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 generate_numba_agg_func(
kwargs: dict[str, Any],
func: Callable[..., Scalar],
engine_kwargs: dict[str, bool] | None,
) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, Any],
np.ndarray]:
"""
Generate a numba jitted agg function specified by values from engine_kwargs.
1. jit the user's function
2. Return a groupby agg function with the jitted function inline
Configurations specified in engine_kwargs apply to both the user's
function _AND_ the groupby evaluation loop.
Parameters
----------
kwargs : dict
**kwargs to be passed into the function
func : function
function to be applied to each window and will be JITed
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
validate_udf(func)
cache_key = (func, "groupby_agg")
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba_func = jit_user_function(func, nopython, nogil, parallel)
numba = import_optional_dependency("numba")
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
def group_agg(
values: np.ndarray,
index: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
num_columns: int,
*args: Any,
) -> np.ndarray:
assert len(begin) == len(end)
num_groups = len(begin)
result = np.empty((num_groups, num_columns))
for i in numba.prange(num_groups):
group_index = index[begin[i]:end[i]]
for j in numba.prange(num_columns):
group = values[begin[i]:end[i], j]
result[i, j] = numba_func(group, group_index, *args)
return result
return group_agg
def generate_numba_apply_func(
args: Tuple,
kwargs: Dict[str, Any],
func: Callable[..., Scalar],
engine_kwargs: Optional[Dict[str, bool]],
):
"""
Generate a numba jitted apply function specified by values from engine_kwargs.
1. jit the user's function
2. Return a rolling apply function with the jitted function inline
Configurations specified in engine_kwargs apply to both the user's
function _AND_ the rolling apply function.
Parameters
----------
args : tuple
*args to be passed into the function
kwargs : dict
**kwargs to be passed into the function
func : function
function to be applied to each window and will be JITed
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
cache_key = (func, "rolling_apply")
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba_func = jit_user_function(func, nopython, nogil, parallel)
numba = import_optional_dependency("numba")
if parallel:
loop_range = numba.prange
else:
loop_range = range
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
def roll_apply(
values: np.ndarray, begin: np.ndarray, end: np.ndarray, minimum_periods: int
) -> np.ndarray:
result = np.empty(len(begin))
for i in loop_range(len(result)):
start = begin[i]
stop = end[i]
window = values[start:stop]
count_nan = np.sum(np.isnan(window))
if len(window) - count_nan >= minimum_periods:
result[i] = numba_func(window, *args)
else:
result[i] = np.nan
return result
return roll_apply
def _aggregate_series_pure_python(
self,
obj: Series,
func: F,
*args,
engine: str = "cython",
engine_kwargs=None,
**kwargs,
):
if engine == "numba":
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
check_kwargs_and_nopython(kwargs, nopython)
validate_udf(func)
cache_key = (func, "groupby_agg")
numba_func = NUMBA_FUNC_CACHE.get(
cache_key, jit_user_function(func, nopython, nogil, parallel)
)
group_index, _, ngroups = self.group_info
counts = np.zeros(ngroups, dtype=int)
result = None
splitter = get_splitter(obj, group_index, ngroups, axis=0)
for label, group in splitter:
if engine == "numba":
values, index = split_for_numba(group)
res = numba_func(values, index, *args)
if cache_key not in NUMBA_FUNC_CACHE:
NUMBA_FUNC_CACHE[cache_key] = numba_func
else:
res = func(group, *args, **kwargs)
if result is None:
if isinstance(res, (Series, Index, np.ndarray)):
if len(res) == 1:
# e.g. test_agg_lambda_with_timezone lambda e: e.head(1)
# FIXME: are we potentially losing important res.index info?
res = res.item()
else:
raise ValueError("Function does not reduce")
result = np.empty(ngroups, dtype="O")
counts[label] = group.shape[0]
result[label] = res
assert result is not None
result = lib.maybe_convert_objects(result, try_float=0)
# TODO: maybe_cast_to_extension_array?
return result, counts
def generate_shared_aggregator(
func: Callable[..., Scalar],
engine_kwargs: dict[str, bool] | None,
cache_key_str: str,
):
"""
Generate a Numba function that loops over the columns 2D object and applies
a 1D numba kernel over each column.
