flee.SimulationSettings.MaxWalkSpeed=5000.0
end_time = 10
e = flee.Ecosystem()
l1 = e.addLocation("A", movechance=0.3)
l2 = e.addLocation("B", movechance=0.0)
l3 = e.addLocation("C", movechance=0.0)
l4 = e.addLocation("D", movechance=0.0)
e.linkUp("A","B","834.0")
e.linkUp("A","C","1368.0")
e.linkUp("A","D","536.0")
d = handle_refugee_data.RefugeeTable(csvformat="generic", data_directory="test_data", start_date="2010-01-01", data_layout="data_layout.csv")
for t in range(0,end_time):
new_refs = d.get_new_refugees(t)
# Insert refugee agents
for i in range(0, new_refs):
e.addAgent(location=l1)
# Propagate the model by one time step.
e.evolve()
print(t, l1.numAgents+l2.numAgents+l3.numAgents+l4.numAgents, l1.numAgents, l2.numAgents, l3.numAgents, l4.numAgents)
assert t==9
movechance="camp",
capacity=4356,
foreign=True)
# GPS 14.684030 1.882720
n3 = e.addLocation("Niamey",
movechance="camp",
capacity=6327,
foreign=True)
n4 = e.addLocation("Tabareybarey",
movechance="camp",
capacity=9189,
foreign=True)
# GPS 14.754761 0.944773
d = handle_refugee_data.RefugeeTable(
csvformat="generic", data_directory="source_data/mali2012/")
# 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 2012.
d.correctLevel1Registrations("Mbera", "2012-12-31")
m1.capacity = d.getMaxFromData("Mbera", last_physical_day)
d.correctLevel1Registrations("Mentao", "2012-10-03")
b1.capacity = d.getMaxFromData("Mentao", last_physical_day)
d.correctLevel1Registrations("Abala", "2012-12-21")
n1.capacity = d.getMaxFromData("Abala", last_physical_day)
d.correctLevel1Registrations("Mangaize", "2012-12-21")
n2.capacity = d.getMaxFromData("Mangaize", last_physical_day)
d.correctLevel1Registrations("Tabareybarey", "2012-12-21")
n4.capacity = d.getMaxFromData("Tabareybarey", last_physical_day)
ig = InputGeography.InputGeography()
ig.ReadLocationsFromCSV("%s/locations.csv" % input_csv_directory)
ig.ReadLinksFromCSV("%s/routes.csv" % input_csv_directory)
ig.ReadClosuresFromCSV("%s/closures.csv" % input_csv_directory)
e, lm = ig.StoreInputGeographyInEcosystem(e)
#print("Network data loaded")
d = handle_refugee_data.RefugeeTable(
csvformat="generic",
data_directory=validation_data_directory,
start_date="2013-12-15",
data_layout="data_layout.csv")
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")
output_header_string = "Day,"
#e.linkUp("N24","Bossobolo","23.0") #Bossobolo has been taken out (see above).
e.linkUp("Bangui", "Mole", "42.0")
e.linkUp("Mole", "Boyabu", "25.0")
e.linkUp("Zemio", "Mboti", "174.0")
e.linkUp("Mobaye", "Mboti", "662.0")
e.linkUp("Gbadolite", "Inke", "42.0")
e.linkUp("Mbaiki", "Betou", "148.0")
e.linkUp("Nola", "Brazaville", "1300.0")
#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()
d = handle_refugee_data.RefugeeTable(csvformat="generic",
data_directory="source_data/car2014/",
start_date="2013-12-01")
#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("Amboko", "2015-09-30")
d.correctLevel1Registrations("Belom", "2015-08-31")
d.correctLevel1Registrations("Dosseye", "2015-09-30")
d.correctLevel1Registrations("Gondje", "2015-09-30")
lm["Moyo"].capacity *= d.correctLevel1Registrations(
"Moyo", "2015-06-02") #also "2014-05-11" and "2015-06-02"
d.correctLevel1Registrations("East", "2014-09-28")
d.correctLevel1Registrations("Adamaoua", "2014-10-19")
#d.correctLevel1Registrations("Mole","2016-02-29") # no clear decrease visible.
e.linkUp("Gisuru", "Nduta", "60.0")
e.linkUp("Commune of Mabanda", "Kagunga", "36.0")
e.linkUp(
"Commune of Mabanda", "Nyarugusu", "71.0"
) #Estimated distance, as exact location of Nyarugusu is uncertain.
e.linkUp("Kagunga", "Nyarugusu", "91.0", forced_redirection=True
) #From Kagunga to Kigoma by ship (Kagunga=Kigoma)
e.linkUp("Kirundo", "Nakivale", "318.0")
e.linkUp("Kayanza", "Nakivale", "413.0")
e.linkUp("Nduta", "Nyarugusu", "150.0",
forced_redirection=True) #distance needs to be checked.
d = handle_refugee_data.RefugeeTable(
csvformat="generic",
data_directory="source_data/burundi2015",
start_date="2015-05-01")
# 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("Mahama", "2015-10-04")
d.correctLevel1Registrations("Nduta", "2016-04-06")
d.correctLevel1Registrations("Nyarugusu", "2015-11-10")
d.correctLevel1Registrations("Nakivale", "2015-08-18")
d.correctLevel1Registrations("Lusenda", "2015-09-30")
locations[26].capacity = d.getMaxFromData("Mahama", last_physical_day)
locations[27].capacity = d.getMaxFromData("Nduta", last_physical_day)
locations[29].capacity = d.getMaxFromData("Nyarugusu", last_physical_day)
ig = InputGeography.InputGeography()
ig.ReadLocationsFromCSV("examples/general_example/locations.csv")
ig.ReadLinksFromCSV("examples/general_example/routes.csv")
ig.ReadClosuresFromCSV("examples/general_example/closures.csv")
e, lm = ig.StoreInputGeographyInEcosystem(e)
# print("Network data loaded")
d = handle_refugee_data.RefugeeTable(
csvformat="generic",
data_directory="examples/general_example/refugee_data/",
start_date="2009-12-24",
data_layout="data_layout.csv")
refugee_debt = 0
refugees_raw = 0 #raw (interpolated) data from TOTAL UNHCR refugee count only.
# Visualization
visoutput = visualization.vis.VisManager(
flee.SimulationSettings.SimulationSettings.DefaultVisPath /
"general.json")
start_date = datetime(2009, 12, 24)
current_date = datetime(2009, 12, 24)
# Visualization end
for t in range(0, end_time):
