More options for eval engine (#68)

* dtype arg for get_expr

* improve get_expr

* add DataTree.eval

* eval_many

* remove rogue print

* with_coords

* enable dotted names with numexpr and python engines

* fix dimension order with numexpr

* default to numexpr usually

Author: jpn--

sharrow/relationships.py

@@ -1,13 +1,16 @@
 import ast
 import logging
 import warnings
+from collections.abc import Mapping, Sequence
+from typing import Literal
 
 import networkx as nx
 import numpy as np
 import pandas as pd
 import xarray as xr
 
 from .dataset import Dataset, construct
+from .tree_branch import DataTreeBranch
 
 try:
     from dask.array import Array as dask_array_type
@@ -69,7 +72,10 @@ def _ixname():
         return f"index{inum}"
 
     for k, v in idxs.items():
-        loaders[k] = xr.DataArray(v, dims=[_ixname() for n in range(v.ndim)])
+        if isinstance(v, xr.DataArray):
+            loaders[k] = v
+        else:
+            loaders[k] = xr.DataArray(v, dims=[_ixname() for n in range(v.ndim)])
     if _names:
         ds = source[_names]
     else:
@@ -91,7 +97,10 @@ def _ixname():
         return f"index{inum}"
 
     for k, v in idxs.items():
-        loaders[k] = xr.DataArray(v, dims=[_ixname() for n in range(v.ndim)])
+        if isinstance(v, xr.DataArray):
+            loaders[k] = v
+        else:
+            loaders[k] = xr.DataArray(v, dims=[_ixname() for n in range(v.ndim)])
     if _names:
         ds = source[_names]
     else:
@@ -575,8 +584,6 @@ def add_dataset(self, name, dataset, relationships=(), as_root=False):
             self.digitize_relationships(inplace=True)
 
     def add_items(self, items):
-        from collections.abc import Mapping, Sequence
-
         if isinstance(items, Sequence):
             for i in items:
                 self.add_items(i)
@@ -621,7 +628,15 @@ def _get_relationship(self, edge):
         )
 
     def __getitem__(self, item):
-        return self.get(item)
+        if hasattr(self, "_eval_cache") and item in self._eval_cache:
+            return self._eval_cache[item]
+        try:
+            return self.get(item)
+        except KeyError as err:
+            s = self._graph.nodes.get(item, {}).get("dataset", None)
+            if s is not None:
+                return DataTreeBranch(self, item)
+            raise err
 
     def get(self, item, default=None, broadcast=True, coords=True):
         """
@@ -687,6 +702,11 @@ def get(self, item, default=None, broadcast=True, coords=True):
                             add_coords[i] = base_dataset.coords[i]
                     if add_coords:
                         result = result.assign_coords(add_coords)
+        if broadcast:
+            if self.dim_order is None:
+                result = result.transpose(*self.root_dims)
+            else:
+                result = result.transpose(*self.dim_order)
         return result
 
     def finditem(self, item, maybe_in=None):
@@ -828,6 +848,32 @@ def _getitem(
                             _positions[r.child_name] = _idx
                         if top_dim_name is not None:
                             top_dim_names[r.child_name] = top_dim_name
+                    if len(top_dim_names) > 1:
+                        if len(set(top_dim_names.values())) == 1:
+                            # capture the situation where all top dims are the same
+                            _positions = {
+                                k: xr.DataArray(v, dims=[top_dim_names[k]])
+                                for (k, v) in _positions.items()
+                            }
+                            _labels = {
+                                k: xr.DataArray(v, dims=[top_dim_names[k]])
+                                for (k, v) in _labels.items()
+                            }
+                            # the top dim names have served their purpose, so clear them
+                            top_dim_names = {}
+                        elif len(set(top_dim_names.values())) < len(top_dim_names):
+                            # capture the situation where some but not all top dims are the same
+                            # same as above?
+                            _positions = {
+                                k: xr.DataArray(v, dims=[top_dim_names[k]])
+                                for (k, v) in _positions.items()
+                            }
+                            _labels = {
+                                k: xr.DataArray(v, dims=[top_dim_names[k]])
+                                for (k, v) in _labels.items()
+                            }
+                            # the top dim names have served their purpose, so clear them
+                            top_dim_names = {}
                     y = xgather(result, _positions, _labels)
                     if len(result.dims) == 1 and len(y.dims) == 1:
                         y = y.rename({y.dims[0]: result.dims[0]})
@@ -844,19 +890,34 @@ def _getitem(
 
         raise KeyError(item)
 
-    def get_expr(self, expression, engine="sharrow", allow_native=True):
+    def get_expr(
+        self,
+        expression,
+        engine="sharrow",
+        allow_native=True,
+        *,
+        dtype="float32",
+        with_coords: bool = True,
+    ):
         """
         Access or evaluate an expression.
 
