`Vg$2 dZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZd ZGd d ZGd d eZGddeZGddeZGddeZGddeZGddeZGddeZy)zLIndexer objects for computing start/end window bounds for rolling operations) annotations) timedeltaN) calculate_variable_window_bounds)Appender)ensure_platform_int)Nanoa Computes the bounds of a window. Parameters ---------- num_values : int, default 0 number of values that will be aggregated over window_size : int, default 0 the number of rows in a window min_periods : int, default None min_periods passed from the top level rolling API center : bool, default None center passed from the top level rolling API closed : str, default None closed passed from the top level rolling API step : int, default None step passed from the top level rolling API .. versionadded:: 1.5 win_type : str, default None win_type passed from the top level rolling API Returns ------- A tuple of ndarray[int64]s, indicating the boundaries of each window cfeZdZdZ d ddZee d ddZy) BaseIndexerz*Base class for window bounds calculations.Nc j||_||_|jD]\}}t|||y)z Parameters ---------- **kwargs : keyword arguments that will be available when get_window_bounds is called N) index_array window_sizeitemssetattr)selfr r kwargskeyvalues ]/var/www/html/hubwallet-dev/venv/lib/python3.12/site-packages/pandas/core/indexers/objects.py__init__zBaseIndexer.__init__.s9'& ,,. &JC D#u % &ctN)NotImplementedErrorr num_values min_periodscenterclosedsteps rget_window_boundszBaseIndexer.get_window_bounds=s "!r)Nrr np.ndarray | Noner intreturnNonerNNNN rr#r int | Nonerz bool | Nonerz str | Nonerr(r$ztuple[np.ndarray, np.ndarray])__name__ __module__ __qualname____doc__rrget_window_bounds_docr rrr r +s4IJ &, &BE &  &#$"&"! " "  " "  "  " ' "% "rr cPeZdZdZee d ddZy)FixedWindowIndexerz3Creates window boundaries that are of fixed length.Nc|r|jdz dz}nd}tjd|z|dz|z|d}||jz }|dvr|dz}|dvr|dz}tj|d|}tj|d|}||fS)Nrint64dtypeleftboth)r8neither)r nparangeclip) rrrrrroffsetendstarts rr z$FixedWindowIndexer.get_window_boundsMs &&*q0FFiiF JNV$;TQd&&& % % QJE ( ( 1HCggc1j)q*-czrr&r'r)r*r+r,rr-r r.rrr0r0Jsh= #$"&"!        '%rr0cPeZdZdZee d ddZy)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.NcJt||j||||jSr)rr r rs rr z'VariableWindowIndexer.get_window_boundsls/0             rr&r'rAr.rrrCrCishX #$"&"!            ' % rrCcxeZdZdZ d dfd Zee d ddZxZS)VariableOffsetWindowIndexerzNCalculate window boundaries based on a non-fixed offset such as a BusinessDay.c Dt|||fi|||_||_yr)superrindexr>)rr r rIr>r __class__s rrz$VariableOffsetWindowIndexer.__init__s' krr)rrrrrr right_closed left_closedindex_growth_signr@r?i end_bound start_boundjs rr z-VariableOffsetWindowIndexer.get_window_boundss  %&WX X ?88AW-rxx/II I > $ 6WFF!22  00 ::j1n % 1 5 "  ! 73 2hhz1  a CFCFq*% A 1 I**Q-*;dkk*IIKtAw& E!H5Q<+ JJqMK/3DDyQR|S E!H  3q1u:&26GG9UV<WQAQUA A! 3 6czr)NrNNr!r&r') r)r*r+r,rrr-r __classcell__rJs@rrFrFsX*. &    #$"&"! EE E E  E  E 'E%ErrFcPeZdZdZee d ddZy)ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()Nctj|tjtjd|dztjfS)Nr5r2)r;zerosr4r<rs rr z"ExpandingIndexer.get_window_boundss8 HHZrxx 0 IIaarxx 8  rr&r'rAr.rrr[r[shE #$"&"!              '  %  rr[cPeZdZdZee d ddZy)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 Nc|r td| td|d}tjd||d}||jz}|jrtj|d|}||fS)Nz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr2rr4r5) ValueErrorr;r<r r=)rrrrrrr@r?s rr z+FixedForwardWindowIndexer.get_window_boundss~ MN N  S  <D !ZW=d&&&   ''#q*-Cczrr&r'rAr.rrr_r_sl0#$"&"!        '%rr_ceZdZdZdddedf dfd Zee d ddZxZ S) GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrc |xsi|_||_|r|jni|_t |d||jj d|d|y)a4 Parameters ---------- index_array : np.ndarray or None np.ndarray of the index of the original object that we are performing a chained groupby operation over. This index has been pre-sorted relative to the groups window_size : int or BaseIndexer window size during the windowing operation groupby_indices : dict or None dict of {group label: [positional index of rows belonging to the group]} window_indexer : BaseIndexer BaseIndexer class determining the start and end bounds of each group indexer_kwargs : dict or None Custom kwargs to be passed to window_indexer **kwargs : keyword arguments that will be available when get_window_bounds is called r r r Nr.)groupby_indiceswindow_indexercopyindexer_kwargsrHrpop)rr r rfrgrirrJs rrzGroupbyIndexer.__init__&sd6 /4",7En1132  #++// {K  rcg}g}d}|jjD]\} } |j%|jjt | } n |j} |j d | |j d|j} | jt| ||||\} }| jtj} |jtj}t| t|k(sJdtj||t| z}|t| z }tj||ddzgjtjd}|j|jt | |j|jt |t|dk(rJtjgtjtjgtjfStj |} tj |}| |fS) Nrrez6these should be equal in length from get_window_boundsrMr2F)rhr5r.)rfrr takerrgr rir lenastyper;r4r<appendarray concatenate)rrrrrr start_arrays end_arrayswindow_indices_startrindicesr indexerr@r?window_indicess rr z GroupbyIndexer.get_window_boundsJs    00668 MLC+"..334G4PQ ".. )d))' ,,%%G !22G k664JE3LL*E**RXX&Cu:" HG H  YY$&:S\&IN !CL 0 YY~r8JQ8N7OPWWuXN    3 34G4N O P   n112Ec2JK L? M@ |  !88Bbhh/"BHH1MM M|,nnZ(czr) r r"r zint | BaseIndexerrf dict | Nonergztype[BaseIndexer]rirxr$r%r&r') r)r*r+r,r rrr-r rXrYs@rrcrc#sW*.)*'+,7&* " &" '" % " * " $ "  " H#$"&"! 33 3 3  3  3 '3%3rrccPeZdZdZee d ddZy)ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)Nctjdgtjtj|gtjfS)Nrr5)r;rpr4rs rr z0ExponentialMovingWindowIndexer.get_window_boundss1xx288,bhh |288.TTTrr&r'rAr.rrrzrzsp9 #$"&"! U U  U U  U  U ' U% Urrz)r, __future__rdatetimernumpyr;pandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonrpandas.tseries.offsetsrr-r r0rCrFr[r_rcrzr.rrrsR"I,9'8"">> K 6U+Up { &1 1h[[[| U[ Ur