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@@ -1060,7 +1060,7 @@ class Statistics:
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graphy = list(range(len(graphx)))
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graphy = list(range(len(graphx)))
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plt.barh(graphy, graphx, width, align='center', linewidth=1, color='red', edgecolor='red')
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plt.barh(graphy, graphx, width, align='center', linewidth=1, color='red', edgecolor='red')
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plt.yticks(graphy, labels)
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plt.yticks(graphy, labels)
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- out = self.pcap_filepath.replace('.pcap', '_in_degree' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-In Degree of an IP' + file_ending)
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plt.tight_layout()
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plt.tight_layout()
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plt.savefig(out,dpi=500)
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plt.savefig(out,dpi=500)
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@@ -1113,7 +1113,7 @@ class Statistics:
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graphy = list(range(len(graphx)))
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graphy = list(range(len(graphx)))
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plt.barh(graphy, graphx, width, align='center', linewidth=1, color='red', edgecolor='red')
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plt.barh(graphy, graphx, width, align='center', linewidth=1, color='red', edgecolor='red')
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plt.yticks(graphy, labels)
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plt.yticks(graphy, labels)
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- out = self.pcap_filepath.replace('.pcap', '_out_degree' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-Out Degree of an IP' + file_ending)
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plt.tight_layout()
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plt.tight_layout()
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plt.savefig(out,dpi=500)
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plt.savefig(out,dpi=500)
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@@ -1166,7 +1166,7 @@ class Statistics:
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graphy = list(range(len(graphx)))
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graphy = list(range(len(graphx)))
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plt.barh(graphy, graphx, width, align='center', linewidth=1, color='red', edgecolor='red')
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plt.barh(graphy, graphx, width, align='center', linewidth=1, color='red', edgecolor='red')
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plt.yticks(graphy, labels)
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plt.yticks(graphy, labels)
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- out = self.pcap_filepath.replace('.pcap', '_overall_degree' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-Overall Degree of an IP' + file_ending)
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plt.tight_layout()
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plt.tight_layout()
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plt.savefig(out,dpi=500)
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plt.savefig(out,dpi=500)
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return out
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return out
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@@ -1363,7 +1363,7 @@ class Statistics:
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# set additional plot parameters
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# set additional plot parameters
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plt.title(title)
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plt.title(title)
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- plt.ylabel("Number of connections")
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+ plt.ylabel("Relative frequency of connections")
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plt.xlabel(label)
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plt.xlabel(label)
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width = 0.5
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width = 0.5
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plt.grid(True)
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plt.grid(True)
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@@ -1374,11 +1374,11 @@ class Statistics:
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for i in range(0, 11):
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for i in range(0, 11):
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bins.append(i * max_val/10)
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bins.append(i * max_val/10)
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- # comment out and set weights to normalize
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- # weights = numpy.ones_like(result)/float(len(result))
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+ # set weights normalize histogram
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+ weights = numpy.ones_like(result)/float(len(result))
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# plot the above data, first use plain numbers as graphy to maintain sorting
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# plot the above data, first use plain numbers as graphy to maintain sorting
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- plt.hist(result, bins=bins, color='red', edgecolor='red', align="mid", rwidth=0.5)
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+ plt.hist(result, bins=bins, weights=weights, color='red', edgecolor='red', align="mid", rwidth=0.5)
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plt.xticks(bins)
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plt.xticks(bins)
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# save created figure
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# save created figure
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@@ -1409,7 +1409,7 @@ class Statistics:
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# set additional plot parameters
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# set additional plot parameters
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plt.title(title)
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plt.title(title)
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- plt.ylabel("Number of IPs")
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+ plt.ylabel("Relative frequency of IPs")
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plt.xlabel(label)
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plt.xlabel(label)
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width = 0.5
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width = 0.5
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plt.grid(True)
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plt.grid(True)
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@@ -1420,11 +1420,11 @@ class Statistics:
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for i in range(0, 11):
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for i in range(0, 11):
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bins.append(int(i * max_val/10))
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bins.append(int(i * max_val/10))
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- # comment out and set weights to normalize
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- # weights = numpy.ones_like(result)/float(len(result))
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+ # set weights normalize histogram
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+ weights = numpy.ones_like(result)/float(len(result))
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# plot the above data, first use plain numbers as graphy to maintain sorting
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# plot the above data, first use plain numbers as graphy to maintain sorting
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- plt.hist(result, bins=bins, color='red', edgecolor='red', align="mid", rwidth=0.5)
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+ plt.hist(result, bins=bins, weights=weights, color='red', edgecolor='red', align="mid", rwidth=0.5)
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plt.xticks(bins)
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plt.xticks(bins)
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# save created figure
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# save created figure
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dustin.born