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  dz  } n�|�J|dk    rt          d¦  «        ‚dt          j        t          j        d¦  «        |z  ¦  «        z
  }d|z  dz
  } n5|�$|dk    s|dk    rt          d	¦  «        ‚d|z
  |z  } nt          d
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ValueErrorÚnpÚexpÚlogr0   )r*   r,   r-   r.   Úvalid_countÚdecays         úPc:\xampp_lite_8_4\www\timesheet\venv\Lib\site-packages\pandas/core/window/ewm.pyÚget_center_of_massr<   G   s#  € õ Ô'¨°°hÀÑFÔF€KØ�Q‚€ÝÐSÑTÔTÐTð ÐØ�AŠ:ˆ:ÝÐ?Ñ@Ô@Ð@ð à	Ð	Ø�!Š8ˆ8ÝÐ;Ñ<Ô<Ð<Ø˜‘(˜a‘ˆˆØ	Ð	Ø�qŠ=ˆ=ÝÐBÑCÔCÐCØ•B”F�2œ6 #™;œ;¨Ñ1Ñ2Ô2Ñ2ˆØ�U‘˜Q‘ˆˆØ	Ð	Ø�AŠ:ˆ:˜ š˜ÝÐAÑBÔBÐBØ�e‘)˜uÑ$ˆˆåÐLÑMÔMÐMå�‰=Œ=Ðó    Útimesúnp.ndarray | NDFrameú(float | TimedeltaConvertibleTypes | Noneúnpt.NDArray[np.float64]c                ór  — t          | j        ¦  «        }t          | t          ¦  «        r| j        } t          j        |                      t
          j        ¦  «        t
          j	        ¬¦  «        }t          t          |¦  «                             |¦  «        j        ¦  «        }t          j        |¦  «        |z  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   rD   Ú
isinstancer   Ú_valuesr6   ÚasarrayÚviewÚint64Úfloat64r0   r   Úas_unitÚ_valueÚdiff)r>   r-   ÚunitÚ_timesÚ	_halflifes        r;   Ú_calculate_deltasrQ   h   s‰   € õ* ˜œÑ%Ô%€DÝ�%�Ñ#Ô#ð Ø”ˆÝŒZ˜Ÿ
š
¥2¤8Ñ,Ô,µB´JÐ?Ñ?Ô?€FÝ•i Ñ)Ô)×1Ò1°$Ñ7Ô7Ô>Ñ?Ô?€IÝŒ7�6‰?Œ?˜YÑ&Ð&r=   c                  ó<  ‡ — e Zd ZdZg d¢Z	 	 	 	 	 	 	 	 	 	 dZddœd[ˆ fd„Zd\d$„Zd]d&„Z	 d^d_d*„Z e	e
d+          ed,¦  «         ed-¦  «        d.d/¬0¦  «        ˆ fd1„¦   «         ZeZ e	e ed2¦  «        e e¦   «          ed3¦  «        e ed4¦  «        e ed5¦  «        e ed6¦  «         ed7¦  «        d8d9d:¬;¦  «        	 	 	 d`dad=„¦   «         Z e	e ed2¦  «        e e¦   «          ed3¦  «        e ed4¦  «        e ed5¦  «        e ed6¦  «         ed>¦  «        d8d?d@¬;¦  «        	 	 	 d`dadA„¦   «         Z e	e ed2¦  «         edB¦  «        e ed3¦  «        e ed4¦  «        e ed6¦  «         edC¦  «        d8dDdE¬;¦  «        dbdcdG„¦   «         Z e	e ed2¦  «         edB¦  «        e ed3¦  «        e ed4¦  «        e ed6¦  «         edH¦  «        d8dIdJ¬;¦  «        dbdcdK„¦   «         Z e	e ed2¦  «         edL¦  «        e ed3¦  «        e ed4¦  «        e ed6¦  «         edM¦  «        d8dNdO¬;¦  «        	 	 	 	 dddedT„¦   «         Z e	e ed2¦  «         edU¦  «        e ed3¦  «        e ed4¦  «        e ed6¦  «         edV¦  «        d8dWdX¬;¦  «        	 	 	 dfdgdY„¦   «         Zˆ xZS )hÚ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
    )