         Parameters
         ----------
         expression : str
-        engine : {'sharrow', 'numexpr'}
+        engine : {'sharrow', 'numexpr', 'python'}
             The engine used to resolve expressions.
         allow_native : bool, default True
             If the expression is an array in a dataset of this tree, return
             that array directly.  Set to false to force evaluation, which
             will also ensure proper broadcasting consistent with this data tree.
+        dtype : str or dtype, default 'float32'
+            The dtype to use when creating new arrays.  This only applies when
+            the expression is not returned as a native variable from the tree.
+        with_coords : bool, default True
+            Attach coordinates from the root node of the tree to the result.
+            If the coordinates are not needed in the result, the process
+            of attaching them can be skipped.
 
         Returns
         -------
@@ -869,21 +930,185 @@ def get_expr(self, expression, engine="sharrow", allow_native=True):
                 raise KeyError
         except (KeyError, IndexError):
             if engine == "sharrow":
+                if dtype is None:
+                    dtype = "float32"
                 result = (
-                    self.setup_flow({expression: expression})
+                    self.setup_flow({expression: expression}, dtype=dtype)
                     .load_dataarray()
                     .isel(expressions=0)
                 )
             elif engine == "numexpr":
                 from xarray import DataArray
 
-                result = DataArray(
-                    pd.eval(expression, resolvers=[self], engine="numexpr"),
-                )
+                self._eval_cache = {}
+                try:
+                    result = DataArray(
+                        pd.eval(expression, resolvers=[self], engine="numexpr"),
+                    ).astype(dtype)
+                except NotImplementedError:
+                    result = DataArray(
+                        pd.eval(expression, resolvers=[self], engine="python"),
+                    ).astype(dtype)
+                else:
+                    # numexpr doesn't carry over the dimension names or coords
+                    result = result.rename(
+                        {result.dims[i]: self.root_dims[i] for i in range(result.ndim)}
+                    )
+                    if with_coords:
+                        result = result.assign_coords(self.root_dataset.coords)
+                finally:
+                    del self._eval_cache
+            elif engine == "python":
+                from xarray import DataArray
+
+                self._eval_cache = {}
+                try:
+                    result = DataArray(
+                        pd.eval(expression, resolvers=[self], engine="python"),
+                    ).astype(dtype)
+                finally:
+                    del self._eval_cache
             else:
                 raise ValueError(f"unknown engine {engine}") from None
         return result
 
