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class numpy.lib.Arrayterator(var, buf_size=None)
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Buffered iterator for big arrays.
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 isbuf_size
elements. Default is None, which will read as many element as possible into memory.See also
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ndenumerate
- Multidimensional array iterator.
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flatiter
- Flat array iterator.
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memmap
- Create a memory-map to an array stored in a binary file on disk.
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. ifbuf_size
is smaller thand1
, 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
>>> 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. Sincebuf_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)
Attributes
shape
The shape of the array to be iterated over. flat
A 1-D flat iterator for Arrayterator objects. var buf_size start stop step -
numpy.lib.Arrayterator()
2017-01-10 18:14:40
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