def _get_corr(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
def _cov(x, y):
return window_aggregations.ewmcov(
x,
np.array([0], dtype=np.int64),
np.array([0], dtype=np.int64),
self.min_periods,
y,
self.com,
self.adjust,
self.ignore_na,
1,
)
x_values = X._prep_values()
y_values = Y._prep_values()
with np.errstate(all="ignore"):
cov = _cov(x_values, y_values)
x_var = _cov(x_values, x_values)
y_var = _cov(y_values, y_values)
corr = cov / zsqrt(x_var * y_var)
return wrap_result(X, corr)
def cov_func(x, y):
x_array = self._prep_values(x)
y_array = self._prep_values(y)
window_indexer = self._get_window_indexer()
min_periods = (self.min_periods if self.min_periods is not None
else window_indexer.window_size)
start, end = window_indexer.get_window_bounds(
num_values=len(x_array),
min_periods=min_periods,
center=self.center,
closed=self.closed,
)
def _cov(X, Y):
return window_aggregations.ewmcov(
X,
start,
end,
min_periods,
Y,
self._com,
self.adjust,
self.ignore_na,
True,
)
with np.errstate(all="ignore"):
cov = _cov(x_array, y_array)
x_var = _cov(x_array, x_array)
y_var = _cov(y_array, y_array)
result = cov / zsqrt(x_var * y_var)
return Series(result, index=x.index, name=x.name)
def std(self,
bias: bool = False,
numeric_only: bool = False,
*args,
**kwargs):
nv.validate_window_func("std", args, kwargs)
if (numeric_only and self._selected_obj.ndim == 1
and not is_numeric_dtype(self._selected_obj.dtype)):
# Raise directly so error message says std instead of var
raise NotImplementedError(
f"{type(self).__name__}.std does not implement numeric_only")
return zsqrt(self.var(bias=bias, numeric_only=numeric_only, **kwargs))
def _get_corr(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
def _cov(x, y):
return window_aggregations.ewmcov(
x,
y,
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
1,
)
x_values = X._prep_values()
y_values = Y._prep_values()
with np.errstate(all="ignore"):
cov = _cov(x_values, y_values)
x_var = _cov(x_values, x_values)
y_var = _cov(y_values, y_values)
corr = cov / zsqrt(x_var * y_var)
return X._wrap_result(corr)
def cov_func(x, y):
x_array = self._prep_values(x)
y_array = self._prep_values(y)
def _cov(X, Y):
return window_aggregations.ewmcov(
X,
np.array([0], dtype=np.int64),
np.array([0], dtype=np.int64),
self.min_periods,
Y,
self.com,
self.adjust,
self.ignore_na,
1,
)
with np.errstate(all="ignore"):
cov = _cov(x_array, y_array)
x_var = _cov(x_array, x_array)
y_var = _cov(y_array, y_array)
result = cov / zsqrt(x_var * y_var)
return Series(result, index=x.index, name=x.name)
def std(self, bias: bool = False, *args, **kwargs):
nv.validate_window_func("std", args, kwargs)
return zsqrt(self.var(bias=bias, **kwargs))
def std(self, bias: bool = False, *args, **kwargs):
"""
Exponential weighted moving stddev.
"""
nv.validate_window_func("std", args, kwargs)
return zsqrt(self.var(bias=bias, **kwargs))