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

The commands – scatter and gather

Now we are ready to look at the entire script for our demo problem, the scalar product:

from mpi4py import MPI
import numpy as np

comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nprocessors = comm.Get_size()
import splitarray as spa

if rank == 0:
# Here we generate data for the example
n = 150
u = 0.1*np.arange(n)
v = - u
u_split = spa.split_array(u, nprocessors)
v_split = spa.split_array(v, nprocessors)
else:
# On all processor we need variables with these names,
# otherwise we would get an Exception "Variable not defined" in
# the scatter command below
u_split = None
v_split = None
# These commands run now on all processors
u_split = comm.scatter(u_split, root=0) # the data is portion wise
# distributed from root
v_split = comm.scatter(v_split, root=0)
# Each processor computes its part of the scalar product
partial_dot = u_split@v_split
# Each processor reports its result...