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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
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core.numeric import array, asanyarray, isnan
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)
# Upgrade bool, unsigned int, and int to float64
if dtype is None and arr.dtype.kind in ['b','u','i']:
ret = um.add.reduce(arr, axis=axis, dtype='f8',
out=out, keepdims=keepdims)
else:
ret = um.add.reduce(arr, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
rcount = _count_reduce_items(arr, axis)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = array(a, subok=True)
mask = isnan(arr)
# Upgrade bool, unsigned int, and int to float64
if dtype is None and arr.dtype.kind in ['b','u','i']:
ret = um.add.reduce(arr, axis=axis, dtype='f8',
out=out, keepdims=keepdims)
else:
mu.copyto(arr, 0.0, where=mask)
ret = um.add.reduce(arr, axis=axis, dtype=dtype,
out=out, keepdims=keepdims)
rcount = (~mask).sum(axis=axis)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0,
keepdims=False):
arr = asanyarray(a)
# First compute the mean, saving 'rcount' for reuse later
if dtype is None and arr.dtype.kind in ['b','u','i']:
arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
else:
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype, keepdims=True)
rcount = _count_reduce_items(arr, axis)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, rcount,
out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean / float(rcount)
# arr - arrmean
x = arr - arrmean
# (arr - arrmean) ** 2
if arr.dtype.kind == 'c':
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
# add.reduce((arr - arrmean) ** 2, axis)
ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
# add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
if not keepdims and isinstance(rcount, mu.ndarray):
rcount = rcount.squeeze(axis=axis)
rcount -= ddof
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(rcount)
return ret
def _nanvar(a, axis=None, dtype=None, out=None, ddof=0,
keepdims=False):
arr = array(a, subok=True)
mask = isnan(arr)
# First compute the mean, saving 'rcount' for reuse later
if dtype is None and arr.dtype.kind in ['b','u','i']:
arrmean = um.add.reduce(arr, axis=axis, dtype='f8', keepdims=True)
else:
mu.copyto(arr, 0.0, where=mask)
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype,
keepdims=True)
rcount = (~mask).sum(axis=axis, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, rcount,
out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean / float(rcount)
# arr - arrmean
x = arr - arrmean
x[mask] = 0.0
# (arr - arrmean) ** 2
if arr.dtype.kind == 'c':
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
# add.reduce((arr - arrmean) ** 2, axis)
ret = um.add.reduce(x, axis=axis, dtype=dtype, out=out,
keepdims=keepdims)
# add.reduce((arr - arrmean) ** 2, axis) / (n - ddof)
if not keepdims and isinstance(rcount, mu.ndarray):
rcount = rcount.squeeze(axis=axis)
rcount -= ddof
if isinstance(ret, mu.ndarray):
ret = um.true_divide(ret, rcount,
out=ret, casting='unsafe', subok=False)
else:
ret = ret / float(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 = um.sqrt(ret)
return ret
def _nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
ret = _nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
else:
ret = um.sqrt(ret)
return ret
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