You're reading from Numpy Beginner's Guide (Update) Build efficient, high-speed programs using the high-performance NumPy mathematical library

Product type Paperback

Published in Jun 2015

Publisher

ISBN-13 9781785281969

Length 348 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Author (1):

Ivan Idris

View More author details

Table of Contents (16) Chapters

Preface

1. NumPy Quick Start FREE CHAPTER

2. Beginning with NumPy Fundamentals

3. Getting Familiar with Commonly Used Functions

4. Convenience Functions for Your Convenience

5. Working with Matrices and ufuncs

6. Moving Further with NumPy Modules

7. Peeking into Special Routines

8. Assuring Quality with Testing

9. Plotting with matplotlib

10. When NumPy Is Not Enough – SciPy and Beyond

11. Playing with Pygame

A. Pop Quiz Answers

B. Additional Online Resources

C. NumPy Functions' References

Index

Time for action – comparing with assert_array_almost_equal_nulp

Let's see the assert_array_almost_equal_nulp() function in action:

Determine the machine epsilon with the finfo() function:
```
eps = np.finfo(float).eps
print("EPS", eps)
```
The epsilon would be as follows:
```
EPS 2.22044604925e-16
```

Compare 1.0 with 1 + epsilon using the assert_almost_equal_nulp() function. Do the same for 1 + 2 * epsilon:

print("1", np.testing.assert_array_almost_equal_nulp(1.0, 1.0 + eps))
print("2", np.testing.assert_array_almost_equal_nulp(1.0, 1.0 + 2 * eps))

The result is as follows:

1 None
2
Traceback (most recent call last):
  …
 assert_array_almost_equal_nulp
    raise AssertionError(msg)
AssertionError: X and Y are not equal to 1 ULP (max is 2)

What just happened?

We determined the machine epsilon with the finfo() function. We then compared 1.0 with 1 + epsilon with the assert_almost_equal_nulp() function. This test passed however, adding another epsilon resulted in...