d)7ddlmZddlZddlmZmZmZddlmZm Z m Z ddl m Z m Z ddlmZmZmZddlmZmZmZer ddlmZdd lmZd ZGd d eeZGd dedee ZGddedee ZdS)) annotationsN)HashableIterableSequence) TYPE_CHECKINGAnyCallable)DataArrayResampleAggregationsDatasetResampleAggregations)DataArrayGroupByBaseDatasetGroupByBaseGroupBy)Dims InterpOptionsT_Xarray) DataArray)Dataset__resample_dim__c~eZdZdZddddfd Z ddfd ZddZdddZeZdddZ e Z dddZ dd dZ dddZ xZS)!ResampleaSAn object that extends the `GroupBy` object with additional logic for handling specialized re-sampling operations. You should create a `Resample` object by using the `DataArray.resample` or `Dataset.resample` methods. The dimension along re-sampling See Also -------- DataArray.resample Dataset.resample N)dim resample_dimrHashable | NonerreturnNonec||krtd|d|d||_tj|i|dS)NzProxy resampling dimension ('z3') cannot have the same name as actual dimension ('z')!) ValueError_dimsuper__init__)selfrrargskwargs __class__s 4lib/python3.11/site-packages/xarray/core/resample.pyr zResample.__init__#sv ,  L LLCFLLL  $)&)))))r keep_attrs bool | Nonerc tjd||d|}|t|ji}|S)N)rr')r _flox_reducerename RESAMPLE_DIM _group_dim)r!rr'r#resultr$s r%r+zResample._flox_reduce3sE &%O#*OOOO do>?? r&c|j}|jD]3\}}||jkr#|j|jvr||}4|S)z=Drop non-dimension coordinates along the resampled dimension.)_objcoordsitemsrdims drop_vars)r!objkvs r% _drop_coordszResample._drop_coords=sZiJ$$&& ' 'DAqDI~~$)qv"5"5mmA&& r& tolerancefloat | Iterable[float] | Nonec|}|j\}||j|jid|S)aTForward fill new values at up-sampled frequency. Parameters ---------- tolerance : float | Iterable[float] | None, default: None Maximum distance between original and new labels to limit the up-sampling method. Up-sampled data with indices that satisfy the equation ``abs(index[indexer] - target) <= tolerance`` are filled by new values. Data with indices that are outside the given tolerance are filled with ``NaN`` s. Returns ------- padded : DataArray or Dataset padmethodr:r9groupersreindexr full_indexr!r:r6groupers r%r=z Resample.padEsJ"!!] {{ Y* +EY   r&c|}|j\}||j|jid|S)aYBackward fill new values at up-sampled frequency. Parameters ---------- tolerance : float | Iterable[float] | None, default: None Maximum distance between original and new labels to limit the up-sampling method. Up-sampled data with indices that satisfy the equation ``abs(index[indexer] - target) <= tolerance`` are filled by new values. Data with indices that are outside the given tolerance are filled with ``NaN`` s. Returns ------- backfilled : DataArray or Dataset backfillr>r@rDs r%rGzResample.backfill^sJ"!!] {{ Y* +J)   r&c|}|j\}||j|jid|S)aTake new values from nearest original coordinate to up-sampled frequency coordinates. Parameters ---------- tolerance : float | Iterable[float] | None, default: None Maximum distance between original and new labels to limit the up-sampling method. Up-sampled data with indices that satisfy the equation ``abs(index[indexer] - target) <= tolerance`` are filled by new values. Data with indices that are outside the given tolerance are filled with ``NaN`` s. Returns ------- upsampled : DataArray or Dataset nearestr>r@rDs r%rIzResample.nearestwsJ$!!] {{ Y* +I   r&linearkindrc.||S)a9Interpolate up-sampled data using the original data as knots. Parameters ---------- kind : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"}, default: "linear" The method used to interpolate. The method should be supported by the scipy interpolator: - ``interp1d``: {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"} - ``interpn``: {"linear", "nearest"} If ``"polynomial"`` is passed, the ``order`` keyword argument must also be provided. Returns ------- interpolated : DataArray or Dataset See Also -------- DataArray.interp Dataset.interp scipy.interpolate.interp1d )rK) _interpolate)r!rKs r% interpolatezResample.interpolates8  d +++r&c|}|j\}||j|jid|ddiS)zz&DatasetResample.map..Bs=LL44,T,,,V,,LLLLLLr&) _iter_grouped_combinerr2r5r-r4r,)r!r^r"rar#appliedrds `` ` r%rczDatasetResample.mapsJMLLLLLt7I7I7K7KLLL==)) 9 ' '))$)44H 8= ( ( di'@AAHr&c \tjdtd|jd|||d|S)z~ Backward compatible implementation of ``map`` See Also -------- DataSetResample.map rfrgrhrjr*rkros r%rpzDatasetResample.applyPsI  ^ %    txJTH4JJ6JJJr&FT)axisr'keepdimsrarrrint | Sequence[int] | Noner'rboolc Dtjd||||||d|S)aReduce the items in this group by applying `func` along the pre-defined resampling dimension. Parameters ---------- func : callable Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of collapsing an np.ndarray over an integer valued axis. dim : "...", str, Iterable of Hashable or None, optional Dimension(s) over which to apply `func`. keep_attrs : bool, optional If True, the datasets's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Dataset Array with summarized data and the indicated dimension(s) removed. )r^rrr'rrar*)rreduce) r!r^rrr'rrar#r$s r%rzDatasetResample.reduce`sFFuww~ !      r&c|||_||jdn|jgS)zReturn values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN. Returns ------- resampled : Dataset Nrrrts r%ruzDatasetResample.asfreqrvr&rw) r^r_r"r`rar(r#rrrrS)r^r_rrrrr'r(rrrarr#rrr)rr) rTrUrVrWrcrprrurZr[s@r%ryrysOO !# $ 11111fKKKK&+ ,0"&+ + + + + + + + Z E E E E E E E Er&ryr) __future__rrlcollections.abcrrrtypingrrr xarray.core._aggregationsr r xarray.core.groupbyr r rxarray.core.typesrrrxarray.core.dataarrayrxarray.core.datasetrr-rr]ryr*r&r%rs""""""8888888888//////////RQQQQQQQQQ;;;;;;;;;;,//////++++++! a a a a a wx a a a J\E\E\E\E\E-/CEb\E\E\E@|E|E|E|E|Ehy)+=?Z|E|E|E|E|Er&