Revert "Add print statements to test_result"

This reverts commit 63cf257.

funtofem/driver/oneway_struct_driver.py

@@ -538,7 +538,6 @@ def _transfer_fixed_aero_loads(self):
                 # the mesh for the new structural shape
                 body.transfer_loads(scenario)
                 body.transfer_heat_flux(scenario)
-
         return
 
     def solve_forward(self):

funtofem/model/composite_function.py

@@ -68,11 +68,6 @@ def __init__(
         self.derivatives = {}
         self.df_dgi = None
 
-    @property
-    def optim_derivatives(self):
-        optim_derivs = {var: self.derivatives[var] for var in self.derivatives if not(var.state)}
-        return optim_derivs
-
     @classmethod
     def external(cls, name, optim=False, plot_name=None):
         return cls(
@@ -99,24 +94,21 @@ def vars_only(self) -> bool:
     @property
     def sparse_gradient(self):
         """used for adjacency constraints, vars only functions"""
-        self.evaluate_gradient()
-        np_array = np.array(
-            [self.derivatives[var] for var in self.optim_derivatives]
-        )
+        np_array = np.array([self.derivatives[var] for var in self.derivatives])
         # return csr_matrix(np_array, shape=(1,np_array.shape[0]))
         nvars = np_array.shape[0]
         cols = np.array(
             [
                 ivar
-                for ivar, var in enumerate(self.optim_derivatives)
+                for ivar, var in enumerate(self.derivatives)
                 if self.derivatives[var] != 0.0
             ]
         )
         rows = np.array([0 for _ in range(cols.shape[0])])
         vals = np.array(
             [
                 self.derivatives[var]
-                for ivar, var in enumerate(self.optim_derivatives)
+                for ivar, var in enumerate(self.derivatives)
                 if self.derivatives[var] != 0.0
             ]
         )

funtofem/model/funtofem_model.py

@@ -255,7 +255,7 @@ def _send_flow_variables(self, base):
             self.flow.set_variables(active_shape_vars, active_aero_vars)
         return
 
-    def get_variables(self, names=None, all=False, optim=False):
+    def get_variables(self, names=None, all=False):
         """
         Get all the coupled and uncoupled variable objects for the entire model.
         Coupled variables only appear once.
@@ -272,9 +272,6 @@ def get_variables(self, names=None, all=False, optim=False):
         var_list: list of variable objects
         """
 
-        if optim:  # return all active and non-state variables
-            return [var for var in self.get_variables() if not (var.state)]
-
         if names is None:
             dv = []
             for scenario in self.scenarios:

funtofem/model/variable.py

@@ -50,7 +50,6 @@ def __init__(
         active=True,
         coupled=False,
         id=0,
-        state=False,
     ):
         """
         Variable object for FUNtoFEM
@@ -71,8 +70,6 @@ def __init__(
             whether or not the design variable is coupled
         id: int
             id number of the design variable
-        state: bool
-            whether this variable is used as a state variable (for TACS only right now)
 
         Examples
         --------
@@ -85,7 +82,6 @@ def __init__(
         self.upper = upper
         self.active = active
         self.coupled = coupled
-        self.state = state
         self.id = id
         self.scale = scale
         self.analysis_type = analysis_type
@@ -100,7 +96,6 @@ def set_bounds(
         scale=None,
         active=None,
         coupled=None,
-        state=None,
     ):
         """
         Update the one or more of the attributes of the design variable
@@ -119,8 +114,6 @@ def set_bounds(
             whether or not the design variable is active
         coupled: bool
             whether or not the design variable is coupled
-        state: bool
-            whether this variable is used as a state variable (for TACS only right now)
         """
 
         if value is not None:
@@ -135,8 +128,6 @@ def set_bounds(
             self.active = active
         if coupled is not None:
             self.coupled = coupled
-        if state is not None:
-            self.state = state
 
         # return the object for method cascading
         return self

funtofem/optimization/optimization_manager.py

@@ -133,7 +133,7 @@ def __init__(
 
             # add all variables (for off-scenario variables to derivatives dict for each function) to analysis functions
             for func in self.model.get_functions():
-                for var in self.model.get_variables(optim=True):
+                for var in self.model.get_variables():
                     func.derivatives[var] = 0.0
 
