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,
)
result = window_aggregations.ewmcov(
x_array,
start,
end,
# error: Argument 4 to "ewmcov" has incompatible type
# "Optional[int]"; expected "int"
self.min_periods, # type: ignore[arg-type]
y_array,
self._com,
self.adjust,
self.ignore_na,
bias,
)
return Series(result, index=x.index, name=x.name)
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,
)
result = window_aggregations.ewmcov(
x_array,
start,
end,
self.min_periods,
y_array,
self.com,
self.adjust,
self.ignore_na,
bias,
)
return Series(result, index=x.index, name=x.name)
def _cov(x, y):
return window_aggregations.ewmcov(
x,
y,
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
1,
)
def f(arg):
return window_aggregations.ewmcov(
arg,
arg,
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
int(bias),
)
def _cov(x, y):
return window_aggregations.ewmcov(
x,
y,
self.com,
self.adjust,
self.ignore_na,
self.min_periods,
1,
)
def f(arg):
return window_aggregations.ewmcov(
arg,
arg,
self.com,
self.adjust,
self.ignore_na,
self.min_periods,
bias,
)
def _cov(X, Y):
return window_aggregations.ewmcov(
X,
start,
end,
min_periods,
Y,
self._com,
self.adjust,
self.ignore_na,
True,
)
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,
)
def _get_cov(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
cov = window_aggregations.ewmcov(
X._prep_values(),
Y._prep_values(),
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
int(bias),
)
return X._wrap_result(cov)
def _get_cov(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
cov = window_aggregations.ewmcov(
X._prep_values(),
Y._prep_values(),
self.com,
self.adjust,
self.ignore_na,
self.min_periods,
bias,
)
return wrap_result(X, cov)
def cov_func(x, y):
x_array = self._prep_values(x)
y_array = self._prep_values(y)
result = window_aggregations.ewmcov(
x_array,
np.array([0], dtype=np.int64),
np.array([0], dtype=np.int64),
self.min_periods,
y_array,
self.com,
self.adjust,
self.ignore_na,
bias,
)
return Series(result, index=x.index, name=x.name)
