nlcpy.nanmin

nlcpy.nanmin(a, axis=None, out=None, keepdims=<no value>)[ソース]

Returns minimum of an array or minimum along an axis, ignoring any NaNs.

When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice.

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.

Returns

nanminndarray: 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.

参考

nanmax: Returns the maximum of an array or maximum along an axis, ignoring any NaNs.
amin: Returns the minimum of an array or maximum along an axis.
fmin: Element-wise minimum of array elements.
minimum: Element-wise minimum of array elements.
isnan: Tests element-wise for NaN and return result as a boolean array.
isfinite: Tests element-wise for finiteness (not infinity or not Not a Number).
amax: Returns the maximum of an array or maximum along an axis.
fmax: Element-wise maximum of array elements.
maximum: Element-wise maximum of array elements.

注釈

NLCPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to amin().

Examples

>>> import nlcpy as vp
>>> a = vp.array([[1, 2], [3, vp.nan]])
>>> vp.nanmin(a)
array(1.)
>>> vp.nanmin(a, axis=0)
array([1., 2.])
>>> vp.nanmin(a, axis=1)
array([1., 3.])

When positive infinity and negative infinity are present:

>>> vp.nanmin([1, 2, vp.nan, vp.inf])
array(1.)
>>> vp.nanmin([1, 2, vp.nan, vp.NINF])
array(-inf)