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d dlmZ d dlm  m  mZ d dlmZ d dlmZmZ d d	lmZ d d
lmZ d dlmZ d dlmZ d dlm Z  d dl!m"Z"m#Z#m$Z$ d dl%m&Z&m'Z' d dl(m)Z) d dl*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2 d dl3m4Z4m5Z5 d dl6m7Z7m8Z8 d dl9m:Z:m;Z; er¼d dl<m=Z=m>Z>m?Z? d dl@mAZAmBZB d dlCmDZD d-dd „ZEd.d%d&„ZFG d'd(„ d(e:ƒZGG d)d*„ d*e;eGƒZHG d+d,„ d,eGƒZIdS )/é    )ÚannotationsN)Úpartial)Údedent)ÚTYPE_CHECKING)Ú	Timedelta)Údoc)Úis_datetime64_dtypeÚis_numeric_dtype)ÚDatetimeTZDtype)Ú	ABCSeries)Úisna)Úcommon)Údtype_to_unit)ÚBaseIndexerÚExponentialMovingWindowIndexerÚGroupbyIndexer)Úget_jit_argumentsÚmaybe_use_numba)Úzsqrt)Ú_shared_docsÚcreate_section_headerÚkwargs_numeric_onlyÚnumba_notesÚtemplate_headerÚtemplate_returnsÚtemplate_see_alsoÚwindow_agg_numba_parameters)Úgenerate_numba_ewm_funcÚgenerate_numba_ewm_table_func)ÚEWMMeanStateÚgenerate_online_numba_ewma_func)Ú
BaseWindowÚBaseWindowGroupby)ÚAxisÚTimedeltaConvertibleTypesÚnpt)Ú	DataFrameÚSeries)ÚNDFrameÚcomassúfloat | NoneÚspanÚhalflifeÚalphaÚreturnÚfloatc                 C  sì   t  | |||¡}|dkrtdƒ‚| d ur | dk rtdƒ‚t| ƒS |d ur6|dk r,tdƒ‚|d d } t| ƒS |d urX|dkrBtdƒ‚dt t d¡| ¡ }d| d } t| ƒS |d urr|dksd|dkrhtd	ƒ‚d| | } t| ƒS td
ƒ‚)Né   z8comass, span, halflife, and alpha are mutually exclusiver   z comass must satisfy: comass >= 0zspan must satisfy: span >= 1é   z#halflife must satisfy: halflife > 0g      à?z"alpha must satisfy: 0 < alpha <= 1z1Must pass one of comass, span, halflife, or alpha)r   Úcount_not_noneÚ
ValueErrorÚnpÚexpÚlogr/   )r)   r+   r,   r-   Úvalid_countÚdecay© r9   úY/home/dinkstrade/pdmp-scanner/venv/lib/python3.10/site-packages/pandas/core/window/ewm.pyÚget_center_of_massG   s0   ðôùþr;   Útimesúnp.ndarray | NDFrameú(float | TimedeltaConvertibleTypes | Noneúnpt.NDArray[np.float64]c                 C  sT   t | jƒ}t| tƒr| j} tj|  tj¡tj	d�}t
t|ƒ |¡jƒ}t |¡| S )aå  
    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
    ©Údtype)r   rA   Ú
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
rN   c                      s`  e Zd ZdZg d¢Z										deddœdf‡ fdd„Zdgd%d&„Zdhd(d)„Z	didjd-d.„Ze	e
d/ ed0ƒed1ƒd2d3d4�‡ fd5d6„ƒZeZe	eed7ƒeeƒ ed8ƒeed9ƒeed:ƒeed;ƒed<ƒd=d>d?d@�			dkdldBdC„ƒZe	eed7ƒeeƒ ed8ƒeed9ƒeed:ƒeed;ƒedDƒd=dEdFd@�			dkdldGdH„ƒZe	eed7ƒedIƒeed8ƒeed9ƒeed;ƒedJƒd=dKdLd@�dmdndNdO„ƒZe	eed7ƒedIƒeed8ƒeed9ƒeed;ƒedPƒd=dQdRd@�dmdndSdT„ƒZe	eed7ƒedUƒeed8ƒeed9ƒeed;ƒedVƒd=dWdXd@�				dodpd]d^„ƒZe	eed7ƒed_ƒeed8ƒeed9ƒeed;ƒed`ƒd=dadbd@�			dqdrdcdd„ƒZ‡  ZS )sÚExponentialMovingWindowaé  
    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
    )
Úcomr+   r,   r-   Úmin_periodsÚadjustÚ	ignore_naÚaxisr<   ÚmethodNr   TFÚsingle©Ú	selectionÚobjr(   rP   r*   r+   r,   r>   r-   rQ   ú
