#!/usr/bin/env python
# coding: utf-8

# > This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.
# 

# # 5.2. Accelerating array computations with Numexpr

# Let's import NumPy and Numexpr.

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import numpy as np
import numexpr as ne


# We generate three large vectors.

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x, y, z = np.random.rand(3, 1000000)


# Now, we evaluate the time taken by NumPy to calculate a complex algebraic expression involving our vectors.

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get_ipython().run_line_magic('timeit', 'x + (y**2 + (z*x + 1)*3)')


# And now, the same calculation performed by Numexpr. We need to give the formula as a string as Numexpr will parse it and compile it.

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get_ipython().run_line_magic('timeit', "ne.evaluate('x + (y**2 + (z*x + 1)*3)')")


# Numexpr also makes use of multicore processors. Here, we have 4 physical cores and 8 virtual threads with hyperthreading. We can specify how many cores we want numexpr to use.

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ne.ncores


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for i in range(1, 5):
    ne.set_num_threads(i)
    get_ipython().run_line_magic('timeit', "ne.evaluate('x + (y**2 + (z*x + 1)*3)')")


# > You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).
# 
# > [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages).