iV[`dZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZdd lmZdd lmZe d Gd d ZGddeZGddeZe d GddeZGddeZe d GddeZGddeZGddeZy)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.indexerscPeZdZdZ d ddZ d ddZy) 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 j||_||_|jD]\}}t|||y)N) index_array window_sizeitemssetattr)selfrrkwargskeyvalues Y/app/cer_product_mecsu/.venv/lib/python3.12/site-packages/pandas/core/indexers/objects.py__init__zBaseIndexer.__init__?s9'& ,,. &JC D#u % &ct)T 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_boundszBaseIndexer.get_window_boundsHs B"!r)Nr)rnp.ndarray | NonerintreturnNonerNNNN rr$r int | Nonerz bool | Noner z str | Noner!r)r%ztuple[np.ndarray, np.ndarray])__name__ __module__ __qualname____doc__rr"rrr r s|'TIJ&,&BE& &"&"! !"!" !" !"  !"  !" '!"rr c:eZdZdZ d ddZy)FixedWindowIndexerz3Creates window boundaries that are of fixed length.Nc8|s|jdk(r|jdz dz}nd}tjd|z|dz|z|d}||jz }|dvr|dz}|dvr|dz}tj|d|}tj|d|}||fS)rrint64dtypeleftboth)r8neither)rnparangeclip) rrrrr r!offsetendstarts rr"z$FixedWindowIndexer.get_window_boundsosB T%%*&&*q0FFiiF JNV$;TQd&&& % % QJE ( ( 1HCggc1j)q*-czrr'r(r*r+r,r-r"r.rrr0r0lsU="&"! 00 0 0  0  0 '0rr0c:eZdZdZ d ddZy)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.Nc|jJt|jx}|krtd|d|dt||j||||jS)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)rrrrr r! index_lengths rr"z'VariableWindowIndexer.get_window_boundssB+++ 0 01 1LZ ?--9N#j\J44 0             rr'r(rAr.rrrCrCsUX"&"! 2 2  2  2  2  2  '2 rrCcjeZdZdZ d dfd Z d ddZxZS)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 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 rFindexrr>)rrrrMr>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) rz/step not implemented for variable offset windowrr4r5rightr9)rPr9r7r2) rr;emptyrMr>fillrranger )rrrrr r! right_closed left_closedindex_growth_sign offset_diffr@r?zeroi end_bound start_boundj start_diffend_diffs rr"z-VariableOffsetWindowIndexer.get_window_bounds(s!B  %&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$rMzDatetimeIndex | Noner>zBaseOffset | Noner%r&r'r()r*r+r,r-rr" __classcell__rNs@rrIrIs:|*.&*$( &$  "  $"&"! cc c c  c  c 'crrIc:eZdZdZ d ddZy)ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()Nctj|tjtjd|dztjfS)rr5r2)r;zerosr4r<rs rr"z"ExpandingIndexer.get_window_boundss9D HHZrxx 0 IIaarxx 8  rr'r(rAr.rrrcrcsUE"&"! $ $  $  $  $  $  '$ rrcc:eZdZdZ d ddZy)FixedForwardWindowIndexera 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|r td| td|d}tjd||d}||jz}|jrtj|d|}||fS)rz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr2rr4r5)rFr;r<rr=)rrrrr r!r@r?s rr"z+FixedForwardWindowIndexer.get_window_boundssB MN N  S  <D !ZW=d&&&   ''#q*-Cczrr'r(rAr.rrrgrgsX*\"&"! // / /  /  / '/rrgcpeZdZdZdddedf dfd Z d ddZxZS) 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_kwargsrKrpop)rrrrmrnrprrNs 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) rrrlz6these should be equal in length from get_window_boundsrQr2F)ror5r.)rmvaluesrtakerrnrrpr"rEastyper;r4r<appendarray concatenate)rrrrr r! start_arrays end_arrayswindow_indices_startindicesrindexerr@r?window_indicess rr"z GroupbyIndexer.get_window_bounds?sH   ++224 MG+"..334G4PQ ".. )d))' ,,%%G !22G k664JE3LL*E**RXX&Cu:S) H )  YY$&:S\&IN !CL 0 YY~r8JQ8N7OPWWuXN    3 34G4N O P   n112Ec2JK L? M@ |  !88Bbhh/"BHH1MM M|,nnZ(czr) rr#rzint | BaseIndexerrm dict | Nonernztype[BaseIndexer]rprr%r&r'r()r*r+r,r-r rr"r`ras@rrjrjsW*.)*'+,7&* " &" '" % " * " $ "  " L"&"! KK K K  K  K 'Krrjc:eZdZdZ d ddZy)ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)Nctjdgtjtj|gtjfS)rrr5)r;rwr4rs rr"z0ExponentialMovingWindowIndexer.get_window_boundss2Bxx288,bhh |288.TTTrr'r(rAr.rrrrs\9"&"! !U!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 r0rCrIrcrgrjrr.rrrsR"*I.97' !"T"T"#T"n33l5 K5 p !"p+p#pf' {' T !"\ \#\~r[rj$U[$Ur