nlcpy.all

nlcpy.all(a, axis=None, out=None, keepdims=<no value>)[source]

Tests whether all array elements along a given axis evaluate to True.

Parameters

aarray_like: Input array or object that can be converted to an array.
axisNone or int or tuple of ints, optional: Axis or axes along which a logical AND reduction is performed. The default (axis = None) is to perform a logical AND over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis. If this is a tuple of ints, a reduction is performed on multiple axes.
outndarray, optional: Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if dtype(out) is float, the result will consist of 0.0’s and 1.0’s).
keepdimsbool, optional: If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Returns

allndarray: A new array is returned unless out is specified, in which case a reference to out is returned.

See also

any: Tests whether any array element along a given axis evaluates to True.

Note

Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero.

>>> import nlcpy as vp
>>> vp.all([[True, False], [True, True]])
array(False)
>>> vp.all([[True,False],[True,True]], axis=0)
array([ True, False])
>>> vp.all([-1, 4, 5])
array(True)
>>> vp.all([1.0, vp.nan])
array(True)
>>> o=vp.array(False)
>>> z=vp.all([-1, 4, 5], out=o)
>>> id(z), id(o), z   
(140052379774144, 140052379774144, array(True)) # may vary