Update docs to talk about how we support Pillow, not PIL.

Author: mdboom

INSTALL

@@ -209,7 +209,13 @@ libpng 1.2 (or later)
     using pip, easy_install or installing from source, the installer
     will attempt to download and install `pyparsing` from PyPI.
 
-**Optional**
+**Optional dependencies**
+
+`Pillow http://python-imaging.github.io/`__
+    If Pillow is installed, matplotlib can read and write a larger
+    selection of image file formats.
+
+**Optional GUI frameworks**
 
 These are optional packages which you may want to install to use
 matplotlib with a user interface toolkit. See

doc/faq/howto_faq.rst

@@ -693,9 +693,9 @@ or by saving to a file handle::
     import sys
     fig.savefig(sys.stdout)
 
-Here is an example using the Python Imaging Library (PIL).  First, the figure
-is saved to a StringIO object which is then fed to PIL for further
-processing::
+Here is an example using `Pillow <http://python-imaging.github.io/>__.
+First, the figure is saved to a StringIO object which is then fed to
+Pillow for further processing::
 
     import StringIO, Image
     imgdata = StringIO.StringIO()

doc/users/image_tutorial.rst

@@ -40,12 +40,12 @@ examples, if you use the -pylab method, you can skip the "mpimg." and
 Importing image data into Numpy arrays
 ===============================================
 
-Plotting image data is supported by the Python Image Library (`PIL 
-<http://www.pythonware.com/products/pil/>`_).  Natively, matplotlib
-only supports PNG images.  The commands shown below fall back on PIL
-if the native read fails.
+Plotting image data is supported by the `Pillow
+<http://python-imaging.github.io/>`_).  Natively, matplotlib only
+supports PNG images.  The commands shown below fall back on Pillow if the
+native read fails.
 
-The image used in this example is a PNG file, but keep that PIL
+The image used in this example is a PNG file, but keep that Pillow
 requirement in mind for your own data.
 
 Here's the image we're going to play with:
@@ -116,13 +116,13 @@ And here we go...
 
 Note the dtype there - float32.  Matplotlib has rescaled the 8 bit
 data from each channel to floating point data between 0.0 and 1.0.  As
-a side note, the only datatype that PIL can work with is uint8.
+a side note, the only datatype that Pillow can work with is uint8.
 Matplotlib plotting can handle float32 and uint8, but image
 reading/writing for any format other than PNG is limited to uint8
 data.  Why 8 bits? Most displays can only render 8 bits per channel
 worth of color gradation.  Why can they only render 8 bits/channel?
 Because that's about all the human eye can see.  More here (from a
-photography standpoint): `Luminous Landscape bit depth tutorial 
+photography standpoint): `Luminous Landscape bit depth tutorial
 <http://www.luminous-landscape.com/tutorials/bit-depth.shtml>`_.
 
 Each inner list represents a pixel.  Here, with an RGB image, there
@@ -179,7 +179,7 @@ channel of our data:
 
     In [6]: lum_img = img[:,:,0]
 
-This is array slicing.  You can read more in the `Numpy tutorial 
+This is array slicing.  You can read more in the `Numpy tutorial
 <http://www.scipy.org/Tentative_NumPy_Tutorial>`_.
 
 .. sourcecode:: ipython
@@ -336,9 +336,9 @@ and the computer has to draw in pixels to fill that space.
 
 .. sourcecode:: ipython
 
-    In [8]: import Image
-    In [9]: img = Image.open('stinkbug.png')    # Open image as PIL image object
-    In [10]: rsize = img.resize((img.size[0]/10,img.size[1]/10)) # Use PIL to resize
+    In [8]: from PIL import Image
+    In [9]: img = Image.open('stinkbug.png')    # Open image as Pillow image object
+    In [10]: rsize = img.resize((img.size[0]/10,img.size[1]/10)) # Use Pillow to resize
     In [11]: rsizeArr = np.asarray(rsize)  # Get array back
     In [12]: imgplot = plt.imshow(rsizeArr)
 
@@ -347,8 +347,8 @@ and the computer has to draw in pixels to fill that space.
     import matplotlib.pyplot as plt
     import matplotlib.image as mpimg
     import numpy as np
-    import Image
-    img = Image.open('../_static/stinkbug.png')  # opens the file using PIL - it's not an array yet
+    from PIL import Image
+    img = Image.open('../_static/stinkbug.png')  # opens the file using Pillow - it's not an array yet
     rsize = img.resize((img.size[0]/10,img.size[1]/10))  # resize the image
     rsizeArr = np.asarray(rsize)
     lum_img = rsizeArr[:,:,0]
@@ -368,8 +368,8 @@ Let's try some others:
         import matplotlib.pyplot as plt
         import matplotlib.image as mpimg
         import numpy as np
-        import Image
-        img = Image.open('../_static/stinkbug.png')  # opens the file using PIL - it's not an array yet
+        from PIL import Image
+        img = Image.open('../_static/stinkbug.png')  # opens the file using Pillow - it's not an array yet
         rsize = img.resize((img.size[0]/10,img.size[1]/10))  # resize the image
         rsizeArr = np.asarray(rsize)
         lum_img = rsizeArr[:,:,0]
@@ -385,8 +385,8 @@ Let's try some others:
         import matplotlib.pyplot as plt
         import matplotlib.image as mpimg
         import numpy as np
-        import Image
-        img = Image.open('../_static/stinkbug.png')  # opens the file using PIL - it's not an array yet
+        from PIL import Image
+        img = Image.open('../_static/stinkbug.png')  # opens the file using Pillow - it's not an array yet
         rsize = img.resize((img.size[0]/10,img.size[1]/10))  # resize the image
         rsizeArr = np.asarray(rsize)
         lum_img = rsizeArr[:,:,0]

examples/pylab_examples/to_numeric.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 """
 Use backend agg to access the figure canvas as an RGB string and then
-convert it to an array and pass the string it to PIL for
-rendering
+convert it to an array and pass the string it to Pillow for
+rendering.
 """
 
 import pylab