#### Overview of this book

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
NumPy Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Winding Along with IPython
Advanced Indexing and Array Concepts
Get to Grips with Commonly Used Functions
Connecting NumPy with the Rest of the World
Audio and Image Processing
Special Arrays and Universal Functions
Profiling and Debugging
Quality Assurance
Speed Up Code with Cython
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.

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...```