Parameters
----------
func : function
aggregation function to be applied to each column
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
cache_key_str: str
string to access the compiled function of the form
<caller_type>_<aggregation_type> e.g. rolling_mean, groupby_mean
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs, None)
cache_key = (func, cache_key_str)
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
if TYPE_CHECKING:
import numba
else:
numba = import_optional_dependency("numba")
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
def column_looper(
values: np.ndarray,
start: np.ndarray,
end: np.ndarray,
min_periods: int,
*args,
):
result = np.empty((len(start), values.shape[1]), dtype=np.float64)
for i in numba.prange(values.shape[1]):
result[:, i] = func(values[:, i], start, end, min_periods, *args)
return result
return column_looper
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 generate_numba_func(
func: Callable,
engine_kwargs: Optional[Dict[str, bool]],
kwargs: dict,
cache_key_str: str,
) -> Tuple[Callable, Tuple[Callable, str]]:
"""
Return a JITed function and cache key for the NUMBA_FUNC_CACHE
This _may_ be specific to groupby (as it's only used there currently).
Parameters
----------
func : function
user defined function
engine_kwargs : dict or None
numba.jit arguments
kwargs : dict
kwargs for func
cache_key_str : str
string representing the second part of the cache key tuple
Returns
-------
(JITed function, cache key)
Raises
------
NumbaUtilError
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
check_kwargs_and_nopython(kwargs, nopython)
validate_udf(func)
cache_key = (func, cache_key_str)
numba_func = NUMBA_FUNC_CACHE.get(
cache_key, jit_user_function(func, nopython, nogil, parallel)
)
return numba_func, cache_key
def generate_online_numba_ewma_func(engine_kwargs: Optional[Dict[str, bool]]):
"""
Generate a numba jitted groupby ewma function specified by values
from engine_kwargs.
Parameters
----------
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
cache_key = (lambda x: x, "online_ewma")
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba = import_optional_dependency("numba")
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
def online_ewma(
values: np.ndarray,
deltas: np.ndarray,
minimum_periods: int,
old_wt_factor: float,
new_wt: float,
old_wt: np.ndarray,
adjust: bool,
ignore_na: bool,
):
"""
Compute online exponentially weighted mean per column over 2D values.
Takes the first observation as is, then computes the subsequent
exponentially weighted mean accounting minimum periods.
"""
result = np.empty(values.shape)
weighted_avg = values[0]
nobs = (~np.isnan(weighted_avg)).astype(np.int64)
result[0] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
for i in range(1, len(values)):
cur = values[i]
is_observations = ~np.isnan(cur)
nobs += is_observations.astype(np.int64)
for j in numba.prange(len(cur)):
if not np.isnan(weighted_avg[j]):
if is_observations[j] or not ignore_na:
# note that len(deltas) = len(vals) - 1 and deltas[i] is to be
# used in conjunction with vals[i+1]
old_wt[j] *= old_wt_factor**deltas[j - 1]
if is_observations[j]:
# avoid numerical errors on constant series
if weighted_avg[j] != cur[j]:
weighted_avg[j] = (
(old_wt[j] * weighted_avg[j]) +
(new_wt * cur[j])) / (old_wt[j] + new_wt)
if adjust:
old_wt[j] += new_wt
else:
old_wt[j] = 1.0
elif is_observations[j]:
weighted_avg[j] = cur[j]
result[i] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
return result, old_wt
return online_ewma
def generate_numba_groupby_ewma_func(
engine_kwargs: Optional[Dict[str, bool]],
com: float,
adjust: bool,
ignore_na: bool,
):
"""
Generate a numba jitted groupby ewma function specified by values
from engine_kwargs.
