#############################################################################################
#data to save
survivors = []
step_time = []
price_df = {}
total_time = []
#if __name__ == '__main__':
for run in range(1):
print ("Run: " + str(run))
#create an instance of the model
start = time.time()
test = NetScape(height = 50, width = 50, initial_population =200, regrow = 1,\
Moore = False)
from distributedspace_mesa.space_distribute_test import Space_Distribute_Test
#create an instance of the pool process, parameter to pass in is
#the model object
d_mesa = Space_Distribute_Test(test, step_finish,\
split = 3)#, resolver = resolver)
#run the program, pass in the steps
results = d_mesa.distro_geo(100)
#print (results[1].schedule.get_breed_count('agent'))
df = results[1].datacollector.get_table_dataframe("Time")
price_df["Run"+str(run)] = results[1].price_record
agents = recorder.survivors(results[1])
#pickle.dump(price_record, open( "price_record.p", "wb" ))
#pickle.dump(test.datacollector.get_table_dataframe("Health"), open("Agent_stats.p", "wb"))
#pickle.dump(test.datacollector.get_table_dataframe("Time"), open("Time_stats.p", "wb"))
'''
#MULTI RUN
survivors = []
time = []
price_df = {}
for run in range(100):
print("Run: " + str(run))
test = NetScape(height=50,
width=50,
initial_population=200,
regrow=1,
seed=42)
for s in range(1000):
test.step()
df = test.datacollector.get_table_dataframe("Time")
price_df["Run" + str(run)] = test.price_record
agents = recorder.survivors(test)
survivors.append(agents)
time.append(df["Time Per Step"].sum())
#pickle.dump(price_df, open("brute_total_price.p", "wb"))
#pickle.dump(survivors, open( "brute_multi_sur.p", "wb" ))
#pickle.dump(time, open("brute_multi_time.p", "wb"))