IjV[`SrSSKJr SSKJr SSKrSSKJr SSK J r SSK J r SSK Jr SS KJr SS KJr \ "S 5"S S 55r"SS\5r"SS\5r\ "S 5"SS\55r"SS\5r\ "S 5"SS\55r"SS\5r"SS\5rg)zLIndexer objects for computing start/end window bounds for rolling operations) annotations) timedeltaN) BaseOffset) calculate_variable_window_bounds) set_module)ensure_platform_int) DatetimeIndex)Nanozpandas.api.indexersc`\rSrSrSrSSSjjrS S SjjrSrg) BaseIndexera Base class for window bounds calculations. Parameters ---------- index_array : np.ndarray, default None Array-like structure representing the indices for the data points. If None, the default indices are assumed. This can be useful for handling non-uniform indices in data, such as in time series with irregular timestamps. window_size : int, default 0 Size of the moving window. This is the number of observations used for calculating the statistic. The default is to consider all observations within the window. **kwargs Additional keyword arguments passed to the subclass's methods. See Also -------- DataFrame.rolling : Provides rolling window calculations on dataframe. Series.rolling : Provides rolling window calculations on series. Examples -------- >>> from pandas.api.indexers import BaseIndexer >>> class CustomIndexer(BaseIndexer): ... def get_window_bounds(self, num_values, min_periods, center, closed, step): ... start = np.arange(num_values, dtype=np.int64) ... end = np.arange(num_values, dtype=np.int64) + 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 fXlX lUR5HupE[XU5 M g)N) index_array window_sizeitemssetattr)selfrrkwargskeyvalues m/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/pandas/core/indexers/objects.py__init__BaseIndexer.__init__?s,'& ,,.JC Du %)c[e) 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 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 )NotImplementedErrorr num_values min_periodscenterclosedsteps rget_window_boundsBaseIndexer.get_window_boundsHs B"!rrr)Nr)rnp.ndarray | NonerintreturnNonerNNNN rr(r int | Noner!z bool | Noner"z str | Noner#r-r)ztuple[np.ndarray, np.ndarray])__name__ __module__ __qualname____firstlineno____doc__rr$__static_attributes__rrr r s'TIJ&,&BE& &"&"! !"!" !" !"  !"  !" '!"!"rr cF\rSrSrSrSSSjjrSrg)FixedWindowIndexerlz3Creates window boundaries that are of fixed length.NcHU(dURS:XaURS- S-nOSn[R"SU-US-U-USS9nXpR- nUS;aUS-nUS;aUS-n[R"USU5n[R"USU5nX4$)rrint64dtypeleftboth)r?neither)rnparangeclip) rrr r!r"r#offsetendstarts rr$$FixedWindowIndexer.get_window_boundsosB T%%*&&*q0FFiiF JNV$;TQ&&& % % QJE ( ( 1HCggc1j)q*-zrr4r+r,r.r/r0r1r2r$r3r4rrr6r6lsZ="&"! 00 0 0  0  0 '00rr6cF\rSrSrSrSSSjjrSrg)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.NcURce[UR5=oa:a[SUSUS35e[UURUUUUR5$)rz\Variable rolling window requires the index to be at least as long as the 'other' index. Got z < z\. Please align 'other' to the rolling object's index using reindex_like() or similar method.)rlen ValueErrorrr)rrr r!r"r# index_lengths rr$'VariableWindowIndexer.get_window_boundssB+++ 0 01 1L ?--9N#j\J44 0             rr4r+r,rIr4rrrKrKsZX"&"! 