As Numexpr was added to the Fedora repositories only recently, it may be time to quickly mention what it is and what is new in version 2.0. According to Numexpr website:
The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime.
Example use:
In [1]: import numpy as np In [2]: import numexpr as ne In [3]: a = np.random.rand(1e6) In [4]: b = np.random.rand(1e6) In [5]: timeit ne.evaluate('a**b') 10 loops, best of 3: 29.5 ms per loop
Also, array slicing can be used with the local_dict argument:
In [10]: timeit ne.evaluate('c**d', \ local_dict={'c':a[1:], 'd':b[:-1]}) 10 loops, best of 3: 28 ms per loop
Computations are multithreaded and bypass the GIL. Version 2.0 comes with a new virtual machine which speeds up broadcasting operations, on-the-fly translation of dtypes and Fortran-ordered arrays. The associated drawback is a slower start of the virtual machine. But overall, the gain is important for large arrays.
With a low number of elements and simple calculations, numexpr should not be used:
In [15]: %timeit a**2 + b**2 - 2*a*b 100000 loops, best of 3: 13 us per loop In [16]: %timeit ne.evaluate('a**2 + b**2 - 2*a*b') 10000 loops, best of 3: 31.1 us per loop
However, with a higher complexity of operations, numexpr can come pretty close to NumPy on a small amount of elements.
Around 10⁶ elements, a good improvement is indeed visible:
In [7]: a = np.random.rand(1e6) In [8]: b = np.random.rand(1e6) In [12]: %timeit a**2 + b**2 - 2*a*b 100 loops, best of 3: 19.5 ms per loop In [10]: %timeit ne.evaluate('a**2 + b**2 - 2*a*b') 100 loops, best of 3: 2.55 ms per loop
The following graph shows the impact of the number of threads on the computation time. numexpr.evaluate() was used with out=an_existing_array to avoid the creation of a new array as output.
Each point was computed 9 times. Each plotted point is the median of this set, and the error bars show the best/worst case of the time ratio.
For some reason, there is a gap around 2000 elements, when the computations are multithreaded. Any suggestion about its origin would be welcome !
Overall, Numexpr is a powerful python module, speeding up array operations while reducing their memory requirements. Thanks to the developers. A list of supported functions is available at Numexpr website. By the way, PyTables makes a good use of Numexpr. I might write about that soon.
Depuis le mois d'octobre, on peut profiter du système 
J'ai ainsi pu prendre le temps d'ajouter quelques chemins sur la carte, dans les environs de Crans ainsi que 
Aperçu de l'interface de pymecavideo. Après avoir sélectionné quelques points sur la vidéo, les vecteurs vitesses sont visibles. Le logiciel propose également des vidéos du mouvement dans l'un ou l'autre des référentiels décrits.
Indication du temps de calcul en fonction de la taille de l'image (cf légende). Utiliser la multi-résolution permet de gagner beaucoup de temps sur les grosses images, ainsi que de l'occupation mémoire.
Panorama depuis Perroy
Lâché de ballons nocturne
Un temps parfois maussade mais très agréable pour la marche !
Le quartier du Vieux-Port vu depuis le sud de Montréal, avec la tour de l'horloge.
Temps de calcul: 1m17s