From e53876ae5a66c29160aceab46f468e289d28a8e0 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sun, 28 May 2017 01:55:38 -0400
Subject: [PATCH 1/5] Use standard NumPy import in pylab examples.

---
 examples/animation/image_slices_viewer.py              |  9 +++++----
 examples/event_handling/pick_event_demo2.py            |  8 ++++----
 examples/event_handling/zoom_window.py                 |  4 ++--
 examples/pylab_examples/ellipse_demo.py                | 10 ++++++----
 examples/pylab_examples/pcolor_demo.py                 |  4 ++--
 examples/pylab_examples/spy_demos.py                   |  4 ++--
 examples/pylab_examples/tricontour_vs_griddata.py      |  7 +++----
 examples/pylab_examples/vline_hline_demo.py            |  3 +--
 examples/subplots_axes_and_figures/subplot_toolbar.py  | 10 +++++-----
 examples/units/units_sample.py                         |  4 ++--
 .../user_interfaces/histogram_demo_canvasagg_sgskip.py |  7 +++----
 examples/userdemo/annotate_text_arrow.py               | 10 +++++-----
 12 files changed, 40 insertions(+), 40 deletions(-)

diff --git a/examples/animation/image_slices_viewer.py b/examples/animation/image_slices_viewer.py
index a6ae072f9560..b8997b531396 100644
--- 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 = np.clip(self.ind + 1, 0, self.slices - 1)
         else:
-            self.ind = numpy.clip(self.ind - 1, 0, self.slices - 1)
+            self.ind = np.clip(self.ind - 1, 0, self.slices - 1)
         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
index a8da484b47bb..48ac59e0ae26 100644
--- 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
index 21c343bc47ec..b84afeb67daf 100644
--- 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/pylab_examples/ellipse_demo.py b/examples/pylab_examples/ellipse_demo.py
index 18606fa55fb4..7830a1c080c2 100644
--- 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/pcolor_demo.py b/examples/pylab_examples/pcolor_demo.py
index 993560ee33e3..68b1b7e1d10c 100644
--- a/examples/pylab_examples/pcolor_demo.py
+++ b/examples/pylab_examples/pcolor_demo.py
@@ -10,15 +10,15 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
-from numpy.random import rand
 from matplotlib.colors import LogNorm
 from matplotlib.mlab import bivariate_normal
 
+
 ###############################################################################
 # A simple pcolor demo
 # --------------------
 
-Z = rand(6, 10)
+Z = np.random.rand(6, 10)
 
 plt.subplot(2, 1, 1)
 c = plt.pcolor(Z)
diff --git a/examples/pylab_examples/spy_demos.py b/examples/pylab_examples/spy_demos.py
index bbf4201ff5ad..a0b6cfd0206a 100644
--- a/examples/pylab_examples/spy_demos.py
+++ b/examples/pylab_examples/spy_demos.py
@@ -7,7 +7,7 @@
 """
 
 from matplotlib.pyplot import figure, show
-import numpy
+import numpy as np
 
 fig = figure()
 ax1 = fig.add_subplot(221)
@@ -15,7 +15,7 @@
 ax3 = fig.add_subplot(223)
 ax4 = fig.add_subplot(224)
 
-x = numpy.random.randn(20, 20)
+x = np.random.randn(20, 20)
 x[5] = 0.
 x[:, 12] = 0.
 
diff --git a/examples/pylab_examples/tricontour_vs_griddata.py b/examples/pylab_examples/tricontour_vs_griddata.py
index 3b3d67f35bac..7ae6bbf84893 100644
--- a/examples/pylab_examples/tricontour_vs_griddata.py
+++ b/examples/pylab_examples/tricontour_vs_griddata.py
@@ -9,16 +9,15 @@
 import matplotlib.pyplot as plt
 import matplotlib.tri as tri
 import numpy as np
-import numpy.random as rnd
 import matplotlib.mlab as mlab
 import time
 
-rnd.seed(0)
+np.random.seed(0)
 npts = 200
 ngridx = 100
 ngridy = 200
-x = rnd.uniform(-2, 2, npts)
-y = rnd.uniform(-2, 2, npts)
+x = np.random.uniform(-2, 2, npts)
+y = np.random.uniform(-2, 2, npts)
 z = x*np.exp(-x**2 - y**2)
 
