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Mastering Python Scientific Computing

You're reading from   Mastering Python Scientific Computing A complete guide for Python programmers to master scientific computing using Python APIs and tools

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781783288823
Length 300 pages
Edition 1st Edition
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Author (1):
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 Kumar Mehta Kumar Mehta
Author Profile Icon Kumar Mehta
Kumar Mehta
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Table of Contents (12) Chapters Close

Preface 1. The Landscape of Scientific Computing – and Why Python? 2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes FREE CHAPTER 3. Efficiently Fabricating and Managing Scientific Data 4. Scientific Computing APIs for Python 5. Performing Numerical Computing 6. Applying Python for Symbolic Computing 7. Data Analysis and Visualization 8. Parallel and Large-scale Scientific Computing 9. Revisiting Real-life Case Studies 10. Best Practices for Scientific Computing Index

The simplify and collect operations


The SymPy module also supports the simplify and collect operations on the given expression. There are options for simplifying various types of functions, including trigonometric functions, Bessel-type functions, combinatorial expressions, and others.

The following program demonstrates the simplification of expressions involving polynomial and trigonometric functions:

from sympy import simplify, cos, sin, trigsimp, cancel
from sympy import sqrt, count_ops, oo, symbols, log
from sympy.abc import x, y

expr = (2*x + 3*x**2)/(4*x*sin(y)**2 + 2*x*cos(y)**2)
expr
simplify(expr)

trigsimp(expr)
cancel(_)

root = 4/(sqrt(2)+3)
simplify(root, ratio=1) == root
count_ops(simplify(root, ratio=oo)) > count_ops(root)
x, y = symbols('x y', positive=True)
expr2 = log(x) + log(y) + log(x)*log(1/y)

expr3 = simplify(expr2)
expr3
count_ops(expr2)
count_ops(expr3)
print(count_ops(expr2, visual=True))
print(count_ops(expr3, visual=True))
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