Use Parameters class in solver

HARK/core.py

@@ -1219,47 +1219,85 @@
         A list of one period solutions for one "cycle" of the AgentType's
         microeconomic model.
     """
-    # Calculate number of periods per cycle, defaults to 1 if all variables are time invariant
-    if len(agent.time_vary) > 0:
-        # name = agent.time_vary[0]
-        # T = len(eval('agent.' + name))
-        T = len(agent.__dict__[agent.time_vary[0]])
-    else:
-        T = 1
 
-    solve_dict = {parameter: agent.__dict__[parameter] for parameter in agent.time_inv}
-    solve_dict.update({parameter: None for parameter in agent.time_vary})
+    # Check if the agent has a 'Parameters' attribute of the 'Parameters' class
+    # if so, take advantage of it. Else, use the old method
+    if hasattr(agent, "params") and isinstance(agent.params, Parameters):
+        T = agent.params._length
 
-    # Initialize the solution for this cycle, then iterate on periods
-    solution_cycle = []
-    solution_next = solution_last
+        # Initialize the solution for this cycle, then iterate on periods
+        solution_cycle = []
+        solution_next = solution_last
 
-    cycles_range = [0] + list(range(T - 1, 0, -1))
-    for k in range(T - 1, -1, -1) if agent.cycles == 1 else cycles_range:
-        # Update which single period solver to use (if it depends on time)
-        if hasattr(agent.solve_one_period, "__getitem__"):
-            solve_one_period = agent.solve_one_period[k]
-        else:
-            solve_one_period = agent.solve_one_period
+        cycles_range = [0] + list(range(T - 1, 0, -1))
+        for k in range(T - 1, -1, -1) if agent.cycles == 1 else cycles_range:
+            # Update which single period solver to use (if it depends on time)
+            if hasattr(agent.solve_one_period, "__getitem__"):
+                solve_one_period = agent.solve_one_period[k]
+            else:
+                solve_one_period = agent.solve_one_period
+
+            if hasattr(solve_one_period, "solver_args"):
+                these_args = solve_one_period.solver_args
+            else:
+                these_args = get_arg_names(solve_one_period)
+
+            # Make a temporary dictionary for this period
+            temp_pars = agent.params[k]
+            temp_dict = {
+                name: solution_next if name == "solution_next" else temp_pars[name]
+                for name in these_args
+            }
 
-        if hasattr(solve_one_period, "solver_args"):
-            these_args = solve_one_period.solver_args
+            # Solve one period, add it to the solution, and move to the next period
+            solution_t = solve_one_period(**temp_dict)
+            solution_cycle.insert(0, solution_t)
+            solution_next = solution_t
+
+    else:
+        # Calculate number of periods per cycle, defaults to 1 if all variables are time invariant
+        if len(agent.time_vary) > 0:
+            # name = agent.time_vary[0]
+            # T = len(eval('agent.' + name))
+            T = len(agent.__dict__[agent.time_vary[0]])
         else:
-            these_args = get_arg_names(solve_one_period)
+            T = 1
+
+        solve_dict = {
+            parameter: agent.__dict__[parameter] for parameter in agent.time_inv
+        }
+        solve_dict.update({parameter: None for parameter in agent.time_vary})
+
+        # Initialize the solution for this cycle, then iterate on periods
+        solution_cycle = []
+        solution_next = solution_last
+
+        cycles_range = [0] + list(range(T - 1, 0, -1))
+        for k in range(T - 1, -1, -1) if agent.cycles == 1 else cycles_range:
+            # Update which single period solver to use (if it depends on time)
+            if hasattr(agent.solve_one_period, "__getitem__"):
+                solve_one_period = agent.solve_one_period[k]
+            else:
+                solve_one_period = agent.solve_one_period
+
+            if hasattr(solve_one_period, "solver_args"):
+                these_args = solve_one_period.solver_args
+            else:
+                these_args = get_arg_names(solve_one_period)
 
-        # Update time-varying single period inputs
-        for name in agent.time_vary:
-            if name in these_args:
-                solve_dict[name] = agent.__dict__[name][k]
-        solve_dict["solution_next"] = solution_next
+            # Update time-varying single period inputs
+            for name in agent.time_vary:
+                if name in these_args:
+                    solve_dict[name] = agent.__dict__[name][k]
+            solve_dict["solution_next"] = solution_next
 
-        # Make a temporary dictionary for this period
-        temp_dict = {name: solve_dict[name] for name in these_args}
+            # Make a temporary dictionary for this period
+            temp_dict = {name: solve_dict[name] for name in these_args}
 
-        # Solve one period, add it to the solution, and move to the next period
-        solution_t = solve_one_period(**temp_dict)
-        solution_cycle.insert(0, solution_t)
-        solution_next = solution_t
+            # Solve one period, add it to the solution, and move to the next period
+            solution_t = solve_one_period(**temp_dict)
+            solution_cycle.insert(0, solution_t)
+            solution_next = solution_t
 
     # Return the list of per-period solutions
     return solution_cycle