在磁盘上保留numpy数组的最佳方法 [英] best way to preserve numpy arrays on disk
问题描述
我正在寻找一种保留大型numpy数组的快速方法.我想将它们以二进制格式保存到磁盘中，然后相对较快地将它们读回到内存中.不幸的是，cPickle不够快.
I am looking for a fast way to preserve large numpy arrays. I want to save them to the disk in a binary format, then read them back into memory relatively fastly. cPickle is not fast enough, unfortunately.
我找到了 numpy.savez 和 numpy.load .但是奇怪的是，numpy.load将一个npy文件加载到内存映射"中.这意味着对数组的常规操作确实很慢.例如，这样的事情真的很慢:
I found numpy.savez and numpy.load. But the weird thing is, numpy.load loads a npy file into "memory-map". That means regular manipulating of arrays really slow. For example, something like this would be really slow:
#!/usr/bin/python
import numpy as np;
import time;
from tempfile import TemporaryFile
n = 10000000;
a = np.arange(n)
b = np.arange(n) * 10
c = np.arange(n) * -0.5
file = TemporaryFile()
np.savez(file,a = a, b = b, c = c);
file.seek(0)
t = time.time()
z = np.load(file)
print "loading time = ", time.time() - t
t = time.time()
aa = z['a']
bb = z['b']
cc = z['c']
print "assigning time = ", time.time() - t;
更准确地说，第一行会非常快，但是将数组分配给obj的其余行却很慢:
more precisely, the first line will be really fast, but the remaining lines that assign the arrays to obj are ridiculously slow:
loading time = 0.000220775604248
assining time = 2.72940087318
有没有更好的方法来保存numpy数组?理想情况下，我希望能够在一个文件中存储多个数组.
Is there any better way of preserving numpy arrays? Ideally, I want to be able to store multiple arrays in one file.
推荐答案
我是hdf5的忠实拥护者，用于存储大型numpy数组.在python中处理hdf5有两种选择:
I'm a big fan of hdf5 for storing large numpy arrays. There are two options for dealing with hdf5 in python:
两者均旨在有效地处理numpy数组.
Both are designed to work with numpy arrays efficiently.
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