def test_add_vcc_five(self):
	src1_data = (1.0+2.0j, 3.0+4.0j, 5.0+6.0j, 7.0+8.0j, 9.0+10.0j)
	src2_data = (11.0+12.0j, 13.0+14.0j, 15.0+16.0j, 17.0+18.0j, 19.0+20.0j)
	src3_data = (21.0+22.0j, 23.0+24.0j, 25.0+26.0j, 27.0+28.0j, 29.0+30.0j)
	expected_result = (33.0+36.0j, 39.0+42.0j, 45.0+48.0j, 51.0+54.0j, 57.0+60.0j)
	op = blocks.add_cc(5)
	self.help_cc(5, (src1_data, src2_data, src3_data), expected_result, op)
 def test_add_vcc_one(self):
     src1_data = (1.0 + 2.0j, )
     src2_data = (3.0 + 4.0j, )
     src3_data = (5.0 + 6.0j, )
     expected_result = (9.0 + 12j, )
     op = blocks.add_cc(1)
     self.help_cc(1, (src1_data, src2_data, src3_data), expected_result, op)
Exemple #3
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    def __init__(self):
        gr.top_block.__init__(self)

        # Make a local QtApp so we can start it from our side
        self.qapp = Qt.QApplication(sys.argv)

        samp_rate = 1e6
        fftsize = 2048
        ampl = 10

        self.src1 = analog.noise_source_c(analog.GR_GAUSSIAN, ampl, seed=0)
        self.src2 = analog.sig_source_c(samp_rate, analog.GR_SAW_WAVE, 400,
                                        ampl)
        self.add = blocks.add_cc()
        self.thr = blocks.throttle(gr.sizeof_gr_complex, samp_rate, True)

        self.snk = qtgui.sink_c(
            fftsize,  #fftsize
            firdes.WIN_BLACKMAN_hARRIS,  #wintype
            0,  #fc
            samp_rate,  #bw
            "",  #name
            True,  #plotfreq
            True,  #plotwaterfall
            True,  #plottime
            True,  #plotconst
        )

        self.connect(self.src1, (self.add, 0))
        self.connect(self.src2, (self.add, 1))
        self.connect(self.add, self.thr, self.snk)

        # Tell the sink we want it displayed
        self.pyobj = sip.wrapinstance(self.snk.pyqwidget(), Qt.QWidget)
        self.pyobj.show()
    def test_add_vcc_one(self):
	src1_data = (1.0+2.0j,)
	src2_data = (3.0+4.0j,)
	src3_data = (5.0+6.0j,)
	expected_result = (9.0+12j,)
	op = blocks.add_cc(1)
	self.help_cc(1, (src1_data, src2_data, src3_data), expected_result, op)
    def check_channelizer(self, channelizer_block):
        signals = list()
        add = blocks.add_cc()
        for i in range(len(self.freqs)):
            f = self.freqs[i] + i*self.fs
            data = sig_source_c(self.ifs, f, 1, self.N)
            signals.append(blocks.vector_source_c(data))
            self.tb.connect(signals[i], (add,i))

        #s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, self.M)

        #self.tb.connect(add, s2ss)
        self.tb.connect(add, channelizer_block)

        snks = list()
        for i in range(self.M):
            snks.append(blocks.vector_sink_c())
            #self.tb.connect((s2ss,i), (channelizer_block,i))
            self.tb.connect((channelizer_block, i), snks[i])

        self.tb.run()

        L = len(snks[0].data())

        expected_data = self.get_expected_data(L)
        received_data = [snk.data() for snk in snks]

        for expected, received in zip(expected_data, received_data):
            self.compare_data(expected, received)
Exemple #6
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	def test_003_t (self):
		# test cut frequency negative freq
		# set up fg
		test_len = 1000
		samp_rate = 2000
		freq1 = -200
		freq2 = -205
		ampl = 1
		packet_len = test_len
		threshold = -100
		samp_protect = 2
		
		src1 = analog.sig_source_c(samp_rate, analog.GR_COS_WAVE, freq1, ampl*0.2)
		src2 = analog.sig_source_c(samp_rate, analog.GR_COS_WAVE, freq2, ampl)
		add = blocks.add_cc();
		head = blocks.head(8,test_len)
		s2ts = blocks.stream_to_tagged_stream(8,1,packet_len,"packet_len")
		fft = radar.ts_fft_cc(packet_len)
		peak = radar.find_max_peak_c(samp_rate, threshold, samp_protect, (-200,200), True)
		debug = blocks.message_debug()
		
		self.tb.connect((src1,0), (add,0))
		self.tb.connect((src2,0), (add,1))
		self.tb.connect(add,head,s2ts,fft,peak)
		self.tb.msg_connect(peak,"Msg out",debug,"store")
		#self.tb.msg_connect(peak,"Msg out",debug,"print")
		self.tb.start()
		sleep(0.5)
		self.tb.stop()
		self.tb.wait()
		
		# check frequency in os_cfar message with given one
		msg = debug.get_message(0)
		self.assertAlmostEqual(freq1,pmt.f32vector_ref(pmt.nth(1,pmt.nth(1,msg)),0),8)
Exemple #7
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 def run_flow_graph(sync_sym1, sync_sym2, data_sym):
     top_block = gr.top_block()
     carr_offset = random.randint(-max_offset / 2, max_offset / 2) * 2
     tx_data = shift_tuple(sync_sym1, carr_offset) + \
               shift_tuple(sync_sym2, carr_offset) + \
               shift_tuple(data_sym,  carr_offset)
     channel = [rand_range(min_chan_ampl, max_chan_ampl) * numpy.exp(1j * rand_range(0, 2 * numpy.pi)) for x in range(fft_len)]
     src = blocks.vector_source_c(tx_data, False, fft_len)
     chan = blocks.multiply_const_vcc(channel)
     noise = analog.noise_source_c(analog.GR_GAUSSIAN, wgn_amplitude)
     add = blocks.add_cc(fft_len)
     chanest = digital.ofdm_chanest_vcvc(sync_sym1, sync_sym2, 1)
     sink = blocks.vector_sink_c(fft_len)
     top_block.connect(src, chan, (add, 0), chanest, sink)
     top_block.connect(noise, blocks.stream_to_vector(gr.sizeof_gr_complex, fft_len), (add, 1))
     top_block.run()
     channel_est = None
     carr_offset_hat = 0
     rx_sym_est = [0,] * fft_len
     tags = sink.tags()
     for tag in tags:
         if pmt.symbol_to_string(tag.key) == 'ofdm_sync_carr_offset':
             carr_offset_hat = pmt.to_long(tag.value)
             self.assertEqual(carr_offset, carr_offset_hat)
         if pmt.symbol_to_string(tag.key) == 'ofdm_sync_chan_taps':
             channel_est = shift_tuple(pmt.c32vector_elements(tag.value), carr_offset)
     shifted_carrier_mask = shift_tuple(carrier_mask, carr_offset)
     for i in range(fft_len):
         if shifted_carrier_mask[i] and channel_est[i]:
             self.assertAlmostEqual(channel[i], channel_est[i], places=0)
             rx_sym_est[i] = (sink.data()[i] / channel_est[i]).real
     return (carr_offset, list(shift_tuple(rx_sym_est, -carr_offset_hat)))
    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 1000
        f2 = 2000

        npts = 2048

        self.qapp = QtGui.QApplication(sys.argv)

        self.filt_taps = [
            1,
        ]

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.1, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.1, 0)
        src = blocks.add_cc()
        channel = channels.channel_model(0.01)
        self.filt = filter.fft_filter_ccc(1, self.filt_taps)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100 * npts)
        self.snk1 = qtgui.freq_sink_c(npts, filter.firdes.WIN_BLACKMAN_hARRIS,
                                      0, Rs, "Complex Freq Example", 1)

        self.connect(src1, (src, 0))
        self.connect(src2, (src, 1))
        self.connect(src, channel, thr, self.filt, (self.snk1, 0))

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt4.QtGui.QWidget
        pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)
        pyWin.show()
Exemple #9
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    def __init__(self, frame, panel, vbox, argv):
        stdgui2.std_top_block.__init__(self, frame, panel, vbox, argv)
        pspectrum_len = 1024
        # build our flow graph
        input_rate = 2e6
        #Generate some noise
        noise = analog.noise_source_c(analog.GR_GAUSSIAN, 1.0 / 10)
        # Generate a complex sinusoid
        #source = gr.file_source(gr.sizeof_gr_complex, 'foobar2.dat', repeat=True)
        src1 = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE, -500e3, 1)
        src2 = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE, 500e3, 1)
        src3 = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE, -250e3, 2)

        # We add these throttle blocks so that this demo doesn't
        # suck down all the CPU available.  Normally you wouldn't use these.
        thr1 = blocks.throttle(gr.sizeof_gr_complex, input_rate)
        sink1 = spectrum_sink_c(panel,
                                title="Spectrum Sink",
                                pspectrum_len=pspectrum_len,
                                sample_rate=input_rate,
                                baseband_freq=0,
                                ref_level=0,
                                y_per_div=20,
                                y_divs=10,
                                m=70,
                                n=3,
                                nsamples=1024)
        vbox.Add(sink1.win, 1, wx.EXPAND)
        combine1 = blocks.add_cc()
        self.connect(src1, (combine1, 0))
        self.connect(src2, (combine1, 1))
        self.connect(src3, (combine1, 2))
        self.connect(noise, (combine1, 3))
        self.connect(combine1, thr1, sink1)
    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 1000
        f2 = 2000

        npts = 2048

        self.qapp = QtGui.QApplication(sys.argv)

        self.filt_taps = [1,]
        
        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.1, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.1, 0)
        src  = blocks.add_cc()
        channel = channels.channel_model(0.01)
        self.filt = filter.fft_filter_ccc(1, self.filt_taps)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100*npts)
        self.snk1 = qtgui.freq_sink_c(npts, filter.firdes.WIN_BLACKMAN_hARRIS,
                                      0, Rs,
                                      "Complex Freq Example", 1)

        self.connect(src1, (src,0))
        self.connect(src2, (src,1))
        self.connect(src,  channel, thr, self.filt, (self.snk1, 0))

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt  = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt4.QtGui.QWidget
        pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)
        pyWin.show()
    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 100
        f2 = 200

        npts = 2048

        self.qapp = QtGui.QApplication(sys.argv)

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.5, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.5, 0)
        src = blocks.add_cc()
        channel = channels.channel_model(0.001)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100 * npts)
        self.snk1 = qtgui.const_sink_c(npts, "Constellation Example", 1)

        self.connect(src1, (src, 0))
        self.connect(src2, (src, 1))
        self.connect(src, channel, thr, (self.snk1, 0))

        self.ctrl_win = control_box()
        self.ctrl_win.attach_signal1(src1)
        self.ctrl_win.attach_signal2(src2)

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt4.QtGui.QWidget
        pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)

        self.main_box = dialog_box(pyWin, self.ctrl_win)
        self.main_box.show()
    def check_channelizer(self, channelizer_block):
        signals = list()
        add = blocks.add_cc()
        for i in range(len(self.freqs)):
            f = self.freqs[i] + i * self.fs
            data = sig_source_c(self.ifs, f, 1, self.N)
            signals.append(blocks.vector_source_c(data))
            self.tb.connect(signals[i], (add, i))

