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DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to support metadata aware operations. The API is similar to that for the pandas Series or DataFrame, but DataArray objects can have any number of dimensions, and their contents have fixed data types. Additional features over raw numpy arrays: - Apply operations over dimensions by name: ``x.sum('time')``. - Select or assign values by integer location (like numpy): ``x[:10]`` or by label (like pandas): ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (known in numpy as "broadcasting") based on dimension names, regardless of their original order. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs`` - Convert to a pandas Series: ``x.to_series()``. Getting items from or doing mathematical operations with a DataArray always returns another DataArray. Parameters ---------- data : array_like Values for this array. Must be an ``numpy.ndarray``, ndarray like, or castable to an ``ndarray``. If a self-described xarray or pandas object, attempts are made to use this array's metadata to fill in other unspecified arguments. A view of the array's data is used instead of a copy if possible. coords : sequence or dict of array_like, optional Coordinates (tick labels) to use for indexing along each dimension. The following notations are accepted: - mapping {dimension name: array-like} - sequence of tuples that are valid arguments for ``xarray.Variable()`` - (dims, data) - (dims, data, attrs) - (dims, data, attrs, encoding) Additionally, it is possible to define a coord whose name does not match the dimension name, or a coord based on multiple dimensions, with one of the following notations: - mapping {coord name: DataArray} - mapping {coord name: Variable} - mapping {coord name: (dimension name, array-like)} - mapping {coord name: (tuple of dimension names, array-like)} dims : Hashable or sequence of Hashable, optional Name(s) of the data dimension(s). Must be either a Hashable (only for 1D data) or a sequence of Hashables with length equal to the number of dimensions. If this argument is omitted, dimension names are taken from ``coords`` (if possible) and otherwise default to ``['dim_0', ... 'dim_n']``. name : str or None, optional Name of this array. attrs : dict_like or None, optional Attributes to assign to the new instance. By default, an empty attribute dictionary is initialized. Examples -------- Create data: >>> np.random.seed(0) >>> temperature = 15 + 8 * np.random.randn(2, 2, 3) >>> lon = [[-99.83, -99.32], [-99.79, -99.23]] >>> lat = [[42.25, 42.21], [42.63, 42.59]] >>> time = pd.date_range("2014-09-06", periods=3) >>> reference_time = pd.Timestamp("2014-09-05") Initialize a dataarray with multiple dimensions: >>> da = xr.DataArray( ... data=temperature, ... dims=["x", "y", "time"], ... coords=dict( ... lon=(["x", "y"], lon), ... lat=(["x", "y"], lat), ... time=time, ... reference_time=reference_time, ... ), ... attrs=dict( ... description="Ambient temperature.", ... units="degC", ... ), ... ) >>> da array([[[29.11241877, 18.20125767, 22.82990387], [32.92714559, 29.94046392, 7.18177696]], [[22.60070734, 13.78914233, 14.17424919], [18.28478802, 16.15234857, 26.63418806]]]) 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 2014-09-08 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Attributes: description: Ambient temperature. units: degC Find out where the coldest temperature was: >>> da.isel(da.argmin(...)) array(7.18177696) Coordinates: lon float64 -99.32 lat float64 42.21 time datetime64[ns] 2014-09-08 reference_time datetime64[ns] 2014-09-05 Attributes: description: Ambient temperature. units: degC zdict[str, Any]_cachezdict[Any, Variable]_coordszCallable[[], None] | None_closezdict[Hashable, Index]_indexeszHashable | None_namer: _variable)rrrrrr __weakref__NFrzISequence[Sequence[Any] | pd.Index | DataArray] | Mapping[Any, Any] | Nonez$Hashable | Sequence[Hashable] | NonezMapping | Nonezdict[Hashable, Index] | Noneboolr)rrrnriattrsindexesfastpathrbc Csb|r0|}|dkst|dks t|dk s,tn|dk r@td|dkrt|trZ|j}nFt|tjrp|jg}n0t|tjr|j|j g}nt|tj t fr|g}|dkrt |dt |dd}|dkrt |dd}|dkrt|t st |dd}t|||}t|}t|j||\}}t|||dd}t|\}}||_t|tsFt||_||_||_d|_dS)Nz/Providing explicit indexes is not supported yetrnrirT)r)AssertionErrorrrrsr_rpdSeriesindex DataFramecolumnsr(r9getattrr/rr;rrr:r1rr}rrrr) rrrrnrirrrvariablercrcrfrsB           zDataArray.__init__ztype[T_DataArray]rrZ)clsrrrirrbcCs,t|}||_||_||_||_d|_|S)zaShortcut around __init__ for internal use when we want to skip costly validation N)object__new__rrrrr)rrrrirobjrcrcrf_construct_directs zDataArray._construct_directzVariable | NonezHashable | None | Default)rrrirbcCsN|dkr|j}|dkr|j}|dkr*|j}|tkr8|j}t|||||ddSNT)rirr)rrrr7ritype)rrrrirrcrcrf_replaceszDataArray._replacecs|j|jkr*|j|jkr*|j}|j}nz|j|jkrrtt|j|jfdd|jD}t|jt |}n2t |jfdd|jD}t|jt |}|j ||||dS)Ncs4i|],\}}|jtfdd|jDkr||qS)c3s|]}|VqdSrjrcrk new_sizesrcrfrosz@DataArray._replace_maybe_drop_dims...)rrzrnrdrrrrcrf sz6DataArray._replace_maybe_drop_dims..cs$i|]\}}t|jkr||qSrc)setrnr) allowed_dimsrcrfrsr) rnrrcopyrr}ryr|r+rr)rrrirrrc)rrrf_replace_maybe_drop_dimss     z"DataArray._replace_maybe_drop_dimszMapping[Any, Index]zMapping[Any, Variable] | Nonezlist[Hashable] | NonezMapping[Any, Any] | None)rrr drop_coords rename_dimsrbc s|s|S|dkri}|dkr g}|j}|j}t|j}|D]}||||<||||<qB|D]}||||qdrtfdd|jD|_|j|||dS)z:Maybe replace indexes and their corresponding coordinates.Nc3s|]}||VqdSrjgetrkrrcrfrosz/DataArray._overwrite_indexes..rrr) rrrr}rpoprzrnr) rrrrrZ new_variablerZ new_indexesrircrrf_overwrite_indexess,      zDataArray._overwrite_indexesr&racCs|jtddS)NF)ri shallow_copy)_to_dataset_whole _THIS_ARRAYrrcrcrf_to_temp_datasetszDataArray._to_temp_dataset)rdatasetrirbcCs*|jt}|j}|j}|j||||dS)Nr) _variablesrrrr)rrrirrrrcrcrf_from_temp_datasets zDataArray._from_temp_dataset)rrbcsxfddfddD}|fddjDtjh}tj|}tj|||j d}|S)z&splits dataarray along dimension 'dim'csj||i}i|_t|Srj)locrr<)rlabelarrayrrcrfsubset*sz+DataArray._to_dataset_split..subsetcsi|]}||qSrcrc)rdr)rrrcrfr/sz/DataArray._to_dataset_split..csi|]\}}|kr||qSrcrcrrrcrfr0s)rr) get_indexupdaterr|rr+rr&rr)rr variables coord_namesrrrc)rrrrf_to_dataset_split's  zDataArray._to_dataset_splitT)rirrbcCs|dkr|j}|dkrtd||jkr0td|j}|j||<|rf|D]}||jdd||<qL|j}t|j}tj |||dS)NzSunable to convert unnamed DataArray to a Dataset without providing an explicit namezUcannot create a Dataset from a DataArray with the same name as one of its coordinatesFdeepr) rirrrrrrrrr&r)rrirrrrrrcrcrfr8s$    zDataArray._to_dataset_whole)ri promote_attrs)rrirrbcCsb|dk r ||jkr t|d|dk rD|dk r8td||}n ||}|r^t|j|_|S)aConvert a DataArray to a Dataset. 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Use .rename() or .swap_dims() instead.)AttributeErrorrrcrcrfrnszMapping[Hashable, Any])rrbcCs,t|r|St||j}tt|j|Srj)rrprrrr}ryrnrrrcrcrf_item_key_to_dict&s zDataArray._item_key_to_dict)rrrbcCsdddlm}z|j|}Wn:tk rTtt|j|j}||j||\}}}YnX|j||dS)Nr)_get_virtual_variablerh) xarray.core.datasetrrKeyErrorr}ryrnrr)rrrrZ dim_sizes_rcrcrf_getitem_coord,s zDataArray._getitem_coordcCs*t|tr||S|j||dSdS)N)indexers)rsrtriselrrrcrcrfr7s  zDataArray.__getitem__)rrrbcCs\t|tr||j|<nB||}t|tr6t||jjdd||D}||j|<dS)NcSs&i|]\}}|t|tr|jn|qSrc)rsr_rrrcrcrfrIsz)DataArray.__setitem__..) rsrtrr_r%rrr|r)rrrrrcrcrfr>s    zDataArray.__setitem__cCs |j|=dSrjrrrcrcrf __delitem__OszDataArray.__delitem__z Iterable[Mapping[Hashable, Any]]ccs|jEdH|jVdS)z2Places to look-up items for attribute-style accessN) _item_sourcesrrrcrcrf _attr_sourcesRs zDataArray._attr_sourcesccs&t|j|jdVt|jidVdS)z*Places to look-up items for key-completion)rwmappingN)r5rrrnrrcrcrfrXszDataArray._item_sourcescCs ||jkSrjrrrcrcrf __contains__aszDataArray.__contains__rcCst|S)z2Attribute for location based indexing like pandas.)rrrcrcrfrdsz DataArray.loczdict[Any, Any]cCs|jjS)z6Dictionary storing arbitrary metadata with this array.)rrrrcrcrfriszDataArray.attrszMapping[Any, Any]cCst||j_dSrj)r}rrrrcrcrfrnscCs|jjS)zWDictionary of format-specific settings for how this array should be serialized.)rencodingrrcrcrfrrszDataArray.encodingcCst||j_dSrj)r}rrrrcrcrfrxsr)cCs |jS)zMapping of pandas.Index objects used for label based indexing. Raises an error if this Dataset has indexes that cannot be coerced to pandas.Index objects. See Also -------- DataArray.xindexes )xindexesZto_pandas_indexesrrcrcrfr|s zDataArray.indexescstjfddjDS)z>Mapping of xarray Index objects used for label based indexing.csi|]}|j|qSrc)rrrrcrfrsz&DataArray.xindexes..)r)rrrcrrfrszDataArray.xindexesr$cCst|S)z/Dictionary-like container of coordinate arrays.)r$rrcrcrfrszDataArray.coordsrOzLiteral[False])rnamesdroprbcCsdSrjrcrr r rcrcrf reset_coordsszDataArray.reset_coordsz Literal[True]cCsdSrjrcr rcrcrfr szT_DataArray | DatasetcCsb|dkrt|jt|j}|j||}|r@|j|jdS|jdkrRtd|j ||j<|S)a3 Given names of coordinates, reset them to become variables. Parameters ---------- names : str, Iterable of Hashable or None, optional Name(s) of non-index coordinates in this dataset to reset into variables. By default, all non-index coordinates are reset. drop : bool, default: False If True, remove coordinates instead of converting them into variables. Returns ------- Dataset, or DataArray if ``drop == True`` Examples -------- >>> temperature = np.arange(25).reshape(5, 5) >>> pressure = np.arange(50, 75).reshape(5, 5) >>> da = xr.DataArray( ... data=temperature, ... dims=["x", "y"], ... coords=dict( ... lon=("x", np.arange(10, 15)), ... lat=("y", np.arange(20, 25)), ... Pressure=(["x", "y"], pressure), ... ), ... name="Temperature", ... ) >>> da 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]]) Coordinates: lon (x) int64 10 11 12 13 14 lat (y) int64 20 21 22 23 24 Pressure (x, y) int64 50 51 52 53 54 55 56 57 ... 67 68 69 70 71 72 73 74 Dimensions without coordinates: x, y Return Dataset with target coordinate as a data variable rather than a coordinate variable: >>> da.reset_coords(names="Pressure") Dimensions: (x: 5, y: 5) Coordinates: lon (x) int64 10 11 12 13 14 lat (y) int64 20 21 22 23 24 Dimensions without coordinates: x, y Data variables: Pressure (x, y) int64 50 51 52 53 54 55 56 57 ... 68 69 70 71 72 73 74 Temperature (x, y) int64 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 23 24 Return DataArray without targeted coordinate: >>> da.reset_coords(names="Pressure", drop=True) 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]]) Coordinates: lon (x) int64 10 11 12 13 14 lat (y) int64 20 21 22 23 24 Dimensions without coordinates: x, y Nrz>> array = xr.DataArray([1, 2, 3], dims="x", coords={"x": ["a", "b", "c"]}) >>> array.copy() array([1, 2, 3]) Coordinates: * x (x) >> array_0 = array.copy(deep=False) >>> array_0[0] = 7 >>> array_0 array([7, 2, 3]) Coordinates: * x (x) >> array array([7, 2, 3]) Coordinates: * x (x) >> array.copy(data=[0.1, 0.2, 0.3]) array([0.1, 0.2, 0.3]) Coordinates: * x (x) >> array array([7, 2, 3]) Coordinates: * x (x) .)rrrr!)rZ all_variablesrcrcrf chunksizesszDataArray.chunksizeszxarray-zrint | Literal['auto'] | tuple[int, ...] | tuple[tuple[int, ...], ...] | Mapping[Any, None | int | tuple[int, ...]]rtz str | None)rr' name_prefixtokenlock inline_array chunks_kwargsrbcKs|dkrtjdtdi}t|tttfr:t|j |}n,t|t t frZtt |j |}n t ||d}|j|||||d}||S)aCoerce this array's data into a dask arrays with the given chunks. If this variable is a non-dask array, it will be converted to dask array. If it's a dask array, it will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along that dimension will not be updated; non-dask arrays will be converted into dask arrays with a single block. Parameters ---------- chunks : int, "auto", tuple of int or mapping of Hashable to int, optional Chunk sizes along each dimension, e.g., ``5``, ``"auto"``, ``(5, 5)`` or ``{"x": 5, "y": 5}``. name_prefix : str, optional Prefix for the name of the new dask array. token : str, optional Token uniquely identifying this array. lock : optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. inline_array: optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. **chunks_kwargs : {dim: chunks, ...}, optional The keyword arguments form of ``chunks``. One of chunks or chunks_kwargs must be provided. Returns ------- chunked : xarray.DataArray See Also -------- DataArray.chunks DataArray.chunksizes xarray.unify_chunks dask.array.from_array Nz]None value for 'chunks' is deprecated. It will raise an error in the future. Use instead '{}'categorychunk)r,r-r.r/)warningswarn FutureWarningrsfloatrtrr}fromkeysrnrzrvryr8rr3r)rr'r,r-r.r/r0rrcrcrfr3s&7 zDataArray.chunkraiserQ)rrr  missing_dimsindexers_kwargsrbc  st||d}tdd|Dr@|j|||d}||S|jj||d}t|j |\}}i} |j D]V\} | |kr|| n4fdd| D} | r| |rj dkrqn| | <qn|j || |d S) aReturn a new DataArray whose data is given by selecting indexes along the specified dimension(s). Parameters ---------- indexers : dict, optional A dict with keys matching dimensions and values given by integers, slice objects or arrays. indexer can be a integer, slice, array-like or DataArray. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See :ref:`indexing` for the details. One of indexers or indexers_kwargs must be provided. drop : bool, default: False If ``drop=True``, drop coordinates variables indexed by integers instead of making them scalar. missing_dims : {"raise", "warn", "ignore"}, default: "raise" What to do if dimensions that should be selected from are not present in the DataArray: - "raise": raise an exception - "warn": raise a warning, and ignore the missing dimensions - "ignore": ignore the missing dimensions **indexers_kwargs : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. Returns ------- indexed : xarray.DataArray See Also -------- Dataset.isel DataArray.sel Examples -------- >>> da = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y")) >>> da 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 >>> tgt_x = xr.DataArray(np.arange(0, 5), dims="points") >>> tgt_y = xr.DataArray(np.arange(0, 5), dims="points") >>> da = da.isel(x=tgt_x, y=tgt_y) >>> da array([ 0, 6, 12, 18, 24]) Dimensions without coordinates: points rcss|]}t|VqdSrj)r-)rdidxrcrcrfrovsz!DataArray.isel..)r r:)r:cs i|]\}}|jkr||qSrcrmrZ coord_valuercrfrs z"DataArray.isel..rr)r8r~rrZ _isel_fancyrrrr,rrr|rr) rrr r:r;rrrZindex_variablesrZ coord_nameZcoord_indexersrcr=rfr7s.=      zDataArray.isel)rrmethodr r;rbcKs(|jf||||d|}||S)aReturn a new DataArray whose data is given by selecting index labels along the specified dimension(s). In contrast to `DataArray.isel`, indexers for this method should use labels instead of integers. Under the hood, this method is powered by using pandas's powerful Index objects. This makes label based indexing essentially just as fast as using integer indexing. It also means this method uses pandas's (well documented) logic for indexing. This means you can use string shortcuts for datetime indexes (e.g., '2000-01' to select all values in January 2000). It also means that slices are treated as inclusive of both the start and stop values, unlike normal Python indexing. .. warning:: Do not try to assign values when using any of the indexing methods ``isel`` or ``sel``:: da = xr.DataArray([0, 1, 2, 3], dims=['x']) # DO NOT do this da.isel(x=[0, 1, 2])[1] = -1 Assigning values with the chained indexing using ``.sel`` or ``.isel`` fails silently. Parameters ---------- indexers : dict, optional A dict with keys matching dimensions and values given by scalars, slices or arrays of tick labels. For dimensions with multi-index, the indexer may also be a dict-like object with keys matching index level names. