U Cv f @sddlmZddlZddlZddlmZddlmZmZm Z m Z m Z ddl Z ddlmZddlmZddlmZddlmZdd lmZdd lmZerdd lmZmZmZd d ZddZddZ ddZ!ddZ"ddZ#dddej$ddfdddd d!d"Z%ej$ddfdddd d#d$Z&dddej$ddfdddd d%d&Z'd'd(Z(ej$ddfddd)d*d+Z)e eed,fZ*dddej$ddfd,d-d.d.d.d/ddd0d1 d2d3Z+d4d5Z,ej$dddddfdddd d6d7Z-gdddej$dddfd8dd9d.d/ddd:d;d< d=d>Z.dS)?) annotationsN)Counter) TYPE_CHECKINGIterableLiteralSequenceUnion)dtypes)concat) DataArray)Dataset)merge)iterate_nested)CombineAttrsOptions CompatOptions JoinOptionscCstt|dS)N)dict _infer_tile_ids_from_nested_list)datasetsrr7/tmp/pip-unpacked-wheel-h316xyqg/xarray/core/combine.py"_infer_concat_order_from_positionssrccsBt|tr4t|D]\}}t|||fEdHqn ||fVdS)a Given a list of lists (of lists...) of objects, returns a iterator which returns a tuple containing the index of each object in the nested list structure as the key, and the object. This can then be called by the dict constructor to create a dictionary of the objects organised by their position in the original nested list. Recursively traverses the given structure, while keeping track of the current position. Should work for any type of object which isn't a list. Parameters ---------- entry : list[list[obj, obj, ...], ...] List of lists of arbitrary depth, containing objects in the order they are to be concatenated. Returns ------- combined_tile_ids : dict[tuple(int, ...), obj] N) isinstancelist enumerater)entryZ current_posiitemrrrrs rcs|jtkrt|t}t|dkrz"ddltfdd|D}Wntk r^d}YnXd dd|D}d|d |d }|r|d }t |dS) Nrc3s|]}t|jVqdSN) issubclassdatetime.0tcftimerr >sz%_ensure_same_types..Fz, css|] }|jVqdSr)__name__r"rrrr'Bsz Cannot combine along dimension 'z' with mixed types. Found: .zZ If importing data directly from a file then setting `use_cftime=True` may fix this issue.) Zdtypeobjectsetmaptypelenr&any ImportErrorjoin TypeError)seriesdimtypesZcftimes error_msgrr%r_ensure_same_types6s   r7c svg}dd|D}|d}|jD] |kr fdd|DtddDrZtdddDtfd dd dDs |td dDrd }n&td dDrd}ntdtddDrtdtddD}|}t ||j d|dd}| t j d }ddt||D}q t|d kr`|s`tdtt||} | |fS)NcSsg|]}dqS)rrr#dsrrr Tsz3_infer_concat_order_from_coords..rcsg|]}|jqSr)Z_indexesgetr8r4rrr:^scss|]}|dkVqdSrrr#indexrrrr'_sz2_infer_concat_order_from_coords..zDEvery dimension needs a coordinate for inferring concatenation ordercSsg|] }|qSr)Zto_pandas_indexr=rrrr:fsc3s|]}|dVqdSrN)equalsr=)indexesrrr'jsrcss|] }|jVqdSr)is_monotonic_increasingr=rrrr'osTcss|] }|jVqdSr)is_monotonic_decreasingr=rrrr'qsFzgCoordinate variable {} is neither monotonically increasing nor monotonically decreasing on all datasetscss|]}|jdkVqdSr?)sizer=rrrr'|sz"Cannot handle size zero dimensionscSsg|] }|dqS)rrr=rrrr:~sZdense)method ascendingZ numeric_onlycSsg|]\}}||fqSrr)r#tile_idpositionrrrr:szWCould not find any dimension coordinates to use to order the datasets for concatenation)Zdimsr/ ValueErrorallappendformatpdZIndexZ to_seriesr7rankZastypeintvalueszipr.r) r concat_dimstile_idsZds0rFZ first_itemsr3rNorder combined_idsr)r4rAr_infer_concat_order_from_coordsQsT   rVcCsB|}dd|D}|s dg}t||dhkr:td||fS)zk Check all tuples are the same length, i.e. check that all lists are nested to the same depth. cSsg|] }t|qSr)r.r#rGrrrr:sz3_check_dimension_depth_tile_ids..rz\The supplied objects do not form a hypercube because sub-lists do not have consistent depths)keysr+rI)combined_tile_idsrSnesting_depthsrrr_check_dimension_depth_tile_idssr[cs`t|\}}t|dD]Bfdd|D}t|}tt|dkrtdtqdS)z4Check all lists along one dimension are same length.rcsg|] }|qSrrrWr<rrr:sz)_check_shape_tile_ids..rzmThe supplied objects do not form a hypercube because sub-lists do not have consistent lengths along dimensionN)r[rangerr.r+rPrIstr)rYrSrZZindices_along_dim occurrencesrr<r_check_shape_tile_idss r_rJZ differentZ no_conflictsouterZdroprrr)compatr1 combine_attrsc Cshtt|}t|} t|| kr8tdt|| |D]} t|| ||||||d}q<|\} | S)a Combines an N-dimensional structure of datasets into one by applying a series of either concat and