+    def eval(
+        self,
+        expression: str,
+        engine: Literal[None, "numexpr", "sharrow", "python"] = None,
+        *,
+        dtype: np.dtype | str | None = None,
+        name: str | None = None,
+        with_coords: bool = True,
+    ):
+        """
+        Evaluate an expression.
+
+        The resulting DataArray will have dimensions that match the root
+        Dataset of this tree, and the content will be broadcast to those
+        dimensions if necessary.  The expression evaluated will be assigned
+        as a scalar coordinate named 'expressions', to facilitate concatenation
+        with other `eval` results if desired.
+
+        Parameters
+        ----------
+        expression : str
+        engine : {None, 'numexpr', 'sharrow', 'python'}
+            The engine used to resolve expressions. If None, the default is
+            to try 'numexpr' first, then 'sharrow' if that fails.
+        dtype : str or dtype, optional
+            The dtype to use for the result.  If the engine is `sharrow` and
+            no value is given, this will default to `float32`, otherwise the
+            default is to use the dtype of the result of the expression.
+        name : str, optional
+            The name to give the resulting DataArray.
+
+        Returns
+        -------
+        DataArray
+        """
+        if not isinstance(expression, str):
+            raise TypeError("expression must be a string")
+        if engine is None:
+            try:
+                result = self.get_expr(
+                    expression,
+                    "numexpr",
+                    allow_native=False,
+                    dtype=dtype,
+                    with_coords=with_coords,
+                )
+            except Exception:
+                result = self.get_expr(
+                    expression,
+                    "sharrow",
+                    allow_native=False,
+                    dtype=dtype,
+                    with_coords=with_coords,
+                )
+        else:
+            result = self.get_expr(
+                expression,
+                engine,
+                allow_native=False,
+                dtype=dtype,
+                with_coords=with_coords,
+            )
+        if with_coords and "expressions" not in result.coords:
+            # add the expression as a scalar coordinate (with no dimension)
+            result = result.assign_coords(expressions=xr.DataArray(expression))
+        if name is not None:
+            result.name = name
+        return result
+
+    def eval_many(
+        self,
+        expressions: Sequence[str] | Mapping[str, str] | pd.Series,
+        *,
+        engine: Literal[None, "numexpr", "sharrow", "python"] = None,
+        dtype=None,
+        result_type: Literal["dataset", "dataarray"] = "dataset",
+        with_coords: bool = True,
+    ) -> xr.Dataset | xr.DataArray:
+        """
+        Evaluate multiple expressions.
+
+        Parameters
+        ----------
+        expressions : Sequence[str] or Mapping[str,str] or pd.Series
+            The expressions to evaluate.  If a sequence, the names of the
+            resulting DataArrays will be the same as the expressions.  If a
+            mapping or Series, the keys or index will be used as the names.
+        engine : {None, 'numexpr', 'sharrow', 'python'}
+            The engine used to resolve expressions. If None, the default is to
+            try 'numexpr' first, then 'sharrow' if that fails.
+        dtype : str or dtype, optional
+            The dtype to use for the result.  If the engine is `sharrow` and
+            no value is given, this will default to `float32`, otherwise the
+            default is to use the dtype of the result of the concatenation of
+            the expressions.
+        result_type : {'dataset', 'dataarray'}
+            Whether to return a Dataset (with a variable for each expression)
+            or a DataArray (with a dimension across all expressions).
+
+        Returns
+        -------
+        Dataset or DataArray
+        """
+        if result_type not in {"dataset", "dataarray"}:
+            raise ValueError("result_type must be one of ['dataset', 'dataarray']")
+        if not isinstance(expressions, (Mapping, pd.Series)):
+            expressions = pd.Series(expressions, index=expressions)
+        if isinstance(expressions, Mapping):
+            expressions = pd.Series(expressions)
+        if result_type == "dataset":
+            arrays = {}
+            for k, v in expressions.items():
+                a = self.eval(
+                    v, engine=engine, dtype=dtype, name=k, with_coords=with_coords
+                )
+                if "expressions" in a.coords:
+                    a = a.drop_vars("expressions")
+                arrays[k] = a.assign_attrs(expression=v)
+            result = xr.Dataset(arrays)
+        else:
+            arrays = {}
+            for k, v in expressions.items():
+                a = self.eval(
+                    v, engine=engine, dtype=dtype, name=k, with_coords=with_coords
+                )
+                if "expressions" in a.coords:
+                    a = a.drop_vars("expressions")
+                a = a.expand_dims("expressions", -1)
+                arrays[k] = a
+            result = xr.concat(list(arrays.values()), "expressions")
+            if with_coords:
+                result = result.assign_coords(
+                    expressions=expressions.index,
+                    source=xr.DataArray(expressions.values, dims="expressions"),
+                )
+        return result
+
     @property
     def subspaces(self):
         """Mapping[str,Dataset] : Direct access to node Dataset objects by name."""
@@ -1583,3 +1808,19 @@ def merged_dataset(self, columns=None, uniquify=False):
         if coords:
             result.assign_coords(coords)
         return result
+
+    def __iter__(self):
+        """Iterate over all the datasets."""
+        import itertools
+
+        if hasattr(self, "_eval_cache"):
+            z = (self._eval_cache,)
+        else:
+            z = ()
+        return itertools.chain(*z, *(v for k, v in self.subspaces_iter()))
+
+    def __setitem__(self, key, value):
+        if hasattr(self, "_eval_cache"):
+            self._eval_cache[key] = value
+        else:
+            raise NotImplementedError("setitem not supported")