Owg9"dZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZdd lmZdd lmZd ZGd d ZGddeZGddeZGddeZGddeZGddeZGddeZGddeZy)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 cfeZdZdZ d ddZee d ddZy) 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 Nc j||_||_|jD]\}}t|||yN) index_array window_sizeitemssetattr)selfrrkwargskeyvalues S/var/www/horilla/myenv/lib/python3.12/site-packages/pandas/core/indexers/objects.py__init__zBaseIndexer.__init__Hs9'& ,,. &JC D#u % &ctr)NotImplementedErrorr num_values min_periodscenterclosedsteps rget_window_boundszBaseIndexer.get_window_boundsQs "!r)Nr)rnp.ndarray | NonerintreturnNonerNNNN rr$r int | Nonerz bool | Noner z str | Noner!r)r%ztuple[np.ndarray, np.ndarray])__name__ __module__ __qualname____doc__rrget_window_bounds_docr"rrr r .s4IJ&,&BE& &#$"&"! "" " "  "  " '"%"rr cPeZdZdZee d ddZy)FixedWindowIndexerz3Creates window boundaries that are of fixed length.Nc8|s|jdk(r|jdz dz}nd}tjd|z|dz|z|d}||jz }|dvr|dz}|dvr|dz}tj|d|}tj|d|}||fS)Nrint64dtypeleftboth)r9neither)rnparangeclip) rrrrr r!offsetendstarts rr"z$FixedWindowIndexer.get_window_bounds`s T%%*&&*q0FFiiF JNV$;TQd&&& % % QJE ( ( 1HCggc1j)q*-czrr'r(r*r+r,r-rr.r"r/rrr1r1]sh= #$"&"!        '%rr1cPeZdZdZee d ddZy)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.NcJt||j||||jSr)rrrrs rr"z'VariableWindowIndexer.get_window_bounds~s/0             rr'r(rBr/rrrDrD{shX #$"&"!            ' % rrDceZdZdZ d dfd Zee d ddZxZS)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 c t|||fi|t|ts t d||_t|t s t d||_y)Nzindex must be a DatetimeIndex.z(offset must be a DateOffset-like object.)superr isinstancer ValueErrorindexrr?)rrrrLr?r __class__s rrz$VariableOffsetWindowIndexer.__init__sS k > &*-GH H rc| td|dkr.tjddtjddfS||jdnd}|dv}|dv}|j|d z |jdkrd }nd }||jz} tj|d} | j d tj|d} | j d d| d<|rd | d<nd| d<t d} td |D]} |j| }|| z }|r|td z}| | | <t| | d z | D]#}|j||z |z}|| kDs|| | <n|j| | d z |z |z}|| k(r|s| | d z d z| | <n|| kr | d z| | <n | | d z | | <|r| | xxd zcc<| | fS) Nz/step not implemented for variable offset windowrr5r6rightr:)rOr:r8r3) rr<emptyrLr?fillrranger )rrrrr r! right_closed left_closedindex_growth_sign offset_diffrAr@zeroi end_bound start_boundj start_diffend_diffs rr"z-VariableOffsetWindowIndexer.get_window_boundss   %&WX X ?88AW-rxx/II I > $ 6WFF!22  00 ::j1n % 1 5 "  ! '$++5 73 2hhz1  a CFCF|q*% A 1 I#k1KtAw& E!H5Q<+ "jjmk9=NN $ E!H   3q1u:.:>OOH4 QUaAT!QAQUA A! ; >czr)NrNN) rr#rr$rLzDatetimeIndex | Noner?zBaseOffset | Noner%r&r'r() r*r+r,r-rrr.r" __classcell__rMs@rrGrGs!J*.&*$( &$  "   #$"&"! JJ J J  J  J 'J%JrrGcPeZdZdZee d ddZy)ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()Nctj|tjtjd|dztjfS)Nr6r3)r<zerosr5r=rs rr"z"ExpandingIndexer.get_window_boundss8 HHZrxx 0 IIaarxx 8  rr'r(rBr/rrrbrbshE #$"&"!              '  %  rrbcPeZdZdZee 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 argumentr3rr5r6)rKr<r=rr>)rrrrr r!rAr@s rr"z+FixedForwardWindowIndexer.get_window_boundsBs~ MN N  S  <D !ZW=d&&&   ''#q*-Cczrr'r(rBr/rrrfrf)sl0#$"&"!        '%rrfceZdZdZdddedf dfd Zee d ddZxZ S) GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrc |xsi|_||_|r|jni|_t |d||jj d|d|y)a4 Parameters ---------- index_array : np.ndarray or None np.ndarray of the index of the original object that we are performing a chained groupby operation over. This index has been pre-sorted relative to the groups window_size : int or BaseIndexer window size during the windowing operation groupby_indices : dict or None dict of {group label: [positional index of rows belonging to the group]} window_indexer : BaseIndexer BaseIndexer class determining the start and end bounds of each group indexer_kwargs : dict or None Custom kwargs to be passed to window_indexer **kwargs : keyword arguments that will be available when get_window_bounds is called rrrNr/)groupby_indiceswindow_indexercopyindexer_kwargsrIrpop)rrrrlrmrorrMs rrzGroupbyIndexer.__init___sd6 /4",7En1132  #++// {K  rcg}g}d}|jjD]\} } |j%|jjt | } n |j} |j d | |j d|j} | jt| ||||\} }| jtj} |jtj}t| t|k(sJdtj||t| z}|t| z }tj||ddzgjtjd}|j|jt | |j|jt |t|dk(rJtjgtjtjgtjfStj |} tj |}| |fS) Nrrkz6these should be equal in length from get_window_boundsrPr3F)rnr6r/)rlrrtakerrmrror"lenastyper<r5r=appendarray concatenate)rrrrr r! start_arrays end_arrayswindow_indices_startrindicesrindexerrAr@window_indicess rr"z GroupbyIndexer.get_window_boundss    00668 MLC+"..334G4PQ ".. )d))' ,,%%G !22G k664JE3LL*E**RXX&Cu:" HG H  YY$&:S\&IN !CL 0 YY~r8JQ8N7OPWWuXN    3 34G4N O P   n112Ec2JK L? M@ |  !88Bbhh/"BHH1MM M|,nnZ(czr) rr#rzint | BaseIndexerrl dict | Nonermztype[BaseIndexer]ror~r%r&r'r() r*r+r,r-r rrr.r"r_r`s@rriri\sW*.)*'+,7&* " &" '" % " * " $ "  " H#$"&"! 22 2 2  2  2 '2%2rricPeZdZdZee d ddZy)ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)Nctjdgtjtj|gtjfS)Nrr6)r<rvr5rs rr"z0ExponentialMovingWindowIndexer.get_window_boundss1xx288,bhh |288.TTTrr'r(rBr/rrrrsp9 #$"&"! UU U U  U  U 'U%Urr)r- __future__rdatetimernumpyr<pandas._libs.tslibsrpandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonrpandas.core.indexes.datetimesr pandas.tseries.offsetsr r.r r1rDrGrbrfrirr/rrrsR"*I,97'8,","^< K 4+D { $0 0fZ[Zz U[ Ur