 # griddata and contour.
diff --git a/examples/pylab_examples/vline_hline_demo.py b/examples/pylab_examples/vline_hline_demo.py
index 1959b4af8e5e..dcb0fb733db9 100644
--- a/examples/pylab_examples/vline_hline_demo.py
+++ b/examples/pylab_examples/vline_hline_demo.py
@@ -9,7 +9,6 @@
 import matplotlib.pyplot as plt
 from matplotlib.transforms import blended_transform_factory as btf
 import numpy as np
-import numpy.random as rnd
 
 
 def f(t):
@@ -19,7 +18,7 @@ def f(t):
 
 t = np.arange(0.0, 5.0, 0.1)
 s = f(t)
-nse = rnd.normal(0.0, 0.3, t.shape) * s
+nse = np.random.normal(0.0, 0.3, t.shape) * s
 
 fig = plt.figure(figsize=(12, 6))
 vax = fig.add_subplot(121)
diff --git a/examples/subplots_axes_and_figures/subplot_toolbar.py b/examples/subplots_axes_and_figures/subplot_toolbar.py
index ed2c7611d8a8..343a35bdfb2b 100644
--- a/examples/subplots_axes_and_figures/subplot_toolbar.py
+++ b/examples/subplots_axes_and_figures/subplot_toolbar.py
@@ -5,17 +5,17 @@
 
 """
 import matplotlib.pyplot as plt
-import numpy.random as rnd
+import numpy as np
 
 fig = plt.figure()
 plt.subplot(221)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 plt.subplot(222)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 plt.subplot(223)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 plt.subplot(224)
-plt.imshow(rnd.random((100, 100)))
+plt.imshow(np.random.random((100, 100)))
 
 plt.subplot_tool()
 plt.show()
diff --git a/examples/units/units_sample.py b/examples/units/units_sample.py
index dfa2333d7dc3..3ab5a9730bb5 100644
--- a/examples/units/units_sample.py
+++ b/examples/units/units_sample.py
@@ -15,9 +15,9 @@
 """
 from basic_units import cm, inch
 import matplotlib.pyplot as plt
-import numpy
+import numpy as np
 
-cms = cm * numpy.arange(0, 10, 2)
+cms = cm * np.arange(0, 10, 2)
 
 fig = plt.figure()
 
diff --git a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
index 07ddaaf669b7..f3cc2cca4e09 100644
--- a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
+++ b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
@@ -15,8 +15,7 @@
 from matplotlib.backends.backend_agg import FigureCanvasAgg
 from matplotlib.figure import Figure
 from matplotlib.mlab import normpdf
-from numpy.random import randn
-import numpy
+import numpy as np
 
 fig = Figure(figsize=(5, 4), dpi=100)
 ax = fig.add_subplot(111)
@@ -24,7 +23,7 @@
 canvas = FigureCanvasAgg(fig)
 
 mu, sigma = 100, 15
-x = mu + sigma*randn(10000)
+x = mu + sigma * np.random.randn(10000)
 
 # the histogram of the data
 n, bins, patches = ax.hist(x, 50, normed=1)
@@ -52,7 +51,7 @@
 
 if 0:
     # convert to a numpy array
-    X = numpy.fromstring(s, numpy.uint8).reshape((h, w, 3))
+    X = np.fromstring(s, np.uint8).reshape((h, w, 3))
 
 if 0:
     # pass off to PIL
diff --git a/examples/userdemo/annotate_text_arrow.py b/examples/userdemo/annotate_text_arrow.py
index b698fd4830a3..193fe52efbba 100644
--- a/examples/userdemo/annotate_text_arrow.py
+++ b/examples/userdemo/annotate_text_arrow.py
@@ -5,16 +5,16 @@
 
 """
 
-import numpy.random
+import numpy as np
 import matplotlib.pyplot as plt
 
 fig, ax = plt.subplots(figsize=(5, 5))
 ax.set_aspect(1)
 
-x1 = -1 + numpy.random.randn(100)
-y1 = -1 + numpy.random.randn(100)
-x2 = 1. + numpy.random.randn(100)
-y2 = 1. + numpy.random.randn(100)
+x1 = -1 + np.random.randn(100)
+y1 = -1 + np.random.randn(100)
+x2 = 1. + np.random.randn(100)
+y2 = 1. + np.random.randn(100)
 
 ax.scatter(x1, y1, color="r")
 ax.scatter(x2, y2, color="g")

From 4df2bb7e28bf5abf8e88064c0fa8625b0743900f Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sun, 28 May 2017 04:38:07 -0400
Subject: [PATCH 2/5] Allow wraparound in image slice viewer example.