        #s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, self.M)

        #self.tb.connect(add, s2ss)
        self.tb.connect(add, channelizer_block)

        snks = list()
        for i in range(self.M):
            snks.append(blocks.vector_sink_c())
            #self.tb.connect((s2ss,i), (channelizer_block,i))
            self.tb.connect((channelizer_block, i), snks[i])

        self.tb.run()

        L = len(snks[0].data())

        expected_data = self.get_expected_data(L)
        received_data = [snk.data() for snk in snks]

        for expected, received in zip(expected_data, received_data):
            self.compare_data(expected, received)
    def __init__(self):
        gr.top_block.__init__(self)
        self.qapp = Qt.QApplication(sys.argv)

        samp_rate = 1e6
        fftsize = 2048

        self.src = analog.sig_source_c(samp_rate, analog.GR_SIN_WAVE, 0.1, 1, 0)
        self.nse = analog.noise_source_c(analog.GR_GAUSSIAN, 0.1)
        self.add = blocks.add_cc()
        self.thr = blocks.throttle(gr.sizeof_gr_complex, samp_rate, True)

        self.snk = qtgui.sink_c(
            fftsize, #fftsize
            firdes.WIN_BLACKMAN_hARRIS, #wintype
            0, #fc
            samp_rate, #bw
            "", #name
            True, #plotfreq
            True, #plotwaterfall
            True, #plottime
            True, #plotconst
        )

        self.connect(self.src, (self.add, 0))
        self.connect(self.nse, (self.add, 1))
        self.connect(self.add, self.thr, self.snk)

        # Se establece como deseamos ver los resultados graficos
        self.pyobj = sip.wrapinstance(self.snk.pyqwidget(), Qt.QWidget)
        self.pyobj.show()
Exemple #14
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    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 100
        f2 = 200

        npts = 2048

        self.qapp = QtGui.QApplication(sys.argv)

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.5, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.5, 0)
        src = blocks.add_cc()
        channel = channels.channel_model(0.001)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100 * npts)
        self.snk1 = qtgui.const_sink_c(npts, "Constellation Example", 1)

        self.connect(src1, (src, 0))
        self.connect(src2, (src, 1))
        self.connect(src, channel, thr, (self.snk1, 0))

        self.ctrl_win = control_box()
        self.ctrl_win.attach_signal1(src1)
        self.ctrl_win.attach_signal2(src2)

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt4.QtGui.QWidget
        pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)

        self.main_box = dialog_box(pyWin, self.ctrl_win)
        self.main_box.show()
Exemple #15
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    def __init__(self, frame, panel, vbox, argv):
        stdgui2.std_top_block.__init__ (self, frame, panel, vbox, argv)
        pspectrum_len = 1024
        # build our flow graph
        input_rate = 2e6
        #Generate some noise
        noise = analog.noise_source_c(analog.GR_GAUSSIAN, 1.0/10)
        # Generate a complex sinusoid
        #source = gr.file_source(gr.sizeof_gr_complex, 'foobar2.dat', repeat=True)
        src1 = analog.sig_source_c (input_rate, analog.GR_SIN_WAVE, -500e3, 1)
        src2 = analog.sig_source_c (input_rate, analog.GR_SIN_WAVE, 500e3, 1)
        src3 = analog.sig_source_c (input_rate, analog.GR_SIN_WAVE, -250e3, 2)

        # We add these throttle blocks so that this demo doesn't
        # suck down all the CPU available.  Normally you wouldn't use these.
        thr1 = blocks.throttle(gr.sizeof_gr_complex, input_rate)
        sink1 = spectrum_sink_c (panel, title="Spectrum Sink", pspectrum_len=pspectrum_len,
                            sample_rate=input_rate, baseband_freq=0,
                            ref_level=0, y_per_div=20, y_divs=10, m = 70, n = 3, nsamples = 1024)
        vbox.Add (sink1.win, 1, wx.EXPAND)
        combine1 = blocks.add_cc()
        self.connect(src1, (combine1, 0))
        self.connect(src2, (combine1, 1))
        self.connect(src3, (combine1, 2))
        self.connect(noise, (combine1, 3))
        self.connect(combine1, thr1, sink1)
 def __init__(self, n_sinusoids=1, SNR=10, samp_rate=32e3, nsamples=2048):
     gr.hier_block2.__init__(self, "ESPRIT/MUSIC signal generator",
                             gr.io_signature(0, 0, gr.sizeof_float),
                             gr.io_signature(1, 1, gr.sizeof_gr_complex))
     sigampl = 10.0**(SNR / 10.0)  # noise power is 1
     self.srcs = list()
     self.n_sinusoids = n_sinusoids
     self.samp_rate = samp_rate
     # create our signals ...
     for s in range(n_sinusoids):
         self.srcs.append(
             analog.sig_source_c(samp_rate, analog.GR_SIN_WAVE,
                                 1000 * s + 2000,
                                 numpy.sqrt(sigampl / n_sinusoids)))
     seed = ord(os.urandom(1))
     self.noise = analog.noise_source_c(analog.GR_GAUSSIAN, 1, seed)
     self.add = blocks.add_cc()
     self.head = blocks.head(gr.sizeof_gr_complex, nsamples)
     self.sink = blocks.vector_sink_f(vlen=n_sinusoids)
     # wire it up ...
     for s in range(n_sinusoids):
         self.connect(self.srcs[s], (self.add, s))
     # Additive noise
     self.connect(self.noise, (self.add, n_sinusoids))
     self.connect(self.add, self.head, self)
    def test_001_t (self):
        # set up fg
	src_data = (-3 + 1j , 4 + 2j, -5 + 5j, 2)
	for i in range(6000):
		src_data = src_data + (-3 + 1j , 4 + 2j, -5 + 5j, 2)
        #expected_result0 = (-3 + 1j, -4 + 2j, -5 + 5j, -2)
	#expected_result1 = (4 + 2j, -3 - 1j , 2 , -5 -5j)
        src = blocks.vector_source_c(src_data)
        mmtx = mimo.alamouti_encode_cc()

	add = blocks.add_cc()

	mmrx = mimo.alamouti_decode_cc()
        dst = blocks.vector_sink_c()

        self.tb.connect(src, mmtx)
        self.tb.connect((mmtx,0), (add,0))
	self.tb.connect((mmtx,1), (add,1))
	self.tb.connect(add, (mmrx,0))
	self.tb.connect(add, (mmrx,1))
	self.tb.connect(mmrx, dst)
        self.tb.run ()
        # check data
	result_data = dst.data()
	print len(result_data)
	#for i in range(len(result_data)):
	#	print result_data[i]
	#	print src_data[i]
        self.assertComplexTuplesAlmostEqual(src_data , result_data, 1025)
Exemple #18
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    def test_003_multiburst (self):
        """ Send several bursts, see if the number of detects is correct.
        Burst lengths and content are random.
        """
        n_bursts = 42
        fft_len = 32
        cp_len = 4
        tx_signal = []
        for i in xrange(n_bursts):
            sync_symbol = [(random.randint(0, 1)*2)-1 for x in range(fft_len/2)] * 2
            tx_signal += [0,] * random.randint(0, 2*fft_len) + \
                         sync_symbol[-cp_len:] + \
                         sync_symbol + \
                         [(random.randint(0, 1)*2)-1 for x in range(fft_len * random.randint(5,23))]
        add = blocks.add_cc()
        sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
        sink_freq   = blocks.vector_sink_f()
        sink_detect = blocks.vector_sink_b()
        channel = channels.channel_model(0.005)
        self.tb.connect(blocks.vector_source_c(tx_signal), channel, sync)
        self.tb.connect((sync, 0), sink_freq)
        self.tb.connect((sync, 1), sink_detect)
        self.tb.run()
        n_bursts_detected = numpy.sum(sink_detect.data())
        # We allow for one false alarm or missed burst
        self.assertTrue(abs(n_bursts_detected - n_bursts) <= 1,
                msg="""Because of statistics, it is possible (though unlikely)
that the number of detected bursts differs slightly. If the number of detects is
off by one or two, run the test again and see what happen.
Detection error was: %d """ % (numpy.sum(sink_detect.data()) - n_bursts)
        )
 def run_flow_graph(sync_sym1, sync_sym2, data_sym):
     top_block = gr.top_block()
     carr_offset = random.randint(-max_offset/2, max_offset/2) * 2
     tx_data = shift_tuple(sync_sym1, carr_offset) + \
               shift_tuple(sync_sym2, carr_offset) + \
               shift_tuple(data_sym,  carr_offset)
     channel = [rand_range(min_chan_ampl, max_chan_ampl) * numpy.exp(1j * rand_range(0, 2 * numpy.pi)) for x in range(fft_len)]
     src = blocks.vector_source_c(tx_data, False, fft_len)
     chan = blocks.multiply_const_vcc(channel)
     noise = analog.noise_source_c(analog.GR_GAUSSIAN, wgn_amplitude)
     add = blocks.add_cc(fft_len)
     chanest = digital.ofdm_chanest_vcvc(sync_sym1, sync_sym2, 1)
     sink = blocks.vector_sink_c(fft_len)
     top_block.connect(src, chan, (add, 0), chanest, sink)
     top_block.connect(noise, blocks.stream_to_vector(gr.sizeof_gr_complex, fft_len), (add, 1))
     top_block.run()
     channel_est = None
     carr_offset_hat = 0
     rx_sym_est = [0,] * fft_len
     tags = sink.tags()
     for tag in tags:
         if pmt.symbol_to_string(tag.key) == 'ofdm_sync_carr_offset':
             carr_offset_hat = pmt.to_long(tag.value)
             self.assertEqual(carr_offset, carr_offset_hat)
         if pmt.symbol_to_string(tag.key) == 'ofdm_sync_chan_taps':
             channel_est = shift_tuple(pmt.c32vector_elements(tag.value), carr_offset)
     shifted_carrier_mask = shift_tuple(carrier_mask, carr_offset)
     for i in range(fft_len):
         if shifted_carrier_mask[i] and channel_est[i]:
             self.assertAlmostEqual(channel[i], channel_est[i], places=0)
             rx_sym_est[i] = (sink.data()[i] / channel_est[i]).real
     return (carr_offset, list(shift_tuple(rx_sym_est, -carr_offset_hat)))
Exemple #20
0
 def test_001_detect(self):
     """ Send two bursts, with zeros in between, and check
     they are both detected at the correct position and no
     false alarms occur """
     n_zeros = 15
     fft_len = 32
     cp_len = 4
     sig_len = (fft_len + cp_len) * 10
     tx_signal = [
         0,
     ] * n_zeros + make_bpsk_burst(fft_len, cp_len, sig_len)
     tx_signal = tx_signal * 2
     add = blocks.add_cc()
     sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
     sink_freq = blocks.vector_sink_f()
     sink_detect = blocks.vector_sink_b()
     self.tb.connect(blocks.vector_source_c(tx_signal), (add, 0))
     self.tb.connect(analog.noise_source_c(analog.GR_GAUSSIAN, .01),
                     (add, 1))
     self.tb.connect(add, sync)
     self.tb.connect((sync, 0), sink_freq)
     self.tb.connect((sync, 1), sink_detect)
     self.tb.run()
     sig1_detect = sink_detect.data()[0:len(tx_signal) // 2]
     sig2_detect = sink_detect.data()[len(tx_signal) // 2:]
     self.assertTrue(
         abs(sig1_detect.index(1) - (n_zeros + fft_len + cp_len)) < cp_len)
     self.assertTrue(
         abs(sig2_detect.index(1) - (n_zeros + fft_len + cp_len)) < cp_len)
     self.assertEqual(numpy.sum(sig1_detect), 1)
     self.assertEqual(numpy.sum(sig2_detect), 1)
 def test_001_detect (self):
     """ Send two bursts, with zeros in between, and check
     they are both detected at the correct position and no
     false alarms occur """
     n_zeros = 15
     fft_len = 32
     cp_len = 4
     sig_len = (fft_len + cp_len) * 10
     sync_symbol = [(random.randint(0, 1)*2)-1 for x in range(fft_len/2)] * 2
     tx_signal = [0,] * n_zeros + \
                 sync_symbol[-cp_len:] + \
                 sync_symbol + \
                 [(random.randint(0, 1)*2)-1 for x in range(sig_len)]
     tx_signal = tx_signal * 2
     add = blocks.add_cc()
     sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
     sink_freq   = blocks.vector_sink_f()
     sink_detect = blocks.vector_sink_b()
     self.tb.connect(blocks.vector_source_c(tx_signal), (add, 0))
     self.tb.connect(analog.noise_source_c(analog.GR_GAUSSIAN, .01), (add, 1))
     self.tb.connect(add, sync)
     self.tb.connect((sync, 0), sink_freq)
     self.tb.connect((sync, 1), sink_detect)
     self.tb.run()
     sig1_detect = sink_detect.data()[0:len(tx_signal)/2]
     sig2_detect = sink_detect.data()[len(tx_signal)/2:]
     self.assertTrue(abs(sig1_detect.index(1) - (n_zeros + fft_len + cp_len)) < cp_len)
     self.assertTrue(abs(sig2_detect.index(1) - (n_zeros + fft_len + cp_len)) < cp_len)
     self.assertEqual(numpy.sum(sig1_detect), 1)
     self.assertEqual(numpy.sum(sig2_detect), 1)
    def test_001_t(self):
        sample_rate = 600e3
        ampl = 0.1
        file_src = blocks.file_source(
            8,
            "/home/hzhua/gr_learn/gr-fch_detection/python/943.125_300kHz.cfile",
            False)
        #src0 = analog.sig_source_c(sample_rate, analog.GR_SIN_WAVE, 60e3, 1.0)
        #src1 = analog.noise_source_c(analog.GR_GAUSSIAN, 0.3)
        add = blocks.add_cc()
        taps = filter.firdes.low_pass(1, sample_rate, 70e3, 10e3)

