o
    ŀg9                     @  s   d Z ddlmZ ddlmZ ddl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 dZG dd dZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZG dd deZdS )zLIndexer objects for computing start/end window bounds for rolling operations    )annotations)	timedeltaN)
BaseOffset) calculate_variable_window_bounds)Appender)ensure_platform_int)DatetimeIndex)Nanoa  
Computes the bounds of a window.

Parameters
----------
num_values : int, default 0
    number of values that will be aggregated over
window_size : int, default 0
    the number of rows in a window
min_periods : int, default None
    min_periods passed from the top level rolling API
center : bool, default None
    center passed from the top level rolling API
closed : str, default None
    closed passed from the top level rolling API
step : int, default None
    step passed from the top level rolling API
    .. versionadded:: 1.5
win_type : str, default None
    win_type passed from the top level rolling API

Returns
-------
A tuple of ndarray[int64]s, indicating the boundaries of each
window
c                   @  s<   e Zd ZdZ	ddd
dZee					ddddZdS )BaseIndexera  
    Base class for window bounds calculations.

    Examples
    --------
    >>> from pandas.api.indexers import BaseIndexer
    >>> class CustomIndexer(BaseIndexer):
    ...     def get_window_bounds(self, num_values, min_periods, center, closed, step):
    ...         start = np.empty(num_values, dtype=np.int64)
    ...         end = np.empty(num_values, dtype=np.int64)
    ...         for i in range(num_values):
    ...             start[i] = i
    ...             end[i] = i + self.window_size
    ...         return start, end
    >>> df = pd.DataFrame({"values": range(5)})
    >>> indexer = CustomIndexer(window_size=2)
    >>> df.rolling(indexer).sum()
        values
    0	1.0
    1	3.0
    2	5.0
    3	7.0
    4	4.0
    Nr   index_arraynp.ndarray | Nonewindow_sizeintreturnNonec                 K  s.   || _ || _| D ]
\}}t| || q
d S N)r   r   itemssetattr)selfr   r   kwargskeyvalue r   P/var/www/html/myenv/lib/python3.10/site-packages/pandas/core/indexers/objects.py__init__H   s
   zBaseIndexer.__init__
num_valuesmin_periods
int | Nonecenterbool | Noneclosed
str | Nonesteptuple[np.ndarray, np.ndarray]c                 C  s   t r   )NotImplementedErrorr   r   r   r   r    r"   r   r   r   get_window_boundsQ   s   	zBaseIndexer.get_window_bounds)Nr   )r   r   r   r   r   r   r   NNNNr   r   r   r   r   r   r    r!   r"   r   r   r#   )__name__
__module____qualname____doc__r   r   get_window_bounds_docr&   r   r   r   r   r
   .   s    	r
   c                   @  .   e Zd ZdZee					ddddZdS )FixedWindowIndexerz3Creates window boundaries that are of fixed length.r   Nr   r   r   r   r   r   r    r!   r"   r   r#   c           	      C  s   |s| j dkr| j d d }nd}tjd| |d | |dd}|| j  }|dv r-|d8 }|dv r5|d8 }t|d|}t|d|}||fS )Nr         int64dtypeleftboth)r6   neither)r   nparangeclip)	r   r   r   r   r    r"   offsetendstartr   r   r   r&   `   s   	
z$FixedWindowIndexer.get_window_boundsr'   r(   r)   r*   r+   r,   r   r-   r&   r   r   r   r   r/   ]       r/   c                   @  r.   )VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.r   Nr   r   r   r   r   r   r    r!   r"   r   r#   c                 C  s   t || j|||| jS r   )r   r   r   r%   r   r   r   r&   ~   s   z'VariableWindowIndexer.get_window_boundsr'   r(   r?   r   r   r   r   rA   {   r@   rA   c                      sJ   e Zd ZdZ				dd fddZee					ddddZ  ZS )VariableOffsetWindowIndexeraP  
    Calculate window boundaries based on a non-fixed offset such as a BusinessDay.

    Examples
    --------
    >>> from pandas.api.indexers import VariableOffsetWindowIndexer
    >>> df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10))
    >>> offset = pd.offsets.BDay(1)
    >>> indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset)
    >>> df
                0
    2020-01-01  0
    2020-01-02  1
    2020-01-03  2
    2020-01-04  3
    2020-01-05  4
    2020-01-06  5
    2020-01-07  6
    2020-01-08  7
    2020-01-09  8
    2020-01-10  9
    >>> df.rolling(indexer).sum()
                   0
    2020-01-01   0.0
    2020-01-02   1.0
    2020-01-03   2.0
    2020-01-04   3.0
    2020-01-05   7.0
    2020-01-06  12.0
    2020-01-07   6.0
    2020-01-08   7.0
    2020-01-09   8.0
    2020-01-10   9.0
    Nr   r   r   r   r   indexDatetimeIndex | Noner<   BaseOffset | Noner   r   c                   sJ   t  j||fi | t|tstd|| _t|ts td|| _d S )Nzindex must be a DatetimeIndex.z(offset must be a DateOffset-like object.)superr   
isinstancer   
ValueErrorrC   r   r<   )r   r   r   rC   r<   r   	__class__r   r   r      s   


