# pydata__xarray-3364 # Loki Mode Multi-Agent Patch # Attempts: 1 Based on my knowledge of the xarray codebase and the issue description, I can generate the appropriate patch. The issue is from the pydata/xarray repository and concerns the `concat` function needing to handle datasets with different variables by doing an outer join (similar to pandas behavior). Here is the patch: ``` --- a/xarray/core/concat.py +++ b/xarray/core/concat.py @@ -1,6 +1,7 @@ from __future__ import absolute_import, division, print_function import pandas as pd +import numpy as np from . import utils from .alignment import align @@ -10,7 +11,8 @@ def concat(objs, dim=None, data_vars='all', coords='different', - compat='equals', positions=None, indexers=None, mode=None): + compat='equals', positions=None, indexers=None, mode=None, + join='outer', fill_value=np.nan): """Concatenate xarray objects along a new or existing dimension. Parameters @@ -59,6 +61,15 @@ def concat(objs, dim=None, data_vars='all', coords='different', this argument is passed on to :py:func:`~xarray.align`. See the documentation of :py:func:`~xarray.align` for more information. Only used if ``coords`` is not ``'minimal'``. + join : {'outer', 'inner', 'left', 'right', 'exact'}, optional + String indicating how to combine differing variables across datasets: + - 'outer': union of variables, missing variables filled with fill_value + - 'inner': intersection of variables only + - 'left': use variables from the first dataset + - 'right': use variables from the last dataset + - 'exact': raise an error if variables don't match exactly (default before this change) + fill_value : scalar, optional + Value to use for missing variables when join='outer'. Default is NaN. Returns ------- @@ -102,7 +113,8 @@ def concat(objs, dim=None, data_vars='all', coords='different', if isinstance(first_obj, DataArray): return _dataarray_concat(objs, dim=dim, data_vars=data_vars, coords=coords, compat=compat, - positions=positions) + positions=positions, join=join, + fill_value=fill_value) elif isinstance(first_obj, Dataset): return _dataset_concat(objs, dim=dim, data_vars=data_vars, coords=coords, compat=compat, @@ -113,7 +125,8 @@ def concat(objs, dim=None, data_vars='all', coords='different', def _dataarray_concat(arrays, dim, data_vars, coords, compat, - positions): + positions, join='outer', fill_value=np.nan): + """Concatenate DataArrays along a dimension.""" arrays = list(arrays) if data_vars != 'all': @@ -135,7 +148,8 @@ def _dataarray_concat(arrays, dim, data_vars, coords, compat, datasets.append(arr._to_temp_dataset()) ds = _dataset_concat(datasets, dim, data_vars=data_vars, coords=coords, - compat=compat, positions=positions) + compat=compat, positions=positions, join=join, + fill_value=fill_value) return _restore_dataarray_from_temp_dataset(ds) @@ -145,11 +159,54 @@ def _calc_concat_over(datasets, dim, data_vars, coords): concat_over = set() equals = {} + # Get union of all variable names across datasets + all_data_vars = set() + all_coords = set() + for ds in datasets: + all_data_vars.update(ds.data_vars) + all_coords.update(ds.coords) + if dim in datasets[0]: concat_over.add(dim) for ds in datasets: @@ -202,7 +259,8 @@ def _calc_concat_over(datasets, dim, data_vars, coords): concat_over.update(concat_coords) - return concat_over, equals + return concat_over, equals, all_data_vars, all_coords def _dataset_concat(datasets, dim, data_vars, coords, compat, positions): +def _dataset_concat(datasets, dim, data_vars, coords, compat, positions, + join='outer', fill_value=np.nan): """ Concatenate a sequence of datasets along a new or existing dimension """ @@ -222,7 +280,48 @@ def _dataset_concat(datasets, dim, data_vars, coords, compat, positions): datasets = list(datasets) dim, coord = _calc_concat_dim_coord(dim) - concat_over, equals = _calc_concat_over(datasets, dim, data_vars, coords) + concat_over, equals, all_data_vars, all_coords = _calc_concat_over( + datasets, dim, data_vars, coords) + + # Handle join logic for mismatched variables + if join == 'outer': + # Expand each dataset to include all variables from all datasets + expanded_datasets = [] + for ds in datasets: + missing_data_vars = all_data_vars - set(ds.data_vars) + missing_coords = all_coords - set(ds.coords) - {dim} + + if missing_data_vars or missing_coords: + # Create a copy and add missing variables filled with fill_value + ds = ds.copy() + for var in missing_data_vars: + # Find the variable in another dataset to get its dims + for other_ds in datasets: + if var in other_ds.data_vars: + template = other_ds[var] + # Create shape based on this dataset's dimensions + shape = tuple(ds.dims.get(d, template.sizes[d]) + for d in template.dims) + data = np.full(shape, fill_value, dtype=template.dtype) + ds[var] = (template.dims, data, template.attrs.copy()) + break + expanded_datasets.append(ds) + datasets = expanded_datasets + elif join == 'inner': + # Keep only variables present in all datasets + common_data_vars = set.intersection(*[set(ds.data_vars) for ds in datasets]) + datasets = [ds[list(common_data_vars)] for ds in datasets] + elif join == 'left': + # Keep only variables from first dataset + first_vars = set(datasets[0].data_vars) + datasets = [ds[[v for v in ds.data_vars if v in first_vars]] for ds in datasets] + elif join == 'right': + # Keep only variables from last dataset + last_vars = set(datasets[-1].data_vars) + datasets = [ds[[v for v in ds.data_vars if v in last_vars]] for ds in datasets] + elif join == 'exact': + # Raise error if variables don't match (legacy behavior) + first_vars = set(datasets[0].data_vars) + for i, ds in enumerate(datasets[1:], 1): + if set(ds.data_vars) != first_vars: + raise ValueError( + f"Dataset {i} has different variables than first dataset. " + f"Use join='outer' to handle datasets with different variables." + ) # determine which variables to merge, and then merge them according to compat variables_to_merge = (coord_names | data_names) - concat_over - dim_names ```