enforce that data types coming from the dataset (e.g. dataframe) to b…

…e set as values in the parametric model in Grasshopper are of type float, int, bool and str for DataReal, DataInt, DataBool and DataCategorical, respectively.

src/aixd_ara/controller.py

@@ -229,6 +229,37 @@ def get_dataobject_names_from_block(self, datablock_nickname):
             return {"msg": "", "names": self.datamodule.output_ml_dblock.names_list}
         return {"msg": f"Wrong block nickname: {datablock_nickname}.", "names": []}
 
+    def cast_to_python_type(self, dataobject_name, value):
+        """
+        Cast the values (usually coming from a dataframe) to the correct python type.
+        Value can be a single value or a list.
+        """
+        if not self.dataset:
+            raise ValueError("Dataset is not loaded.")
+
+        dobj = self.dataset.get_data_objects_by_name([dataobject_name])[0]
+
+        if not isinstance(value, list):
+            value = [value]
+            single = True
+        else:
+            single = False
+
+        if isinstance(dobj, DataInt):
+            castvalue = [int(v) for v in value]
+        elif isinstance(dobj, DataReal):
+            castvalue = [float(v) for v in value]
+        elif isinstance(dobj, DataBool):
+            castvalue = [bool(v) for v in value]
+        elif isinstance(dobj, DataCategorical):
+            castvalue = [str(v) for v in value]
+        else:
+            raise ValueError(f"Dataobject type not recognized: {dobj.type}")
+
+        if single:
+            castvalue = castvalue[0]
+        return castvalue
+
     def get_dataobject_types(self):
         """
         Returns names of the data types of the dataobjects in the dataset.
@@ -462,12 +493,12 @@ def get_one_sample(self, item):
 
         dct = reformat_dataframeflat_to_dict(self.dataset.design_par.data, self.dataset.design_par.dobj_list)
         for key, values in dct.items():
-            dct[key] = values[item]
+            dct[key] = self.cast_to_python_type(key, values[item])
         sample["design_parameters"] = dct
 
         dct = reformat_dataframeflat_to_dict(self.dataset.perf_attributes.data, self.dataset.perf_attributes.dobj_list)
         for key, values in dct.items():
-            dct[key] = values[item]
+            dct[key] = self.cast_to_python_type(key, values[item])
         sample["performance_attributes"] = dct
 
         # for single-value entries, unpack them from a list [123] -> 123