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int | NonerV   ÚboolrW   rX   r$   r>   únp.ndarray | NDFrame | NonerY   Ústrr/   ÚNonec          
     ó†  •— t          ¦   «                              ||€dnt          t          |¦  «        d¦  «        d dd ||	|¬¦  «         || _        || _        || _        || _        || _        || _	        |
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k    r(t;          | j        | j        d | j        ¦  «        | _        d S d| _        d S | j        �@t          | j        t$          t&          j        t*          j        f¦  «        rt!          d¦  «        ‚t+          j        t          | j         j!        | j"                 dz
  d
¦  «        t*          j#        ¬¦  «        | _        t;          | j        | j        | j        | j        ¦  «        | _        d S )Nr2   F)r]   rU   ÚonÚcenterÚclosedrY   rX   r\   z)times is not supported with adjust=False.rD   ztimes must be datetime64 dtype.z,times must be the same length as the object.z/halflife must be a timedelta convertible objectz$Cannot convert NaT values to integerr   g      ð?zKhalflife can only be a timedelta convertible argument if times is not None.rC   )$ÚsuperÚ__init__ÚmaxÚintrT   r,   r-   r.   rV   rW   r>   ÚNotImplementedErrorÚgetattrr	   rE   r   r5   Úlenra   ÚdatetimeÚ	timedeltar6   Útimedelta64r   ÚanyrQ   Ú_deltasr   r4   r<   Ú_comÚonesr]   ÚshaperX   rJ   )Úselfr]   rT   r,   r-   r.   rU   rV   rW   rX   r>   rY   r\   Útimes_dtypeÚ	__class__s                 €r;   rh   z ExponentialMovingWindow.__init__P  sˆ  ø€ õ  	‰Œ×ÒØØ(Ð0˜˜µc½#¸kÑ:JÔ:JÈAÑ6NÔ6NØØØØØØð 	ñ 		
ô 		
ð 		
ð ˆŒØˆŒ	Ø ˆŒØˆŒ
ØˆŒØ"ˆŒØˆŒ
ØŒ:Ñ!Ø”;ð WÝ)Ð*UÑVÔVÐVÝ! $¤*¨g°tÑ<Ô<ˆKå# KÑ0Ô0ðDå˜k­?Ñ;Ô;ðDõ !Ð!BÑCÔCÐCÝ�4”:‰Œ¥# c¡(¤(Ò*Ð*Ý Ð!OÑPÔPÐPÝ˜dœm­cµ8Ô3EÅrÄ~Ð-VÑWÔWð TÝ Ð!RÑSÔSÐSÝ�D”JÑÔ×#Ò#Ñ%Ô%ð IÝ Ð!GÑHÔHÐHÝ,¨T¬Z¸¼ÑGÔGˆDŒLõ Ô$ T¤X¨t¬y¸$¼*ÑEÔEÈÒIÐIÝ.¨t¬x¸¼ÀDÈ$Ì*ÑUÔU�”	�	�	à�”	�	�	àŒ}Ð(­ZØ”¥¥XÔ%7½¼ÐHñ.ô .Ð(õ !ð)ñô ð õ
 œ7Ý�D”H”N 4¤9Ô-°Ñ1°1Ñ5Ô5½R¼Zðñ ô ˆDŒLõ +ð ”Ø”	Ø”Ø”
ñô ˆDŒIˆIˆIr=   Ústartú
np.ndarrayÚendÚnum_valsrj   c                ó   — d S ©N© )rv   ry   r{   r|   s       r;   Ú_check_window_boundsz,ExponentialMovingWindow._check_window_bounds�  s	   € ð
 	ˆr=   r   c                ó   — t          ¦   «         S )z[
        Return an indexer class that will compute the window start and end bounds
        )r   ©rv   s    r;   Ú_get_window_indexerz+ExponentialMovingWindow._get_window_indexer¤  s   € õ .Ñ/Ô/Ð/r=   ÚnumbaÚengineÚOnlineExponentialMovingWindowc                ó¨   — t          | j        | j        | j        | j        | j        | j        | j        | j        | j	        | j
        ||| j        ¬¦  «        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