        detect = fch_detection.detect2(taps)

        #dst = file.sink(sample_rate, "")fff
        dst = blocks.vector_sink_c()
        #dst= fftsink2.fft_sink_c(panel, title="FFT display", fft_size=512, sample_rate=sample_rate)
        #dst = qtgui.sink_c(512, gr.firdes.WIN_BLACKMAN_hARRIS)

        #self.tb.connect(src0, (add,0));
        #self.tb.connect(src1, (add,1));
        self.tb.connect(file_src, detect)
        #self.tb.connect(add, detect)
        self.tb.connect(detect, dst)
        self.tb.run()
    def test_001_t(self):
        # set up fg
        src_data = (-3 + 1j, 4 + 2j, -5 + 5j, 2)
        for i in range(6000):
            src_data = src_data + (-3 + 1j, 4 + 2j, -5 + 5j, 2)

    #expected_result0 = (-3 + 1j, -4 + 2j, -5 + 5j, -2)
        #expected_result1 = (4 + 2j, -3 - 1j , 2 , -5 -5j)
        src = blocks.vector_source_c(src_data)
        mmtx = mimo.alamouti_encode_cc()

        add = blocks.add_cc()

        mmrx = mimo.alamouti_decode_cc()
        dst = blocks.vector_sink_c()

        self.tb.connect(src, mmtx)
        self.tb.connect((mmtx, 0), (add, 0))
        self.tb.connect((mmtx, 1), (add, 1))
        self.tb.connect(add, (mmrx, 0))
        self.tb.connect(add, (mmrx, 1))
        self.tb.connect(mmrx, dst)
        self.tb.run()
        # check data
        result_data = dst.data()
        print len(result_data)
        #for i in range(len(result_data)):
        #	print result_data[i]
        #	print src_data[i]
        self.assertComplexTuplesAlmostEqual(src_data, result_data, 1025)
    def __init__(self):
        gr.top_block.__init__(self)  # otra vez la herencia
        self.qapp = Qt.QApplication(sys.argv)
        sample_rate = 32000
        fftsize = 2048
        ampl = 0.5

        self.src0 = analog.sig_source_c(sample_rate, analog.GR_SIN_WAVE, 1000,
                                        ampl)
        self.src1 = analog.sig_source_c(sample_rate, analog.GR_SIN_WAVE, 500,
                                        ampl)
        self.add = blocks.add_cc()
        self.thr = blocks.throttle(gr.sizeof_gr_complex, sample_rate, True)
        self.snk = qtgui.sink_c(
            fftsize,  #fftsize
            firdes.WIN_BLACKMAN_hARRIS,  #wintype
            0,  #fc
            sample_rate,  #bw
            "",  #name
            True,  #plotfreq
            False,  #plotwaterfall
            True,  #plottime
            False,  #plotconst
        )
        self.connect(self.src0, (self.add, 0))
        self.connect(self.src1, (self.add, 1))
        self.connect(self.add, self.thr, self.snk)
        # Se establece como deseamos ver los resultados graficos
        self.pyobj = sip.wrapinstance(self.snk.pyqwidget(), Qt.QWidget)
        self.pyobj.show()
Exemple #25
0
def run_test(tb, channel, fft_rotate, fft_filter):
    N = 1000  # number of samples to use
    M = 5  # Number of channels
    fs = 5000.0  # baseband sampling rate
    ifs = M * fs  # input samp rate to decimator

    taps = filter.firdes.low_pass_2(1,
                                    ifs,
                                    fs / 2,
                                    fs / 10,
                                    attenuation_dB=80,
                                    window=filter.firdes.WIN_BLACKMAN_hARRIS)

    signals = list()
    add = blocks.add_cc()
    freqs = [-230., 121., 110., -513., 203.]
    Mch = ((len(freqs) - 1) // 2 + channel) % len(freqs)
    for i in range(len(freqs)):
        f = freqs[i] + (M // 2 - M + i + 1) * fs
        data = sig_source_c(ifs, f, 1, N)
        signals.append(blocks.vector_source_c(data))
        tb.connect(signals[i], (add, i))

    s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, M)
    pfb = filter.pfb_decimator_ccf(M, taps, channel, fft_rotate, fft_filter)
    snk = blocks.vector_sink_c()

    tb.connect(add, s2ss)
    for i in range(M):
        tb.connect((s2ss, i), (pfb, i))
    tb.connect(pfb, snk)
    tb.run()

    L = len(snk.data())

    # Adjusted phase rotations for data
    phase = [
        0.11058476216852586, 4.5108246571401693, 3.9739891674564594,
        2.2820531095511924, 1.3782797467397869
    ]
    phase = phase[channel]

    # Filter delay is the normal delay of each arm
    tpf = math.ceil(len(taps) / float(M))
    delay = -(tpf - 1.0) / 2.0
    delay = int(delay)

    # Create a time scale that's delayed to match the filter delay
    t = [float(x) / fs for x in range(delay, L + delay)]

    # Create known data as complex sinusoids for the baseband freq
    # of the extracted channel is due to decimator output order.
    expected_data = [
        math.cos(2. * math.pi * freqs[Mch] * x + phase) +
        1j * math.sin(2. * math.pi * freqs[Mch] * x + phase) for x in t
    ]
    dst_data = snk.data()

    return (dst_data, expected_data)
Exemple #26
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    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 100
        f2 = 200

        npts = 2048

        self.qapp = QtWidgets.QApplication(sys.argv)
        ss = open(gr.prefix() + '/share/gnuradio/themes/dark.qss')
        sstext = ss.read()
        ss.close()
        self.qapp.setStyleSheet(sstext)

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.1, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.1, 0)
        src = blocks.add_cc()
        channel = channels.channel_model(0.01)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100 * npts)
        self.snk1 = qtgui.time_sink_c(npts, Rs, "Complex Time Example", 1,
                                      None)

        self.connect(src1, (src, 0))
        self.connect(src2, (src, 1))
        self.connect(src, channel, thr, (self.snk1, 0))
        #self.connect(src1, (self.snk1, 1))
        #self.connect(src2, (self.snk1, 2))

        self.ctrl_win = control_box()
        self.ctrl_win.attach_signal1(src1)
        self.ctrl_win.attach_signal2(src2)

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt = self.snk1.qwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt5.QtWidgets.QWidget
        pyWin = sip.wrapinstance(pyQt, QtWidgets.QWidget)

        # Example of using signal/slot to set the title of a curve
        # FIXME: update for Qt5
        # pyWin.setLineLabel.connect(pyWin.setLineLabel)
        #pyWin.emit(QtCore.SIGNAL("setLineLabel(int, QString)"), 0, "Re{sum}")
        self.snk1.set_line_label(0, "Re{Sum}")
        self.snk1.set_line_label(1, "Im{Sum}")
        #self.snk1.set_line_label(2, "Re{src1}")
        #self.snk1.set_line_label(3, "Im{src1}")
        #self.snk1.set_line_label(4, "Re{src2}")
        #self.snk1.set_line_label(5, "Im{src2}")

        # Can also set the color of a curve
        #self.snk1.set_color(5, "blue")

        self.snk1.set_update_time(0.5)

        # pyWin.show()
        self.main_box = dialog_box(pyWin, self.ctrl_win)
        self.main_box.show()
Exemple #27
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    def test_001_t (self):
        """AGC on random noisy QPSK symbols"""

        # Parameters
        snr_db     = 0.0
        const_mult = 1.5
        agc_rate   = 1e-4
        agc_ref    = 1.0
        agc_gain   = 1.0
        N_last     = 1000 # analyze the last symbols

        # Constants
        n_symbols = int(10*(1/agc_rate))
        noise_v   = 1/sqrt((10**(float(snr_db)/10)))
        rndm      = random.Random()