Also, drop the more expensive `np.clip` call.
---
 examples/animation/image_slices_viewer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/animation/image_slices_viewer.py b/examples/animation/image_slices_viewer.py
index b8997b531396..70646973ec98 100644
--- a/examples/animation/image_slices_viewer.py
+++ b/examples/animation/image_slices_viewer.py
@@ -25,9 +25,9 @@ def __init__(self, ax, X):
     def onscroll(self, event):
         print("%s %s" % (event.button, event.step))
         if event.button == 'up':
-            self.ind = np.clip(self.ind + 1, 0, self.slices - 1)
+            self.ind = (self.ind + 1) % self.slices
         else:
-            self.ind = np.clip(self.ind - 1, 0, self.slices - 1)
+            self.ind = (self.ind - 1) % self.slices
         self.update()
 
     def update(self):

From 5b8efe1b6689cb2c1ef3f211faa958111fb1c137 Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sun, 28 May 2017 05:27:58 -0400
Subject: [PATCH 3/5] Avoid leaks with cbook.get_sample_data.

---
 examples/frontpage/membrane.py                |  4 +--
 .../image_clip_path.py                        |  4 +--
 .../images_contours_and_fields/image_demo.py  |  8 ++---
 examples/pylab_examples/mri_demo.py           |  7 ++--
 examples/pylab_examples/mri_with_eeg.py       | 10 +++---
 .../showcase/bachelors_degrees_by_gender.py   |  4 +--
 .../topographic_hillshading.py                | 32 ++++++++++---------
 7 files changed, 34 insertions(+), 35 deletions(-)

diff --git a/examples/frontpage/membrane.py b/examples/frontpage/membrane.py
index 4a99e6de1429..4e126eceda5f 100644
--- 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
index e005b6538c51..e444ee1195bf 100644
--- 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
index 40cc412bd38f..d811ef4fe30a 100644
--- 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)
@@ -53,8 +53,8 @@
 
 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()
 
diff --git a/examples/pylab_examples/mri_demo.py b/examples/pylab_examples/mri_demo.py
index 7a94529be9d1..0f2e873c1ae9 100644
--- 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
index da92dd0aea8b..0fd3be3d6eda 100644
--- 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
 
diff --git a/examples/showcase/bachelors_degrees_by_gender.py b/examples/showcase/bachelors_degrees_by_gender.py
index 9bca3da11a96..1305770f584f 100644
--- a/examples/showcase/bachelors_degrees_by_gender.py
+++ b/examples/showcase/bachelors_degrees_by_gender.py
@@ -14,8 +14,8 @@
 from matplotlib.mlab import csv2rec
 from matplotlib.cbook import get_sample_data
 
-fname = get_sample_data('percent_bachelors_degrees_women_usa.csv')
-gender_degree_data = csv2rec(fname)
+with get_sample_data('percent_bachelors_degrees_women_usa.csv') as fname:
+    gender_degree_data = csv2rec(fname)
 
 # These are the colors that will be used in the plot
 color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
diff --git a/examples/specialty_plots/topographic_hillshading.py b/examples/specialty_plots/topographic_hillshading.py
index fd9fd90e13fd..cd197e09af72 100644
--- a/examples/specialty_plots/topographic_hillshading.py
+++ b/examples/specialty_plots/topographic_hillshading.py
@@ -21,22 +21,24 @@
 from matplotlib.cbook import get_sample_data
 from matplotlib.colors import LightSource
 
-dem = np.load(get_sample_data('jacksboro_fault_dem.npz'))
-z = dem['elevation']
 