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ƒtj#d�| _t| j| j| j| jƒ| _d S )Nr0   F)rY   rQ   ÚonÚcenterÚclosedrU   rT   rX   z)times is not supported with adjust=False.rA   ztimes must be datetime64 dtype.z,times must be the same length as the object.z/halflife must be a timedelta convertible objectz$Cannot convert NaT values to integerr   g      ð?zKhalflife can only be a timedelta convertible argument if times is not None.r@   )$ÚsuperÚ__init__ÚmaxÚintrP   r+   r,   r-   rR   rS   r<   ÚNotImplementedErrorÚgetattrr   rB   r
   r3   Úlenr]   ÚdatetimeÚ	timedeltar4   Útimedelta64r   ÚanyrN   Ú_deltasr   r2   r;   Ú_comÚonesrY   ÚshaperT   rG   )ÚselfrY   rP   r+   r,   r-   rQ   rR   rS   rT   r<   rU   rX   Útimes_dtype©Ú	__class__r9   r:   rc   P  sf   ø

ÿþ
ÿÿÿ
ùz ExponentialMovingWindow.__init__Ústartú
np.ndarrayÚendÚnum_valsre   c                 C  s   d S ©Nr9   )rq   ru   rw   rx   r9   r9   r:   Ú_check_window_bounds�  s   z,ExponentialMovingWindow._check_window_boundsr   c                 C  s   t ƒ S )z[
        Return an indexer class that will compute the window start and end bounds
        )r   ©rq   r9   r9   r:   Ú_get_window_indexer¤  s   z+ExponentialMovingWindow._get_window_indexerÚnumbaÚengineÚOnlineExponentialMovingWindowc                 C  s8   t | j| j| j| j| j| j| j| j| j	| j
||| jd�S )aª  
        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
        )rY   rP   r+   r,   r-   rQ   rR   rS   rT   r<   r~   Úengine_kwargsrX   )r   rY   rP   r+   r,   r-   rQ   rR   rS   rT   r<   Ú
_selection)rq   r~   r€   r9   r9   r:   Úonlineª  s   ózExponentialMovingWindow.onlineÚ	aggregatezV
        See Also
        --------
        pandas.DataFrame.rolling.aggregate
        aœ  
        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
        zSeries/DataframeÚ )Úsee_alsoÚexamplesÚklassrT   c                   s   t ƒ j|g|¢R i |¤ŽS ry   )rb   rƒ   ©rq   ÚfuncÚargsÚkwargsrs   r9   r:   rƒ   Ö  s   z!ExponentialMovingWindow.aggregateÚ
ParametersÚReturnszSee AlsoÚNotesÚExampleszÆ        >>> 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
        Úewmz"(exponential weighted moment) meanÚmean)Úwindow_methodÚaggregation_descriptionÚ
agg_methodÚnumeric_onlyc              	   C  s¸   t |ƒr,| jdkrt}nt}|d
i t|ƒ¤| j| j| jt| j	ƒddœ¤Ž}| j
|dd�S |dv rX|d ur8tdƒ‚| jd u r?d n| j	}ttj| j| j| j|dd�}| j
|d|d�S td	ƒ‚)NrV   T©rP   rR   rS   ÚdeltasÚ	normalizer‘   ©Úname©ÚcythonNú+cython engine does not accept engine_kwargs©rš   r•   ú)engine must be either 'numba' or 'cython'r9   )r   rU   r   r   r   rn   rR   rS   Útuplerm   Ú_applyr3   r<   r   Úwindow_aggregationsr�   ©rq   r•   r~   r€   r‰   Úewm_funcr—   Úwindow_funcr9   r9   r:   r‘   ù  s8   !
