Speed Up Code with Cython | NumPy Cookbook

Sign In Start Free Trial

Book Overview & Buying
Table Of Contents

NumPy Cookbook

By : Ivan Idris

4 (17)

NumPy Cookbook

4 (17)

By: Ivan Idris

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

NumPy Cookbook

Credits

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Preface

Free Chapter

Winding Along with IPython

Winding Along with IPython

Introduction

Installing IPython

Using IPython as a shell

Reading manual pages

Installing Matplotlib

Running a web notebook

Exporting a web notebook

Importing a web notebook

Configuring a notebook server

Exploring the SymPy profile

Advanced Indexing and Array Concepts

Advanced Indexing and Array Concepts

Introduction

Installing SciPy

Installing PIL

Resizing images

Creating views and copies

Flipping Lena

Fancy indexing

Indexing with a list of locations

Indexing with booleans

Stride tricks for Sudoku

Broadcasting arrays

Get to Grips with Commonly Used Functions

Get to Grips with Commonly Used Functions

Introduction

Summing Fibonacci numbers

Finding prime factors

Finding palindromic numbers

The steady state vector determination

Discovering a power law

Trading periodically on dips

Simulating trading at random

Sieving integers with the Sieve of Erasthothenes

Connecting NumPy with the Rest of the World

Connecting NumPy with the Rest of the World

Introduction

Using the buffer protocol

Using the array interface

Exchanging data with MATLAB and Octave

Installing RPy2

Interfacing with R

Installing JPype

Sending a NumPy array to JPype

Installing Google App Engine

Deploying NumPy code in the Google cloud

Running NumPy code in a Python Anywhere web console

Setting up PiCloud

Audio and Image Processing

Audio and Image Processing

Introduction

Loading images into memory map

Combining images

Blurring images

Repeating audio fragments

Generating sounds

Designing an audio filter

Edge detection with the Sobel filter

Special Arrays and Universal Functions

Special Arrays and Universal Functions

Introduction

Creating a universal function

Finding Pythagorean triples

Performing string operations with chararray

Creating a masked array

Ignoring negative and extreme values

Creating a scores table with recarray

Profiling and Debugging

Profiling and Debugging

Introduction

Profiling with timeit

Profiling with IPython

Installing line_profiler

Profiling code with line_profiler

Profiling code with the cProfile extension

Debugging with IPython

Debugging with pudb

Quality Assurance

Quality Assurance

Introduction

Installing Pyflakes

Performing static analysis with Pyflakes

Analyzing code with Pylint

Speed Up Code with Cython

Speed Up Code with Cython

Introduction

Installing Cython

Building a Hello World program

Using Cython with NumPy

Calling C functions

Profiling Cython code

Approximating factorials with Cython

Fun with Scikits

Fun with Scikits

Introduction

Installing scikits-learn

Loading an example dataset

Clustering Dow Jones stocks with scikits-learn

Installing scikits-statsmodels

Performing a normality test with scikits-statsmodels

Installing scikits-image

Detecting corners

Detecting edges

Installing Pandas

Estimating stock returns correlation with Pandas

Loading data as pandas objects from statsmodels

Resampling time series data

Index

Index

Calling C functions

We can call C functions from Cython. For instance, in this example, we will call the C log function. This function works on a single number only. Remember that the NumPy log function can also work with arrays. We will compute the so-called log returns of stock prices.

How to do it...

We will start by writing some Cython code:

Write the .pyx file.
First, we need to import the C log function from the libc namespace. Second, we will apply this function to numbers in a for loop. Finally, we will use the NumPy diff function to get the first order difference between the log values in the second step.
```
from libc.math cimport log
import numpy

def logrets(numbers):
   logs = [log(x) for x in numbers] 
   return numpy.diff(logs)
```
Building has been covered in the previous recipes already. We only need to change some values in the setup.py file.
Plot the log returns.
Let's download stock price data with matplotlib, again. Apply the Cython logrets function that we just created on the...

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

NumPy Cookbook

Search

Your notes and bookmarks