        # Input data
        in_vec  = tuple([rndm.randint(0,1) for i in range(0, n_symbols)])

        # Flowgraph
        src     = blocks.vector_source_b(in_vec)
        pack    = blocks.repack_bits_bb(1, 2, "", False, gr.GR_MSB_FIRST)
        const   = digital.constellation_qpsk().base()
        cmap    = digital.chunks_to_symbols_bc(const.points())
        mult    = blocks.multiply_const_cc(const_mult)
        nadder  = blocks.add_cc()
        noise   = analog.noise_source_c(analog.GR_GAUSSIAN, noise_v, 0)
        agc     = blocksat.agc_cc(agc_rate, agc_ref, agc_gain)
        snk     = blocks.vector_sink_c()
        snk2    = blocks.vector_sink_c()
        # Reference AGC approach
        rms_cf  = blocks.rms_cf(0.0001)
        f2c     = blocks.float_to_complex()
        div     = blocks.divide_cc()
        snk3    = blocks.vector_sink_c()
        self.tb.connect(src, pack, cmap, mult)
        self.tb.connect(mult, (nadder, 0))
        self.tb.connect(noise, (nadder, 1))
        self.tb.connect(nadder, agc, snk)
        self.tb.connect(nadder, snk2)
        self.tb.connect(nadder, (div, 0))
        self.tb.connect(nadder, rms_cf, (f2c, 0), (div, 1))
        self.tb.connect(div, snk3)
        self.tb.run()

        # Collect results
        agc_syms     = snk.data()
        pre_agc_syms = snk2.data()
        ref_agc_syms = snk3.data()
        rms_agc      = self.rms(agc_syms, N_last)
        rms_pre_agc  = self.rms(pre_agc_syms, N_last)
        rms_agc_ref  = self.rms(ref_agc_syms, N_last)

        print('RMS before AGC: %f' %(rms_pre_agc))
        print('RMS after AGC: %f' %(rms_agc))
        print('RMS after reference AGC: %f' %(rms_agc_ref))

        # Check results
        self.assertAlmostEqual(rms_agc, 1.0, places=1)
    def test_002_t(self):
        """Encoder to Decoder - noisy BPSK w/ soft constellation de-mapper"""

        # Parameters
        N = 18444
        K = 6144
        pct_en = False
        n_ite = 6
        M = 2
        snr_db = SNR_DB
        max_len = 10 * K
        flip_llrs = False
        sym_rate = 2 * 156250

        # NOTE: The soft decoder block outputs positive LLR if bit "1" is more
        # likely, and negative LLR otherwise (bit = 0). That is, the soft
        # de-mapper's convention is the oposite of the one adopted in the Turbo
        # Decoder. To compensate that, we choose to flip the LLRs (multiply them
        # by "-1") inside the decoder wrapper.

        # Constants
        noise_v = 1 / math.sqrt((10**(float(snr_db) / 10)))
        rndm = random.Random()

        # Input data
        in_vec = tuple([rndm.randint(0, 1) for i in range(0, max_len)])

        # Flowgraph
        src = blocks.vector_source_b(in_vec)
        enc = blocksattx.turbo_encoder(K, pct_en)
        const = digital.constellation_bpsk().base()
        cmap = digital.chunks_to_symbols_bc(const.points())
        nadder = blocks.add_cc()
        noise = analog.noise_source_c(analog.GR_GAUSSIAN, noise_v, 0)
        cdemap = blocksat.soft_decoder_cf(M, noise_v)
        dec = blocksat.turbo_decoder(K, pct_en, n_ite, flip_llrs)
        snk = blocks.vector_sink_b()
        snk_sym = blocks.vector_sink_c()
        snk_llr = blocks.vector_sink_f()
        self.tb.connect(src, enc, cmap)
        self.tb.connect(cmap, (nadder, 0))
        self.tb.connect(noise, (nadder, 1))
        self.tb.connect(nadder, cdemap, dec, snk)
        self.tb.connect(cmap, snk_sym)
        self.tb.connect(cdemap, snk_llr)
        self.tb.run()

        # Collect results
        out_vec = snk.data()
        llrs = snk_llr.data()
        syms = snk_sym.data()
        diff = array(in_vec) - array(out_vec)
        err = [0 if i == 0 else 1 for i in diff]

        self.dump(const.points(), in_vec, syms, llrs, out_vec)
        print('Number of errors: %d' % (sum(err)))

        # Check results
        self.assertEqual(sum(err), 0)
    def test_001_t(self):
        # We check if with a constant source, the SNR is measured correctly
        sample_rate = 1e6
        frame_duration = 1.0e-3
        test_duration = 1.5 * frame_duration
        snr = np.random.rand() * 1000
        zc_seq_len = 71

        samples_per_frame = int(round(frame_duration * sample_rate))
        samples_per_test = int(round(test_duration * sample_rate))
        snr_estim_sample_window = zc_seq_len  #int(round(samples_per_frame/20))
        random_samples_skip = np.random.randint(
            samples_per_frame / 2) + samples_per_frame / 2
        preamble_seq = zadoffchu.generate_sequence(zc_seq_len, 1, 0)
        preamble_pwr_sum = np.sum(np.abs(preamble_seq)**2)

        print "***** Start test 001 *****"
        framer = specmonitor.framer_c(sample_rate, frame_duration,
                                      preamble_seq)
        const_source = blocks.vector_source_c([1.0 / snr], True)
        add = blocks.add_cc()
        skiphead = blocks.skiphead(gr.sizeof_gr_complex, random_samples_skip)
        corr_est = specmonitor.corr_est_norm_cc(
            preamble_seq, 1, 0)  #digital.corr_est_cc(preamble_seq, 1, 0)
        snr_est = specmonitor.framer_snr_est_cc(snr_estim_sample_window,
                                                preamble_seq.size)
        # tag_db = blocks.tag_debug(gr.sizeof_gr_complex, "tag debugger")
        head = blocks.head(gr.sizeof_gr_complex, samples_per_test)
        dst = blocks.vector_sink_c()

        self.tb.connect(framer, (add, 0))
        self.tb.connect(const_source, (add, 1))
        self.tb.connect(add, skiphead)
        self.tb.connect(skiphead, head)
        self.tb.connect(head, corr_est)
        self.tb.connect(corr_est, snr_est)
        # self.tb.connect(snr_est,tag_db)
        self.tb.connect(snr_est, dst)

        self.tb.run()
        x_data = dst.data()

        snrdB = snr_est.SNRdB()
        y_data = np.abs(x_data)**2

        # print "Random Initial Skip: ", random_samples_skip
        # print "y_data size: ", len(y_data)

        start_preamble_idx = samples_per_frame - random_samples_skip + preamble_seq.size
        sig_range = np.arange(start_preamble_idx,
                              start_preamble_idx + preamble_seq.size)
        floor_range = np.arange(sig_range[-1] + 1,
                                sig_range[-1] + 1 + snr_estim_sample_window)
        y_pwr = np.mean(y_data[sig_range])
        floor_pwr = np.mean(y_data[floor_range])
        py_snrdB = 10 * np.log10(y_pwr / floor_pwr)

        self.assertAlmostEqual(py_snrdB, 20 * np.log10(snr), 1)
        self.assertAlmostEqual(snrdB, 20 * np.log10(snr), 1)
Exemple #30
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    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 100
        f2 = 200

        npts = 2048

        self.qapp = QtWidgets.QApplication(sys.argv)
        ss = open(gr.prefix() + '/share/gnuradio/themes/dark.qss')
        sstext = ss.read()
        ss.close()
        self.qapp.setStyleSheet(sstext)

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.1, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.1, 0)
        src  = blocks.add_cc()
        channel = channels.channel_model(0.01)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100*npts)
        self.snk1 = qtgui.time_sink_c(npts, Rs,
                                      "Complex Time Example", 1)

        self.connect(src1, (src,0))
        self.connect(src2, (src,1))
        self.connect(src,  channel, thr, (self.snk1, 0))
        #self.connect(src1, (self.snk1, 1))
        #self.connect(src2, (self.snk1, 2))

        self.ctrl_win = control_box()
        self.ctrl_win.attach_signal1(src1)
        self.ctrl_win.attach_signal2(src2)

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt  = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt5.QtWidgets.QWidget
        pyWin = sip.wrapinstance(pyQt, QtWidgets.QWidget)

        # Example of using signal/slot to set the title of a curve
        # FIXME: update for Qt5
        #pyWin.setLineLabel.connect(pyWin.setLineLabel)
        #pyWin.emit(QtCore.SIGNAL("setLineLabel(int, QString)"), 0, "Re{sum}")
        self.snk1.set_line_label(0, "Re{Sum}")
        self.snk1.set_line_label(1, "Im{Sum}")
        #self.snk1.set_line_label(2, "Re{src1}")
        #self.snk1.set_line_label(3, "Im{src1}")
        #self.snk1.set_line_label(4, "Re{src2}")
        #self.snk1.set_line_label(5, "Im{src2}")

        # Can also set the color of a curve
        #self.snk1.set_color(5, "blue")

        self.snk1.set_update_time(0.5)

        #pyWin.show()
        self.main_box = dialog_box(pyWin, self.ctrl_win)
        self.main_box.show()
Exemple #31
0
    def test_002_t(self):
        # Generate frames with AWGN and test SNR estimation
        sample_rate = 10.0e6
        frame_duration = 1.0e-3
        test_duration = 0.1 * frame_duration
        snr = 10
        scale_value = 15.0
        samples_per_frame = int(round(frame_duration * sample_rate))
        samples_per_test = int(round(test_duration * sample_rate))
        random_samples_skip = 0  #np.random.randint(samples_per_frame)
        preamble_seq = zadoffchu(63, 1, 0)
        preamble_pwr_sum = np.sum(np.abs(preamble_seq)**2)

        print "***** Start test 002 *****"
        framer = specmonitor.framer_c(sample_rate, frame_duration,
                                      preamble_seq)
        awgn = analog.noise_source_c(analog.GR_GAUSSIAN, 1.0 / snr)
        add = blocks.add_cc()
        scaler = blocks.multiply_const_vcc([complex(scale_value)])
        mag2 = blocks.complex_to_mag_squared()
        mavg = blocks.moving_average_ff(
            len(preamble_seq), 1.0 / len(preamble_seq)
        )  # i need to divide by the preamble size in case the preamble seq has amplitude 1 (sum of power is len)
        sqrtavg = blocks.transcendental("sqrt")