2 2  2  2  2  2  '2 2 rrKc|^\rSrSrSrSSU4SjjjrSS SjjrSrU=r$) VariableOffsetWindowIndexera Calculate window boundaries based on a non-fixed offset such as a BusinessDay. Parameters ---------- index_array : np.ndarray, default 0 Array-like structure specifying the indices for data points. This parameter is currently not used. window_size : int, optional, default 0 Specifies the number of data points in each window. This parameter is currently not used. index : DatetimeIndex, optional ``DatetimeIndex`` of the labels of each observation. offset : BaseOffset, optional ``DateOffset`` representing the size of the window. **kwargs Additional keyword arguments passed to the parent class ``BaseIndexer``. See Also -------- api.indexers.BaseIndexer : Base class for all indexers. DataFrame.rolling : Rolling window calculations on DataFrames. offsets : Module providing various time offset classes. Examples -------- >>> from pandas.api.indexers import VariableOffsetWindowIndexer >>> df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10)) >>> offset = pd.offsets.BDay(1) >>> indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset) >>> df 0 2020-01-01 0 2020-01-02 1 2020-01-03 2 2020-01-04 3 2020-01-05 4 2020-01-06 5 2020-01-07 6 2020-01-08 7 2020-01-09 8 2020-01-10 9 >>> df.rolling(indexer).sum() 0 2020-01-01 0.0 2020-01-02 1.0 2020-01-03 2.0 2020-01-04 3.0 2020-01-05 7.0 2020-01-06 12.0 2020-01-07 6.0 2020-01-08 7.0 2020-01-09 8.0 2020-01-10 9.0 c >[TU]"X40UD6 [U[5(d [ S5eX0l[U[ 5(d [ S5eX@lg)Nzindex must be a DatetimeIndex.z(offset must be a DateOffset-like object.)superr isinstancer rOindexrrE)rrrrXrEr __class__s rr$VariableOffsetWindowIndexer.__init__sS  > &*--GH H rcUb [S5eUS::a*[R"SSS9[R"SSS94$UcURbSOSnUS;nUS;nURUS - URS:aS nOS nXR-n [R"USS9n U R S 5 [R"USS9n U R S 5 SU S'U(aS U S'OSU S'[ S5n [S U5Hn URU nX- nU(aU[S 5-nXU '[XS - U 5H$nURUU- U-nUU :dM UX' O URXS - U- U-nUU :XaU(d XS - S -X'OUU ::aU S -X'O XS - X'U(aMX==S -ss'M X4$) rz/step not implemented for variable offset windowrr;r<rightr@)r\r@r>r9) rrBemptyrXrEfillrranger )rrr r!r"r# right_closed left_closedindex_growth_sign offset_diffrGrFzeroi end_bound start_boundj start_diffend_diffs rr$-VariableOffsetWindowIndexer.get_window_bounds(sB  %&WX X ?88AW-rxx/II I > $ 6WFF!22  00 ::j1n % 1 5 "  ! '++5 73 2hhz1  a CFCF|q*%A 1 I#1KtAw& !H5Q<+"jjmk9=NN $ EH , 31u:.:>OOH4 UaT!QU <! ;&>zr)rXrE)NrNN) rr'rr(rXzDatetimeIndex | NonerEzBaseOffset | Noner)r*r+r,) r.r/r0r1r2rr$r3 __classcell__rYs@rrSrSs:|*.&*$( &$  "  $"&"! cc c c  c  c 'ccrrScF\rSrSrSrSSSjjrSrg)ExpandingIndexeriz;Calculate expanding window bounds, mimicking df.expanding()Nc[R"U[RS9[R"SUS-[RS94$)rr<r9)rBzerosr;rCrs rr$"ExpandingIndexer.get_window_boundss9D HHZrxx 0 IIaarxx 8  rr4r+r,rIr4rrrprpsZE"&"! $ $  $  $  $  $  '$ $ rrpcF\rSrSrSrSSSjjrSrg)FixedForwardWindowIndexeria^ Creates window boundaries for fixed-length windows that include the current row. Parameters ---------- index_array : np.ndarray, default None Array-like structure representing the indices for the data points. If None, the default indices are assumed. This can be useful for handling