def generate_cdf(fb, hk):
cdf_fb = Cdf(degrees(fb))
cdf_hk = Cdf(degrees(hk))
thinkplot.Cdf(cdf_fb, color='gray', label="Facebook CDF")
thinkplot.Cdf(cdf_hk, label='RPA CDF')
thinkplot.config(xlabel='degree', xscale='log', ylabel='CDF')
plt.savefig('CDFGraphs_Modified.png')
def generate_ccdf(fb, hk):
cdf_fb = Cdf(degrees(fb))
cdf_hk = Cdf(degrees(hk))
thinkplot.Cdf(cdf_fb, label='Facebook CCDF', color='gray', complement=True)
thinkplot.Cdf(cdf_hk, label="RPA CCDF", complement=True)
thinkplot.config(xlabel='degree',
xscale='log',
ylabel='CCDF',
yscale='log')
plt.savefig("CCDFGraphs_Modified.png")
# viewer = SugarscapeViewer(env)
# anim = viewer.animate(frames=100)
# # plt.show()
RandomSeed(17)
env = Sugarscape(50,
num_agents=250,
min_lifespan=60,
max_lifespan=100,
replace=True)
cdfs = []
for i in range(5):
[env.step() for i in range(100)]
cdf = Cdf(agent.sugar for agent in env.agents)
cdfs.append(cdf)
thinkplot.preplot(cols=2)
thinkplot.Cdfs(cdfs[:-1], color='gray', alpha=0.3)
thinkplot.Cdf(cdfs[-1])
thinkplot.config(xlabel='Wealth', ylabel='CDF')
thinkplot.bigger_text()
thinkplot.subplot(2)
thinkplot.Cdfs(cdfs[:-1], color='gray', alpha=0.3)
thinkplot.Cdf(cdfs[-1])
thinkplot.config(xlabel='Wealth', ylabel='CDF', xscale='log')
thinkplot.bigger_text()
thinkplot.save('chap09-4')
thinkplot.Plot(history[0, 0, :], label="Susceptible")
thinkplot.Plot(history[0, 1, :], label="Infected")
thinkplot.Plot(history[0, 2, :], label="Dead")
thinkplot.Plot(history[0, 3, :], label="R")
plt.title("Population distribution in one city")
plt.xlabel("Timestep")
plt.ylabel("Population")
thinkplot.config(legend=True)
thinkplot.show()
print("num infected", history[:, 4, :])
thinkplot.plot(np.sum(history[:, 3, :], axis=1))
thinkplot.show()
""" Plot CDF of infections number """
infection_cdf = Cdf(np.sum(history[:, 3, :], axis=1))
thinkplot.Cdf(infection_cdf)
plt.title("Sum Reinfections Cdf")
plt.xlabel("Total Reinfections")
plt.ylabel("CDF")
thinkplot.show()
# print("G Len nodes:", len(G.nodes()))
# print("G Len edges:", len(G.edges()))
# print("G transitivity:", nx.transitivity(G))
# print("G Degree Centrality:", np.mean(list(nx.degree_centrality(G).values())))
# # print("G Closeness Centrality:", np.mean(list(nx.closeness_centrality(G).values())))
# print("G Clustering:", nx.average_clustering(G))
""" Plot infections vs degree and closeness """
def vel_distribution(filename,
fps,
speed_limit=25,
dir=None,
only_vehicle=True):
"""
Arguments
---------
filename: str, path to database
fps: frame rate of the video, in frames per second
speed_limit: speed limit of the intersection
dir: directory to save image, if None don't automatically save
only_vehicle: only show velocities of vehicles, default True
"""
connection = sqlite3.connect(filename)
cursor = connection.cursor()
if only_vehicle:
queryStatement = '''SELECT object_trajectories.object_id AS object_id, frame, x, y, x_v, y_v
FROM object_trajectories INNER JOIN objects ON object_trajectories.object_id = objects.object_id
WHERE road_user_type = 1
ORDER BY object_id, frame'''
else:
queryStatement = 'SELECT object_id, frame, x, y, x_v, y_v FROM object_trajectories ORDER BY object_id, frame'
cursor.execute(queryStatement)
obj_id = 0
obj_vels = []
xvels = []
yvels = []
for row in cursor:
xvel = row[4]
yvel = row[5]
xvels.append(xvel * fps * MPS_MPH_CONVERSION)
yvels.append(yvel * fps * MPS_MPH_CONVERSION)
# reading new object
if (row[0] != obj_id):
# save velocity information for old object before moving onward
xvels = [abs(x) for x in xvels]
yvels = [abs(y) for y in yvels]
speeds = [
math.sqrt(vels[0]**2 + vels[1]**2)
for vels in zip(xvels, yvels)
]
avg_xv = sum(xvels) / len(xvels)
avg_yv = sum(yvels) / len(yvels)
avg_vel = math.sqrt(avg_xv**2 + avg_yv**2)
obj_vels.append(avg_vel)
obj_id = row[0]
xvels = []
yvels = []
cdf = Cdf(obj_vels)
kdepdf = EstimatedPdf(obj_vels)
pr = cdf.PercentileRank(speed_limit)
titlestring = "{:0.1f} % of {} are exceeding the {} mph limit".format(
100 - pr, 'vehicles' if only_vehicle else 'road users', speed_limit)
thinkplot.PrePlot(1)
thinkplot.Pdf(kdepdf)
thinkplot.Vlines(speed_limit, 0, 0.05)
thinkplot.Config(title=titlestring, xlabel='Velocity (mph)', ylabel='PDF')
if dir is not None:
thinkplot.Save(os.path.join(dir, 'velocityPDF'),
formats=['jpg'],
bbox_inches='tight')
else:
thinkplot.Show()
plt.xlabel("Degree t")
plt.ylabel("Mortality %")
# plt.yscale('log')
thinkplot.show()
thinkplot.Scatter(degree_vs_infections[:, 0], degree_vs_infections[:, 1])
plt.title("Degree vs Reinfections")
plt.xlabel("Degree t")
plt.ylabel("Reinfections")
thinkplot.show()
thinkplot.plot(np.sum(history[:, 4, :], axis=0))
plt.title("Infections across t")
thinkplot.show()
thinkplot.Cdf(Cdf(history[:, 0, -2]), label="S")
thinkplot.Cdf(Cdf(history[:, 1, -2]), label="I")
thinkplot.Cdf(Cdf(history[:, 2, -2]), label="Dead")
thinkplot.Cdf(Cdf(history[:, 3, -2]), label="R")
plt.title("D in each city")
thinkplot.show()
city_i = 5
thinkplot.plot(history[city_i, 4, :])
plt.title("Infected at each timestep, degree" + str(len(G[node_list[city_i]])))
thinkplot.show()
# thinkplot.plot(history[1,:3,:100].T)
# thinkplot.show()
thinkplot.Plot(np.sum(history[:, 3, :], axis=0), label="NYS")