def test_001_filter_delay_one_input(self):
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data)
dst2 = blocks.vector_sink_c()
# calculate taps
taps = filter.firdes.hilbert(ntaps, filter.firdes.WIN_HAMMING)
hd = filter.filter_delay_fc(taps)
expected_result = fir_filter(data, taps, (ntaps-1) // 2)
tb.connect(src1, hd)
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_002_filter_delay_two_inputs(self):
# giving the same signal to both the inputs should fetch the same results
# as above
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data)
dst2 = blocks.vector_sink_c()
# calculate taps
taps = filter.firdes.hilbert(ntaps, filter.firdes.WIN_HAMMING)
hd = filter.filter_delay_fc(taps)
expected_result = fir_filter2(data, data, taps, (ntaps-1) // 2)
tb.connect(src1, (hd,0))
tb.connect(src1, (hd,1))
tb.connect(hd,dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_002_filter_delay_two_inputs(self):
# giving the same signal to both the inputs should fetch the same results
# as above
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data)
dst2 = blocks.vector_sink_c()
# calculate taps
taps = filter.firdes.hilbert(ntaps, filter.firdes.WIN_HAMMING)
hd = filter.filter_delay_fc(taps)
expected_result = fir_filter2(data, data, taps, (ntaps-1)/2)
tb.connect(src1, (hd,0))
tb.connect(src1, (hd,1))
tb.connect(hd,dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_003_filter_delay_two_inputs(self):
# give two different inputs
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data1 = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
data2 = cos_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data1)
src2 = blocks.vector_source_f(data2)
taps = filter.firdes.hilbert(ntaps, filter.firdes.WIN_HAMMING)
hd = filter.filter_delay_fc(taps)
expected_result = fir_filter2(data1, data2, taps, (ntaps-1) // 2)
dst2 = blocks.vector_sink_c()
tb.connect(src1, (hd,0))
tb.connect(src2, (hd,1))
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_001_filter_delay_one_input(self):
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data)
dst2 = blocks.vector_sink_c()
# calculate taps
taps = filter.firdes.hilbert(ntaps, filter.firdes.WIN_HAMMING)
hd = filter.filter_delay_fc(taps)
expected_result = fir_filter(data, taps, (ntaps-1)/2)
tb.connect(src1, hd)
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_003_filter_delay_two_inputs(self):
# give two different inputs
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data1 = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
data2 = cos_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data1)
src2 = blocks.vector_source_f(data2)
taps = filter.firdes.hilbert(ntaps, filter.firdes.WIN_HAMMING)
hd = filter.filter_delay_fc(taps)
expected_result = fir_filter2(data1, data2, taps, (ntaps-1)/2)
dst2 = blocks.vector_sink_c()
tb.connect(src1, (hd,0))
tb.connect(src2, (hd,1))
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_001_filter_delay_one_input(self):
# expected result
expected_result = (-1.4678005338941702e-11j, -0.0011950774351134896j,
-0.0019336787518113852j, -0.0034673355985432863j,
-0.0036765895783901215j, -0.004916108213365078j,
-0.0042778430506587029j, -0.006028641015291214j,
-0.005476709920912981j, -0.0092810001224279404j,
-0.0095402700826525688j, -0.016060983762145042j,
-0.016446959227323532j, -0.02523401565849781j,
-0.024382550269365311j, -0.035477779805660248j,
-0.033021725714206696j, -0.048487484455108643j,
-0.04543270543217659j, -0.069477587938308716j,
-0.066984444856643677j, -0.10703597217798233j,
-0.10620346665382385j, -0.1852707713842392j,
-0.19357112050056458j, (7.2191945754696007e-09 -
0.50004088878631592j),
(0.58778399229049683 - 0.6155126690864563j),
(0.95105588436126709 - 0.12377222627401352j),
(0.95105588436126709 + 0.41524654626846313j),
(0.5877838134765625 + 0.91611981391906738j),
(5.8516356205018383e-09 + 1.0670661926269531j),
(-0.5877840518951416 +
0.87856143712997437j), (-0.95105588436126709 +
