Sindbad~EG File Manager
from __future__ import annotations
import datetime
from functools import partial
from textwrap import dedent
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs.tslibs import Timedelta
import pandas._libs.window.aggregations as window_aggregations
from pandas.util._decorators import doc
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_numeric_dtype,
)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.dtypes.missing import isna
from pandas.core import common
from pandas.core.arrays.datetimelike import dtype_to_unit
from pandas.core.indexers.objects import (
BaseIndexer,
ExponentialMovingWindowIndexer,
GroupbyIndexer,
)
from pandas.core.util.numba_ import (
get_jit_arguments,
maybe_use_numba,
)
from pandas.core.window.common import zsqrt
from pandas.core.window.doc import (
_shared_docs,
create_section_header,
kwargs_numeric_only,
numba_notes,
template_header,
template_returns,
template_see_also,
window_agg_numba_parameters,
)
from pandas.core.window.numba_ import (
generate_numba_ewm_func,
generate_numba_ewm_table_func,
)
from pandas.core.window.online import (
EWMMeanState,
generate_online_numba_ewma_func,
)
from pandas.core.window.rolling import (
BaseWindow,
BaseWindowGroupby,
)
if TYPE_CHECKING:
from pandas._typing import (
Axis,
TimedeltaConvertibleTypes,
npt,
)
from pandas import (
DataFrame,
Series,
)
from pandas.core.generic import NDFrame
def get_center_of_mass(
comass: float | None,
span: float | None,
halflife: float | None,
alpha: float | None,
) -> float:
valid_count = common.count_not_none(comass, span, halflife, alpha)
if valid_count > 1:
raise ValueError("comass, span, halflife, and alpha are mutually exclusive")
# Convert to center of mass; domain checks ensure 0 < alpha <= 1
if comass is not None:
if comass < 0:
raise ValueError("comass must satisfy: comass >= 0")
elif span is not None:
if span < 1:
raise ValueError("span must satisfy: span >= 1")
comass = (span - 1) / 2
elif halflife is not None:
if halflife <= 0:
raise ValueError("halflife must satisfy: halflife > 0")
decay = 1 - np.exp(np.log(0.5) / halflife)
comass = 1 / decay - 1
elif alpha is not None:
if alpha <= 0 or alpha > 1:
raise ValueError("alpha must satisfy: 0 < alpha <= 1")
comass = (1 - alpha) / alpha
else:
raise ValueError("Must pass one of comass, span, halflife, or alpha")
return float(comass)
def _calculate_deltas(
times: np.ndarray | NDFrame,
halflife: float | TimedeltaConvertibleTypes | None,
) -> npt.NDArray[np.float64]:
"""
Return the diff of the times divided by the half-life. These values are used in
the calculation of the ewm mean.
Parameters
----------
times : np.ndarray, Series
Times corresponding to the observations. Must be monotonically increasing
and ``datetime64[ns]`` dtype.
halflife : float, str, timedelta, optional
Half-life specifying the decay
Returns
-------
np.ndarray
Diff of the times divided by the half-life
"""
unit = dtype_to_unit(times.dtype)
if isinstance(times, ABCSeries):
times = times._values
_times = np.asarray(times.view(np.int64), dtype=np.float64)
_halflife = float(Timedelta(halflife).as_unit(unit)._value)
return np.diff(_times) / _halflife
class ExponentialMovingWindow(BaseWindow):
r"""
Provide exponentially weighted (EW) calculations.
Exactly one of ``com``, ``span``, ``halflife``, or ``alpha`` must be
provided if ``times`` is not provided. If ``times`` is provided,
``halflife`` and one of ``com``, ``span`` or ``alpha`` may be provided.
Parameters
----------
com : float, optional
Specify decay in terms of center of mass
:math:`\alpha = 1 / (1 + com)`, for :math:`com \geq 0`.
span : float, optional
Specify decay in terms of span
:math:`\alpha = 2 / (span + 1)`, for :math:`span \geq 1`.
halflife : float, str, timedelta, optional
Specify decay in terms of half-life
:math:`\alpha = 1 - \exp\left(-\ln(2) / halflife\right)`, for
:math:`halflife > 0`.
If ``times`` is specified, a timedelta convertible unit over which an
observation decays to half its value. Only applicable to ``mean()``,
and halflife value will not apply to the other functions.
alpha : float, optional
Specify smoothing factor :math:`\alpha` directly
:math:`0 < \alpha \leq 1`.
min_periods : int, default 0
Minimum number of observations in window required to have a value;
otherwise, result is ``np.nan``.
adjust : bool, default True
Divide by decaying adjustment factor in beginning periods to account
for imbalance in relative weightings (viewing EWMA as a moving average).
