@derived_from(scipy.stats)
def moment(a, moment=1, axis=0, nan_policy="propagate"):
if nan_policy != "propagate":
raise NotImplementedError(
"`nan_policy` other than 'propagate' have not been implemented.")
return da.moment(a, moment, axis=axis)
# -------
# Helpers
# -------
# Don't really want to do all of scipy.special (or do we?)
_xlogy = wrap_elemwise(special.xlogy, source=special)
_fdtrc = wrap_elemwise(special.fdtrc, source=special)
def _equal_var_ttest_denom(v1, n1, v2, n2):
df = n1 + n2 - 2.0
svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
denom = da.sqrt(svar * (1.0 / n1 + 1.0 / n2)) # XXX: np -> da
return df, denom
def _unequal_var_ttest_denom(v1, n1, v2, n2):
vn1 = v1 / n1
vn2 = v2 / n2
with np.errstate(divide="ignore", invalid="ignore"):
df = (vn1 + vn2)**2 / (vn1**2 / (n1 - 1) + vn2**2 / (n2 - 1))
@doc_wraps(scipy.stats.moment)
def moment(a, moment=1, axis=0, nan_policy='propagate'):
if nan_policy != 'propagate':
raise NotImplementedError("`nan_policy` other than 'propagate' "
"have not been implemented.")
return da.moment(a, moment, axis=axis)
# -------
# Helpers
# -------
# Don't really want to do all of scipy.special (or do we?)
_xlogy = wrap_elemwise(special.xlogy)
_fdtrc = wrap_elemwise(special.fdtrc)
def _equal_var_ttest_denom(v1, n1, v2, n2):
df = n1 + n2 - 2.0
svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
denom = da.sqrt(svar * (1.0 / n1 + 1.0 / n2)) # XXX: np -> da
return df, denom
def _unequal_var_ttest_denom(v1, n1, v2, n2):
vn1 = v1 / n1
vn2 = v2 / n2
with np.errstate(divide='ignore', invalid='ignore'):
df = (vn1 + vn2)**2 / (vn1**2 / (n1 - 1) + vn2**2 / (n2 - 1))
@doc_wraps(scipy.stats.moment)
def moment(a, moment=1, axis=0, nan_policy="propagate"):
if nan_policy != "propagate":
raise NotImplementedError(
"`nan_policy` other than 'propagate' " "have not been implemented."
)
return da.moment(a, moment, axis=axis)
# -------
# Helpers
# -------
# Don't really want to do all of scipy.special (or do we?)
_xlogy = wrap_elemwise(special.xlogy)
_fdtrc = wrap_elemwise(special.fdtrc)
def _equal_var_ttest_denom(v1, n1, v2, n2):
df = n1 + n2 - 2.0
svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
denom = da.sqrt(svar * (1.0 / n1 + 1.0 / n2)) # XXX: np -> da
return df, denom
def _unequal_var_ttest_denom(v1, n1, v2, n2):
vn1 = v1 / n1
vn2 = v2 / n2
with np.errstate(divide="ignore", invalid="ignore"):
df = (vn1 + vn2) ** 2 / (vn1 ** 2 / (n1 - 1) + vn2 ** 2 / (n2 - 1))