code: #!/usr/bin/env python
  #
  # Copyright 2007,2010,2012 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, analog, blocks
  import numpy
  
  
  class test_fastnoise_source(gr_unittest.TestCase):
  
      def setUp (self):
  
          self.num = 2**22
          self.num_items = 10**6
          self.default_args = {"samples": self.num, "seed": 43, "ampl": 1}
  
      def tearDown (self):
          pass
  
      def run_test_real(self, form):
          """ Run test case with float input/output
          """
          tb = gr.top_block()
          src = analog.fastnoise_source_f(type=form, **self.default_args)
          head = blocks.head(nitems=self.num_items, sizeof_stream_item=gr.sizeof_float)
          sink = blocks.vector_sink_f()
          tb.connect(src, head, sink)
          tb.run()
          return numpy.array(sink.data())
  
      def run_test_complex(self, form):
          """ Run test case with complex input/output
          """
          tb = gr.top_block()
          src = analog.fastnoise_source_c(type=form, **self.default_args)
          head = blocks.head(nitems=self.num_items, sizeof_stream_item=gr.sizeof_gr_complex)
          sink = blocks.vector_sink_c()
          tb.connect(src, head, sink)
          tb.run()
          return numpy.array(sink.data())
  
      def test_001_real_uniform_moments(self):
  
          data = self.run_test_real(analog.GR_UNIFORM)
  
          self.assertAlmostEqual(min(data), -1, places=4)
          self.assertAlmostEqual(max(data), 1, places=4)
  
          # mean, variance
          self.assertAlmostEqual(data.mean(), 0, places=2)
          self.assertAlmostEqual(data.var(), (1-(-1))**2./12, places=3)
  
      def test_001_real_gaussian_moments(self):
          data = self.run_test_real(analog.GR_GAUSSIAN)
  
          # mean, variance
          self.assertAlmostEqual(data.mean(), 0, places=2)
          self.assertAlmostEqual(data.var(), 1, places=2)
  
      def test_001_real_laplacian_moments(self):
          data = self.run_test_real(analog.GR_LAPLACIAN)
  
          # mean, variance
          self.assertAlmostEqual(data.mean(), 0, places=2)
          self.assertAlmostEqual(data.var(), 2, places=2)
  
      def test_001_complex_uniform_moments(self):
          data = self.run_test_complex(analog.GR_UNIFORM)
  
          # mean, variance
          self.assertAlmostEqual(data.real.mean(), 0, places=2)
          self.assertAlmostEqual(data.real.var(), 0.5*(1-(-1))**2./12, places=3)
  
          self.assertAlmostEqual(data.imag.mean(), 0, places=2)
          self.assertAlmostEqual(data.imag.var(), 0.5*(1-(-1))**2./12, places=3)
  
      def test_001_complex_gaussian_moments(self):
          data = self.run_test_complex(analog.GR_GAUSSIAN)
  
          # mean, variance
          self.assertAlmostEqual(data.real.mean(), 0, places=2)
          self.assertAlmostEqual(data.real.var(), 0.5, places=2)
  
          self.assertAlmostEqual(data.imag.mean(), 0, places=2)
          self.assertAlmostEqual(data.imag.var(), 0.5, places=2)
  
      def test_002_real_uniform_reproducibility(self):
          data1 = self.run_test_real(analog.GR_UNIFORM)
          data2 = self.run_test_real(analog.GR_UNIFORM)
  
          # It's pseudoramdo thus must be equal
          self.assertTrue(numpy.array_equal(data1, data2))
  
      def test_002_real_gaussian_reproducibility(self):
          data1 = self.run_test_real(analog.GR_GAUSSIAN)
          data2 = self.run_test_real(analog.GR_GAUSSIAN)
  
          self.assertTrue(numpy.array_equal(data1, data2))
  
      def test_003_real_uniform_pool(self):
          src = analog.fastnoise_source_f(type=analog.GR_UNIFORM, **self.default_args)
          src2 = analog.fastnoise_source_f(type=analog.GR_UNIFORM, **self.default_args)
          self.assertTrue(numpy.array_equal(numpy.array(src.samples()), numpy.array(src2.samples())))
      def test_003_real_gaussian_pool(self):
          src = analog.fastnoise_source_f(type=analog.GR_GAUSSIAN, **self.default_args)
          src2 = analog.fastnoise_source_f(type=analog.GR_GAUSSIAN, **self.default_args)
          self.assertTrue(numpy.array_equal(numpy.array(src.samples()), numpy.array(src2.samples())))
      def test_003_cmplx_gaussian_pool(self):
          src = analog.fastnoise_source_c(type=analog.GR_GAUSSIAN, **self.default_args)
          src2 = analog.fastnoise_source_c(type=analog.GR_GAUSSIAN, **self.default_args)
          self.assertTrue(numpy.array_equal(numpy.array(src.samples()), numpy.array(src2.samples())))
      def test_003_cmplx_uniform_pool(self):
          src = analog.fastnoise_source_c(type=analog.GR_UNIFORM, **self.default_args)
          src2 = analog.fastnoise_source_c(type=analog.GR_UNIFORM, **self.default_args)
          self.assertTrue(numpy.array_equal(numpy.array(src.samples()), numpy.array(src2.samples())))
      def test_003_real_laplacian_pool(self):
          src = analog.fastnoise_source_f(type=analog.GR_LAPLACIAN, **self.default_args)
          src2 = analog.fastnoise_source_f(type=analog.GR_LAPLACIAN, **self.default_args)
          self.assertTrue(numpy.array_equal(numpy.array(src.samples()), numpy.array(src2.samples())))
  if __name__ == '__main__':
      gr_unittest.run(test_fastnoise_source, "test_fastnoise_source.xml")
  
apis: numpy.array_equal