- When ``adjust=True`` (default), the EW function is calculated using weights
:math:`w_i = (1 - \alpha)^i`. For example, the EW moving average of the series
[:math:`x_0, x_1, ..., x_t`] would be:
.. math::
y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 -
\alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
- When ``adjust=False``, the exponentially weighted function is calculated
recursively:
.. math::
\begin{split}
y_0 &= x_0\\
y_t &= (1 - \alpha) y_{t-1} + \alpha x_t,
\end{split}
ignore_na : bool, default False
Ignore missing values when calculating weights.
- When ``ignore_na=False`` (default), weights are based on absolute positions.
For example, the weights of :math:`x_0` and :math:`x_2` used in calculating
the final weighted average of [:math:`x_0`, None, :math:`x_2`] are
:math:`(1-\alpha)^2` and :math:`1` if ``adjust=True``, and
:math:`(1-\alpha)^2` and :math:`\alpha` if ``adjust=False``.
- When ``ignore_na=True``, weights are based
on relative positions. For example, the weights of :math:`x_0` and :math:`x_2`
used in calculating the final weighted average of
[:math:`x_0`, None, :math:`x_2`] are :math:`1-\alpha` and :math:`1` if
``adjust=True``, and :math:`1-\alpha` and :math:`\alpha` if ``adjust=False``.
axis : {0, 1}, default 0
If ``0`` or ``'index'``, calculate across the rows.
If ``1`` or ``'columns'``, calculate across the columns.
For `Series` this parameter is unused and defaults to 0.
times : np.ndarray, Series, default None
Only applicable to ``mean()``.
Times corresponding to the observations. Must be monotonically increasing and
``datetime64[ns]`` dtype.
If 1-D array like, a sequence with the same shape as the observations.
method : str {'single', 'table'}, default 'single'
.. versionadded:: 1.4.0
Execute the rolling operation per single column or row (``'single'``)
or over the entire object (``'table'``).
This argument is only implemented when specifying ``engine='numba'``
in the method call.
Only applicable to ``mean()``
Returns
-------
pandas.api.typing.ExponentialMovingWindow
See Also
--------
rolling : Provides rolling window calculations.
expanding : Provides expanding transformations.
Notes
-----
See :ref:`Windowing Operations <window.exponentially_weighted>`
for further usage details and examples.
Examples
--------
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
B
0 0.0
1 1.0
2 2.0
3 NaN
4 4.0
>>> df.ewm(com=0.5).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
>>> df.ewm(alpha=2 / 3).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
**adjust**
>>> df.ewm(com=0.5, adjust=True).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
>>> df.ewm(com=0.5, adjust=False).mean()
B
0 0.000000
1 0.666667
2 1.555556
3 1.555556
4 3.650794
**ignore_na**
>>> df.ewm(com=0.5, ignore_na=True).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.225000
>>> df.ewm(com=0.5, ignore_na=False).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
**times**
Exponentially weighted mean with weights calculated with a timedelta ``halflife``
relative to ``times``.
>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
B
0 0.000000
1 0.585786
2 1.523889
3 1.523889
4 3.233686
"""
_attributes = [
"com",
"span",
"halflife",
"alpha",
"min_periods",
"adjust",
"ignore_na",
"axis",
"times",
"method",
]
def __init__(
self,
obj: NDFrame,
com: float | None = None,
span: float | None = None,
halflife: float | TimedeltaConvertibleTypes | None = None,
alpha: float | None = None,
min_periods: int | None = 0,
adjust: bool = True,
ignore_na: bool = False,
axis: Axis = 0,
times: np.ndarray | NDFrame | None = None,
method: str = "single",
*,
selection=None,
) -> None:
super().__init__(
obj=obj,
min_periods=1 if min_periods is None else max(int(min_periods), 1),
on=None,
center=False,
closed=None,
method=method,
axis=axis,
selection=selection,
)
self.com = com
self.span = span
self.halflife = halflife
self.alpha = alpha
self.adjust = adjust
self.ignore_na = ignore_na
self.times = times
if self.times is not None:
if not self.adjust:
raise NotImplementedError("times is not supported with adjust=False.")
times_dtype = getattr(self.times, "dtype", None)
if not (
is_datetime64_dtype(times_dtype)
or isinstance(times_dtype, DatetimeTZDtype)
):
raise ValueError("times must be datetime64 dtype.")
if len(self.times) != len(obj):
raise ValueError("times must be the same length as the object.")
if not isinstance(self.halflife, (str, datetime.timedelta, np.timedelta64)):
raise ValueError("halflife must be a timedelta convertible object")
if isna(self.times).any():
raise ValueError("Cannot convert NaT values to integer")
self._deltas = _calculate_deltas(self.times, self.halflife)
# Halflife is no longer applicable when calculating COM
# But allow COM to still be calculated if the user passes other decay args
if common.count_not_none(self.com, self.span, self.alpha) > 0:
self._com = get_center_of_mass(self.com, self.span, None, self.alpha)
else:
self._com = 1.0
else:
if self.halflife is not None and isinstance(
self.halflife, (str, datetime.timedelta, np.timedelta64)
):
raise ValueError(
"halflife can only be a timedelta convertible argument if "
"times is not None."
