CSVMixin: Enable support for ensembles with one member

src/rtctools/optimization/csv_mixin.py

@@ -98,6 +98,9 @@ def check_initial_state_array(initial_state):
                 names=True,
                 encoding=None,
             )
+            if len(self.__ensemble.shape) == 0:
+                # If there is only one ensemble member, the array is 0-dimensional.
+                self.__ensemble = np.expand_dims(self.__ensemble, 0)
 
             logger.debug("CSVMixin: Read ensemble description")
 

tests/optimization/data/one-member-ensemble/ensemble.csv

@@ -0,0 +1,2 @@
+name,probability
+member0,1.0

tests/optimization/data/one-member-ensemble/member0/.gitignore

@@ -0,0 +1,2 @@
+timeseries_export.xml
+timeseries_export.csv

tests/optimization/data/one-member-ensemble/member0/initial_state.csv

@@ -0,0 +1,2 @@
+x
+1.02

tests/optimization/data/one-member-ensemble/member0/parameters.csv

@@ -0,0 +1,2 @@
+k
+1.01

tests/optimization/data/one-member-ensemble/member0/timeseries_import.csv

@@ -0,0 +1,23 @@
+Time,u_Min,u_Max,constant_input
+2013-05-19 22:00:00,-2.0,2.0,1.0
+2013-05-19 23:00:00,-2.0,2.0,1.0
+2013-05-20 00:00:00,-2.0,2.0,1.0
+2013-05-20 01:00:00,-2.0,2.0,1.0
+2013-05-20 02:00:00,-2.0,2.0,1.0
+2013-05-20 03:00:00,-2.0,2.0,1.0
+2013-05-20 04:00:00,-2.0,2.0,1.0
+2013-05-20 05:00:00,-2.0,2.0,1.0
+2013-05-20 06:00:00,-2.0,2.0,1.0
+2013-05-20 07:00:00,-2.0,2.0,1.0
+2013-05-20 08:00:00,-2.0,2.0,1.0
+2013-05-20 09:00:00,-2.0,2.0,1.0
+2013-05-20 10:00:00,-2.0,2.0,1.0
+2013-05-20 11:00:00,-2.0,2.0,1.0
+2013-05-20 12:00:00,-2.0,2.0,1.0
+2013-05-20 13:00:00,-2.0,2.0,1.0
+2013-05-20 14:00:00,-2.0,2.0,1.0
+2013-05-20 15:00:00,-2.0,2.0,1.0
+2013-05-20 16:00:00,-2.0,2.0,1.0
+2013-05-20 17:00:00,-2.0,2.0,1.0
+2013-05-20 18:00:00,-2.0,2.0,1.0
+2013-05-20 19:00:00,-2.0,2.0,1.0

tests/optimization/test_csv_mixin.py

@@ -1,4 +1,5 @@
 import logging
+import os
 
 import numpy as np
 from rtctools.optimization.collocated_integrated_optimization_problem import (
@@ -17,9 +18,9 @@
 class Model(CSVMixin, ModelicaMixin, CollocatedIntegratedOptimizationProblem):
     def __init__(self, **kwargs):
         kwargs["model_name"] = kwargs.get("model_name", "Model")
-        kwargs["input_folder"] = data_path()
-        kwargs["output_folder"] = data_path()
-        kwargs["model_folder"] = data_path()
+        kwargs["input_folder"] = kwargs.get("input_folder", data_path())
+        kwargs["output_folder"] = kwargs.get("output_folder", data_path())
+        kwargs["model_folder"] = kwargs.get("model_folder", data_path())
         super().__init__(**kwargs)
 
     def objective(self, ensemble_member):
@@ -42,10 +43,10 @@ def compiler_options(self):
 class ModelEnsemble(Model):
     csv_ensemble_mode = True
 
-    def __init__(self):
+    def __init__(self, io_folder):
         super().__init__(
-            input_folder=data_path(),
-            output_folder=data_path(),
+            input_folder=io_folder,
+            output_folder=io_folder,
             model_name="Model",
             model_folder=data_path(),
             lookup_tables=[],
@@ -105,7 +106,18 @@ def test_interpolate(self):
 
 class TestCSVMixinEnsemble(TestCase):
     def setUp(self):
-        self.problem = ModelEnsemble()
+        self.problem = ModelEnsemble(data_path())
+        self.problem.optimize()
+        self.tolerance = 1e-6
+
+    def test_objective_value(self):
+        objective_value_tol = 1e-6
+        self.assertTrue(abs(self.problem.objective_value) < objective_value_tol)
+
+
+class TestCSVMixinOneMemberEnsemble(TestCase):
+    def setUp(self):
+        self.problem = ModelEnsemble(os.path.join(data_path(), "one-member-ensemble"))
         self.problem.optimize()
         self.tolerance = 1e-6