Implement a parallelized version of starter_one

start.py

@@ -169,7 +169,7 @@ def save_simulation(t, sim, sim_param, exit_now=False):
         exit(0)
 
 # main entry point
-if __name__ == "__main__":
+def entry_point(passed_args=None):
 
     """ use argparse to handle command line arguments"""
     parser = argparse.ArgumentParser(description='Model the Insurance sector')
@@ -189,7 +189,8 @@ def save_simulation(t, sim, sim_param, exit_now=False):
     parser.add_argument("-p", "--showprogress", action="store_true", help="show timesteps")
     parser.add_argument("-v", "--verbose", action="store_true", help="more detailed output")
     parser.add_argument("--save_iterations", type=int, help="number of iterations to iterate before saving world state")
-    args = parser.parse_args()
+    parser.add_argument("--multiprocess", action="store_true", help="run this script as part of a parallel computation")
+    args = parser.parse_args(passed_args)
 
     if args.abce:
         isleconfig.use_abce = True
@@ -238,6 +239,9 @@ def save_simulation(t, sim, sim_param, exit_now=False):
     [general_rc_event_schedule, general_rc_event_damage, np_seeds, random_seeds] = setup.obtain_ensemble(1)   #Only one ensemble. This part will only be run locally (laptop).
 
     log = main(simulation_parameters, general_rc_event_schedule[0], general_rc_event_damage[0], np_seeds[0], random_seeds[0], save_iter)
+
+    if args.multiprocess:
+        return log
 
     """ Restore the log at the end of the single simulation run for saving and for potential further study """
     L = logger.Logger()
@@ -248,3 +252,5 @@ def save_simulation(t, sim, sim_param, exit_now=False):
     CS = calibrationscore.CalibrationScore(L)
     score = CS.test_all()
 
+if __name__ == "__main__":
+    entry_point()

starter_one.py

@@ -0,0 +1,52 @@
+import os
+import datetime
+import logger
+import multiprocessing as mp
+
+import numpy as np
+
+from start import entry_point
+import calibrationscore
+
+# Rename files
+LOG_TYPES = 'operational contracts cash reinoperational reincontracts reincash premium'.split(' ')
+timestamp = datetime.datetime.now().strftime('%Y_%h_%d_%H_%M')
+for log_type in LOG_TYPES:
+    fname = 'data/one_%s.dat' % log_type
+    try:
+        os.rename(fname, fname + timestamp)
+    except FileNotFoundError:
+        print('file not found:', fname)
+
+# Now run the ensemble
+
+N = 300
+N = 10
+PROCS = 4
+# separate into PROCS procceses
+epochs = np.array_split(np.arange(N), PROCS)
+print([list(e) for e in epochs])
+manager = mp.Manager()
+par_logs = manager.list()
+
+def _fn(_epoch):
+    logs = [entry_point(('--replicid %d --replicating --oneriskmodel --multiprocess' % i).split(' ')) for i in _epoch]
+    par_logs.append(logs)
+
+procs = []
+for epoch in epochs:
+    proc = mp.Process(target=_fn, args=(epoch,))
+    proc.start()
+    procs.append(proc)
+
+for p in procs:
+    p.join()
+
+for logs in par_logs:
+    for log in logs:
+        L = logger.Logger()
+        L.restore_logger_object(log)
+        L.save_log(True)
+
+        CS = calibrationscore.CalibrationScore(L)
+        score = CS.test_all()