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Python High Performance, Second Edition

You're reading from   Python High Performance, Second Edition Build high-performing, concurrent, and distributed applications

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787282896
Length 270 pages
Edition 2nd Edition
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Author (1):
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Dr. Gabriele Lanaro Dr. Gabriele Lanaro
Author Profile Icon Dr. Gabriele Lanaro
Dr. Gabriele Lanaro
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Table of Contents (10) Chapters Close

Preface Benchmarking and Profiling FREE CHAPTER Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Distributed Processing Designing for High Performance

Particle simulator in Cython

Now that we have a basic understanding of how Cython works, we can rewrite the ParticleSimulator.evolve method. Thanks to Cython, we can convert our loops in C, thus removing the overhead introduced by the Python interpreter.

In Chapter 3, Fast Array Operations with NumPy and Pandas, we wrote a fairly efficient version of the evolve method using NumPy. We can rename the old version as evolve_numpy to differentiate it from the new version:

    def evolve_numpy(self, dt): 
timestep = 0.00001
nsteps = int(dt/timestep)

r_i = np.array([[p.x, p.y] for p in self.particles])
ang_speed_i = np.array([p.ang_speed for p in self.particles])
v_i = np.empty_like(r_i)

for i in range(nsteps):
norm_i = np.sqrt((r_i ** 2).sum(axis=1))

v_i = r_i[:, [1, 0]]
v_i[:, 0] *= -1
v_i /= norm_i[:, np.newaxis] ...
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