def plotme(out_dir, data, name):
plt.clf()
data_x = []
data_y = []
d = handle_refugee_data.DataTable("mali2012/refugees.csv",
csvformat="mali-portal")
for day in range(0, len(data["%s data" % name])):
if d.is_interpolated(name, day) == False:
#draw a point
data_x.append(day)
data_y.append(data.at[day, "%s data" % name])
# data.loc[:,["%s sim" % name,"%s data" % name]]).as_matrix()
y1 = data["%s sim" % name].as_matrix()
y2 = data["%s data" % name].as_matrix()
days = np.arange(len(y1))
#plt.ylabel("Number of refugees")
plt.xlabel("Days elapsed")
#matplotlib.rc('xtick', labelsize=16)
#matplotlib.rc('ytick', labelsize=16)
matplotlib.rcParams.update({'font.size': 22})
country = {
"Bobo-Dioulasso": "(BF)",
"Mentao": "(BF)",
"Mbera": "(MAU)",
"Fassala": "(MAU)",
"Abala": "(NI)",
"Mangaize": "(NI)",
"Niamey": "(NI)",
"Tabareybarey": "(NI)"
}
labelsim, = plt.plot(days,
y1,
linewidth=10,
label="%s %s simulation" %
(name.title(), country[name.title()]))
labeldata, = plt.plot(days,
y2,
linewidth=10,
label="%s %s UNHCR data" %
(name.title(), country[name.title()]))
plt.plot(data_x, data_y, 'ob')
plt.legend(handles=[labelsim, labeldata], loc=4, prop={'size': 20})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(12, 8)
#adjust margins.
set_margins()
fig.savefig("%s/%s.png" % (out_dir, name))
def plotme_minimal(out_dir, data, name):
"""
Explaining minimal graphs: populating data points to generate graphs and an example
"""
plt.clf()
data_x = []
data_y = []
d = handle_refugee_data.DataTable("mali2012/refugees.csv", csvformat="mali-portal")
#Loop - taking the length of dataset for x and y rays
for day in range(0, len(data["%s data" % name])):
if d.is_interpolated(name, day) == False:
#draw a point
data_x.append(day)
data_y.append(data.at[day,"%s data" % name])
# data.loc[:,["%s sim" % name,"%s data" % name]]).as_matrix()
y1 = data["%s sim" % name].as_matrix()
y2 = data["%s data" % name].as_matrix()
days = np.arange(len(y1))
matplotlib.rcParams.update({'font.size': 18})
max_val = max([max(y1),max(y2)])
#Graph labels
plt.xticks([])
plt.yticks([2000,5000])
plt.ylim([0, 1.1*max_val])
#Plotting lines representing simulation results and UNHCR data
labelsim, = plt.plot(days,y1, linewidth=10, label="%s simulation" % (name.title()))
labeldata, = plt.plot(days,y2, linewidth=10, label="%s UNHCR data" % (name.title()))
plt.plot(data_x,data_y,'ob')
#Text labels
#plt.legend(handles=[labelsim, labeldata],loc=4,prop={'size':20})
plt.gca().legend_ = None
plt.text(295, 0.02*plt.ylim()[1], "%s" % (name.title()), size=24, ha='right')
#plt.text(200, 0.02*plt.ylim()[1], "Max: %s" % (max(y1)), size=24)
#Size of plots/graphs
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(7, 6)
#adjust margins.
set_margins(l=0.14,b=0.13,r=0.96,t=0.96)
fig.savefig("%s/min-%s.png" % (out_dir, name))
def plotme_minimal(out_dir, data, name):
plt.clf()
data_x = []
data_y = []
d = handle_refugee_data.DataTable("mali2012/refugees.csv",
csvformat="mali-portal")
for day in range(0, len(data["%s data" % name])):
if d.is_interpolated(name, day) == False:
#draw a point
data_x.append(day)
data_y.append(data.at[day, "%s data" % name])
# data.loc[:,["%s sim" % name,"%s data" % name]]).as_matrix()
y1 = data["%s sim" % name].as_matrix()
y2 = data["%s data" % name].as_matrix()
days = np.arange(len(y1))
#plt.ylabel("Number of refugees")
#plt.xlabel("Days elapsed")
#matplotlib.rc('xtick', labelsize=16)
#matplotlib.rc('ytick', labelsize=16)
matplotlib.rcParams.update({'font.size': 28})
country = {
"Bobo-Dioulasso": "(BF)",
"Mentao": "(BF)",
"Mbera": "(MAU)",
"Fassala": "(MAU)",
"Abala": "(NI)",
"Mangaize": "(NI)",
"Niamey": "(NI)",
"Tabareybarey": "(NI)"
}
max_val = max([max(y1), max(y2)])
plt.xticks([])
plt.yticks([2000, 5000])
plt.ylim([0, 1.1 * max_val])
labelsim, = plt.plot(days,
y1,
linewidth=10,
label="%s %s simulation" %
(name.title(), country[name.title()]))
labeldata, = plt.plot(days,
y2,
linewidth=10,
label="%s %s UNHCR data" %
(name.title(), country[name.title()]))
plt.plot(data_x, data_y, 'ob')
#plt.legend(handles=[labelsim, labeldata],loc=4,prop={'size':20})
plt.gca().legend_ = None
plt.text(295,
0.02 * plt.ylim()[1],
"%s %s" % (name.title(), country[name.title()]),
size=24,
ha='right')
#plt.text(200, 0.02*plt.ylim()[1], "Max: %s" % (max(y1)), size=24)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(8, 6)
#adjust margins.
set_margins(l=0.14, b=0.13, r=0.96, t=0.96)
fig.savefig("%s/min-%s.png" % (out_dir, name))
print("Simulating Mali.")
end_time = 80
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.DataTable("source-data-unhcr.txt",
csvformat="mali-pdf")
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()
e.printInfo()
print(t, l1.numAgents, l2.numAgents, l3.numAgents, l4.numAgents)
"""
l2_data = d.get_field("Mauritania", t) - d.get_field("Mauritania", 0)