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See Also -------- numpy.clip : equivalent function r apply_ufuncNTdefaultallowed)rdask)xarray.core.computationrrr[clip)r:rrrrr/r/r=rs  zDataWithCoords.clipr zpd.Index)keyrNcCsT||jkrt|z|j|WStk rNtjt|j||dYSXdS)zDGet an index for a dimension, with fall-back to a default RangeIndex)rN)r}rrto_pandas_indexpdr rlr)r:rr/r/r= get_indexs  zDataWithCoords.get_indexr,z"Mapping[Any, T | Callable[[C], T]]zdict[Hashable, T])r:r;rNcsfdd|DS)Ncs&i|]\}}|t|r|n|qSr/)callablervrvrQr/r=rsz7DataWithCoords._calc_assign_results..)r)r:r;r/rQr=_calc_assign_resultssz#DataWithCoords._calc_assign_resultszMapping[Any, Any] | Noner)r:coords coords_kwargsrNcKs2t||d}|jdd}||}|j||S)aQAssign new coordinates to this object. Returns a new object with all the original data in addition to the new coordinates. Parameters ---------- coords : dict-like or None, optional A dict where the keys are the names of the coordinates with the new values to assign. If the values are callable, they are computed on this object and assigned to new coordinate variables. If the values are not callable, (e.g. a ``DataArray``, scalar, or array), they are simply assigned. A new coordinate can also be defined and attached to an existing dimension using a tuple with the first element the dimension name and the second element the values for this new coordinate. **coords_kwargs : optional The keyword arguments form of ``coords``. One of ``coords`` or ``coords_kwargs`` must be provided. Returns ------- assigned : same type as caller A new object with the new coordinates in addition to the existing data. Examples -------- Convert `DataArray` longitude coordinates from 0-359 to -180-179: >>> da = xr.DataArray( ... np.random.rand(4), ... coords=[np.array([358, 359, 0, 1])], ... dims="lon", ... ) >>> da array([0.5488135 , 0.71518937, 0.60276338, 0.54488318]) Coordinates: * lon (lon) int64 358 359 0 1 >>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180)) array([0.5488135 , 0.71518937, 0.60276338, 0.54488318]) Coordinates: * lon (lon) int64 -2 -1 0 1 The function also accepts dictionary arguments: >>> da.assign_coords({"lon": (((da.lon + 180) % 360) - 180)}) array([0.5488135 , 0.71518937, 0.60276338, 0.54488318]) Coordinates: * lon (lon) int64 -2 -1 0 1 New coordinate can also be attached to an existing dimension: >>> lon_2 = np.array([300, 289, 0, 1]) >>> da.assign_coords(lon_2=("lon", lon_2)) array([0.5488135 , 0.71518937, 0.60276338, 0.54488318]) Coordinates: * lon (lon) int64 358 359 0 1 lon_2 (lon) int64 300 289 0 1 Note that the same result can also be obtained with a dict e.g. >>> _ = da.assign_coords({"lon_2": ("lon", lon_2)}) Note the same method applies to `Dataset` objects. Convert `Dataset` longitude coordinates from 0-359 to -180-179: >>> temperature = np.linspace(20, 32, num=16).reshape(2, 2, 4) >>> precipitation = 2 * np.identity(4).reshape(2, 2, 4) >>> ds = xr.Dataset( ... data_vars=dict( ... temperature=(["x", "y", "time"], temperature), ... precipitation=(["x", "y", "time"], precipitation), ... ), ... coords=dict( ... lon=(["x", "y"], [[260.17, 260.68], [260.21, 260.77]]), ... lat=(["x", "y"], [[42.25, 42.21], [42.63, 42.59]]), ... time=pd.date_range("2014-09-06", periods=4), ... reference_time=pd.Timestamp("2014-09-05"), ... ), ... attrs=dict(description="Weather-related data"), ... ) >>> ds Dimensions: (x: 2, y: 2, time: 4) Coordinates: lon (x, y) float64 260.2 260.7 260.2 260.8 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 ... 2014-09-09 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 20.0 20.8 21.6 22.4 ... 30.4 31.2 32.0 precipitation (x, y, time) float64 2.