From d46e9fd9e2256a8fe955a7f54835072d2fab99f8 Mon Sep 17 00:00:00 2001
From: Raphael Bauduin <raphael.bauduin@uclouvain.be>
Date: Tue, 23 Jun 2015 23:52:59 -0700
Subject: [PATCH] added mpRefreshSetCsv.py

---
 src/mpRefreshSetCsv.py | 61 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 src/mpRefreshSetCsv.py

diff --git a/src/mpRefreshSetCsv.py b/src/mpRefreshSetCsv.py
new file mode 100644
index 0000000..24b182a
--- /dev/null
+++ b/src/mpRefreshSetCsv.py
@@ -0,0 +1,61 @@
+
+import numpy as np
+import os as os
+import matplotlib.pyplot as plt
+from subprocess import call,  check_call
+from subprocess import check_output
+
+class RefreshSetCsvAggregator:
+	def __init__(self, yml, test_name, dest_dir):
+		# csv_file="c2s_seq_1.csv"
+		# minimum delay to observe to identify the beginning of a block
+
+		self.paths_used=open("paths_used.log", "a")
+		self.pcap_file=dest_dir+"/client.pcap"
+                self.base_dir = os.path.dirname(self.pcap_file)
+                working_dir =  os.getcwd()
+
+                os.chdir(self.base_dir)
+		check_call(["/usr/local/bin/mptcptrace" , "-f", os.path.basename(self.pcap_file) , "-G20",  "-F3",  "-r7",  "-s",  "-S", "-a", "-w2"])
+		csv_content = check_output(["sudo", "tcptrace", "-l", "--csv", os.path.basename(self.pcap_file)])
+		with (open("tcptrace.csv", "w")) as f:
+			f.write(csv_content)
+		time=os.system("cat stats_1.csv | grep Time | cut -d \";\" -f 4 >> /home/mininet/minitopo/src/times.log")
+                os.chdir(working_dir)
+
+
+		# for refresh_set:
+		#self.headers = [ "decision", "port", "metric", "path" ] 
+		#self.log = open(dest_dir+"/refresh_set_aggregator.log","w")
+		#self.csv_file=dest_dir+"/"+"refresh_set.csv"
+		#csv_content = check_output(["grep", "csv", dest_dir+"/upmc.log"])
+		#with open(self.csv_file,"w") as f:
+		#	f.write(csv_content)
+		#self.a = np.genfromtxt (self.csv_file, delimiter=",")[:,1:]
+		#self.a=np.array(self.a, dtype=int)
+		#self.extract_data()
+	def __del__(self):
+		self.paths_used.close()
+	def extract_data(self):
+		decisions = np.unique(self.a[:,0])
+		print decisions
+		paths_used = []
+		
+		for decision in decisions:
+			paths = np.array(filter(lambda row: row[0]==decision, self.a))
+			print "paths for decision " + str(decision)
+			print paths
+			unique_paths = np.unique(paths[:, 3])
+			print "unique paths:"
+			print unique_paths
+			print len(unique_paths)
+			paths_used.append(len(unique_paths))
+		print >>self.paths_used, paths_used
+		print np.array(paths_used).argmax()
+
+
+	def __str__(self):
+		return "Nothing here, change it in mpRefreshSetCsv.py"
+		#return self.csv_file
+	def plot(self):
+		pass