)
# Without times, points are equally spaced
self._deltas = np.ones(
max(self.obj.shape[self.axis] - 1, 0), dtype=np.float64
)
self._com = get_center_of_mass(
# error: Argument 3 to "get_center_of_mass" has incompatible type
# "Union[float, Any, None, timedelta64, signedinteger[_64Bit]]";
# expected "Optional[float]"
self.com,
self.span,
self.halflife, # type: ignore[arg-type]
self.alpha,
)
def _check_window_bounds(
self, start: np.ndarray, end: np.ndarray, num_vals: int
) -> None:
# emw algorithms are iterative with each point
# ExponentialMovingWindowIndexer "bounds" are the entire window
pass
def _get_window_indexer(self) -> BaseIndexer:
"""
Return an indexer class that will compute the window start and end bounds
"""
return ExponentialMovingWindowIndexer()
def online(
self, engine: str = "numba", engine_kwargs=None
) -> OnlineExponentialMovingWindow:
"""
Return an ``OnlineExponentialMovingWindow`` object to calculate
exponentially moving window aggregations in an online method.
.. versionadded:: 1.3.0
Parameters
----------
engine: str, default ``'numba'``
Execution engine to calculate online aggregations.
Applies to all supported aggregation methods.
engine_kwargs : dict, default None
Applies to all supported aggregation methods.
* For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
and ``parallel`` dictionary keys. The values must either be ``True`` or
``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
applied to the function
Returns
-------
OnlineExponentialMovingWindow
"""
return OnlineExponentialMovingWindow(
obj=self.obj,
com=self.com,
span=self.span,
halflife=self.halflife,
alpha=self.alpha,
min_periods=self.min_periods,
adjust=self.adjust,
ignore_na=self.ignore_na,
axis=self.axis,
times=self.times,
engine=engine,
engine_kwargs=engine_kwargs,
selection=self._selection,
)
@doc(
_shared_docs["aggregate"],
see_also=dedent(
"""
See Also
--------
pandas.DataFrame.rolling.aggregate
"""
),
examples=dedent(
"""
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
>>> df
A B C
0 1 4 7
1 2 5 8
2 3 6 9
>>> df.ewm(alpha=0.5).mean()
A B C
0 1.000000 4.000000 7.000000
1 1.666667 4.666667 7.666667
2 2.428571 5.428571 8.428571
"""
),
klass="Series/Dataframe",
axis="",
)
def aggregate(self, func, *args, **kwargs):
return super().aggregate(func, *args, **kwargs)
agg = aggregate
@doc(
template_header,
create_section_header("Parameters"),
kwargs_numeric_only,
window_agg_numba_parameters(),
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also,
create_section_header("Notes"),
numba_notes,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).mean()
0 1.000000
1 1.555556
2 2.147541
3 2.775068
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) mean",
agg_method="mean",
)
def mean(
self,
numeric_only: bool = False,
engine=None,
engine_kwargs=None,
):
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")
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'")
@doc(
template_header,
create_section_header("Parameters"),
kwargs_numeric_only,
window_agg_numba_parameters(),
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also,
create_section_header("Notes"),
numba_notes,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).sum()
0 1.000
1 2.800
2 5.240
3 8.192
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) sum",
agg_method="sum",
)
def sum(
self,
numeric_only: bool = False,
engine=None,
engine_kwargs=None,
):
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, name="sum")
elif engine in ("cython", None):
if engine_kwargs is not None:
raise ValueError("cython engine does not accept engine_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, name="sum", numeric_only=numeric_only)
else:
raise ValueError("engine must be either 'numba' or 'cython'")
@doc(
template_header,
create_section_header("Parameters"),
dedent(
"""\
bias : bool, default False
Use a standard estimation bias correction.
"""
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).std()
0 NaN
1 0.707107
2 0.995893
3 1.277320
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) standard deviation",
agg_method="std",
)
def std(self, bias: bool = False, numeric_only: bool = False):
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))
@doc(
template_header,
create_section_header("Parameters"),
dedent(
"""\
bias : bool, default False
Use a standard estimation bias correction.
"""
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).var()
0 NaN
1 0.500000
2 0.991803
3 1.631547
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) variance",
agg_method="var",
)
def var(self, bias: bool = False, numeric_only: bool = False):
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)
@doc(
template_header,
create_section_header("Parameters"),
dedent(
"""\
other : Series or DataFrame , optional
If not supplied then will default to self and produce pairwise
output.
pairwise : bool, default None
If False then only matching columns between self and other will be
used and the output will be a DataFrame.