        )r]   rT   r,   r-   r.   rU   rV   rW   rX   r>   r…   Úengine_kwargsr\   )r†   r]   rT   r,   r-   r.   rU   rV   rW   rX   r>   Ú
_selection)rv   r…   rˆ   s      r;   ÚonlinezExponentialMovingWindow.onlineª  sY   € õ8 -Ø”Ø”Ø”Ø”]Ø”*ØÔ(Ø”;Ø”nØ”Ø”*ØØ'Ø”oð
ñ 
ô 
ð 	
r=   Ú	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klassrX   c                ó>   •—  t          ¦   «         j        |g|¢R i |¤ŽS r~   )rg   r‹   )rv   ÚfuncÚargsÚkwargsrx   s       €r;   r‹   z!ExponentialMovingWindow.aggregateÖ  s,   ø€ ð> !�u‰wŒwÔ  Ð7¨Ð7Ð7Ð7°Ð7Ð7Ð7r=   Ú
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           
     óò  — t          |¦  «        ro| j        dk    rt          }nt          } |d
i t	          |¦  «        ¤| j        | j        | j        t          | j	        ¦  «        ddœ¤Ž}|  
                    |d¬¦  «        S |dv rg|�t          d¦  «        ‚| j        €d n| j	        }t          t          j        | j        | j        | j        |d¬¦  «        }|  
                    |d|¬¦  «        S t          d	¦  «        ‚)NrZ   T©rT   rV   rW   ÚdeltasÚ	normalizer™   ©Úname©ÚcythonNú+cython engine does not accept engine_kwargs©r£   r�   ú)engine must be either 'numba' or 'cython'r   )r   rY   r   r   r   rs   rV   rW   Útuplerr   Ú_applyr5   r>   r   Úwindow_aggregationsr˜   ©rv   r�   r…   rˆ   r‘   Úewm_funcr    Úwindow_funcs           r;   r™   zExponentialMovingWindow.meanù  s&  € õB ˜6Ñ"Ô"ð 	JØŒ{˜hÒ&Ð&Ý.��å4�Ø�tð ð Ý# MÑ2Ô2ðà”IØ”{Øœ.Ý˜Tœ\Ñ*Ô*Øðð ð ð ˆHð —;’;˜x¨f�;Ñ5Ô5Ð5ØÐ'Ð'Ð'ØÐ(Ý Ð!NÑOÔOÐOà!œZÐ/�T�T°T´\ˆFÝ!Ý#Ô'Ø”IØ”{Øœ.ØØðñ ô ˆKð —;’;˜{°Àl�;ÑSÔSÐSåÐHÑIÔIÐIr=   z¹        >>> 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           
     ó  — | j         st          d¦  «        ‚t          |¦  «        ro| j        dk    rt          }nt
          } |di t          |¦  «        ¤| j        | j         | j        t          | j
        ¦  «        ddœ¤Ž}|                      |d¬¦  «        S |dv rg|�t          d¦  «        ‚| j        €d n| j
        }t          t          j        | j        | j         | j        |d¬¦  «        }|                      |d|¬	¦  «        S t          d
¦  «        ‚)Nz(sum is not implemented with adjust=FalserZ   FrŸ   r¯   r¢   r¤   r¦   r§   r¨   r   )rV   rk   r   rY   r   r   r   rs   rW   r©   rr   rª   r5   r>   r   r«   r˜   r¬   s           r;   r¯   zExponentialMovingWindow.sum9  s@  € ðB Œ{ð 	RÝ%Ð&PÑQÔQÐQÝ˜6Ñ"Ô"ð 	JØŒ{˜hÒ&Ð&Ý.��å4�Ø�tð ð Ý# MÑ2Ô2ðà”IØ”{Øœ.Ý˜Tœ\Ñ*Ô*Øðð ð ð ˆHð —;’;˜x¨e�;Ñ4Ô4Ð4ØÐ'Ð'Ð'ØÐ(Ý Ð!NÑOÔOÐOà!œZÐ/�T�T°T´\ˆFÝ!Ý#Ô'Ø”IØ”{Øœ.ØØðñ ô ˆKð —;’;˜{°À\�;ÑRÔRÐRåÐHÑIÔIÐIr=   zb        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                óè   — |rM| j         j        dk    r=t          | j         j        ¦  «        s$t	          t          | ¦  «        j        › d�¦  «        ‚t          |                      ||¬¦  «        ¦  «        S )Nr2   z$.std does not implement numeric_only)r²   r�   )	Ú_selected_objÚndimr