        # I have to compensate the amplitude of the input signal (scale) either through a feedback normalization loop that computes the scale, or manually (not practical)
        # additionally I have to scale down by the len(preamble)==sum(abs(preamble)^2) because the cross-corr does not divide by the preamble length
        #scaler2 = blocks.multiply_const_vcc([complex(1.0)/scale_value/len(preamble_seq)]) # if no feedback normalization loop, I have to scale the signal compensating additionally the scaling factor
        corr_est = specmonitor.corr_est_norm_cc(
            preamble_seq, 1, 0)  #digital.corr_est_cc(preamble_seq, 1, 0)
        tag_db = blocks.tag_debug(gr.sizeof_gr_complex, "tag debugger")
        head = blocks.head(gr.sizeof_gr_complex, samples_per_test)
        dst = blocks.vector_sink_c()

        skiphead = blocks.skiphead(gr.sizeof_float, random_samples_skip)
        skiphead2 = blocks.skiphead(gr.sizeof_gr_complex, random_samples_skip)
        skiphead3 = blocks.skiphead(gr.sizeof_gr_complex, random_samples_skip)
        debug_vec = blocks.vector_sink_f()
        debug_vec2 = blocks.vector_sink_c()
        debug_vec3 = blocks.vector_sink_c()

        self.tb.connect(framer, (add, 0))
        self.tb.connect(awgn, (add, 1))
        self.tb.connect(add, scaler)
        self.tb.connect(scaler, mag2, mavg, sqrtavg)

        self.tb.connect(scaler, corr_est)
        self.tb.connect(corr_est, tag_db)
        self.tb.connect(corr_est, head, dst)

        self.tb.connect(sqrtavg, skiphead, debug_vec)
        self.tb.connect(scaler, skiphead2, debug_vec2)

        self.tb.run()
        result_data = dst.data()
        debug_vec_data = debug_vec.data()
        debug_vec_data2 = debug_vec2.data()
        debug_vec_data3 = debug_vec3.data()
Exemple #32
0
 def __init__(self, config, tb):
     self.name = config['name']
     self.sample_rate = config['rate']
     self.args = config['args']
     self.frequency = config['frequency']
     self.tb = tb
     self.sum = blocks.add_cc()
     self.sum_count = 0
     self.output_throttle = None
    def test_003_t(self):
        """Encoder to Decoder - noisy QPSK w/ soft constellation de-mapper"""

        # Parameters
        N = 18444
        K = 6144
        pct_en = False
        n_ite = 6
        M = 4
        snr_db = SNR_DB
        max_len = 10 * K
        flip_llrs = False
        sym_rate = 2 * 156250

        # NOTE: just like in the previous example, flipping of the LLRs is
        # necessary here.

        # Constants
        noise_v = 1 / math.sqrt((10**(float(snr_db) / 10)))
        rndm = random.Random()

        # Input data
        in_vec = tuple([rndm.randint(0, 1) for i in range(0, max_len)])

        # Flowgraph
        src = blocks.vector_source_b(in_vec)
        enc = blocksattx.turbo_encoder(K, pct_en)
        pack = blocks.repack_bits_bb(1, 2, "", False, gr.GR_MSB_FIRST)
        const = digital.constellation_qpsk().base()
        cmap = digital.chunks_to_symbols_bc(const.points())
        nadder = blocks.add_cc()
        noise = analog.noise_source_c(analog.GR_GAUSSIAN, noise_v, 0)
        cdemap = blocksat.soft_decoder_cf(M, noise_v)
        dec = blocksat.turbo_decoder(K, pct_en, n_ite, flip_llrs)
        snk = blocks.vector_sink_b()
        snk_sym = blocks.vector_sink_c()
        snk_llr = blocks.vector_sink_f()
        self.tb.connect(src, enc, pack, cmap)
        self.tb.connect(cmap, (nadder, 0))
        self.tb.connect(noise, (nadder, 1))
        self.tb.connect(nadder, cdemap, dec, snk)
        self.tb.connect(cmap, snk_sym)
        self.tb.connect(cdemap, snk_llr)
        self.tb.run()

        # Collect results
        out_vec = snk.data()
        llrs = snk_llr.data()
        syms = snk_sym.data()
        diff = array(in_vec) - array(out_vec)
        err = [0 if i == 0 else 1 for i in diff]

        self.dump(const.points(), in_vec, syms, llrs, out_vec)
        print('Number of errors: %d' % (sum(err)))

        # Check results
        self.assertEqual(sum(err), 0)
Exemple #34
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    def __init__(self, context, mode, angle=0.0):
        gr.hier_block2.__init__(
            self,
            type(self).__name__,
            gr.io_signature(1, 1, gr.sizeof_float * 1),
            gr.io_signature(1, 1, gr.sizeof_gr_complex * 1),
        )

        self.__angle = 0.0  # dummy statically visible value will be overwritten

        # TODO: My signal level parameters are probably wrong because this signal doesn't look like a real VOR signal

        vor_30 = analog.sig_source_f(self.__audio_rate, analog.GR_COS_WAVE,
                                     self.__vor_sig_freq, 1, 0)
        vor_add = blocks.add_cc(1)
        vor_audio = blocks.add_ff(1)
        # Audio/AM signal
        self.connect(
            vor_30,
            blocks.multiply_const_ff(0.3),  # M_n
            (vor_audio, 0))
        self.connect(
            self,
            blocks.multiply_const_ff(audio_modulation_index),  # M_i
            (vor_audio, 1))
        # Carrier component
        self.connect(analog.sig_source_c(0, analog.GR_CONST_WAVE, 0, 0, 1),
                     (vor_add, 0))
        # AM component
        self.__delay = blocks.delay(gr.sizeof_gr_complex,
                                    0)  # configured by set_angle
        self.connect(
            vor_audio,
            make_resampler(self.__audio_rate, self.__rf_rate
                           ),  # TODO make a complex version and do this last
            blocks.float_to_complex(1),
            self.__delay,
            (vor_add, 1))
        # FM component
        vor_fm_mult = blocks.multiply_cc(1)
        self.connect(  # carrier generation
            analog.sig_source_f(self.__rf_rate,
                                analog.GR_COS_WAVE, fm_subcarrier, 1, 0),
            blocks.float_to_complex(1), (vor_fm_mult, 1))
        self.connect(  # modulation
            vor_30,
            make_resampler(self.__audio_rate, self.__rf_rate),
            analog.frequency_modulator_fc(2 * math.pi * fm_deviation /
                                          self.__rf_rate),
            blocks.multiply_const_cc(0.3),  # M_d
            vor_fm_mult,
            (vor_add, 2))
        self.connect(vor_add, self)

        # calculate and initialize delay
        self.set_angle(angle)
Exemple #35
0
    def __init__(self):
        gr.top_block.__init__(self)

        self._nsamples = 1000000
        self._audio_rate = 8000

        # Set up N channels with their own baseband and IF frequencies
        self._N = 5
        chspacing = 16000
        freq = [10, 20, 30, 40, 50]
        f_lo = [0, 1*chspacing, -1*chspacing, 2*chspacing, -2*chspacing]

        self._if_rate = 4*self._N*self._audio_rate

        # Create a signal source and frequency modulate it
        self.sum = blocks.add_cc()
        for n in range(self._N):
            sig = analog.sig_source_f(self._audio_rate, analog.GR_SIN_WAVE, freq[n], 0.5)
            fm = fmtx(f_lo[n], self._audio_rate, self._if_rate)
            self.connect(sig, fm)
            self.connect(fm, (self.sum, n))

        self.head = blocks.head(gr.sizeof_gr_complex, self._nsamples)
        self.snk_tx = blocks.vector_sink_c()
        self.channel = channels.channel_model(0.1)

        self.connect(self.sum, self.head, self.channel, self.snk_tx)


        # Design the channlizer
        self._M = 10
        bw = chspacing / 2.0
        t_bw = chspacing / 10.0
        self._chan_rate = self._if_rate / self._M
        self._taps = filter.firdes.low_pass_2(1, self._if_rate, bw, t_bw,
                                              attenuation_dB=100,
                                              window=filter.firdes.WIN_BLACKMAN_hARRIS)
        tpc = math.ceil(float(len(self._taps)) / float(self._M))

        print("Number of taps:     ", len(self._taps))
        print("Number of channels: ", self._M)
        print("Taps per channel:   ", tpc)

        self.pfb = filter.pfb.channelizer_ccf(self._M, self._taps)

        self.connect(self.channel, self.pfb)

        # Create a file sink for each of M output channels of the filter and connect it
        self.fmdet = list()
        self.squelch = list()
        self.snks = list()
        for i in range(self._M):
            self.fmdet.append(analog.nbfm_rx(self._audio_rate, self._chan_rate))
            self.squelch.append(analog.standard_squelch(self._audio_rate*10))
            self.snks.append(blocks.vector_sink_f())
            self.connect((self.pfb, i), self.fmdet[i], self.squelch[i], self.snks[i])
Exemple #36
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    def __init__(self):
        gr.top_block.__init__(self)

        self._nsamples = 1000000
        self._audio_rate = 8000

        # Set up N channels with their own baseband and IF frequencies
        self._N = 5
        chspacing = 16000
        freq = [10, 20, 30, 40, 50]
        f_lo = [0, 1*chspacing, -1*chspacing, 2*chspacing, -2*chspacing]

        self._if_rate = 4*self._N*self._audio_rate

        # Create a signal source and frequency modulate it
        self.sum = blocks.add_cc()
        for n in xrange(self._N):
            sig = analog.sig_source_f(self._audio_rate, analog.GR_SIN_WAVE, freq[n], 0.5)
            fm = fmtx(f_lo[n], self._audio_rate, self._if_rate)
            self.connect(sig, fm)
            self.connect(fm, (self.sum, n))

        self.head = blocks.head(gr.sizeof_gr_complex, self._nsamples)
        self.snk_tx = blocks.vector_sink_c()
        self.channel = channels.channel_model(0.1)

        self.connect(self.sum, self.head, self.channel, self.snk_tx)


        # Design the channlizer
        self._M = 10
        bw = chspacing/2.0
        t_bw = chspacing/10.0
        self._chan_rate = self._if_rate / self._M
        self._taps = filter.firdes.low_pass_2(1, self._if_rate, bw, t_bw,
                                              attenuation_dB=100,
                                              window=filter.firdes.WIN_BLACKMAN_hARRIS)
        tpc = math.ceil(float(len(self._taps)) /  float(self._M))

        print "Number of taps:     ", len(self._taps)
        print "Number of channels: ", self._M
        print "Taps per channel:   ", tpc

        self.pfb = filter.pfb.channelizer_ccf(self._M, self._taps)

        self.connect(self.channel, self.pfb)

        # Create a file sink for each of M output channels of the filter and connect it
        self.fmdet = list()
        self.squelch = list()
        self.snks = list()
        for i in xrange(self._M):
            self.fmdet.append(analog.nbfm_rx(self._audio_rate, self._chan_rate))
            self.squelch.append(analog.standard_squelch(self._audio_rate*10))
            self.snks.append(blocks.vector_sink_f())
            self.connect((self.pfb, i), self.fmdet[i], self.squelch[i], self.snks[i])
def test_add_cc():
    top = gr.top_block()
    src = blocks.null_source(gr.sizeof_gr_complex)
    add = blocks.add_cc()
    probe = blocks.probe_rate(gr.sizeof_gr_complex)
    top.connect((src, 0), (add, 0))
    top.connect((src, 0), (add, 1))
    top.connect(add, probe)

    return top, probe
Exemple #38
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def test_add_cc():
    top = gr.top_block()
    src = blocks.null_source(gr.sizeof_gr_complex)
    add = blocks.add_cc()
    probe = blocks.probe_rate(gr.sizeof_gr_complex)
    top.connect((src, 0), (add, 0))
    top.connect((src, 0), (add, 1))
    top.connect(add, probe)

    return top, probe
Exemple #39
0
    def __init__(self, frame, panel, vbox, argv):
        stdgui2.std_top_block.__init__(self, frame, panel, vbox, argv)

        fft_size = 256

        # build our flow graph
        input_rate = 2048.0e3

        #Generate some noise
        noise = analog.noise_source_c(analog.GR_UNIFORM, 1.0 / 10)

