Book Image

Scientific Computing with Python 3

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python 3

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
References

NumPy methods


NumPy has built-in methods for reading and writing NumPy array data to text files. These are numpy.loadtxt and numpy.savetxt.

savetxt

Writing an array to a text file is simple:

savetxt(filename,data)

There are two useful parameters given as strings, fmt and delimiter, which control the format and the delimiter between columns. The defaults are space for the delimiter and %.18e for the format, which corresponds to the exponential format with all digits. The formatting parameters are used as follows:

x = range(100) # 100 integers
savetxt('test.txt',x,delimiter=',')   # use comma instead of space
savetxt('test.txt',x,fmt='%d') # integer format instead of float with e

 loadtxt

Reading to an array from a text file is done with the help of the following syntax:

filename = 'test.txt'
data = loadtxt(filename)

Due to the fact that each row in an array must have the same length, each row in the text file must have the same number of elements. Similar to savetxt, the default values...