   rD   rk   ÚtypeÚ__name__r   Úvar©rv   r²   r�   s      r;   r±   zExponentialMovingWindow.std{  s}   € ð@ ð	àÔ"Ô'¨1Ò,Ð,Ý$ TÔ%7Ô%=Ñ>Ô>ð -õ &Ý˜‘:”:Ô&ÐLÐLÐLñô ð õ �T—X’X 4°l�XÑCÔCÑDÔDÐDr=   zÅ        >>> 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                óœ   ‡— t           j        }t          || j        | j        | j        |¬¦  «        Šˆfd„}|                      |d|¬¦  «        S )N)rT   rV   rW   r²   c                ó"   •—  ‰| |||| ¦  «        S r~   r   )ÚvaluesÚbeginr{   rU   Úwfuncs       €r;   Úvar_funcz-ExponentialMovingWindow.var.<locals>.var_funcÍ  s   ø€ Ø�5˜ ¨¨[¸&ÑAÔAÐAr=   r¸   r§   )r«   Úewmcovr   rs   rV   rW   rª   )rv   r²   r�   r®   r¿   r¾   s        @r;   r¸   zExponentialMovingWindow.var¥  sn   ø€ õ> *Ô0ˆÝØØ”	Ø”;Ø”nØð
ñ 
ô 
ˆð	Bð 	Bð 	Bð 	Bð 	Bð �{Š{˜8¨%¸lˆ{ÑKÔKÐKr=   a¦          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                óŠ   ‡ ‡‡— ddl mŠ ‰                      d|¦  «         ˆˆˆ fd„}‰                      ‰ j        ||||¦  «        S )Nr   ©r(   rÁ   c                ó®  •— ‰                      | ¦  «        }‰                      |¦  «        }‰                     ¦   «         }‰j        �‰j        n|j        }|                     t          |¦  «        |‰j        ‰j        ‰j        ¬¦  «        \  }}t          j
        |||‰j        |‰j        ‰j        ‰j        ‰
¦	  «	        } ‰	|| j        | j        d¬¦  «        S )N©Ú
num_valuesrU   re   rf   ÚstepF©Úindexr£   Úcopy)Ú_prep_valuesrƒ   rU   Úwindow_sizeÚget_window_boundsrm   re   rf   rË   r«   rÀ   rs   rV   rW   rÍ   r£   )ÚxÚyÚx_arrayÚy_arrayÚwindow_indexerrU   ry   r{   Úresultr(   r²   rv   s            €€€r;   Úcov_funcz-ExponentialMovingWindow.cov.<locals>.cov_func  sï   ø€ Ø×'Ò'¨Ñ*Ô*ˆGØ×'Ò'¨Ñ*Ô*ˆGØ!×5Ò5Ñ7Ô7ˆNð Ô#Ð/ð Ô Ð à#Ô/ð ð
 (×9Ò9Ý˜w™<œ<Ø'Ø”{Ø”{Ø”Yð :ñ ô ‰JˆE�3õ )Ô/ØØØð Ô ØØ”	Ø”Ø”Øñô ˆFð �6˜&¨¬°a´fÀ5ÐIÑIÔIÐIr=   ©Úpandasr(   Ú_validate_numeric_onlyÚ_apply_pairwiser´   )rv   rÂ   rÄ   r²   r�   rØ   r(   s   `  `  @r;   rÁ   zExponentialMovingWindow.covÒ  s‚   øøø€ ð` 	"Ð!Ð!Ð!Ð!Ð!à×#Ò# E¨<Ñ8Ô8Ð8ð	Jð 	Jð 	Jð 	Jð 	Jð 	Jð 	Jð> ×#Ò#ØÔ  x°¸<ñ
ô 
ð 	
r=   aK          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                ó†   ‡ ‡— ddl mŠ ‰                      d|¦  «         ˆˆ fd„}‰                      ‰ j        ||||¦  «        S )Nr   rÇ   rÝ   c                ó0  •‡
