def sum(self, *args, engine=None, engine_kwargs=None, **kwargs):
if not self.adjust:
raise NotImplementedError(
"sum is not implemented with adjust=False")
if maybe_use_numba(engine):
if self.method == "single":
func = generate_numba_ewm_func
else:
func = generate_numba_ewm_table_func
ewm_func = func(
**get_jit_arguments(engine_kwargs),
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
deltas=tuple(self._deltas),
normalize=False,
)
return self._apply(ewm_func)
elif engine in ("cython", None):
if engine_kwargs is not None:
raise ValueError("cython engine does not accept engine_kwargs")
nv.validate_window_func("sum", args, kwargs)
deltas = None if self.times is None else self._deltas
window_func = partial(
window_aggregations.ewm,
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
deltas=deltas,
normalize=False,
)
return self._apply(window_func)
else:
raise ValueError("engine must be either 'numba' or 'cython'")
def mean(self, *args, engine=None, engine_kwargs=None, **kwargs):
if maybe_use_numba(engine):
if self.method == "single":
ewma_func = generate_numba_ewma_func(engine_kwargs, self._com,
self.adjust,
self.ignore_na,
self._deltas)
numba_cache_key = (lambda x: x, "ewma")
else:
ewma_func = generate_ewma_numba_table_func(
engine_kwargs, self._com, self.adjust, self.ignore_na,
self._deltas)
numba_cache_key = (lambda x: x, "ewma_table")
return self._apply(
ewma_func,
numba_cache_key=numba_cache_key,
)
elif engine in ("cython", None):
if engine_kwargs is not None:
raise ValueError("cython engine does not accept engine_kwargs")
nv.validate_window_func("mean", args, kwargs)
window_func = partial(
window_aggregations.ewma,
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
deltas=self._deltas,
)
return self._apply(window_func)
else:
raise ValueError("engine must be either 'numba' or 'cython'")
def mean(self, *args, **kwargs):
"""
Exponential weighted moving average.
Parameters
----------
*args, **kwargs
Arguments and keyword arguments to be passed into func.
"""
nv.validate_window_func("mean", args, kwargs)
if self.times is not None:
window_func = window_aggregations.ewma_time
window_func = partial(
window_func,
times=self.times,
halflife=self.halflife,
)
else:
window_func = window_aggregations.ewma
window_func = partial(
window_func,
com=self.com,
adjust=self.adjust,
ignore_na=self.ignore_na,
)
return self._apply(window_func)
def mean(self, *args, **kwargs):
"""
Exponential weighted moving average.
Parameters
----------
*args, **kwargs
Arguments and keyword arguments to be passed into func.
"""
nv.validate_window_func("mean", args, kwargs)
return self._apply("ewma", **kwargs)
def mean(self, *args, **kwargs):
nv.validate_window_func("mean", args, kwargs)
window_func = window_aggregations.ewma
window_func = partial(
window_func,
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
deltas=self._deltas,
)
return self._apply(window_func)
def var(self, bias: bool = False, *args, **kwargs):
"""
Exponential weighted moving variance.
"""
nv.validate_window_func("var", args, kwargs)
def f(arg):
return window_aggregations.ewmcov(
arg, arg, self.com, self.adjust, self.ignore_na, self.min_periods, bias
)
return self._apply(f)
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 var(self, bias: bool = False, *args, **kwargs):
nv.validate_window_func("var", args, kwargs)
window_func = window_aggregations.ewmcov
wfunc = partial(
window_func,
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
bias=bias,
)
def var_func(values, begin, end, min_periods):
return wfunc(values, begin, end, min_periods, values)
return self._apply(var_func)
def mean(self, *args, **kwargs):
nv.validate_window_func("mean", args, kwargs)
if self.times is not None:
window_func = window_aggregations.ewma_time
window_func = partial(
window_func,
times=self.times,
halflife=self.halflife,
)
else:
window_func = window_aggregations.ewma
window_func = partial(
window_func,
com=self.com,
adjust=self.adjust,
ignore_na=self.ignore_na,
)
return self._apply(window_func)
def var(self, bias=False, *args, **kwargs):
"""
Exponential weighted moving variance.
"""
nv.validate_window_func("var", args, kwargs)
def f(arg):
return libwindow.ewmcov(
arg,
arg,
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
int(bias),
)
return self._apply(f, **kwargs)
def var(self, bias: bool = False, *args, **kwargs):
"""
Exponential weighted moving variance.
"""
nv.validate_window_func("var", args, kwargs)
window_func = self._get_roll_func("ewmcov")
window_func = partial(
window_func,
com=self.com,
adjust=self.adjust,
ignore_na=self.ignore_na,
bias=bias,
)
def var_func(values, begin, end, min_periods):
return window_func(values, begin, end, min_periods, values)
return self._apply(var_func)
def mean(self, *args, **kwargs):
"""
Exponential weighted moving average.
Parameters
----------
*args, **kwargs
Arguments and keyword arguments to be passed into func.
"""
nv.validate_window_func("mean", args, kwargs)
window_func = self._get_roll_func("ewma")
window_func = partial(
window_func,
com=self.com,
adjust=self.adjust,
ignore_na=self.ignore_na,
minp=self.min_periods,
)
return self._apply(window_func)
def mean(
self,
numeric_only: bool = False,
*args,
engine=None,
engine_kwargs=None,
**kwargs,
):
maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
if maybe_use_numba(engine):
if self.method == "single":
func = generate_numba_ewm_func
else:
func = generate_numba_ewm_table_func
ewm_func = func(
**get_jit_arguments(engine_kwargs),
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
deltas=tuple(self._deltas),
normalize=True,
)
return self._apply(ewm_func, name="mean")
elif engine in ("cython", None):
if engine_kwargs is not None:
raise ValueError("cython engine does not accept engine_kwargs")
nv.validate_window_func("mean", args, kwargs)
deltas = None if self.times is None else self._deltas
window_func = partial(
window_aggregations.ewm,
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
deltas=deltas,
normalize=True,
)
return self._apply(window_func,
name="mean",
numeric_only=numeric_only)
else:
raise ValueError("engine must be either 'numba' or 'cython'")
def mean(self, *args, **kwargs):
nv.validate_window_func("mean", args, kwargs)
if self.times is not None:
com = 1.0
times = self.times.astype(np.float64)
halflife = float(self.halflife)
else:
com = self.com
times = np.arange(len(self.obj), dtype=np.float64)
halflife = 1.0
window_func = window_aggregations.ewma
window_func = partial(
window_func,
com=com,
adjust=self.adjust,
ignore_na=self.ignore_na,
times=times,
halflife=halflife,
)
return self._apply(window_func)
def var(self,
bias: bool = False,
numeric_only: bool = False,
*args,
**kwargs):
maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
nv.validate_window_func("var", args, kwargs)
window_func = window_aggregations.ewmcov
wfunc = partial(
window_func,
com=self._com,
adjust=self.adjust,
ignore_na=self.ignore_na,
bias=bias,
)
def var_func(values, begin, end, min_periods):
return wfunc(values, begin, end, min_periods, values)
return self._apply(var_func, name="var", numeric_only=numeric_only)
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))