        # Generate a complex sinusoid
        #src1 = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE, 2e3, 1)
        src1 = analog.sig_source_c(input_rate, analog.GR_CONST_WAVE, 57.50e3,
                                   1)

        # We add these throttle blocks so that this demo doesn't
        # suck down all the CPU available.  Normally you wouldn't use these.
        thr1 = blocks.throttle(gr.sizeof_gr_complex, input_rate)

        sink1 = fft_sink_c(panel,
                           title="Complex Data",
                           fft_size=fft_size,
                           sample_rate=input_rate,
                           baseband_freq=100e3,
                           ref_level=0,
                           y_per_div=20,
                           y_divs=10)
        vbox.Add(sink1.win, 1, wx.EXPAND)

        combine1 = blocks.add_cc()
        self.connect(src1, (combine1, 0))
        self.connect(noise, (combine1, 1))
        self.connect(combine1, thr1, sink1)

        #src2 = analog.sig_source_f(input_rate, analog.GR_SIN_WAVE, 2e3, 1)
        src2 = analog.sig_source_f(input_rate, analog.GR_CONST_WAVE, 57.50e3,
                                   1)
        thr2 = blocks.throttle(gr.sizeof_float, input_rate)
        sink2 = fft_sink_f(panel,
                           title="Real Data",
                           fft_size=fft_size * 2,
                           sample_rate=input_rate,
                           baseband_freq=100e3,
                           ref_level=0,
                           y_per_div=20,
                           y_divs=10)
        vbox.Add(sink2.win, 1, wx.EXPAND)

        combine2 = blocks.add_ff()
        c2f2 = blocks.complex_to_float()

        self.connect(src2, (combine2, 0))
        self.connect(noise, c2f2, (combine2, 1))
        self.connect(combine2, thr2, sink2)
def run_test(tb, channel, fft_rotate, fft_filter):
        N = 1000         # number of samples to use
        M = 5            # Number of channels
        fs = 5000.0      # baseband sampling rate
        ifs = M*fs       # input samp rate to decimator

        taps = filter.firdes.low_pass_2(1, ifs, fs/2, fs/10,
                                        attenuation_dB=80,
                                        window=filter.firdes.WIN_BLACKMAN_hARRIS)

        signals = list()
        add = blocks.add_cc()
        freqs = [-230., 121., 110., -513., 203.]
        Mch = ((len(freqs)-1)/2 + channel) % len(freqs)
        for i in xrange(len(freqs)):
            f = freqs[i] + (M/2-M+i+1)*fs
            data = sig_source_c(ifs, f, 1, N)
            signals.append(blocks.vector_source_c(data))
            tb.connect(signals[i], (add,i))

        s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, M)
        pfb = filter.pfb_decimator_ccf(M, taps, channel, fft_rotate, fft_filter)
        snk = blocks.vector_sink_c()

        tb.connect(add, s2ss)
        for i in xrange(M):
            tb.connect((s2ss,i), (pfb,i))
        tb.connect(pfb, snk)
        tb.run()

        L = len(snk.data())

        # Adjusted phase rotations for data
        phase = [ 0.11058476216852586,
                  4.5108246571401693,
                  3.9739891674564594,
                  2.2820531095511924,
                  1.3782797467397869]
        phase = phase[channel]

        # Filter delay is the normal delay of each arm
        tpf = math.ceil(len(taps) / float(M))
        delay = -(tpf - 1.0) / 2.0
        delay = int(delay)

        # Create a time scale that's delayed to match the filter delay
        t = map(lambda x: float(x)/fs, xrange(delay, L+delay))

        # Create known data as complex sinusoids for the baseband freq
        # of the extracted channel is due to decimator output order.
        expected_data = map(lambda x: math.cos(2.*math.pi*freqs[Mch]*x+phase) + \
                                1j*math.sin(2.*math.pi*freqs[Mch]*x+phase), t)
        dst_data = snk.data()

        return (dst_data, expected_data)
Exemple #41
0
    def __init__(self):
        gr.top_block.__init__(self)

        self._N = 2000000  # number of samples to use
        self._fs = 1000  # initial sampling rate
        self._M = M = 9  # Number of channels to channelize
        self._ifs = M * self._fs  # initial sampling rate

        # Create a set of taps for the PFB channelizer
        self._taps = filter.firdes.low_pass_2(
            1,
            self._ifs,
            475.50,
            50,
            attenuation_dB=100,
            window=filter.firdes.WIN_BLACKMAN_hARRIS)

        # Calculate the number of taps per channel for our own information
        tpc = numpy.ceil(float(len(self._taps)) / float(self._M))
        print("Number of taps:     ", len(self._taps))
        print("Number of channels: ", self._M)
        print("Taps per channel:   ", tpc)

        # Create a set of signals at different frequencies
        #   freqs lists the frequencies of the signals that get stored
        #   in the list "signals", which then get summed together
        self.signals = list()
        self.add = blocks.add_cc()
        freqs = [-70, -50, -30, -10, 10, 20, 40, 60, 80]
        for i in range(len(freqs)):
            f = freqs[i] + (M / 2 - M + i + 1) * self._fs
            self.signals.append(
                analog.sig_source_c(self._ifs, analog.GR_SIN_WAVE, f, 1))
            self.connect(self.signals[i], (self.add, i))

        self.head = blocks.head(gr.sizeof_gr_complex, self._N)

        # Construct the channelizer filter
        self.pfb = filter.pfb.channelizer_ccf(self._M, self._taps, 1)

        # Construct a vector sink for the input signal to the channelizer
        self.snk_i = blocks.vector_sink_c()

        # Connect the blocks
        self.connect(self.add, self.head, self.pfb)
        self.connect(self.add, self.snk_i)

        # Use this to play with the channel mapping
        #self.pfb.set_channel_map([5,6,7,8,0,1,2,3,4])

        # Create a vector sink for each of M output channels of the filter and connect it
        self.snks = list()
        for i in range(self._M):
            self.snks.append(blocks.vector_sink_c())
            self.connect((self.pfb, i), self.snks[i])
Exemple #42
0
 def __init__(self, context, mode, angle=0.0):
     gr.hier_block2.__init__(
         self, 'SimulatedDevice VOR modulator',
         gr.io_signature(1, 1, gr.sizeof_float * 1),
         gr.io_signature(1, 1, gr.sizeof_gr_complex * 1),
     )
     
     self.__angle = 0.0  # dummy statically visible value will be overwritten
     
     # TODO: My signal level parameters are probably wrong because this signal doesn't look like a real VOR signal
     
     vor_30 = analog.sig_source_f(self.__audio_rate, analog.GR_COS_WAVE, self.__vor_sig_freq, 1, 0)
     vor_add = blocks.add_cc(1)
     vor_audio = blocks.add_ff(1)
     # Audio/AM signal
     self.connect(
         vor_30,
         blocks.multiply_const_ff(0.3),  # M_n
         (vor_audio, 0))
     self.connect(
         self,
         blocks.multiply_const_ff(audio_modulation_index),  # M_i
         (vor_audio, 1))
     # Carrier component
     self.connect(
         analog.sig_source_c(0, analog.GR_CONST_WAVE, 0, 0, 1),
         (vor_add, 0))
     # AM component
     self.__delay = blocks.delay(gr.sizeof_gr_complex, 0)  # configured by set_angle
     self.connect(
         vor_audio,
         make_resampler(self.__audio_rate, self.__rf_rate),  # TODO make a complex version and do this last
         blocks.float_to_complex(1),
         self.__delay,
         (vor_add, 1))
     # FM component
     vor_fm_mult = blocks.multiply_cc(1)
     self.connect(  # carrier generation
         analog.sig_source_f(self.__rf_rate, analog.GR_COS_WAVE, fm_subcarrier, 1, 0), 
         blocks.float_to_complex(1),
         (vor_fm_mult, 1))
     self.connect(  # modulation
         vor_30,
         make_resampler(self.__audio_rate, self.__rf_rate),
         analog.frequency_modulator_fc(2 * math.pi * fm_deviation / self.__rf_rate),
         blocks.multiply_const_cc(0.3),  # M_d
         vor_fm_mult,
         (vor_add, 2))
     self.connect(
         vor_add,
         self)
     
     # calculate and initialize delay
     self.set_angle(angle)
Exemple #43
0
    def test_001_t(self):
        """MER of noisy QPSK symbols"""

        # Parameters
        snr_db = 10.0
        alpha = 0.01
        M = 2
        frame_len = 10000

        # Constants
        bits_per_sym = int(log(M, 2))
        n_bits = int(bits_per_sym * frame_len)
        rndm = random.Random()

        # Iterate over SNR levels
        for snr_db in reversed(range(3, 15)):
            print("Trying SNR: %f dB" % (float(snr_db)))

            # Random input data
            in_vec = tuple([rndm.randint(0, 1) for i in range(0, n_bits)])
            src = blocks.vector_source_b(in_vec)

            # Random noise
            noise_var = 1.0 / (10**(float(snr_db) / 10))
            noise_std = sqrt(noise_var)
            print("Noise amplitude: %f" % (noise_std))
            noise = analog.noise_source_c(analog.GR_GAUSSIAN, noise_std, 0)

            # Fixed flowgraph blocks
            pack = blocks.repack_bits_bb(1, bits_per_sym, "", False,
                                         gr.GR_MSB_FIRST)
            const = digital.constellation_bpsk().base()
            cmap = digital.chunks_to_symbols_bc(const.points())
            nadder = blocks.add_cc()
            mer = blocksat.mer_measurement(alpha, M, frame_len)
            snk = blocks.vector_sink_f()
            snk_n = blocks.vector_sink_c()

            # Connect source into flowgraph
            self.tb.connect(src, pack, cmap)
            self.tb.connect(cmap, (nadder, 0))
            self.tb.connect(noise, (nadder, 1))
            self.tb.connect(nadder, mer, snk)
            self.tb.connect(noise, snk_n)
            self.tb.run()

            # Collect results
            mer_measurements = snk.data()
            noise_samples = snk_n.data()
            print(mer_measurements)
            avg_mer = np.mean(mer_measurements)

            # Check results
            self.assertAlmostEqual(round(avg_mer), snr_db, places=1)
Exemple #44
0
    def __init__(self):
        gr.top_block.__init__(self)

        self._N = 2000000        # number of samples to use
        self._fs = 1000          # initial sampling rate
        self._M = M = 9          # Number of channels to channelize
        self._ifs = M*self._fs   # initial sampling rate

        # Create a set of taps for the PFB channelizer
        self._taps = filter.firdes.low_pass_2(1, self._ifs, 475.50, 50,
                                              attenuation_dB=100,
                                              window=filter.firdes.WIN_BLACKMAN_hARRIS)

