numpy.lib.arrayterator.Arrayterator

class documentation

class Arrayterator:

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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 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

See Also

ndenumerate : Multidimensional array iterator. flatiter : Flat array iterator. 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. 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

>>> 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) # doctest: +SKIP
>>> # [[[[0 1]]]] (1, 1, 1, 2)
Method __array__ Return corresponding data.
Method __getattr__ Undocumented
Method __getitem__ Return a new arrayterator.
Method __init__ Undocumented
Method __iter__ Undocumented
Instance Variable buf​_size Undocumented
Instance Variable start Undocumented
Instance Variable step Undocumented
Instance Variable stop Undocumented
Instance Variable var Undocumented
Property flat A 1-D flat iterator for Arrayterator objects.
Property shape The shape of the array to be iterated over.
def __array__(self):
Return corresponding data.
def __getattr__(self, attr):

Undocumented

def __getitem__(self, index):
Return a new arrayterator.
def __init__(self, var, buf_size=None):

Undocumented

def __iter__(self):

Undocumented

buf_size =

Undocumented

start =

Undocumented

step =

Undocumented

stop =

Undocumented

var =

Undocumented

@property
flat =

A 1-D flat iterator for Arrayterator objects.

This iterator returns elements of the array to be iterated over in Arrayterator one by one. It is similar to flatiter.

See Also

Arrayterator flatiter

Examples

>>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
>>> a_itor = np.lib.Arrayterator(a, 2)
>>> for subarr in a_itor.flat:
...     if not subarr:
...         print(subarr, type(subarr))
...
0 <class 'numpy.int64'>
@property
shape =

The shape of the array to be iterated over.

For an example, see Arrayterator.