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B �5�g�r � @ s� d dl mZ d dlZd dlmZ d dlmZ d dlZd dlZ d dl m Z d dl m m mZ d dlmZmZmZmZ d dlmZ d dlmZ d d lm Z d d l m Z d dl m m!Z! d d l"m#Z# d d l$m%Z% d d l&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/ d dl0m1Z1m2Z2m3Z3 d dl4m5Z5 d dl6m7Z7m8Z8 d dl9m:Z:m;Z; dddddd�dd�Z<dddd�dd �Z=G d d � d e:�Z>G d d � d e;e>�Z?G d!d"� d"e>�Z@dS )#� )� annotationsN)�partial)�dedent)� Timedelta)�Axis� FrameOrSeries�FrameOrSeriesUnion�TimedeltaConvertibleTypes)�function)�doc)�is_datetime64_ns_dtype)�isna)�maybe_use_numba)�zsqrt) � _shared_docs� args_compat�create_section_header� kwargs_compat� numba_notes�template_header�template_returns�template_see_also�window_agg_numba_parameters)� BaseIndexer� ExponentialMovingWindowIndexer�GroupbyIndexer)�generate_numba_ewma_func)� EWMMeanState� generate_online_numba_ewma_func)� BaseWindow�BaseWindowGroupbyz float | None�float)�comass�span�halflife�alpha�returnc C s� t �| |||�}|dkr td��| d k r:| dk r�td��n�|d k r`|dk rRtd��|d d } nt|d k r�|dkrxtd��dt�t�d�| � }d| d } n6|d k r�|dks�|dkr�td ��d| | } ntd ��t| �S ) N� z8comass, span, halflife, and alpha are mutually exclusiver z comass must satisfy: comass >= 0z span must satisfy: span >= 1� z#halflife must satisfy: halflife > 0g �?z"alpha must satisfy: 0 < alpha <= 1z1Must pass one of comass, span, halflife, or alpha)�common�count_not_none� ValueError�np�exp�logr! )r" r# r$ r% Z valid_countZdecay� r/ �L/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/window/ewm.py�get_center_of_mass6 s* r1 z'str | np.ndarray | FrameOrSeries | Nonez(float | TimedeltaConvertibleTypes | Nonez np.ndarray)�timesr$ r& c C s4 t j| �t j�t jd�}tt|�j�}t �|�| S )a� Return the diff of the times divided by the half-life. These values are used in the calculation of the ewm mean. Parameters ---------- times : str, np.ndarray, Series, default None 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 )�dtype) r, Zasarray�viewZint64�float64r! r �value�diff)r2 r$ Z_timesZ _halflifer/ r/ r0 �_calculate_deltasW s r8 c sD e Zd ZdZdddddddd d g ZdQd d�ddddddddddd� � fdd�Zdd�d d �ZdRd d �Zee d! e d"�e d#�d$d%d&�� fd'd(��Z e Z ee ed)�eeeed*�eed+�eed,�e�d-d%d.�d/d0d1d2�d d d3�d4d5��Zee ed)�e d6��d-d%d.�eeed*�eed+�ed d7� d/d8d9d2� dSdd:�d;d<��ZdTdd:�d=d>�Zee ed)�e d6��d-d%d.�eeed*�eed+�ed d7� d/d?d@d2� dUdd:�dAdB��Zee ed)�e dC��d-d%d.�eed*�eed+�ed d7� d/dDdEd2� dVdFdGddH�dIdJ��Zee ed)�e dK��d-d%d.�eed*�eed+�ed d7� d/dLdMd2� dWdFdGdN�dOdP��Z� Z S )X�ExponentialMovingWindowa� Provide exponential weighted (EW) functions. Available EW functions: ``mean()``, ``var()``, ``std()``, ``corr()``, ``cov()``. Exactly one parameter: ``com``, ``span``, ``halflife``, or ``alpha`` must 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, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable to ``mean()`` and halflife value will not apply to the other functions. .. versionadded:: 1.1.0 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 NA). 