             # initialize funcs, sens in case of failure on first design iteration of hot start
@@ -143,7 +143,7 @@ def __init__(
             self._sens = {}
             for func in self.model.get_functions(optim=True):
                 self._sens[func.full_name] = {}
-                for var in self.model.get_variables(optim=True):
+                for var in self.model.get_variables():
                     if self.sparse:
                         self._sens[func.full_name][self.SPARSE_VARS_GROUP] = None
                     else:
@@ -176,7 +176,7 @@ def _gatekeeper(self, x_dict):
                 if self.sparse:  # all vars in same group
                     regular_dict = {
                         var.name: float(x_dict[self.SPARSE_VARS_GROUP][ivar])
-                        for ivar, var in enumerate(self.model.get_variables(optim=True))
+                        for ivar, var in enumerate(self.model.get_variables())
                     }
                 else:
                     regular_dict = {key: float(x_dict[key]) for key in x_dict}
@@ -240,14 +240,13 @@ def _gatekeeper(self, x_dict):
                     for func in self.model.get_functions(optim=True)
                     if func._plot
                 }
-                if self.driver.solvers.flow is not None:
-                    forward_res = self.driver.solvers.flow.get_forward_residual()
-                    forward_steps = self.driver.solvers.flow._last_forward_step
-                    adjoint_res = self.driver.solvers.flow.get_adjoint_residual()
-                    adjoint_steps = self.driver.solvers.flow._last_adjoint_step
-                    self._design_hdl.write(
-                        f"Forward resid {forward_res:2.5e} in {forward_steps} steps, Adjoint resid {adjoint_res:2.5e} in {adjoint_steps} steps and {_total_time:.4f} {time_units}\n"
-                    )
+                forward_res = self.driver.solvers.flow.get_forward_residual()
+                forward_steps = self.driver.solvers.flow._last_forward_step
+                adjoint_res = self.driver.solvers.flow.get_adjoint_residual()
+                adjoint_steps = self.driver.solvers.flow._last_adjoint_step
+                self._design_hdl.write(
+                    f"Forward resid {forward_res:2.5e} in {forward_steps} steps, Adjoint resid {adjoint_res:2.5e} in {adjoint_steps} steps and {_total_time:.4f} {time_units}\n"
+                )
                 self._design_hdl.write(f"Functions = {plot_funcs}\n")
                 self._design_hdl.flush()
 
@@ -285,7 +284,7 @@ def _gatekeeper(self, x_dict):
                     if not func._plot:
                         continue
                     self._func_history[func.plot_name] += [func.value.real]
-                #self._plot_history()
+                self._plot_history()
         return fail
 
     def _run_complete_analysis(self):
@@ -295,12 +294,12 @@ def _run_complete_analysis(self):
 
         # update the model design variables
         if self.sparse:
-            variables = self.model.get_variables(optim=True)
+            variables = self.model.get_variables()
             for ivar, val in enumerate(self._x_dict[self.SPARSE_VARS_GROUP]):
                 var = variables[ivar]
                 var.value = float(val)
         else:
-            for var in self.model.get_variables(optim=True):
+            for var in self.model.get_variables():
                 for var_key in self._x_dict:
                     if var.full_name == var_key:
                         # assumes here that only pyoptsparse single variables (no var groups are made)
@@ -318,8 +317,6 @@ def _run_complete_analysis(self):
         self._sens = {}
         # get only functions with optim=True, set with func.optimize() method (can method cascade it)
         for func in self.model.get_functions(optim=True):
-            if func.vars_only and self.sparse:
-                continue
             self._funcs[func.full_name] = func.value.real
             self._sens[func.full_name] = {}
             # if self.sparse and isinstance(func, CompositeFunction) and func.vars_only:
@@ -328,13 +325,10 @@ def _run_complete_analysis(self):
             #     ) # linear constraints define their jacobians up front
             if self.sparse:
                 self._sens[func.full_name][self.SPARSE_VARS_GROUP] = np.array(
-                    [
-                        func.derivatives[var].real
-                        for var in self.model.get_variables(optim=True)
-                    ]
+                    [func.derivatives[var].real for var in func.derivatives]
                 )
             else:
-                for var in self.model.get_variables(optim=True):
+                for var in self.model.get_variables():
                     self._sens[func.full_name][var.full_name] = (
                         func.get_gradient_component(var).real
                     )
@@ -345,7 +339,8 @@ def register_to_problem(self, opt_problem):
         add funtofem model variables and functions to a pyoptsparse optimization problem
         """
         if self.sparse:
-            variables = self.model.get_variables(optim=True)
+            variables = self.model.get_variables()
+            values = np.array([var.value for var in variables])
             opt_problem.addVarGroup(
                 self.SPARSE_VARS_GROUP,
                 len(variables),
@@ -356,7 +351,7 @@ def register_to_problem(self, opt_problem):
                 scale=np.array([var.scale for var in variables]),
             )
         else:
-            for var in self.model.get_variables(optim=True):
+            for var in self.model.get_variables():
                 opt_problem.addVar(
                     var.full_name,
                     lower=var.lower,

tests/fun3d_tests/fully_coupled_disc_deriv/inviscid/meshes/plate_flow_quads/Flow/miniMesh.b8.ugrid

@@ -0,0 +1 @@
+../../quads.b8.ugrid

tests/fun3d_tests/fully_coupled_disc_deriv/inviscid/meshes/plate_flow_tris/Flow/miniMesh.b8.ugrid

@@ -0,0 +1 @@
+../../tris.b8.ugrid

tests/fun3d_tests/fully_coupled_disc_deriv/inviscid/meshes/plate_grid_quads/Flow/miniMesh.b8.ugrid

@@ -0,0 +1 @@
+../../quads.b8.ugrid

tests/fun3d_tests/fully_coupled_disc_deriv/inviscid/meshes/plate_grid_tris/Flow/miniMesh.b8.ugrid

@@ -0,0 +1 @@
+../../tris.b8.ugrid

tests/fun3d_tests/fully_coupled_disc_deriv/inviscid/meshes/plate_quads/Flow/miniMesh.b8.ugrid

@@ -0,0 +1 @@
+../../quads.b8.ugrid

tests/fun3d_tests/fully_coupled_disc_deriv/inviscid/meshes/plate_tris/Flow/miniMesh.b8.ugrid

@@ -0,0 +1 @@
+../../tris.b8.ugrid