def mid_range_f(a, axis=None, masked=False):
"""Return the minimum and maximum of an array or the minimum and
maximum along an axis.
``mid_range_f(a, axis=axis)`` is equivalent to ``(numpy.amin(a,
axis=axis), numpy.amax(a, axis=axis))``
:Parameters:
a: numpy array_like
Input array
axis: `int`, optional
Axis along which to operate. By default, flattened input
is used.
kwargs: ignored
:Returns:
3-`tuple`
The sample size, minimum and maximum inside a 3-tuple.
"""
(N,) = sample_size_f(a, axis=axis, masked=masked)
amin = numpy_amin(a, axis=axis)
amax = numpy_amax(a, axis=axis)
if not numpy_ndim(amin):
# Make sure that we have a numpy array (as opposed to, e.g. a
# numpy.float64)
amin = numpy_asanyarray(amin)
amax = numpy_asanyarray(amax)
return asanyarray(N, amin, amax)
def min_f(a, axis=None, masked=False):
'''Return the minimum of an array, or the minima of an array along an
axis.
:Parameters:
a: numpy array_like
Input array
axis: `int`, optional
Axis along which to operate. By default, flattened input
is used.
masked: `bool`
:Returns:
out: 2-tuple of `numpy.ndarray`
The sample sizes and the minima.
'''
N, = sample_size_f(a, axis=axis, masked=masked)
amin = numpy_amin(a, axis=axis)
return asanyarray(N, amin)
def min_f(a, axis=None, masked=False):
"""Return the minimum of an array or minimum along a specified axis.
:Parameters:
a: numpy array_like
Input array
axis: `int`, optional
Axis along which to operate. By default, flattened input
is used.
masked: `bool`, optional
:Returns:
2-`tuple` of `numpy.ndarray`
The sample size and the minimum.
"""
(N,) = sample_size_f(a, axis=axis, masked=masked)
amin = numpy_amin(a, axis=axis)
return asanyarray(N, amin)