## Thursday, December 29, 2011

### Book review: Numpy 1.5 Beginner's Guide

I got the chance to read the book NumPy 1.5 Beginner's Guide written by Ivan Idris and published by Packt Publishing last month. My impression of the book was quite positive. It's a book based on examples, which incrementally introduce all the main features of the library. It is written with a simple language, and it is always easy to understand.

## Contents and structure

The organization and flow are good. The ten chapters contain well thought out examples that you can use as building blocks for your scientific computing projects. Every example is structured in this way:
• An introduction to the problem that the example will solve.
• The code, commented line by line.
• The result of the code.
• A short recap of how the problem has been solved.
• And, sometimes, a multiple choice question to help the reader to test his own understanding.
There is no attempt at teaching the mathematics behind the examples. Every example is a "how to" that can help you to learn how to use the library and can save hours of searching through the official documentation and more complicated texts.

The chapters 1,2 and 3 contain the starting points to use NumPy. They explain how to install NumPy, how to handle the NumPy arrays and how to use some of the basic mathematical/statistical functions provided by the library. Chapters 4 through 7 cover the basics about handling matrices, how to load and write data, how to write universal functions and cover some of the basic modules that are discussed. Chapter 8 explains how to use the unit test functions provided by NumPy. Finally, chapters 9 and 10 (my favorites!) introduce how to integrate NumPy with Matplotlib and SciPy.

## Who is this book for?

This book is aimed at people who know Python and need to start using scientific computing in their programs. It is also suitable for people who use another scientific computing environment, such as Matlab, and want quick-start introduction to NumPy.

## Thursday, December 15, 2011

### Polar Charts with matplolib

A polar system is a two-dimensional coordinate system, where there are two coordinates: the radial and the angular coordinates. The radial coordinate denotes the point distance from a central point (pole) and the angular coordinate denotes the angle required to reach the point from the 0 degree ray (polar axis). Let's see an example of how to make polar charts with matplotlib:
```from pylab import figure,polar,show
from numpy import arange,pi,cos

theta = arange(0, 2, 1./180)*pi # angular coordinates
figure(1)
polar(3*theta, theta/5) # drawing a spiral
figure(2)
polar(theta, cos(4*theta)) # drawing the polar rose
show()
```
The result of this script consists of two charts. The first with the spiral
and the second with the polar rose

## Thursday, December 8, 2011

### Lissajous curves

And after the Epitrochoids, we're going to see another family of wonderful figures: The Lissajous curves. The equations that describe these curves are the following

the curves vary with respect the parameter t and their appearance is determined by the ratio a/b and the value of δ.
As usual, I made a snippet to visualize them:
```from numpy import sin,pi,linspace
from pylab import plot,show,subplot

a = [1,3,5,3] # plotting the curves for
b = [1,5,7,4] # different values of a/b
delta = pi/2
t = linspace(-pi,pi,300)

for i in range(0,4):
x = sin(a[i] * t + delta)
y = sin(b[i] * t)
subplot(2,2,i+1)
plot(x,y)

show()
```
This is the result

## Wednesday, December 7, 2011

### Waiting for NumPy 1.5 Beginner’s Guide

I'm waiting to receive a copy of NumPy 1.5 Beginner’s Guide. Lately, I have read a sample chapter of the book and I was surprised because it contains a lot of code examples and clearly explains how to use the code and what it means.

I found on the publisher web site that the book is supposed to cover a lot of topics, from the NumPy installation to the integration of NumPy in real scientific computing projects. If the entire book is like the sample chapter, it will be one of my favorite resources about the NumPy. I can't wait to have the book!

Stay tuned for the complete review ;)