ÿ
úúzExponentialMovingWindow.meanz¹        >>> 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
        z!(exponential weighted moment) sumÚsumc              	   C  sÆ   | j stdƒ‚t|ƒr3| jdkrt}nt}|di t|ƒ¤| j| j | jt	| j
ƒddœ¤Ž}| j|dd�S |dv r_|d ur?tdƒ‚| jd u rFd n| j
}ttj| j| j | j|dd�}| j|d|d	�S td
ƒ‚)Nz(sum is not implemented with adjust=FalserV   Fr–   r¦   r™   r›   r�   rž   rŸ   r9   )rR   rf   r   rU   r   r   r   rn   rS   r    rm   r¡   r3   r<   r   r¢   r�   r£   r9   r9   r:   r¦   9  s<   !
ÿ
úúzExponentialMovingWindow.sumzb        bias : bool, default False
            Use a standard estimation bias correction.
        zÅ        >>> 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
        z0(exponential weighted moment) standard deviationÚstdÚbiasc                 C  sB   |r| j jdkrt| j jƒstt| ƒj› d�ƒ‚t| j||d�ƒS )Nr0   z$.std does not implement numeric_only)r¨   r•   )	Ú_selected_objÚndimr	   rA   rf   ÚtypeÚ__name__r   Úvar©rq   r¨   r•   r9   r9   r:   r§   {  s    ÿ
ÿÿzExponentialMovingWindow.stdzÅ        >>> 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
        z&(exponential weighted moment) variancer­   c                   s:   t j}t|| j| j| j|d�‰ ‡ fdd„}| j|d|d�S )N)rP   rR   rS   r¨   c                   s   ˆ | |||| ƒS ry   r9   )ÚvaluesÚbeginrw   rQ   ©Úwfuncr9   r:   Úvar_funcÍ  s   z-ExponentialMovingWindow.var.<locals>.var_funcr­   rž   )r¢   Úewmcovr   rn   rR   rS   r¡   )rq   r¨   r•   r¥   r³   r9   r±   r:   r­   ¥  s   ûzExponentialMovingWindow.vara¦          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.
        zú        >>> 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
        z/(exponential weighted moment) sample covarianceÚcovÚotherúDataFrame | Series | NoneÚpairwiseúbool | Nonec                   s<   ddl m‰  ˆ d|¡ ‡ ‡‡fdd„}ˆ ˆj||||¡S )Nr   ©r'   rµ   c           	        sŠ   ˆ  | ¡}ˆ  |¡}ˆ ¡ }ˆjd urˆjn|j}|jt|ƒ|ˆjˆjˆjd�\}}t	 
|||ˆj|ˆjˆjˆjˆ¡	}ˆ || j| jdd�S )N©Ú
num_valuesrQ   r`   ra   ÚstepF©Úindexrš   Úcopy)Ú_prep_valuesr|   rQ   Úwindow_sizeÚget_window_boundsrh   r`   ra   r½   r¢   r´   rn   rR   rS   r¿   rš   )	ÚxÚyÚx_arrayÚy_arrayÚwindow_indexerrQ   ru   rw   Úresult©r'   r¨   rq   r9   r:   Úcov_func  s4   


ÿý
ûõz-ExponentialMovingWindow.cov.<locals>.cov_func©Úpandasr'   Ú_validate_numeric_onlyÚ_apply_pairwiser©   )rq   r¶   r¸   r¨   r•   rË   r9   rÊ   r:   rµ   Ò  s   0ÿzExponentialMovingWindow.covaK          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.
