j^dZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZdd lmZdd lmZe d Gd d ZGddeZGddeZe d GddeZGddeZe d GddeZGddeZGddeZdS)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.indexersc2eZdZdZ ddd Z dddZdS) 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 Nr index_arraynp.ndarray | None window_sizeintreturnNonec x||_||_|D]\}}t|||dS)N)r ritemssetattr)selfr rkwargskeyvalues JC:\Projects\DSCons\.venv\Lib\site-packages\pandas\core\indexers\objects.py__init__zBaseIndexer.__init__?sN'& ,,.. & &JC D#u % % % % & & num_values min_periods int | Nonecenter bool | Noneclosed str | Nonesteptuple[np.ndarray, np.ndarray]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 )NotImplementedErrorrrrr r"r$s rget_window_boundszBaseIndexer.get_window_boundsHs B"!r)Nr)r rrrrrrNNNN rrrrr r!r"r#r$rrr%)__name__ __module__ __qualname____doc__rr*rrr r si''TIJ&&&&&"&"! !"!"!"!"!"!"!"rr c&eZdZdZ dddZdS)FixedWindowIndexerz3Creates window boundaries that are of fixed length.rNrrrrr r!r"r#r$rr%c|s |jdkr|jdz dz}nd}tjd|z|dz|z|d}||jz }|dvr|dz}|dvr|dz}tj|d|}tj|d|}||fS)r'rint64dtypeleftboth)r;neither)rnparangeclip) rrrr r"r$offsetendstarts rr*z$FixedWindowIndexer.get_window_boundsosB  T%**&*q0FFFiF JNV$;TQQQd&& % % % QJE ( ( ( 1HCgc1j))q*--czrr+r,r-r.r/r0r*r1rrr3r3lsD=="&"! 0000000rr3c&eZdZdZ dddZdS)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.rNrrrrr r!r"r#r$rr%c|jJt|jx}|krtd|d|dt||j||||jS)r'Nz\Variable rolling window requires the index to be at least as long as the 'other' index. Got z < z\. Please align 'other' to the rolling object's index using reindex_like() or similar method.)r len ValueErrorrr)rrrr r"r$ index_lengths rr*z'VariableWindowIndexer.get_window_boundssB+++ 011 1LZ ? ?4-944>H444 0            rr+r,rDr1rrrFrFsDXX"&"! 2 2 2 2 2 2 2 rrFcBeZdZdZ ddfd Z dddZxZS)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 Nrr rrrindexDatetimeIndex | NonerABaseOffset | Nonerrc tj||fi|t|tst d||_t|t st d||_dS)Nzindex must be a DatetimeIndex.z(offset must be a DateOffset-like object.)superr isinstancer rIrMrrA)rr rrMrAr __class__s rrz$VariableOffsetWindowIndexer.__init__s{ k<> > &*-- IGHH H rrrrr r!r"r#r$r%c|td|dkr,tjddtjddfS| |jdnd}|dv}|d v}|j|d z |jdkrd }nd }||jz} tj|d} | d tj|d} | d d| d<|rd | d<nd| d<t d} td |D]} |j| }|| z }|r|td z}| | | <t| | d z | D]"}|j||z |z}|| kr|| | <n#|j| | d z |z |z}|| kr|s| | d z d z| | <n|| kr | d z| | <n| | d z | | <|s| | xxd zcc<| | fS) r'Nz/step not implemented for variable offset windowrr7r8rightr<)rUr<r:r5) r(r>emptyrMrAfillrranger )rrrr r"r$ right_closed left_closedindex_growth_sign offset_diffrCrBzeroi end_bound start_boundj start_diffend_diffs rr*z-VariableOffsetWindowIndexer.get_window_bounds(sqB  %&WXX X ??8AW---rx/I/I/II I > $ 6WWFF!22  00 :j1n % 1 5 5 "   ! '$+5 7333 2hz111  a  CFFCF||q*%%  A 1 I#k1K 'tAww& E!H5Q<++  "jmk9=NN $$ E!HE%  3q1u:.:>OOH4 QUaAT!!QAQUA  A! czr)NrNN) r rrrrMrNrArOrrr+r,)r-r.r/r0rr* __classcell__rSs@rrLrLs::|*.