0.35447561740875244j),
(-0.95105588436126709 -
0.26055556535720825j), (-0.5877838134765625 -
0.77606213092803955j),
(-8.7774534307527574e-09 -
0.96460390090942383j), (0.58778399229049683 -
0.78470128774642944j),
(0.95105588436126709 -
0.28380891680717468j), (0.95105588436126709 +
0.32548999786376953j),
(0.5877838134765625 +
0.82514488697052002j), (1.4629089051254596e-08 +
1.0096219778060913j),
(-0.5877840518951416 +
0.81836479902267456j), (-0.95105588436126709 +
0.31451958417892456j),
(-0.95105588436126709 -
0.3030143678188324j), (-0.5877838134765625 -
0.80480599403381348j),
(-1.7554906861505515e-08 -
0.99516552686691284j), (0.58778399229049683 -
0.80540722608566284j),
(0.95105582475662231 -
0.30557557940483093j), (0.95105588436126709 +
0.31097668409347534j),
(0.5877838134765625 +
0.81027895212173462j), (2.3406542482007353e-08 +
1.0000816583633423j),
(-0.5877840518951416 +
0.80908381938934326j), (-0.95105588436126709 +
0.30904293060302734j),
(-0.95105588436126709 -
0.30904296040534973j), (-0.5877838134765625 -
0.80908387899398804j),
(-2.6332360292258272e-08 -
1.0000815391540527j), (0.58778399229049683 -
0.80908381938934326j),
(0.95105582475662231 -
0.30904299020767212j), (0.95105588436126709 +
0.30904293060302734j),
(0.5877838134765625 +
0.80908381938934326j), (3.218399768911695e-08 +
1.0000815391540527j))
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data)
dst2 = blocks.vector_sink_c()
# calculate taps
taps = filter.firdes_hilbert(ntaps)
hd = filter.filter_delay_fc(taps)
tb.connect(src1, hd)
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_003_filter_delay_two_inputs(self):
# give two different inputs
# expected result
expected_result = (-0.0020331963896751404j, -0.0016448829555884004j,
-0.0032375147566199303j, -0.0014826074475422502j,
-0.0033034090884029865j, -0.00051144487224519253j,
-0.0043686260469257832j, -0.0010198024101555347j,
-0.0082517862319946289j, -0.003456643782556057j,
-0.014193611219525337j, -0.005875137634575367j,
-0.020293503999710083j, -0.0067503536120057106j,
-0.026798896491527557j, -0.0073488112539052963j,
-0.037041611969470978j, -0.010557252913713455j,
-0.055669989436864853j, -0.018332764506340027j,
-0.089904911816120148j, -0.033361352980136871j,
-0.16902604699134827j, -0.074318811297416687j,
-0.58429563045501709j, (7.2191945754696007e-09 -
0.35892376303672791j),
(0.58778399229049683 + 0.63660913705825806j),
(0.95105588436126709 + 0.87681591510772705j),
(0.95105588436126709 + 0.98705857992172241j),
(0.5877838134765625 + 0.55447429418563843j),
(5.8516356205018383e-09 + 0.026006083935499191j),
(-0.5877840518951416 -
0.60616838932037354j), (-0.95105588436126709 -
0.9311758279800415j),
(-0.95105588436126709 -
0.96169203519821167j), (-0.5877838134765625 -
0.57292771339416504j),
(-8.7774534307527574e-09 -
0.0073488391935825348j), (0.58778399229049683 +
0.59720659255981445j),
(0.95105588436126709 +
0.94438445568084717j), (0.95105588436126709 +
0.95582199096679688j),
(0.5877838134765625 +
0.58196049928665161j), (1.4629089051254596e-08 +
0.0026587247848510742j),
(-0.5877840518951416 -
0.59129220247268677j), (-0.95105588436126709 -
0.94841635227203369j),
(-0.95105588436126709 -
0.95215457677841187j), (-0.5877838134765625 -
0.58535969257354736j),
(-1.7554906861505515e-08 -
0.00051158666610717773j), (0.58778399229049683 +
0.58867418766021729j),
(0.95105582475662231 +
0.94965213537216187j), (0.95105588436126709 +
0.95050644874572754j),
(0.5877838134765625 +
0.58619076013565063j), (2.3406542482007353e-08 +
1.1920928955078125e-07j),
(-0.5877840518951416 -
0.58783555030822754j), (-0.95105588436126709 -
0.95113480091094971j),
(-0.95105588436126709 -
0.95113474130630493j), (-0.5877838134765625 -
0.58783555030822754j),
(-2.6332360292258272e-08 -
8.1956386566162109e-08j), (0.58778399229049683 +
0.58783555030822754j),
(0.95105582475662231 +
0.95113474130630493j), (0.95105588436126709 +
0.95113474130630493j),
(0.5877838134765625 +