merge operations along each dimension. No checks are performed on the consistency of the datasets, concat_dims or tile_IDs, because it is assumed that this has already been done. Parameters ---------- combined_ids : Dict[Tuple[int, ...]], xarray.Dataset] Structure containing all datasets to be concatenated with "tile_IDs" as keys, which specify position within the desired final combined result. concat_dims : sequence of str The dimensions along which the datasets should be concatenated. Must be in order, and the length must match the length of the tuples used as keys in combined_ids. If the string is a dimension name then concat along that dimension, if it is None then merge. Returns ------- combined_ds : xarray.Dataset zZconcat_dims has length {} but the datasets passed are nested in a {}-dimensional structurer4 data_varscoordsra fill_valuer1rb)nextiterrXr.rIrL_combine_all_along_first_dimrP) rUrRrdrerarfr1rbZexample_tile_idZn_dims concat_dimZ combined_dsrrr _combine_nds,!   rkc Csjtt|td}tj|td}i} |D]6\} } tt| }|} t| |||||||| | <q.| S)Nkey)rsorteditems _new_tile_id itertoolsgroupbyrP _combine_1d) rUr4rdrerarfr1rbZgroupedZnew_combined_idsZnew_idgrouprrrrris"   ric Csx|dk rbzt||||||||d}Wqttk r^} zdt| krLtdnW5d} ~ XYqtXnt|||||d}|S)zd Applies either concat or merge to 1D list of datasets depending on value of concat_dim Nrczencountered unexpected variablezThese objects cannot be combined using only xarray.combine_nested, instead either use xarray.combine_by_coords, or do it manually with xarray.concat, xarray.merge and xarray.alignrarfr1rb)r rIr]r ) rrjrardrerfr1rbcombinederrrrrrss4  rscCs|\}}|ddS)Nrr)Zsingle_id_ds_pairrGr9rrrrpKsrp)r1rbc CsRt|dkrtS|s t|} ntt||} t| t| |||||||d} | S)Nr)rardrerfr1rb)r.r rrrQr_rk) rrRrardreidsrfr1rbrUrvrrr_nested_combinePs"   ryDATASET_HYPERCUBEzDstr | DataArray | None | Sequence[str | DataArray | pd.Index | None]r]r*r ) rrjrardrerfr1rbreturnc Csntddt|Do*tddt|D}|r8tdt|ttfsN|dkrT|g}t|||||d|||d S)a Explicitly combine an N-dimensional grid of datasets into one by using a succession of concat and merge operations along each dimension of the grid. Does not sort the supplied datasets under any circumstances, so the datasets must be passed in the order you wish them to be concatenated. It does align coordinates, but different variables on datasets can cause it to fail under some scenarios. In complex cases, you may need to clean up your data and use concat/merge explicitly. To concatenate along multiple dimensions the datasets must be passed as a nested list-of-lists, with a depth equal to the length of ``concat_dims``. ``combine_nested`` will concatenate along the top-level list first. Useful for combining datasets from a set of nested directories, or for collecting the output of a simulation parallelized along multiple dimensions. Parameters ---------- datasets : list or nested list of Dataset Dataset objects to combine. If concatenation or merging along more than one dimension is desired, then datasets must be supplied in a nested list-of-lists. concat_dim : str, or list of str, DataArray, Index or None Dimensions along which to concatenate variables, as used by :py:func:`xarray.concat`. Set ``concat_dim=[..., None, ...]`` explicitly to disable concatenation and merge instead along a particular dimension. The position of ``None`` in the list specifies the dimension of the nested-list input along which to merge. Must be the same length as the depth of the list passed to ``datasets``. compat : {"identical", "equals", "broadcast_equals", "no_conflicts", "override"}, optional String indicating how to compare variables of the same name for potential merge conflicts: - "broadcast_equals": all values must be equal when variables are broadcast against each other to ensure common dimensions. - "equals": all values and dimensions must be the same. - "identical": all values, dimensions