nlcpy.amin

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

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

Parameters

aarray_like: Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.
axisNone 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.
outndarray, optional: Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output.
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.
initialscalar, optional: The maximum value of an output element. Must be present to allow computation on empty slice. See nlcpy.ufunc.reduce() for details.
wherearray_like of bool, optional: Elements to compare for the minimum. See nlcpy.ufunc.reduce() for details.

Returns

aminndarray: Minimum of a. An array with the same shape as a, with the specified axis removed. If a is a scalar, or if axis is None, this function returns the result as a 0-dimention array. The same dtype as a is returned.

See also

amax: Returns the maximum of an array or maximum along an axis.
nanmin: Returns minimum of an array or minimum along an axis, ignoring any NaNs.
minimum: Element-wise minimum of array elements.
fmin: Element-wise minimum of array elements.
argmin: Returns the indices of the minimum values along an axis.
nanmax: Returns the maximum of an array or maximum along an axis, ignoring any NaNs.
maximum: Element-wise maximum of array elements.
fmax: Element-wise maximum of array elements.

Note

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, 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).

Restriction

If an ndarray is passed to where and where.shape != a.shape, NotImplementedError occurs.
If an ndarray is passed to out and out.shape != amin.shape, NotImplementedError occurs.

Examples

>>> import nlcpy as vp
>>> a = vp.arange(4).reshape((2,2))
>>> a
array([[0, 1],
       [2, 3]])
>>> vp.amin(a)           # Minimum of the flattened array
array(0)
>>> vp.amin(a, axis=0)   # Minima along the first axis
array([0, 1])
>>> vp.amin(a, axis=1)   # Minima along the second axis
array([0, 2])
>>> b = vp.arange(5, dtype=float)
>>> b[2] = vp.NaN
>>> vp.amin(b)
array(nan)
>>> vp.amin(b, where=~vp.isnan(b), initial=10)
array(0.)
>>> vp.nanmin(b)
array(0.)