for i in camp_locations:
camps += [locations[i]]
camp_names = ["Mahama", "Nduta", "Nyarugusu", "Nakivale", "Lusenda"]
refugees_in_camps_sim = 0
for c in camps:
refugees_in_camps_sim += c.numAgents
# calculate errors
for i in range(0, len(camp_locations)):
camp_number = camp_locations[i]
errors += [
a.rel_error(locations[camp_number].numAgents, loc_data[i])
]
abs_errors += [
a.abs_error(locations[camp_number].numAgents, loc_data[i])
]
output = "%s" % t
for i in range(0, len(errors)):
camp_number = camp_locations[i]
output += ",%s,%s,%s" % (locations[camp_number].numAgents,
loc_data[i], errors[i])
if refugees_raw > 0:
#output_string += ",%s,%s,%s,%s" % (float(np.sum(abs_errors))/float(refugees_raw), int(sum(loc_data)), e.numAgents(), refugees_raw)
output += ",%s,%s,%s,%s,%s,%s" % (
float(np.sum(abs_errors)) / float(refugees_raw),
int(sum(loc_data)), e.numAgents(), refugees_raw,
refugees_in_camps_sim, refugee_debt)
camps = []
for i in camp_locations:
camps += [lm[i]]
loc_data += [d.get_field(i, t)]
# calculate retrofitted time.
refugees_in_camps_sim = 0
for c in camps:
refugees_in_camps_sim += c.numAgents
# calculate errors
j=0
for i in camp_locations:
errors += [a.rel_error(lm[i].numAgents, loc_data[j])]
abs_errors += [a.abs_error(lm[i].numAgents, loc_data[j])]
j += 1
output = "%s" % t
for i in range(0,len(errors)):
output += ",%s,%s,%s" % (lm[camp_locations[i]].numAgents, loc_data[i], errors[i])
if refugees_raw>0:
output += ",%s,%s,%s,%s,%s,%s" % (float(np.sum(abs_errors))/float(refugees_raw), int(sum(loc_data)), e.numAgents(), refugees_raw, refugees_in_camps_sim, refugee_debt)
else:
output += ",0,0,0,0,0,0"
print(output)
def run_micro_macro_model(e, c, submodel, ig, d, camp_locations, end_time):
# Set up a mechanism to incorporate temporary decreases in refugees
refugee_debt = 0
# raw (interpolated) data from TOTAL UNHCR refugee count only.
refugees_raw = 0
while c.reuse_coupling():
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 = d.get_daily_difference(
t, FullInterpolation=True, SumFromCamps=False) - refugee_debt
refugees_raw += d.get_daily_difference(t,
FullInterpolation=True,
SumFromCamps=False)
if log_exchange_data is True:
c.logNewAgents(t, new_refs)
if new_refs < 0:
refugee_debt = -new_refs
new_refs = 0
elif refugee_debt > 0:
refugee_debt = 0
# Insert refugee agents
if submodel == "macro":
print("t={}, inserting {} new agents".format(t, new_refs),
file=sys.stderr)
# e.add_agents_to_conflict_zones(new_refs)
for i in range(0, new_refs):
e.addAgent(e.pick_conflict_location())
e.updateNumAgents(log=False)
# e.printInfo()
# exchange data with other code.
# immediately after agent insertion to ensure ghost locations
# work correctly.
c.Couple(t)
e.refresh_conflict_weights()
t_data = t
# e.enact_border_closures(t)
e.evolve()
# Calculation of error terms
errors = []
abs_errors = []
loc_data = []
camps = []
for i in camp_locations:
camps += [lm[i]]
loc_data += [d.get_field(i, t)]
# calculate retrofitted time.
refugees_in_camps_sim = 0
for camp in camps:
refugees_in_camps_sim += camp.numAgents
if e.getRankN(t):
output = "{}".format(t)
j = 0
for i in camp_locations:
errors += [a.rel_error(lm[i].numAgents, loc_data[j])]
abs_errors += [a.abs_error(lm[i].numAgents, loc_data[j])]
j += 1
output = "{}".format(t)
for i in range(0, len(errors)):
output += ",{},{},{}".format(
lm[camp_locations[i]].numAgents, loc_data[i],
errors[i])
if refugees_raw > 0:
output += ",{},{},{},{},{},{}".format(
round(
float(np.sum(abs_errors)) / float(refugees_raw),
a.ROUND_NDIGITS), int(sum(loc_data)),
e.numAgents(), refugees_raw, refugees_in_camps_sim,
refugee_debt)
else:
output += ",0,0,0,0,0,0"
# print(output)
with open(out_csv_file, "a+") as f:
f.write(output)
f.write("\n")
f.flush()
# exit()
# break while-loop(c.reuse_coupling) if coupling_type == file
if not hasattr(c, "instance"):
break
