#
# This file is part of Hooke.
#
-# Hooke is free software: you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation, either
-# version 3 of the License, or (at your option) any later version.
+# Hooke is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Lesser General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
#
-# Hooke 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 Lesser General Public License for more details.
+# Hooke 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 Lesser General
+# Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with Hooke. If not, see
--------
>>> data = numpy.arange(-3, 4)
- >>> above_noise(data, side='both', min_deviations=1.0, mean=0, std=1.0)
- array([ True, False, False, False, False, False, True], dtype=bool)
- >>> above_noise(data, side='positive', min_deviations=1.0, mean=0, std=1.0)
- array([False, False, False, False, False, False, True], dtype=bool)
- >>> above_noise(data, side='negative', min_deviations=1.0, mean=0, std=1.0)
- array([ True, False, False, False, False, False, False], dtype=bool)
+ >>> above_noise(data, side='both', min_deviations=1.1, mean=0, std=1.0)
+ array([ True, True, False, False, False, True, True], dtype=bool)
+ >>> above_noise(data, side='positive', min_deviations=1.1, mean=0, std=1.0)
+ array([False, False, False, False, False, True, True], dtype=bool)
+ >>> above_noise(data, side='negative', min_deviations=1.1, mean=0, std=1.0)
+ array([ True, True, False, False, False, False, False], dtype=bool)
"""
if mean == None:
mean = data.mean()