def nanmean(self, a: BlockArray, axis=None, keepdims=False, dtype=None):
if not array_utils.is_float(a):
a = a.astype(np.float64)
num_summed = self.sum(~a.ufunc("isnan"),
axis=axis,
dtype=a.dtype,
keepdims=keepdims)
if num_summed.ndim == 0 and num_summed == 0:
return self.scalar(np.nan)
if num_summed.ndim > 0:
num_summed = self.where(
num_summed == 0,
self.empty(num_summed.shape, num_summed.block_shape) * np.nan,
num_summed,
)
res = (self.reduce(
"nansum", a, axis=axis, dtype=dtype, keepdims=keepdims) /
num_summed)
if dtype is not None:
res = res.astype(dtype)
return res
def nanvar(self, a: BlockArray, axis=None, ddof=0, keepdims=False, dtype=None):
mean = self.nanmean(a, axis=axis, keepdims=True)
ss = self.reduce(
"nansum", (a - mean) ** self.two, axis=axis, dtype=dtype, keepdims=keepdims
)
num_summed = (
self.sum(~a.ufunc("isnan"), axis=axis, dtype=a.dtype, keepdims=keepdims)
- ddof
)
res = ss / num_summed
if dtype is not None:
res = res.astype(dtype)
return res
def abs(self, X: BlockArray):
return X.ufunc("abs")
def exp(self, X: BlockArray):
return X.ufunc("exp")
def log(self, X: BlockArray):
return X.ufunc("log")
def sqrt(self, X: BlockArray) -> BlockArray:
if X.dtype not in (float, np.float32, np.float64):
X = X.astype(np.float64)
return X.ufunc("sqrt")