and attributes must be the same. - "no_conflicts": only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. - "override": skip comparing and pick variable from first dataset data_vars : {"minimal", "different", "all" or list of str}, optional Details are in the documentation of concat coords : {"minimal", "different", "all" or list of str}, optional Details are in the documentation of concat fill_value : scalar or dict-like, optional Value to use for newly missing values. If a dict-like, maps variable names to fill values. Use a data array's name to refer to its values. join : {"outer", "inner", "left", "right", "exact"}, optional String indicating how to combine differing indexes (excluding concat_dim) in objects - "outer": use the union of object indexes - "inner": use the intersection of object indexes - "left": use indexes from the first object with each dimension - "right": use indexes from the last object with each dimension - "exact": instead of aligning, raise `ValueError` when indexes to be aligned are not equal - "override": if indexes are of same size, rewrite indexes to be those of the first object with that dimension. Indexes for the same dimension must have the same size in all objects. combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", "override"} or callable, default: "drop" A callable or a string indicating how to combine attrs of the objects being merged: - "drop": empty attrs on returned Dataset. - "identical": all attrs must be the same on every object. - "no_conflicts": attrs from all objects are combined, any that have the same name must also have the same value. - "drop_conflicts": attrs from all objects are combined, any that have the same name but different values are dropped. - "override": skip comparing and copy attrs from the first dataset to the result. If a callable, it must expect a sequence of ``attrs`` dicts and a context object as its only parameters. Returns ------- combined : xarray.Dataset Examples -------- A common task is collecting data from a parallelized simulation in which each process wrote out to a separate file. A domain which was decomposed into 4 parts, 2 each along both the x and y axes, requires organising the datasets into a doubly-nested list, e.g: >>> x1y1 = xr.Dataset( ... { ... "temperature": (("x", "y"), np.random.randn(2, 2)), ... "precipitation": (("x", "y"), np.random.randn(2, 2)), ... } ... ) >>> x1y1 Dimensions: (x: 2, y: 2) Dimensions without coordinates: x, y Data variables: temperature (x, y) float64 1.764 0.4002 0.9787 2.241 precipitation (x, y) float64 1.868 -0.9773 0.9501 -0.1514 >>> x1y2 = xr.Dataset( ... { ... "temperature": (("x", "y"), np.random.randn(2, 2)), ... "precipitation": (("x", "y"), np.random.randn(2, 2)), ... } ... ) >>> x2y1 = xr.Dataset( ... { ... "temperature": (("x", "y"), np.random.randn(2, 2)), ... "precipitation": (("x", "y"), np.random.randn(2, 2)), ... } ... ) >>> x2y2 = xr.Dataset( ... { ... "temperature": (("x", "y"), np.random.randn(2, 2)), ... "precipitation": (("x", "y"), np.random.randn(2, 2)), ... } ... ) >>> ds_grid = [[x1y1, x1y2], [x2y1, x2y2]] >>> combined = xr.combine_nested(ds_grid, concat_dim=["x", "y"]) >>> combined Dimensions: (x: 4, y: 4) Dimensions without coordinates: x, y Data variables: temperature (x, y) float64 1.764 0.4002 -0.1032 ... 