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See :ref:`indexing` for the details. One of indexers or indexers_kwargs must be provided. method : {None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional Method to use for inexact matches: - None (default): only exact matches - pad / ffill: propagate last valid index value forward - backfill / bfill: propagate next valid index value backward - nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. drop : bool, optional If ``drop=True``, drop coordinates variables in `indexers` instead of making them scalar. **indexers_kwargs : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. One of indexers or indexers_kwargs must be provided. Returns ------- obj : DataArray A new DataArray with the same contents as this DataArray, except the data and each dimension is indexed by the appropriate indexers. If indexer DataArrays have coordinates that do not conflict with this object, then these coordinates will be attached. In general, each array's data will be a view of the array's data in this DataArray, unless vectorized indexing was triggered by using an array indexer, in which case the data will be a copy. See Also -------- Dataset.sel DataArray.isel Examples -------- >>> da = xr.DataArray( ... np.arange(25).reshape(5, 5), ... coords={"x": np.arange(5), "y": np.arange(5)}, ... dims=("x", "y"), ... ) >>> da 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]]) Coordinates: * x (x) int64 0 1 2 3 4 * y (y) int64 0 1 2 3 4 >>> tgt_x = xr.DataArray(np.linspace(0, 4, num=5), dims="points") >>> tgt_y = xr.DataArray(np.linspace(0, 4, num=5), dims="points") >>> da = da.sel(x=tgt_x, y=tgt_y, method="nearest") >>> da array([ 0, 6, 12, 18, 24]) Coordinates: x (points) int64 0 1 2 3 4 y (points) int64 0 1 2 3 4 Dimensions without coordinates: points )rr r> tolerance)rrr)rrr>r?r r;rrcrcrfrsn z DataArray.selzMapping[Any, int] | int | None)rrr;rbcKs|j|f|}||S)aReturn a new DataArray whose data is given by the the first `n` values along the specified dimension(s). Default `n` = 5 See Also -------- Dataset.head DataArray.tail DataArray.thin Examples -------- >>> da = xr.DataArray( ... np.arange(25).reshape(5, 5), ... dims=("x", "y"), ... ) >>> da 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 >>> da.head(x=1) array([[0, 1, 2, 3, 4]]) Dimensions without coordinates: x, y >>> da.head({"x": 2, "y": 2}) array([[0, 1], [5, 6]]) Dimensions without coordinates: x, y )rheadrrrr;rrcrcrfr@ s(zDataArray.headcKs|j|f|}||S)a2Return a new DataArray whose data is given by the the last `n` values along the specified dimension(s). Default `n` = 5 See Also -------- Dataset.tail DataArray.head DataArray.thin Examples -------- >>> da = xr.DataArray( ... np.arange(25).reshape(5, 5), ... dims=("x", "y"), ... ) >>> da 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 >>> da.tail(y=1) array([[ 4], [ 9], [14], [19], [24]]) Dimensions without coordinates: x, y >>> da.tail({"x": 2, "y": 2}) array([[18, 19], [23, 24]]) Dimensions without coordinates: x, y )rtailrrArcrcrfrB4s,zDataArray.tailcKs|j|f|}||S)a:Return a new DataArray whose data is given by each `n` value along the specified dimension(s). Examples -------- >>> x_arr = np.arange(0, 26) >>> x_arr 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, 25]) >>> x = xr.DataArray( ... np.reshape(x_arr, (2, 13)), ... dims=("x", "y"), ... coords={"x": [0, 1], "y": np.arange(0, 13)}, ... ) >>> x 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, 25]]) Coordinates: * x (x) int64 0 1 * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 >>> >>> x.thin(3) array([[ 0, 3, 6, 9, 12]]) Coordinates: * x (x) int64 0 * y (y) int64 0 3 6 9 12 >>> x.thin({"x": 2, "y": 5}) array([[ 0, 5, 10]]) Coordinates: * x (x) int64 0 * y (y) int64 0 5 10 See Also -------- Dataset.thin DataArray.head DataArray.tail )rthinrrArcrcrfrCcs/zDataArray.thinzDataArray | DatasetzIterable[Hashable] | None)rotherexcluderbcCsP|dkrt}nt|}t||dd|d}t||\}}ttd|d|||S)aGBroadcast this DataArray against another Dataset or DataArray. This is equivalent to xr.broadcast(other, self)[1] xarray objects are broadcast against each other in arithmetic operations, so this method is not be necessary for most uses. If no change is needed, the input data is returned to the output without being copied. If new coords are added by the broadcast, their values are NaN filled. Parameters ---------- other : Dataset or DataArray Object against which to broadcast this array. exclude : iterable of Hashable, optional Dimensions that must not be broadcasted Returns ------- new_da : DataArray The caller broadcasted against ``other``. Examples -------- >>> arr1 = xr.DataArray( ... np.random.randn(2, 3), ... dims=("x", "y"), ... coords={"x": ["a", "b"], "y": ["a", "b", "c"]}, ... ) >>> arr2 = xr.DataArray( ... np.random.randn(3, 2), ... dims=("x", "y"), ... coords={"x": ["a", "b", "c"], "y": ["a", "b"]}, ... ) >>> arr1 array([[ 1.76405235, 0.40015721, 0.97873798], [ 2.2408932 , 1.86755799, -0.97727788]]) Coordinates: * x (x) >> arr2 array([[ 0.95008842, -0.15135721], [-0.10321885, 0.4105985 ], [ 0.14404357, 1.45427351]]) Coordinates: * x (x) >> arr1.broadcast_like(arr2) array([[ 1.76405235, 0.40015721, 0.97873798], [ 2.2408932 , 1.86755799, -0.97727788], [ nan, nan, nan]]) Coordinates: * x (x) r?rrbcCstj||||||dS)uConform this object onto the indexes of another object, filling in missing values with ``fill_value``. The default fill value is NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordinates upon which to index the variables in this dataset. The indexes on this other object need not be the same as the indexes on this dataset. Any mis-matched index values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional Method to use for filling index values from other not found on this data array: - None (default): don't fill gaps - pad / ffill: propagate last valid index value forward - backfill / bfill: propagate next valid index value backward - nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like must be the same size as the index and its dtype must exactly match the index’s type. copy : bool, default: True If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. fill_value : scalar or dict-like, optional Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values. Use this data array's name to refer to the data array's values. Returns ------- reindexed : DataArray Another dataset array, with this array's data but coordinates from the other object. Examples -------- >>> data = np.arange(12).reshape(4, 3) >>> da1 = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]}, ... ) >>> da1 array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) int64 10 20 30 40 * y (y) int64 70 80 90 >>> da2 = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [40, 30, 20, 10], "y": [90, 80, 70]}, ... ) >>> da2 array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) int64 40 30 20 10 * y (y) int64 90 80 70 Reindexing with both DataArrays having the same coordinates set, but in different order: >>> da1.reindex_like(da2) array([[11, 10, 9], [ 8, 7, 6], [ 5, 4, 3], [ 2, 1, 0]]) Coordinates: * x (x) int64 40 30 20 10 * y (y) int64 90 80 70 Reindexing with the other array having coordinates which the source array doesn't have: >>> data = np.arange(12).reshape(4, 3) >>> da1 = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]}, ... ) >>> da2 = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [20, 10, 29, 39], "y": [70, 80, 90]}, ... ) >>> da1.reindex_like(da2) array([[ 3., 4., 5.], [ 0., 1., 2.], [nan, nan, nan], [nan, nan, nan]]) Coordinates: * x (x) int64 20 10 29 39 * y (y) int64 70 80 90 Filling missing values with the previous valid index with respect to the coordinates' value: >>> da1.reindex_like(da2, method="ffill") array([[3, 4, 5], [0, 1, 2], [3, 4, 5], [6, 7, 8]]) Coordinates: * x (x) int64 20 10 29 39 * y (y) int64 70 80 90 Filling missing values while tolerating specified error for inexact matches: >>> da1.reindex_like(da2, method="ffill", tolerance=5) array([[ 3., 4., 5.], [ 0., 1., 2.], [nan, nan, nan], [nan, nan, nan]]) Coordinates: * x (x) int64 20 10 29 39 * y (y) int64 70 80 90 Filling missing values with manually specified values: >>> da1.reindex_like(da2, fill_value=19) array([[ 3, 4, 5], [ 0, 1, 2], [19, 19, 19], [19, 19, 19]]) Coordinates: * x (x) int64 20 10 29 39 * y (y) int64 70 80 90 See Also -------- DataArray.reindex align )rDr>r?rrK)r reindex_like)rrDr>r?rrKrcrcrfrPs"zDataArray.reindex_likezfloat | Iterable[float] | None)rrr>r?rr;rbcKs$t||d}tj||||||dS)u< Conform this object onto the indexes of another object, filling in missing values with ``fill_value``. The default fill value is NaN. Parameters ---------- indexers : dict, optional Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. One of indexers or indexers_kwargs must be provided. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values in ``indexers`` not found on this data array: - None (default): don't fill gaps - pad / ffill: propagate last valid index value forward - backfill / bfill: propagate next valid index value backward - nearest: use nearest valid index value tolerance : float | Iterable[float] | None, default: None Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like must be the same size as the index and its dtype must exactly match the index’s type. fill_value : scalar or dict-like, optional Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values. Use this data array's name to refer to the data array's values. **indexers_kwargs : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. One of indexers or indexers_kwargs must be provided. Returns ------- reindexed : DataArray Another dataset array, with this array's data but replaced coordinates. Examples -------- Reverse latitude: >>> da = xr.DataArray( ... np.arange(4), ... coords=[np.array([90, 89, 88, 87])], ... dims="lat", ... ) >>> da array([0, 1, 2, 3]) Coordinates: * lat (lat) int64 90 89 88 87 >>> da.reindex(lat=da.lat[::-1]) array([3, 2, 1, 0]) Coordinates: * lat (lat) int64 87 88 89 90 See Also -------- DataArray.reindex_like align reindex)rr>r?rrK)rr8rrQ)rrr>r?rrKr;rcrcrfrQsQzDataArray.reindexlinearrRzMapping[str, Any] | None)rrr> assume_sortedr coords_kwargsrbcKsD|jjdkrtd|j|j|f|||d|}||S)a*Interpolate a DataArray onto new coordinates Performs univariate or multivariate interpolation of a DataArray onto new coordinates using scipy's interpolation routines. If interpolating along an existing dimension, :py:class:`scipy.interpolate.interp1d` is called. When interpolating along multiple existing dimensions, an attempt is made to decompose the interpolation into multiple 1-dimensional interpolations. If this is possible, :py:class:`scipy.interpolate.interp1d` is called. Otherwise, :py:func:`scipy.interpolate.interpn` is called. Parameters ---------- coords : dict, optional Mapping from dimension names to the new coordinates. New coordinate can be a scalar, array-like or DataArray. If DataArrays are passed as new coordinates, their dimensions are used for the broadcasting. Missing values are skipped. method : {"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. assume_sorted : bool, default: False If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values. kwargs : dict-like or None, default: None Additional keyword arguments passed to scipy's interpolator. Valid options and their behavior depend whether ``interp1d`` or ``interpn`` is used. **coords_kwargs : {dim: coordinate, ...}, optional The keyword arguments form of ``coords``. One of coords or coords_kwargs must be provided. Returns ------- interpolated : DataArray New dataarray on the new coordinates. Notes ----- scipy is required. See Also -------- scipy.interpolate.interp1d scipy.interpolate.interpn Examples -------- >>> da = xr.DataArray( ... data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]], ... dims=("x", "y"), ... coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]}, ... ) >>> da array([[ 1., 4., 2., 9.], [ 2., 7., 6., nan], [ 6., nan, 5., 8.]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 10 12 14 16 1D linear interpolation (the default): >>> da.interp(x=[0, 0.75, 1.25, 1.75]) array([[1. , 4. , 2. , nan], [1.75, 6.25, 5. , nan], [3. , nan, 5.75, nan], [5. , nan, 5.25, nan]]) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 0.0 0.75 1.25 1.75 1D nearest interpolation: >>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest") array([[ 1., 4., 2., 9.], [ 2., 7., 6., nan], [ 2., 7., 6., nan], [ 6., nan, 5., 8.]]) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 0.0 0.75 1.25 1.75 1D linear extrapolation: >>> da.interp( ... x=[1, 1.5, 2.5, 3.5], ... method="linear", ... kwargs={"fill_value": "extrapolate"}, ... ) array([[ 2. , 7. , 6. , nan], [ 4. , nan, 5.5, nan], [ 8. , nan, 4.5, nan], [12. , nan, 3.5, nan]]) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 1.0 1.5 2.5 3.5 2D linear interpolation: >>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear") array([[2.5 , 3. , nan], [4. , 5.625, nan], [ nan, nan, nan], [ nan, nan, nan]]) Coordinates: * x (x) float64 0.0 0.75 1.25 1.75 * y (y) int64 11 13 15 uifc5interp only works for a numeric type array. Given {}.r>rrS)rkindr{formatrinterpr)rrr>rSrrTrrcrcrfrZs" zDataArray.interp)rrDr>rSrrbcCs<|jjdkrtd|j|j||||d}||S)aInterpolate this object onto the coordinates of another object, filling out of range values with NaN. If interpolating along a single existing dimension, :py:class:`scipy.interpolate.interp1d` is called. When interpolating along multiple existing dimensions, an attempt is made to decompose the interpolation into multiple 1-dimensional interpolations. If this is possible, :py:class:`scipy.interpolate.interp1d` is called. Otherwise, :py:func:`scipy.interpolate.interpn` is called. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to an 1d array-like, which provides coordinates upon which to index the variables in this dataset. Missing values are skipped. method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"}, default: "linear" The method used to interpolate. The method should be supported by the scipy interpolator: - {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"} when ``interp1d`` is called. - {"linear", "nearest"} when ``interpn`` is called. If ``"polynomial"`` is passed, the ``order`` keyword argument must also be provided. assume_sorted : bool, default: False If False, values of coordinates that are interpolated over can be in any order and they are sorted first. If True, interpolated coordinates are assumed to be an array of monotonically increasing values. kwargs : dict, optional Additional keyword passed to scipy's interpolator. Returns ------- interpolated : DataArray Another dataarray by interpolating this dataarray's data along the coordinates of the other object. Examples -------- >>> data = np.arange(12).reshape(4, 3) >>> da1 = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]}, ... ) >>> da1 array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) int64 10 20 30 40 * y (y) int64 70 80 90 >>> da2 = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [10, 20, 29, 39], "y": [70, 80, 90]}, ... ) >>> da2 array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) int64 10 20 29 39 * y (y) int64 70 80 90 Interpolate the values in the coordinates of the other DataArray with respect to the source's values: >>> da2.interp_like(da1) array([[0. , 1. , 2. ], [3. , 4. , 5. ], [6.3, 7.3, 8.3], [nan, nan, nan]]) Coordinates: * x (x) int64 10 20 30 40 * y (y) int64 70 80 90 Could also extrapolate missing values: >>> da2.interp_like(da1, kwargs={"fill_value": "extrapolate"}) array([[ 0. , 1. , 2. ], [ 3. , 4. , 5. ], [ 6.3, 7.3, 8.3], [ 9.3, 10.3, 11.3]]) Coordinates: * x (x) int64 10 20 30 40 * y (y) int64 70 80 90 Notes ----- scipy is required. If the dataarray has object-type coordinates, reindex is used for these coordinates instead of the interpolation. See Also -------- DataArray.interp DataArray.reindex_like rUrVrW)rrXr{rYr interp_liker)rrDr>rSrrrcrcrfr[sr zDataArray.interp_likez(Hashable | Mapping[Any, Hashable] | None)new_name_or_name_dictr rbcKs|dkr|s|jddSt|s*|dkrNt||d}||}||St|r|r||}||}|j|dS|j|dS)a8Returns a new DataArray with renamed coordinates, dimensions or a new name. Parameters ---------- new_name_or_name_dict : str or dict-like, optional If the argument is dict-like, it used as a mapping from old names to new names for coordinates or dimensions. Otherwise, use the argument as the new name for this array. **names : Hashable, optional The keyword arguments form of a mapping from old names to new names for coordinates or dimensions. One of new_name_or_name_dict or names must be provided. Returns ------- renamed : DataArray Renamed array or array with renamed coordinates. See Also -------- Dataset.rename DataArray.swap_dims Nrhrename)rrrpr8rZ_renamerZhashable)rr\r Z name_dictrZ dataarrayrcrcrfr] s      zDataArray.renamezMapping[Any, Hashable] | None)r dims_dictrbcKs$t||d}||}||S)amReturns a new DataArray with swapped dimensions. Parameters ---------- dims_dict : dict-like Dictionary whose keys are current dimension names and whose values are new names. **dims_kwargs : {existing_dim: new_dim, ...}, optional The keyword arguments form of ``dims_dict``. One of dims_dict or dims_kwargs must be provided. Returns ------- swapped : DataArray DataArray with swapped dimensions. Examples -------- >>> arr = xr.DataArray( ... data=[0, 1], ... dims="x", ... coords={"x": ["a", "b"], "y": ("x", [0, 1])}, ... ) >>> arr array([0, 1]) Coordinates: * x (x) >> arr.swap_dims({"x": "y"}) array([0, 1]) Coordinates: x (y) >> arr.swap_dims({"x": "z"}) array([0, 1]) Coordinates: x (z) >> da = xr.DataArray(np.arange(5), dims=("x")) >>> da array([0, 1, 2, 3, 4]) Dimensions without coordinates: x Add new dimension of length 2: >>> da.expand_dims(dim={"y": 2}) array([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]) Dimensions without coordinates: y, x >>> da.expand_dims(dim={"y": 2}, axis=1) array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4]]) Dimensions without coordinates: x, y Add a new dimension with coordinates from array: >>> da.expand_dims(dim={"y": np.arange(5)}, axis=0) array([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]) Coordinates: * y (y) int64 0 1 2 3 4 Dimensions without coordinates: x z7dim should be Hashable or sequence/mapping of Hashablesz)dims should not contain duplicate values.rN expand_dims)rsrr{r rtrqrrrr}r8r r rr8rrbr)rrr`rarrcrcrfrb~ sQ   zDataArray.expand_dimsz2Mapping[Any, Hashable | Sequence[Hashable]] | NonezHashable | Sequence[Hashable])rappendindexes_kwargsrbcKs$|j|fd|i|}||S)a;Set DataArray (multi-)indexes using one or more existing coordinates. This legacy method is limited to pandas (multi-)indexes and 1-dimensional "dimension" coordinates. See :py:meth:`~DataArray.set_xindex` for setting a pandas or a custom Xarray-compatible index from one or more arbitrary coordinates. Parameters ---------- indexes : {dim: index, ...} Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. append : bool, default: False If True, append the supplied index(es) to the existing index(es). Otherwise replace the existing index(es). **indexes_kwargs : optional The keyword arguments form of ``indexes``. One of indexes or indexes_kwargs must be provided. Returns ------- obj : DataArray Another DataArray, with this data but replaced coordinates. Examples -------- >>> arr = xr.DataArray( ... data=np.ones((2, 3)), ... dims=["x", "y"], ... coords={"x": range(2), "y": range(3), "a": ("x", [3, 4])}, ... ) >>> arr array([[1., 1., 1.], [1., 1., 1.]]) Coordinates: * x (x) int64 0 1 * y (y) int64 0 1 2 a (x) int64 3 4 >>> arr.set_index(x="a") array([[1., 1., 1.], [1., 1., 1.]]) Coordinates: * x (x) int64 3 4 * y (y) int64 0 1 2 See Also -------- DataArray.reset_index DataArray.set_xindex rc)r set_indexr)rrrcrdrrcrcrfre s<zDataArray.set_index)dims_or_levelsr rbcCs|j||d}||S)aReset the specified index(es) or multi-index level(s). This legacy method is specific to pandas (multi-)indexes and 1-dimensional "dimension" coordinates. See the more generic :py:meth:`~DataArray.drop_indexes` and :py:meth:`~DataArray.set_xindex` method to respectively drop and set pandas or custom indexes for arbitrary coordinates. Parameters ---------- dims_or_levels : Hashable or sequence of Hashable Name(s) of the dimension(s) and/or multi-index level(s) that will be reset. drop : bool, default: False If True, remove the specified indexes and/or multi-index levels instead of extracting them as new coordinates (default: False). Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. See Also -------- DataArray.set_index DataArray.set_xindex DataArray.drop_indexes r )r reset_indexr)rrfr rrcrcrfrh s"zDataArray.reset_indexzstr | Sequence[Hashable]ztype[Index] | None)rr index_clsrbcKs|j||f|}||S)aSet a new, Xarray-compatible index from one or more existing coordinate(s). Parameters ---------- coord_names : str or list Name(s) of the coordinate(s) used to build the index. If several names are given, their order matters. index_cls : subclass of :class:`~xarray.indexes.Index` The type of index to create. By default, try setting a pandas (multi-)index from the supplied coordinates. **options Options passed to the index constructor. Returns ------- obj : DataArray Another dataarray, with this dataarray's data and with a new index. )r set_xindexr)rrrioptionsrrcrcrfrjD szDataArray.set_xindexz-Mapping[Any, Sequence[int | Hashable]] | NonezSequence[int | Hashable])r dim_orderdim_order_kwargsrbcKs|j|f|}||S)aRearrange index levels using input order. Parameters ---------- dim_order dict-like of Hashable to int or Hashable: optional Mapping from names matching dimensions and values given by lists representing new level orders. Every given dimension must have a multi-index. **dim_order_kwargs : optional The keyword arguments form of ``dim_order``. One of dim_order or dim_order_kwargs must be provided. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. )rreorder_levelsr)rrlrmrrcrcrfrna szDataArray.reorder_levelsz'Mapping[Any, Sequence[Hashable]] | Nonez bool | Nonez type[Index]zSequence[Hashable])r dimensions create_indexridimensions_kwargsrbcKs&|j|f||d|}||S)a Stack any number of existing dimensions into a single new dimension. New dimensions will be added at the end, and the corresponding coordinate variables will be combined into a MultiIndex. Parameters ---------- dimensions : mapping of Hashable to sequence of Hashable Mapping of the form `new_name=(dim1, dim2, ...)`. Names of new dimensions, and the existing dimensions that they replace. An ellipsis (`...`) will be replaced by all unlisted dimensions. Passing a list containing an ellipsis (`stacked_dim=[...]`) will stack over all dimensions. create_index : bool or None, default: True If True, create a multi-index for each of the stacked dimensions. If False, don't create any index. If None, create a multi-index only if exactly one single (1-d) coordinate index is found for every dimension to stack. index_cls: class, optional Can be used to pass a custom multi-index type. Must be an Xarray index that implements `.stack()`. By default, a pandas multi-index wrapper is used. **dimensions_kwargs The keyword arguments form of ``dimensions``. One of dimensions or dimensions_kwargs must be provided. Returns ------- stacked : DataArray DataArray with stacked data. Examples -------- >>> arr = xr.DataArray( ... np.arange(6).reshape(2, 3), ... coords=[("x", ["a", "b"]), ("y", [0, 1, 2])], ... ) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) >> stacked = arr.stack(z=("x", "y")) >>> stacked.indexes["z"] MultiIndex([('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)], name='z') See Also -------- DataArray.unstack )rpri)rstackr)rrorprirqrrcrcrfrr{ sAzDataArray.stack)rrKsparserbcCs||||}||S)a Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. New dimensions will be added at the end. Parameters ---------- dim : str, Iterable of Hashable or None, optional Dimension(s) over which to unstack. By default unstacks all MultiIndexes. fill_value : scalar or dict-like, default: nan Value to be filled. If a dict-like, maps variable names to fill values. Use the data array's name to refer to its name. If not provided or if the dict-like does not contain all variables, the dtype's NA value will be used. sparse : bool, default: False Use sparse-array if True Returns ------- unstacked : DataArray Array with unstacked data. Examples -------- >>> arr = xr.DataArray( ... np.arange(6).reshape(2, 3), ... coords=[("x", ["a", "b"]), ("y", [0, 1, 2])], ... ) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) >> stacked = arr.stack(z=("x", "y")) >>> stacked.indexes["z"] MultiIndex([('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)], name='z') >>> roundtripped = stacked.unstack() >>> arr.identical(roundtripped) True See Also -------- DataArray.stack )runstackr)rrrKrsrrcrcrfrt s<zDataArray.unstackrzint | Hashable)rlevelrbc Cs||j|}t|tjs*td|d||}|j|}|j|}i}|D]"}|j ||iddj dd||<qPt |S)aUnstack DataArray expanding to Dataset along a given level of a stacked coordinate. This is the inverse operation of Dataset.to_stacked_array. Parameters ---------- dim : Hashable Name of existing dimension to unstack level : int or Hashable, default: 0 The MultiIndex level to expand to a dataset along. Can either be the integer index of the level or its name. Returns ------- unstacked: Dataset Examples -------- >>> arr = xr.DataArray( ... np.arange(6).reshape(2, 3), ... coords=[("x", ["a", "b"]), ("y", [0, 1, 2])], ... ) >>> data = xr.Dataset({"a": arr, "b": arr.isel(y=0)}) >>> data Dimensions: (x: 2, y: 3) Coordinates: * x (x) >> stacked = data.to_stacked_array("z", ["x"]) >>> stacked.indexes["z"] MultiIndex([('a', 0.0), ('a', 1.0), ('a', 2.0), ('b', nan)], name='z') >>> roundtripped = stacked.to_unstacked_dataset(dim="z") >>> data.identical(roundtripped) True See Also -------- Dataset.to_stacked_array 'z' is not a stacked coordinateTrg) rZto_pandas_indexrsrZ MultiIndexrrZ_get_level_numberlevelsr rsqueezer&) rrrur< level_numberrZ variable_dimZ data_dictrrcrcrfto_unstacked_dataset s1     zDataArray.to_unstacked_dataset)transpose_coordsr:)rrnr{r:rbcs|rtt||j|}|jj|}|rri}|jD].\}tfdd|D}jj|||<q6|||S||SdS)aqReturn a new DataArray object with transposed dimensions. Parameters ---------- *dims : Hashable, optional By default, reverse the dimensions. Otherwise, reorder the dimensions to this order. transpose_coords : bool, default: True If True, also transpose the coordinates of this DataArray. missing_dims : {"raise", "warn", "ignore"}, default: "raise" What to do if dimensions that should be selected from are not present in the DataArray: - "raise": raise an exception - "warn": raise a warning, and ignore the missing dimensions - "ignore": ignore the missing dimensions Returns ------- transposed : DataArray The returned DataArray's array is transposed. Notes ----- This operation returns a view of this array's data. It is lazy for dask-backed DataArrays but not for numpy-backed DataArrays -- the data will be fully loaded. See Also -------- numpy.transpose Dataset.transpose c3s|]}|jkr|VqdSrjrmrdrrrcrfror s z&DataArray.transpose..N) rzrZ infix_dimsrnr transposerr|r)rr{r:rnrrriZ coord_dimsrcr}rfr~F s&  zDataArray.transposecCs|Srj)r~rrcrcrfTx sz DataArray.TerrorszHashable | Iterable[Hashable]rP)r rrbcCs|j||d}||S)aReturns an array with dropped variables. Parameters ---------- names : Hashable or iterable of Hashable Name(s) of variables to drop. errors : {"raise", "ignore"}, default: "raise" If 'raise', raises a ValueError error if any of the variable passed are not in the dataset. If 'ignore', any given names that are in the DataArray are dropped and no error is raised. Returns ------- dropped : Dataset New Dataset copied from `self` with variables removed. Examples ------- >>> data = np.arange(12).reshape(4, 3) >>> da = xr.DataArray( ... data=data, ... dims=["x", "y"], ... coords={"x": [10, 20, 30, 40], "y": [70, 80, 90]}, ... ) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) int64 10 20 30 40 * y (y) int64 70 80 90 Removing a single variable: >>> da.drop_vars("x") array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * y (y) int64 70 80 90 Dimensions without coordinates: x Removing a list of variables: >>> da.drop_vars(["x", "y"]) array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Dimensions without coordinates: x, y r)r drop_varsr)rr rrrcrcrfr~ s>zDataArray.drop_vars)rrrrbcCs|j||d}||S)a;Drop the indexes assigned to the given coordinates. Parameters ---------- coord_names : hashable or iterable of hashable Name(s) of the coordinate(s) for which to drop the index. errors : {"raise", "ignore"}, default: "raise" If 'raise', raises a ValueError error if any of the coordinates passed have no index or are not in the dataset. If 'ignore', no error is raised. Returns ------- dropped : DataArray A new dataarray with dropped indexes. r)r drop_indexesr)rrrrrcrcrfr szDataArray.drop_indexes)rrrrrbcKs&|j||fd|i|}||S)zBackward compatible method based on `drop_vars` and `drop_sel` Using either `drop_vars` or `drop_sel` is encouraged See Also -------- DataArray.drop_vars DataArray.drop_sel r)rr r)rrrr labels_kwargsrrcrcrfr  szDataArray.drop)rrrrbcKs6|st|trt||d}|j||d}||S)aDrop index labels from this DataArray. Parameters ---------- labels : mapping of Hashable to Any Index labels to drop errors : {"raise", "ignore"}, default: "raise" If 'raise', raises a ValueError error if any of the index labels passed are not in the dataset. If 'ignore', any given labels that are in the dataset are dropped and no error is raised. **labels_kwargs : {dim: label, ...