Prototype that works

force_bdss/core_plugins/dummy/dummy_dakota/dakota_communicator.py

@@ -8,12 +8,15 @@ class DummyDakotaCommunicator(BaseMCOCommunicator):
     def receive_from_mco(self):
         data = sys.stdin.read()
         values = list(map(float, data.split()))
+        value_names = [p.name for p in self.model.parameters]
+        value_types = [p.value_type for p in self.model.parameters]
+
         return DataSourceParameters(
-            value_types=["DUMMY"]*len(values),
+            value_names=value_names,
+            value_types=value_types,
             values=numpy.array(values)
         )
 
-
     def send_to_mco(self, kpi_results):
         data = " ".join(
             [" ".join(list(map(str, r.values.tolist()))) for r in kpi_results]

force_bdss/core_plugins/dummy/dummy_dakota/dakota_optimizer.py

@@ -14,17 +14,16 @@ def rotated_range(start, stop, starting_value):
     return list(d)
 
 
-class DakotaOptimizer(BaseMultiCriteriaOptimizer):
-
+class DummyDakotaOptimizer(BaseMultiCriteriaOptimizer):
     def run(self):
         parameters = self.model.parameters
 
         values = []
         for p in parameters:
             values.append(
-                rotated_range(p.lower_bound,
-                              p.upper_bound,
-                              p.initial_value)
+                rotated_range(int(p.lower_bound),
+                              int(p.upper_bound),
+                              int(p.initial_value))
             )
 
         value_iterator = itertools.product(*values)

force_bdss/core_plugins/dummy/dummy_kpi_calculator/dummy_kpi_calculator.py

@@ -5,9 +5,12 @@ class DummyKPICalculator(BaseKPICalculator):
     id = bundle_id("enthought", "dummy_kpi_calculator")
 
     def run(self, data_source_results):
-        return KPICalculatorResult(
+        res = KPICalculatorResult(
             originator=self,
             value_names=data_source_results[0].value_names,
             value_types=data_source_results[0].value_types,
             values=data_source_results[0].values.reshape(
                 data_source_results[0].values.shape[:-1]))
+
+        print(res)
+        return res

force_bdss/io/workflow_reader.py

@@ -3,6 +3,7 @@ import logging
 
 from traits.api import HasStrictTraits, Instance
 
+from ..workspecs.mco_parameters import RangedMCOParameter
 from ..workspecs.workflow import Workflow
 from ..bundle_registry_plugin import BundleRegistryPlugin
 
@@ -115,8 +116,11 @@ class WorkflowReader(HasStrictTraits):
 
         mco_id = mco_data["id"]
         mco_bundle = registry.mco_bundle_by_id(mco_id)
-        return mco_bundle.create_model(
+        model = mco_bundle.create_model(
             wf_data["multi_criteria_optimizer"]["model_data"])
+        model.parameters = self._extract_mco_parameters(
+            wf_data["multi_criteria_optimizer"]["parameters"])
+        return model
 
     def _extract_data_sources(self, wf_data):
         """Extracts the data sources from the workflow dictionary data.
@@ -166,3 +170,7 @@ class WorkflowReader(HasStrictTraits):
                 kpic_bundle.create_model(kpic_entry["model_data"]))
 
         return kpi_calculators
+
+    def _extract_mco_parameters(self, parameters_data):
+        return [RangedMCOParameter(**d) for d in parameters_data]
+

force_bdss/kpi/kpi_calculator_result.py

@@ -10,3 +10,32 @@ class KPICalculatorResult(HasTraits):
     values = Array(shape=(None, ))
     accuracy = ArrayOrNone(shape=(None, ))
     quality = ArrayOrNone(shape=(None, ))
+
+    def __str__(self):
+        return """
+        DataSourceResults
+
+        originator:
+        {}
+
+        value_names:
+        {}
+
+        value_types:
+        {}
+
+        values:
+        {}
+
+        Accuracy:
+        {}
+
+        Quality:
+        {}
+        """.format(
+                self.originator,
+                self.value_names,
+                self.value_types,
+                self.values,
+                self.accuracy,
+                self.quality)