U c$2@sdZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZd ZGd d d ZGd d d eZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZdS)zLIndexer objects for computing start/end window bounds for rolling operations) annotations) timedeltaN) calculate_variable_window_bounds)Appender)ensure_platform_int)Nanoa Computes the bounds of a window. Parameters ---------- num_values : int, default 0 number of values that will be aggregated over window_size : int, default 0 the number of rows in a window min_periods : int, default None min_periods passed from the top level rolling API center : bool, default None center passed from the top level rolling API closed : str, default None closed passed from the top level rolling API step : int, default None step passed from the top level rolling API .. versionadded:: 1.5 win_type : str, default None win_type passed from the top level rolling API Returns ------- A tuple of ndarray[int64]s, indicating the boundaries of each window c @sFeZdZdZddddddd Zeeddd d d d d dddZdS) BaseIndexerz*Base class for window bounds calculations.Nrnp.ndarray | NoneintNone index_array window_sizereturncKs.||_||_|D]\}}t|||qdS)z Parameters ---------- **kwargs : keyword arguments that will be available when get_window_bounds is called N)r ritemssetattr)selfr rkwargskeyvaluer@/tmp/pip-unpacked-wheel-g7fro6k3/pandas/core/indexers/objects.py__init__.s zBaseIndexer.__init__ int | None bool | None str | Nonetuple[np.ndarray, np.ndarray] num_values min_periodscenterclosedsteprcCstdSN)NotImplementedErrorrrrr r!r"rrrget_window_bounds=s zBaseIndexer.get_window_bounds)Nr)rNNNN)__name__ __module__ __qualname____doc__rrget_window_bounds_docr&rrrrr+src @s2eZdZdZeed ddddddd d d ZdS) FixedWindowIndexerz3Creates window boundaries that are of fixed length.rNr rrrrrc Cs|r|jdd}nd}tjd||d||dd}||j}|dkrP|d8}|dkr`|d8}t|d|}t|d|}||fS)Nrint64Zdtypeleftboth)r2Zneither)rnparangeclip) rrrr r!r"offsetendstartrrrr&Ms  z$FixedWindowIndexer.get_window_bounds)rNNNNr'r(r)r*rr+r&rrrrr,Jsr,c @s2eZdZdZeed ddddddd d d ZdS) VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.rNr rrrrrcCst||j||||jSr#)rrr r%rrrr&lsz'VariableWindowIndexer.get_window_bounds)rNNNNr:rrrrr;isr;c sNeZdZdZdddddfdd Zeeddd d d d d dddZZS)VariableOffsetWindowIndexerzNCalculate window boundaries based on a non-fixed offset such as a BusinessDay.Nrr r r r c s"tj||f|||_||_dSr#)superrindexr7)rr rr>r7r __class__rrrsz$VariableOffsetWindowIndexer.__init__rrrrrcCs|dk rtd|dkr4tjdddtjdddfS|dkrN|jdk rJdnd}|dk}|dk}|j|d |jdkr|d }nd }tj|dd} | d tj|dd} | d d| d<|rd | d<nd| d<td |D]} |j| } |j| ||j} |r | td 8} | | | <t| | d | D]0}|j|| |tdkr&|| | <qXq&|j| | d | |tdkr| d | | <n| | d | | <|s| | d 8<q| | fS) Nz/step not implemented for variable offset windowrr/r0rightr3)rAr3r1r-) r$r4emptyr>fillranger7rr)rrrr r!r"Z right_closedZ left_closedZindex_growth_signr9r8iZ end_boundZ start_boundjrrrr&sF       $z-VariableOffsetWindowIndexer.get_window_bounds)NrNN)rNNNN) r'r(r)r*rrr+r& __classcell__rrr?rr<s r<c @s2eZdZdZeed ddddddd d d ZdS) ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()rNr rrrrrcCs&tj|tjdtjd|dtjdfS)Nr0r-)r4zerosr/r5r%rrrr&s z"ExpandingIndexer.get_window_bounds)rNNNNr:rrrrrIsrIc @s2eZdZdZeed ddddddd 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 rrrrrcCs`|r td|dk rtd|dkr(d}tjd||dd}||j}|jrXt|d|}||fS)Nz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr-rr/r0) ValueErrorr4r5rr6)rrrr r!r"r9r8rrrr&s  z+FixedForwardWindowIndexer.get_window_bounds)rNNNNr:rrrrrKsrKc s^eZdZdZdddedfddddddd fd d Zeedd d ddd ddddZZ S)GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrr zint | BaseIndexerz dict | Noneztype[BaseIndexer]r )r rgroupby_indiceswindow_indexerindexer_kwargsrc 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 rr rN)rNrOcopyrPr=rpop)rr rrNrOrPrr?rrr&s  zGroupbyIndexer.__init__r rrrrrcCsZg}g}d}|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|qt |dkr>t jgt j dt jgt j dfSt |} t |}| |fS) NrrQz6these should be equal in length from get_window_boundsrBr-F)rRr0)rNrr ZtakerrOrrPr&lenZastyper4r/AssertionErrorr5appendarrayZ concatenate)rrrr r!r"Z start_arraysZ end_arraysZwindow_indices_startrindicesr Zindexerr9r8Zwindow_indicesrrrr&JsV         z GroupbyIndexer.get_window_bounds)rNNNN) r'r(r)r*rrrr+r&rHrrr?rrM#s$rMc @s2eZdZdZeed ddddddd d d ZdS) ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)rNr rrrrrcCs$tjdgtjdtj|gtjdfS)Nrr0)r4rWr/r%rrrr&s z0ExponentialMovingWindowIndexer.get_window_bounds)rNNNNr:rrrrrYsrY)r* __future__rdatetimerZnumpyr4Zpandas._libs.window.indexersrZpandas.util._decoratorsrZpandas.core.dtypes.commonrZpandas.tseries.offsetsrr+rr,r;r<rIrKrMrYrrrrs       X4^