}, optional The keyword arguments form of ``dim`` and ``labels`` Returns ------- dropped : DataArray Examples -------- >>> da = xr.DataArray( ... np.arange(25).reshape(5, 5), ... coords={"x": np.arange(0, 9, 2), "y": np.arange(0, 13, 3)}, ... dims=("x", "y"), ... ) >>> da 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]]) Coordinates: * x (x) int64 0 2 4 6 8 * y (y) int64 0 3 6 9 12 >>> da.drop_sel(x=[0, 2], y=9) array([[10, 11, 12, 14], [15, 16, 17, 19], [20, 21, 22, 24]]) Coordinates: * x (x) int64 4 6 8 * y (y) int64 0 3 6 12 >>> da.drop_sel({"x": 6, "y": [0, 3]}) array([[ 2, 3, 4], [ 7, 8, 9], [12, 13, 14], [22, 23, 24]]) Coordinates: * x (x) int64 0 2 4 8 * y (y) int64 6 9 12 r r)rsr}r8rdrop_selr)rrrrrrcrcrfr s> zDataArray.drop_sel)rrrbcKs&|}|jfd|i|}||S)a%Drop index positions from this DataArray. Parameters ---------- indexers : mapping of Hashable to Any or None, default: None Index locations to drop **indexers_kwargs : {dim: position, ...}, optional The keyword arguments form of ``dim`` and ``positions`` Returns ------- dropped : DataArray Raises ------ IndexError Examples -------- >>> da = xr.DataArray(np.arange(25).reshape(5, 5), dims=("X", "Y")) >>> da 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 >>> da.drop_isel(X=[0, 4], Y=2) array([[ 5, 6, 8, 9], [10, 11, 13, 14], [15, 16, 18, 19]]) Dimensions without coordinates: X, Y >>> da.drop_isel({"X": 3, "Y": 3}) array([[ 0, 1, 2, 4], [ 5, 6, 7, 9], [10, 11, 12, 14], [20, 21, 22, 24]]) Dimensions without coordinates: X, Y r)r drop_iselr)rrr;rrcrcrfr0 s/zDataArray.drop_iselr~zLiteral[('any', 'all')]z int | None)rrhowthreshrbcCs|j|||d}||S)a Returns a new array with dropped labels for missing values along the provided dimension. Parameters ---------- dim : Hashable Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supported. how : {"any", "all"}, default: "any" - any : if any NA values are present, drop that label - all : if all values are NA, drop that label thresh : int or None, default: None If supplied, require this many non-NA values. Returns ------- dropped : DataArray Examples -------- >>> temperature = [ ... [0, 4, 2, 9], ... [np.nan, np.nan, np.nan, np.nan], ... [np.nan, 4, 2, 0], ... [3, 1, 0, 0], ... ] >>> da = xr.DataArray( ... data=temperature, ... dims=["Y", "X"], ... coords=dict( ... lat=("Y", np.array([-20.0, -20.25, -20.50, -20.75])), ... lon=("X", np.array([10.0, 10.25, 10.5, 10.75])), ... ), ... ) >>> da array([[ 0., 4., 2., 9.], [nan, nan, nan, nan], [nan, 4., 2., 0.], [ 3., 1., 0., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 lon (X) float64 10.0 10.25 10.5 10.75 Dimensions without coordinates: Y, X >>> da.dropna(dim="Y", how="any") array([[0., 4., 2., 9.], [3., 1., 0., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.75 lon (X) float64 10.0 10.25 10.5 10.75 Dimensions without coordinates: Y, X Drop values only if all values along the dimension are NaN: >>> da.dropna(dim="Y", how="all") array([[ 0., 4., 2., 9.], [nan, 4., 2., 0.], [ 3., 1., 0., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.5 -20.75 lon (X) float64 10.0 10.25 10.5 10.75 Dimensions without coordinates: Y, X )rr)rdropnar)rrrrrrcrcrfrc sIzDataArray.dropna)rrrbcCs"t|rtdt||}|S)aFill missing values in this object. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value : scalar, ndarray or DataArray Used to fill all matching missing values in this array. If the argument is a DataArray, it is first aligned with (reindexed to) this array. Returns ------- filled : DataArray Examples -------- >>> da = xr.DataArray( ... np.array([1, 4, np.nan, 0, 3, np.nan]), ... dims="Z", ... coords=dict( ... Z=("Z", np.arange(6)), ... height=("Z", np.array([0, 10, 20, 30, 40, 50])), ... ), ... ) >>> da array([ 1., 4., nan, 0., 3., nan]) Coordinates: * Z (Z) int64 0 1 2 3 4 5 height (Z) int64 0 10 20 30 40 50 Fill all NaN values with 0: >>> da.fillna(0) array([1., 4., 0., 0., 3., 0.]) Coordinates: * Z (Z) int64 0 1 2 3 4 5 height (Z) int64 0 10 20 30 40 50 Fill NaN values with corresponding values in array: >>> da.fillna(np.array([2, 9, 4, 2, 8, 9])) array([1., 4., 4., 0., 3., 9.]) Coordinates: * Z (Z) int64 0 1 2 3 4 5 height (Z) int64 0 10 20 30 40 50 zDcannot provide fill value as a dictionary with fillna on a DataArray)rrpr{rfillna)rroutrcrcrfr s 6  zDataArray.fillnaz bool | strzMNone | int | float | str | pd.Timedelta | np.timedelta64 | datetime.timedelta) rrr>limituse_coordinatemax_gap keep_attrsrrbc Ks*ddlm}||f||||||d|S)aFill in NaNs by interpolating according to different methods. Parameters ---------- dim : Hashable or None, optional Specifies the dimension along which to interpolate. method : {"linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial", "barycentric", "krog", "pchip", "spline", "akima"}, default: "linear" String indicating which method to use for interpolation: - 'linear': linear interpolation. Additional keyword arguments are passed to :py:func:`numpy.interp` - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial': are passed to :py:func:`scipy.interpolate.interp1d`. If ``method='polynomial'``, the ``order`` keyword argument must also be provided. - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their respective :py:class:`scipy.interpolate` classes. use_coordinate : bool or str, default: True Specifies which index to use as the x values in the interpolation formulated as `y = f(x)`. If False, values are treated as if eqaully-spaced along ``dim``. If True, the IndexVariable `dim` is used. If ``use_coordinate`` is a string, it specifies the name of a coordinate variariable to use as the index. limit : int or None, default: None Maximum number of consecutive NaNs to fill. Must be greater than 0 or None for no limit. This filling is done regardless of the size of the gap in the data. To only interpolate over gaps less than a given length, see ``max_gap``. max_gap : int, float, str, pandas.Timedelta, numpy.timedelta64, datetime.timedelta, default: None Maximum size of gap, a continuous sequence of NaNs, that will be filled. Use None for no limit. When interpolating along a datetime64 dimension and ``use_coordinate=True``, ``max_gap`` can be one of the following: - a string that is valid input for pandas.to_timedelta - a :py:class:`numpy.timedelta64` object - a :py:class:`pandas.Timedelta` object - a :py:class:`datetime.timedelta` object Otherwise, ``max_gap`` must be an int or a float. Use of ``max_gap`` with unlabeled dimensions has not been implemented yet. Gap length is defined as the difference between coordinate values at the first data point after a gap and the last value before a gap. For gaps at the beginning (end), gap length is defined as the difference between coordinate values at the first (last) valid data point and the first (last) NaN. For example, consider:: array([nan, nan, nan, 1., nan, nan, 4., nan, nan]) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 The gap lengths are 3-0 = 3; 6-3 = 3; and 8-6 = 2 respectively keep_attrs : bool or None, default: None If True, the dataarray's attributes (`attrs`) will be copied from the original object to the new one. If False, the new object will be returned without attributes. **kwargs : dict, optional parameters passed verbatim to the underlying interpolation function Returns ------- interpolated: DataArray Filled in DataArray. See Also -------- numpy.interp scipy.interpolate Examples -------- >>> da = xr.DataArray( ... [np.nan, 2, 3, np.nan, 0], dims="x", coords={"x": [0, 1, 2, 3, 4]} ... ) >>> da array([nan, 2., 3., nan, 0.]) Coordinates: * x (x) int64 0 1 2 3 4 >>> da.interpolate_na(dim="x", method="linear") array([nan, 2. , 3. , 1.5, 0. ]) Coordinates: * x (x) int64 0 1 2 3 4 >>> da.interpolate_na(dim="x", method="linear", fill_value="extrapolate") array([1. , 2. , 3. , 1.5, 0. ]) Coordinates: * x (x) int64 0 1 2 3 4 r) interp_na)rr>rrrr)xarray.core.missingr) rrr>rrrrrrrcrcrfinterpolate_na so zDataArray.interpolate_na)rrrrbcCsddlm}||||dS)ae Fill NaN values by propagating values forward *Requires bottleneck.* Parameters ---------- dim : Hashable Specifies the dimension along which to propagate values when filling. limit : int or None, default: None The maximum number of consecutive NaN values to forward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Must be None or greater than or equal to axis length if filling along chunked axes (dimensions). Returns ------- filled : DataArray Examples -------- >>> temperature = np.array( ... [ ... [np.nan, 1, 3], ... [0, np.nan, 5], ... [5, np.nan, np.nan], ... [3, np.nan, np.nan], ... [0, 2, 0], ... ] ... ) >>> da = xr.DataArray( ... data=temperature, ... dims=["Y", "X"], ... coords=dict( ... lat=("Y", np.array([-20.0, -20.25, -20.50, -20.75, -21.0])), ... lon=("X", np.array([10.0, 10.25, 10.5])), ... ), ... ) >>> da array([[nan, 1., 3.], [ 0., nan, 5.], [ 5., nan, nan], [ 3., nan, nan], [ 0., 2., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0 lon (X) float64 10.0 10.25 10.5 Dimensions without coordinates: Y, X Fill all NaN values: >>> da.ffill(dim="Y", limit=None) array([[nan, 1., 3.], [ 0., 1., 5.], [ 5., 1., 5.], [ 3., 1., 5.], [ 0., 2., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0 lon (X) float64 10.0 10.25 10.5 Dimensions without coordinates: Y, X Fill only the first of consecutive NaN values: >>> da.ffill(dim="Y", limit=1) array([[nan, 1., 3.], [ 0., 1., 5.], [ 5., nan, 5.], [ 3., nan, nan], [ 0., 2., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0 lon (X) float64 10.0 10.25 10.5 Dimensions without coordinates: Y, X r)ffillr)rr)rrrrrcrcrfri sR zDataArray.ffillcCsddlm}||||dS)ab Fill NaN values by propagating values backward *Requires bottleneck.* Parameters ---------- dim : str Specifies the dimension along which to propagate values when filling. limit : int or None, default: None The maximum number of consecutive NaN values to backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Must be None or greater than or equal to axis length if filling along chunked axes (dimensions). Returns ------- filled : DataArray Examples -------- >>> temperature = np.array( ... [ ... [0, 1, 3], ... [0, np.nan, 5], ... [5, np.nan, np.nan], ... [3, np.nan, np.nan], ... [np.nan, 2, 0], ... ] ... ) >>> da = xr.DataArray( ... data=temperature, ... dims=["Y", "X"], ... coords=dict( ... lat=("Y", np.array([-20.0, -20.25, -20.50, -20.75, -21.0])), ... lon=("X", np.array([10.0, 10.25, 10.5])), ... ), ... ) >>> da array([[ 0., 1., 3.], [ 0., nan, 5.], [ 5., nan, nan], [ 3., nan, nan], [nan, 2., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0 lon (X) float64 10.0 10.25 10.5 Dimensions without coordinates: Y, X Fill all NaN values: >>> da.bfill(dim="Y", limit=None) array([[ 0., 1., 3.], [ 0., 2., 5.], [ 5., 2., 0.], [ 3., 2., 0.], [nan, 2., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0 lon (X) float64 10.0 10.25 10.5 Dimensions without coordinates: Y, X Fill only the first of consecutive NaN values: >>> da.bfill(dim="Y", limit=1) array([[ 0., 1., 3.], [ 0., nan, 5.], [ 5., nan, nan], [ 3., 2., 0.], [nan, 2., 0.]]) Coordinates: lat (Y) float64 -20.0 -20.25 -20.5 -20.75 -21.0 lon (X) float64 10.0 10.25 10.5 Dimensions without coordinates: Y, X r)bfillr)rr)rrrrrcrcrfr sR zDataArray.bfill)rrDrbcCstj||ddS)aCombine two DataArray objects, with union of coordinates. This operation follows the normal broadcasting and alignment rules of ``join='outer'``. Default to non-null values of array calling the method. Use np.nan to fill in vacant cells after alignment. Parameters ---------- other : DataArray Used to fill all matching missing values in this array. Returns ------- DataArray rF)rG)rrrrDrcrcrf combine_firstszDataArray.combine_first)r`rkeepdimszCallable[..., Any]zint | Sequence[int] | None)rrrr`rrrrbcKs"|jj|||||f|}||S)a6Reduce this array by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `f(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : "...", str, Iterable of Hashable or None, optional Dimension(s) over which to apply `func`. By default `func` is applied over all dimensions. axis : int or sequence of int, optional Axis(es) over which to repeatedly apply `func`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling `f(x)` without an axis argument). keep_attrs : bool or None, optional If True, the variable'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. keepdims : bool, default: False If True, the dimensions which are reduced are left in the result as dimensions of size one. Coordinates that use these dimensions are removed. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray DataArray with this object's array replaced with an array with summarized data and the indicated dimension(s) removed. )rreducer)rrrr`rrrrrcrcrfr's,zDataArray.reducez$DataArray | pd.Series | pd.DataFramecsnddtjtjd}z|j}Wn&tk rHtdjdYnXfddjD}|jf|S)aConvert this array into a pandas object with the same shape. The type of the returned object depends on the number of DataArray dimensions: * 0D -> `xarray.DataArray` * 1D -> `pandas.Series` * 2D -> `pandas.DataFrame` Only works for arrays with 2 or fewer dimensions. The DataArray constructor performs the inverse transformation. Returns ------- result : DataArray | Series | DataFrame DataArray, pandas Series or pandas DataFrame. cSs|Srjrc)xrcrcrfkz%DataArray.to_pandas..)rrzCannot convert arrays with z@ dimensions into pandas objects. Requires 2 or fewer dimensions.csg|]}|qSrc)rr|rrcrfrgssz'DataArray.to_pandas..)rrrrrrrrnr)r constructors constructorrrcrrf to_pandasVs zDataArray.to_pandaszSequence[Hashable] | Nonez pd.DataFrame)rirlrbcsdkr|jdkrtd|jdkr0tdd|jd}|dkr\tt|j|j}n |j|d}| |}fdd |j D|_ |S) aiConvert this array and its coordinates into a tidy pandas.DataFrame. The DataFrame is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). Other coordinates are included as columns in the DataFrame. For 1D and 2D DataArrays, see also :py:func:`DataArray.to_pandas` which doesn't rely on a MultiIndex to build the DataFrame. Parameters ---------- name: Hashable or None, optional Name to give to this array (required if unnamed). dim_order: Sequence of Hashable or None, optional Hierarchical dimension order for the resulting dataframe. Array content is transposed to this order and then written out as flat vectors in contiguous order, so the last dimension in this list will be contiguous in the resulting DataFrame. This has a major influence on which operations are efficient on the resulting dataframe. If provided, must include all dimensions of this DataArray. By default, dimensions are sorted according to the DataArray dimensions order. Returns ------- result: DataFrame DataArray as a pandas DataFrame. See also -------- DataArray.to_pandas DataArray.to_series NzNcannot convert an unnamed DataArray to a DataFrame: use the ``name`` parameterrz&cannot convert a scalar to a DataFrameZ,__unique_name_identifier_z98xfz98xugfg73ho__rh)rlcsg|]}|krn|qSrcrcr)riZ unique_namercrfrgsz*DataArray.to_dataframe..) rirrrrr}ryrnrZ_normalize_dim_orderZ _to_dataframer)rrirlrZ ordered_dimsdfrcrrf to_dataframevs %    zDataArray.to_dataframez pd.SeriescCs$|j}tj|jd||jdS)ayConvert this array into a pandas.Series. The Series is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). Returns ------- result : Series DataArray as a pandas Series. See also -------- DataArray.to_pandas DataArray.to_dataframe )rri)rrrrrZreshaperi)rrrcrcrf to_seriess zDataArray.to_seriesznp.ma.MaskedArray)rrbcCs$|}t|}tjj|||dS)aConvert this array into a numpy.ma.MaskedArray Parameters ---------- copy : bool, default: True If True make a copy of the array in the result. If False, a MaskedArray view of DataArray.values is returned. Returns ------- result : MaskedArray Masked where invalid values (nan or inf) occur. )rmaskr)rrisnullrmaZ MaskedArray)rrrrrcrcrfto_masked_arrays zDataArray.to_masked_arraywzLiteral[('w', 'a')]zT_NetcdfTypes | NonezT_NetcdfEngine | Nonez+Mapping[Hashable, Mapping[str, Any]] | Nonebytes) pathmoderYgroupenginerunlimited_dimsrinvalid_netcdfrbc CsdSrjrc rrrrYrrrrrrrcrcrf to_netcdfs zDataArray.to_netcdfzstr | PathLikec CsdSrjrcrrcrcrfrs )rrBc CsdSrjrcrrcrcrfrszstr | PathLike | Nonezbytes | Delayed | Nonec  Csddlm} m} m} |jdkr,|j| d} n:|j|jksD|j|jkr^|j| d} |j| j| <n|} | | ||||||||d| d S)aWrite dataset contents to a netCDF file. Parameters ---------- path : str, path-like or None, optional Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resulting netCDF file as bytes; in this case, we need to use scipy, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT). mode : {"w", "a"}, default: "w" Write ('w') or append ('a') mode. If mode='w', any existing file at this location will be overwritten. If mode='a', existing variables will be overwritten. format : {"NETCDF4", "NETCDF4_CLASSIC", "NETCDF3_64BIT", "NETCDF3_CLASSIC"}, optional File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). group : str, optional Path to the netCDF4 group in the given file to open (only works for format='NETCDF4'). The group(s) will be created if necessary. engine : {"netcdf4", "scipy", "h5netcdf"}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4' if writing to a file on disk. encoding : dict, optional Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., ``{"my_variable": {"dtype": "int16", "scale_factor": 0.1, "zlib": True}, ...}`` The `h5netcdf` engine supports both the NetCDF4-style compression encoding parameters ``{"zlib": True, "complevel": 9}`` and the h5py ones ``{"compression": "gzip", "compression_opts": 9}``. This allows using any compression plugin installed in the HDF5 library, e.g. LZF. unlimited_dims : iterable of Hashable, optional Dimension(s) that should be serialized as unlimited dimensions. By default, no dimensions are treated as unlimited dimensions. Note that unlimited_dims may also be set via ``dataset.encoding["unlimited_dims"]``. compute: bool, default: True If true compute immediately, otherwise return a ``dask.delayed.Delayed`` object that can be computed later. invalid_netcdf: bool, default: False Only valid along with ``engine="h5netcdf"``. If True, allow writing hdf5 files which are invalid netcdf as described in https://github.com/h5netcdf/h5netcdf. Returns ------- store: bytes or Delayed or None * ``bytes`` if path is None * ``dask.delayed.Delayed`` if compute is False * None otherwise Notes ----- Only xarray.Dataset objects can be written to netCDF files, so the xarray.DataArray is converted to a xarray.Dataset object containing a single variable. If the DataArray has no name, or if the name is the same as a coordinate name, then it is given the name ``"__xarray_dataarray_variable__"``. See Also -------- Dataset.to_netcdf r)DATAARRAY_NAMEDATAARRAY_VARIABLErNrhF) rrYrrrrrZ multifiler) xarray.backends.apirrrrirrrnr)rrrrYrrrrrrrrrrrcrcrfr s(b  )rrrbcCs`|jj|d}|i|jd|jD]\}}|jj|d|d|<q*|r\t|j|d<|S)a Convert this xarray.DataArray into a dictionary following xarray naming conventions. Converts all variables and attributes to native Python objects. Useful for converting to json. To avoid datetime incompatibility use decode_times=False kwarg in xarray.open_dataset. Parameters ---------- data : bool, default: True Whether to include the actual data in the dictionary. When set to False, returns just the schema. encoding : bool, default: False Whether to include the Dataset's encoding in the dictionary. Returns ------- dict: dict See Also -------- DataArray.from_dict Dataset.to_dict r)rrirr)rto_dictrrirr|r}r)rrrrlrrrcrcrfrszDataArray.to_dictzMapping[str, Any])rrlrbc Csd}d|krdzdd|dD}Wn<tk rb}ztdjt|jddW5d}~XYnXz |d}Wntk rtd Yn$X||||d |d |d }|j|d i|S)aqConvert a dictionary into an xarray.DataArray Parameters ---------- d : dict Mapping with a minimum structure of {"dims": [...], "data": [...]} Returns ------- obj : xarray.DataArray See Also -------- DataArray.to_dict Dataset.from_dict Examples -------- >>> d = {"dims": "t", "data": [1, 2, 3]} >>> da = xr.DataArray.from_dict(d) >>> da array([1, 2, 3]) Dimensions without coordinates: t >>> d = { ... "coords": { ... "t": {"dims": "t", "data": [0, 1, 2], "attrs": {"units": "s"}} ... }, ... "attrs": {"title": "air temperature"}, ... "dims": "t", ... "data": [10, 20, 30], ... "name": "a", ... } >>> da = xr.DataArray.from_dict(d) >>> da array([10, 20, 30]) Coordinates: * t (t) int64 0 1 2 Attributes: title: air temperature NrcSs*i|]"\}}||d|d|dfqS)rnrrrrrcrcrfrsz'DataArray.from_dict..zAcannot convert dict when coords are missing the key '{dims_data}'r)Z dims_datarz+cannot convert dict without the key 'data''rnrirr) r|rrrrYrtrrrr)rrlrerrrcrcrf from_dicts&-   "zDataArray.from_dict)seriesrsrbcCs:d}t||i}tj||d}tt||}|j|_|S)aConvert a pandas.Series into an xarray.DataArray. If the series's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional coordinates (filling in missing values with NaN). Thus this operation should be the inverse of the `to_series` method. Parameters ---------- series : Series Pandas Series object to convert. sparse : bool, default: False If sparse=True, creates a sparse array instead of a dense NumPy array. Requires the pydata/sparse package. See Also -------- DataArray.to_series Dataset.from_dataframe Z__temporary_name)rs)rrr&Zfrom_dataframer r_ri)rrrsZ temp_namerrrrcrcrf from_seriess zDataArray.from_seriescdms2_VariablecCsddlm}||S)z(Convert this array into a cdms2.Variabler)to_cdms2)xarray.convertr)rrrcrcrfr s zDataArray.to_cdms2)rrbcCsddlm}||S)z1Convert a cdms2.Variable into an xarray.DataArrayr) from_cdms2)rr)rrrrcrcrfrs zDataArray.from_cdms2 iris_CubecCsddlm}||S)z(Convert this array into a iris.cube.Cuber)to_iris)rr)rrrcrcrfrs zDataArray.to_iris)cuberbcCsddlm}||S)z1Convert a iris.cube.Cube into an xarray.DataArrayr) from_iris)rr)rrrrcrcrfr s zDataArray.from_iris)rrD compat_strrbcs*fdd}tj|j|j|do(|||S)z;Helper function for equals, broadcast_equals, and identicalcst|j|jSrj)rr)ryrrcrfcompat*sz%DataArray._all_compat..compat)r)rZ dict_equivr)rrDrrrcrrf _all_compat's  zDataArray._all_compatc Cs.z||dWSttfk r(YdSXdS)aTwo DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions. Parameters ---------- other : DataArray DataArray to compare to. Returns ---------- equal : bool True if the two DataArrays are broadcast equal. See Also -------- DataArray.equals DataArray.identical Examples -------- >>> a = xr.DataArray([1, 2], dims="X") >>> b = xr.DataArray([[1, 1], [2, 2]], dims=["X", "Y"]) >>> a array([1, 2]) Dimensions without coordinates: X >>> b array([[1, 1], [2, 2]]) Dimensions without coordinates: X, Y .equals returns True if two DataArrays have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two DataArrays against eachother have the same values, dimensions, and coordinates. >>> a.equals(b) False >>> a2, b2 = xr.broadcast(a, b) >>> a2.equals(b2) True >>> a.broadcast_equals(b) True broadcast_equalsFNrr{rrrcrcrfr1s,zDataArray.broadcast_equalsc Cs.z||dWSttfk r(YdSXdS)aTrue if two DataArrays have the same dimensions, coordinates and values; otherwise False. DataArrays can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for ``DataArray`` does element-wise comparisons (like numpy.ndarrays). Parameters ---------- other : DataArray DataArray to compare to. Returns ---------- equal : bool True if the two DataArrays are equal. See Also -------- DataArray.broadcast_equals DataArray.identical Examples -------- >>> a = xr.DataArray([1, 2, 3], dims="X") >>> b = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="m")) >>> c = xr.DataArray([1, 2, 3], dims="Y") >>> d = xr.DataArray([3, 2, 1], dims="X") >>> a array([1, 2, 3]) Dimensions without coordinates: X >>> b array([1, 2, 3]) Dimensions without coordinates: X Attributes: units: m >>> c array([1, 2, 3]) Dimensions without coordinates: Y >>> d array([3, 2, 1]) Dimensions without coordinates: X >>> a.equals(b) True >>> a.equals(c) False >>> a.equals(d) False equalsFNrrrcrcrfrbs9zDataArray.equalsc Cs:z|j|jko||dWSttfk r4YdSXdS)a_Like equals, but also checks the array name and attributes, and attributes on all coordinates. Parameters ---------- other : DataArray DataArray to compare to. Returns ---------- equal : bool True if the two DataArrays are identical. See Also -------- DataArray.broadcast_equals DataArray.equals Examples -------- >>> a = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="m"), name="Width") >>> b = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="m"), name="Width") >>> c = xr.DataArray([1, 2, 3], dims="X", attrs=dict(units="ft"), name="Width") >>> a array([1, 2, 3]) Dimensions without coordinates: X Attributes: units: m >>> b array([1, 2, 3]) Dimensions without coordinates: X Attributes: units: m >>> c array([1, 2, 3]) Dimensions without coordinates: X Attributes: units: ft >>> a.equals(b) True >>> a.identical(b) True >>> a.equals(c) True >>> a.identical(c) False identicalFN)rirr{rrrcrcrfrs5zDataArray.identical)rDrbcCs,t|dt}|tks||jkr$|jSdSdS)Nri)rr7ri)rrDZ other_namercrcrf _result_names zDataArray._result_namecCs|j||}||Srj)r__array_wrap__r)rrcontextZnew_varrcrcrfrszDataArray.__array_wrap__)rrrbcCs ||Srj)dot)rrrcrcrf __matmul__szDataArray.__matmul__cCs t||Srj)rrrrcrcrf __rmatmul__szDataArray.__rmatmul__r)rfrbc Os|dd}|dkrtdd}tltddtjddtdtjdd |||j j f||}W5QRX|r|j |_ |W5QRSQRXdS) NrT)defaultignorez All-NaN (slice|axis) encounteredzMean of empty slicer1)all) rr3r4catch_warningsfilterwarningsRuntimeWarningrZerrstaterrrr)rrrrrdarcrcrf _unary_ops    $zDataArray._unary_op)rrDr reflexiverbc Csddlm}t|t|frtSt|trDtd}t|||dd\}}t|d|}t|dd}|sl||j |n |||j }|j ||\} } | |} |j || | | dS) NrGroupByZarithmetic_joinF)rGrrrr)xarray.core.groupbyrrsr&NotImplementedr_r2rrrrZ _merge_rawrr) rrDrrrZ align_typeother_variable other_coordsrrrrircrcrf _binary_ops      zDataArray._binary_op)rrDrrbc Csddlm}t||rtdt|dd}t|d|}z(|j|||j|W5QRXWn,tk r}ztd|W5d}~XYnX|S)NrrzNin-place operations between a DataArray and a grouped object are not permittedrrzAutomatic alignment is not supported for in-place operations. Consider aligning the indices manually or using a not-in-place operation. See https://github.com/pydata/xarray/issues/3910 for more explanations.) rrrsr{rrZ_merge_inplacerr0)rrDrrrrexcrcrcrf_inplace_binary_ops"    zDataArray._inplace_binary_opzDataArray | Dataset | VariablecCs |j|_dSrj)rrrcrcrf_copy_attrs_from1szDataArray._copy_attrs_from2)truncaterbcCspg}|jD]2\}}|jdkr|dj|t|jt|dqd|}t ||krl|d|dd}|S)a` If the dataarray has 1 dimensional coordinates or comes from a slice we can show that info in the title Parameters ---------- truncate : int, default: 50 maximum number of characters for title Returns ------- title : string Can be used for plot titles rz{dim} = {v}{unit})rrunitz, Nz...) rr|rrcrYr'rr>rGrq)rrZone_dimsrrtitlercrcrf_title_for_slice6s   zDataArray._title_for_slicerupperzLiteral[('upper', 'lower')])rrrerrbcCs|j|||d}||S)aCalculate the n-th order discrete difference along given axis. Parameters ---------- dim : Hashable Dimension over which to calculate the finite difference. n : int, default: 1 The number of times values are differenced. label : {"upper", "lower"}, default: "upper" The new coordinate in dimension ``dim`` will have the values of either the minuend's or subtrahend's coordinate for values 'upper' and 'lower', respectively. Returns ------- difference : DataArray The n-th order finite difference of this object. Notes ----- `n` matches numpy's behavior and is different from pandas' first argument named `periods`. Examples -------- >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ["x"]) >>> arr.diff("x") array([0, 1, 0]) Coordinates: * x (x) int64 2 3 4 >>> arr.diff("x", 2) array([ 1, -1]) Coordinates: * x (x) int64 3 4 See Also -------- DataArray.differentiate )rerr)rdiffr)rrrerrrcrcrfrWs/zDataArray.diffzMapping[Any, int] | None)rshiftsrK shifts_kwargsrbcKs$|jjf||d|}|j|dS)a!Shift this DataArray by an offset along one or more dimensions. Only the data is moved; coordinates stay in place. This is consistent with the behavior of ``shift`` in pandas. Values shifted from beyond array bounds will appear at one end of each dimension, which are filled according to `fill_value`. For periodic offsets instead see `roll`. Parameters ---------- shifts : mapping of Hashable to int or None, optional Integer offset to shift along each of the given dimensions. Positive offsets shift to the right; negative offsets shift to the left. fill_value : scalar, optional Value to use for newly missing values **shifts_kwargs The keyword arguments form of ``shifts``. One of shifts or shifts_kwargs must be provided. Returns ------- shifted : DataArray DataArray with the same coordinates and attributes but shifted data. See Also -------- roll Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims="x") >>> arr.shift(x=1) array([nan, 5., 6.]) Dimensions without coordinates: x )rrKr()rshiftr)rrrKrrrcrcrfrs-zDataArray.shiftzMapping[Hashable, int] | None)rr roll_coordsrrbcKs$|jf||d|}||S)a"Roll this array by an offset along one or more dimensions. Unlike shift, roll treats the given dimensions as periodic, so will not create any missing values to be filled. Unlike shift, roll may rotate all variables, including coordinates if specified. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- shifts : mapping of Hashable to int, optional Integer offset to rotate each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. roll_coords : bool, default: False Indicates whether to roll the coordinates by the offset too. **shifts_kwargs : {dim: offset, ...