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| author | Stefan <stefan.wunsch@student.kit.edu> | 2015-09-01 13:21:51 +0200 |
|---|---|---|
| committer | Stefan <stefan.wunsch@student.kit.edu> | 2015-09-01 13:21:51 +0200 |
| commit | ebad2162b40b2b144449ff2927dbe89c683c4972 (patch) | |
| tree | 5a8219cd5001f0402a21ac9eb28670c22aefeefb /gnuradio-runtime/python | |
| parent | 1206251231696359270a260508551e044f3af33a (diff) | |
fix wrong laplacian random numbers and add testcase
Diffstat (limited to 'gnuradio-runtime/python')
| -rw-r--r-- | gnuradio-runtime/python/gnuradio/gr/qa_random.py | 51 |
1 files changed, 33 insertions, 18 deletions
diff --git a/gnuradio-runtime/python/gnuradio/gr/qa_random.py b/gnuradio-runtime/python/gnuradio/gr/qa_random.py index 39d75f3afa..ee4018327b 100644 --- a/gnuradio-runtime/python/gnuradio/gr/qa_random.py +++ b/gnuradio-runtime/python/gnuradio/gr/qa_random.py @@ -22,78 +22,93 @@ from gnuradio import gr, gr_unittest import numpy as np -from scipy.stats import norm, laplace +from scipy.stats import norm, laplace, rayleigh class test_random(gr_unittest.TestCase): + num_tests = 10000 + # Disclaimer def test_0(self): - print 'NOTE: Following tests are not statistically significant! Check out fulltest_random.py for full testing.' + print 'NOTE: Following tests are not statistically significant!' + print 'Realisations per test:',self.num_tests 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) + values = np.zeros(self.num_tests) rndm = gr.random() - for k in range(num_tests): + for k in range(self.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) + print 'Uniform random numbers (num/min/max):', self.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) + values = np.zeros(self.num_tests) rndm = gr.random() - for k in range(num_tests): + for k in range(self.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) + print hist[1][k], hist[1][k+1], hist[0][k], float(self.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) + values = np.zeros(self.num_tests) rndm = gr.random() - for k in range(num_tests): + for k in range(self.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 + print hist[1][k], hist[1][k+1], hist[0][k], float(norm.cdf(hist[1][k+1])-norm.cdf(hist[1][k]))*self.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) + values = np.zeros(self.num_tests) rndm = gr.random() - for k in range(num_tests): + for k in range(self.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 + print hist[1][k], hist[1][k+1], hist[0][k], float(laplace.cdf(hist[1][k+1])-laplace.cdf(hist[1][k]))*self.num_tests + + # Check distribution of laplacian (mean=0, variance=1) distributed random numbers (no assert) + def test_5(self): + print '# TEST 5' + print 'Rayleigh random numbers: Distribution' + num_bins = 11 + hist_range = [0,10] + values = np.zeros(self.num_tests) + rndm = gr.random() + for k in range(self.num_tests): + values[k] = rndm.rayleigh() + 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(rayleigh.cdf(hist[1][k+1])-rayleigh.cdf(hist[1][k]))*self.num_tests if __name__ == '__main__': gr_unittest.run(test_random, "test_random.xml") |