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diff --git a/src/cython/example/witness_complex_from_file_example.py b/src/cython/example/witness_complex_from_file_example.py
new file mode 100755
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+++ b/src/cython/example/witness_complex_from_file_example.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+
+import gudhi
+import pandas
+import argparse
+
+"""This file is part of the Gudhi Library. The Gudhi library
+   (Geometric Understanding in Higher Dimensions) is a generic C++
+   library for computational topology.
+
+   Author(s):       Vincent Rouvreau
+
+   Copyright (C) 2016 INRIA
+
+   This program is free software: you can redistribute it and/or modify
+   it under the terms of the GNU General Public License as published by
+   the Free Software Foundation, either version 3 of the License, or
+   (at your option) any later version.
+
+   This program is distributed in the hope that it will be useful,
+   but WITHOUT ANY WARRANTY; without even the implied warranty of
+   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+   GNU General Public License for more details.
+
+   You should have received a copy of the GNU General Public License
+   along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+__author__ = "Vincent Rouvreau"
+__copyright__ = "Copyright (C) 2016 INRIA"
+__license__ = "GPL v3"
+
+print("#####################################################################")
+print("WitnessComplex creation from points read in a file")
+
+parser = argparse.ArgumentParser(description='WitnessComplex creation from '
+                                 'points read in a file.',
+                                 epilog='Example: '
+                                 'example/witness_complex_from_file_example.py'
+                                 ' data/2000_random_points_on_3D_Torus.csv '
+                                 '- Constructs a witness complex with the '
+                                 'points from the given file. File format '
+                                 'is X1, X2, ..., Xn')
+parser.add_argument('file', type=argparse.FileType('r'))
+args = parser.parse_args()
+
+points = pandas.read_csv(args.file, header=None)
+
+print("WitnessComplex with number_of_landmarks=100")
+
+witness_complex = gudhi.WitnessComplex(points=points.values,
+                                       number_of_landmarks=100)
+
+witness_complex.initialize_filtration()
+
+print("filtered_tree=", witness_complex.get_filtered_tree())