ujXdZddlmZddlmZddlmZddlZddl m Z ddl m Z ddl mZdd lmZdd lmZdd lmZer dd lmZdd lmZedGddZGddeZGddeZedGddeZGddeZedGddeZGddeZGddeZy)zLIndexer objects for computing start/end window bounds for rolling operations) annotations) timedelta) TYPE_CHECKINGN) BaseOffset) calculate_variable_window_bounds) set_module)ensure_platform_int) DatetimeIndex)Nano)Hashable)Anyzpandas.api.indexerscPeZdZdZ d ddZ d ddZy) BaseIndexera7 Base class for window bounds calculations. Subclasses should implement ``get_window_bounds`` to define custom windowing logic for use with :meth:`DataFrame.rolling` or :meth:`Series.rolling`. 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 g/var/www/html/python-recuperauto-other/env/lib/python3.12/site-packages/pandas/core/indexers/objects.py__init__zBaseIndexer.__init__Hs9'& ,,. &JC D#u % &ct) Computes the bounds of a window. Parameters ---------- num_values : int, default 0 number of values that will be aggregated over 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 Returns ------- A tuple of ndarray[int64]s, indicating the boundaries of each window )NotImplementedErrorr num_values min_periodscenterclosedsteps rget_window_boundszBaseIndexer.get_window_boundsQs :"!r)Nr)rnp.ndarray | NonerintreturnNonerNNNN r r'r! int | Noner"z bool | Noner#z str | Noner$r,r(ztuple[np.ndarray, np.ndarray])__name__ __module__ __qualname____doc__rr%rrrrs|+\IJ&,&BE& &"&"! "" " "  "  " '"rrc:eZdZdZ d ddZy)FixedWindowIndexerz3Creates window boundaries that are of fixed length.Nc8|s|jdk(r|jdz dz}nd}tjd|z|dz|z|d}||jz }|dvr|dz}|dvr|dz}tj|d|}tj|d|}||fS)rrint64dtypeleftboth)r;neither)rnparangeclip) rr r!r"r#r$offsetendstarts rr%z$FixedWindowIndexer.get_window_boundsts: T%%*&&*q0FFiiF JNV$;TQd&&& % % QJE ( ( 1HCggc1j)q*-czrr*r+r-r.r/r0r%r1rrr3r3qsU="&"! ,, , ,  ,  , ',rr3c: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)rr r!r"r#r$ index_lengths rr%z'VariableWindowIndexer.get_window_boundss:+++ 0 01 1LZ ?--9N#j\J44 0             rr*r+rDr1rrrFrFsUX"&"! . .  .  .  .  .  '. rrFcjeZdZdZ d dfd Z d ddZxZS)VariableOffsetWindowIndexeraH Calculate window boundaries based on a non-fixed offset such as a BusinessDay. Unlike the default fixed-size window, this indexer uses a :class:`DateOffset` to determine variable-width window boundaries based on the datetime index of the data. 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 rIindexrrA)rrrrPrAr __class__s rrz$VariableOffsetWindowIndexer.__init__sS k > &*-GH H rc| td|dkr.tjddtjddfS||jdnd}|dv}|dv}|j|d z |jdkrd }nd }||jz} tj|d} | j d tj|d} | j d d| d<|rd | d<nd| d<t d} td |D]} |j| }|| z }|r|td z}| | | <t| | d z | D]#}|j||z |z}|| kDs|| | <n|j| | d z |z |z}|| k(r|s| | d z d z| | <n|| kr | d z| | <n | | d z | | <|r| | xxd zcc<| | fS) rz/step not implemented for variable offset windowrr7r8rightr<)rSr<r:r5) rr>emptyrPrAfillrranger )rr r!r"r#r$ right_closed left_closedindex_growth_sign offset_diffrCrBzeroi end_bound start_boundj start_diffend_diffs rr%z-VariableOffsetWindowIndexer.get_window_bounds)s :  %&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'rPzDatetimeIndex | NonerAzBaseOffset | Noner(r)r*r+)r-r.r/r0rr% __classcell__rQs@rrLrLs>D*.&*$( &$  "  $"&"! __ _ _  _  _ '_rrLc:eZdZdZ d ddZy)ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()Nctj|tjtjd|dztjfS)rr8r5)r>zerosr7r?rs rr%z"ExpandingIndexer.get_window_boundss8< HHZrxx 0 IIaarxx 8  rr*r+rDr1rrrfrfsUE"&"!              '  rrfc:eZdZdZ d ddZy)FixedForwardWindowIndexera Creates window boundaries for fixed-length windows that include the current row. This indexer produces forward-looking windows, where each window starts at the current row and extends ``window_size`` rows ahead, unlike the default backward-looking behavior of :meth:`DataFrame.rolling`. Parameters ---------- index_array : np.ndarray, default None Array-like structure representing the indices for the data points. If None, the default indices are assumed. This can be useful for handling non-uniform indices in data, such as in time series with irregular timestamps. window_size : int, default 0 Size of the moving window. This is the number of observations used for calculating the statistic. The default is to consider all observations within the window. **kwargs Additional keyword arguments passed to the subclass's methods. See Also -------- DataFrame.rolling : Provides rolling window calculations. api.indexers.VariableWindowIndexer : Calculate window bounds based on variable-sized windows. Examples -------- >>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0 1 1.0 2 2.0 3 NaN 4 4.0 >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2) >>> df.rolling(window=indexer, min_periods=1).sum() B 0 1.0 1 3.0 2 2.0 3 4.0 4 4.0 Nc|r td| td|d}tjd||d}||jz}|jrtj|d|}||fS)rz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr5rr7r8)rIr>r?rr@)rr r!r"r#r$rCrBs rr%z+FixedForwardWindowIndexer.get_window_boundss~: MN N  S  <D !ZW=d&&&   ''#q*-Cczrr*r+rDr1rrrjrjsX.d"&"! ++ + +  +  + '+rrjcpeZdZdZdddedf 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 rrrNr1)groupby_indiceswindow_indexercopyindexer_kwargsrNrpop)rrrrprqrsrrQs 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) rrroz6these should be equal in length from get_window_boundsrTr5F)rrr8r1)rpvaluesrtaker rqrrsr%rHastyper>r7r?appendarray concatenate)rr r!r"r#r$ start_arrays end_arrayswindow_indices_startindicesrindexerrCrBwindow_indicess rr%z GroupByIndexer.get_window_bounds8s@   ++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 | BaseIndexerrpz!dict[Hashable, np.ndarray] | Nonerqztype[BaseIndexer]rszdict[str, Any] | Noner(r)r*r+)r-r.r/r0rrr%rcrds@rrmrmsW*.)*=A,704 " &" '" ; " * " . "  " L"&"! GG G G  G  G 'Grrmc:eZdZdZ d ddZy)ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)Nctjdgtjtj|gtjfS)rrr8)r>rzr7rs rr%z0ExponentialMovingWindowIndexer.get_window_boundss1:xx288,bhh |288.TTTrr*r+rDr1rrrrs\9"&"! UU U U  U  U 'Urr) r0 __future__rdatetimertypingrnumpyr>pandas._libs.tslibsrpandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonr pandas.core.indexes.datetimesr pandas.tseries.offsetsr collections.abcr r rr3rFrLrfrjrmrr1rrrsR" *I.97'( !"T"T"#T"n//d1 K1 h !"p+p#pf# {# L !"\ \#\~n[nb U[ Ur