+ xȇi?ZbRt^RIHt^RIHt^RIt^RIHt^RI H t ^RI H t ^RI Ht^RIHt^R IHt] !R 4!R R 44t!R R]4t!RR]4t] !R 4!RR]44t!RR]4t] !R 4!RR]44t!RR]4t!RR]4tR#)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:]tRt^tRtRRRlltR RRlltRtR#) 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$V^8dQhRRRRRR/#) index_arraynp.ndarray | None window_sizeintreturnNone)formats"l/Users/max/.openclaw/workspace/postharvest/venv/lib/python3.14/site-packages/pandas/core/indexers/objects.py __annotate__BaseIndexer.__annotate__?s$&&,&BE& &c hWnW nVP4FwrE\WV4K R#)N)rritemssetattr)selfrrkwargskeyvalues&&&, r__init__BaseIndexer.__init__?s,'& ,,.JC Du %)rc 0V^8dQhRRRRRRRRR RR R /# r num_valuesr min_periodsz int | Nonecenterz bool | Noneclosedz str | Nonesteprztuple[np.ndarray, np.ndarray]r)rs"rrrHsF!"!"!" !" !"  !"  !" '!"rc \h 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 )NotImplementedErrorrr%r&r'r(r)s&&&&&&rget_window_boundsBaseIndexer.get_window_boundsHs B"!r)rr)Nr1NNNN)__name__ __module__ __qualname____firstlineno____doc__r!r/__static_attributes__rrrr r s'R&!"!"rr c*]tRt^ltRtRRRlltRtR#)FixedWindowIndexerz3Creates window boundaries that are of fixed length.Nc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rrFixedWindowIndexer.__annotate__osF000 0 0  0  0 '0rc V'gVP^8XdVP^, ^,pM^p\P!^V,V^,V,VRR7pWpP, pVR9d V^,pVR9d V^,p\P!V^V4p\P!V^V4pW3#)r,int64dtypeleftboth)rBneither)rnparangeclip) rr%r&r'r(r)offsetendstarts &&&&&& rr/$FixedWindowIndexer.get_window_boundsosB T%%*&&*q0FFiiF JNV$;TQ&&& % % QJE ( ( 1HCggc1j)q*-zrrr2r3r4r5r6r7r/r8rrrr:r:ls=00rr:c*]tRt^tRtRRRlltRtR#)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.Nc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rr"VariableWindowIndexer.__annotate__sF, , ,  ,  ,  ,  ,  ', rc J\VVPVVVVP4#r+)rrrr.s&&&&&&rr/'VariableWindowIndexer.get_window_boundss0J0             rrr2rLrrrrNrNsX, , rrNcFa]tRt^tRtRRV3RllltRRRlltRtV;t#) 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 ,V^8dQhRRRRRRRRR R /#) r rrrrindexzDatetimeIndex | NonerHzBaseOffset | Nonerrr)rs"rr(VariableOffsetWindowIndexer.__annotate__s<&$  "  rc <\SV`!W3/VB\V\4'g \ R4hW0n\V\ 4'g \ R4hW@nR#)zindex must be a DatetimeIndex.z(offset must be a DateOffset-like object.N)superr! isinstancer ValueErrorrVrrH)rrrrVrHr __class__s&&&&&,rr!$VariableOffsetWindowIndexer.__init__sS  > &*--GH H rc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rrrW"sNccc c c  c  c 'crc Ve \R4hV^8:d1\P!^RR7\P!^RR73#VfVPeRMRpVR9pVR9pVPV^, ,VP^,8dRpM^pWP,p \P!VRR7p V P R4\P!VRR7p V P R4^V ^&V'd^V ^&M^V ^&\ ^4p \^V4EF$p VPV ,pW, pV'dV\^4,pWV &\W^, ,V 4F1pVPV,V, V,pVV 8gK-VW&M VPW^, ,,V, V,pVV 8Xd"V'gW^, ,^,W&M$VV 8:d V ^,W&MW^, ,W&V'dEKW;;,^,uu&EK' W3#) r,z/step not implemented for variable offset windowr>r?rightrC)r`rCrA) r-rEemptyrVrHfillrranger )rr%r&r'r(r) right_closed left_closedindex_growth_sign offset_diffrJrIzeroi 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)rVrH)Nr1NNr2) r3r4r5r6r7r!r/r8 __classcell__r\s@rrTrTs:x ccrrTc*]tRtRtRtRRRlltRtR#)ExpandingIndexeriz;Calculate expanding window bounds, mimicking df.expanding()Nc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rrExpandingIndexer.