        # Calculate the number of taps per channel for our own information
        tpc = scipy.ceil(float(len(self._taps)) /  float(self._M))
        print "Number of taps:     ", len(self._taps)
        print "Number of channels: ", self._M
        print "Taps per channel:   ", tpc

        # Create a set of signals at different frequencies
        #   freqs lists the frequencies of the signals that get stored
        #   in the list "signals", which then get summed together
        self.signals = list()
        self.add = blocks.add_cc()
        freqs = [-70, -50, -30, -10, 10, 20, 40, 60, 80]
        for i in xrange(len(freqs)):
            f = freqs[i] + (M/2-M+i+1)*self._fs
            self.signals.append(analog.sig_source_c(self._ifs, analog.GR_SIN_WAVE, f, 1))
            self.connect(self.signals[i], (self.add,i))

        self.head = blocks.head(gr.sizeof_gr_complex, self._N)

        # Construct the channelizer filter
        self.pfb = filter.pfb.channelizer_ccf(self._M, self._taps, 1)

        # Construct a vector sink for the input signal to the channelizer
        self.snk_i = blocks.vector_sink_c()

        # Connect the blocks
        self.connect(self.add, self.head, self.pfb)
        self.connect(self.add, self.snk_i)

        # Use this to play with the channel mapping
        #self.pfb.set_channel_map([5,6,7,8,0,1,2,3,4])

        # Create a vector sink for each of M output channels of the filter and connect it
        self.snks = list()
        for i in xrange(self._M):
            self.snks.append(blocks.vector_sink_c())
            self.connect((self.pfb, i), self.snks[i])
Exemple #45
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		def add_vor(freq, angle):
			compensation = math.pi / 180 * -6.5  # empirical, calibrated against VOR receiver (and therefore probably wrong)
			angle = angle + compensation
			angle = angle % (2 * math.pi)
			vor_sig_freq = 30
			phase_shift = int(rf_rate / vor_sig_freq * (angle / (2 * math.pi)))
			vor_dev = 480
			vor_channel = make_channel(freq)
			vor_30 = analog.sig_source_f(audio_rate, analog.GR_COS_WAVE, vor_sig_freq, 1, 0)
			vor_add = blocks.add_cc(1)
			vor_audio = blocks.add_ff(1)
			# Audio/AM signal
			self.connect(
				vor_30,
				blocks.multiply_const_ff(0.3),  # M_n
				(vor_audio, 0))
			self.connect(audio_signal,
				blocks.multiply_const_ff(0.07),  # M_i
				(vor_audio, 1))
			# Carrier component
			self.connect(
				analog.sig_source_c(0, analog.GR_CONST_WAVE, 0, 0, 1),
				(vor_add, 0))
			# AM component
			self.connect(
				vor_audio,
				blocks.float_to_complex(1),
				make_interpolator(),
				blocks.delay(gr.sizeof_gr_complex, phase_shift),
				(vor_add, 1))
			# FM component
			vor_fm_mult = blocks.multiply_cc(1)
			self.connect(  # carrier generation
				analog.sig_source_f(rf_rate, analog.GR_COS_WAVE, 9960, 1, 0), 
				blocks.float_to_complex(1),
				(vor_fm_mult, 1))
			self.connect(  # modulation
				vor_30,
				filter.interp_fir_filter_fff(interp, interp_taps),  # float not complex
				analog.frequency_modulator_fc(2 * math.pi * vor_dev / rf_rate),
				blocks.multiply_const_cc(0.3),  # M_d
				vor_fm_mult,
				(vor_add, 2))
			self.connect(
				vor_add,
				vor_channel)
			signals.append(vor_channel)
Exemple #46
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    def __init__(self):
        gr.top_block.__init__(self)

        self._N = 10000000      # number of samples to use
        self._fs = 10000        # initial sampling rate
        self._decim = 20        # Decimation rate

        # Generate the prototype filter taps for the decimators with a 200 Hz bandwidth
        self._taps = filter.firdes.low_pass_2(1, self._fs,
                                              200, 150,
                                              attenuation_dB=120,
                                              window=filter.firdes.WIN_BLACKMAN_hARRIS)

        # Calculate the number of taps per channel for our own information
        tpc = scipy.ceil(float(len(self._taps)) /  float(self._decim))
        print "Number of taps:     ", len(self._taps)
        print "Number of filters:  ", self._decim
        print "Taps per channel:   ", tpc

        # Build the input signal source
        # We create a list of freqs, and a sine wave is generated and added to the source
        # for each one of these frequencies.
        self.signals = list()
        self.add = blocks.add_cc()
        freqs = [10, 20, 2040]
        for i in xrange(len(freqs)):
            self.signals.append(analog.sig_source_c(self._fs, analog.GR_SIN_WAVE, freqs[i], 1))
            self.connect(self.signals[i], (self.add,i))

        self.head = blocks.head(gr.sizeof_gr_complex, self._N)

        # Construct a PFB decimator filter
        self.pfb = filter.pfb.decimator_ccf(self._decim, self._taps, 0)

        # Construct a standard FIR decimating filter
        self.dec = filter.fir_filter_ccf(self._decim, self._taps)

        self.snk_i = blocks.vector_sink_c()

        # Connect the blocks
        self.connect(self.add, self.head, self.pfb)
        self.connect(self.add, self.snk_i)

        # Create the sink for the decimated siganl
        self.snk = blocks.vector_sink_c()
        self.connect(self.pfb, self.snk)
    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 100
        f2 = 2000

        npts = 2048

        taps = filter.firdes.complex_band_pass_2(1, Rs, 1500, 2500, 100, 60)

        self.qapp = QtGui.QApplication(sys.argv)
        ss = open(gr.prefix() + '/share/gnuradio/themes/dark.qss')
        sstext = ss.read()
        ss.close()
        self.qapp.setStyleSheet(sstext)

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.1, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.1, 0)
        src  = blocks.add_cc()
        channel = channels.channel_model(0.01)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100*npts)
        filt = filter.fft_filter_ccc(1, taps)
        self.snk1 = qtgui.waterfall_sink_c(npts, filter.firdes.WIN_BLACKMAN_hARRIS,
                                           0, Rs,
                                           "Complex Waterfall Example", 2)

        self.connect(src1, (src,0))
        self.connect(src2, (src,1))
        self.connect(src,  channel, thr, (self.snk1, 0))
        self.connect(thr, filt, (self.snk1, 1))

        self.ctrl_win = control_box()
        self.ctrl_win.attach_signal1(src1)
        self.ctrl_win.attach_signal2(src2)

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt  = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt4.QtGui.QWidget
        pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)

        #pyWin.show()
        self.main_box = dialog_box(pyWin, self.ctrl_win)
        self.main_box.show()
 def __init__(self, frame, panel, vbox, argv):
     stdgui2.std_top_block.__init__(self, frame, panel, vbox, argv)
     self.frame = frame
     self.panel = panel
     parser = OptionParser(usage="%prog: [options]")
     parser.add_option("-N", "--dpsslength", type="int",
             help="Length of the DPSS", default="512")
     parser.add_option("-B", "--timebandwidthproduct", type="float",
             help="Time Bandwidthproduct used to calculate the DPSS", default="3")
     parser.add_option("-K", "--tapers", type="int",
             help="Number of Tapers used to calculate the Spectrum", default="5")
     parser.add_option("-W", "--weighting", type="choice", choices=("unity", "eigenvalues" ,"adaptive" ),
             help="weighting-type to be used (unity, eigenvalues, adaptive) ", default="adaptive")
     (options, args) = parser.parse_args ()
     self.options = options
     #setting up our signal
     self.samplingrate = 48000
     self.source = analog.sig_source_c(self.samplingrate, analog.GR_SIN_WAVE, 3000, 1)
     self.noise = analog.noise_source_c(analog.GR_GAUSSIAN,0.5)
     self.add = blocks.add_cc()
     self.throttle = blocks.throttle(gr.sizeof_gr_complex, self.samplingrate)
     #the actual spectrum estimator
     self.v2s = blocks.vector_to_stream(gr.sizeof_float, self.options.dpsslength)
     self.mtm = specest.mtm(
             N = self.options.dpsslength,
             NW = self.options.timebandwidthproduct,
             K = self.options.tapers,
             weighting = self.options.weighting
     )
     self.scope = specest.spectrum_sink_f(
             panel,
             title='Spectrum with %s weighting, Length %i and %i Tapers' % (
                 self.options.weighting, self.options.dpsslength, self.options.tapers
             ),
             spec_size=self.options.dpsslength,
             sample_rate=self.samplingrate,
             ref_level=80,
             avg_alpha=0.8,
             y_per_div=20
     )
     self.connect(self.source, (self.add, 0) )
     self.connect(self.noise, (self.add, 1) )
     self.connect(self.add, self.throttle, self.mtm)
     self.connect(self.mtm, self.v2s, self.scope)
     self._build_gui(vbox)
Exemple #49
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    def __init__(self, frame, panel, vbox, argv):
        stdgui2.std_top_block.__init__ (self, frame, panel, vbox, argv)

        default_input_rate = 1e6
        if len(argv) > 1:
            input_rate = int(argv[1])
        else:
            input_rate = default_input_rate

        if len(argv) > 2:
            v_scale = float(argv[2])  # start up at this v_scale value
        else:
            v_scale = None  # start up in autorange mode, default

        if len(argv) > 3:
            t_scale = float(argv[3])  # start up at this t_scale value
        else:
            t_scale = .00003*default_input_rate/input_rate # old behavior

        print "input rate %s  v_scale %s  t_scale %s" % (input_rate,v_scale,t_scale)


        # Generate a complex sinusoid
        ampl=1.0e3
        self.src0 = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE,
					25.1e3*input_rate/default_input_rate, ampl)
        self.noise = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE,
					 11.1*25.1e3*input_rate/default_input_rate,
                                         ampl/10)
        #self.noise = analog.noise_source_c(analog.GR_GAUSSIAN, ampl/10)
        self.combine = blocks.add_cc()

        # We add this throttle block so that this demo doesn't suck down
        # all the CPU available.  You normally wouldn't use it...
        self.thr = blocks.throttle(gr.sizeof_gr_complex, input_rate)

        scope = scope_sink_c(panel,"Secret Data",sample_rate=input_rate,
			     v_scale=v_scale, t_scale=t_scale)
        vbox.Add(scope.win, 1, wx.EXPAND)

        # Ultimately this will be
        # self.connect("src0 throttle scope")
	self.connect(self.src0,(self.combine,0))
        self.connect(self.noise,(self.combine,1))
        self.connect(self.combine, self.thr, scope)
    def test_000(self):
        N = 1000         # number of samples to use
        M = 5            # Number of channels
        fs = 1000        # baseband sampling rate
        ifs = M*fs       # input samp rate to decimator
        channel = 0      # Extract channel 0

        taps = filter.firdes.low_pass_2(1, ifs, fs/2, fs/10,
                                        attenuation_dB=80,
                                        window=filter.firdes.WIN_BLACKMAN_hARRIS)

        signals = list()
        add = blocks.add_cc()
        freqs = [-200, -100, 0, 100, 200]
        for i in xrange(len(freqs)):
            f = freqs[i] + (M/2-M+i+1)*fs
            data = sig_source_c(ifs, f, 1, N)
            signals.append(blocks.vector_source_c(data))
            self.tb.connect(signals[i], (add,i))

        head = blocks.head(gr.sizeof_gr_complex, N)
        s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, M)
        pfb = filter.pfb_decimator_ccf(M, taps, channel)
        snk = blocks.vector_sink_c()

        self.tb.connect(add, head, s2ss)
        for i in xrange(M):
            self.tb.connect((s2ss,i), (pfb,i))
        self.tb.connect(pfb, snk)

        self.tb.run() 