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; specify ``True`` to reproduce pre-0.15.0 behavior. - 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`` (reproducing pre-0.15.0 behavior), 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 The axis to use. The value 0 identifies the rows, and 1 identifies the columns. times : str, np.ndarray, Series, default None .. versionadded:: 1.1.0 Times corresponding to the observations. Must be monotonically increasing and ``datetime64[ns]`` dtype. If str, the name of the column in the DataFrame representing the times. If 1-D array like, a sequence with the same shape as the observations. Only applicable to ``mean()``. Returns ------- DataFrame A Window sub-classed for the particular operation. See Also -------- rolling : Provides rolling window calculations. expanding : Provides expanding transformations. Notes ----- More details can be found at: :ref:`Exponentially weighted windows <window.exponentially_weighted>`. 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 Specifying ``times`` with a timedelta ``halflife`` when computing mean. >>> 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 �comr# r$ r% � min_periods�adjust� ignore_na�axisr2 Nr TF)� selectionr z float | Nonez(float | TimedeltaConvertibleTypes | Nonez int | None�boolr z'str | np.ndarray | FrameOrSeries | None) �objr: r# r$ r% r; r< r= r> r2 c s� t � j||d krdn tt|�d�d dd d| | d� || _|| _|| _|| _|| _|| _ | | _ | j d k �r6| jsvt d��t | j t �r�| j| j | _ t| j �s�td��t| j �t|�kr�td��t | jt tjf�s�td��t| j ��� r�td ��t| j | j�| _t�| j| j| j�d k�r.t| j| jd | j�| _nd | _nb| jd k �r^t | jt tjf��r^td ��t j tt| j �d d �t j d �| _t| j| j| j| j�| _d S )Nr' FZsingle)rA r; �on�center�closed�methodr> r? z)times is not supported with adjust=False.z#times must be datetime64[ns] dtype.z,times must be the same length as the object.z6halflife must be a string or datetime.timedelta objectz$Cannot convert NaT values to integerr g �?zKhalflife can only be a timedelta convertible argument if times is not None.)r3 ) �super�__init__�max�intr: r# r$ r% r< r= r2 �NotImplementedError� isinstance�str� _selected_objr r+ �len�datetime� timedeltar �anyr8 �_deltasr) r* r1 �_comr, �onesrA r5 ) �selfrA r: r# r$ r% r; r< r= r> r2 r? )� __class__r/ r0 rG sX "z ExponentialMovingWindow.__init__r )r& c C s t � S )z[ Return an indexer class that will compute the window start and end bounds )r )rU r/ r/ r0 �_get_window_indexerS s z+ExponentialMovingWindow._get_window_indexer�numbac C s8 t | j| j| j| j| j| j| j| j| j | j ||| j d� S )a� 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 ) rA r: r# r$ r% r; r< r= r> r2 �engine� engine_kwargsr? ) � OnlineExponentialMovingWindowrA r: r# r$ r% r; r< r= r> r2 Z _selection)rU rY rZ r/ r/ r0 �onlineY s z ExponentialMovingWindow.online� aggregatezV See Also -------- pandas.DataFrame.rolling.aggregate a� 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 zSeries/Dataframe� )Zsee_alsoZexamples�klassr> c s t � j|f|�|�S )N)rF r] )rU �func�args�kwargs)rV r/ r0 r] � s z!ExponentialMovingWindow.aggregateZ ParametersZReturnszSee AlsoZNotes� r' Zewmz"(exponential weighted moment) mean�mean)Z window_methodZaggregation_descriptionZ agg_method)rY rZ c O s� t |�r6t|| j| j| j| j�}| j|dd� dfd�S |dkr�|d k rNtd��t� d||� | j d krjd n| j}t t j | j| j| j|d�}| �|�S td ��d S ) Nc S s | S )Nr/ )�xr/ r/ r0 �<lambda>� � z.ExponentialMovingWindow.mean.