‡‡— ‰                      | ¦  «        }‰                      |¦  «        }‰                     ¦   «         }‰j        �‰j        n|j        Š|                     t          |¦  «        ‰‰j        ‰j        ‰j        ¬¦  «        \  ŠŠ
ˆ
ˆˆˆfd„}t          j
        d¬¦  «        5   |||¦  «        } |||¦  «        } |||¦  «        }|t          ||z  ¦  «        z  }	d d d ¦  «         n# 1 swxY w Y    ‰|	| j        | j        d¬¦  «        S )NrÉ   c                óZ   •— t          j        | ‰‰‰|‰j        ‰j        ‰j        d¦	  «	        S )NT)r«   rÀ   rs   rV   rW   )ÚXÚYr{   rU   rv   ry   s     €€€€r;   Ú_covz<ExponentialMovingWindow.corr.<locals>.cov_func.<locals>._covk  s9   ø€ Ý*Ô1ØØØØØØ”IØ”KØ”NØñ
ô 
ð 
r=   Úignore)ÚallFrÌ   )rÏ   rƒ   rU   rÐ   rÑ   rm   re   rf   rË   r6   Úerrstater   rÍ   r£   )rÒ   rÓ   rÔ   rÕ   rÖ   rã   rÁ   Úx_varÚy_varr×   r{   rU   ry   r(   rv   s             @@@€€r;   rØ   z.ExponentialMovingWindow.corr.<locals>.cov_funcZ  s“  øøøø€ Ø×'Ò'¨Ñ*Ô*ˆGØ×'Ò'¨Ñ*Ô*ˆGØ!×5Ò5Ñ7Ô7ˆNð Ô#Ð/ð Ô Ð à#Ô/ð ð
 (×9Ò9Ý˜w™<œ<Ø'Ø”{Ø”{Ø”Yð :ñ ô ‰JˆE�3ðð ð ð ð ð ð ð õ ” Ð*Ñ*Ô*ð 4ð 4Ø�d˜7 GÑ,Ô,�Ø˜˜W gÑ.Ô.�Ø˜˜W gÑ.Ô.�Ø�u U¨U¡]Ñ3Ô3Ñ3�ð	4ð 4ð 4ñ 4ô 4ð 4ð 4ð 4ð 4ð 4ð 4øøøð 4ð 4ð 4ð 4ð
 �6˜&¨¬°a´fÀ5ÐIÑIÔIÐIs   Â.:C4Ã4C8Ã;C8rÙ   )rv   rÂ   rÄ   r�   rØ   r(   s   `    @r;   rÝ   zExponentialMovingWindow.corr)  s|   øø€ ðZ 	"Ð!Ð!Ð!Ð!Ð!à×#Ò# F¨LÑ9Ô9Ð9ð#	Jð #	Jð #	Jð #	Jð #	Jð #	JðJ ×#Ò#ØÔ  x°¸<ñ
ô 
ð 	
r=   )
NNNNr   TFr   NrZ   )r]   r)   rT   r+   r,   r+   r-   r@   r.   r+   rU   r^   rV   r_   rW   r_   rX   r$   r>   r`   rY   ra   r/   rb   )ry   rz   r{   rz   r|   rj   r/   rb   )r/   r   )r„   N)r…   ra   r/   r†   )FNN)r�   r_   ©FF©r²   r_   r�   r_   ©NNFF©rÂ   rÃ   rÄ   rÅ   r²   r_   r�   r_   ©NNF©rÂ   rÃ   rÄ   rÅ   r�   r_   )r·   Ú
__module__Ú__qualname__Ú__doc__Ú_attributesrh   r€   rƒ   rŠ   r   r   r   r‹   Úaggr   r   r   r   r   r   r   r™   r¯   r±   r¸   rÁ   rÝ   Ú__classcell__©rx   s   @r;   rS   rS   …   sà  ø€ € € € € ð{ð {ðzð ð €Kð  !Ø!Ø=AØ"Ø"#ØØØØ-1ØðKð ðKð Kð Kð Kð Kð Kð Kð KðZð ð ð ð0ð 0ð 0ð 0ð 48ð*
ð *
ð *
ð *
ð *
ðX 	€SØ�[Ô!Ø�ðñ
ô 
ð �ðñ
ô 
ð$ !Øð9ñ ô ð<8ð 8ð 8ð 8ñ=ô ð<8ð €Cà€SØØÐ˜lÑ+Ô+ØØ#Ð#Ñ%Ô%ØÐ˜iÑ(Ô(ØØÐ˜jÑ)Ô)ØØÐ˜gÑ&Ô&ØØÐ˜jÑ)Ô)Øˆðñ
	
ô 
	
ð Ø DØð3ñ ô ð: #ØØð	#Jð #Jð #Jð #Jñ7ô ð6#JðJ 	€SØØÐ˜lÑ+Ô+ØØ#Ð#Ñ%Ô%ØÐ˜iÑ(Ô(ØØÐ˜jÑ)Ô)ØØÐ˜gÑ&Ô&ØØÐ˜jÑ)Ô)Øˆðñ
	
ô 
	
ð Ø CØð3ñ ô ð: #ØØð	%Jð %Jð %Jð %Jñ7ô ð6%JðN 	€SØØÐ˜lÑ+Ô+Øˆðñ	
ô 	
ð 	ØÐ˜iÑ(Ô(ØØÐ˜jÑ)Ô)ØØÐ˜jÑ)Ô)Øˆðñ
	
ô 
	
ð Ø RØð9ñ ô ð<
Eð 
Eð 
Eð 
Eñ=ô ð<
Eð 	€SØØÐ˜lÑ+Ô+Øˆðñ	
ô 	
ð 	ØÐ˜iÑ(Ô(ØØÐ˜jÑ)Ô)ØØÐ˜jÑ)Ô)Øˆðñ
	
ô 
	
ð Ø HØð9ñ ô ð<Lð Lð Lð Lñ=ô ð<Lð 	€SØØÐ˜lÑ+Ô+Øˆðñ	
ô 	
ð  	ØÐ˜iÑ(Ô(ØØÐ˜jÑ)Ô)ØØÐ˜jÑ)Ô)Øˆð	ñ	
ô 	
ð Ø QØðO(ñ (ô (ðV ,0Ø $ØØ"ð,
ð ,
ð ,
ð ,
ñS(ô (ðR,
ð\ 	€SØØÐ˜lÑ+Ô+Øˆðñ	
ô 	
ð 	ØÐ˜iÑ(Ô(ØØÐ˜jÑ)Ô)ØØÐ˜jÑ)Ô)Øˆð	ñ	
ô 	
ð Ø RØðK&ñ &ô &ðR ,0Ø $Ø"ð	1