Parameters
----------
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
com : float
adjust : bool
ignore_na : bool
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
cache_key = (lambda x: x, "groupby_ewma")
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba = import_optional_dependency("numba")
if parallel:
loop_range = numba.prange
else:
loop_range = range
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
def groupby_ewma(
values: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
minimum_periods: int,
) -> np.ndarray:
result = np.empty(len(values))
alpha = 1.0 / (1.0 + com)
for i in loop_range(len(begin)):
start = begin[i]
stop = end[i]
window = values[start:stop]
sub_result = np.empty(len(window))
old_wt_factor = 1.0 - alpha
new_wt = 1.0 if adjust else alpha
weighted_avg = window[0]
nobs = int(not np.isnan(weighted_avg))
sub_result[0] = weighted_avg if nobs >= minimum_periods else np.nan
old_wt = 1.0
for j in range(1, len(window)):
cur = window[j]
is_observation = not np.isnan(cur)
nobs += is_observation
if not np.isnan(weighted_avg):
if is_observation or not ignore_na:
old_wt *= old_wt_factor
if is_observation:
# avoid numerical errors on constant series
if weighted_avg != cur:
weighted_avg = (
(old_wt * weighted_avg) +
(new_wt * cur)) / (old_wt + new_wt)
if adjust:
old_wt += new_wt
else:
old_wt = 1.0
elif is_observation:
weighted_avg = cur
sub_result[
j] = weighted_avg if nobs >= minimum_periods else np.nan
result[start:stop] = sub_result
return result
return groupby_ewma
def generate_numba_transform_func(
args: Tuple,
kwargs: Dict[str, Any],
func: Callable[..., Scalar],
engine_kwargs: Optional[Dict[str, bool]],
) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, int], np.ndarray]:
"""
Generate a numba jitted transform function specified by values from engine_kwargs.
1. jit the user's function
2. Return a groupby agg function with the jitted function inline
Configurations specified in engine_kwargs apply to both the user's
function _AND_ the rolling apply function.
Parameters
----------
args : tuple
*args to be passed into the function
kwargs : dict
**kwargs to be passed into the function
func : function
function to be applied to each window and will be JITed
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
check_kwargs_and_nopython(kwargs, nopython)
validate_udf(func)
numba_func = jit_user_function(func, nopython, nogil, parallel)
numba = import_optional_dependency("numba")
if parallel:
loop_range = numba.prange
else:
loop_range = range
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
def group_transform(
values: np.ndarray,
index: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
num_groups: int,
num_columns: int,
) -> np.ndarray:
result = np.empty((len(values), num_columns))
for i in loop_range(num_groups):
group_index = index[begin[i] : end[i]]
for j in loop_range(num_columns):
group = values[begin[i] : end[i], j]
result[begin[i] : end[i], j] = numba_func(group, group_index, *args)
return result
return group_transform
def generate_numba_ewm_func(
engine_kwargs: dict[str, bool] | None,
com: float,
adjust: bool,
ignore_na: bool,
deltas: np.ndarray,
normalize: bool,
):
"""
Generate a numba jitted ewm mean or sum function specified by values
from engine_kwargs.
Parameters
----------
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
com : float
adjust : bool
ignore_na : bool
deltas : numpy.ndarray
normalize : bool
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
str_key = "ewm_mean" if normalize else "ewm_sum"
cache_key = (lambda x: x, str_key)
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba = import_optional_dependency("numba")
# error: Untyped decorator makes function "ewma" untyped
@numba.jit(nopython=nopython, nogil=nogil,
parallel=parallel) # type: ignore[misc]
def ewm(
values: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
minimum_periods: int,
) -> np.ndarray:
result = np.empty(len(values))
alpha = 1.0 / (1.0 + com)
old_wt_factor = 1.0 - alpha
new_wt = 1.0 if adjust else alpha
for i in numba.prange(len(begin)):
start = begin[i]
stop = end[i]
window = values[start:stop]
sub_result = np.empty(len(window))
weighted = window[0]
nobs = int(not np.isnan(weighted))
sub_result[0] = weighted if nobs >= minimum_periods else np.nan
old_wt = 1.0
for j in range(1, len(window)):
cur = window[j]
is_observation = not np.isnan(cur)
nobs += is_observation
if not np.isnan(weighted):
if is_observation or not ignore_na:
if normalize:
# note that len(deltas) = len(vals) - 1 and deltas[i]
# is to be used in conjunction with vals[i+1]
old_wt *= old_wt_factor**deltas[start + j - 1]
else:
weighted = old_wt_factor * weighted
if is_observation:
if normalize:
# avoid numerical errors on constant series
if weighted != cur:
weighted = old_wt * weighted + new_wt * cur
if normalize:
weighted = weighted / (old_wt + new_wt)
if adjust:
old_wt += new_wt
else:
old_wt = 1.0
else:
weighted += cur
elif is_observation:
weighted = cur
sub_result[j] = weighted if nobs >= minimum_periods else np.nan
result[start:stop] = sub_result
return result
return ewm
def generate_numba_ewm_table_func(
engine_kwargs: dict[str, bool] | None,
com: float,
adjust: bool,
ignore_na: bool,
deltas: np.ndarray,
normalize: bool,
):
"""
Generate a numba jitted ewm mean or sum function applied table wise specified
by values from engine_kwargs.