<locals>.<lambda>�ewma)Znumba_cache_key)�cythonNz+cython engine does not accept engine_kwargsrd )r: r< r= �deltasz)engine must be either 'numba' or 'cython')r r rS r< r= rR �_applyr+ �nv�validate_window_funcr2 r �window_aggregationsrh )rU rY rZ ra rb � ewma_funcrj � window_funcr/ r/ r0 rd � s& z ExponentialMovingWindow.meanzc bias : bool, default False Use a standard estimation bias correction. ���z0(exponential weighted moment) standard deviation�std)�biasc O s& t �d||� t| jf d|i|���S )Nrr rs )rl rm r �var)rU rs ra rb r/ r/ r0 rr � s zExponentialMovingWindow.stdc O s" t jdtdd� | j|f|�|�S )NzGvol is deprecated will be removed in a future version. Use std instead.r( )� stacklevel)�warnings�warn� FutureWarningrr )rU rs ra rb r/ r/ r0 �vol� s zExponentialMovingWindow.volz&(exponential weighted moment) variancert c sB t �d||� tj}t|| j| j| j|d�� � fdd�}| �|�S )Nrt )r: r< r= rs c s � | |||| �S )Nr/ )�values�begin�endr; )�wfuncr/ r0 �var_func s z-ExponentialMovingWindow.var.<locals>.var_func) rl rm rn �ewmcovr rS r< r= rk )rU rs ra rb rp r~ r/ )r} r0 rt � s zExponentialMovingWindow.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. z/(exponential weighted moment) sample covariance�covzFrameOrSeriesUnion | Nonez bool | None)�other�pairwisers c s. ddl m� � ��fdd�}���j|||�S )Nr )�Seriesc s� �� | �}�� |�}��� }�jd k r,�jn|j}|jt|�|�j�jd�\}}t� |||�j|�j �j �j �� }� || j | jd�S )N)� num_valuesr; rC rD )�index�name)� _prep_valuesrW r; � window_size�get_window_boundsrN rC rD rn r rS r< r= r� r� ) re �y�x_array�y_array�window_indexerr; �startr| �result)r� rs rU r/ r0 �cov_func: s* z-ExponentialMovingWindow.cov.<locals>.cov_func)�pandasr� �_apply_pairwiserM )rU r� r� rs rb r� r/ )r� rs rU r0 r� s # zExponentialMovingWindow.covaL 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. z0(exponential weighted moment) sample correlation�corr)r� r� c s, ddl m� � �fdd�}���j|||�S )Nr )r� c s� �� | �}�� |�}��� }�jd k r,�jn|j�|jt|���j�jd�\�� � ���fdd�}tj dd��4 |||�}|||�}|||�}|t || � } W d Q R X �| | j | j d�S )N)r� r; rC rD c s t �| �� �|�j�j�jd� S )NT)rn r rS r< r= )�X�Y)r| r; rU r� r/ r0 �_cov� s z<ExponentialMovingWindow.corr.<locals>.cov_func.<locals>._cov�ignore)�all)r� r� ) r� rW r; r� r� rN rC rD r, Zerrstater r� r� ) re r� r� r� r� r� r� Zx_varZy_varr� )r� rU )r| r; r� r0 r� | s" z.ExponentialMovingWindow.corr.<locals>.cov_func)r� r� r� rM )rU r� r� rb r� r/ )r� rU r0 r� Z s $z ExponentialMovingWindow.corr) NNNNr TFr N)rX N)F)F)F)NNF)NN) �__name__� __module__� __qualname__�__doc__� _attributesrG rW r\ r r r r] Zaggr r r r r r r r �replacerd rr ry rt r� r� � __classcell__r/ r/ )rV r0 r9 w s� *? * % r9 c s@ e Zd ZdZejej Zdd�� fdd� Zdd�dd �Z� Z S ) � ExponentialMovingWindowGroupbyzF Provide an exponential moving window groupby implementation. N)�_grouperc s\ t � j|f|�d|i|�� |jsX| jd k rXt�t| jj� � ��}t | j� |�| j �| _ d S )Nr� )rF rG �emptyr2 r, � concatenate�listr� �indicesrz r8 Ztaker$ rR )rU rA r� ra rb Z groupby_order)rV r/ r0 rG � s z'ExponentialMovingWindowGroupby.