Update on Overleaf.

figs/analyse_hash.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+
+import csv
+import matplotlib.pyplot as plt
+import numpy as np
+
+targets = []
+hashvalues = []
+diff = []
+distrib=[]
+ratio=[]
+
+with open("headers.csv", "r") as f:
+    reader = csv.reader(f, delimiter=";")
+    for row in reader:
+        if row[0][0] != '#':
+            targets.append(int(row[3]))
+            hashvalues.append(int(row[5]))
+            diff.append(int(row[3]) - int(row[5]))
+            ratio.append(int(row[5])/int(row[3]))
+
+plt.plot(hashvalues, '+', label="hash")
+plt.plot(targets, 'o', label="target")
+plt.legend(loc="upper right")
+plt.savefig("target_hash.pdf")
+
+plt.clf()
+plt.plot(diff, '+', label="difference")
+plt.legend()
+plt.savefig("diff.pdf")
+
+sorted_diff = np.sort(diff)
+p = 1. * np.arange(len(sorted_diff)) / (len(sorted_diff) - 1)
+
+
+plt.plot(sorted_diff, p, label="proba de dépassement de k 0")
+plt.legend()
+plt.xlabel("k")
+plt.savefig("distribution.pdf")
+
+ratio=[]
+for v in range(len(targets)):
+    ratio.append(hashvalues[v]/targets[v])
+
+plt.clf()
+plt.plot(ratio)
+
+sorted_ratio = np.sort(ratio)
+p = 1. * np.arange(len(sorted_ratio))/(len(sorted_ratio) -1)
+plt.clf()
+plt.plot(range(len(p)), p, label="$\mathfrak{diff}(b)/\mathfrak{target}(b)$")
+plt.legend(loc="lower right")
+plt.savefig("distribution_ratio.pdf")