Interface fix

flume/base_classes/system.py

@@ -59,26 +59,22 @@ def __init__(
 
         return
 
-    def reset_analysis_flags(self, it_counter):
+    def reset_analysis_flags(self):
         """
-        Resets all of the analysis flags for all analyses within the system to be False. This is done when variable values are updated, as all systems must be analyzed again to propagate chanes in design variable values.
+        Resets all of the analysis flags for all analyses within the system to be False. This is done when variable values are updated, as all systems must be analyzed again to propagate changes in design variable values.
         """
 
-        # If it_counter is zero, skip this setp
-        if it_counter == 0:
-            pass
-        else:
-            # Loop through each top-level analysis, resetting each analysis in the stack
-            for top_level in self.top_level_analysis_list:
+        # Loop through each top-level analysis, resetting each analysis in the stack
+        for top_level in self.top_level_analysis_list:
 
-                # If the stack does not exist, make it
-                if not hasattr(top_level, "stack"):
-                    # Assemble the stack
-                    top_level.stack = top_level._make_stack()
+            # If the stack does not exist, make it
+            if not hasattr(top_level, "stack"):
+                # Assemble the stack
+                top_level.stack = top_level._make_stack()
 
-                # Loop through each object within the current top-level's analysis stack and set the analyzed attribute to be False
-                for analysis in top_level.stack:
-                    analysis.analyzed = False
+            # Loop through each object within the current top-level's analysis stack and set the analyzed attribute to be False
+            for analysis in top_level.stack:
+                analysis.analyzed = False
 
         return
 

flume/interfaces/paropt_interface.py

@@ -201,20 +201,18 @@ def getVarsAndBounds(self, x, lb, ub):
 
         return
 
-    def set_system_variables(self, x, it_counter):
+    def set_system_variables(self, x):
         """
         Sets the variable values for the analysis objects contained within the System.
 
         Parameters
         ----------
         x : ParOptVec
             Design variable vector at the current iteration
-        it_counter : int
-            Current iteration for the optimization
         """
 
         # Since system variables are being set, all analysis objects must be recomputed
-        self.flume_sys.reset_analysis_flags(it_counter)
+        self.flume_sys.reset_analysis_flags()
 
         # Loop through the design variables for the system and set for their components
         for var in self.flume_sys.design_vars_info:
@@ -279,7 +277,7 @@ def evalObjCon(self, x):
             )
 
         # Set the variable values for the various analyses
-        self.set_system_variables(x, self.it_counter)
+        self.set_system_variables(x)
 
         # Perform the analysis for the objective function
         self.flume_sys.obj_analysis.analyze(debug_print=False)

flume/interfaces/pyoptsparse_interface.py

@@ -51,20 +51,18 @@ def user_update(it_number):
         # Initialize the iteration counter
         self.it_counter = 0
 
-    def _set_system_variables(self, xdict: dict, it_counter: int):
+    def _set_system_variables(self, xdict: dict):
         """
         Private method that is used to set the design variables values provided in the input dictionary into the various Flume Analysis objects.
 
         Parameters
         ----------
         xdict : dict
             Dictionary that specifies the current design point for the optimization problem. The keys are the global variable names, and the values are the numeric values for the design variables in the System
-        it_counter : int
-            Iteration number for the optimization
         """
 
         # Since system variables are being set, all analysis objects must be recomputed
-        self.flume_sys.reset_analysis_flags(it_counter)
+        self.flume_sys.reset_analysis_flags()
 
         # Loop through the design variables for the system and set the values for their components
         for var in self.flume_sys.design_vars_info:
@@ -122,7 +120,7 @@ def _objconfun(self, xdict: dict):
             )
 
         # Set the variable values for the various analyses
-        self._set_system_variables(xdict, self.it_counter)
+        self._set_system_variables(xdict)
 
         # Perform the analysis for the objective function
         self.flume_sys.obj_analysis.analyze(debug_print=False)
@@ -250,10 +248,6 @@ def _objconGradFun(self, xdict: dict, funcs: dict):
                         .deriv
                     )
 
-                    # Apply the scaling, if necessary
-                    if rhs != 0.0:
-                        gradc_i /= abs(rhs)
-
                     # Assign the gradient value into the grad_vals dictionary
                     if i > 0:
                         current = grad_vals[con_name][var]
@@ -359,7 +353,7 @@ def _addConGroups(self, optProb: Optimization):
                 scale = 1.0
             else:
                 scale = 1 / abs(rhs_val)
-                bound_val = np.sign(rhs_val)
+                bound_val = rhs_val
 
             # Greater than inequality constraints
             if direction == "geq":
@@ -577,6 +571,7 @@ def _update_options_output_directory(self, optimizer: str, opt: Optimizer):
             options["Summary file"] = os.path.join(
                 self.flume_sys.log_prefix, "SNOPT_summary.out"
             )
+            options["Verify level"] = 3
         elif optimizer.lower() == "ipopt":
             options = opt.getOptions()
             options["output_file"] = os.path.join(

flume/interfaces/scipy_interface.py

@@ -142,13 +142,13 @@ def _get_dv_bounds(self, x):
 
         return bounds
 
-    def _set_system_variables(self, x, it_counter):
+    def _set_system_variables(self, x):
         """
         Sets the variable values for the analysis objects contained within the system.
         """
 
         # Since system variables are being set, all analysis objects must be recomputed
-        self.flume_sys.reset_analysis_flags(it_counter - 1)
+        self.flume_sys.reset_analysis_flags()
 
         # Loop through the design variables for the system and set for their components
         for var in self.flume_sys.design_vars_info:
@@ -229,7 +229,7 @@ def _objective_func(self, x, method):
             )
 
         # Set the variable values for the various analyses
-        self._set_system_variables(x, self.it_counter)
+        self._set_system_variables(x)
 
         # Perform the analysis for the objective function
         self.flume_sys.obj_analysis.analyze(debug_print=False)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "flume-smdo"
-version = "1.0.5"
+version = "1.0.6"
 description = "A framework for performing forward and derivative analyses for systems following directed acylic graph structure."
 readme = "README.md"
 authors = [{ name = "Cameron Smith", email = "csmith763@gatech.edu" }]

tests/test_paropt_interface.py

@@ -247,7 +247,9 @@ def optimize_system(self, n_p: int, maxit: int = 100):
         self.positions.set_var_values(variables={"theta": theta0, "phi": phi0})
 
         # Optimize the problem with ParOpt's TR method
-        x, fstar, con_star = interface.optimize_system(algorithm="tr")
+        x, fstar, con_star, opt = interface.optimize_system(
+            algorithm="tr", check_gradients=False
+        )
 
         # Check that the potential energy at the final point matches the expected value
         obj_val = fstar

tests/test_pyoptsparse_interface.py

@@ -134,7 +134,7 @@ def setUp(self):
 
         # Declare the constraint
         sys.declare_constraints(
-            global_con_name={"con.g": {"direction": "leq", "rhs": 1.0}}
+            global_con_name={"con.g": {"direction": "geq", "rhs": -2.0}}
         )
 
         # Store the system as an attribute
@@ -165,7 +165,7 @@ def test_optimization_pyoptsparse_slsqp(self):
         x = np.concatenate((sol.xStar["dvs.x_dv"], sol.xStar["dvs.y_dv"]))
 
         # Set the expected optimal values
-        xstar = np.array([0.786, 0.618])
+        xstar = np.array([1.0, 1.0])
 
         # Check that the values match
         np.testing.assert_allclose(