Buffered iterator for big arrays.
Arrayteratorcreates 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.
Arrayteratorcan be used with any object that supports multidimensional slices. This includes NumPy arrays, but also variables from Scientific.IO.NetCDF or pynetcdf for example.
- 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.
- Multidimensional array iterator.
- Flat array iterator.
- Create a memory-map to an array stored in a binary file on disk.
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*d2the 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.
>>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6) >>> a_itor = np.lib.Arrayterator(a, 2) >>> a_itor.shape (3, 4, 5, 6)
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:
>>> for subarr in a_itor: ... if not subarr.all(): ... print(subarr, subarr.shape) ... [[[[0 1]]]] (1, 1, 1, 2)