#### 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
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

## Indexing with booleans

Boolean indexing is indexing based on a boolean array and falls in the category fancy indexing.

### How to do it...

We will apply this indexing technique to an image:

1. Image with dots on the diagonal.

This is in some way similar to the Fancy indexing recipe, in this chapter. This time we select modulo `4` points on the diagonal of the image:

```def get_indices(size):
arr = numpy.arange(size)
return arr % 4 == 0```

Then we just apply this selection and plot the points:

```lena1 = lena.copy()
xindices = get_indices(lena.shape[0])
yindices = get_indices(lena.shape[1])
lena1[xindices, yindices] = 0
matplotlib.pyplot.subplot(211)
matplotlib.pyplot.imshow(lena1)```
2. Set to `0` based on value.

Select array values between quarter and three-quarters of the maximum value and set them to `0`:

`lena2[(lena > lena.max()/4) & (lena < 3 * lena.max()/4)] = 0`

The plot with the two new images will look like the following screenshot:

The following is the complete code for this recipe:

`import scipy.misc...`