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"""
Array methods which are called by the both the C-code for the method
and the Python code for the NumPy-namespace function
"""
from __future__ import division, absolute_import, print_function
import warnings
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core.numeric import asanyarray
from numpy.core import numerictypes as nt
def _amax(a, axis=None, out=None, keepdims=False):
return um.maximum.reduce(a, axis=axis,
out=out, keepdims=keepdims)
def _amin(a, axis=None, out=None, keepdims=False):
return um.minimum.reduce(a, axis=axis,
out=out, keepdims=keepdims)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
return um.add.reduce(a, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
return um.multiply.reduce(a, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
def _any(a, axis=None, dtype=None, out=None, keepdims=False):
return um.logical_or.reduce(a, axis=axis, dtype=dtype, out=out,
keepdims=keepdims)
def _all(a, axis=None, dtype=None, out=None, keepdims=False):
return um.logical_and.reduce(a, axis=axis, dtype=dtype, out=out,
keepdims=keepdims)
def _count_reduce_items(arr, axis):
if axis is None:
axis = tuple(range(arr.ndim))
if not isinstance(axis, tuple):
axis = (axis,)
items = 1
for ax in axis:
items *= arr.shape[ax]
return items
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
else:
ret = ret.dtype.type(ret / rcount)
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis)
# Make this warning show up on top.
if ddof >= rcount:
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(
arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean.dtype.type(arrmean / rcount)
# Compute sum of squared deviations from mean
# Note that x may not be inexact
x = arr - arrmean
if issubclass(arr.dtype.type, nt.complexfloating):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
# Compute degrees of freedom and make sure it is not negative.
rcount = max([rcount - ddof, 0])
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
else:
ret = ret.dtype.type(ret / rcount)
return ret
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
else:
ret = ret.dtype.type(um.sqrt(ret))
return ret
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