        zû        >>> 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
        z0(exponential weighted moment) sample correlationÚcorrc                   s:   ddl m‰  ˆ d|¡ ‡ ‡fdd„}ˆ ˆj||||¡S )Nr   rº   rÐ   c           
        sÔ   ˆ  | ¡}ˆ  |¡}ˆ ¡ }ˆjd urˆjn|j‰|jt|ƒˆˆjˆjˆjd�\‰‰ ‡ ‡‡‡fdd„}t	j
dd�� |||ƒ}|||ƒ}|||ƒ}|t|| ƒ }	W d   ƒ n1 s[w   Y  ˆ|	| j| jdd�S )Nr»   c                   s    t  | ˆˆ ˆ|ˆjˆjˆjd¡	S )NT)r¢   r´   rn   rR   rS   )ÚXÚY)rw   rQ   rq   ru   r9   r:   Ú_covk  s   ÷z<ExponentialMovingWindow.corr.<locals>.cov_func.<locals>._covÚignore)ÚallFr¾   )rÁ   r|   rQ   rÂ   rÃ   rh   r`   ra   r½   r4   Úerrstater   r¿   rš   )
rÄ   rÅ   rÆ   rÇ   rÈ   rÓ   rµ   Úx_varÚy_varrÉ   ©r'   rq   )rw   rQ   ru   r:   rË   Z  s,   


ÿý
û


üz.ExponentialMovingWindow.corr.<locals>.cov_funcrÌ   )rq   r¶   r¸   r•   rË   r9   rÙ   r:   rÐ   )  s   -%ÿzExponentialMovingWindow.corr)
NNNNr   TFr   NrV   )rY   r(   rP   r*   r+   r*   r,   r>   r-   r*   rQ   rZ   rR   r[   rS   r[   rT   r#   r<   r\   rU   r]   r.   r^   )ru   rv   rw   rv   rx   re   r.   r^   )r.   r   )r}   N)r~   r]   r.   r   )FNN)r•   r[   ©FF©r¨   r[   r•   r[   ©NNFF©r¶   r·   r¸   r¹   r¨   r[   r•   r[   ©NNF©r¶   r·   r¸   r¹   r•   r[   )r¬   Ú
__module__Ú__qualname__Ú__doc__Ú_attributesrc   rz   r|   r‚   r   r   r   rƒ   Úaggr   r   r   r   r   r   r   r‘   r¦   r§   r­   rµ   rÐ   Ú__classcell__r9   r9   rs   r:   rO   …   sN    >ôò
M
ÿ,ÿÿäÿçü%ÿçü'ÿÿäÿÿäÿÿÙ+û.ÿÿÛ)ürO   c                      s>   e Zd ZdZejej Zddœd‡ fdd„Zdd	d
„Z‡  Z	S )ÚExponentialMovingWindowGroupbyzF
    Provide an exponential moving window groupby implementation.
    N)Ú_grouperr.   r^   c                  sf   t ƒ j|g|¢R d|i|¤Ž |js/| jd ur1t t| jj 	¡ ƒ¡}t
| j |¡| jƒ| _d S d S d S )Nrç   )rb   rc   Úemptyr<   r4   ÚconcatenateÚlistrç   Úindicesr¯   rN   Útaker,   rm   )rq   rY   rç   rŠ   r‹   Úgroupby_orderrs   r9   r:   rc   ‹  s   

þýz'ExponentialMovingWindowGroupby.__init__r   c                 C  s   t | jjtd�}|S )z“
        Return an indexer class that will compute the window start and end bounds

        Returns
        -------
        GroupbyIndexer
        )Úgroupby_indicesrÈ   )r   rç   rë   r   )rq   rÈ   r9   r9   r:   r|   –  s
   þz2ExponentialMovingWindowGroupby._get_window_indexer©r.   r^   )r.   r   )
r¬   rà   rá   râ   rO   rã   r"   rc   r|   rå   r9   r9   rs   r:   ræ   „  s
    ræ   c                      sœ   e Zd Z											d5ddœd6‡ fdd„Zd7d d!„Zd"d#„ Zd8d9d%d&„Z			d:d;d,d-„Z				d<d=d.d/„Zd>d?d0d1„Z	ddd2œd3d4„Z
‡  ZS )@r   Nr   TFr}   rW   rY   r(   rP   r*   r+   r,   r>   r-   rQ   rZ   rR   r[   rS   rT   r#   r<   r\   r~   r]   r€   údict[str, bool] | Noner.   r^   c                  sn   |
d urt dƒ‚tƒ j|||||||||	|
|d� t| j| j| j| j|jƒ| _	t
|ƒr3|| _|| _d S tdƒ‚)Nz0times is not implemented with online operations.)rY   rP   r+   r,   r-   rQ   rR   rS   rT   r<   rX   z$'numba' is the only supported engine)rf   rb   rc   r   rn   rR   rS   rT   rp   Ú_meanr   r~   r€   r3   )rq   rY   rP   r+   r,   r-   rQ   rR   rS   rT   r<   r~   r€   rX   rs   r9   r:   rc   ¦  s0   ÿõÿ
z&OnlineExponentialMovingWindow.__init__c                 C  s   | j  ¡  dS )z=
        Reset the state captured by `update` calls.