ð 1
ð 1
ð 1
ñO&ô &ðN1
ð 1
ð 1
ð 1
ð 1
r=   rS   c                  óP   ‡ — e Zd ZdZej        ej        z   Zddœd	ˆ fd„Zd
d„Zˆ xZ	S )ÚExponentialMovingWindowGroupbyzF
    Provide an exponential moving window groupby implementation.
    N)Ú_grouperr/   rb   c               óH  •—  t          ¦   «         j        |g|¢R d|i|¤Ž |j        sx| j        �st	          j        t          | j        j         	                    ¦   «         ¦  «        ¦  «        }t          | j                             |¦  «        | j        ¦  «        | _        d S d S d S )Nrø   )rg   rh   Úemptyr>   r6   ÚconcatenateÚlistrø   Úindicesr¼   rQ   Útaker-   rr   )rv   r]   rø   r’   r“   Úgroupby_orderrx   s         €r;   rh   z'ExponentialMovingWindowGroupby.__init__‹  s£   ø€ Ø�‰ŒÔ˜ÐA˜tÐAÐAÐA¨hÐA¸&ÐAÐAÐAàŒyð 	˜TœZÐ3åœN­4°´Ô0E×0LÒ0LÑ0NÔ0NÑ+OÔ+OÑPÔPˆMÝ,Ø”
—’ Ñ.Ô.Ø”ñô ˆDŒLˆLˆLð	ð 	Ð3Ð3r=   r   c                óF   — t          | j        j        t          ¬¦  «        }|S )z“
        Return an indexer class that will compute the window start and end bounds

        Returns
        -------
        GroupbyIndexer
        )Úgroupby_indicesrÖ   )r   rø   rý   r   )rv   rÖ   s     r;   rƒ   z2ExponentialMovingWindowGroupby._get_window_indexer–  s+   € õ (Ø œMÔ1Ý9ð
ñ 
ô 
ˆð Ðr=   ©r/   rb   )r/   r   )
r·   rï   rð   rñ   rS   rò   r#   rh   rƒ   rô   rõ   s   @r;   r÷   r÷   „  s{   ø€ € € € € ðð ð *Ô5Ð8IÔ8UÑU€Kà,0ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ðð ð ð ð ð ð ð r=   r÷   c                  ó�   ‡ — e Zd Z	 	 	 	 	 	 	 	 	 	 	 d-ddœd.ˆ fd„Zd/d„Zd „ Zd0d1d"„Z	 	 	 d2d3d(„Z	 	 	 	 d4d5d)„Zd6d7d*„Z	ddd+œd,„Z
ˆ xZS )8r†   Nr   TFr„   r[   r]   r)   rT   r+   r,   r-   r@   r.   rU   r^   rV   r_   rW   rX   r$   r>   r`   r…   ra   rˆ   údict[str, bool] | Noner/   rb   c               ó<  •— |
�t          d¦  «        ‚t          ¦   «                              |||||||||	|
|¬¦  «         t          | j        | j        | j        | j        |j        ¦  «        | _	        t          |¦  «        r|| _        || _        d S t          d¦  «        ‚)Nz0times is not implemented with online operations.)r]   rT   r,   r-   r.   rU   rV   rW   rX   r>   r\   z$'numba' is the only supported engine)rk   rg   rh   r    rs   rV   rW   rX   ru   Ú_meanr   r…   rˆ   r5   )rv   r]   rT   r,   r-   r.   rU   rV   rW   rX   r>   r…   rˆ   r\   rx   s                 €r;   rh   z&OnlineExponentialMovingWindow.__init__¦  sÃ   ø€ ð" ÐÝ%ØBñô ð õ 	‰Œ×ÒØØØØØØ#ØØØØØð 	ñ 	
ô 	
ð 	
õ "ØŒI�t”{ D¤N°D´I¸s¼yñ
ô 
ˆŒ
õ ˜6Ñ"Ô"ð 	EØ ˆDŒKØ!.ˆDÔÐÐåÐCÑDÔDÐDr=   c                ó8   — | j                              ¦   «          dS )z=
        Reset the state captured by `update` calls.