}, optional The keyword arguments form of ``shifts``. One of shifts or shifts_kwargs must be provided. Returns ------- rolled : DataArray DataArray with the same attributes but rolled data and coordinates. See Also -------- shift Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims="x") >>> arr.roll(x=1) array([7, 5, 6]) Dimensions without coordinates: x )rr)rrollr)rrrrrrcrcrfrs, zDataArray.rollcCs||jjS)zk The real part of the array. See Also -------- numpy.ndarray.real )rrrealrrcrcrfrs zDataArray.realcCs||jjS)zp The imaginary part of the array. See Also -------- numpy.ndarray.imag )rrimagrrcrcrfrs zDataArray.imag)rrDrnrbcCs4t|trtdt|ts$tdtj|||dS)a_Perform dot product of two DataArrays along their shared dims. Equivalent to taking taking tensordot over all shared dims. Parameters ---------- other : DataArray The other array with which the dot product is performed. dims : ..., str, Iterable of Hashable or None, optional Which dimensions to sum over. Ellipsis (`...`) sums over all dimensions. If not specified, then all the common dimensions are summed over. Returns ------- result : DataArray Array resulting from the dot product over all shared dimensions. See Also -------- dot numpy.tensordot Examples -------- >>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4)) >>> da = xr.DataArray(da_vals, dims=["x", "y", "z"]) >>> dm_vals = np.arange(4) >>> dm = xr.DataArray(dm_vals, dims=["z"]) >>> dm.dims ('z',) >>> da.dims ('x', 'y', 'z') >>> dot_result = da.dot(dm) >>> dot_result.dims ('x', 'y') z8dot products are not yet supported with Dataset objects.z dot only operates on DataArrays.rm)rsr&NotImplementedErrorr_r{rr)rrDrnrcrcrfrs-  z DataArray.dotz5Hashable | DataArray | Sequence[Hashable | DataArray])r ascendingrbcCs|j||d}||S)a Sort object by labels or values (along an axis). Sorts the dataarray, either along specified dimensions, or according to values of 1-D dataarrays that share dimension with calling object. If the input variables are dataarrays, then the dataarrays are aligned (via left-join) to the calling object prior to sorting by cell values. NaNs are sorted to the end, following Numpy convention. If multiple sorts along the same dimension is given, numpy's lexsort is performed along that dimension: https://numpy.org/doc/stable/reference/generated/numpy.lexsort.html and the FIRST key in the sequence is used as the primary sort key, followed by the 2nd key, etc. Parameters ---------- variables : Hashable, DataArray, or sequence of Hashable or DataArray 1D DataArray objects or name(s) of 1D variable(s) in coords whose values are used to sort this array. ascending : bool, default: True Whether to sort by ascending or descending order. Returns ------- sorted : DataArray A new dataarray where all the specified dims are sorted by dim labels. See Also -------- Dataset.sortby numpy.sort pandas.sort_values pandas.sort_index Examples -------- >>> da = xr.DataArray( ... np.random.rand(5), ... coords=[pd.date_range("1/1/2000", periods=5)], ... dims="time", ... ) >>> da array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05 >>> da.sortby(da) array([0.4236548 , 0.54488318, 0.5488135 , 0.60276338, 0.71518937]) Coordinates: * time (time) datetime64[ns] 2000-01-05 2000-01-04 ... 2000-01-02 )r)rsortbyr)rrrrrcrcrfr:s=zDataArray.sortbyrArUzQuantileMethods | None)rqrr>rskipna interpolationrbcCs$|j||||||d}||S)aCompute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements. Parameters ---------- q : float or array-like of float Quantile to compute, which must be between 0 and 1 inclusive. dim : str or Iterable of Hashable, optional Dimension(s) over which to apply quantile. method : str, default: "linear" This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points. The options sorted by their R type as summarized in the H&F paper [1]_ are: 1. "inverted_cdf" (*) 2. "averaged_inverted_cdf" (*) 3. "closest_observation" (*) 4. "interpolated_inverted_cdf" (*) 5. "hazen" (*) 6. "weibull" (*) 7. "linear" (default) 8. "median_unbiased" (*) 9. "normal_unbiased" (*) The first three methods are discontiuous. The following discontinuous variations of the default "linear" (7.) option are also available: * "lower" * "higher" * "midpoint" * "nearest" See :py:func:`numpy.quantile` or [1]_ for details. The "method" argument was previously called "interpolation", renamed in accordance with numpy version 1.22.0. (*) These methods require numpy version 1.22 or newer. keep_attrs : bool or None, optional If True, the dataset'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. skipna : bool or None, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). Returns ------- quantiles : DataArray If `q` is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile Examples -------- >>> da = xr.DataArray( ... data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]], ... coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]}, ... dims=("x", "y"), ... ) >>> da.quantile(0) # or da.quantile(0, dim=...) array(0.7) Coordinates: quantile float64 0.0 >>> da.quantile(0, dim="x") array([0.7, 4.2, 2.6, 1.5]) Coordinates: * y (y) float64 1.0 1.5 2.0 2.5 quantile float64 0.0 >>> da.quantile([0, 0.5, 1]) array([0.7, 3.4, 9.4]) Coordinates: * quantile (quantile) float64 0.0 0.5 1.0 >>> da.quantile([0, 0.5, 1], dim="x") array([[0.7 , 4.2 , 2.6 , 1.5 ], [3.6 , 5.75, 6. , 1.7 ], [6.5 , 7.3 , 9.4 , 1.9 ]]) Coordinates: * y (y) float64 1.0 1.5 2.0 2.5 * quantile (quantile) float64 0.0 0.5 1.0 References ---------- .. [1] R. J. Hyndman and Y. Fan, "Sample quantiles in statistical packages," The American Statistician, 50(4), pp. 361-365, 1996 )rrr>rr)rquantiler)rrrr>rrrrrcrcrfrzsnzDataArray.quantile)rrpctrrbcCs|j|||d}||S)aRanks the data. Equal values are assigned a rank that is the average of the ranks that would have been otherwise assigned to all of the values within that set. Ranks begin at 1, not 0. If pct, computes percentage ranks. NaNs in the input array are returned as NaNs. The `bottleneck` library is required. Parameters ---------- dim : Hashable Dimension over which to compute rank. pct : bool, default: False If True, compute percentage ranks, otherwise compute integer ranks. keep_attrs : bool or None, optional If True, the dataset'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. Returns ------- ranked : DataArray DataArray with the same coordinates and dtype 'float64'. Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims="x") >>> arr.rank("x") array([1., 2., 3.]) Dimensions without coordinates: x )rr)rrankr)rrrrrrcrcrfrs)zDataArray.rankzLiteral[(1, 2)]rN)rr edge_order datetime_unitrbcCs||||}||S)a Differentiate the array with the second order accurate central differences. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- coord : Hashable The coordinate to be used to compute the gradient. edge_order : {1, 2}, default: 1 N-th order accurate differences at the boundaries. datetime_unit : {"Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as", None}, optional Unit to compute gradient. Only valid for datetime coordinate. Returns ------- differentiated: DataArray See also -------- numpy.gradient: corresponding numpy function Examples -------- >>> da = xr.DataArray( ... np.arange(12).reshape(4, 3), ... dims=["x", "y"], ... coords={"x": [0, 0.1, 1.1, 1.2]}, ... ) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.differentiate("x") array([[30. , 30. , 30. ], [27.54545455, 27.54545455, 27.54545455], [27.54545455, 27.54545455, 27.54545455], [30. , 30. , 30. ]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y )r differentiater)rrrrrrcrcrfrs;zDataArray.differentiate)rrrbcCs|||}||S)aTIntegrate along the given coordinate using the trapezoidal rule. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- coord : Hashable, or sequence of Hashable Coordinate(s) used for the integration. datetime_unit : {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as', None}, optional Specify the unit if a datetime coordinate is used. Returns ------- integrated : DataArray See also -------- Dataset.integrate numpy.trapz : corresponding numpy function Examples -------- >>> da = xr.DataArray( ... np.arange(12).reshape(4, 3), ... dims=["x", "y"], ... coords={"x": [0, 0.1, 1.1, 1.2]}, ... ) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.integrate("x") array([5.4, 6.6, 7.8]) Dimensions without coordinates: y )r integraterrrrrrcrcrfr^s3zDataArray.integratecCs|||}||S)aIntegrate cumulatively along the given coordinate using the trapezoidal rule. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. The first entry of the cumulative integral is always 0, in order to keep the length of the dimension unchanged between input and output. Parameters ---------- coord : Hashable, or sequence of Hashable Coordinate(s) used for the integration. datetime_unit : {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as', None}, optional Specify the unit if a datetime coordinate is used. Returns ------- integrated : DataArray See also -------- Dataset.cumulative_integrate scipy.integrate.cumulative_trapezoid : corresponding scipy function Examples -------- >>> da = xr.DataArray( ... np.arange(12).reshape(4, 3), ... dims=["x", "y"], ... coords={"x": [0, 0.1, 1.1, 1.2]}, ... ) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.cumulative_integrate("x") array([[0. , 0. , 0. ], [0.15, 0.25, 0.35], [4.65, 5.75, 6.85], [5.4 , 6.6 , 7.8 ]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y )rcumulative_integraterrrcrcrfrs;zDataArray.cumulative_integratecCs t|dS)zUnify chunk size along all chunked dimensions of this DataArray. Returns ------- DataArray with consistent chunk sizes for all dask-array variables See Also -------- dask.array.core.unify_chunks rr"rrcrcrfr#s zDataArray.unify_chunksrczCallable[..., T_Xarray]z Sequence[Any]zDataArray | Dataset | Noner[)rrrtemplaterbcCsddlm}||||||S)aQ Apply a function to each block of this DataArray. .. warning:: This method is experimental and its signature may change. Parameters ---------- func : callable User-provided function that accepts a DataArray as its first parameter. The function will receive a subset or 'block' of this DataArray (see below), corresponding to one chunk along each chunked dimension. ``func`` will be executed as ``func(subset_dataarray, *subset_args, **kwargs)``. This function must return either a single DataArray or a single Dataset. This function cannot add a new chunked dimension. args : sequence Passed to func after unpacking and subsetting any xarray objects by blocks. xarray objects in args must be aligned with this object, otherwise an error is raised. kwargs : mapping Passed verbatim to func after unpacking. xarray objects, if any, will not be subset to blocks. Passing dask collections in kwargs is not allowed. template : DataArray or Dataset, optional xarray object representing the final result after compute is called. If not provided, the function will be first run on mocked-up data, that looks like this object but has sizes 0, to determine properties of the returned object such as dtype, variable names, attributes, new dimensions and new indexes (if any). ``template`` must be provided if the function changes the size of existing dimensions. When provided, ``attrs`` on variables in `template` are copied over to the result. Any ``attrs`` set by ``func`` will be ignored. Returns ------- A single DataArray or Dataset with dask backend, reassembled from the outputs of the function. Notes ----- This function is designed for when ``func`` needs to manipulate a whole xarray object subset to each block. Each block is loaded into memory. In the more common case where ``func`` can work on numpy arrays, it is recommended to use ``apply_ufunc``. If none of the variables in this object is backed by dask arrays, calling this function is equivalent to calling ``func(obj, *args, **kwargs)``. See Also -------- dask.array.map_blocks, xarray.apply_ufunc, xarray.Dataset.map_blocks xarray.DataArray.map_blocks Examples -------- Calculate an anomaly from climatology using ``.groupby()``. Using ``xr.map_blocks()`` allows for parallel operations with knowledge of ``xarray``, its indices, and its methods like ``.groupby()``. >>> def calculate_anomaly(da, groupby_type="time.month"): ... gb = da.groupby(groupby_type) ... clim = gb.mean(dim="time") ... return gb - clim ... >>> time = xr.cftime_range("1990-01", "1992-01", freq="M") >>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"]) >>> np.random.seed(123) >>> array = xr.DataArray( ... np.random.rand(len(time)), ... dims=["time"], ... coords={"time": time, "month": month}, ... ).chunk() >>> array.map_blocks(calculate_anomaly, template=array).compute() array([ 0.12894847, 0.11323072, -0.0855964 , -0.09334032, 0.26848862, 0.12382735, 0.22460641, 0.07650108, -0.07673453, -0.22865714, -0.19063865, 0.0590131 , -0.12894847, -0.11323072, 0.0855964 , 0.09334032, -0.26848862, -0.12382735, -0.22460641, -0.07650108, 0.07673453, 0.22865714, 0.19063865, -0.0590131 ]) Coordinates: * time (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00 month (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Note that one must explicitly use ``args=[]`` and ``kwargs={}`` to pass arguments to the function being applied in ``xr.map_blocks()``: >>> array.map_blocks( ... calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array ... ) # doctest: +ELLIPSIS dask.array<-calculate_anomaly, shape=(24,), dtype=float64, chunksize=(24,), chunktype=numpy.ndarray> Coordinates: * time (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00 month (time) int64 dask.array r) map_blocks)Zxarray.core.parallelr)rrrrrrrcrcrfrsd zDataArray.map_blocksz float | NonezHashable | Any | Nonezbool | Literal['unscaled'])rdegrrcondrrcovrbc Cs|j|||||||dS)a Least squares polynomial fit. This replicates the behaviour of `numpy.polyfit` but differs by skipping invalid values when `skipna = True`. Parameters ---------- dim : Hashable Coordinate along which to fit the polynomials. deg : int Degree of the fitting polynomial. skipna : bool or None, optional If True, removes all invalid values before fitting each 1D slices of the array. Default is True if data is stored in a dask.array or if there is any invalid values, False otherwise. rcond : float or None, optional Relative condition number to the fit. w : Hashable, array-like or None, optional Weights to apply to the y-coordinate of the sample points. Can be an array-like object or the name of a coordinate in the dataset. full : bool, default: False Whether to return the residuals, matrix rank and singular values in addition to the coefficients. cov : bool or "unscaled", default: False Whether to return to the covariance matrix in addition to the coefficients. The matrix is not scaled if `cov='unscaled'`. Returns ------- polyfit_results : Dataset A single dataset which contains: polyfit_coefficients The coefficients of the best fit. polyfit_residuals The residuals of the least-square computation (only included if `full=True`). When the matrix rank is deficient, np.nan