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)