__init__r )r& c C s t | jjtd�}|S )z� Return an indexer class that will compute the window start and end bounds Returns ------- GroupbyIndexer )Zgroupby_indicesr� )r r� r� r )rU r� r/ r/ r0 rW � s z2ExponentialMovingWindowGroupby._get_window_indexer) r� r� r� r� r9 r� r rG rW r� r/ r/ )rV r0 r� � s r� c s� e Zd Zd'dd�dddd dd d d d d ddd� � fdd�Zdd� Zdd� Zd(d d�dd�Zd)ddd �d d �Zd*ddd d �d d!�Zd+d d�d"d#�Z ddd$�d%d&�Z � Z S ),r[ Nr TFrX )r? r z float | Nonez(float | TimedeltaConvertibleTypes | Nonez int | Noner@ r z'str | np.ndarray | FrameOrSeries | NonerL zdict[str, bool] | None) rA r: r# r$ r% r; r< r= r> r2 rY rZ c sp | d k rt d��t� j||||||||| | | d� t| j| j| j| j|j�| _ t | �rd| | _ | | _ nt d��d S )Nz0times is not implemented with online operations.) rA r: r# r$ r% r; r< r= r> r2 r? z$'numba' is the only supported engine)rJ rF rG r rS r< r= r> �shape�_meanr rY rZ r+ )rU rA r: r# r$ r% r; r< r= r> r2 rY rZ r? )rV r/ r0 rG � s* z&OnlineExponentialMovingWindow.__init__c C s | j �� dS )z= Reset the state captured by `update` calls. N)r� �reset)rU r/ r/ r0 r� � s z#OnlineExponentialMovingWindow.resetc O s t S )N)rJ )rU r` ra rb r/ r/ r0 r] � s z'OnlineExponentialMovingWindow.aggregate)rs c O s t S )N)rJ )rU rs ra rb r/ r/ r0 rr � s z!OnlineExponentialMovingWindow.stdzFrameOrSeriesUnion | Nonez bool | None)r� r� c K s t S )N)rJ )rU r� r� rb r/ r/ r0 r� � s z"OnlineExponentialMovingWindow.corr)r� r� rs c K s t S )N)rJ )rU r� r� rs rb r/ r/ r0 r� s z!OnlineExponentialMovingWindow.covc O s t S )N)rJ )rU rs ra rb r/ r/ r0 rt s z!OnlineExponentialMovingWindow.var)�update� update_timesc O sl i }| j jdkrdnd}|dk r*td��n(tjt| j j| jd d d�tjd�}|dk r�| j j dkrnt d ��d}|j |d < |r�| j j tj dd�f } |j|d < n| j j } |j|d < t�| |�� f�} n@d}| j j |d < |r�| j j|d < n | j j|d < | j �tj��� } t| j�} | j �|�r"| n| dd�tj f || j| �} |�sL| �� } | |d� } | j j| f|�} | 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 r( TFNz update_times is not implemented.r' r )r3 z;Must call mean with update=None first before passing updater� �columnsr� )rM �ndimrJ r, rT rH r� r> r5 r� Zlast_ewmr+ r� Znewaxisr� r� r� Zto_numpyZastyper rZ Zrun_ewmr; ZsqueezeZ _constructor) rU r� r� ra rb Z result_kwargsZis_frameZ update_deltasZ result_from� last_valueZnp_arrayro r� r/ r/ r0 rd sF , $ z"OnlineExponentialMovingWindow.mean) NNNNr TFr NrX N)F)NN)NNF)F) r� r� r� rG r� r] rr r� r� rt rd r� r/ r/ )rV r0 r[ � s, . r[ )A� __future__r rO � functoolsr �textwrapr rv �numpyr, Zpandas._libs.tslibsr Z pandas._libs.window.aggregationsZ_libsZwindowZ aggregationsrn Zpandas._typingr r r r Zpandas.compat.numpyr rl Zpandas.util._decoratorsr Zpandas.core.dtypes.commonr Zpandas.core.dtypes.missingr Zpandas.core.common�corer) Zpandas.core.util.numba_r Zpandas.core.window.commonr Zpandas.core.window.docr r r r r r r r r Zpandas.core.window.indexersr r r Zpandas.core.window.numba_r Zpandas.core.window.onliner r Zpandas.core.window.rollingr r r1 r8 r9 r� r[ r/ r/ r/ r0 �<module> s: , ! 0!
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