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| author | Stefan <stefan.wunsch@student.kit.edu> | 2015-09-01 12:46:09 +0200 |
|---|---|---|
| committer | Stefan <stefan.wunsch@student.kit.edu> | 2015-09-01 12:46:09 +0200 |
| commit | 1206251231696359270a260508551e044f3af33a (patch) | |
| tree | 79c8f65de7d2e31301e051cbcaedf95b4e31d9c6 /gnuradio-runtime/python | |
| parent | a06420691493534ca268ce52e1f16504c216828d (diff) | |
include random.h in swig; add qa_random testcase
Diffstat (limited to 'gnuradio-runtime/python')
| -rw-r--r-- | gnuradio-runtime/python/gnuradio/gr/qa_random.py | 99 |
1 files changed, 99 insertions, 0 deletions
diff --git a/gnuradio-runtime/python/gnuradio/gr/qa_random.py b/gnuradio-runtime/python/gnuradio/gr/qa_random.py new file mode 100644 index 0000000000..39d75f3afa --- /dev/null +++ b/gnuradio-runtime/python/gnuradio/gr/qa_random.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python +# +# Copyright 2006,2007,2010 Free Software Foundation, Inc. +# +# This file is part of GNU Radio +# +# GNU Radio 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, or (at your option) +# any later version. +# +# GNU Radio 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 GNU Radio; see the file COPYING. If not, write to +# the Free Software Foundation, Inc., 51 Franklin Street, +# Boston, MA 02110-1301, USA. +# + +from gnuradio import gr, gr_unittest +import numpy as np +from scipy.stats import norm, laplace + +class test_random(gr_unittest.TestCase): + + # Disclaimer + def test_0(self): + print 'NOTE: Following tests are not statistically significant! Check out fulltest_random.py for full testing.' + self.assertEqual(1,1) + + # Check for range [0,1) of uniform distributed random numbers and print minimal and maximal value + def test_1(self): + print '# TEST 1' + print 'Uniform distributed numbers: Range' + num_tests = 10000 + values = np.zeros(num_tests) + rndm = gr.random() + for k in range(num_tests): + values[k] = rndm.ran1() + for value in values: + self.assertLess(value, 1) + self.assertGreaterEqual(value, 0) + print 'Uniform random numbers (num/min/max):', num_tests, min(values), max(values) + + # Check uniformly distributed random numbers on uniformity (without assert, only printing) + def test_2(self): + print '# TEST 2' + print 'Uniform random numbers: Distribution' + num_tests = 10000 + num_bins = 11 + values = np.zeros(num_tests) + rndm = gr.random() + for k in range(num_tests): + values[k] = rndm.ran1() + bins = np.linspace(0,1,num_bins) # These are the bin edges! + hist = np.histogram(values,bins) + print 'Lower edge bin / upper edge bin / count / expected' + for k in range(len(hist[0])): + print hist[1][k], hist[1][k+1], hist[0][k], float(num_tests)/(num_bins-1) + + # Check distribution of normally (gaussian, mean=0, variance=1) distributed random numbers (no assert) + def test_3(self): + print '# TEST 3' + print 'Normal random numbers: Distribution' + num_tests = 10000 + num_bins = 11 + hist_range = [-5,5] + values = np.zeros(num_tests) + rndm = gr.random() + for k in range(num_tests): + values[k] = rndm.gasdev() + bins = np.linspace(hist_range[0],hist_range[1],num_bins) + hist = np.histogram(values,bins) + print 'Lower edge bin / upper edge bin / count / expected' + for k in range(len(hist[0])): + print hist[1][k], hist[1][k+1], hist[0][k], float(norm.cdf(hist[1][k+1])-norm.cdf(hist[1][k]))*num_tests + + # Check distribution of laplacian (mean=0, variance=1) distributed random numbers (no assert) + def test_4(self): + print '# TEST 4' + print 'Laplacian random numbers: Distribution' + num_tests = 100000 + num_bins = 11 + hist_range = [-5,5] + values = np.zeros(num_tests) + rndm = gr.random() + for k in range(num_tests): + values[k] = rndm.laplacian() + bins = np.linspace(hist_range[0],hist_range[1],num_bins) + hist = np.histogram(values,bins) + print 'Lower edge bin / upper edge bin / count / expected' + for k in range(len(hist[0])): + print hist[1][k], hist[1][k+1], hist[0][k], float(laplace.cdf(hist[1][k+1])-laplace.cdf(hist[1][k]))*num_tests + +if __name__ == '__main__': + gr_unittest.run(test_random, "test_random.xml") |