def _mean(a, axis=None, dtype=None, out=None, skipna=False, 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, skipna=skipna, keepdims=keepdims)
else:
ret = um.add.reduce(arr, axis=axis, dtype=dtype,
out=out, skipna=skipna, keepdims=keepdims)
rcount = mu.count_reduce_items(arr, axis=axis,
skipna=skipna, keepdims=keepdims)
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,
skipna=False, 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',
skipna=skipna, keepdims=True)
else:
arrmean = um.add.reduce(arr, axis=axis, dtype=dtype,
skipna=skipna, keepdims=True)
rcount = mu.count_reduce_items(arr, axis=axis,
skipna=skipna, 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
# (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,
skipna=skipna, 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
