Book Image

Scientific Computing with Python - Second Edition

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

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
20
About Packt
22
References

5.6.3 Sparse matrix methods

There are methods to convert one sparse type into another or into an array:

AS.toarray # converts sparse formats to a numpy array 
AS.tocsr
AS.tocsc
AS.tolil

The type of a sparse matrix can be inspected by the methods issparse, isspmatrix_lil, isspmatrix_csr, and isspmatrix_csc.

Elementwise operations +, *, /, and ** on sparse matrices are defined as for NumPy arrays. Regardless of the sparse matrix format of the operands, the result is always csr_matrix. Applying elementwise operating functions to sparse matrices requires first transforming them to either CSR or CSC format and applying the functions to their data attribute, as demonstrated by the next example.

The elementwise sine of a sparse matrix can be defined by an operation on its data attribute:

import scipy.sparse as sp
def sparse_sin(A):
    if not (sp.isspmatrix_csr(A) or sp.isspmatrix_csc(A)):
        A = A.tocsr()
    A.data = sin(A.data...