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 2.0 Attributes: description: Weather-related data >>> ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180)) Dimensions: (x: 2, y: 2, time: 4) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 ... 2014-09-09 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 20.0 20.8 21.6 22.4 ... 30.4 31.2 32.0 precipitation (x, y, time) float64 2.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 2.0 Attributes: description: Weather-related data Notes ----- Since ``coords_kwargs`` is a dictionary, the order of your arguments may not be preserved, and so the order of the new variables is not well defined. Assigning multiple variables within the same ``assign_coords`` is possible, but you cannot reference other variables created within the same ``assign_coords`` call. See Also -------- Dataset.assign Dataset.swap_dims Dataset.set_coords assign_coordsFdeep)rcopyrrupdate)r:rrZcoords_combinedriresultsr/r/r=rs     zDataWithCoords.assign_coords)r:argsr;rNcOs|jdd}|jj|||S)aAssign new attrs to this object. Returns a new object equivalent to ``self.attrs.update(*args, **kwargs)``. Parameters ---------- *args positional arguments passed into ``attrs.update``. **kwargs keyword arguments passed into ``attrs.update``. Returns ------- assigned : same type as caller A new object with the new attrs in addition to the existing data. See Also -------- Dataset.assign Fr)rattrsr)r:rr;outr/r/r= assign_attrsjs zDataWithCoords.assign_attrsz/Callable[..., T] | tuple[Callable[..., T], str]r-)r2rr;rNcOsNt|tr:|\}}||kr(t|d|||<|||S||f||SdS)a Apply ``func(self, *args, **kwargs)`` This method replicates the pandas method of the same name. Parameters ---------- func : callable function to apply to this xarray object (Dataset/DataArray). ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the xarray object. *args positional arguments passed into ``func``. **kwargs a dictionary of keyword arguments passed into ``func``. Returns ------- object : Any the return type of ``func``. Notes ----- Use ``.pipe`` when chaining together functions that expect xarray or pandas objects, e.g., instead of writing .. code:: python f(g(h(ds), arg1=a), arg2=b, arg3=c) You can write .. code:: python (ds.pipe(h).pipe(g, arg1=a).pipe(f, arg2=b, arg3=c)) If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``f`` takes its data as ``arg2``: .. code:: python (ds.pipe(h).pipe(g, arg1=a).pipe((f, "arg2"), arg1=a, arg3=c)) Examples -------- >>> x = xr.Dataset( ... { ... "temperature_c": ( ... ("lat", "lon"), ... 20 * np.random.rand(4).reshape(2, 2), ... ), ... "precipitation": (("lat", "lon"), np.random.rand(4).reshape(2, 2)), ... }, ... coords={"lat": [10, 20], "lon": [150, 160]}, ... ) >>> x Dimensions: (lat: 2, lon: 2) Coordinates: * lat (lat) int64 10 20 * lon (lon) int64 150 160 Data variables: temperature_c (lat, lon) float64 10.98 14.3 12.06 10.9 precipitation (lat, lon) float64 0.4237 0.6459 0.4376 0.8918 >>> def adder(data, arg): ... return data + arg ... >>> def div(data, arg): ... return data / arg ... >>> def sub_mult(data, sub_arg, mult_arg): ... return (data * mult_arg) - sub_arg ... >>> x.pipe(adder, 2) Dimensions: (lat: 2, lon: 2) Coordinates: * lat (lat) int64 10 20 * lon (lon) int64 150 160 Data variables: temperature_c (lat, lon) float64 12.98 16.3 14.06 12.9 precipitation (lat, lon) float64 2.424 2.646 2.438 2.892 >>> x.pipe(adder, arg=2) Dimensions: (lat: 2, lon: 2) Coordinates: * lat (lat) int64 10 20 * lon (lon) int64 150 160 Data variables: temperature_c (lat, lon) float64 12.98 16.3 14.06 12.9 precipitation (lat, lon) float64 2.424 2.646 2.438 2.892 >>> ( ... x.pipe(adder, arg=2) ... .pipe(div, arg=2) ... .pipe(sub_mult, sub_arg=2, mult_arg=2) ... ) Dimensions: (lat: 2, lon: 2) Coordinates: * lat (lat) int64 10 20 * lon (lon) int64 150 160 Data variables: temperature_c (lat, lon) float64 10.98 14.3 12.06 10.9 precipitation (lat, lon) float64 0.4237 0.6459 0.4376 0.8918 See Also -------- pandas.DataFrame.pipe z/ is both the pipe target and a keyword argumentN)ryrzr)r:r2rr;targetr/r/r=pipesy  zDataWithCoords.pipespanzMapping[Any, int] | Noner^zRollingExp[T_DataWithCoords])r:window window_typerNcKs8ddlm}d|krtdt||d}||||S)a Exponentially-weighted moving window. Similar to EWM in pandas Requires the optional Numbagg dependency. Parameters ---------- window : mapping of hashable to int, optional A mapping from the name of the dimension to create the rolling exponential window along (e.g. `time`) to the size of the moving window. window_type : {"span", "com", "halflife", "alpha"}, default: "span" The format of the previously supplied window. Each is a simple numerical transformation of the others. Described in detail: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html **window_kwargs : optional The keyword arguments form of ``window``. One of window or window_kwargs must be provided. See Also -------- core.rolling_exp.RollingExp r) rolling_exprzPassing ``keep_attrs`` to ``rolling_exp`` has no effect. Pass ``keep_attrs`` directly to the applied function, e.g. ``rolling_exp(...).mean(keep_attrs=False)``.r) xarray.corerrrrr")r:rrZ window_kwargsrr/r/r=r s  zDataWithCoords.rolling_expztype[T_Resample]zMapping[Any, str] | NonezSideOptions | Nonez int | Nonez.pd.Timedelta | datetime.timedelta | str | Nonezstr | DatetimeLikezdatetime.timedelta | str | Noner*) resample_clsindexerr7closedlabelbaseoffsetoriginrloffsetrestore_coord_dimsindexer_kwargsrNc  KsRddlm} ddlm}ddlm}| dk r6td|dk rHt|t rld| krZd|j ksld| krtd|j krtt d t || d }t |d krtd tt|\}}|}||}thtjd dtdt|j|| rddlm}|||||| ||d}ntj||||||| d}W5QRX|||j|j |d}||||||| dS)aReturns a Resample object for performing resampling operations. Handles both downsampling and upsampling. The resampled dimension must be a datetime-like coordinate. If any intervals contain no values from the original object, they will be given the value ``NaN``. Parameters ---------- indexer : {dim: freq}, optional Mapping from the dimension name to resample frequency [1]_. The dimension must be datetime-like. skipna : bool, optional Whether to skip missing values when aggregating in downsampling. closed : {"left", "right"}, optional Side of each interval to treat as closed. label : {"left", "right"}, optional Side of each interval to use for labeling. base : int, optional For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for "24H" frequency, base could range from 0 through 23. origin : {'epoch', 'start', 'start_day', 'end', 'end_day'}, pd.Timestamp, datetime.datetime, np.datetime64, or cftime.datetime, default 'start_day' The datetime on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a datetime is not used, these values are also supported: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day offset : pd.Timedelta, datetime.timedelta, or str, default is None An offset timedelta added to the origin. loffset : timedelta or str, optional Offset used to adjust the resampled time labels. Some pandas date offset strings are supported. restore_coord_dims : bool, optional If True, also restore the dimension order of multi-dimensional coordinates. **indexer_kwargs : {dim: freq} The keyword arguments form of ``indexer``. One of indexer or indexer_kwargs must be provided. Returns ------- resampled : same type as caller This object resampled. Examples -------- Downsample monthly time-series data to seasonal data: >>> da = xr.DataArray( ... np.linspace(0, 