1 files changed, 0 insertions, 56 deletions
diff --git a/src/cython/example/witness_complex_from_file_example.py b/src/cython/example/witness_complex_from_file_example.py
deleted file mode 100755
index 82a5b49a..00000000
--- a/src/cython/example/witness_complex_from_file_example.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/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())