples/mri_with_eeg.py b/examples/pylab_examples/mri_with_eeg.py
@@ -20,9 +20,8 @@
 fig = plt.figure("MRI_with_EEG")
 
 # Load the MRI data (256x256 16 bit integers)
-dfile = cbook.get_sample_data('s1045.ima.gz')
-im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
-dfile.close()
+with cbook.get_sample_data('s1045.ima.gz') as dfile:
+    im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
 
 # Plot the MRI image
 ax0 = fig.add_subplot(2, 2, 1)
@@ -43,9 +42,8 @@
 
 # Load the EEG data
 numSamples, numRows = 800, 4
-eegfile = cbook.get_sample_data('eeg.dat', asfileobj=False)
-print('Loading EEG %s' % eegfile)
-data = np.fromfile(eegfile, dtype=float)
+with cbook.get_sample_data('eeg.dat') as eegfile:
+    data = np.fromfile(eegfile, dtype=float)
 data.shape = (numSamples, numRows)
 t = 10.0 * np.arange(numSamples) / numSamples