-#-- Optional dx and dy for accurate vertical exaggeration --------------------
-# If you need topographically accurate vertical exaggeration, or you don't want
-# to guess at what *vert_exag* should be, you'll need to specify the cellsize
-# of the grid (i.e. the *dx* and *dy* parameters).  Otherwise, any *vert_exag*
-# value you specify will be relative to the grid spacing of your input data
-# (in other words, *dx* and *dy* default to 1.0, and *vert_exag* is calculated
-# relative to those parameters).  Similarly, *dx* and *dy* are assumed to be in
-# the same units as your input z-values.  Therefore, we'll need to convert the
-# given dx and dy from decimal degrees to meters.
-dx, dy = dem['dx'], dem['dy']
-dy = 111200 * dy
-dx = 111200 * dx * np.cos(np.radians(dem['ymin']))
-#-----------------------------------------------------------------------------
+with np.load(get_sample_data('jacksboro_fault_dem.npz')) as dem:
+    z = dem['elevation']
+
+    #-- Optional dx and dy for accurate vertical exaggeration ----------------
+    # If you need topographically accurate vertical exaggeration, or you don't
+    # want to guess at what *vert_exag* should be, you'll need to specify the
+    # cellsize of the grid (i.e. the *dx* and *dy* parameters).  Otherwise, any
+    # *vert_exag* value you specify will be relative to the grid spacing of
+    # your input data (in other words, *dx* and *dy* default to 1.0, and
+    # *vert_exag* is calculated relative to those parameters).  Similarly, *dx*
+    # and *dy* are assumed to be in the same units as your input z-values.
+    # Therefore, we'll need to convert the given dx and dy from decimal degrees
+    # to meters.
+    dx, dy = dem['dx'], dem['dy']
+    dy = 111200 * dy
+    dx = 111200 * dx * np.cos(np.radians(dem['ymin']))
+    #-------------------------------------------------------------------------
 
 # Shade from the northwest, with the sun 45 degrees from horizontal
 ls = LightSource(azdeg=315, altdeg=45)

From 426a712d99387ff8d09749f5d2d05af51067731f Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sun, 28 May 2017 05:41:38 -0400
Subject: [PATCH 4/5] Fix some typos in merged examples.

---
 examples/images_contours_and_fields/image_demo.py           | 6 +++---
 examples/pylab_examples/fancybox_demo.py                    | 6 +++---
 examples/pylab_examples/psd_demo.py                         | 2 +-
 examples/user_interfaces/histogram_demo_canvasagg_sgskip.py | 4 ++--
 4 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
index d811ef4fe30a..d55bd905a4e2 100644
--- a/examples/images_contours_and_fields/image_demo.py
+++ b/examples/images_contours_and_fields/image_demo.py
@@ -138,9 +138,9 @@
 
 ###############################################################################
 # 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))
 
diff --git a/examples/pylab_examples/fancybox_demo.py b/examples/pylab_examples/fancybox_demo.py
index 9b3b02314c2f..9d5465d51f2e 100644
--- 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/psd_demo.py b/examples/pylab_examples/psd_demo.py
index ba7cda9c086e..a60cefe9a5e3 100644
--- a/examples/pylab_examples/psd_demo.py
+++ b/examples/pylab_examples/psd_demo.py
@@ -5,7 +5,7 @@
 
 Plotting Power Spectral Density (PSD) in Matplotlib.
 
-The PSD is a common plot in the field of signal processing. Numpy has
+The PSD is a common plot in the field of signal processing. NumPy has
 many useful libraries for computing a PSD. Below we demo a few examples
 of how this can be accomplished and visualized with Matplotlib.
 """
diff --git a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
index f3cc2cca4e09..5fe6e82d1076 100644
--- a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
+++ b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
@@ -44,13 +44,13 @@
 
 s = canvas.tostring_rgb()  # save this and convert to bitmap as needed
 
-# get the figure dimensions for creating bitmaps or numpy arrays,
+# Get the figure dimensions for creating bitmaps or NumPy arrays,
 # etc.
 l, b, w, h = fig.bbox.bounds
 w, h = int(w), int(h)
 
 if 0:
-    # convert to a numpy array
+    # Convert to a NumPy array
     X = np.fromstring(s, np.uint8).reshape((h, w, 3))
 
 if 0:

From 869bd95b3d8eb4e333f303a7fd25cb84f1b66c6f Mon Sep 17 00:00:00 2001
From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
Date: Sun, 28 May 2017 06:02:36 -0400
Subject: [PATCH 5/5] Use OO interface more in merged examples.

---
 .../images_contours_and_fields/image_demo.py  | 32 ++++-----
 examples/pylab_examples/line_collection.py    |  5 +-
 examples/pylab_examples/major_minor_demo.py   | 10 +--
 examples/pylab_examples/pcolor_demo.py        | 71 +++++++++----------
 examples/pylab_examples/spy_demos.py          | 16 ++---
 examples/pylab_examples/stackplot_demo.py     |  4 +-
 examples/statistics/barchart_demo.py          | 39 +++++-----
 examples/statistics/boxplot_demo.py           | 68 ++++++++++--------
 examples/statistics/hexbin_demo.py            | 19 +++--
 .../subplot_toolbar.py                        | 18 ++---
 .../legend_demo.py                            | 46 ++++++------
 11 files changed, 164 insertions(+), 164 deletions(-)

diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
index d55bd905a4e2..6400f6391109 100644
--- 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
index 211285027d77..b55ecb5e3f60 100644
--- 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
index 76561779b3b6..d5f0b5b869ae 100644
--- 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
index 68b1b7e1d10c..ce56253dbef0 100644
--- 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/spy_demos.py b/examples/pylab_examples/spy_demos.py
index a0b6cfd0206a..2a518375ec6e 100644
--- a/examples/pylab_examples/spy_demos.py
+++ b/examples/pylab_examples/spy_demos.py
@@ -6,17 +6,17 @@
 Plot the sparsity pattern of arrays
 """
 
-from matplotlib.pyplot import figure, show
+import matplotlib.pyplot as plt
 import numpy as np
 
-fig = figure()
-ax1 = fig.add_subplot(221)
-ax2 = fig.add_subplot(222)
-ax3 = fig.add_subplot(223)
-ax4 = fig.add_subplot(224)
+fig, axs = plt.subplots(2, 2)
+ax1 = axs[0, 0]
+ax2 = axs[0, 1]
+ax3 = axs[1, 0]
+ax4 = axs[1, 1]
 
 x = np.random.randn(20, 20)
-x[5] = 0.
+x[5, :] = 0.
 x[:, 12] = 0.
 
 ax1.spy(x, markersize=5)
@@ -25,4 +25,4 @@
 ax3.spy(x)
 ax4.spy(x, precision=0.1)
 
-show()
+plt.show()
diff --git a/examples/pylab_examples/stackplot_demo.py b/examples/pylab_examples/stackplot_demo.py
index 609c0d0c1902..a9dceac9b913 100644
--- 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
index 42123185fb9b..e4ea9022806c 100644
--- 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
index b8893e5c81f3..aac441baa4f8 100644
--- 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='+')
@@ -142,11 +148,11 @@
     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
@@ -154,8 +160,8 @@
 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
index 70333d88b772..33446d686549 100644
--- a/examples/statistics/hexbin_demo.py
+++ b/examples/statistics/hexbin_demo.py
@@ -79,18 +79,17 @@
 
 gridsize = 30
 
-plt.subplot(211)
-plt.hexbin(x, y, C=z, gridsize=gridsize, marginals=True, cmap=plt.cm.RdBu,
-           vmax=abs(z).max(), vmin=-abs(z).max())
-plt.axis([xmin, xmax, ymin, ymax])
-cb = plt.colorbar()
-cb.set_label('mean value')
+fig, (ax0, ax1) = plt.subplots(2, 1)
 
+c = ax0.hexbin(x, y, C=z, gridsize=gridsize, marginals=True, cmap=plt.cm.RdBu,
+               vmax=abs(z).max(), vmin=-abs(z).max())
+ax0.axis([xmin, xmax, ymin, ymax])
+cb = fig.colorbar(c, ax=ax0)
+cb.set_label('mean value')
 
-plt.subplot(212)
-plt.hexbin(x, y, gridsize=gridsize, cmap=plt.cm.Blues_r)
-plt.axis([xmin, xmax, ymin, ymax])
-cb = plt.colorbar()
+c = ax1.hexbin(x, y, gridsize=gridsize, cmap=plt.cm.Blues_r)
+ax1.axis([xmin, xmax, ymin, ymax])
+cb = fig.colorbar(c, ax=ax1)
 cb.set_label('N observations')
 
 plt.show()
diff --git a/examples/subplots_axes_and_figures/subplot_toolbar.py b/examples/subplots_axes_and_figures/subplot_toolbar.py
index 343a35bdfb2b..dcaa6d8927fb 100644
--- a/examples/subplots_axes_and_figures/subplot_toolbar.py
+++ b/examples/subplots_axes_and_figures/subplot_toolbar.py
@@ -7,15 +7,15 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-fig = plt.figure()
-plt.subplot(221)
-plt.imshow(np.random.random((100, 100)))
-plt.subplot(222)
-plt.imshow(np.random.random((100, 100)))
-plt.subplot(223)
-plt.imshow(np.random.random((100, 100)))
-plt.subplot(224)
-plt.imshow(np.random.random((100, 100)))
+fig, axs = plt.subplots(2, 2)
+
+axs[0, 0].imshow(np.random.random((100, 100)))
+
+axs[0, 1].imshow(np.random.random((100, 100)))
+
+axs[1, 0].imshow(np.random.random((100, 100)))
+
+axs[1, 1].imshow(np.random.random((100, 100)))
 
 plt.subplot_tool()
 plt.show()
diff --git a/examples/text_labels_and_annotations/legend_demo.py b/examples/text_labels_and_annotations/legend_demo.py
index 2e42e36e0022..77db192ebe85 100644
--- a/examples/text_labels_and_annotations/legend_demo.py
+++ b/examples/text_labels_and_annotations/legend_demo.py
@@ -23,17 +23,19 @@
 t1 = np.arange(0.0, 2.0, 0.1)
 t2 = np.arange(0.0, 2.0, 0.01)
 
