o 5c$2@sdZddlmZddlmZddlZddlmZddl m Z ddl m Z ddl mZd ZGd d d ZGd d d eZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZdS)zLIndexer objects for computing start/end window bounds for rolling operations) annotations) timedeltaN) calculate_variable_window_bounds)Appender)ensure_platform_int)Nanoa Computes the bounds of a window. Parameters ---------- num_values : int, default 0 number of values that will be aggregated over window_size : int, default 0 the number of rows in a window min_periods : int, default None min_periods passed from the top level rolling API center : bool, default None center passed from the top level rolling API closed : str, default None closed passed from the top level rolling API step : int, default None step passed from the top level rolling API .. versionadded:: 1.5 win_type : str, default None win_type passed from the top level rolling API Returns ------- A tuple of ndarray[int64]s, indicating the boundaries of each window c@s<eZdZdZ ddd d Zee     ddddZdS) BaseIndexerz*Base class for window bounds calculations.Nr index_arraynp.ndarray | None window_sizeintreturnNonecKs.||_||_|D] \}}t|||q dS)z Parameters ---------- **kwargs : keyword arguments that will be available when get_window_bounds is called N)r r itemssetattr)selfr r kwargskeyvaluerR/var/www/html/gps/gps/lib/python3.10/site-packages/pandas/core/indexers/objects.py__init__.s  zBaseIndexer.__init__ num_values min_periods int | Nonecenter bool | Noneclosed str | Nonesteptuple[np.ndarray, np.ndarray]cCstN)NotImplementedErrorrrrrrrrrrget_window_bounds=s zBaseIndexer.get_window_bounds)Nrr r r r r rrNNNN rr rrrrrrrrr r )__name__ __module__ __qualname____doc__rrget_window_bounds_docr$rrrrr+s rc@.eZdZdZee     ddddZdS)FixedWindowIndexerz3Creates window boundaries that are of fixed length.rNrr rrrrrrrr r c Cs|r |jdd}nd}tjd||d||dd}||j}|dvr(|d8}|dvr0|d8}t|d|}t|d|}||fS)Nrint64dtypeleftboth)r5neither)r nparangeclip) rrrrrroffsetendstartrrrr$Ms  z$FixedWindowIndexer.get_window_boundsr&r'r(r)r*r+rr,r$rrrrr.Jr.c@r-)VariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.rNrr rrrrrrrr r cCst||j||||jSr!)rr r r#rrrr$lsz'VariableWindowIndexer.get_window_boundsr&r'r>rrrrr@ir?r@csJeZdZdZ    ddfd d Zee     ddddZZS)VariableOffsetWindowIndexerzNCalculate window boundaries based on a non-fixed offset such as a BusinessDay.Nrr r r r r rc s&tj||fi|||_||_dSr!)superrindexr;)rr r rCr;r __class__rrrs z$VariableOffsetWindowIndexer.__init__rrrrrrrrr cCs|durtd|dkrtjdddtjdddfS|dur'|jdur%dnd}|dv}|dv}|j|d |jdkr>d }nd }tj|dd} | d tj|dd} | d d| d<|rcd | d<nd| d<td |D]f} |j| } |j| ||j} |r| td 8} | | | <t| | d | D]}|j|| |tdkr|| | <nq|j| | d | |tdkr| d | | <n| | d | | <|s| | d 8<ql| | fS) Nz/step not implemented for variable offset windowrr1r2rightr6)rFr6r4r/) r"r8emptyrCfillranger;rr)rrrrrr right_closed left_closedindex_growth_signr=r<i end_bound start_boundjrrrr$sJ      "z-VariableOffsetWindowIndexer.get_window_bounds)NrNNr%r&r') r(r)r*r+rrr,r$ __classcell__rrrDrrAs rAc@r-)ExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()rNrr rrrrrrrr r cCs&tj|tjdtjd|dtjdfS)Nr2r/)r8zerosr1r9r#rrrr$s z"ExpandingIndexer.get_window_boundsr&r'r>rrrrrSr?rSc@r-)FixedForwardWindowIndexera Creates window boundaries for fixed-length windows that include the current row. Examples -------- >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0 1 1.0 2 2.0 3 NaN 4 4.0 >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2) >>> df.rolling(window=indexer, min_periods=1).sum() B 0 1.0 1 3.0 2 2.0 3 4.0 4 4.0 rNrr rrrrrrrr r cCs`|rtd|durtd|durd}tjd||dd}||j}|jr,t|d|}||fS)Nz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr/rr1r2) ValueErrorr8r9r r:)rrrrrrr=r<rrrr$s  z+FixedForwardWindowIndexer.get_window_boundsr&r'r>rrrrrUsrUcsLeZdZdZdddedfdfdd Zee     ddddZZ S) GroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nrr r r int | BaseIndexergroupby_indices dict | Nonewindow_indexertype[BaseIndexer]indexer_kwargsr rc sH|pi|_||_|r|ni|_tjd||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 r r r Nr)rYr[copyr]rBrpop)rr r rYr[r]rrDrrr&s   zGroupbyIndexer.__init__rr rrrrrrrr cCsXg}g}d}|jD]|\} } |jdur|jt| } n|j} |jd | |jd|j} | t | ||||\} }| t j } | t j }t | t |ksRJdt ||t | }|t | 7}t ||ddgj t j dd}||t| ||t|q t |dkrt jgt j dt jgt j dfSt |} t |}| |fS) Nrr^z6these should be equal in length from get_window_boundsrGr/F)r_r2r)rYrr takerr[r r]r$lenastyper8r1r9appendarray concatenate)rrrrrr start_arrays end_arrayswindow_indices_startrindicesr indexerr=r<window_indicesrrrr$JsJ          z GroupbyIndexer.get_window_bounds) r r r rXrYrZr[r\r]rZr rr&r') r(r)r*r+rrrr,r$rRrrrDrrW#s$rWc@r-)ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)rNrr rrrrrrrr r cCs$tjdgtjdtj|gtjdfS)Nrr2)r8rer1r#rrrr$s$ z0ExponentialMovingWindowIndexer.get_window_boundsr&r'r>rrrrrmr?rm)r+ __future__rdatetimernumpyr8pandas._libs.window.indexersrpandas.util._decoratorsrpandas.core.dtypes.commonrpandas.tseries.offsetsrr,rr.r@rArSrUrWrmrrrrs"      X4^