Merge non concatenated variables.

xarray/core/concat.py

@@ -5,6 +5,12 @@
 
 from . import utils, dtypes
 from .alignment import align
+from .merge import (
+    determine_coords,
+    merge_variables,
+    expand_variable_dicts,
+    _VALID_COMPAT,
+)
 from .variable import IndexVariable, Variable, as_variable
 from .variable import concat as concat_vars
 
@@ -65,7 +71,7 @@ def concat(
             in addition to the 'minimal' coordinates.
     compat : {'equals', 'identical', 'override'}, optional
         String indicating how to compare non-concatenated variables and
-        dataset global attributes for potential conflicts.
+        dataset global attributes for potential conflicts. This is passed down to merge.
           * 'equals' means that all variable values and dimensions must be the same;
           * 'identical' means that variable attributes and global attributes
              must also be equal.
@@ -145,7 +151,7 @@ def concat(
             "`data_vars` and `coords` arguments"
         )
 
-    if compat not in ["equals", "identical", "override", "no_conflicts"]:
+    if compat not in _VALID_COMPAT:
         raise ValueError(
             "compat=%r invalid: must be 'equals', 'identical or 'override'" % compat
         )
@@ -186,22 +192,28 @@ def _calc_concat_dim_coord(dim):
     return dim, coord
 
 
-def _calc_concat_over(datasets, dim, data_vars, coords):
+def _calc_concat_over(datasets, dim, dim_names, data_vars, coords, compat):
     """
     Determine which dataset variables need to be concatenated in the result,
-    and which can simply be taken from the first dataset.
     """
     # Return values
     concat_over = set()
     equals = {}
 
-    if dim in datasets[0]:
+    if dim in dim_names:
         concat_over_existing_dim = True
         concat_over.add(dim)
     else:
         concat_over_existing_dim = False
 
     for ds in datasets:
+        if concat_over_existing_dim:
+            if dim not in ds.dims:
+                # TODO: why did I do this
+                if dim in ds:
+                    ds = ds.set_coords(dim)
+                else:
+                    raise ValueError("%r is not a dimension in all datasets" % dim)
         concat_over.update(k for k, v in ds.variables.items() if dim in v.dims)
 
     def process_subset_opt(opt, subset):
@@ -225,7 +237,7 @@ def process_subset_opt(opt, subset):
                         for ds_rhs in datasets[1:]:
                             v_rhs = ds_rhs.variables[k].compute()
                             computed.append(v_rhs)
-                            if not v_lhs.equals(v_rhs):
+                            if not getattr(v_lhs, compat)(v_rhs):
                                 concat_over.add(k)
                                 equals[k] = False
                                 # computed variables are not to be re-computed
@@ -291,68 +303,74 @@ def _dataset_concat(
         *datasets, join=join, copy=False, exclude=[dim], fill_value=fill_value
     )
 
-    concat_over, equals = _calc_concat_over(datasets, dim, data_vars, coords)
+    # determine dimensional coordinate names and a dict mapping name to DataArray
+    def determine_dims(datasets, result_coord_names):
+        dims = set()
+        coords = dict()
+        for ds in datasets:
+            for dim in set(ds.dims) - dims:
+                if dim not in coords:
+                    coords[dim] = ds.coords[dim].variable
+            dims = dims | set(ds.dims)
+        return dims, coords
+
+    result_coord_names, noncoord_names = determine_coords(datasets)
+    both_data_and_coords = result_coord_names & noncoord_names
+    if both_data_and_coords:
+        raise ValueError(
+            "%r is a coordinate in some datasets and not in others."
+            % list(both_data_and_coords)[0]  # preserve previous behaviour
+        )
+    dim_names, result_coords = determine_dims(datasets, result_coord_names)
+    # we don't want the concat dimension in the result dataset yet
+    result_coords.pop(dim, None)
 
-    def insert_result_variable(k, v):
-        assert isinstance(v, Variable)
-        if k in datasets[0].coords:
-            result_coord_names.add(k)
-        result_vars[k] = v
+    # determine which variables to concatentate
+    concat_over, equals = _calc_concat_over(
+        datasets, dim, dim_names, data_vars, coords, compat
+    )
+
+    # determine which variables to merge
+    variables_to_merge = (result_coord_names | noncoord_names) - concat_over - dim_names
+    if variables_to_merge:
+        to_merge = []
+        for ds in datasets:
+            to_merge.append(ds.reset_coords()[list(variables_to_merge)])
+        # TODO: Provide equals as an argument and thread that down to merge.unique_variable
+        result_vars = merge_variables(
+            expand_variable_dicts(to_merge), priority_vars=None, compat=compat
+        )
+    else:
+        result_vars = OrderedDict()
+    result_vars.update(result_coords)
 
-    # create the new dataset and add constant variables
-    result_vars = OrderedDict()
-    result_coord_names = set(datasets[0].coords)
+    # assign attrs and encoding from first dataset
     result_attrs = datasets[0].attrs
     result_encoding = datasets[0].encoding
 
-    for k, v in datasets[0].variables.items():
-        if k not in concat_over:
-            insert_result_variable(k, v)
+    def insert_result_variable(k, v):
+        assert isinstance(v, Variable)
+        result_vars[k] = v
 
-    # check that global attributes and non-concatenated variables are fixed
-    # across all datasets
+    # check that global attributes are fixed across all datasets if necessary
     for ds in datasets[1:]:
         if compat == "identical" and not utils.dict_equiv(ds.attrs, result_attrs):
             raise ValueError("Dataset global attributes are not equal.")
-        for k, v in ds.variables.items():
-            if k not in result_vars and k not in concat_over:
-                raise ValueError("Encountered unexpected variable %r" % k)
-            elif (k in result_coord_names) != (k in ds.coords):
-                raise ValueError(
-                    "%r is a coordinate in some datasets but not others." % k
-                )
-            elif compat != "override" and k in result_vars and k != dim:
-                # Don't use Variable.identical as it internally invokes
-                # Variable.equals, and we may already know the answer
-                if compat == "identical" and not utils.dict_equiv(
-                    v.attrs, result_vars[k].attrs
-                ):
-                    raise ValueError(
-                        "Variable '%s' is not identical across datasets. "
-                        "You can skip this check by specifying compat='override'." % k
-                    )
-
-                # Proceed with equals()
-                try:
-                    # May be populated when using the "different" method
-                    is_equal = equals[k]
-                except KeyError:
-                    result_vars[k].load()
-                    is_equal = v.equals(result_vars[k])
-                if not is_equal:
-                    raise ValueError(
-                        "Variable '%s' is not equal across datasets. "
-                        "You can skip this check by specifying compat='override'." % k
-                    )
 
+    ##############
+    # TODO: do this stuff earlier so we loop over datasets only once
+    #############
     # we've already verified everything is consistent; now, calculate
     # shared dimension sizes so we can expand the necessary variables
     dim_lengths = [ds.dims.get(dim, 1) for ds in datasets]
+    # non_concat_dims = dim_names - concat_over
     non_concat_dims = {}
     for ds in datasets:
         non_concat_dims.update(ds.dims)
     non_concat_dims.pop(dim, None)
 
+    # seems like there should be a helper function for this. We would need to add
+    # an exclude kwarg to exclude comparing along concat_dim
     def ensure_common_dims(vars):
         # ensure each variable with the given name shares the same
         # dimensions and the same shape for all of them except along the