Redo simulations correct rln gen proof

rln-delay-simulations/plot_paper_distribution.py

@@ -15,10 +15,10 @@ num_bins = 50
 # Best (1 hop) and worst (4 hops) latencies in ms
 # See Table 2 from paper
 multi_host_simulations = {
-    "2kb": [364, 709],
-    "25kb": [436, 1084],
-    "100kb": [471, 1922],
-    "500kb": [564, 2988]
+    "2kb": [364, 477],
+    "25kb": [436, 945],
+    "100kb": [471, 1689],
+    "500kb": [564, 2468]
 }
 
 with plt.style.context(['science', 'ieee']):
@@ -32,20 +32,28 @@ with plt.style.context(['science', 'ieee']):
 
     for (size, pos) in possitions:
         # Plot single host results
-        latencies.hist(size, bins=num_bins, ax=pos, density=True)
+        latencies.hist(size, bins=num_bins, ax=pos, density=True, label="1) single-host")
 
         # Plot multi host results
-        pos.axvline(x=multi_host_simulations[size][0], color='red', linestyle='--')
+        pos.axvline(x=multi_host_simulations[size][0], color='red', linestyle='--', label="2) multi-host")
         pos.axvline(x=multi_host_simulations[size][1], color='red', linestyle='--')
 
         pos.grid(False)
-        title = ('size={size}\n' + r'$\mu$={mean:.0f} $p_{{95}}$={p95:.0f} min={min:.0f} max={max:.0f}').format(
+        title = ('size={size}\n' +
+                 r'1) $\mu$={mean:.0f} $p_{{95}}$={p95:.0f} min={min:.0f} max={max:.0f}' + '\n' +
+                 r'2) min={min_multi:.0f} max={max_multi:.0f}').format(
             size=size,
             mean=latencies[size].mean(axis=0),
             p95=np.percentile(latencies[size], 95),
             min=latencies[size].min(),
-            max=latencies[size].max())
+            max=latencies[size].max(),
+            min_multi=multi_host_simulations[size][0],
+            max_multi=multi_host_simulations[size][1])
         pos.set_title(title, fontsize=8)
+        if size == "25kb":
+            pos.legend(fontsize="5", loc="upper left")
+        else:
+            pos.legend(fontsize="5", loc="best")
 
     ax[0][0].set(ylabel='Cumulative message share')
     ax[1][0].set(xlabel='Latency (ms)', ylabel='Cumulative message share')