`numpy.lib:Arrayterator`

source: /numpy/lib/__init__.py :16

Signature

class Arrayterator ( var , buf_size = None )

Summary

Buffered iterator for big arrays.

Extended Summary

Arrayterator creates a buffered iterator for reading big arrays in small contiguous blocks. The class is useful for objects stored in the file system. It allows iteration over the object without reading everything in memory; instead, small blocks are read and iterated over.

Arrayterator can be used with any object that supports multidimensional slices. This includes NumPy arrays, but also variables from Scientific.IO.NetCDF or pynetcdf for example.

Parameters

var : array_like: The object to iterate over.
buf_size : int, optional: The buffer size. If buf_size is supplied, the maximum amount of data that will be read into memory is buf_size elements. Default is None, which will read as many element as possible into memory.

Attributes

var
buf_size
start
stop
step
shape
flat

Notes

The algorithm works by first finding a "running dimension", along which the blocks will be extracted. Given an array of dimensions (d1, d2, ..., dn), e.g. if buf_size is smaller than d1, the first dimension will be used. If, on the other hand, d1 < buf_size < d1*d2 the second dimension will be used, and so on. Blocks are extracted along this dimension, and when the last block is returned the process continues from the next dimension, until all elements have been read.

Examples

import numpy as np
a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
a_itor = np.lib.Arrayterator(a, 2)
a_itor.shape

✓

Now we can iterate over ``a_itor``, and it will return arrays of size two. Since `buf_size` was smaller than any dimension, the first dimension will be iterated over first:

Aliases

numpy.lib.Arrayterator