/
mscalecity.py
137 lines (99 loc) · 3.59 KB
/
mscalecity.py
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from flee import pflee #parallel implementation
from flee import coupling #coupling interface for multiscale models
from datamanager import handle_refugee_data
from datamanager import DataTable #DataTable.subtract_dates()
from flee import InputGeography
import numpy as np
import outputanalysis.analysis as a
import sys
import analyze_graph
def AddInitialRefugees(e, loc):
""" Add the initial refugees to a location, using the location name"""
num_refugees = 10000
for i in range(0, num_refugees):
e.addAgent(location=loc)
def date_to_sim_days(date):
return DataTable.subtract_dates(date,"2010-01-01")
if __name__ == "__main__":
end_time = 10
last_physical_day = 10
submodel_id = 0
if len(sys.argv)>1:
if (sys.argv[1]).isnumeric():
submodel_id = int(sys.argv[1])
#duration = pflee.SimulationSettings.SimulationSettings.ReadFromCSV(sys.argv[1])
#if duration>0:
# end_time = duration
# last_physical_day = end_time
e = pflee.Ecosystem()
c = coupling.CouplingInterface(e)
c.setCouplingFilenames("in","out")
if(submodel_id > 0):
c.setCouplingFilenames("out","in")
ig = InputGeography.InputGeography()
ig.ReadLocationsFromCSV("examples/testmscale/locations-%s.csv" % submodel_id)
ig.ReadLinksFromCSV("examples/testmscale/routes-%s.csv" % submodel_id)
ig.ReadClosuresFromCSV("examples/testmscale/closures-%s.csv" % submodel_id)
e,lm = ig.StoreInputGeographyInEcosystem(e)
#DEBUG: print graph and show it on an image.
vertices, edges = e.export_graph()
#analyze_graph.print_graph_nx(vertices, edges, print_dist=True)
#sys.exit()
#print("Network data loaded")
#d = handle_refugee_data.RefugeeTable(csvformat="generic", data_directory="test_data/test_input_csv/refugee_data", start_date="2010-01-01", data_layout="data_layout.csv")
output_header_string = "Day,"
coupled_locations = ["N","E","S","W"]
camp_locations = list(lm.keys())
#print(camp_locations)
#camp_locations = ["N","E","S","W"]
#if(submodel_id > 0):
# camp_locations = ["A","B"]
#TODO: Add Camps from CSV based on their location type.
if submodel_id == 0:
AddInitialRefugees(e,lm["A"])
for l in coupled_locations:
c.addCoupledLocation(lm[l], l)
for l in camp_locations:
output_header_string += "%s sim," % (lm[l].name)
if e.getRankN(0):
output_header_string += "num agents,num agents in camps"
print(output_header_string)
# Set up a mechanism to incorporate temporary decreases in refugees
refugee_debt = 0
refugees_raw = 0 #raw (interpolated) data from TOTAL UNHCR refugee count only.
for t in range(0,end_time):
#if t>0:
ig.AddNewConflictZones(e,t)
# Determine number of new refugees to insert into the system.
new_refs = 0
if submodel_id == 0:
new_refs = 0
refugees_raw += new_refs
#Insert refugee agents
if submodel_id == 0:
e.add_agents_to_conflict_zones(new_refs)
e.refresh_conflict_weights()
t_data = t
#e.enact_border_closures(t)
e.evolve()
#Calculation of error terms
errors = []
abs_errors = []
camps = []
for i in camp_locations:
camps += [lm[i]]
# calculate retrofitted time.
refugees_in_camps_sim = 0
for camp in camps:
refugees_in_camps_sim += camp.numAgents
if e.getRankN(t):
output = "%s" % t
for i in range(0,len(camp_locations)):
output += ",%s" % (lm[camp_locations[i]].numAgents)
if refugees_raw>0:
output += ",%s,%s" % (e.numAgents(), refugees_in_camps_sim)
else:
output += ",0,0"
print(output)
#exchange data with other code.
c.Couple(t)