11, num=12), ... coords=[ ... pd.date_range( ... "1999-12-15", ... periods=12, ... freq=pd.DateOffset(months=1), ... ) ... ], ... dims="time", ... ) >>> da array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.]) Coordinates: * time (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15 >>> da.resample(time="QS-DEC").mean() array([ 1., 4., 7., 10.]) Coordinates: * time (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01 Upsample monthly time-series data to daily data: >>> da.resample(time="1D").interpolate("linear") # +doctest: ELLIPSIS array([ 0. , 0.03225806, 0.06451613, 0.09677419, 0.12903226, 0.16129032, 0.19354839, 0.22580645, 0.25806452, 0.29032258, 0.32258065, 0.35483871, 0.38709677, 0.41935484, 0.4516129 , ... 10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387, 10.96774194, 11. ]) Coordinates: * time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15 Limit scope of upsampling method >>> da.resample(time="1D").nearest(tolerance="1D") array([ 0., 0., nan, ..., nan, 11., 11.]) Coordinates: * time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15 See Also -------- pandas.Series.resample pandas.DataFrame.resample References ---------- .. [1] https://pandas.pydata.org/docs/user_guide/timeseries.html#dateoffset-objects r) CFTimeIndexr) RESAMPLE_DIMNzPassing ``keep_attrs`` to ``resample`` has no effect and will raise an error in xarray 0.20. Pass ``keep_attrs`` directly to the applied function, e.g. ``resample(...).mean(keep_attrs=True)``.howr5zresample() no longer supports the `how` or `dim` arguments. Instead call methods on resample objects, e.g., data.resample(time='1D').mean()Zresamplerz2Resampling only supported along single dimensions.ignorezC'(base|loffset)' in .resample\(\) and in Grouper\(\) is deprecated.)category) CFTimeGrouper)freqrrrrrr)rrrrrrr)rr}r)groupr5grouperZ resample_dimr)Zxarray.coding.cftimeindexrxarray.core.dataarrayrxarray.core.resamplerrrryr0r}rqrrmrnextiterrcatch_warningsfilterwarningsrrrZxarray.core.resample_cftimerrZGrouperr)r:rrr7rrrrrrrrrrrrr5rZdim_nameZ dim_coordrrrr/r/r= _resample3sz         zDataWithCoords._resample)r:condotherrrNc sddlm}ddlm}ddlm}tr4||rt||fs`tdd|d|||\}dd d fd d }dd d fd d }t|r|n|} i} j D]} | | | | <q|j f| }j f| t ||S)al Filter elements from this object according to a condition. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic. Parameters ---------- cond : DataArray, Dataset, or callable Locations at which to preserve this object's values. dtype must be `bool`. If a callable, it must expect this object as its only parameter. other : scalar, DataArray or Dataset, optional Value to use for locations in this object where ``cond`` is False. By default, these locations filled with NA. drop : bool, default: False If True, coordinate labels that only correspond to False values of the condition are dropped from the result. Returns ------- DataArray or Dataset Same xarray type as caller, with dtype float64. Examples -------- >>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y")) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) Dimensions without coordinates: x, y >>> a.where(a.x + a.y < 4) array([[ 0., 1., 2., 3., nan], [ 5., 6., 7., nan, nan], [10., 11., nan, nan, nan], [15., nan, nan, nan, nan], [nan, nan, nan, nan, nan]]) Dimensions without coordinates: x, y >>> a.where(a.x + a.y < 5, -1) array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, -1], [10, 11, 12, -1, -1], [15, 16, -1, -1, -1], [20, -1, -1, -1, -1]]) Dimensions without coordinates: x, y >>> a.where(a.x + a.y < 4, drop=True) array([[ 0., 1., 2., 3.], [ 5., 6., 7., nan], [10., 11., nan, nan], [15., nan, nan, nan]]) Dimensions without coordinates: x, y >>> a.where(lambda x: x.x + x.y < 4, drop=True) array([[ 0., 1., 2., 3.], [ 5., 6., 7., nan], [10., 11., nan, nan], [15., nan, nan, nan]]) Dimensions without coordinates: x, y >>> a.where(a.x + a.y < 4, -1, drop=True) array([[ 0, 1, 2, 3], [ 5, 6, 7, -1], [10, 11, -1, -1], [15, -1, -1, -1]]) Dimensions without coordinates: x, y See Also -------- numpy.where : corresponding numpy function where : equivalent function r)alignrrzcond argument is z but must be a z or r rrscsjfddjDdS)Nc3s|]}|kr|VqdSrOr/rur5r/r=rxZszCDataWithCoords.where.._dataarray_indexer..r )rr}r rr r=_dataarray_indexerYsz0DataWithCoords.where.._dataarray_indexercsHfddD}|jfddjDd}|dS)Nc3s |]}|jkr|VqdSrO)r})rvvar)rr5r/r=rx]szADataWithCoords.where.._dataset_indexer..c3s|]}|kr|VqdSrOr/rur r/r=rx`sr variable)Z drop_varsrr}rZto_array)r5Z cond_wdimZkeepanyr r r=_dataset_indexer\s  z.DataWithCoords.where.._dataset_indexer)Zxarray.core.alignmentr rrxarray.core.datasetrrryrqrrrrZ where_method) r:rr rr rrr rZ _get_indexerZindexersr5r/r r=wheres*T     zDataWithCoords.wherer)closerNcCs ||_dS)aRegister the function that releases any resources linked to this object. This method controls how xarray cleans up resources associated with this object when the ``.close()`` method is called. It is mostly intended for backend developers and it is rarely needed by regular end-users. Parameters ---------- close : callable The function that when called like ``close()`` releases any resources linked to this object. Nr)r:rr/r/r= set_closepszDataWithCoords.set_closer_cCs|jdk r|d|_dS)z,Release any resources linked to this object.NrrQr/r/r=rs zDataWithCoords.close)r:rrNcCs0ddlm}|dkrtdd}|tj|d|dS)aTest each value in the array for whether it is a missing value. Parameters ---------- keep_attrs : bool or None, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False, the new object will be returned without attributes. Returns ------- isnull : DataArray or Dataset Same type and shape as object, but the dtype of the data is bool. See Also -------- pandas.isnull Examples -------- >>> array = xr.DataArray([1, np.nan, 3], dims="x") >>> array array([ 1., nan, 3.]) Dimensions without coordinates: x >>> array.isnull() array([False, True, False]) Dimensions without coordinates: x rrNFrrrr)rrrrisnullr:rrr/r/r=rs!  zDataWithCoords.isnullcCs0ddlm}|dkrtdd}|tj|d|dS)aTest each value in the array for whether it is not a missing value. Parameters ---------- keep_attrs : bool or None, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False, the new object will be returned without attributes. Returns ------- notnull : DataArray or Dataset Same type and shape as object, but the dtype of the data is bool. See Also -------- pandas.notnull Examples -------- >>> array = xr.DataArray([1, np.nan, 3], dims="x") >>> array array([ 1., nan, 3.]) Dimensions without coordinates: x >>> array.notnull() array([ True, False, True]) Dimensions without coordinates: x rrNFrrr)rrrrnotnullrr/r/r=rs!  zDataWithCoords.notnull)r: test_elementsrNcCsvddlm}ddlm}ddlm}ddlm}t||rJt d |nt|||fr^|j }|t j |t|ddd S) aTests each value in the array for whether it is in test elements. Parameters ---------- test_elements : array_like The values against which to test each value of `element`. This argument is flattened if an array or array_like. See numpy notes for behavior with non-array-like parameters. Returns ------- isin : DataArray or Dataset Has the same type and shape as this object, but with a bool dtype. Examples -------- >>> array = xr.DataArray([1, 2, 3], dims="x") >>> array.isin([1, 3]) array([ True, False, True]) Dimensions without coordinates: x See Also -------- numpy.isin rrrrr(z3isin() argument must be convertible to an array: {})rr)r;r)rrrrrrxarray.core.variabler)ryrqrririsinr)r:rrrrr)r/r/r=rs$     zDataWithCoords.isinT)ordercastingsubokrrrMc CsDddlm}t||||d}dd|D}|tj||||ddS)u Copy of the xarray object, with data cast to a specified type. Leaves coordinate dtype unchanged. