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Advanced Python Programming

You're reading from   Advanced Python Programming Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns

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Product type Course
Published in Feb 2019
Publisher Packt
ISBN-13 9781838551216
Length 672 pages
Edition 1st Edition
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Authors (3):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Sakis Kasampalis Sakis Kasampalis
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Sakis Kasampalis
Dr. Gabriele Lanaro Dr. Gabriele Lanaro
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Dr. Gabriele Lanaro
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Table of Contents (41) Chapters Close

Title Page
Copyright
About Packt
Contributors
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 Advanced Introduction to Concurrent and Parallel Programming Amdahl's Law Working with Threads in Python Using the with Statement in Threads Concurrent Web Requests Working with Processes in Python Reduction Operators in Processes Concurrent Image Processing Introduction to Asynchronous Programming Implementing Asynchronous Programming in Python Building Communication Channels with asyncio Deadlocks Starvation Race Conditions The Global Interpreter Lock The Factory Pattern The Builder Pattern Other Creational Patterns The Adapter Pattern The Decorator Pattern The Bridge Pattern The Facade Pattern Other Structural Patterns The Chain of Responsibility Pattern The Command Pattern The Observer Pattern 1. Appendix 2. Other Books You May Enjoy Index

Using Cython with Jupyter


Optimizing Cython code requires substantial trial and error. Fortunately, Cython tools can be conveniently accessed through the Jupyter notebook for a more streamlined and integrated experience.

You can launch a notebook session by typing jupyter notebook in the command line and you can load the Cython magic by typing %load_ext cython in a cell.

As already mentioned earlier, the %%cython magic can be used to compile and load the Cython code inside the current session. As an example, we may copy the contents of cheb.py into a notebook cell:

    %%cython
    import numpy as np

    cdef int max(int a, int b):
        return a if a > b else b

    cdef int chebyshev(int x1, int y1, int x2, int y2):
        return max(abs(x1 - x2), abs(y1 - y2))

    def c_benchmark():
        a = np.random.rand(1000, 2)
        b = np.random.rand(1000, 2)

        for x1, y1 in a:
           for x2, y2 in b:
               chebyshev(x1, x2, y1, y2)

A useful feature of the %%cython magic...

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