diff --git a/tools/cumdist.py b/tools/cumdist.py
new file mode 100644
index 000000000..e7dc3a836
--- /dev/null
+++ b/tools/cumdist.py
@@ -0,0 +1,63 @@
+import csv
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+import re
+
+# check if called correctly
+if len(sys.argv) < 2:
+	sys.exit('Usage: %s FILE1 FILE2' % sys.argv[0])
+
+plt.figure(figsize=(8,4))
+
+for fn in sys.argv[1:]:
+
+#	m = re.match('[a-zA-Z-]+[-_](\d+)[-_](\d+).', fn)
+#	rate = m.group(1)
+#	values = m.group(2)
+#	print 'Processing file %s (rate=%s, values=%s)' % (fn, rate, values)
+
+	# read data from file
+	data = [ ]
+	with open(fn) as f:
+		reader = csv.reader(f, delimiter='\t')
+
+		for row in reader:
+			offset = float(row[0])
+
+#			if fn != 'nrel-test1_offset.log':
+#				offset = offset * 0.001
+
+			if offset > 100:
+				continue
+
+			data.append(offset)
+
+	# evaluate the histogram
+	values, base = np.histogram(data, bins='fd')
+
+	# evaluate the cumulative
+	cumulative = np.cumsum(values)
+	cumscaled = [ float(x) / len(data) for x in cumulative ]
+
+	# plot the cumulative function
+	plt.plot(base[:-1], cumscaled, label=fn, linewidth=1)
+
+	# plot the distribution
+	#valscaled = [ float(x) / len(data) for x in values ]
+	#plt.plot(base[:-1], valscaled, label=fn, linewidth=1)
+
+plt.xlabel('RTT (s)')
+plt.ylabel('Cum. Probability')
+plt.grid(color='0.75')
+
+#plt.yscale('log')
+
+#plt.ylim([0, 1.03])
+#plt.xlim([0.025, 0.05])
+
+lgd = plt.legend(title='Rate (p/s)', loc='center left', bbox_to_anchor=(1, 0.5))
+
+plt.show()
+
+#plt.savefig('cumdist.png', dpi=600, bbox_extra_artists=(lgd,), bbox_inches='tight')
diff --git a/tools/extract_rtt.py b/tools/extract_rtt.py
new file mode 100644
index 000000000..aaf51b337
--- /dev/null
+++ b/tools/extract_rtt.py
@@ -0,0 +1,22 @@
+import csv
+import sys
+import re
+
+# check if called correctly
+if len(sys.argv) != 2:
+	sys.exit('Usage: %s FILE' % sys.argv[0])
+
+prog = re.compile('(\d+)(?:\.(\d+))?([-+]\d+(?:\.\d+)?(?:e[+-]?\d+)?)?(?:\((\d+)\))?')
+
+with open(sys.argv[1]) as f:
+	reader = csv.reader(f, delimiter='\t')
+
+	for row in reader:
+		m = prog.match(row[0])
+
+		if m == None or m.group(3) == None:
+			 continue
+
+		offset = float(m.group(3))
+
+		print(offset)
diff --git a/tools/file-filter.py b/tools/file-filter.py
new file mode 100755
index 000000000..de9e145d6
--- /dev/null
+++ b/tools/file-filter.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+
+import sys
+
+import villas
+from villas import *
+
+def main():
+	if len(sys.argv) != 3:
+		print "Usage: %s from to" % (sys.argv[0])
+		sys.exit(-1)
+
+	start = villas.Timestamp.parse(sys.argv[1])
+	end = villas.Timestamp.parse(sys.argv[2])
+
+	for line in sys.stdin:
+		msg = villas.Message.parse(line)
+
+		if start <= msg.ts <= end:
+			print msg
+
+if __name__ == "__main__":
+	main()
diff --git a/tools/file-merge.py b/tools/file-merge.py
new file mode 100755
index 000000000..3b67b12b0
--- /dev/null
+++ b/tools/file-merge.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+
+import sys
+
+import villas
+from villas import *
+
+def main():
+	files = sys.argv[1:]
+
+	all = [ ]
+	last = { }
+
+	for file in files:
+		handle = sys.stdin if file == '-' else open(file, "r")
+
+		msgs = [ ]
+		for line in handle.xreadlines():
+			msgs.append(villas.Message.parse(line, file))
+
+		all += msgs
+		last[file] = villas.Message(villas.Timestamp(), [0] * len(msgs[0].values), file)
+
+	all.sort()
+	for msg in all:
+		last[msg.source] = msg
+
+		values = [ ]
+		for file in files:
+			values += last[file].values
+
+		print villas.Message(msg.ts, values, "")
+
+if __name__ == "__main__":
+	main()
diff --git a/tools/plots/plot_dp.py b/tools/plots/plot_dp.py
new file mode 100644
index 000000000..5b75cb2af
--- /dev/null
+++ b/tools/plots/plot_dp.py
@@ -0,0 +1,17 @@
+#!/usr/bin/python
+
+import sys
+import villas.dataprocessing.readtools as rt
+import villas.dataprocessing.plottools as pt
+import matplotlib.pyplot as plt
+
+if len(sys.argv) != 2:
+    sys.exit(-1)
+
+filename = sys.argv[1]
+
+timeseries = rt.read_timeseries_villas(filename)
+
+pt.plot_timeseries(1, timeseries)
+
+plt.show()