text: # -*- coding: utf-8 -*-
  # test_plotSpectrum.py
  # This module provides the tests for the plotSpectrum function.
  # Copyright 2014 Giuseppe Venturini
  # This file is part of python-deltasigma.
  #
  # python-deltasigma is a 1:1 Python replacement of Richard Schreier's 
  # MATLAB delta sigma toolbox (aka "delsigma"), upon which it is heavily based.
  # The delta sigma toolbox is (c) 2009, Richard Schreier.
  #
  # python-deltasigma 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
  # LICENSE file for the licensing terms.
  
  """This module provides the test class for the plotSpectrum() function.
  """
  
  import unittest
  import numpy as np
  import deltasigma as ds
  import pylab as plt
  
  from numpy.fft import fft
  
  class TestPlotSpectrum(unittest.TestCase):
      """Test class for plotSpectrum()"""
  
      def setUp(self):
          pass
  
      def test_plotSpectrum(self):
          """Test function for plotSpectrum()"""
          f0 = 0
          osr = 32
          quadrature = False
          Hinf = 1.5
          order = 3
          ntf = ds.synthesizeNTF(order, osr, 0, Hinf, f0)
          f1, f2 = ds.ds_f1f2(osr, f0, quadrature)
          delta = 2
          Amp = ds.undbv(-3)
          f = 0.3
          N = 2**12
          f1_bin = np.round(f1*N)
          f2_bin = np.round(f2*N)
          fin = np.round(((1 - f)/2*f1 + (f + 1)/2*f2) * N)
          t = np.arange(0, N)
          u = Amp*np.cos((2*np.pi/N)*fin*t)
          v, xn, xmax, y = ds.simulateDSM(u, ntf, 2)
          window = ds.ds_hann(N)
          NBW = 1.5/N
          spec0 = fft(v * window)/(N/4)
          freq = np.linspace(0, 0.5, N/2 + 1)
          # plotting
          plt.subplot(211)
          plt.plot(freq, ds.dbv(spec0[:N/2 + 1]), 'c', linewidth=1, label='$S$')
          plt.hold(True)
          spec_smoothed = ds.circ_smooth(np.abs(spec0)**2., 16)
          plt.plot(freq, ds.dbp(spec_smoothed[:N/2 + 1]), 'b--', linewidth=2,
                   label='$\\mathrm{circ\\_smooth}(S)$')
          ds.plotSpectrum(spec0, fin, 'r', linewidth=2,
                          label='$\\mathrm{plotSpectrum}(S)$')
          Snn = np.abs(ds.evalTF(ntf, np.exp(2j*np.pi*freq)))**2 * 2/12*(delta)**2
          plt.plot(freq, ds.dbp(Snn*NBW), 'm', linewidth=1.5,
                   label='$\mathrm{from\\ NTF}$')
          plt.text(0.5, -3, 'NBW = %.1e ' % NBW, horizontalalignment='right',
                   verticalalignment='top')
          ds.figureMagic((0, 0.5), None, None, (-140, 0), 20, None)
          plt.ylabel('Spectrum [dB]')
          ax = plt.gca()
          ax.set_title('Smoothing and plotting for LOG and LIN axes')
          plt.legend(loc=4)
          plt.subplot(212)
          plt.plot(freq, ds.dbv(spec0[:N/2 + 1]), 'c', linewidth=1, label='$S$')
          plt.hold(True)
          ds.plotSpectrum(spec0, fin, '--r', linewidth=2,
                          label='$\\mathrm{plotSpectrum}(S)$')
          plt.plot(freq, ds.dbp(spec_smoothed[:N/2 + 1]), 'b', linewidth=2,
                   label='$\\mathrm{circ\\_smooth}(S)$')
          plt.plot(freq, ds.dbp(Snn*NBW), 'm', linewidth=1.5,
                   label='$\mathrm{from\\ NTF}$')
          plt.text(0.5, -3, 'NBW = %.1e ' % NBW, horizontalalignment='right',
                   verticalalignment='top')
          ds.figureMagic((0, 0.5), None, None, (-140, 0), 20, None)
          ax = plt.gca()
          ax.set_xscale('linear')
          plt.ylabel('Spectrum [dB]')
          plt.xlabel('Normalized frequency ($f_s \\rightarrow 1$)')
          plt.legend(loc=4)
  
  