Book Image

Python High Performance, Second Edition - Second Edition

By : Dr. Gabriele Lanaro
Book Image

Python High Performance, Second Edition - Second Edition

By: Dr. Gabriele Lanaro

Overview of this book

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.
Table of Contents (10 chapters)

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] ...