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

Shelves


Objects in dictionaries can be accessed by keys. There is a similar way to access particular data in a file by first assigning it a key. This is possible by using the module shelve:

from contextlib import closing
import shelve as sv
# opens a data file (creates it before if necessary)
with closing(sv.open('datafile')) as data:
    A = array([[1,2,3],[4,5,6]])     
    data['my_matrix'] = A  # here we created a key

In the section File handling, we saw that the built-in open command generates a context manager, and we saw why this is important for handling external resources, such as files. In contrast to this command, sv.open does not create a context manager by itself. The closing command from the contextlib module is needed to transform it into an appropriate context manager. Consider the following example of restoring the file:

from contextlib import closing
import shelve as sv
with closing(sv.open('datafile')) as data: # opens a data file
...