        N)r  Úresetr‚   s    r;   r  z#OnlineExponentialMovingWindow.resetÑ  s   € ð 	Œ
×ÒÑÔÐÐÐr=   c                ó    — t          d¦  «        ‚)Nzaggregate is not implemented.©rk   )rv   r‘   r’   r“   s       r;   r‹   z'OnlineExponentialMovingWindow.aggregate×  s   € Ý!Ð"AÑBÔBÐBr=   r²   c                ó    — t          d¦  «        ‚)Nzstd is not implemented.r
  )rv   r²   r’   r“   s       r;   r±   z!OnlineExponentialMovingWindow.stdÚ  ó   € Ý!Ð";Ñ<Ô<Ð<r=   rÂ   rÃ   rÄ   rÅ   r�   c                ó    — t          d¦  «        ‚)Nzcorr is not implemented.r
  )rv   rÂ   rÄ   r�   s       r;   rÝ   z"OnlineExponentialMovingWindow.corrÝ  s   € õ "Ð"<Ñ=Ô=Ð=r=   c                ó    — t          d¦  «        ‚)Nzcov is not implemented.r
  )rv   rÂ   rÄ   r²   r�   s        r;   rÁ   z!OnlineExponentialMovingWindow.covå  s   € õ "Ð";Ñ<Ô<Ð<r=   c                ó    — t          d¦  «        ‚)Nzvar is not implemented.r
  r¹   s      r;   r¸   z!OnlineExponentialMovingWindow.varî  r  r=   )ÚupdateÚupdate_timesc               óò  — i }| j         j        dk    }|�t          d¦  «        ‚t          j        t          | j         j        | j        dz
           dz
  d¦  «        t          j        ¬¦  «        }|�“| j	        j
        €t          d¦  «        ‚d}|j        |d<   |r+| j	        j
        t          j        dd…f         }	|j        |d	<   n| j	        j
        }	|j        |d
<   t          j        |	|                     ¦   «         f¦  «        }
njd}| j         j        |d<   |r| j         j        |d	<   n| j         j        |d
<   | j                              t          j        d¬¦  «                             ¦   «         }
t'          di t)          | j        ¦  «        ¤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
        r3   Nz update_times is not implemented.r2   r   rC   z;Must call mean with update=None first before passing updaterÍ   Úcolumnsr£   F)rÎ   r   )r´   rµ   rk   r6   rt   ri   ru   rX   rJ   r  Úlast_ewmr5   rÍ   Únewaxisr  r£   rû   Úto_numpyÚastyper!   r   rˆ   Úrun_ewmrU   ÚsqueezeÚ_constructor)rv   r  r  r’   r“   Úresult_kwargsÚis_frameÚupdate_deltasÚresult_fromÚ
last_valueÚnp_arrayÚ	ewma_funcr×   s                r;   r™   z"OnlineExponentialMovingWindow.meanñ  s'  € ðX ˆØÔ%Ô*¨aÒ/ˆØÐ#Ý%Ð&HÑIÔIÐIÝœÝ�Ô"Ô(¨¬°Q©Ô7¸!Ñ;¸QÑ?Ô?ÅrÄzð