If True then all pairwise combinations will be calculated and the
output will be a MultiIndex DataFrame in the case of DataFrame
inputs. In the case of missing elements, only complete pairwise
observations will be used.
bias : bool, default False
Use a standard estimation bias correction.
"""
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).cov(ser2)
0 NaN
1 0.500000
2 1.524590
3 3.408836
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) sample covariance",
agg_method="cov",
)
def cov(
self,
other: DataFrame | Series | None = None,
pairwise: bool | None = None,
bias: bool = False,
numeric_only: bool = False,
):
from pandas import Series
self._validate_numeric_only("cov", numeric_only)
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,
step=self.step,
)
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, copy=False)
return self._apply_pairwise(
self._selected_obj, other, pairwise, cov_func, numeric_only
)
@doc(
template_header,
create_section_header("Parameters"),
dedent(
"""\
other : Series or DataFrame, optional
If not supplied then will default to self and produce pairwise
output.
pairwise : bool, default None
If False then only matching columns between self and other will be
used and the output will be a DataFrame.
If True then all pairwise combinations will be calculated and the
output will be a MultiIndex DataFrame in the case of DataFrame
inputs. In the case of missing elements, only complete pairwise
observations will be used.
"""
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).corr(ser2)
0 NaN
1 1.000000
2 0.982821
3 0.977802
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) sample correlation",
agg_method="corr",
)
def corr(
self,
other: DataFrame | Series | None = None,
pairwise: bool | None = None,
numeric_only: bool = False,
):
from pandas import Series
self._validate_numeric_only("corr", numeric_only)
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,
step=self.step,
)
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, copy=False)
return self._apply_pairwise(
self._selected_obj, other, pairwise, cov_func, numeric_only
)
class ExponentialMovingWindowGroupby(BaseWindowGroupby, ExponentialMovingWindow):
"""
Provide an exponential moving window groupby implementation.
"""
_attributes = ExponentialMovingWindow._attributes + BaseWindowGroupby._attributes
def __init__(self, obj, *args, _grouper=None, **kwargs) -> None:
super().__init__(obj, *args, _grouper=_grouper, **kwargs)
if not obj.empty and self.times is not None:
# sort the times and recalculate the deltas according to the groups
groupby_order = np.concatenate(list(self._grouper.indices.values()))
self._deltas = _calculate_deltas(
self.times.take(groupby_order),
self.halflife,
)
def _get_window_indexer(self) -> GroupbyIndexer:
"""
Return an indexer class that will compute the window start and end bounds
Returns
-------
GroupbyIndexer
"""
window_indexer = GroupbyIndexer(
groupby_indices=self._grouper.indices,
window_indexer=ExponentialMovingWindowIndexer,
)
return window_indexer
class OnlineExponentialMovingWindow(ExponentialMovingWindow):
def __init__(
self,
obj: NDFrame,
com: float | None = None,
span: float | None = None,
halflife: float | TimedeltaConvertibleTypes | None = None,
alpha: float | None = None,
min_periods: int | None = 0,
adjust: bool = True,
ignore_na: bool = False,
axis: Axis = 0,
times: np.ndarray | NDFrame | None = None,
engine: str = "numba",
engine_kwargs: dict[str, bool] | None = None,
*,
selection=None,
) -> None:
if times is not None:
raise NotImplementedError(
"times is not implemented with online operations."
)
super().__init__(
obj=obj,
com=com,
span=span,
halflife=halflife,
alpha=alpha,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na,
axis=axis,
times=times,
selection=selection,
)
self._mean = EWMMeanState(
self._com, self.adjust, self.ignore_na, self.axis, obj.shape
)
if maybe_use_numba(engine):
self.engine = engine
self.engine_kwargs = engine_kwargs
else:
raise ValueError("'numba' is the only supported engine")
def reset(self) -> None:
"""
Reset the state captured by `update` calls.
"""
self._mean.reset()
def aggregate(self, func, *args, **kwargs):
raise NotImplementedError("aggregate is not implemented.")
def std(self, bias: bool = False, *args, **kwargs):
raise NotImplementedError("std is not implemented.")
def corr(
self,
other: DataFrame | Series | None = None,
pairwise: bool | None = None,
numeric_only: bool = False,
):
raise NotImplementedError("corr is not implemented.")
def cov(
self,
other: DataFrame | Series | None = None,
pairwise: bool | None = None,
bias: bool = False,
numeric_only: bool = False,
):
raise NotImplementedError("cov is not implemented.")
def var(self, bias: bool = False, numeric_only: bool = False):
raise NotImplementedError("var is not implemented.")
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 = self._selected_obj.ndim == 2
if update_times is not None:
raise NotImplementedError("update_times is not implemented.")
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, copy=False).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
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