flopscope.numpy.fromiter

fnp.fromiter(*args, **kwargs)[flopscope source]

Create a new 1-dimensional array from an iterable object.

Adapted from NumPy docs np.fromiter

Areacore

Typecustom

NumPy Refnp.fromiter

Cost

16×per-operation

Flopscope Context

Create array from an iterable. Cost: numel(output).

Parameters

iter:iterable object: An iterable object providing data for the array.
dtype:data-type: The data-type of the returned array.
Changed in version 1.23.
count:int, optional: The number of items to read from iterable. The default is -1, which means all data is read.
like:array_like, optional: Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.
Added in version 1.20.0.

Returns

out:ndarray: The output array.

Notes

Specify count to improve performance. It allows fromiter to pre-allocate the output array, instead of resizing it on demand.

Examples

>>> import flopscope.numpy as fnp
>>> iterable = (x*x for x in range(5))
>>> flops.fromiter(iterable, float)
array([  0.,   1.,   4.,   9.,  16.])

A carefully constructed subarray dtype will lead to higher dimensional results:

>>> iterable = ((x+1, x+2) for x in range(5))
>>> flops.fromiter(iterable, dtype=flops.dtype((int, 2)))
array([[1, 2],
       [2, 3],
       [3, 4],
       [4, 5],
       [5, 6]])