"Changed structure to full_model_flow tests"

adopt_net0/modelhub.py

@@ -77,7 +77,6 @@ def read_data(
         log.info(log_msg)
         self.data.set_settings(data_path, start_period, end_period)
         self.data.read_data()
-        self._perform_preprocessing_checks()
 
         log_msg = "--- Reading in data complete ---"
         log.info(log_msg)
@@ -231,6 +230,8 @@ def construct_model(self):
         log.info(log_msg)
         start = time.time()
 
+        self._perform_preprocessing_checks()
+
         # Determine aggregation
         config = self.data.model_config
 
@@ -473,7 +474,7 @@ def add_technology(self, investment_period: str, node: str, technologies: list):
         }
         for technology in technologies:
             # read in technology data
-            tec_data = read_tec_data(
+            tec_data = create_technology_class(
                 technology,
                 self.data.data_path
                 / investment_period

conftest.py

@@ -32,7 +32,7 @@ def pytest_configure(config):
     config.docu_notebook_folder_path = Path("tests")
     config.docu_notebook_data_path = Path("tests/notebook_data")
     config.root_folder_path = Path(".")
-    config.solver = "glpk"
+    config.solver = "gurobi"
 
 
 @pytest.fixture(autouse=True)

tests/test_full_model_flow.py

@@ -40,15 +40,15 @@ def test_full_model_flow(request):
 
     pyhub = ModelHub()
     pyhub.read_data(path, start_period=0, end_period=1)
-    pyhub.construct_model()
-    pyhub.construct_balances()
     pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
     pyhub.data.model_config["reporting"]["save_summary_path"][
         "value"
     ] = request.config.result_folder_path
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+    pyhub.construct_model()
+    pyhub.construct_balances()
     pyhub.solve()
 
     m = pyhub.model["full"]
@@ -105,15 +105,16 @@ def test_clustering_algo(request):
 
     pyhub = ModelHub()
     pyhub.read_data(path, start_period=0, end_period=2 * 24)
-    pyhub.construct_model()
-    pyhub.construct_balances()
     pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
     pyhub.data.model_config["reporting"]["save_summary_path"][
         "value"
     ] = request.config.result_folder_path
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+
+    pyhub.construct_model()
+    pyhub.construct_balances()
     pyhub.solve()
 
     m = pyhub.model["full"]
@@ -131,6 +132,7 @@ def test_clustering_algo(request):
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+    pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
     for method in methods:
         for n in N:
             pyhub.data.model_config["optimization"]["typicaldays"]["N"]["value"] = n
@@ -174,15 +176,16 @@ def test_average_algo(request):
 
     pyhub = ModelHub()
     pyhub.read_data(path, start_period=0, end_period=2 * 24)
-    pyhub.construct_model()
-    pyhub.construct_balances()
     pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
     pyhub.data.model_config["reporting"]["save_summary_path"][
         "value"
     ] = request.config.result_folder_path
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+
+    pyhub.construct_model()
+    pyhub.construct_balances()
     pyhub.solve()
 
     m = pyhub.model["full"]
@@ -200,6 +203,7 @@ def test_average_algo(request):
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+    pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
 
     pyhub.data._read_time_series()
     pyhub.data._read_node_locations()
@@ -236,20 +240,23 @@ def test_objective_functions(request):
 
     pyhub = ModelHub()
     pyhub.read_data(path, start_period=0, end_period=1)
-    pyhub.construct_model()
-    pyhub.construct_balances()
+
     pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
     pyhub.data.model_config["reporting"]["save_summary_path"][
         "value"
     ] = request.config.result_folder_path
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+
+    pyhub.construct_model()
+    pyhub.construct_balances()
     pyhub._define_solver_settings()
 
     pyhub._optimize_emissions_net()
     pyhub._optimize_costs_minE()
     pyhub._optimize_costs_emissionslimit()
+
     pyhub._solve_pareto()
 
 
@@ -266,15 +273,16 @@ def test_monte_carlo(request):
     # Monte Carlo type normal
     pyhub.data.model_config["optimization"]["monte_carlo"]["N"]["value"] = 2
 
-    pyhub.construct_model()
-    pyhub.construct_balances()
     pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
     pyhub.data.model_config["reporting"]["save_summary_path"][
         "value"
     ] = request.config.result_folder_path
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+
+    pyhub.construct_model()
+    pyhub.construct_balances()
     pyhub.solve()
 
     termination = pyhub.solution.solver.termination_condition
@@ -300,13 +308,16 @@ def test_scaling(request):
 
     pyhub = ModelHub()
     pyhub.read_data(path, start_period=0, end_period=1)
-    pyhub.construct_model()
-    pyhub.construct_balances()
+
     pyhub.data.model_config["scaling"]["scaling_on"]["value"] = 1
     pyhub.data.model_config["reporting"]["save_summary_path"][
         "value"
     ] = request.config.result_folder_path
     pyhub.data.model_config["reporting"]["save_path"][
         "value"
     ] = request.config.result_folder_path
+    pyhub.data.model_config["solveroptions"]["solver"]["value"] = request.config.solver
+
+    pyhub.construct_model()
+    pyhub.construct_balances()
     pyhub.solve()