def test_2_same_seed(self): num = 5 # Init with fixed seed. rndm0 = gr.random(42) rndm1 = gr.random(42) for k in range(num): x = rndm0.ran1() y = rndm1.ran1() self.assertEqual(x, y)
def test_1(self): 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)
def test_004_integer(self): nitems = 100000 minimum = 2 maximum = 42 rng = gr.random(1, minimum, maximum) rnd_vals = np.zeros(nitems, dtype=int) for i in range(nitems): rnd_vals[i] = rng.ran_int() self.assertGreaterEqual(minimum, np.min(rnd_vals)) self.assertLess(np.max(rnd_vals), maximum)
def test_003_reseed(self): num = 5 x = np.zeros(num) y = np.zeros(num) rndm = gr.random(43) # init with fix seed 1 for k in range(num): x[k] = rndm.ran1() rndm.reseed(43) # init with fix seed 2 for k in range(num): y[k] = rndm.ran1() self.assertFloatTuplesAlmostEqual(x, y)
def test_2(self): num = 5 rndm0 = gr.random(42); # init with time rndm1 = gr.random(42); # init with fix seed for k in range(num): x = rndm0.ran1(); y = rndm1.ran1(); self.assertEqual(x,y) x = np.zeros(num) y = np.zeros(num) rndm0 = gr.random(42); # init with fix seed 1 for k in range(num): x[k] = rndm0.ran1(); rndm1.reseed(43); # init with fix seed 2 for k in range(num): y[k] = rndm0.ran1(); for k in range(num): self.assertNotEqual(x[k],y[k])
def test_2(self): num = 5 rndm0 = gr.random(42) # init with time rndm1 = gr.random(42) # init with fix seed for k in range(num): x = rndm0.ran1() y = rndm1.ran1() self.assertEqual(x, y) x = np.zeros(num) y = np.zeros(num) rndm0 = gr.random(42) # init with fix seed 1 for k in range(num): x[k] = rndm0.ran1() rndm1.reseed(43) # init with fix seed 2 for k in range(num): y[k] = rndm0.ran1() for k in range(num): self.assertNotEqual(x[k], y[k])
from matplotlib import pyplot as plt # NOTE: scipy and matplotlib are optional packages and not included in the default gnuradio dependencies #*** SETUP ***# # Number of realisations per histogram num_tests = 1000000 # Set number of bins in histograms uniform_num_bins = 31 gauss_num_bins = 31 rayleigh_num_bins = 31 laplace_num_bins = 31 rndm = gr.random() # instance of gnuradio random class (gr::random) print('All histograms contain', num_tests, 'realisations.') #*** GENERATE DATA ***# uniform_values = np.zeros(num_tests) gauss_values = np.zeros(num_tests) rayleigh_values = np.zeros(num_tests) laplace_values = np.zeros(num_tests) for k in range(num_tests): uniform_values[k] = rndm.ran1() gauss_values[k] = rndm.gasdev() rayleigh_values[k] = rndm.rayleigh() laplace_values[k] = rndm.laplacian()
from matplotlib import pyplot as plt # NOTE: scipy and matplotlib are optional packages and not included in the default gnuradio dependencies #*** SETUP ***# # Number of realisations per histogram num_tests = 1000000 # Set number of bins in histograms uniform_num_bins = 31 gauss_num_bins = 31 rayleigh_num_bins = 31 laplace_num_bins = 31 rndm = gr.random() # instance of gnuradio random class (gr::random) print 'All histograms contain',num_tests,'realisations.' #*** GENERATE DATA ***# uniform_values = np.zeros(num_tests) gauss_values = np.zeros(num_tests) rayleigh_values = np.zeros(num_tests) laplace_values = np.zeros(num_tests) for k in range(num_tests): uniform_values[k] = rndm.ran1() gauss_values[k] = rndm.gasdev() rayleigh_values[k] = rndm.rayleigh() laplace_values[k] = rndm.laplacian()