        N)rñ   Úresetr{   r9   r9   r:   rò   Ñ  s   z#OnlineExponentialMovingWindow.resetc                 O  ó   t dƒ‚)Nzaggregate is not implemented.©rf   rˆ   r9   r9   r:   rƒ   ×  ó   z'OnlineExponentialMovingWindow.aggregater¨   c                 O  ró   )Nzstd is not implemented.rô   )rq   r¨   rŠ   r‹   r9   r9   r:   r§   Ú  rõ   z!OnlineExponentialMovingWindow.stdr¶   r·   r¸   r¹   r•   c                 C  ró   )Nzcorr is not implemented.rô   )rq   r¶   r¸   r•   r9   r9   r:   rÐ   Ý  s   z"OnlineExponentialMovingWindow.corrc                 C  ró   )Nzcov is not implemented.rô   )rq   r¶   r¸   r¨   r•   r9   r9   r:   rµ   å  s   z!OnlineExponentialMovingWindow.covc                 C  ró   )Nzvar is not implemented.rô   r®   r9   r9   r:   r­   î  rõ   z!OnlineExponentialMovingWindow.var)ÚupdateÚupdate_timesc                O  sp  i }| j jdk}|durtdƒ‚tjt| j j| jd  d dƒtjd�}|dur_| j	j
du r2tdƒ‚d}|j|d< |rL| j	j
tjdd…f }	|j|d	< n	| j	j
}	|j|d
< t |	| ¡ f¡}
n"d}| j j|d< |rp| j j|d	< n| j j|d
< | j jtjdd� ¡ }
tdi t| jƒ¤Ž}| j	 |r’|
n|
dd…tjf || j|¡}|s¦| ¡ }||d… }| j j|fi |¤Ž}|S )a[  
        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
        r1   Nz update_times is not implemented.r0   r   r@   z;Must call mean with update=None first before passing updater¿   Úcolumnsrš   F)rÀ   r9   )r©   rª   rf   r4   ro   rd   rp   rT   rG   rñ   Úlast_ewmr3   r¿   Únewaxisrø   rš   ré   Úto_numpyÚastyper    r   r€   Úrun_ewmrQ   ÚsqueezeÚ_constructor)rq   rö   r÷   rŠ   r‹   Úresult_kwargsÚis_frameÚupdate_deltasÚresult_fromÚ
last_valueÚnp_arrayÚ	ewma_funcrÉ   r9   r9   r:   r‘   ñ  sP   ,ÿÿ

ÿüz"OnlineExponentialMovingWindow.mean)NNNNr   TFr   Nr}   N)rY   r(   rP   r*   r+   r*   r,   r>   r-   r*   rQ   rZ   rR   r[   rS   r[   rT   r#   r<   r\   r~   r]   r€   rð   r.   r^   rï   )F)r¨   r[   rÞ   rß   rÜ   rÝ   rÚ   rÛ   )r¬   rà   rá   rc   rò   rƒ   r§   rÐ   rµ   r­   r‘   rå   r9   r9   rs   r:   r   ¥  s:    óñ
+ü
û	r   )
r)   r*   r+   r*   r,   r*   r-   r*   r.   r/   )r<   r=   r,   r>   r.   r?   )JÚ
__future__r   ri   Ú	functoolsr   Útextwrapr   Útypingr   Únumpyr4   Úpandas._libs.tslibsr   Ú pandas._libs.window.aggregationsÚ_libsÚwindowÚaggregationsr¢   Úpandas.util._decoratorsr   Úpandas.core.dtypes.commonr   r	   Úpandas.core.dtypes.dtypesr
   Úpandas.core.dtypes.genericr   Úpandas.core.dtypes.missingr   Úpandas.corer   Úpandas.core.arrays.datetimeliker   Úpandas.core.indexers.objectsr   r   r   Úpandas.core.util.numba_r   r   Úpandas.core.window.commonr   Úpandas.core.window.docr   r   r   r   r   r   r   r   Úpandas.core.window.numba_r   r   Úpandas.core.window.onliner   r    Úpandas.core.window.rollingr!   r"   Úpandas._typingr#   r$   r%   rÍ   r&   r'   Úpandas.core.genericr(   r;   rN   rO   ræ   r   r9   r9   r9   r:   Ú<module>   sJ    (
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