added viz for states, agents h and v

run2.py

@@ -9,6 +9,7 @@
 pd.set_option('display.width', None)
 pd.set_option('display.max_colwidth', None)
 
+from src.sim import config 
 
 def run(drop_midsteps=True):
     print()

src/sim/config.py

@@ -11,8 +11,8 @@
 import pandas as pd
 import itertools
 
-time_periods_per_run = 10
-monte_carlo_runs = 1
+time_periods_per_run = 100
+monte_carlo_runs = 3
 E = 0.45  # to be reviewed
 
 KAPPA = [2]

src/sim/model/parts/private_beliefs.py

@@ -25,7 +25,7 @@ def update_private_price(params, substep, state_history, prev_state, policy_inpu
         # 'period': ['N/A', 2000, 2000, 2000]
         'dP': P0[0]/4,
         'period': 2000,
-        'sigma': [.005],  # , 'N/A', 'N/A', 'N/A']
+        'sigma': .005,  # , 'N/A', 'N/A', 'N/A']
     }
 
     #params = params[0]

src/sim/run.py

@@ -1,29 +1,101 @@
 
+# # The following imports NEED to be in the exact order
+# from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
+# from cadCAD import configs
+# import pandas as pd
+
+
+# def run(drop_midsteps=True):
+#     exec_mode = ExecutionMode()
+#     multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
+#     run = Executor(exec_context=multi_proc_ctx, configs=configs)
+#     results = pd.DataFrame()
+#     i = 0
+#     for raw_result, _ in run.execute():
+#         params = configs[i].sim_config['M']
+#         result_record = pd.DataFrame.from_records(
+#             [tuple([i for i in params.values()])], columns=list(params.keys()))
+
+#         df = pd.DataFrame(raw_result)
+#         # keep only last substep of each timestep
+#         if drop_midsteps:
+#             max_substep = max(df.substep)
+#             is_droppable = (df.substep != max_substep) & (df.substep != 0)
+#             df.drop(df[is_droppable].index, inplace=True)
+
+#         result_record['dataset'] = [df]
+#         results = results.append(result_record)
+#         i += 1
+#     return results.reset_index()
+
+
+
 # The following imports NEED to be in the exact order
-from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
 from cadCAD import configs
-import pandas as pd
+######### ADD FOR PRINTING CONFIG
+from cadCAD.configuration.utils import *
+from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
+
+from src.sim import config
 
+exec_mode = ExecutionMode()
+exec_ctx = ExecutionContext(context=exec_mode.multi_proc)
+simulation = Executor(exec_context=exec_ctx, configs=configs)
+raw_system_events, tensor_field, session = simulation.execute()
+df = pd.DataFrame(raw_system_events)
+
+def get_M(k, v):
+    if k == 'sim_config':
+        k, v = 'M', v['M']
+    return k, v
+config_ids = [
+    dict(
+        get_M(k, v) for k, v in config.__dict__.items() if k in ['simulation_id', 'run_id', 'sim_config']
+    ) for config in configs
+]
 
 def run(drop_midsteps=True):
-    exec_mode = ExecutionMode()
-    multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
-    run = Executor(exec_context=multi_proc_ctx, configs=configs)
+    print('config_ids = ', config_ids)
+    result_records_list, sim_id_records = [], []
     results = pd.DataFrame()
-    i = 0
-    for raw_result, _ in run.execute():
-        params = configs[i].sim_config['M']
-        result_record = pd.DataFrame.from_records(
-            [tuple([i for i in params.values()])], columns=list(params.keys()))
+    sim_ids = list(set([_id['simulation_id'] for _id in config_ids]))
+
+    # print(sim_ids)
+    sim_dfs = {_id: [] for _id in sim_ids}
+    for i, config_id in enumerate(config_ids):
+        sim_id, run_id = config_id['simulation_id'], config_id['run_id']
+        params = config_id['M']
+        result_record = pd.DataFrame.from_records([tuple([i for i in params.values()])], columns=list(params.keys()))
 
-        df = pd.DataFrame(raw_result)
+        mod_record = {'sim_id': sim_id, 'meta': result_record}
+        if sim_id not in sim_id_records:
+            sim_id_records.append(sim_id)
+            result_records_list.append(mod_record)
+
+        sim_id = config_id['simulation_id']
+        # print('sim id first loop = ',sim_id)
+
+        sub_df = df[df.simulation == config_id['simulation_id']][df.run == config_id['run_id'] + 1]
+        sim_dfs[sim_id].append(sub_df)
+        # print(sub_df[['simulation', 'run', 'substep', 'timestep']].tail(5))
+        # print(sub_df.tail(5))
+
+    for sim_id in sim_ids:
+        result_record = [d for d in result_records_list if d['sim_id'] == sim_id][0]['meta']
+        sim_dfs[sim_id] = pd.concat(sim_dfs[sim_id])
+        sub_df = sim_dfs[sim_id]
+
+        # print('sim id second loop = ',sim_id)
         # keep only last substep of each timestep
         if drop_midsteps:
-            max_substep = max(df.substep)
-            is_droppable = (df.substep != max_substep) & (df.substep != 0)
-            df.drop(df[is_droppable].index, inplace=True)
+            max_substep = max(sub_df.substep)
+            is_droppable = (sub_df.substep != max_substep) & (sub_df.substep != 0)
+            sub_df.drop(sub_df[is_droppable].index, inplace=True)
 
-        result_record['dataset'] = [df]
+        # print(sub_df.head(3))
+        # print(sub_df.tail(3))
+        result_record['dataset'] = [sub_df]
         results = results.append(result_record)
-        i += 1
-    return results.reset_index()
+        # print(sub_df[['simulation', 'run', 'substep', 'timestep']].tail(5))
+
+    return results.reset_index()