nums.numpy.api.sort module

nums.numpy.api.sort.amax(a, axis=None, out=None, keepdims=False, initial=None, where=None)

Return the maximum of an array or maximum along an axis.

This docstring was copied from numpy.max.

Some inconsistencies with the NumS version may exist.

Parameters

a (BlockArray) – Input data.
axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before.
out (BlockArray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. 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 amax 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.
initial (scalar, optional) – The minimum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.
where (BlockArray of bool, optional) – Elements to compare for the maximum. See ~numpy.ufunc.reduce for details.

Returns

amax – Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

BlockArray or scalar

See also

amin: The minimum value of an array along a given axis, propagating any NaNs.
nanmax: The maximum value of an array along a given axis, ignoring any NaNs.
maximum: Element-wise maximum of two arrays, propagating any NaNs.
fmax: Element-wise maximum of two arrays, ignoring any NaNs.
argmax: Return the indices of the maximum values.

nanmin, minimum, fmin

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax.

Don’t use amax for element-wise comparison of 2 arrays; when a.shape[0] is 2, maximum(a[0], a[1]) is faster than amax(a, axis=0).

‘initial’ is currently not supported.

‘where’ is currently not supported.

‘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().

>>> a = nps.arange(4).reshape((2,2))  
>>> a.get()  
array([[0, 1],
       [2, 3]])
>>> nps.amax(a).get()           # Maximum of the flattened array  
array(3)
>>> nps.amax(a, axis=0).get()   # Maxima along the first axis  
array([2, 3])
>>> nps.amax(a, axis=1).get()   # Maxima along the second axis  
>>> b = nps.arange(5, dtype=float)  
>>> b[2] = nps.NaN  
>>> nps.amax(b).get()  
array(nan)
>>> nps.nanmax(b).get()  
array(4.)

nums.numpy.api.sort.amin(a, axis=None, out=None, keepdims=False, initial=None, where=None)

Return the minimum of an array or minimum along an axis.

This docstring was copied from numpy.min.

Some inconsistencies with the NumS version may exist.

Parameters

a (BlockArray) – Input data.
axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.
out (BlockArray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output.
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 amin 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.
initial (scalar, optional) – The maximum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.
where (BlockArray of bool, optional) – Elements to compare for the minimum. See ~numpy.ufunc.reduce for details.

Returns

amin – Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

BlockArray

See also

amax: The maximum value of an array along a given axis, propagating any NaNs.
nanmin: The minimum value of an array along a given axis, ignoring any NaNs.
minimum: Element-wise minimum of two arrays, propagating any NaNs.
fmin: Element-wise minimum of two arrays, ignoring any NaNs.
argmin: Return the indices of the minimum values.

nanmax, maximum, fmax

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Don’t use amin for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than amin(a, axis=0).

‘initial’ is currently not supported.

‘where’ is currently not supported.

‘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().

>>> a = nps.arange(4).reshape((2,2))  
>>> a.get()  
array([[0, 1],
       [2, 3]])
>>> nps.amin(a).get()           # Minimum of the flattened array  
array(0)
>>> nps.amin(a, axis=0).get()   # Minima along the first axis  
array([0, 1])
>>> nps.amin(a, axis=1).get()   # Minima along the second axis  
array([0, 2])

>>> nps.nanmin(b).get()  
0.0

nums.numpy.api.sort.argmax(a, axis=None, out=None)[source]

Returns the indices of the maximum values along an axis.

This docstring was copied from numpy.argmax.

Some inconsistencies with the NumS version may exist.

Parameters

a (BlockArray) – Input array.
axis (int, optional) – By default, the index is into the flattened array, otherwise along the specified axis.
out (array, optional) – If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.

Returns

index_array – Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed.

Return type

BlockArray of ints

See also

argmax, argmin

amax: The maximum value along a given axis.

Notes

In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned.

argmax currently only supports one-dimensional arrays.

‘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().

Indexes of the maximal elements of a N-dimensional array:

>>> b = nps.arange(6)  
>>> b[1] = 5  
>>> b.get()  
array([0, 5, 2, 3, 4, 5])
>>> nps.argmax(b).get()  # Only the first occurrence is returned.  
array(1)

nums.numpy.api.sort.argmin(a, axis=None, out=None)[source]

Returns the indices of the minimum values along an axis.

This docstring was copied from numpy.argmin.

Some inconsistencies with the NumS version may exist.

Parameters

a (BlockArray) – Input array.
axis (int, optional) – By default, the index is into the flattened array, otherwise along the specified axis.
out (array, optional) – If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.

Returns

index_array – Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed.

Return type

BlockArray of ints

See also

argmin, argmax

amin: The minimum value along a given axis.

Notes

In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.

‘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().

