matplotlib · QuLogic · Oct 9, 2018 · Oct 9, 2018
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -8,7 +8,6 @@
 The most common way to plot images in Matplotlib is with
 :meth:`~.axes.Axes.imshow`. The following examples demonstrate much of the
 functionality of imshow and the many images you can create.
-
 """
 
 import numpy as np
@@ -54,7 +53,7 @@
 
 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 = np.frombuffer(s, np.uint16).astype(float).reshape((w, h))
 A /= A.max()
 
 fig, ax = plt.subplots()

diff --git a/examples/misc/agg_buffer.py b/examples/misc/agg_buffer.py
@@ -21,7 +21,7 @@
 s, (width, height) = agg.print_to_buffer()
 
 # Convert to a NumPy array.
-X = np.fromstring(s, np.uint8).reshape((height, width, 4))
+X = np.frombuffer(s, np.uint8).reshape((height, width, 4))
 
 # Pass off to PIL.
 from PIL import Image

diff --git a/examples/specialty_plots/mri_demo.py b/examples/specialty_plots/mri_demo.py
@@ -3,24 +3,21 @@
 MRI
 ===
 
-
 This example illustrates how to read an image (of an MRI) into a NumPy
 array, and display it in greyscale using `imshow`.
-
 """
 
 import matplotlib.pyplot as plt
 import matplotlib.cbook as cbook
-import matplotlib.cm as cm
 import numpy as np
 
 
-# Data are 256x256 16 bit integers
+# Data are 256x256 16 bit integers.
 with cbook.get_sample_data('s1045.ima.gz') as dfile:
-    im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
+    im = np.frombuffer(dfile.read(), np.uint16).reshape((256, 256))
 
 fig, ax = plt.subplots(num="MRI_demo")
-ax.imshow(im, cmap=cm.gray)
+ax.imshow(im, cmap="gray")
 ax.axis('off')
 
 plt.show()
diff --git a/examples/specialty_plots/mri_with_eeg.py b/examples/specialty_plots/mri_with_eeg.py
@@ -7,7 +7,6 @@
 histogram and some EEG traces.
 """
 
-
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.cbook as cbook
@@ -20,7 +19,7 @@
 
 # Load the MRI data (256x256 16 bit integers)
 with cbook.get_sample_data('s1045.ima.gz') as dfile:
-    im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
+    im = np.frombuffer(dfile.read(), np.uint16).reshape((256, 256))
 
 # Plot the MRI image
 ax0 = fig.add_subplot(2, 2, 1)

diff --git a/examples/user_interfaces/canvasagg.py b/examples/user_interfaces/canvasagg.py
@@ -42,7 +42,7 @@
 s, (width, height) = canvas.print_to_buffer()
 
 # Option 2a: Convert to a NumPy array.
-X = np.fromstring(s, np.uint8).reshape((height, width, 4))
+X = np.frombuffer(s, np.uint8).reshape((height, width, 4))
 
 # Option 2b: Pass off to PIL.
 from PIL import Image