Fix 7717, Improve handling of data variables with inferred parameters + tests

docs/changes/newsfragments/7725.improved

@@ -0,0 +1,6 @@
+Parameters using ``has_control_of`` are now correctly handled when exporting to
+xarray. Controlled parameters are no longer treated as independent top-level
+parameters, preventing duplicate data rows. Additionally, inferred parameters
+are now included as data variables in the xarray dataset when exporting via the
+pandas-based path, and a warning is logged when the inferred parameter data size
+does not match the expected xarray dataset dimensions.

src/qcodes/dataset/descriptions/dependencies.py

@@ -287,6 +287,16 @@ def top_level_parameters(self) -> tuple[ParamSpecBase, ...]:
             for node_id, in_degree in self._dependency_subgraph.in_degree
             if in_degree == 0
         }
+        # Parameters that are inferred from other parameters (have outgoing
+        # edges in the inference subgraph) should not be independent top-level
+        # parameters, since their data is part of the tree of the parameter
+        # they are inferred from.
+        parameters_inferred_from_others = {
+            self._node_to_paramspec(node_id)
+            for node_id, out_degree in self._inference_subgraph.out_degree
+            if out_degree > 0
+        }
+        dependency_top_level = dependency_top_level - parameters_inferred_from_others
         standalone_top_level = {
             self._node_to_paramspec(node_id)
             for node_id, degree in self._graph.degree

src/qcodes/dataset/exporters/export_to_xarray.py

@@ -6,6 +6,7 @@
 from math import prod
 from typing import TYPE_CHECKING, Literal
 
+import numpy as np
 from packaging import version as p_version
 
 from qcodes.dataset.linked_datasets.links import links_to_str
@@ -61,6 +62,68 @@ def _calculate_index_shape(idx: pd.Index | pd.MultiIndex) -> dict[Hashable, int]
     return expanded_shape
 
 
+def _add_inferred_data_vars(
+    dataset: DataSetProtocol,
+    name: str,
+    sub_dict: Mapping[str, npt.NDArray],
+    xr_dataset: xr.Dataset,
+) -> xr.Dataset:
+    """Add inferred parameters as data variables to an xarray dataset.
+
+    Parameters that are inferred from the top-level measurement parameter
+    and present in sub_dict but not yet in the dataset are added as data
+    variables along the existing dimensions.
+    """
+
+    interdeps = dataset.description.interdeps
+    meas_paramspec = interdeps.graph.nodes[name]["value"]
+    _, deps, inferred = interdeps.all_parameters_in_tree_by_group(meas_paramspec)
+
+    dep_names = {dep.name for dep in deps}
+    dims = tuple(d for d in xr_dataset.dims)
+
+    for inf in inferred:
+        if inf.name in dep_names:
+            continue
+        if inf.name in xr_dataset:
+            continue
+        if inf.name not in sub_dict:
+            continue
+
+        inf_data = sub_dict[inf.name]
+        if inf_data.dtype == np.dtype("O"):
+            try:
+                flat = np.concatenate(inf_data)
+            except ValueError:
+                flat = inf_data.ravel()
+        else:
+            flat = inf_data.ravel()
+
+        # Only add if the flattened data can be reshaped to the dataset
+        # dimensions. This is more robust than checking individual parent
+        # sizes because an inferred parameter may have multiple parents
+        # with different sizes.
+        expected_shape = tuple(xr_dataset.sizes[d] for d in dims)
+        expected_size = prod(expected_shape)
+        if flat.shape[0] == expected_size:
+            xr_dataset[inf.name] = (dims, flat.reshape(expected_shape))
+        else:
+            _LOG.warning(
+                "Cannot add inferred parameter '%s' to xarray dataset for '%s' "
+                "(run_id=%s): data size %d does not match the dataset "
+                "dimensions %s (size %d). This is likely a user error in the "
+                "measurement setup.",
+                inf.name,
+                name,
+                dataset.run_id,
+                flat.shape[0],
+                dict(zip(dims, expected_shape)),
+                expected_size,
+            )
+
+    return xr_dataset
+
+
 def _load_to_xarray_dataset_dict_no_metadata(
     dataset: DataSetProtocol,
     datadict: Mapping[str, Mapping[str, npt.NDArray]],
@@ -100,25 +163,33 @@ def _load_to_xarray_dataset_dict_no_metadata(
                     interdeps=dataset.description.interdeps,
                     dependent_parameter=name,
                 ).to_xarray()
-                xr_dataset_dict[name] = xr_dataset
+                xr_dataset_dict[name] = _add_inferred_data_vars(
+                    dataset, name, sub_dict, xr_dataset
+                )
             elif index_is_unique:
                 df = _data_to_dataframe(
                     sub_dict,
                     index,
                     interdeps=dataset.description.interdeps,
                     dependent_parameter=name,
                 )
-                xr_dataset_dict[name] = _xarray_data_set_from_pandas_multi_index(
+                xr_dataset = _xarray_data_set_from_pandas_multi_index(
                     dataset, use_multi_index, name, df, index
                 )
+                xr_dataset_dict[name] = _add_inferred_data_vars(
+                    dataset, name, sub_dict, xr_dataset
+                )
             else:
                 df = _data_to_dataframe(
                     sub_dict,
                     index,
                     interdeps=dataset.description.interdeps,
                     dependent_parameter=name,
                 )
-                xr_dataset_dict[name] = df.reset_index().to_xarray()
+                xr_dataset = df.reset_index().to_xarray()
+                xr_dataset_dict[name] = _add_inferred_data_vars(
+                    dataset, name, sub_dict, xr_dataset
+                )
 
     return xr_dataset_dict