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NumPy Cookbook

You're reading from   NumPy Cookbook If you're a Python developer with basic NumPy skills, the 70+ recipes in this brilliant cookbook will boost your skills in no time. Learn to raise productivity levels and code faster and cleaner with the open source mathematical library.

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Product type Paperback
Published in Oct 2012
Publisher Packt
ISBN-13 9781849518925
Length 226 pages
Edition 1st Edition
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Toc

Table of Contents (17) Chapters Close

NumPy Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Winding Along with IPython 2. Advanced Indexing and Array Concepts FREE CHAPTER 3. Get to Grips with Commonly Used Functions 4. Connecting NumPy with the Rest of the World 5. Audio and Image Processing 6. Special Arrays and Universal Functions 7. Profiling and Debugging 8. Quality Assurance 9. Speed Up Code with Cython 10. Fun with Scikits Index

Using the buffer protocol


C-based Python objects have a so called "buffer interface". Python objects can expose their data for direct access without the need to copy it. The buffer protocol enables us to communicate with other Python software such as the Python Imaging Library (PIL) . We will see an example of saving a PIL image from a NumPy array.

Getting ready

Install PIL and SciPy, if necessary. Check the See Also section of this recipe for instructions.

How to do it...

First, we need a NumPy array with which to play.

  1. Create an array from image data.

    In previous chapters, we saw how to load the "Lena" sample image of Lena Soderberg. We will create an array filled with zeroes, and populate the alpha channel with the image data:

    lena = scipy.misc.lena()
    data = numpy.zeros((lena.shape[0], lena.shape[1], 4), dtype=numpy.int8)
    data[:,:,3] = lena.copy()
  2. Save the data as a PIL image.

    Now, we will use the PIL API to save the data as a RGBA image:

    img = Image.frombuffer("RGBA", lena.shape, data)
    img.save...
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