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.
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 :None:None:`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.
The object to iterate over.
The buffer size. If :None:None:`buf_size` is supplied, the maximum amount of data that will be read into memory is :None:None:`buf_size` elements. Default is None, which will read as many element as possible into memory.
Buffered iterator for big arrays.
flatiter
Flat array iterator.
memmap
Create a memory-map to an array stored in a binary file on disk.
ndenumerate
Multidimensional array iterator.
>>> 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 :None:None:`buf_size` was smaller than any dimension, the first dimension will be iterated over first:
>>> for subarr in a_itor:See :
... if not subarr.all():
... print(subarr, subarr.shape) # doctest: +SKIP
... # [[[[0 1]]]] (1, 1, 1, 2)
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.lib.arrayterator
numpy.lib.arrayterator.Arrayterator
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