Avoid leaks with cbook.get_sample_data.

QuLogic · QuLogic · commit 5b8efe1b6689 · 2017-05-29T04:01:50.000-04:00
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)
@@ -53,8 +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
@@ -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
 
diff --git 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
@@ -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)