__annotate__sF$ $ $  $  $  $  $  '$ rc \P!V\PR7\P!^V^,\PR73#r,r?)rEzerosr>rFr.s&&&&&&rr/"ExpandingIndexer.get_window_boundss9D HHZrxx 0 IIaarxx 8  rrr2rLrrrrtrtsE$ $ rrtc*]tRtRtRtRRRlltRtR#)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 Nc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rr&FixedForwardWindowIndexer.__annotate__sF/// / /  /  / '/rc V'd \R4hVe \R4hVf^p\P!^WRR7pW`P,pVP'd\P!V^V4pWg3#)r,z.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr>r?)r[rErFrrG)rr%r&r'r(r)rJrIs&&&&&& rr/+FixedForwardWindowIndexer.get_window_boundss|B MN N  S  <D !ZW=&&&    ''#q*-Czrrr2rLrrrr|r|s*X//rr|cPa]tRtRtRtR^R]R3RV3RllltR RRlltRtV;t #) GroupbyIndexerizMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nc 0V^8dQhRRRRRRRRR RR R /#) r rrrzint | BaseIndexergroupby_indicesz dict | Nonewindow_indexerztype[BaseIndexer]indexer_kwargsrrr)rs"rrGroupbyIndexer.__annotate__sF" " &" '" % " * " $ "  " rc <T;'g/VnW@nV'dVP4M/Vn\SV`!RRVRVPP RV4/VBR#)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 rrNr)rrcopyrrYr!pop)rrrrrrrr\s&&&&&&,rr!GroupbyIndexer.__init__sj6 /44",7En1132  # ++// {K  rc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rrr9sNKKK K K  K  K 'Krc .p.p^pVPP4EFp VPe&VPP\ V 44p M VPp VP !RRV RVP /VPBp V P\V 4W#WE4wrV P\P4p V P\P4p \V 4\V 48XgQR4h\P!W\V 4,4pV\V 4, p\P!WR,^,.4P\PRR7pVPVP\ V 444VPVP\ V 444EK \V4^8XdM\P!.\PR7\P!.\PR73#\P !V4p \P !V4p W3#) r,rrz6these should be equal in length from get_window_boundsF)rr?rra)rvaluesrtakerrrrr/lenastyperEr>rFappendarray concatenate)rr%r&r'r(r) start_arrays end_arrayswindow_indices_startindicesrindexerrJrIwindow_indicess&&&&&& rr/ GroupbyIndexer.get_window_bounds9sH   ++224G+"..334G4PQ ".. ))' ,,%%G !22G k6JELL*E**RXX&Cu:S) H )  YY$S\&IN !CL 0 YY~r8JQ8N7OPWWuXN    3 34G4N O P   n112Ec2JK L?5@ |  !88Bbhh/"BHH1MM M|,nnZ(zr)rrrr2) r3r4r5r6r7r r!r/r8rqrrs@rrrs.W*.)*'+,7&* " " HKKrrc*]tRtRtRtRRRlltRtR#)ExponentialMovingWindowIndexeriz/Calculate ewm window bounds (the entire window)Nc 0V^8dQhRRRRRRRRR RR R /#r$r)rs"rr+ExponentialMovingWindowIndexer.__annotate__sN!U!U!U !U !U  !U  !U '!Urc \P!^.\PR7\P!V.\PR73#rx)rErr>r.s&&&&&&rr/0ExponentialMovingWindowIndexer.get_window_boundss2Bxx288,bhh |288.TTTrrr2rLrrrrrs9!U!Urr)r7 __future__rdatetimernumpyrEpandas._libs.tslibsrpandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonrpandas.core.indexes.datetimesrpandas.tseries.offsetsr r r:rNrTrtr|rrrrrrsR"*I.97' !"T"T"#T"n33l/ K/ d !"p+p#pf' {' T !"\ \#\~r[rj$U[$Ur