is returned. [dim]_matrix_rank The effective rank of the scaled Vandermonde coefficient matrix (only included if `full=True`) [dim]_singular_value The singular values of the scaled Vandermonde coefficient matrix (only included if `full=True`) polyfit_covariance The covariance matrix of the polynomial coefficient estimates (only included if `full=False` and `cov=True`) See Also -------- numpy.polyfit numpy.polyval xarray.polyval )rrrrr )rpolyfit)rrrrrrrr rcrcrfr Js=zDataArray.polyfitconstantz*Mapping[Any, int | tuple[int, int]] | NonerSz>> arr = xr.DataArray([5, 6, 7], coords=[("x", [0, 1, 2])]) >>> arr.pad(x=(1, 2), constant_values=0) array([0, 5, 6, 7, 0, 0]) Coordinates: * x (x) float64 nan 0.0 1.0 2.0 nan nan >>> da = xr.DataArray( ... [[0, 1, 2, 3], [10, 11, 12, 13]], ... dims=["x", "y"], ... coords={"x": [0, 1], "y": [10, 20, 30, 40], "z": ("x", [100, 200])}, ... ) >>> da.pad(x=1) array([[nan, nan, nan, nan], [ 0., 1., 2., 3.], [10., 11., 12., 13.], [nan, nan, nan, nan]]) Coordinates: * x (x) float64 nan 0.0 1.0 nan * y (y) int64 10 20 30 40 z (x) float64 nan 100.0 200.0 nan Careful, ``constant_values`` are coerced to the data type of the array which may lead to a loss of precision: >>> da.pad(x=1, constant_values=1.23456789) array([[ 1, 1, 1, 1], [ 0, 1, 2, 3], [10, 11, 12, 13], [ 1, 1, 1, 1]]) Coordinates: * x (x) float64 nan 0.0 1.0 nan * y (y) int64 10 20 30 40 z (x) float64 nan 100.0 200.0 nan )r rr rrrr)rpadr) rr rr rrrrrrrcrcrfrs  z DataArray.pad)rrrKrrbcCstj|dd||||dS)a Return the coordinate label of the minimum value along a dimension. Returns a new `DataArray` named after the dimension with the values of the coordinate labels along that dimension corresponding to minimum values along that dimension. In comparison to :py:meth:`~DataArray.argmin`, this returns the coordinate label while :py:meth:`~DataArray.argmin` returns the index. Parameters ---------- dim : str, optional Dimension over which to apply `idxmin`. This is optional for 1D arrays, but required for arrays with 2 or more dimensions. skipna : bool or None, default: None If True, skip missing values (as marked by NaN). By default, only skips missing values for ``float``, ``complex``, and ``object`` dtypes; other dtypes either do not have a sentinel missing value (``int``) or ``skipna=True`` has not been implemented (``datetime64`` or ``timedelta64``). fill_value : Any, default: NaN Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if ``skipna`` is False. 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 ------- reduced : DataArray New `DataArray` object with `idxmin` applied to its data and the indicated dimension removed. See Also -------- Dataset.idxmin, DataArray.idxmax, DataArray.min, DataArray.argmin Examples -------- >>> array = xr.DataArray( ... [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]} ... ) >>> array.min() array(-2) >>> array.argmin(...) {'x': array(4)} >>> array.idxmin() array('e', dtype='>> array = xr.DataArray( ... [ ... [2.0, 1.0, 2.0, 0.0, -2.0], ... [-4.0, np.NaN, 2.0, np.NaN, -2.0], ... [np.NaN, np.NaN, 1.0, np.NaN, np.NaN], ... ], ... dims=["y", "x"], ... coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2}, ... ) >>> array.min(dim="x") array([-2., -4., 1.]) Coordinates: * y (y) int64 -1 0 1 >>> array.argmin(dim="x") array([4, 0, 2]) Coordinates: * y (y) int64 -1 0 1 >>> array.idxmin(dim="x") array([16., 0., 4.]) Coordinates: * y (y) int64 -1 0 1 c_s |j||Srj)argminrrrrcrcrfrrz"DataArray.idxmin..rrrrrKrrZ_calc_idxminmaxrrrrKrrcrcrfidxmin3sWzDataArray.idxmincCstj|dd||||dS)a Return the coordinate label of the maximum value along a dimension. Returns a new `DataArray` named after the dimension with the values of the coordinate labels along that dimension corresponding to maximum values along that dimension. In comparison to :py:meth:`~DataArray.argmax`, this returns the coordinate label while :py:meth:`~DataArray.argmax` returns the index. Parameters ---------- dim : Hashable, optional Dimension over which to apply `idxmax`. This is optional for 1D arrays, but required for arrays with 2 or more dimensions. skipna : bool or None, default: None If True, skip missing values (as marked by NaN). By default, only skips missing values for ``float``, ``complex``, and ``object`` dtypes; other dtypes either do not have a sentinel missing value (``int``) or ``skipna=True`` has not been implemented (``datetime64`` or ``timedelta64``). fill_value : Any, default: NaN Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if ``skipna`` is False. 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 ------- reduced : DataArray New `DataArray` object with `idxmax` applied to its data and the indicated dimension removed. See Also -------- Dataset.idxmax, DataArray.idxmin, DataArray.max, DataArray.argmax Examples -------- >>> array = xr.DataArray( ... [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]} ... ) >>> array.max() array(2) >>> array.argmax(...) {'x': array(1)} >>> array.idxmax() array('b', dtype='>> array = xr.DataArray( ... [ ... [2.0, 1.0, 2.0, 0.0, -2.0], ... [-4.0, np.NaN, 2.0, np.NaN, -2.0], ... [np.NaN, np.NaN, 1.0, np.NaN, np.NaN], ... ], ... dims=["y", "x"], ... coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2}, ... ) >>> array.max(dim="x") array([2., 2., 1.]) Coordinates: * y (y) int64 -1 0 1 >>> array.argmax(dim="x") array([0, 2, 2]) Coordinates: * y (y) int64 -1 0 1 >>> array.idxmax(dim="x") array([0., 4., 4.]) Coordinates: * y (y) int64 -1 0 1 c_s |j||Srj)argmaxrrcrcrfrrz"DataArray.idxmax..rrrrcrcrfidxmaxsWzDataArray.idxmaxz%DataArray | dict[Hashable, DataArray])rr`rrrbcs@j||||}t|tr2fdd|DS|SdS)aC Index or indices of the minimum of the DataArray over one or more dimensions. If a sequence is passed to 'dim', then result returned as dict of DataArrays, which can be passed directly to isel(). If a single str is passed to 'dim' then returns a DataArray with dtype int. If there are multiple minima, the indices of the first one found will be returned. Parameters ---------- dim : "...", str, Iterable of Hashable or None, optional The dimensions over which to find the minimum. By default, finds minimum over all dimensions - for now returning an int for backward compatibility, but this is deprecated, in future will return a dict with indices for all dimensions; to return a dict with all dimensions now, pass '...'. axis : int or None, optional Axis over which to apply `argmin`. Only one of the 'dim' and 'axis' arguments can be supplied. 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. skipna : bool or None, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). Returns ------- result : DataArray or dict of DataArray See Also -------- Variable.argmin, DataArray.idxmin Examples -------- >>> array = xr.DataArray([0, 2, -1, 3], dims="x") >>> array.min() array(-1) >>> array.argmin(...) {'x': array(2)} >>> array.isel(array.argmin(...)) array(-1) >>> array = xr.DataArray( ... [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, -5, 1], [2, 3, 1]]], ... dims=("x", "y", "z"), ... ) >>> array.min(dim="x") array([[ 1, 2, 1], [ 2, -5, 1], [ 2, 1, 1]]) Dimensions without coordinates: y, z >>> array.argmin(dim="x") array([[1, 0, 0], [1, 1, 1], [0, 0, 1]]) Dimensions without coordinates: y, z >>> array.argmin(dim=["x"]) {'x': array([[1, 0, 0], [1, 1, 1], [0, 0, 1]]) Dimensions without coordinates: y, z} >>> array.min(dim=("x", "z")) array([ 1, -5, 1]) Dimensions without coordinates: y >>> array.argmin(dim=["x", "z"]) {'x': array([0, 1, 0]) Dimensions without coordinates: y, 'z': array([2, 1, 1]) Dimensions without coordinates: y} >>> array.isel(array.argmin(dim=["x", "z"])) array([ 1, -5, 1]) Dimensions without coordinates: y csi|]\}}||qSrcrrrrcrfrUsz$DataArray.argmin..N)rrrsr}r|rrrr`rrrrcrrfrs^ zDataArray.argmincs@j||||}t|tr2fdd|DS|SdS)a1 Index or indices of the maximum of the DataArray over one or more dimensions. If a sequence is passed to 'dim', then result returned as dict of DataArrays, which can be passed directly to isel(). If a single str is passed to 'dim' then returns a DataArray with dtype int. If there are multiple maxima, the indices of the first one found will be returned. Parameters ---------- dim : "...", str, Iterable of Hashable or None, optional The dimensions over which to find the maximum. By default, finds maximum over all dimensions - for now returning an int for backward compatibility, but this is deprecated, in future will return a dict with indices for all dimensions; to return a dict with all dimensions now, pass '...'. axis : int or None, optional Axis over which to apply `argmax`. Only one of the 'dim' and 'axis' arguments can be supplied. 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. skipna : bool or None, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). Returns ------- result : DataArray or dict of DataArray See Also -------- Variable.argmax, DataArray.idxmax Examples -------- >>> array = xr.DataArray([0, 2, -1, 3], dims="x") >>> array.max() array(3) >>> array.argmax(...) {'x': array(3)} >>> array.isel(array.argmax(...)) array(3) >>> array = xr.DataArray( ... [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, 5, 1], [2, 3, 1]]], ... dims=("x", "y", "z"), ... ) >>> array.max(dim="x") array([[3, 3, 2], [3, 5, 2], [2, 3, 3]]) Dimensions without coordinates: y, z >>> array.argmax(dim="x") array([[0, 1, 1], [0, 1, 0], [0, 1, 0]]) Dimensions without coordinates: y, z >>> array.argmax(dim=["x"]) {'x': array([[0, 1, 1], [0, 1, 0], [0, 1, 0]]) Dimensions without coordinates: y, z} >>> array.max(dim=("x", "z")) array([3, 5, 3]) Dimensions without coordinates: y >>> array.argmax(dim=["x", "z"]) {'x': array([0, 1, 0]) Dimensions without coordinates: y, 'z': array([0, 1, 2]) Dimensions without coordinates: y} >>> array.isel(array.argmax(dim=["x", "z"])) array([3, 5, 3]) Dimensions without coordinates: y csi|]\}}||qSrcrrrrcrfrsz$DataArray.argmax..N)rrrsr}r|rrrcrrfr[s^ zDataArray.argmaxpandasrWrV)queriesparserrr:queries_kwargsrbcKs0|jdd}|jf||||d|}||jS)u Return a new data array indexed along the specified dimension(s), where the indexers are given as strings containing Python expressions to be evaluated against the values in the array. Parameters ---------- queries : dict-like or None, optional A dict-like with keys matching dimensions and values given by strings containing Python expressions to be evaluated against the data variables in the dataset. The expressions will be evaluated using the pandas eval() function, and can contain any valid Python expressions but cannot contain any Python statements. parser : {"pandas", "python"}, default: "pandas" The parser to use to construct the syntax tree from the expression. The default of 'pandas' parses code slightly different than standard Python. Alternatively, you can parse an expression using the 'python' parser to retain strict Python semantics. engine : {"python", "numexpr", None}, default: None The engine used to evaluate the expression. Supported engines are: - None: tries to use numexpr, falls back to python - "numexpr": evaluates expressions using numexpr - "python": performs operations as if you had eval’d in top level python missing_dims : {"raise", "warn", "ignore"}, default: "raise" What to do if dimensions that should be selected from are not present in the DataArray: - "raise": raise an exception - "warn": raise a warning, and ignore the missing dimensions - "ignore": ignore the missing dimensions **queries_kwargs : {dim: query, ...}, optional The keyword arguments form of ``queries``. One of queries or queries_kwargs must be provided. Returns ------- obj : DataArray A new DataArray with the same contents as this dataset, indexed by the results of the appropriate queries. See Also -------- DataArray.isel Dataset.query pandas.eval Examples -------- >>> da = xr.DataArray(np.arange(0, 5, 1), dims="x", name="a") >>> da array([0, 1, 2, 3, 4]) Dimensions without coordinates: x >>> da.query(x="a > 2") array([3, 4]) Dimensions without coordinates: x T)r)rrrr:)rqueryri)rrrrr:r rrcrcrfr!sE zDataArray.queryz+str | DataArray | Iterable[str | DataArray]zdict[str, Any] | NonezSequence[str] | None) rr reduce_dimsrp0bounds param_namesrrbc Cs|j||||||||dS)ag Curve fitting optimization for arbitrary functions. Wraps `scipy.optimize.curve_fit` with `apply_ufunc`. Parameters ---------- coords : Hashable, DataArray, or sequence of DataArray or Hashable Independent coordinate(s) over which to perform the curve fitting. Must share at least one dimension with the calling object. When fitting multi-dimensional functions, supply `coords` as a sequence in the same order as arguments in `func`. To fit along existing dimensions of the calling object, `coords` can also be specified as a str or sequence of strs. func : callable User specified function in the form `f(x, *params)` which returns a numpy array of length `len(x)`. `params` are the fittable parameters which are optimized by scipy curve_fit. `x` can also be specified as a sequence containing multiple coordinates, e.g. `f((x0, x1), *params)`. reduce_dims : str, Iterable of Hashable or None, optional Additional dimension(s) over which to aggregate while fitting. For example, calling `ds.curvefit(coords='time', reduce_dims=['lat', 'lon'], ...)` will aggregate all lat and lon points and fit the specified function along the time dimension. skipna : bool, default: True Whether to skip missing values when fitting. Default is True. p0 : dict-like or None, optional Optional dictionary of parameter names to initial guesses passed to the `curve_fit` `p0` arg. If none or only some parameters are passed, the rest will be assigned initial values following the default scipy behavior. bounds : dict-like or None, optional Optional dictionary of parameter names to bounding values passed to the `curve_fit` `bounds` arg. If none or only some parameters are passed, the rest will be unbounded following the default scipy behavior. param_names : sequence of Hashable or None, optional Sequence of names for the fittable parameters of `func`. If not supplied, this will be automatically determined by arguments of `func`. `param_names` should be manually supplied when fitting a function that takes a variable number of parameters. **kwargs : optional Additional keyword arguments to passed to scipy curve_fit. Returns ------- curvefit_results : Dataset A single dataset which contains: [var]_curvefit_coefficients The coefficients of the best fit. [var]_curvefit_covariance The covariance matrix of the coefficient estimates. See Also -------- DataArray.polyfit scipy.optimize.curve_fit )r"rr#r$r%r)rcurvefit) rrrr"rr#r$r%rrcrcrfr&sCzDataArray.curvefitfirstz!Literal[('first', 'last', False)])rrkeeprbcCs|j||d}||S)aReturns a new DataArray with duplicate dimension values removed. Parameters ---------- dim : dimension label or labels Pass `...