0.04576 -0.1872 precipitation (x, y) float64 1.868 -0.9773 0.761 ... -0.7422 0.1549 0.3782 ``combine_nested`` can also be used to explicitly merge datasets with different variables. For example if we have 4 datasets, which are divided along two times, and contain two different variables, we can pass ``None`` to ``concat_dim`` to specify the dimension of the nested list over which we wish to use ``merge`` instead of ``concat``: >>> t1temp = xr.Dataset({"temperature": ("t", np.random.randn(5))}) >>> t1temp Dimensions: (t: 5) Dimensions without coordinates: t Data variables: temperature (t) float64 -0.8878 -1.981 -0.3479 0.1563 1.23 >>> t1precip = xr.Dataset({"precipitation": ("t", np.random.randn(5))}) >>> t1precip Dimensions: (t: 5) Dimensions without coordinates: t Data variables: precipitation (t) float64 1.202 -0.3873 -0.3023 -1.049 -1.42 >>> t2temp = xr.Dataset({"temperature": ("t", np.random.randn(5))}) >>> t2precip = xr.Dataset({"precipitation": ("t", np.random.randn(5))}) >>> ds_grid = [[t1temp, t1precip], [t2temp, t2precip]] >>> combined = xr.combine_nested(ds_grid, concat_dim=["t", None]) >>> combined Dimensions: (t: 10) Dimensions without coordinates: t Data variables: temperature (t) float64 -0.8878 -1.981 -0.3479 ... -0.5097 -0.4381 -1.253 precipitation (t) float64 1.202 -0.3873 -0.3023 ... -0.2127 -0.8955 0.3869 See also -------- concat merge css|]}t|tVqdSr)rr r#objrrrr'>sz!combine_nested..css"|]}t|to|jdkVqdSrrr namer|rrrr'@sz+Can't combine datasets with unnamed arrays.NF)rRrardrerxrfr1rb)r/rrIrr]r ry) rrjrardrerfr1rbZmixed_datasets_and_arraysrrrcombine_nesteds.@rcCs tt|Sr)tuplern)r9rrr vars_as_keysXsrc Cst|dkrtdtt|\}}|dkr6t|nt|t||||||||d} |D]*} | j| } | j sZ| j sZtd | qZ| S)af Attempt to combine a list of Datasets into a hypercube using their coordinates. All provided Datasets must belong to a single variable, ie. must be assigned the same variable name. This precondition is not checked by this function, so the caller is assumed to know what it's doing. This function is NOT part of the public API. rzRAt least one Dataset is required to resolve variable names for combined hypercube.N)rRrdrerarfr1rbzJResulting object does not have monotonic global indexes along dimension {}) r.rIrVrr_r[rkrAr;rBrCrL) rrfrdrerar1rbrUrR concatenatedr4rArrr"_combine_single_variable_hypercube\s6     rzIterable[Dataset | DataArray]z4Literal[('all', 'minimal', 'different')] | list[str]zIterable[Dataset] | NonezDataset | DataArray) data_objectsrardrerfr1rbrr{c Cs|dk rtd|}|s tSdd|D}t|rxt|rndd|D} t| ||||||d} t| Stdn^dd|D}t |t d } t j | t d } g} | D],\}}tt |||||||d}| |qt| ||||d S) a/) Attempt to auto-magically combine the given datasets (or data arrays) into one by using dimension coordinates. This function attempts to combine a group of datasets along any number of dimensions into a single entity by inspecting coords and metadata and using a combination of concat and merge. Will attempt to order the datasets such that the values in their dimension coordinates are monotonic along all dimensions. If it cannot determine the order in which to concatenate the datasets, it will raise a ValueError. Non-coordinate dimensions will be ignored, as will any coordinate dimensions which do not vary between each dataset. Aligns coordinates, but different variables on datasets can cause it to fail under some scenarios. In complex cases, you may need to clean up your data and use concat/merge explicitly (also see `combine_nested`). Works well if, for example, you have N years of data and M data variables, and each combination of a distinct time period and set of data variables is saved as its own dataset. Also useful for if you have a simulation which is parallelized in multiple dimensions, but has global coordinates saved in each file specifying the positions of points within the global domain. Parameters ---------- data_objects : Iterable of Datasets or DataArrays Data objects to combine. compat : {"identical", "equals", "broadcast_equals", "no_conflicts", "override"}, optional String indicating how to compare variables of the same name for potential conflicts: - "broadcast_equals": all values must be equal when variables are broadcast against each other to ensure common dimensions. - "equals": all values and dimensions must be the same. - "identical": all values, dimensions and attributes must be the same. - "no_conflicts": only values which are not null in both