        Ntest = 50
        L = len(snk.data())
        t = map(lambda x: float(x)/fs, xrange(L))

        # Create known data as complex sinusoids for the baseband freq
        # of the extracted channel is due to decimator output order.
        phase = 0
        expected_data = map(lambda x: math.cos(2.*math.pi*freqs[2]*x+phase) + \
                                1j*math.sin(2.*math.pi*freqs[2]*x+phase), t)

        dst_data = snk.data()

        self.assertComplexTuplesAlmostEqual(expected_data[-Ntest:], dst_data[-Ntest:], 4)
Exemple #51
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 def get_input_data(self):
     """
     Get the raw data generated by addition of sinusoids.
     Useful for debugging.
     """
     tb = gr.top_block()
     signals = []
     add = blocks.add_cc()
     for i in range(len(self.freqs)):
         f = self.freqs[i] + i*self.fs
         signals.append(analog.sig_source_c(self.ifs, analog.GR_SIN_WAVE, f, 1))
         tb.connect(signals[i], (add,i))
     head = blocks.head(gr.sizeof_gr_complex, self.N)
     snk = blocks.vector_sink_c()
     tb.connect(add, head, snk)
     tb.run()
     input_data = snk.data()
     return input_data
Exemple #52
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  def sim ( self, arity, snr_db, N ):
    
    vlen = 1
    N = int( N )
    snr = 10.0**(snr_db/10.0)
    
    sigpow = 1.0
    noise_pow = sigpow / snr
    
    demapper = ofdm.generic_demapper_vcb( vlen )
    const = demapper.get_constellation( arity )
    assert( len( const ) == 2**arity )
    
    symsrc = ofdm.symbol_random_src( const, vlen )
    noise_src = ofdm.complex_white_noise( 0.0, sqrt( noise_pow ) )
    channel = blocks.add_cc()
    bitmap_src = blocks.vector_source_b( [arity] * vlen, True, vlen )
    bm_trig_src = blocks.vector_source_b( [1], True )
    ref_bitstream = blocks.unpack_k_bits_bb( arity )
    bitstream_xor = blocks.xor_bb()
    bitstream_c2f = blocks.char_to_float()
    acc_biterr = ofdm.accumulator_ff()
    skiphead = blocks.skiphead( gr.sizeof_float, N-1 )
    limit = blocks.head( gr.sizeof_float, 1 )
    dst = blocks.vector_sink_f()
    
    tb = gr.top_block ( "test_block" )
    
    tb.connect( (symsrc,0), (channel,0) )
    tb.connect( noise_src,  (channel,1) )
    tb.connect( channel,     (demapper,0), (bitstream_xor,0) )
    tb.connect( bitmap_src,  (demapper,1) )
    tb.connect( bm_trig_src, (demapper,2) )
    tb.connect( (symsrc,1), ref_bitstream, (bitstream_xor,1) )
    tb.connect( bitstream_xor, bitstream_c2f, acc_biterr )
    tb.connect( acc_biterr, skiphead, limit, dst )

    tb.run()
    
    bit_errors = numpy.array( dst.data() )
    assert( len( bit_errors ) == 1 )
    bit_errors = bit_errors[0]
    
    return bit_errors / N
Exemple #53
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    def __init__(self, frame, panel, vbox, argv):
        stdgui2.std_top_block.__init__(self, frame, panel, vbox, argv)

        fft_size = 256

        # build our flow graph
        input_rate = 2048.0e3

        #Generate some noise
        noise = analog.noise_source_c(analog.GR_UNIFORM, 1.0/10)

        # Generate a complex sinusoid
        #src1 = analog.sig_source_c(input_rate, analog.GR_SIN_WAVE, 2e3, 1)
        src1 = analog.sig_source_c(input_rate, analog.GR_CONST_WAVE, 57.50e3, 1)

        # We add these throttle blocks so that this demo doesn't
        # suck down all the CPU available.  Normally you wouldn't use these.
        thr1 = blocks.throttle(gr.sizeof_gr_complex, input_rate)

        sink1 = fft_sink_c(panel, title="Complex Data", fft_size=fft_size,
			   sample_rate=input_rate, baseband_freq=100e3,
			   ref_level=0, y_per_div=20, y_divs=10)
        vbox.Add(sink1.win, 1, wx.EXPAND)

        combine1 = blocks.add_cc()
        self.connect(src1, (combine1,0))
        self.connect(noise,(combine1,1))
        self.connect(combine1,thr1, sink1)

        #src2 = analog.sig_source_f(input_rate, analog.GR_SIN_WAVE, 2e3, 1)
        src2 = analog.sig_source_f (input_rate, analog.GR_CONST_WAVE, 57.50e3, 1)
        thr2 = blocks.throttle(gr.sizeof_float, input_rate)
        sink2 = fft_sink_f(panel, title="Real Data", fft_size=fft_size*2,
			   sample_rate=input_rate, baseband_freq=100e3,
			   ref_level=0, y_per_div=20, y_divs=10)
        vbox.Add(sink2.win, 1, wx.EXPAND)

        combine2 = blocks.add_ff()
        c2f2 = blocks.complex_to_float()

        self.connect(src2, (combine2,0))
        self.connect(noise,c2f2,(combine2,1))
        self.connect(combine2, thr2,sink2)
Exemple #54
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    def __init__(self, constellation, f, N0=0.25, seed=-666L):
        """
        constellation - a constellation object used for modulation.
        f - a finite state machine specification used for coding.
        N0 - noise level
        seed - random seed
        """
        super(trellis_tb, self).__init__()
        # packet size in bits (make it multiple of 16 so it can be packed in a short)
        packet_size = 1024*16
        # bits per FSM input symbol
        bitspersymbol = int(round(math.log(f.I())/math.log(2))) # bits per FSM input symbol
        # packet size in trellis steps
        K = packet_size/bitspersymbol

        # TX
        src = blocks.lfsr_32k_source_s()
        # packet size in shorts
        src_head = blocks.head(gr.sizeof_short, packet_size/16)
        # unpack shorts to symbols compatible with the FSM input cardinality
        s2fsmi = blocks.packed_to_unpacked_ss(bitspersymbol, gr.GR_MSB_FIRST)
        # initial FSM state = 0
        enc = trellis.encoder_ss(f, 0)
        mod = digital.chunks_to_symbols_sc(constellation.points(), 1)

        # CHANNEL
        add = blocks.add_cc()
        noise = analog.noise_source_c(analog.GR_GAUSSIAN,math.sqrt(N0/2),seed)

        # RX
        # data preprocessing to generate metrics for Viterbi
        metrics = trellis.constellation_metrics_cf(constellation.base(), digital.TRELLIS_EUCLIDEAN)
        # Put -1 if the Initial/Final states are not set.
        va = trellis.viterbi_s(f, K, 0, -1)
        # pack FSM input symbols to shorts
        fsmi2s = blocks.unpacked_to_packed_ss(bitspersymbol, gr.GR_MSB_FIRST)
        # check the output
        self.dst = blocks.check_lfsr_32k_s()

        self.connect (src, src_head, s2fsmi, enc, mod)
        self.connect (mod, (add, 0))
        self.connect (noise, (add, 1))
        self.connect (add, metrics, va, fsmi2s, self.dst)
    def __init__(self):
        gr.top_block.__init__(self)

        Rs = 8000
        f1 = 1000
        f2 = 2000

        fftsize = 2048

        self.qapp = QtGui.QApplication(sys.argv)
        ss = open('dark.qss')
        sstext = ss.read()
        ss.close()
        self.qapp.setStyleSheet(sstext)

        src1 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f1, 0.1, 0)
        src2 = analog.sig_source_c(Rs, analog.GR_SIN_WAVE, f2, 0.1, 0)
        src  = blocks.add_cc()
        channel = channels.channel_model(0.001)
        thr = blocks.throttle(gr.sizeof_gr_complex, 100*fftsize)
        self.snk1 = qtgui.sink_c(fftsize, filter.firdes.WIN_BLACKMAN_hARRIS,
                                 0, Rs,
                                 "Complex Signal Example",
                                 True, True, True, False)

        self.connect(src1, (src,0))
        self.connect(src2, (src,1))
        self.connect(src,  channel, thr, self.snk1)

        self.ctrl_win = control_box()
        self.ctrl_win.attach_signal1(src1)
        self.ctrl_win.attach_signal2(src2)

        # Get the reference pointer to the SpectrumDisplayForm QWidget
        pyQt  = self.snk1.pyqwidget()

        # Wrap the pointer as a PyQt SIP object
        # This can now be manipulated as a PyQt4.QtGui.QWidget
        pyWin = sip.wrapinstance(pyQt, QtGui.QWidget)

        self.main_box = dialog_box(pyWin, self.ctrl_win)

        self.main_box.show()
    def __init__(self, noise_voltage, freq, timing):
        gr.hier_block2.__init__(self, "channel_model",
                                gr.io_signature(1, 1, gr.sizeof_gr_complex),
                                gr.io_signature(1, 1, gr.sizeof_gr_complex))


        timing_offset = filter.mmse_resampler_cc(0, timing)
        noise_adder = blocks.add_cc()
        noise = analog.noise_source_c(analog.GR_GAUSSIAN,
                                      noise_voltage, 0)
        freq_offset = analog.sig_source_c(1, analog.GR_SIN_WAVE,
                                          freq, 1.0, 0.0)
        mixer_offset = blocks.multiply_cc();

        self.connect(self, timing_offset)
        self.connect(timing_offset, (mixer_offset,0))
        self.connect(freq_offset, (mixer_offset,1))
        self.connect(mixer_offset, (noise_adder,1))
        self.connect(noise, (noise_adder,0))
        self.connect(noise_adder, self)
    def __init__(self, sample_rate):
        gr.hier_block2.__init__(self, "example_signal_1",
                                gr.io_signature(0, 0, 0),                    # Input signature
                                gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature

        src0 = analog.sig_source_c(sample_rate,        # sample rate
                                   analog.GR_SIN_WAVE, # waveform type
                                   350,                # frequency
                                   1.0,                # amplitude
                                   0)                  # DC Offset

        src1 = analog.sig_source_c(sample_rate,        # sample rate
                                   analog.GR_SIN_WAVE, # waveform type
                                   440,                # frequency
                                   1.0,                # amplitude
                                   0)                  # DC Offset
        sum = blocks.add_cc()
        self.connect(src0, (sum, 0))
        self.connect(src1, (sum, 1))
        self.connect(sum, self)