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array. copy : bool, optional By default, astype always returns a newly allocated array. If this is set to False and the `dtype` requirement is satisfied, the input array is returned instead of a copy. keep_attrs : bool, optional By default, astype keeps attributes. Set to False to remove attributes in the returned object. Returns ------- out : same as object New object with data cast to the specified type. Notes ----- The ``order``, ``casting``, ``subok`` and ``copy`` arguments are only passed through to the ``astype`` method of the underlying array when a value different than ``None`` is supplied. Make sure to only supply these arguments if the underlying array class supports them. See Also -------- numpy.ndarray.astype dask.array.Array.astype sparse.COO.astype rr)rrrrcSsi|]\}}|dk r||qSrOr/rr/r/r=rSsz)DataWithCoords.astype..r)r;rr)rrrrrastype) r:rYrrrrrrr;r/r/r=rs> zDataWithCoords.astypecCs|SrOr/rQr/r/r= __enter__^szDataWithCoords.__enter__cCs |dSrO)r)r:exc_type exc_value tracebackr/r/r=__exit__aszDataWithCoords.__exit__cCs tdSrO)rk)r:rr/r/r= __getitem__dszDataWithCoords.__getitem__)NFN)NN)N)Nr)N)N)rBrCrDr__annotations__rErrrrrrrrrrNArrrrrrrr r$r%r/r/r/r=rzsZ #+ *&Ez.-6Lrrrr$)r  fill_valuerYrNcCsdSrOr/r r(rYr/r/r= full_likeisr*rDTypeMaybeMappingcCsdSrOr/r)r/r/r=r*psr)cCsdSrOr/r)r/r/r=r*wszDataset | DataArraycCsdSrOr/r)r/r/r=r*~szDataset | DataArray | VariablecCsdSrOr/r)r/r/r=r*scsJddlm}ddlm}ddlm}tsPt||r@ttsPt ddt||rtts|fdd|j Dtt sfd d|j Dnfd d|j D}|||j|jd St||rtt rt d |t|j|j|j|j|jd St||r>tt r2t dt|StddS)a Return a new object with the same shape and type as a given object. Parameters ---------- other : DataArray, Dataset or Variable The reference object in input fill_value : scalar or dict-like Value to fill the new object with before returning it. If other is a Dataset, may also be a dict-like mapping data variables to fill values. dtype : dtype or dict-like of dtype, optional dtype of the new array. If a dict-like, maps dtypes to variables. If omitted, it defaults to other.dtype. Returns ------- out : same as object New object with the same shape and type as other, with the data filled with fill_value. Coords will be copied from other. If other is based on dask, the new one will be as well, and will be split in the same chunks. Examples -------- >>> x = xr.DataArray( ... np.arange(6).reshape(2, 3), ... dims=["lat", "lon"], ... coords={"lat": [1, 2], "lon": [0, 1, 2]}, ... ) >>> x array([[0, 1, 2], [3, 4, 5]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.full_like(x, 1) array([[1, 1, 1], [1, 1, 1]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.full_like(x, 0.5) array([[0, 0, 0], [0, 0, 0]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.full_like(x, 0.5, dtype=np.double) array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.full_like(x, np.nan, dtype=np.double) array([[nan, nan, nan], [nan, nan, nan]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> ds = xr.Dataset( ... {"a": ("x", [3, 5, 2]), "b": ("x", [9, 1, 0])}, coords={"x": [2, 4, 6]} ... ) >>> ds Dimensions: (x: 3) Coordinates: * x (x) int64 2 4 6 Data variables: a (x) int64 3 5 2 b (x) int64 9 1 0 >>> xr.full_like(ds, fill_value={"a": 1, "b": 2}) Dimensions: (x: 3) Coordinates: * x (x) int64 2 4 6 Data variables: a (x) int64 1 1 1 b (x) int64 2 2 2 >>> xr.full_like(ds, fill_value={"a": 1, "b": 2}, dtype={"a": bool, "b": float}) Dimensions: (x: 3) Coordinates: * x (x) int64 2 4 6 Data variables: a (x) bool True True True b (x) float64 2.0 2.0 2.0 See Also -------- zeros_like ones_like rrrr(zBfill_value must be scalar or, for datasets, a dict-like. Received z instead.csi|] }|qSr/r/r)r(r/r=rszfull_like..csi|] }|qSr/r/rrZr/r=r sc s2i|]*\}}|t|j|tj|dqSrO)_full_like_variablergetrr'r)dtype_r(r/r=rs )rrz4'dtype' cannot be dict-like when passing a DataArray)r}rrrz3'dtype' cannot be dict-like when passing a Variablez(Expected DataArray, Dataset, or VariableN)rrrrrr)rryrr data_varsrr rrrr,rr}rrq)r r(rYrrr)r/r/)rYr.r(r=r*sHl              rcCsddlm}|tjkr.t|dk r&|n|j}t|jrjddl}|dkrN|j}|j j |j |||jj d}nt j|j||d}||j||jdS)z;Inner function of full_like, where other must be a variablerr(N)rYchunksrZ)r}rir)rr)rr'Zget_fill_valuerYrriZ dask.arrayarrayfullrhr0r[r*r}r)r r(rYr)rrir/r/r=r,(s   r,)r rYrNcCsdSrOr/r rYr/r/r= zeros_like?sr4cCsdSrOr/r3r/r/r=r4DscCsdSrOr/r3r/r/r=r4IscCsdSrOr/r3r/r/r=r4NscCsdSrOr/r3r/r/r=r4UscCs t|d|S)aBReturn a new object of zeros with the same shape and type as a given dataarray or dataset. Parameters ---------- other : DataArray, Dataset or Variable The reference object. The output will have the same dimensions and coordinates as this object. dtype : dtype, optional dtype of the new array. If omitted, it defaults to other.dtype. Returns ------- out : DataArray, Dataset or Variable New object of zeros with the same shape and type as other. Examples -------- >>> x = xr.DataArray( ... np.arange(6).reshape(2, 3), ... dims=["lat", "lon"], ... coords={"lat": [1, 2], "lon": [0, 1, 2]}, ... ) >>> x array([[0, 1, 2], [3, 4, 5]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.zeros_like(x) array([[0, 0, 0], [0, 0, 0]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.zeros_like(x, dtype=float) array([[0., 0., 0.], [0., 0., 0.]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 See Also -------- ones_like full_like rr*r3r/r/r=r4\s7cCsdSrOr/r3r/r/r= ones_likesr6cCsdSrOr/r3r/r/r=r6scCsdSrOr/r3r/r/r=r6scCsdSrOr/r3r/r/r=r6scCsdSrOr/r3r/r/r=r6scCs t|d|S)aZReturn a new object of ones with the same shape and type as a given dataarray or dataset. Parameters ---------- other : DataArray, Dataset, or Variable The reference object. The output will have the same dimensions and coordinates as this object. dtype : dtype, optional dtype of the new array. If omitted, it defaults to other.dtype. Returns ------- out : same as object New object of ones with the same shape and type as other. Examples -------- >>> x = xr.DataArray( ... np.arange(6).reshape(2, 3), ... dims=["lat", "lon"], ... coords={"lat": [1, 2], "lon": [0, 1, 2]}, ... ) >>> x array([[0, 1, 2], [3, 4, 5]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 >>> xr.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) Coordinates: * lat (lat) int64 1 2 * lon (lon) int64 0 1 2 See Also -------- zeros_like full_like rr5r3r/r/r=r6s/zIterable[Variable]zMapping[Any, tuple[int, ...]]) variablesrNcCsbi}|D]P}t|jdr|jD]4\}}||krN|||krNtd|d|||<q"qt|S)Nr0z/Object has inconsistent chunks along dimension z.. This can be fixed by calling unify_chunks().)r_dataZ chunksizesrrr)r7r0rr5cr/r/r=get_chunksizess   r:r0)rYrNcCst|tjpt|tjS)z:Check if a dtype is a subclass of the numpy datetime types)r[ issubdtypeZ datetime64 timedelta64rZr/r/r=is_np_datetime_likesr=cCst|tjS)z0Check whether dtype is of the timedelta64 dtype.)r[r;r<rZr/r/r=is_np_timedelta_likesr>r_cCsntdkr dS|jtdkrf|jdkrft|jd}t|rZ|}t|tj rZ| }t|tj SdSdS)z2Check if an array contains cftime.datetime objectsNFOr) cftimerYr[sizer\ZflatrZcomputeryZndarrayrdatetime)r1sampler/r/r=_contains_cftime_datetimess  rDcCs,|jtdkr$|jdkr$t|jSdSdS)zrDrFrGr/r/r/r=s    0            ',[m!v :2