ñ 
ô 
ˆð ÐØŒzÔ"Ð*Ý ØQñô ð ð ˆKØ%+¤\ˆM˜'Ñ"Øð 4Ø!œZÔ0µ´¸Q¸Q¸Q°Ô?�
Ø+1¬>�˜iÑ(Ð(à!œZÔ0�
Ø(.¬�˜fÑ%Ý”~ z°6·?²?Ñ3DÔ3DÐ&EÑFÔFˆHˆHàˆKØ%)Ô%7Ô%=ˆM˜'Ñ"Øð @Ø+/Ô+=Ô+E�˜iÑ(Ð(à(,Ô(:Ô(?�˜fÑ%ØÔ)×0Ò0µ´À%Ð0ÑHÔH×QÒQÑSÔSˆHÝ3ð 
ð 
Ý Ô 2Ñ3Ô3ð
ð 
ˆ	ð ”×#Ò#Ø Ð=ˆHˆH h¨q¨q¨qµ"´*¨}Ô&=ØØÔØñ	
ô 
ˆð ð 	&Ø—^’^Ñ%Ô%ˆFØ˜˜˜Ô%ˆØ0�Ô#Ô0°ÐIÐI¸=ÐIÐIˆØˆr=   )NNNNr   TFr   Nr„   N)r]   r)   rT   r+   r,   r+   r-   r@   r.   r+   rU   r^   rV   r_   rW   r_   rX   r$   r>   r`   r…   ra   rˆ   r  r/   rb   r  )F)r²   r_   rí   rî   rë   rì   ré   rê   )r·   rï   rð   rh   r  r‹   r±   rÝ   rÁ   r¸   r™   rô   rõ   s   @r;   r†   r†   ¥  sJ  ø€ € € € € ð !Ø!Ø=AØ"Ø"#ØØØØ-1ØØ04ð)Eð ð)Eð )Eð )Eð )Eð )Eð )Eð )Eð )EðVð ð ð ðCð Cð Cð=ð =ð =ð =ð =ð
 ,0Ø $Ø"ð	>ð >ð >ð >ð >ð ,0Ø $ØØ"ð=ð =ð =ð =ð =ð=ð =ð =ð =ð =ð "&°Dð Vð Vð Vð Vð Vð Vð Vð Vð Vr=   r†   )
r*   r+   r,   r+   r-   r+   r.   r+   r/   r0   )r>   r?   r-   r@   r/   rA   )JÚ
__future__r   rn   Ú	functoolsr   Útextwrapr   Útypingr   Únumpyr6   Úpandas._libs.tslibsr   Ú pandas._libs.window.aggregationsÚ_libsÚwindowÚaggregationsr«   Úpandas.util._decoratorsr   Úpandas.core.dtypes.commonr	   r
   Úpandas.core.dtypes.dtypesr   Úpandas.core.dtypes.genericr   Úpandas.core.dtypes.missingr   Úpandas.corer   Úpandas.core.arrays.datetimeliker   Úpandas.core.indexers.objectsr   r   r   Úpandas.core.util.numba_r   r   Úpandas.core.window.commonr   Úpandas.core.window.docr   r   r   r   r   r   r   r   Úpandas.core.window.numba_r   r   Úpandas.core.window.onliner    r!   Úpandas.core.window.rollingr"   r#   Úpandas._typingr$   r%   r&   rÚ   r'   r(   Úpandas.core.genericr)   r<   rQ   rS   r÷   r†   r   r=   r;   ú<module>r<     sº  ðØ "Ð "Ð "Ð "Ð "Ð "à €€€Ø Ð Ð Ð Ð Ð Ø Ð Ð Ð Ð Ð Ø  Ð  Ð  Ð  Ð  Ð  à Ð Ð Ð à )Ð )Ð )Ð )Ð )Ð )Ø >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ð >Ø 'Ð 'Ð 'Ð 'Ð 'Ð 'ðð ð ð ð ð ð ð ð 6Ð 5Ð 5Ð 5Ð 5Ð 5Ø 0Ð 0Ð 0Ð 0Ð 0Ð 0Ø +Ð +Ð +Ð +Ð +Ð +à Ð Ð Ð Ð Ð Ø 9Ð 9Ð 9Ð 9Ð 9Ð 9ðð ð ð ð ð ð ð ð ð ð
ð ð ð ð ð ð ð ð ,Ð +Ð +Ð +Ð +Ð +ð	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ð 	ðð ð ð ð ð ð ð ðð ð ð ð ð ð ð ðð ð ð ð ð ð ð ð
 ð ,ðð ð ð ð ð ð ð ð ð ðð ð ð ð ð ð ð ð ,Ð+Ð+Ð+Ð+Ð+ðð ð ð ðB'ð 'ð 'ð 'ð:|
ð |
ð |
ð |
ð |
˜jñ |
ô |
ð |
ð~ð ð ð ð Ð%6Ð8Oñ ô ð ðBbð bð bð bð bÐ$;ñ bô bð bð bð br=   