non-uniform indices in data, such as in time series with irregular timestamps. window_size : int, default 0 Size of the moving window. This is the number of observations used for calculating the statistic. The default is to consider all observations within the window. **kwargs Additional keyword arguments passed to the subclass's methods. See Also -------- DataFrame.rolling : Provides rolling window calculations. api.indexers.VariableWindowIndexer : Calculate window bounds based on variable-sized windows. 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 NcU(a [S5eUb [S5eUcSn[R"SXSS9nX`R-nUR(a[R"USU5nXg4$)rz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr9rr;r<)rOrBrCrrD)rrr r!r"r#rGrFs rr$+FixedForwardWindowIndexer.get_window_boundssyB MN N  S  <D !ZW=&&&   ''#q*-Czrr4r+r,rIr4rrrurus]*\"&"! // / /  /  / '//rruc^\rSrSrSrSSS\S4SU4SjjjrS S SjjrSrU=r $) GroupbyIndexerizMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrc >U=(d 0UlX@lU(aUR5O0Ul[TU]"SUURR SU5S.UD6 g)a Parameters ---------- index_array : np.ndarray or None np.ndarray of the index of the original object that we are performing a chained groupby operation over. This index has been pre-sorted relative to the groups window_size : int or BaseIndexer window size during the windowing operation groupby_indices : dict or None dict of {group label: [positional index of rows belonging to the group]} window_indexer : BaseIndexer BaseIndexer class determining the start and end bounds of each group indexer_kwargs : dict or None Custom kwargs to be passed to window_indexer **kwargs : keyword arguments that will be available when get_window_bounds is called rr&Nr4)groupby_indiceswindow_indexercopyindexer_kwargsrVrpop)rrrr{r|r~rrYs rrGroupbyIndexer.__init__sb6 /4",7En1132  #++// {K  rc/n/nSnURR5GHn URb%URR[ U 55n O URn UR "S U UR S.URD6n U R[U 5X#XE5upU R[R5n U R[R5n [U 5[U 5:XdS5e[R"X[U 5-5nU[U 5- n[R"XSS-/5R[RSS9nURUR[ U 555 URUR[ U 555 GM [U5S:XaF[R"/[RS9[R"/[RS94$[R "U5n [R "U5n X4$) rrr&z6these should be equal in length from get_window_boundsr]r9F)r}r<r4)r{valuesrtakerr|rr~r$rNastyperBr;rCappendarray concatenate)rrr r!r"r# start_arrays end_arrayswindow_indices_startindicesrindexerrGrFwindow_indicess rr$ GroupbyIndexer.get_window_bounds?sH   ++224G+"..334G4PQ ".. ))' ,,%%G !22G k6JELL*E**RXX&Cu:S) H )  YY$S\&IN !CL 0 YY~r8JQ8N7OPWWuXN    3 34G4N O P   n112Ec2JK L?5@ |  !88Bbhh/"BHH1MM M|,nnZ(zr)r{r~r|) rr'rzint | BaseIndexerr{ dict | Noner|ztype[BaseIndexer]r~rr)r*r+r,) r.r/r0r1r2r rr$r3rmrns@rryrysW*.)*'+,7&* " &" '" % " * " $ "  " " L"&"! KK K K  K  K 'KKrrycF\rSrSrSrSSSjjrSrg)ExponentialMovingWindowIndexeriz/Calculate ewm window bounds (the entire window)Nc[R"S/[RS9[R"U/[RS94$)rrr<)rBrr;rs rr$0ExponentialMovingWindowIndexer.get_window_boundss2Bxx288,bhh |288.TTTrr4r+r,rIr4rrrrsb9"&"! !U!U !U !U  !U  !U '!U!Urr)r2 __future__rdatetimernumpyrBpandas._libs.tslibsrpandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonrpandas.core.indexes.datetimesr pandas.tseries.offsetsr r r6rKrSrpruryrr4rrrsR"*I.97' !"T"T"#T"n33l5 K5 p !"p+p#pf' {' T !"\ \#\~r[rj$U[$Ur