` to drop duplicates along all dimensions. keep : {"first", "last", False}, default: "first" Determines which duplicates (if any) to keep. - ``"first"`` : Drop duplicates except for the first occurrence. - ``"last"`` : Drop duplicates except for the last occurrence. - False : Drop all duplicates. Returns ------- DataArray See Also -------- Dataset.drop_duplicates Examples -------- >>> da = xr.DataArray( ... np.arange(25).reshape(5, 5), ... dims=("x", "y"), ... coords={"x": np.array([0, 0, 1, 2, 3]), "y": np.array([0, 1, 2, 3, 3])}, ... ) >>> da 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]]) Coordinates: * x (x) int64 0 0 1 2 3 * y (y) int64 0 1 2 3 3 >>> da.drop_duplicates(dim="x") array([[ 0, 1, 2, 3, 4], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) Coordinates: * x (x) int64 0 1 2 3 * y (y) int64 0 1 2 3 3 >>> da.drop_duplicates(dim="x", keep="last") array([[ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) Coordinates: * x (x) int64 0 1 2 3 * y (y) int64 0 1 2 3 3 Drop all duplicate dimension values: >>> da.drop_duplicates(dim=...) array([[ 0, 1, 2, 3], [10, 11, 12, 13], [15, 16, 17, 18], [20, 21, 22, 23]]) Coordinates: * x (x) int64 0 1 2 3 * y (y) int64 0 1 2 3 )r()rdrop_duplicatesr)rrr(Z deduplicatedrcrcrfr)\sLzDataArray.drop_duplicatestimez Any | None)calendarralign_onmissing use_cftimerbcCst||||||dS)aConvert the DataArray to another calendar. Only converts the individual timestamps, does not modify any data except in dropping invalid/surplus dates or inserting missing dates. If the source and target calendars are either no_leap, all_leap or a standard type, only the type of the time array is modified. When converting to a leap year from a non-leap year, the 29th of February is removed from the array. In the other direction the 29th of February will be missing in the output, unless `missing` is specified, in which case that value is inserted. For conversions involving `360_day` calendars, see Notes. This method is safe to use with sub-daily data as it doesn't touch the time part of the timestamps. Parameters --------- calendar : str The target calendar name. dim : str Name of the time coordinate. align_on : {None, 'date', 'year'} Must be specified when either source or target is a `360_day` calendar, ignored otherwise. See Notes. missing : Optional[any] By default, i.e. if the value is None, this method will simply attempt to convert the dates in the source calendar to the same dates in the target calendar, and drop any of those that are not possible to represent. If a value is provided, a new time coordinate will be created in the target calendar with the same frequency as the original time coordinate; for any dates that are not present in the source, the data will be filled with this value. Note that using this mode requires that the source data have an inferable frequency; for more information see :py:func:`xarray.infer_freq`. For certain frequency, source, and target calendar combinations, this could result in many missing values, see notes. use_cftime : boolean, optional Whether to use cftime objects in the output, only used if `calendar` is one of {"proleptic_gregorian", "gregorian" or "standard"}. If True, the new time axis uses cftime objects. If None (default), it uses :py:class:`numpy.datetime64` values if the date range permits it, and :py:class:`cftime.datetime` objects if not. If False, it uses :py:class:`numpy.datetime64` or fails. Returns ------- DataArray Copy of the dataarray with the time coordinate converted to the target calendar. If 'missing' was None (default), invalid dates in the new calendar are dropped, but missing dates are not inserted. If `missing` was given, the new data is reindexed to have a time axis with the same frequency as the source, but in the new calendar; any missing datapoints are filled with `missing`. Notes ----- Passing a value to `missing` is only usable if the source's time coordinate as an inferable frequencies (see :py:func:`~xarray.infer_freq`) and is only appropriate if the target coordinate, generated from this frequency, has dates equivalent to the source. It is usually **not** appropriate to use this mode with: - Period-end frequencies : 'A', 'Y', 'Q' or 'M', in opposition to 'AS' 'YS', 'QS' and 'MS' - Sub-monthly frequencies that do not divide a day evenly : 'W', 'nD' where `N != 1` or 'mH' where 24 % m != 0). If one of the source or target calendars is `"360_day"`, `align_on` must be specified and two options are offered. - "year" The dates are translated according to their relative position in the year, ignoring their original month and day information, meaning that the missing/surplus days are added/removed at regular intervals. From a `360_day` to a standard calendar, the output will be missing the following dates (day of year in parentheses): To a leap year: January 31st (31), March 31st (91), June 1st (153), July 31st (213), September 31st (275) and November 30th (335). To a non-leap year: February 6th (36), April 19th (109), July 2nd (183), September 12th (255), November 25th (329). From a standard calendar to a `"360_day"`, the following dates in the source array will be dropped: From a leap year: January 31st (31), April 1st (92), June 1st (153), August 1st (214), September 31st (275), December 1st (336) From a non-leap year: February 6th (37), April 20th (110), July 2nd (183), September 13th (256), November 25th (329) This option is best used on daily and subdaily data. - "date" The month/day information is conserved and invalid dates are dropped from the output. This means that when converting from a `"360_day"` to a standard calendar, all 31st (Jan, March, May, July, August, October and December) will be missing as there is no equivalent dates in the `"360_day"` calendar and the 29th (on non-leap years) and 30th of February will be dropped as there are no equivalent dates in a standard calendar. This option is best used with data on a frequency coarser than daily. )rr,r-r.)r)rr+rr,r-r.rcrcrfrsrzDataArray.convert_calendarz*pd.DatetimeIndex | CFTimeIndex | DataArray)targetrrbcCst|||dS)aInterpolates the DataArray to another calendar based on decimal year measure. Each timestamp in `source` and `target` are first converted to their decimal year equivalent then `source` is interpolated on the target coordinate. The decimal year of a timestamp is its year plus its sub-year component converted to the fraction of its year. For example "2000-03-01 12:00" is 2000.1653 in a standard calendar or 2000.16301 in a `"noleap"` calendar. This method should only be used when the time (HH:MM:SS) information of time coordinate is not important. Parameters ---------- target: DataArray or DatetimeIndex or CFTimeIndex The target time coordinate of a valid dtype (np.datetime64 or cftime objects) dim : str The time coordinate name. Return ------ DataArray The source interpolated on the decimal years of target, r)r)rr/rrcrcrfr&szDataArray.interp_calendarz$Hashable | DataArray | IndexVariablerG)rrxrestore_coord_dimsrbcCs6ddlm}t|ts&td|d|||||dS)aReturns a DataArrayGroupBy object for performing grouped operations. Parameters ---------- group : Hashable, DataArray or IndexVariable Array whose unique values should be used to group this array. If a Hashable, must be the name of a coordinate contained in this dataarray. squeeze : bool, default: True If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. restore_coord_dims : bool, default: False If True, also restore the dimension order of multi-dimensional coordinates. Returns ------- grouped : DataArrayGroupBy A `DataArrayGroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. Examples -------- Calculate daily anomalies for daily data: >>> da = xr.DataArray( ... np.linspace(0, 1826, num=1827), ... coords=[pd.date_range("2000-01-01", "2004-12-31", freq="D")], ... dims="time", ... ) >>> da array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03, 1.826e+03]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31 >>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time") array([-730.8, -730.8, -730.8, ..., 730.2, 730.2, 730.5]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31 dayofyear (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366 See Also -------- DataArray.groupby_bins Dataset.groupby core.groupby.DataArrayGroupBy pandas.DataFrame.groupby rrFz%`squeeze` must be True or False, but z was supplied)rxr0)rrGrsrr{)rrrxr0rGrcrcrfgroupbyEs8   zDataArray.groupbyrz!ArrayLike | Literal[False] | None) rbinsrightr precisioninclude_lowestrxr0rbc Cs*ddlm} | |||||||||ddS)aK Returns a DataArrayGroupBy object for performing grouped operations. Rather than using all unique values of `group`, the values are discretized first by applying `pandas.cut` [1]_ to `group`. Parameters ---------- group : Hashable, DataArray or IndexVariable Array whose binned values should be used to group this array. If a Hashable, must be the name of a coordinate contained in this dataarray. bins : int or array-like If bins is an int, it defines the number of equal-width bins in the range of x. However, in this case, the range of x is extended by .1% on each side to include the min or max values of x. If bins is a sequence it defines the bin edges allowing for non-uniform bin width. No extension of the range of x is done in this case. right : bool, default: True Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4]. labels : array-like, False or None, default: None Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, string bin labels are assigned by `pandas.cut`. precision : int, default: 3 The precision at which to store and display the bins labels. include_lowest : bool, default: False Whether the first interval should be left-inclusive or not. squeeze : bool, default: True If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. restore_coord_dims : bool, default: False If True, also restore the dimension order of multi-dimensional coordinates. Returns ------- grouped : DataArrayGroupBy A `DataArrayGroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. The name of the group has the added suffix `_bins` in order to distinguish it from the original variable. See Also -------- DataArray.groupby Dataset.groupby_bins core.groupby.DataArrayGroupBy pandas.DataFrame.groupby References ---------- .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html rrF)r3rr4r5)rxr2r0Z cut_kwargs)rrG) rrr2r3rr4r5rxr0rGrcrcrf groupby_binssB zDataArray.groupby_binsr])weightsrbcCsddlm}|||S)ag Weighted DataArray operations. Parameters ---------- weights : DataArray An array of weights associated with the values in this Dataset. Each value in the data contributes to the reduction operation according to its associated weight. Notes ----- ``weights`` must be a DataArray and cannot contain missing values. Missing values can be replaced by ``weights.fillna(0)``. Returns ------- core.weighted.DataArrayWeighted See Also -------- Dataset.weighted rr\)xarray.core.weightedr])rr7r]rcrcrfweighteds zDataArray.weightedzbool | Mapping[Any, bool]rK)r min_periodscenter window_kwargsrbcKs(ddlm}t||d}|||||dS)a Rolling window object for DataArrays. Parameters ---------- dim : dict, optional Mapping from the dimension name to create the rolling iterator along (e.g. `time`) to its moving window size. min_periods : int or None, default: None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : bool or Mapping to int, default: False Set the labels at the center of the window. **window_kwargs : optional The keyword arguments form of ``dim``. One of dim or window_kwargs must be provided. Returns ------- core.rolling.DataArrayRolling Examples -------- Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON: >>> 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.rolling(time=3, center=True).mean() array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., nan]) Coordinates: * time (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15 Remove the NaNs using ``dropna()``: >>> da.rolling(time=3, center=True).mean().dropna("time") array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]) Coordinates: * time (time) datetime64[ns] 2000-01-15 2000-02-15 ... 2000-10-15 See Also -------- core.rolling.DataArrayRolling Dataset.rolling r)rKrolling)r:r;)xarray.core.rollingrKr8)rrr:r;r<rKrcrcrfr=sD  zDataArray.rollingexactleftmeanrLz'SideOptions | Mapping[Any, SideOptions]z-str | Callable | Mapping[Any, str | Callable]rJ)rboundaryside coord_funcr<rbcKs*ddlm}t||d}||||||dS)a Coarsen object for DataArrays. Parameters ---------- dim : mapping of hashable to int, optional Mapping from the dimension name to the window size. boundary : {"exact", "trim", "pad"}, default: "exact" If 'exact', a ValueError will be raised if dimension size is not a multiple of the window size. If 'trim', the excess entries are dropped. If 'pad', NA will be padded. side : {"left", "right"} or mapping of str to {"left", "right"}, default: "left" coord_func : str or mapping of hashable to str, default: "mean" function (name) that is applied to the coordinates, or a mapping from coordinate name to function (name). Returns ------- core.rolling.DataArrayCoarsen Examples -------- Coarsen the long time series by averaging over every four days. >>> da = xr.DataArray( ... np.linspace(0, 364, num=364), ... dims="time", ... coords={"time": pd.date_range("1999-12-15", periods=364)}, ... ) >>> da # +doctest: ELLIPSIS array([ 0. , 1.00275482, 2.00550964, 3.00826446, 4.01101928, 5.0137741 , 6.01652893, 7.01928375, 8.02203857, 9.02479339, 10.02754821, 11.03030303, ... 356.98071625, 357.98347107, 358.9862259 , 359.98898072, 360.99173554, 361.99449036, 362.99724518, 364. ]) Coordinates: * time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12 >>> da.coarsen(time=3, boundary="trim").mean() # +doctest: ELLIPSIS array([ 1.00275482, 4.01101928, 7.01928375, 10.02754821, 13.03581267, 16.04407713, 19.0523416 , 22.06060606, 25.06887052, 28.07713499, 31.08539945, 34.09366391, ... 349.96143251, 352.96969697, 355.97796143, 358.9862259 , 361.99449036]) Coordinates: * time (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10 >>> See Also -------- core.rolling.DataArrayCoarsen Dataset.coarsen r)rJcoarsen)rBrCrD)r>rJr8)rrrBrCrDr<rJrcrcrfrEEs@  zDataArray.coarsen start_dayzMapping[Any, str] | NonezSideOptions | Nonez.pd.Timedelta | datetime.timedelta | str | Nonezstr | DatetimeLikezdatetime.timedelta | str | NonerI) indexerrclosedrbaseoffsetoriginrloffsetr0indexer_kwargsrbc Ks4ddlm} |jf| ||||||||| | d | S)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 : Mapping of Hashable to str, 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 : str The keyword arguments form of ``indexer``. One of indexer or indexer_kwargs must be provided. Returns ------- resampled : core.resample.DataArrayResample 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 -------- Dataset.resample pandas.Series.resample pandas.DataFrame.resample References ---------- .. 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