Add plot Python scripts (#82)

rln-delay-simulations/message_hops.py

@@ -0,0 +1,47 @@
+from math import ceil
+from numpy import log
+from numpy import ceil
+import matplotlib.pyplot as plt
+import numpy as np
+import scienceplots
+
+L = 1      # latency in ms (msg prop + rln proof verif, etc)
+N1 = 1000    # number of nodes in the network (1)
+N2 = 10000   # number of nodes in the network (2)
+N3 = 100000  # number of nodes in the network (3)
+
+def delay_last_hop(n, d, l):
+    # multiply by l for latencies
+    return ceil(log(n)/log(d))
+
+ds = np.arange(2,15)
+ls = np.ones(len(ds))*L
+
+# astype(int) is ok since ceil() returns integers
+delays_1 = delay_last_hop(np.ones(len(ds))*N1, ds, ls).astype(int)
+delays_2 = delay_last_hop(np.ones(len(ds))*N2, ds, ls).astype(int)
+delays_3 = delay_last_hop(np.ones(len(ds))*N3, ds, ls).astype(int)
+
+with plt.style.context(['science', 'ieee']):
+    fig, ax1 = plt.subplots()
+    ax2 = ax1.twinx()
+
+    ax1.plot(ds, delays_1, color='r', label=r"$N_1=$"+str(N1))
+    ax1.plot(ds, delays_2, color='g', label=r"$N_2=$"+str(N2))
+    ax1.plot(ds, delays_3, color='b', label=r"$N_3=$"+str(N3))
+    ax2.plot(ds, ds, color='y', label="Bandwidth ampl.")
+    print(delays_1)
+    print(ds)
+    ax1.autoscale(tight=True)
+    ax2.autoscale(tight=True)
+    ax1.legend(loc=0)
+    ax2.legend(loc=0)
+    ax1.set(title='Worst case maximum hops vs bandwidth')
+    ax1.set(**dict(xlabel='Outbound degree (D)', ylabel='Amount of hops'))
+    ax2.set(**dict(ylabel='Bandwidth amplification'))
+    #ax2.set_xlim([2, 14])
+    fig.savefig('message_hops.svg', dpi=300)
+
+    plt.close()
+
+

rln-delay-simulations/plot_latencies_d6.py

@@ -5,9 +5,8 @@ from analyze import load
 
 latencies = [load("raw/latency_10kb.txt", "arrival_diff="),
              load("raw/latency_100kb.txt", "arrival_diff="),
-             load("raw/latency_500kb.txt", "arrival_diff=")]
-
-print(latencies)
+             load("raw/latency_500kb.txt", "arrival_diff="),
+             ]
 
 with plt.style.context(['science', 'ieee']):
     fig, ax = plt.subplots()
@@ -30,4 +29,4 @@ for i, line in enumerate(bp['medians']):
     ax.annotate(text, xy=(x + 0.1, y), fontsize=6)
 
 fig.set_size_inches(4, 3)
-fig.savefig('plot.jpg', dpi=600)
+fig.savefig('latencies_d6.svg', dpi=600)

rln-delay-simulations/plot_latencies_d6_vs_d3.py

@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+import scienceplots
+import numpy as np
+from analyze import load
+
+latencies = [load("raw/latency_10kb.txt", "arrival_diff="),
+             load("raw/latency_scenario2_10kb.txt", "arrival_diff="),
+             load("raw/latency_100kb.txt", "arrival_diff="),
+             load("raw/latency_scenario2_100kb.txt", "arrival_diff="),
+             #load("raw/latency_500kb.txt", "arrival_diff="),
+             #load("raw/latency_scenario2_500kb.txt", "arrival_diff=")
+             ]
+
+with plt.style.context(['science', 'ieee']):
+    fig, ax = plt.subplots()
+    bp = ax.boxplot(latencies,notch=True, vert=True, patch_artist=True,
+                    showfliers=True, whis=100000000000)
+
+    for patch, color in zip(bp['boxes'], ['red', 'blue', 'gold', 'yellowgreen']):
+        patch.set_facecolor(color)
+
+    ax.set(title="Message latencies distribution\nD=6 vs D=3 nodes=1000 samples="+str(latencies[1].size), xlabel='Scenario', ylabel='Message propagation time (ms)')
+    ax.grid(linestyle='-')
+    my_legend = []
+    for msg_size in [10, 100]:
+        for d in [6, 3]:
+          my_legend.append("Message size: " + str(msg_size) + " kB"+ " D="+ str(d))
+    ax.legend([bp["boxes"][i] for i in range(len(my_legend))], my_legend, loc='upper left', fontsize=5)
+
+for i, line in enumerate(bp['medians']):
+    x, y = line.get_xydata()[1]
+    text = r' $ \mu =$ ' + '{:.0f} ms\n'.format(latencies[i].mean(axis=0)) + r' $ p_{95} = $ ' + '{:.0f} ms'.format(np.percentile(latencies[i], 95))
+    ax.annotate(text, xy=(x + 0.1, y), fontsize=6)
+
+fig.set_size_inches(4, 3)
+fig.savefig('latencies_d6_vs_d3.svg', dpi=600)