>>> b = nps.arange(6) + 10  
>>> b[4] = 10  
>>> b.get()  
array([10, 11, 12, 13, 10, 15])
>>> nps.argmin(b).get()  # Only the first occurrence is returned.  
array(0)

nums.numpy.api.sort.max(a, axis=None, out=None, keepdims=False, initial=None, where=None)[source]

Return the maximum of an array or maximum along an axis.

This docstring was copied from numpy.max.

Some inconsistencies with the NumS version may exist.

Parameters

a (BlockArray) – Input data.
axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before.
out (BlockArray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. 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 amax 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.
initial (scalar, optional) – The minimum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.
where (BlockArray of bool, optional) – Elements to compare for the maximum. See ~numpy.ufunc.reduce for details.

Returns

amax – Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

BlockArray or scalar

See also

amin: The minimum value of an array along a given axis, propagating any NaNs.
nanmax: The maximum value of an array along a given axis, ignoring any NaNs.
maximum: Element-wise maximum of two arrays, propagating any NaNs.
fmax: Element-wise maximum of two arrays, ignoring any NaNs.
argmax: Return the indices of the maximum values.

nanmin, minimum, fmin

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax.

Don’t use amax for element-wise comparison of 2 arrays; when a.shape[0] is 2, maximum(a[0], a[1]) is faster than amax(a, axis=0).

‘initial’ is currently not supported.

‘where’ is currently not supported.

‘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().

>>> a = nps.arange(4).reshape((2,2))  
>>> a.get()  
array([[0, 1],
       [2, 3]])
>>> nps.amax(a).get()           # Maximum of the flattened array  
array(3)
>>> nps.amax(a, axis=0).get()   # Maxima along the first axis  
array([2, 3])
>>> nps.amax(a, axis=1).get()   # Maxima along the second axis  
>>> b = nps.arange(5, dtype=float)  
>>> b[2] = nps.NaN  
>>> nps.amax(b).get()  
array(nan)
>>> nps.nanmax(b).get()  
array(4.)

nums.numpy.api.sort.min(a, axis=None, out=None, keepdims=False, initial=None, where=None)[source]

Return the minimum of an array or minimum along an axis.

This docstring was copied from numpy.min.

Some inconsistencies with the NumS version may exist.

Parameters

a (BlockArray) – Input data.
axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.
out (BlockArray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output.
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 amin 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.
initial (scalar, optional) – The maximum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.
where (BlockArray of bool, optional) – Elements to compare for the minimum. See ~numpy.ufunc.reduce for details.

Returns

amin – Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

BlockArray

See also

amax: The maximum value of an array along a given axis, propagating any NaNs.
nanmin: The minimum value of an array along a given axis, ignoring any NaNs.
minimum: Element-wise minimum of two arrays, propagating any NaNs.
fmin: Element-wise minimum of two arrays, ignoring any NaNs.
argmin: Return the indices of the minimum values.

nanmax, maximum, fmax

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Don’t use amin for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than amin(a, axis=0).

‘initial’ is currently not supported.

‘where’ is currently not supported.

‘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().

>>> a = nps.arange(4).reshape((2,2))  
>>> a.get()  
array([[0, 1],
       [2, 3]])
>>> nps.amin(a).get()           # Minimum of the flattened array  
array(0)
>>> nps.amin(a, axis=0).get()   # Minima along the first axis  
array([0, 1])
>>> nps.amin(a, axis=1).get()   # Minima along the second axis  
array([0, 2])

>>> nps.nanmin(b).get()  
0.0

nums.numpy.api.sort.top_k(a, k, largest=True, sorted=False)[source]

Find the k largest or smallest elements of a BlockArray.

If there are multiple kth elements that are equal in value, then no guarantees are made as to which ones are included in the top k.

Parameters

a (BlockArray) – A BlockArray.
k (int) – Number of top elements to return.
largest – Whether to return largest or smallest elements.

Return type

Tuple[BlockArray, BlockArray]

Returns

A tuple containing two BlockArrays, (values, indices). values: Values of the top k elements, unsorted. indices: Indices of the top k elements, ordered by their corresponding values.

nums.numpy.api.sort.where(condition, x=None, y=None)[source]

Return elements chosen from x or y depending on condition.

This docstring was copied from numpy.where.

Some inconsistencies with the NumS version may exist.

Parameters

condition (BlockArray, bool) – Where True, yield x, otherwise yield y.
x (BlockArray) – Values from which to choose. x, y and condition need to be broadcastable to some shape.
y (BlockArray) – Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns

out – An array with elements from x where condition is True, and elements from y elsewhere.

Return type

BlockArray

Notes

If all the arrays are 1-D, where is equivalent to:

[xv if c else yv
 for c, xv, yv in zip(condition, x, y)]

Examples

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

>>> a = nps.arange(10)  
>>> a.get()  
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> nps.where(a < 5, a, 10*a).get()  
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])