nums.numpy.api.logic module

nums.numpy.api.logic.all(a, axis=None, out=None, keepdims=False)[source]

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

This docstring was copied from numpy.all.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Input array or object that can be converted to an array.

  • axis (None 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, instead of a single axis or all the axes as before.

  • out (BlockArray, 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). See ufuncs-output-type for more details.

  • keepdims (bool, 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.

    If the default value is passed, then keepdims will not be passed through to the all method of sub-classes of BlockArray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

Returns

all – A new boolean or array is returned unless out is specified, in which case a reference to out is returned.

Return type

BlockArray, bool

See also

all

equivalent method

any

Test whether any element along a given axis evaluates to True.

Notes

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

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.all(nps.array([[True,False],[True,True]])).get()  
array(False)

>>> nps.all(nps.array([[True,False],[True,True]]), axis=0).get()  
array([ True, False])

>>> nps.all(nps.array([-1, 4, 5])).get()  
array(True)

>>> nps.all(nps.array([1.0, nps.nan])).get()  
array(True)
nums.numpy.api.logic.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]

Returns True if two arrays are element-wise equal within a tolerance.

This docstring was copied from numpy.allclose.

Some inconsistencies with the NumS version may exist.

The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.

NaNs are treated as equal if they are in the same place and if equal_nan=True. Infs are treated as equal if they are in the same place and of the same sign in both arrays.

Parameters

  • a (BlockArray) – Input arrays to compare.

  • b (BlockArray) – Input arrays to compare.

  • rtol (float) – The relative tolerance parameter (see Notes).

  • atol (float) – The absolute tolerance parameter (see Notes).

  • equal_nan (bool) – Whether to compare NaN’s as equal. If True, NaN’s in a will be considered equal to NaN’s in b in the output array.

Returns

allclose – Returns True if the two arrays are equal within the given tolerance; False otherwise.

Return type

bool

See also

isclose, all, any, equal

Notes

If the following equation is element-wise True, then allclose returns True.

absolute(a - b) <= (atol + rtol * absolute(b))

The above equation is not symmetric in a and b, so that allclose(a, b) might be different from allclose(b, a) in some rare cases.

The comparison of a and b uses standard broadcasting, which means that a and b need not have the same shape in order for allclose(a, b) to evaluate to True. The same is true for equal but not array_equal.

equal_nan=True not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.allclose(nps.array([1e10,1e-7]), nps.array([1.00001e10,1e-8])).get()  
array(False)
>>> nps.allclose(nps.array([1e10,1e-8]), nps.array([1.00001e10,1e-9])).get()  
array(True)
>>> nps.allclose(nps.array([1e10,1e-8]), nps.array([1.0001e10,1e-9])).get()  
array(False)
>>> nps.allclose(nps.array([1.0, nps.nan]), nps.array([1.0, nps.nan])).get()  
array(False)
nums.numpy.api.logic.alltrue(a, axis=None, out=None, dtype=None, keepdims=False)[source]

Check if all elements of input array are true.

This docstring was copied from numpy.alltrue.

Some inconsistencies with the NumS version may exist.

See also

all

Equivalent function; see for details.

nums.numpy.api.logic.any(a, axis=None, out=None, keepdims=False)[source]

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

This docstring was copied from numpy.any.

Some inconsistencies with the NumS version may exist.

Returns single boolean unless axis is not None

Parameters

  • a (BlockArray) – Input array or object that can be converted to an array.

  • axis (None or int or tuple of ints, optional) – Axis or axes along which a logical OR reduction is performed. The default (axis=None) is to perform a logical OR 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, instead of a single axis or all the axes as before.

  • out (BlockArray, 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 it is of type float, then it will remain so, returning 1.0 for True and 0.0 for False, regardless of the type of a).

  • keepdims (bool, 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.

    If the default value is passed, then keepdims will not be passed through to the any method of sub-classes of BlockArray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

Returns

any – A new BlockArray is returned unless out is specified, in which case a reference to out is returned.

Return type

BlockArray

See also

any

equivalent method

all

Test whether all elements along a given axis evaluate to True.

Notes

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

‘out’ is currently not supported

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.any(nps.array([[True, False], [True, True]])).get()  
array(True)

>>> nps.any(nps.array([[True, False], [False, False]]), axis=0).get()  
array([ True, False])

>>> nps.any(nps.array([-1, 0, 5])).get()  
array(True)

>>> nps.any(nps.array(nps.nan)).get()  
array(True)
nums.numpy.api.logic.array_equal(a, b, equal_nan=False)[source]

True if two arrays have the same shape and elements, False otherwise.

This docstring was copied from numpy.array_equal.

Some inconsistencies with the NumS version may exist.

Parameters

  • a1 (BlockArray) – Input arrays.

  • a2 (BlockArray) – Input arrays.

  • equal_nan (bool) – Whether to compare NaN’s as equal. If the dtype of a1 and a2 is complex, values will be considered equal if either the real or the imaginary component of a given value is nan.

Returns

b – Returns True if the arrays are equal.

Return type

bool

See also

allclose

Returns True if two arrays are element-wise equal within a tolerance.

array_equiv

Returns True if input arrays are shape consistent and all elements equal.

Notes

equal_nan=True not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.array_equal(nps.array([1, 2]), nps.array([1, 2])).get()  
array(True)
>>> a = nps.array([1, nps.nan])  
>>> nps.array_equal(a, a).get()  
array(False)
nums.numpy.api.logic.array_equiv(a, b)[source]

Returns True if input arrays are shape consistent and all elements equal.

This docstring was copied from numpy.array_equiv.

Some inconsistencies with the NumS version may exist.

Shape consistent means they are either the same shape, or one input array can be broadcasted to create the same shape as the other one.

Parameters
Returns

out – True if equivalent, False otherwise.

Return type

BlockArray

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.array_equiv(nps.array([1, 2]), nps.array([1, 2])).get()  
array(True)
>>> nps.array_equiv(nps.array([1, 2]), nps.array([1, 3])).get()  
array(False)