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#!/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, rayleigh
class test_random(gr_unittest.TestCase):
num_tests = 10000
# Disclaimer
def test_0(self):
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'
values = np.zeros(self.num_tests)
rndm = gr.random()
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):', 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_bins = 11
values = np.zeros(self.num_tests)
rndm = gr.random()
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(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_bins = 11
hist_range = [-5,5]
values = np.zeros(self.num_tests)
rndm = gr.random()
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]))*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_bins = 11
hist_range = [-5,5]
values = np.zeros(self.num_tests)
rndm = gr.random()
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]))*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")
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