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ssudan-csv.py
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/
ssudan-csv.py
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from flee import flee
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
def AddInitialRefugees(e, d, loc):
""" Add the initial refugees to a location, using the location name"""
num_refugees = int(d.get_field(loc.name, 0, FullInterpolation=True))
for i in range(0, num_refugees):
e.addAgent(location=loc)
def date_to_sim_days(date):
return DataTable.subtract_dates(date,"2013-12-15")
if __name__ == "__main__":
end_time = 604
last_physical_day = 604
if len(sys.argv)>1:
if (sys.argv[1]).isnumeric():
end_time = int(sys.argv[1])
last_physical_day = int(sys.argv[1])
else:
end_time = 604
last_physical_day = 604
duration = flee.SimulationSettings.SimulationSettings.ReadFromCSV(sys.argv[1])
if duration>0:
end_time = duration
last_physical_day = end_time
e = flee.Ecosystem()
ig = InputGeography.InputGeography()
ig.ReadLocationsFromCSV("examples/ssudan_input_csv/locations.csv")
ig.ReadLinksFromCSV("examples/ssudan_input_csv/routes.csv")
ig.ReadClosuresFromCSV("examples/ssudan_input_csv/closures.csv")
e,lm = ig.StoreInputGeographyInEcosystem(e)
#print("Network data loaded")
d = handle_refugee_data.RefugeeTable(csvformat="generic", data_directory="source_data/ssudan2014/", start_date="2013-12-15", data_layout="data_layout.csv")
#Correcting for overestimations due to inaccurate level 1 registrations in five of the camps.
#These errors led to a perceived large drop in refugee population in all of these camps.
#We correct by linearly scaling the values down to make the last level 1 registration match the first level 2 registration value.
#To our knowledge, all level 2 registration procedures were put in place by the end of 2016.
d.correctLevel1Registrations("Tierkidi","2014-08-08")
d.correctLevel1Registrations("Pugnido","2015-06-26")
d.correctLevel1Registrations("Jewi","2015-07-31")
d.correctLevel1Registrations("Kule","2014-09-12")
d.correctLevel1Registrations("Kakuma","2014-06-26")
d.correctLevel1Registrations("Khartoum","2014-04-27")
d.correctLevel1Registrations("West_Kordofan","2015-08-05")
d.correctLevel1Registrations("Rhino","2014-05-21")
d.correctLevel1Registrations("Kiryandongo","2014-05-27")
lm["Tierkidi"].capacity = d.getMaxFromData("Tierkidi", last_physical_day)
lm["Pugnido"].capacity = d.getMaxFromData("Pugnido", last_physical_day)
lm["Jewi"].capacity = d.getMaxFromData("Jewi", last_physical_day)
lm["Kule"].capacity = d.getMaxFromData("Kule", last_physical_day)
lm["Kakuma"].capacity = d.getMaxFromData("Kakuma", last_physical_day)
lm["Khartoum"].capacity = d.getMaxFromData("Khartoum", last_physical_day)
lm["West_Kordofan"].capacity = d.getMaxFromData("West_Kordofan", last_physical_day)
lm["Adjumani"].capacity = d.getMaxFromData("Adjumani", last_physical_day)
lm["Rhino"].capacity = d.getMaxFromData("Rhino", last_physical_day)
lm["Kiryandongo"].capacity = d.getMaxFromData("Kiryandongo", last_physical_day)
output_header_string = "Day,"
camp_locations = ["Tierkidi","Pugnido","Jewi","Kule","Kakuma","Khartoum","West_Kordofan","Adjumani","Rhino","Kiryandongo"]
#TODO: Add Camps from CSV based on their location type.
for l in camp_locations:
#AddInitialRefugees(e,d,lm[l])
output_header_string += "%s sim,%s data,%s error," % (lm[l].name, lm[l].name, lm[l].name)
output_header_string += "Total error,refugees in camps (UNHCR),total refugees (simulation),raw UNHCR refugee count,refugees in camps (simulation),refugee_debt"
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):
ig.AddNewConflictZones(e,t)
# Determine number of new refugees to insert into the system.
new_refs = d.get_daily_difference(t, FullInterpolation=True) - refugee_debt
refugees_raw += d.get_daily_difference(t, FullInterpolation=True)
if new_refs < 0:
refugee_debt = -new_refs
new_refs = 0
elif refugee_debt > 0:
refugee_debt = 0
# Insert refugee agents
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 = []
loc_data = []
camps = []
for i in camp_locations:
camps += [lm[i]]
loc_data += [d.get_field(i, t)]
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_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)
else:
output += ",0,0,0,0,0,0"
#output_string += ",0"
print(output)