+fig, ax = plt.subplots()
+
 # note that plot returns a list of lines.  The "l1, = plot" usage
 # extracts the first element of the list into l1 using tuple
 # unpacking.  So l1 is a Line2D instance, not a sequence of lines
-l1, = plt.plot(t2, np.exp(-t2))
-l2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--o', t1, np.log(1 + t1), '.')
-l4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 's-.')
-
-plt.legend((l2, l4), ('oscillatory', 'damped'), loc='upper right', shadow=True)
-plt.xlabel('time')
-plt.ylabel('volts')
-plt.title('Damped oscillation')
+l1, = ax.plot(t2, np.exp(-t2))
+l2, l3 = ax.plot(t2, np.sin(2 * np.pi * t2), '--o', t1, np.log(1 + t1), '.')
+l4, = ax.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 's-.')
+
+ax.legend((l2, l4), ('oscillatory', 'damped'), loc='upper right', shadow=True)
+ax.set_xlabel('time')
+ax.set_ylabel('volts')
+ax.set_title('Damped oscillation')
 plt.show()
 
 
@@ -42,21 +44,21 @@
 
 x = np.linspace(0, 1)
 
+fig, (ax0, ax1) = plt.subplots(2, 1)
+
 # Plot the lines y=x**n for n=1..4.
-ax = plt.subplot(2, 1, 1)
 for n in range(1, 5):
-    plt.plot(x, x**n, label="n={0}".format(n))
-plt.legend(loc="upper left", bbox_to_anchor=[0, 1],
-           ncol=2, shadow=True, title="Legend", fancybox=True)
-ax.get_legend().get_title().set_color("red")
+    ax0.plot(x, x**n, label="n={0}".format(n))
+leg = ax0.legend(loc="upper left", bbox_to_anchor=[0, 1],
+                 ncol=2, shadow=True, title="Legend", fancybox=True)
+leg.get_title().set_color("red")
 
 # Demonstrate some more complex labels.
-ax = plt.subplot(2, 1, 2)
-plt.plot(x, x**2, label="multi\nline")
+ax1.plot(x, x**2, label="multi\nline")
 half_pi = np.linspace(0, np.pi / 2)
-plt.plot(np.sin(half_pi), np.cos(half_pi), label=r"$\frac{1}{2}\pi$")
-plt.plot(x, 2**(x**2), label="$2^{x^2}$")
-plt.legend(shadow=True, fancybox=True)
+ax1.plot(np.sin(half_pi), np.cos(half_pi), label=r"$\frac{1}{2}\pi$")
+ax1.plot(x, 2**(x**2), label="$2^{x^2}$")
+ax1.legend(shadow=True, fancybox=True)
 
 plt.show()
 
@@ -166,12 +168,12 @@ def create_artists(self, legend, orig_handle,
 
 x = np.linspace(0, 5, 100)
 
-plt.figure()
+fig, ax = plt.subplots()
 colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][:5]
 styles = ['solid', 'dashed', 'dashed', 'dashed', 'solid']
 lines = []
 for i, color, style in zip(range(5), colors, styles):
-    plt.plot(x, np.sin(x) - .1 * i, c=color, ls=style)
+    ax.plot(x, np.sin(x) - .1 * i, c=color, ls=style)
 
 
 # make proxy artists
@@ -180,7 +182,7 @@ def create_artists(self, legend, orig_handle,
 # set up the proxy artist
 lc = mcol.LineCollection(5 * line, linestyles=styles, colors=colors)
 # create the legend
-plt.legend([lc], ['multi-line'], handler_map={type(lc): HandlerDashedLines()},
-           handlelength=2.5, handleheight=3)
+ax.legend([lc], ['multi-line'], handler_map={type(lc): HandlerDashedLines()},
+          handlelength=2.5, handleheight=3)
 
 plt.show()