Parameters
----------
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
com : float
adjust : bool
ignore_na : bool
deltas : numpy.ndarray
normalize: bool
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
str_key = "ewm_mean_table" if normalize else "ewm_sum_table"
cache_key = (lambda x: x, str_key)
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba = import_optional_dependency("numba")
# error: Untyped decorator makes function "ewm_table" untyped
@numba.jit(nopython=nopython, nogil=nogil,
parallel=parallel) # type: ignore[misc]
def ewm_table(
values: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
minimum_periods: int,
) -> np.ndarray:
alpha = 1.0 / (1.0 + com)
old_wt_factor = 1.0 - alpha
new_wt = 1.0 if adjust else alpha
old_wt = np.ones(values.shape[1])
result = np.empty(values.shape)
weighted = values[0].copy()
nobs = (~np.isnan(weighted)).astype(np.int64)
result[0] = np.where(nobs >= minimum_periods, weighted, np.nan)
for i in range(1, len(values)):
cur = values[i]
is_observations = ~np.isnan(cur)
nobs += is_observations.astype(np.int64)
for j in numba.prange(len(cur)):
if not np.isnan(weighted[j]):
if is_observations[j] or not ignore_na:
if normalize:
# note that len(deltas) = len(vals) - 1 and deltas[i]
# is to be used in conjunction with vals[i+1]
old_wt[j] *= old_wt_factor**deltas[i - 1]
else:
weighted[j] = old_wt_factor * weighted[j]
if is_observations[j]:
if normalize:
# avoid numerical errors on constant series
if weighted[j] != cur[j]:
weighted[j] = (old_wt[j] * weighted[j] +
new_wt * cur[j])
if normalize:
weighted[j] = weighted[j] / (
old_wt[j] + new_wt)
if adjust:
old_wt[j] += new_wt
else:
old_wt[j] = 1.0
else:
weighted[j] += cur[j]
elif is_observations[j]:
weighted[j] = cur[j]
result[i] = np.where(nobs >= minimum_periods, weighted, np.nan)
return result
return ewm_table
def generate_numba_apply_func(
kwargs: dict[str, Any],
func: Callable[..., Scalar],
engine_kwargs: dict[str, bool] | None,
name: str,
):
"""
Generate a numba jitted apply function specified by values from engine_kwargs.
1. jit the user's function
2. Return a rolling apply function with the jitted function inline
Configurations specified in engine_kwargs apply to both the user's
function _AND_ the rolling apply function.
Parameters
----------
kwargs : dict
**kwargs to be passed into the function
func : function
function to be applied to each window and will be JITed
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
name: str
name of the caller (Rolling/Expanding)
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
cache_key = (func, f"{name}_apply_single")
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba_func = jit_user_function(func, nopython, nogil, parallel)
numba = import_optional_dependency("numba")
# error: Untyped decorator makes function "roll_apply" untyped
@numba.jit(nopython=nopython, nogil=nogil,
parallel=parallel) # type: ignore[misc]
def roll_apply(
values: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
minimum_periods: int,
*args: Any,
) -> np.ndarray:
result = np.empty(len(begin))
for i in numba.prange(len(result)):
start = begin[i]
stop = end[i]
window = values[start:stop]
count_nan = np.sum(np.isnan(window))
if len(window) - count_nan >= minimum_periods:
result[i] = numba_func(window, *args)
else:
result[i] = np.nan
return result
return roll_apply
def generate_numba_table_func(
kwargs: dict[str, Any],
func: Callable[..., np.ndarray],
engine_kwargs: dict[str, bool] | None,
name: str,
):
"""
Generate a numba jitted function to apply window calculations table-wise.