datasets must be equal. The returned dataset then contains the combination of all non-null values. - "override": skip comparing and pick variable from first dataset data_vars : {"minimal", "different", "all" or list of str}, optional These data variables will be concatenated together: - "minimal": Only data variables in which the dimension already appears are included. - "different": Data variables which are not equal (ignoring attributes) across all datasets are also concatenated (as well as all for which dimension already appears). Beware: this option may load the data payload of data variables into memory if they are not already loaded. - "all": All data variables will be concatenated. - list of str: The listed data variables will be concatenated, in addition to the "minimal" data variables. If objects are DataArrays, `data_vars` must be "all". coords : {"minimal", "different", "all"} or list of str, optional As per the "data_vars" kwarg, but for coordinate variables. fill_value : scalar or dict-like, optional Value to use for newly missing values. If a dict-like, maps variable names to fill values. Use a data array's name to refer to its values. If None, raises a ValueError if the passed Datasets do not create a complete hypercube. join : {"outer", "inner", "left", "right", "exact"}, optional String indicating how to combine differing indexes in objects - "outer": use the union of object indexes - "inner": use the intersection of object indexes - "left": use indexes from the first object with each dimension - "right": use indexes from the last object with each dimension - "exact": instead of aligning, raise `ValueError` when indexes to be aligned are not equal - "override": if indexes are of same size, rewrite indexes to be those of the first object with that dimension. Indexes for the same dimension must have the same size in all objects. combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", "override"} or callable, default: "drop" A callable or a string indicating how to combine attrs of the objects being merged: - "drop": empty attrs on returned Dataset. - "identical": all attrs must be the same on every object. - "no_conflicts": attrs from all objects are combined, any that have the same name must also have the same value. - "drop_conflicts": attrs from all objects are combined, any that have the same name but different values are dropped. - "override": skip comparing and copy attrs from the first dataset to the result. If a callable, it must expect a sequence of ``attrs`` dicts and a context object as its only parameters. datasets : Iterable of Datasets Returns ------- combined : xarray.Dataset or xarray.DataArray Will return a Dataset unless all the inputs are unnamed DataArrays, in which case a DataArray will be returned. See also -------- concat merge combine_nested Examples -------- Combining two datasets using their common dimension coordinates. Notice they are concatenated based on the values in their dimension coordinates, not on their position in the list passed to `combine_by_coords`. >>> x1 = xr.Dataset( ... { ... "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)), ... "precipitation": (("y", "x"), np.random.rand(6).reshape(2, 3)), ... }, ... coords={"y": [0, 1], "x": [10, 20, 30]}, ... ) >>> x2 = xr.Dataset( ... { ... "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)), ... "precipitation": (("y", "x"), np.random.rand(6).reshape(2, 3)), ... }, ... coords={"y": [2, 3], "x": [10, 20, 30]}, ... ) >>> x3 = xr.Dataset( ... { ... "temperature": (("y", "x"), 20 * np.random.rand(6).reshape(2, 3)), ... "precipitation": (("y", "x"), np.random.rand(6).reshape(2, 3)), ... }, ... coords={"y": [2, 3], "x": [40, 50, 60]}, ... ) >>> x1 Dimensions: (y: 2, x: 3) Coordinates: * y (y) int64 0 1 * x (x) int64 10 20 30 Data variables: temperature (y, x) float64 10.98 14.3 12.06 10.9 