B W0d/@sdZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZd ZGd d d ZGd d d eZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZdS)zLIndexer objects for computing start/end window bounds for rolling operations) annotations) timedeltaN) calculate_variable_window_bounds)Appender)ensure_platform_int)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 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@sBeZdZdZddddddZeeddd d d d d ddZdS) BaseIndexerz*Base class for window bounds calculations.Nrznp.ndarray | Noneint) index_array window_sizecKs2||_||_x |D]\}}t|||qWdS)z Parameters ---------- **kwargs : keyword arguments that will be available when get_window_bounds is called N)r r itemssetattr)selfr r kwargskeyvaluerM/var/www/html/venv/lib/python3.7/site-packages/pandas/core/window/indexers.py__init__+s zBaseIndexer.__init__z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray]) num_values min_periodscenterclosedreturncCstdS)N)NotImplementedError)rrrrrrrrget_window_bounds:s zBaseIndexer.get_window_bounds)Nr)rNNN)__name__ __module__ __qualname____doc__rrget_window_bounds_docrrrrrr(src@s0eZdZdZeed dddddd d d ZdS) FixedWindowIndexerz3Creates window boundaries that are of fixed length.rNr z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCs|r|jdd}nd}tjd||d|dd}||j}|dkrN|d8}|dkr^|d8}t|d|}t|d|}||fS)Nrint64)dtype)leftboth)r&Zneither)r nparangeZclip)rrrrroffsetendstartrrrrIs  z$FixedWindowIndexer.get_window_bounds)rNNN)rrrrrr rrrrrr!Fs r!c@s0eZdZdZeed dddddd d d ZdS) VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.rNr z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCst||j||||jS)N)rr r )rrrrrrrrrgs z'VariableWindowIndexer.get_window_bounds)rNNN)rrrrrr rrrrrr-ds r-csJeZdZdZddddfdd Zeeddd d d d d ddZZS)VariableOffsetWindowIndexerzMCalculate window boundaries based on a non-fixed offset such as a BusinessDayNrznp.ndarray | Noner )r r c s"tj||f|||_||_dS)N)superrindexr*)rr r r0r*r) __class__rrrsz$VariableOffsetWindowIndexer.__init__z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCs|dkr|jdk rdnd}|dk}|dk}|j|d|jdkrHd}nd}tj|dd }|dtj|dd } | dd|d<|rd| d<nd| d<xtd|D]} |j| } |j| ||j} |r| td8} | || <x>t|| d| D](} |j| | |tdkr| || <PqW|j| | d| |tdkrR| d| | <n| | d| | <|s| | d8<qW|| fS) Nrightr')r2r')r&r'r"rr$)r%)r0r(emptyfillranger*rr)rrrrrZ right_closedZ left_closedZindex_growth_signr,r+iZ end_boundZ start_boundjrrrrs>      $z-VariableOffsetWindowIndexer.get_window_bounds)NrNN)rNNN) rrrrrrr r __classcell__rr)r1rr.~sr.c@s0eZdZdZeed dddddd d d ZdS) ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()rNr z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCs&tj|tjdtjd|dtjdfS)N)r%r")r(Zzerosr$r))rrrrrrrrrs z"ExpandingIndexer.get_window_bounds)rNNN)rrrrrr rrrrrr:s r:c@s0eZdZdZeed dddddd d d ZdS) 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 rNr z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCsR|r td|dk rtdtj|dd}||j}|jrJ|||j d<||fS)Nz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr$)r%) ValueErrorr(r)r )rrrrrr,r+rrrrs  z+FixedForwardWindowIndexer.get_window_bounds)rNNN)rrrrrr rrrrrr;s r;csZeZdZdZdddedfddddddfd d Zeedd d d dddddZZ S)GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrznp.ndarray | Nonezint | BaseIndexerz dict | Noneztype[BaseIndexer])r r groupby_indiceswindow_indexerindexer_kwargsc sH|pi|_||_|r|ni|_tjf||jd|d|dS)a4 Parameters ---------- index_array : np.ndarray or None np.ndarray of the index of the original object that we are performing a chained groupby operation over. This index has been pre-sorted relative to the groups window_size : int or BaseIndexer window size during the windowing operation groupby_indices : dict or None dict of {group label: [positional index of rows belonging to the group]} window_indexer : BaseIndexer BaseIndexer class determining the start and end bounds of each group indexer_kwargs : dict or None Custom kwargs to be passed to window_indexer **kwargs : keyword arguments that will be available when get_window_bounds is called r )r r N)r>r?copyr@r/rpop)rr r r>r?r@r)r1rrrs zGroupbyIndexer.__init__r z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCs0g}g}d}x|jD]\}} |jdk r>|jt| } n|j} |jf| |jd|j} | t | |||\} } | t j } | t j } t | t | kst dt ||t | }|t | 7}t ||ddgj t j dd}||t| ||t| qWt |} t |} | | fS)Nr)r r z6these should be equal in length from get_window_boundsr3r"F)rA)r>r r Ztakerr?r r@rlenZastyper(r$AssertionErrorr)appendZ concatenate)rrrrrZ start_arraysZ end_arraysZwindow_indices_startrindicesr Zindexerr,r+Zwindow_indicesrrrr;s8          z GroupbyIndexer.get_window_bounds)rNNN) rrrrrrrr rr9rr)r1rr=sr=c@s0eZdZdZeed dddddd d d ZdS) ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)rNr z int | Nonez bool | Nonez str | Noneztuple[np.ndarray, np.ndarray])rrrrrcCs$tjdgtjdtj|gtjdfS)Nr)r%)r(arrayr$)rrrrrrrrrqs z0ExponentialMovingWindowIndexer.get_window_bounds)rNNN)rrrrrr rrrrrrGns rG)r __future__rdatetimernumpyr(Zpandas._libs.window.indexersrZpandas.util._decoratorsrZpandas.core.dtypes.commonrZpandas.tseries.offsetsrr rr!r-r.r:r;r=rGrrrrs       R2Z