Func will be passed a M window size x N number of columns array, and
must return a 1 x N number of columns array. Func is intended to operate
row-wise, but the result will be transposed for axis=1.
1. jit the user's function
2. Return a rolling apply function with the jitted function inline
Parameters
----------
kwargs : dict
**kwargs to be passed into the function
func : function
function to be applied to each window and will be JITed
engine_kwargs : dict
dictionary of arguments to be passed into numba.jit
name : str
caller (Rolling/Expanding) and original method name for numba cache key
Returns
-------
Numba function
"""
nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
cache_key = (func, f"{name}_table")
if cache_key in NUMBA_FUNC_CACHE:
return NUMBA_FUNC_CACHE[cache_key]
numba_func = jit_user_function(func, nopython, nogil, parallel)
numba = import_optional_dependency("numba")
# error: Untyped decorator makes function "roll_table" untyped
@numba.jit(nopython=nopython, nogil=nogil,
parallel=parallel) # type: ignore[misc]
def roll_table(
values: np.ndarray,
begin: np.ndarray,
end: np.ndarray,
minimum_periods: int,
*args: Any,
):
result = np.empty(values.shape)
min_periods_mask = np.empty(values.shape)
for i in numba.prange(len(result)):
start = begin[i]
stop = end[i]
window = values[start:stop]
count_nan = np.sum(np.isnan(window), axis=0)
sub_result = numba_func(window, *args)
nan_mask = len(window) - count_nan >= minimum_periods
min_periods_mask[i, :] = nan_mask
result[i, :] = sub_result
result = np.where(min_periods_mask, result, np.nan)
return result
return roll_table
def mean(self, *args, update=None, update_times=None, **kwargs):
"""
Calculate an online exponentially weighted mean.
Parameters
----------
update: DataFrame or Series, default None
New values to continue calculating the
exponentially weighted mean from the last values and weights.
Values should be float64 dtype.
``update`` needs to be ``None`` the first time the
exponentially weighted mean is calculated.
update_times: Series or 1-D np.ndarray, default None
New times to continue calculating the
exponentially weighted mean from the last values and weights.
If ``None``, values are assumed to be evenly spaced
in time.
This feature is currently unsupported.
Returns
-------
DataFrame or Series
Examples
--------
>>> df = pd.DataFrame({"a": range(5), "b": range(5, 10)})
>>> online_ewm = df.head(2).ewm(0.5).online()
>>> online_ewm.mean()
a b
0 0.00 5.00
1 0.75 5.75
>>> online_ewm.mean(update=df.tail(3))
a b
2 1.615385 6.615385
3 2.550000 7.550000
4 3.520661 8.520661
>>> online_ewm.reset()
>>> online_ewm.mean()
a b
0 0.00 5.00
1 0.75 5.75
"""
result_kwargs = {}
is_frame = True if self._selected_obj.ndim == 2 else False
if update_times is not None:
raise NotImplementedError("update_times is not implemented.")
else:
update_deltas = np.ones(max(
self._selected_obj.shape[self.axis - 1] - 1, 0),
dtype=np.float64)
if update is not None:
if self._mean.last_ewm is None:
raise ValueError(
"Must call mean with update=None first before passing update"
)
result_from = 1
result_kwargs["index"] = update.index
if is_frame:
last_value = self._mean.last_ewm[np.newaxis, :]
result_kwargs["columns"] = update.columns
else:
last_value = self._mean.last_ewm
result_kwargs["name"] = update.name
np_array = np.concatenate((last_value, update.to_numpy()))
else:
result_from = 0
result_kwargs["index"] = self._selected_obj.index
if is_frame:
result_kwargs["columns"] = self._selected_obj.columns
else:
result_kwargs["name"] = self._selected_obj.name
np_array = self._selected_obj.astype(np.float64).to_numpy()
ewma_func = generate_online_numba_ewma_func(
**get_jit_arguments(self.engine_kwargs))
result = self._mean.run_ewm(
np_array if is_frame else np_array[:, np.newaxis],
update_deltas,
self.min_periods,
ewma_func,
)
if not is_frame:
result = result.squeeze()
result = result[result_from:]
result = self._selected_obj._constructor(result, **result_kwargs)
return result