8.473 12.92 precipitation (y, x) float64 0.4376 0.8918 0.9637 0.3834 0.7917 0.5289 >>> x2 Dimensions: (y: 2, x: 3) Coordinates: * y (y) int64 2 3 * x (x) int64 10 20 30 Data variables: temperature (y, x) float64 11.36 18.51 1.421 1.743 0.4044 16.65 precipitation (y, x) float64 0.7782 0.87 0.9786 0.7992 0.4615 0.7805 >>> x3 Dimensions: (y: 2, x: 3) Coordinates: * y (y) int64 2 3 * x (x) int64 40 50 60 Data variables: temperature (y, x) float64 2.365 12.8 2.867 18.89 10.44 8.293 precipitation (y, x) float64 0.2646 0.7742 0.4562 0.5684 0.01879 0.6176 >>> xr.combine_by_coords([x2, x1]) Dimensions: (y: 4, x: 3) Coordinates: * y (y) int64 0 1 2 3 * x (x) int64 10 20 30 Data variables: temperature (y, x) float64 10.98 14.3 12.06 10.9 ... 1.743 0.4044 16.65 precipitation (y, x) float64 0.4376 0.8918 0.9637 ... 0.7992 0.4615 0.7805 >>> xr.combine_by_coords([x3, x1]) Dimensions: (y: 4, x: 6) Coordinates: * y (y) int64 0 1 2 3 * x (x) int64 10 20 30 40 50 60 Data variables: temperature (y, x) float64 10.98 14.3 12.06 nan ... nan 18.89 10.44 8.293 precipitation (y, x) float64 0.4376 0.8918 0.9637 ... 0.5684 0.01879 0.6176 >>> xr.combine_by_coords([x3, x1], join="override") Dimensions: (y: 2, x: 6) Coordinates: * y (y) int64 0 1 * x (x) int64 10 20 30 40 50 60 Data variables: temperature (y, x) float64 10.98 14.3 12.06 2.365 ... 18.89 10.44 8.293 precipitation (y, x) float64 0.4376 0.8918 0.9637 ... 0.5684 0.01879 0.6176 >>> xr.combine_by_coords([x1, x2, x3]) Dimensions: (y: 4, x: 6) Coordinates: * y (y) int64 0 1 2 3 * x (x) int64 10 20 30 40 50 60 Data variables: temperature (y, x) float64 10.98 14.3 12.06 nan ... 18.89 10.44 8.293 precipitation (y, x) float64 0.4376 0.8918 0.9637 ... 0.5684 0.01879 0.6176 You can also combine DataArray objects, but the behaviour will differ depending on whether or not the DataArrays are named. If all DataArrays are named then they will be promoted to Datasets before combining, and then the resultant Dataset will be returned, e.g. >>> named_da1 = xr.DataArray( ... name="a", data=[1.0, 2.0], coords={"x": [0, 1]}, dims="x" ... ) >>> named_da1 array([1., 2.]) Coordinates: * x (x) int64 0 1 >>> named_da2 = xr.DataArray( ... name="a", data=[3.0, 4.0], coords={"x": [2, 3]}, dims="x" ... ) >>> named_da2 array([3., 4.]) Coordinates: * x (x) int64 2 3 >>> xr.combine_by_coords([named_da1, named_da2]) Dimensions: (x: 4) Coordinates: * x (x) int64 0 1 2 3 Data variables: a (x) float64 1.0 2.0 3.0 4.0 If all the DataArrays are unnamed, a single DataArray will be returned, e.g. >>> unnamed_da1 = xr.DataArray(data=[1.0, 2.0], coords={"x": [0, 1]}, dims="x") >>> unnamed_da2 = xr.DataArray(data=[3.0, 4.0], coords={"x": [2, 3]}, dims="x") >>> xr.combine_by_coords([unnamed_da1, unnamed_da2]) array([1., 2., 3., 4.]) Coordinates: * x (x) int64 0 1 2 3 Finally, if you attempt to combine a mix of unnamed DataArrays with either named DataArrays or Datasets, a ValueError will be raised (as this is an ambiguous operation). NzxThe datasets argument has been renamed to `data_objects`. From 0.21 on passing a value for datasets will raise an error.cSs g|]}t|to|jdkqSrr~)r#Z data_objectrrrr:sz%combine_by_coords..cSsg|] }|qSr)Z_to_temp_dataset)r#Zunnamed_dataarrayrrrr:s)rfrdrerar1rbzXCan't automatically combine unnamed DataArrays with either named DataArrays or Datasets.cSs"g|]}t|tr|n|qSr)rr Z to_datasetr|rrrr:srlru)warningswarnr r/rJrr Z_from_temp_datasetrIrnrrqrrrrKr )rrardrerfr1rbrZobjs_are_unnamed_dataarraysZ temp_datasetsZcombined_temp_datasetZsorted_datasetsZgrouped_by_varsZ!concatenated_grouped_by_data_varsvarsZdatasets_with_same_varsrrrrcombine_by_coordssl     r)/ __future__rrqr collectionsrtypingrrrrrZpandasrMZ xarray.corer Zxarray.core.concatr Zxarray.core.dataarrayr Zxarray.core.datasetr Zxarray.core.merger Zxarray.core.utilsrZxarray.core.typesrrrrrr7rVr[r_ZNArkrirsrpryrzrrrrrrrrs        ND2 , Z=