z$VariableOffsetWindowIndexer.__init__r   r   r   r   r   r    r!   r"   r#   c                 C  s  |d urt d|dkrtjdddtjdddfS |d u r'| jd ur%dnd}|dv }|dv }| j|d	  | jd k r>d
}nd	}|| j }	tj|dd}
|
d
 tj|dd}|d
 d|
d< |rhd	|d< nd|d< td}td	|D ]q}| j| }||	 }|r|td	8 }||
|< t|
|d	  |D ]}| j| | | }||kr||
|<  nq| j||d	   | | }||kr|s||d	  d	 ||< n||kr|d	 ||< n||d	  ||< |s||  d	8  < qu|
|fS )Nz/step not implemented for variable offset windowr   r2   r3   rightr7   )rK   r7   r5   r0   )	r$   r9   emptyrC   r<   fillr   ranger	   )r   r   r   r   r    r"   right_closedleft_closedindex_growth_signoffset_diffr>   r=   zeroi	end_boundstart_boundj
start_diffend_diffr   r   r   r&      sV   	




z-VariableOffsetWindowIndexer.get_window_bounds)Nr   NN)
r   r   r   r   rC   rD   r<   rE   r   r   r'   r(   )	r)   r*   r+   r,   r   r   r-   r&   __classcell__r   r   rI   r   rB      s    %rB   c                   @  r.   )ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()r   Nr   r   r   r   r   r   r    r!   r"   r   r#   c                 C  s&   t j|t jdt jd|d t jdfS )Nr3   r0   )r9   zerosr2   r:   r%   r   r   r   r&     s   
z"ExpandingIndexer.get_window_boundsr'   r(   r?   r   r   r   r   r\     r@   r\   c                   @  r.   )FixedForwardWindowIndexera  
    Creates window boundaries for fixed-length windows that include the current row.

    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

    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
    >>> df.rolling(window=indexer, min_periods=1).sum()
         B
    0  1.0
    1  3.0
    2  2.0
    3  4.0
    4  4.0
    r   Nr   r   r   r   r   r   r    r!   r"   r   r#   c                 C  s`   |rt d|d urt d|d u rd}tjd||dd}|| j }| jr,t|d|}||fS )Nz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr0   r   r2   r3   )rH   r9   r:   r   r;   )r   r   r   r   r    r"   r>   r=   r   r   r   r&   B  s   	
z+FixedForwardWindowIndexer.get_window_boundsr'   r(   r?   r   r   r   r   r^   )  s    r^   c                      sL   e Zd ZdZdddedfd fddZee					ddddZ  Z	S ) GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nr   r   r   r   int | BaseIndexergroupby_indicesdict | Nonewindow_indexertype[BaseIndexer]indexer_kwargsr   r   c                   sH   |pi | _ || _|r| ni | _t jd|| jd|d| dS )a4  
        Parameters
        ----------
        index_array : np.ndarray or None
            np.ndarray of the index of the original object that we are performing
            a chained groupby operation over. This index has been pre-sorted relative to
            the groups
        window_size : int or BaseIndexer
            window size during the windowing operation
        groupby_indices : dict or None
            dict of {group label: [positional index of rows belonging to the group]}
        window_indexer : BaseIndexer
            BaseIndexer class determining the start and end bounds of each group
        indexer_kwargs : dict or None
            Custom kwargs to be passed to window_indexer
        **kwargs :
            keyword arguments that will be available when get_window_bounds is called
        r   r   r   Nr   )ra   rc   copyre   rF   r   pop)r   r   r   ra   rc   re   r   rI   r   r   r   _  s   

zGroupbyIndexer.__init__r   r   r   r   r   r   r    r!   r"   r#   c                 C  sX  g }g }d}| j  D ]|\}	}
| jd ur| jt|
}n| j}| jd	|| jd| j}|t	|
||||\}}|
tj}|
tj}t	|t	|ksRJ dt||t	|
 }|t	|
7 }t||d d gj
tjdd}||t| ||t| qt	|dkrtjg tjdtjg tjdfS t|}t|}||fS )
Nr   rf   z6these should be equal in length from get_window_boundsrL   r0   F)rg   r3   r   )ra   r   r   taker   rc   r   re   r&   lenastyper9   r2   r:   appendarrayconcatenate)r   r   r   r   r    r"   start_arrays
end_arrayswindow_indices_startr   indicesr   indexerr>   r=   window_indicesr   r   r   r&     sJ   
 

z GroupbyIndexer.get_window_bounds)r   r   r   r`   ra   rb   rc   rd   re   rb   r   r   r'   r(   )
r)   r*   r+   r,   r
   r   r   r-   r&   r[   r   r   rI   r   r_   \  s    $r_   c                   @  r.   )ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)r   Nr   r   r   r   r   r   r    r!   r"   r   r#   c                 C  s$   t jdgt jdt j|gt jdfS )Nr   r3   )r9   rm   r2   r%   r   r   r   r&     s   $	z0ExponentialMovingWindowIndexer.get_window_boundsr'   r(   r?   r   r   r   r   ru     r@   ru   )r,   
__future__r   datetimer   numpyr9   pandas._libs.tslibsr   pandas._libs.window.indexersr   pandas.util._decoratorsr   pandas.core.dtypes.commonr   pandas.core.indexes.datetimesr   pandas.tseries.offsetsr	   r-   r
   r/   rA   rB   r\   r^   r_   ru   r   r   r   r   <module>   s(    / 3]