U Cv ft9@sddlmZddlZddlmZmZmZmZmZm Z ddl Z ddl m Z mZddlmZmZmZddlmZmZmZerddlmZddlmZd ZGd d d eeZGd d d edee ZGdddedeeZdS)) annotationsN) TYPE_CHECKINGAnyCallableHashableIterableSequence)DataArrayResampleAggregationsDatasetResampleAggregations)DataArrayGroupByBaseDatasetGroupByBaseGroupBy)Dims InterpOptionsT_Xarray DataArray)DatasetZ__resample_dim__cseZdZdZdddddddfddZd!d d d d fd d Zd dddZd"dd dddZeZd#dd dddZ e Z d$dd dddZ d%dd dddZ d&d ddd Z ZS)'ResampleaSAn object that extends the `GroupBy` object with additional logic for handling specialized re-sampling operations. You should create a `Resample` object by using the `DataArray.resample` or `Dataset.resample` methods. The dimension along re-sampling See Also -------- DataArray.resample Dataset.resample N)dim resample_dimzHashable | NoneNone)rrreturncs6||krtd|d|d||_tj||dS)NzProxy resampling dimension ('z3') cannot have the same name as actual dimension ('z')!) ValueError_dimsuper__init__)selfrrargskwargs __class__8/tmp/pip-unpacked-wheel-h316xyqg/xarray/core/resample.pyr$s zResample.__init__r bool | Noner)r keep_attrsrc  sddlm}|ddg}|jD]@}t|ts4t|jdk rD|jn |jj |j }| ||j q"t |jj|}|||j f|jjd} tjf|| |d|} || } | t|j i} | S)NrrmethodZcohorts)dimsname)rgroupr%)xarray.core.dataarrayr setdefaultZ_group_indices isinstancesliceAssertionErrorstop_objZsizesZ _group_dimappendstartnprepeatZ _unique_coorddatar(r _flox_reduceZ_maybe_restore_empty_groupsrename RESAMPLE_DIM) rrr%rrZrepeatsZslicerr/labelsr)resultr r"r#r65s,     zResample._flox_reducercCs>|j}|jD](\}}||jkr|j|jkr||}q|S)z=Drop non-dimension coordinates along the resampled dimension.)r0coordsitemsrr' drop_vars)robjkvr"r"r# _drop_coordsTs  zResample._drop_coordszfloat | Iterable[float] | None) tolerancercCs |}|j|j|jid|dS)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 padr&rCrBZreindexr _full_indexrrCr?r"r"r#rD\s  z Resample.padcCs |}|j|j|jid|dS)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 backfillrErFrHr"r"r#rIts  zResample.backfillcCs |}|j|j|jid|dS)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 nearestrErFrHr"r"r#rJs  zResample.nearestlinearr)kindrcCs |j|dS)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 )rL) _interpolate)rrLr"r"r# interpolateszResample.interpolatecCs&|}|j|j|jid|ddidS)zr8r'r7)rrXrrYrcombinedr r"r#rZs 2   zDataArrayResample.mapNcKs(tjdtdd|jf|||d|S)z Backward compatible implementation of ``map`` See Also -------- DataArrayResample.map PResample.apply may be deprecated in the future. Using Resample.map is encouraged stacklevelrXrYrwarningswarnPendingDeprecationWarningrZrrXrrYrr"r"r#applys zDataArrayResample.applyr;cCs&||_||jdkrdn|jgS)zReturn values of original object at the new up-sampling frequency; essentially a re-index with new times set to NaN. Returns ------- resampled : DataArray NrBr0Zmeanrrr"r"r#asfreq s zDataArrayResample.asfreq)r"F)r"N)rOrPrQrRrZrfrirSr"r"r r#rTs ? rTrc sreZdZdZddddddd d d Zdd d ZdddddddddddddddfddZddddZZS)DatasetResamplezEDatasetGroupBy object specialized to resampling a specified dimensionr"NrUrVr$rrrWc sZfdd|D}||}|j|jkr<||j}t|jkrV|t|ji}|S)a^Apply a function over each Dataset in the groups generated for resampling and concatenate them together into a new Dataset. `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds` in this group. Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how to stack together the datasets. The rule is: 1. If the dimension along which the group coordinate is defined is still in the first grouped item after applying `func`, then stack over this dimension. 2. Otherwise, stack over the new dimension given by name of this grouping (the argument to the `groupby` function). Parameters ---------- func : callable Callable to apply to each sub-dataset. args : tuple, optional Positional arguments passed on to `func`. **kwargs Used to call `func(ds, **kwargs)` for each sub-dataset `ar`. Returns ------- applied : Dataset The result of splitting, applying and combining this dataset. c3s|]}|fVqdS)Nr").0ZdsrrXrr"r# Usz&DatasetResample.map..)Z _iter_groupedZ_combinerr<r>r8r'r7)rrXrrYrZappliedr[r"rlr#rZ0s%    zDatasetResample.mapcKs(tjdtdd|jf|||d|S)z~ Backward compatible implementation of ``map`` See Also -------- DataSetResample.map r\r]r^r`rarer"r"r#rfcs zDatasetResample.applyFT)axisr%keepdimsrYrzint | Sequence[int] | Nonebool)rXrrnr%rorYrrc s tjf||||||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. )rXrrnr%rorY)rreduce)rrXrrnr%rorYrr r"r#rqss#zDatasetResample.reducer;cCs&||_||jdkrdn|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 Nrgrhr"r"r#ris zDatasetResample.asfreq)r"N)r"N)N) rOrPrQrRrZrfrqrirSr"r"r r#rj-s3 $-rjr) __future__rrbtypingrrrrrrZnumpyr3Zxarray.core._aggregationsr r Zxarray.core.groupbyr r r Zxarray.core.typesrrrr*rZxarray.core.datasetrr8rrTrjr"r"r"r#s    8`