def min(a, axis=None, out=None):
"""
Return the minimum of an array or minimum along an axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which a min is performed.
The default (`axis` = `None`) is perform a min 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 min is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result. Must
be of the same shape and buffer length as the expected output.
See `doc.ufuncs` (Section "Output arguments") for more details.
Returns
-------
min : ndarray or scalar
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``.
See Also
--------
min :
The minimum 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.
nanmin, minimum, fmin
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 `min` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
``min(a, axis=0)``.
Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.min(a) # minimum of the flattened array
3
>>> np.min(a, axis=0) # minima along the first axis
array([2, 3])
>>> np.min(a, axis=1) # minima along the second axis
array([1, 3])
>>> b = np.arange(5, dtype=np.float)
>>> b[2] = np.NaN
>>> np.min(b)
nan
>>> np.nanmin(b)
4.0
"""
if not bhary.check(a) and not bhary.check(out):
return numpy.min(a, axis=axis,
out=out) # NumPy 1.6 doesn't support axis=None
else:
return ufuncs.minimum.reduce(a, axis=axis, out=out)
def min(a, axis=None, out=None):
"""
Return the minimum of an array or minimum along an axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which a min is performed.
The default (`axis` = `None`) is perform a min 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 min is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result. Must
be of the same shape and buffer length as the expected output.
See `doc.ufuncs` (Section "Output arguments") for more details.
Returns
-------
min : ndarray or scalar
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``.
See Also
--------
min :
The minimum 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.
nanmin, minimum, fmin
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 `min` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
``min(a, axis=0)``.
Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.min(a) # minimum of the flattened array
3
>>> np.min(a, axis=0) # minima along the first axis
array([2, 3])
>>> np.min(a, axis=1) # minima along the second axis
array([1, 3])
>>> b = np.arange(5, dtype=np.float)
>>> b[2] = np.NaN
>>> np.min(b)
nan
>>> np.nanmin(b)
4.0
"""
if not ndarray.check(a) and not ndarray.check(out):
return numpy.min(a, axis=axis, out=out)#NumPy 1.6 doesn't support axis=None
else:
return ufunc.minimum.reduce(a, axis=axis, out=out)