flopscope.

flopscope.numpy.compress

fnp.compress(condition, a, axis=None, out=None)[flopscope source][numpy source]

Return selected slices of an array along given axis.

Adapted from NumPy docs np.compress

Areacore
Typecustom
NumPy Refnp.compress
Cost
4×per-operation
Flopscope Context

Return selected slices along axis. Cost: numel(input).

When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, compress is equivalent to extract.

Parameters

condition:1-D array of bools

Array that selects which entries to return. If len(condition) is less than the size of a along the given axis, then output is truncated to the length of the condition array.

a:array_like

Array from which to extract a part.

axis:int, optional

Axis along which to take slices. If None (default), work on the flattened array.

out:ndarray, optional

Output array. Its type is preserved and it must be of the right shape to hold the output.

Returns

compressed_array:ndarray

A copy of a without the slices along axis for which condition is false.

See also

Examples

>>> import flopscope.numpy as fnp
>>> a = flops.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> flops.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> flops.compress([False, True, True], a, axis=0)
array([[3, 4],
       [5, 6]])
>>> flops.compress([False, True], a, axis=1)
array([[2],
       [4],
       [6]])

Working on the flattened array does not return slices along an axis but selects elements.

>>> flops.compress([False, True], a)
array([2])