&*$( $"&"! cccccccccrrLc&eZdZdZ dddZdS)ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()rNrrrrr r!r"r#r$rr%ctj|tjtjd|dztjfS)r'r8r5)r>zerosr7r?r)s rr*z"ExpandingIndexer.get_window_boundss=D HZrx 0 0 0 Iaarx 8 8 8  rr+r,rDr1rrrhrhsDEE"&"! $ $ $ $ $ $ $ rrhc&eZdZdZ dddZdS)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 rNrrrrr r!r"r#r$rr%c|rtd|td|d}tjd||d}||jz}|jrtj|d|}||fS)r'z.Forward-looking windows can't have center=TrueNzAForward-looking windows don't support setting the closed argumentr5rr7r8)rIr>r?rr@)rrrr r"r$rCrBs rr*z+FixedForwardWindowIndexer.get_window_boundssB  OMNN N  S  <D !ZW===d&&   .'#q*--Cczrr+r,rDr1rrrlrlsI**\"&"! ///////rrlcDeZdZdZdddedfdfd Z dddZxZS)GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrr rrint | BaseIndexergroupby_indices dict | Nonewindow_indexertype[BaseIndexer]indexer_kwargsrrc |pi|_||_|r|ni|_t jd||jd|d|dS)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 rr rNr1)rqrscopyrurQrpop)rr rrqrsrurrSs rrzGroupbyIndexer.__init__s6 /4",7EMn113332 #+// {KK       rrrrrr r!r"r#r$r%cg}g}d}|jD]} |j(|jt | } n|j} |jd | |jd|j} | t| ||||\} } | tj } | tj } t| t| ks Jdtj ||t| z}|t| z }tj||ddzg tj d}||t | ||t | t|dkr@tjgtj tjgtj fStj|} tj|} | | fS) r'rNrwz6these should be equal in length from get_window_boundsrVr5F)rxr8r1)rqvaluesr takerrsrrur*rHastyper>r7r?appendarray concatenate)rrrr r"r$ start_arrays end_arrayswindow_indices_startindicesr indexerrCrBwindow_indicess rr*z GroupbyIndexer.get_window_bounds?sNH   +2244 M MG+".334G4P4PQQ ". )d)' ,%G !22G k664JE3LL**E**RX&&Cu::S)))H*))  Y$&:S\\&IN !CLL 0 Y~r8JQ8N7OPPWWuXN    3 34G4N4N O O P P P   n112Ec2J2JKK L L L L |   ! !8Bbh///"BH1M1M1MM M|,,nZ((czr) r rrrprqrrrsrtrurrrrr+r,)r-r.r/r0r rr*rerfs@rrorosWW*.)*'+,7&* " " " " " " " L"&"! KKKKKKKKKrroc&eZdZdZ dddZdS)ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)rNrrrrr r!r"r#r$rr%ctjdgtjtj|gtjfS)r'rr8)r>rr7r)s rr*z0ExponentialMovingWindowIndexer.get_window_boundss6Bx28,,,bh |28.T.T.TTTrr+r,rDr1rrrrsK99"&"! !U!U!U!U!U!U!Urr)r0 __future__rdatetimernumpyr>pandas._libs.tslibsrpandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonrpandas.core.indexes.datetimesr pandas.tseries.offsetsr r r3rFrLrhrlrorr1rrrsxRR""""""******IIIIII......999999777777''''''  !""T"T"T"T"T"T"T"#"T"n33333333l5 5 5 5 5 K5 5 5 p  !""ppppp+pp#"pf' ' ' ' ' {' ' ' T  !""\\\\\ \\#"\~rrrrr[rrrj$U$U$U$U$U[$U$U$U$U$Ur