0.58783560991287231j), (3.218399768911695e-08 +
1.1920928955078125e-07j))
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data1 = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
data2 = cos_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data1)
src2 = blocks.vector_source_f(data2)
taps = filter.firdes_hilbert(ntaps)
hd = filter.filter_delay_fc(taps)
dst2 = blocks.vector_sink_c()
tb.connect(src1, (hd, 0))
tb.connect(src2, (hd, 1))
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def __init__(self):
gr.top_block.__init__(self, "Delay Test")
Qt.QWidget.__init__(self)
self.setWindowTitle("Delay Test")
qtgui.util.check_set_qss()
try:
self.setWindowIcon(Qt.QIcon.fromTheme('gnuradio-grc'))
except:
pass
self.top_scroll_layout = Qt.QVBoxLayout()
self.setLayout(self.top_scroll_layout)
self.top_scroll = Qt.QScrollArea()
self.top_scroll.setFrameStyle(Qt.QFrame.NoFrame)
self.top_scroll_layout.addWidget(self.top_scroll)
self.top_scroll.setWidgetResizable(True)
self.top_widget = Qt.QWidget()
self.top_scroll.setWidget(self.top_widget)
self.top_layout = Qt.QVBoxLayout(self.top_widget)
self.top_grid_layout = Qt.QGridLayout()
self.top_layout.addLayout(self.top_grid_layout)
self.settings = Qt.QSettings("GNU Radio", "delay_test")
if StrictVersion(Qt.qVersion()) < StrictVersion("5.0.0"):
self.restoreGeometry(self.settings.value("geometry").toByteArray())
else:
self.restoreGeometry(
self.settings.value("geometry", type=QtCore.QByteArray))
##################################################
# Variables
##################################################
self.sps = sps = 4
self.nfilts = nfilts = 32
self.samp_rate = samp_rate = 6e6
self.rrc_taps = rrc_taps = firdes.root_raised_cosine(
nfilts, nfilts, 1.0 / float(sps), 0.35, 11 * sps * nfilts)
self.constel = constel = digital.constellation_calcdist(
(digital.psk_4()[0]), (digital.psk_4()[1]), 4, 1).base()
self.constel.gen_soft_dec_lut(8)
##################################################
# Blocks
##################################################
self.qtgui_time_sink_x_1_0_1_0 = qtgui.time_sink_f(
1024, #size
samp_rate, #samp_rate
"Filter", #name
2 #number of inputs
)
self.qtgui_time_sink_x_1_0_1_0.set_update_time(0.10)
self.qtgui_time_sink_x_1_0_1_0.set_y_axis(-1, 1)
self.qtgui_time_sink_x_1_0_1_0.set_y_label('Amplitude', "")
self.qtgui_time_sink_x_1_0_1_0.enable_tags(-1, True)
self.qtgui_time_sink_x_1_0_1_0.set_trigger_mode(
qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "")
self.qtgui_time_sink_x_1_0_1_0.enable_autoscale(False)
self.qtgui_time_sink_x_1_0_1_0.enable_grid(False)
self.qtgui_time_sink_x_1_0_1_0.enable_axis_labels(True)
self.qtgui_time_sink_x_1_0_1_0.enable_control_panel(False)
self.qtgui_time_sink_x_1_0_1_0.enable_stem_plot(False)
if not True:
self.qtgui_time_sink_x_1_0_1_0.disable_legend()
labels = ['', '', '', '', '', '', '', '', '', '']
widths = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
colors = [
"blue", "red", "green", "black", "cyan", "magenta", "yellow",
"dark red", "dark green", "blue"
]
styles = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
markers = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
alphas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
for i in xrange(2):
if len(labels[i]) == 0:
self.qtgui_time_sink_x_1_0_1_0.set_line_label(
i, "Data {0}".format(i))
else:
self.qtgui_time_sink_x_1_0_1_0.set_line_label(i, labels[i])
self.qtgui_time_sink_x_1_0_1_0.set_line_width(i, widths[i])
self.qtgui_time_sink_x_1_0_1_0.set_line_color(i, colors[i])
self.qtgui_time_sink_x_1_0_1_0.set_line_style(i, styles[i])
self.qtgui_time_sink_x_1_0_1_0.set_line_marker(i, markers[i])
self.qtgui_time_sink_x_1_0_1_0.set_line_alpha(i, alphas[i])
self._qtgui_time_sink_x_1_0_1_0_win = sip.wrapinstance(
self.qtgui_time_sink_x_1_0_1_0.pyqwidget(), Qt.QWidget)
self.top_layout.addWidget(self._qtgui_time_sink_x_1_0_1_0_win)
self.qtgui_time_sink_x_1_0_1 = qtgui.time_sink_f(
1024, #size
samp_rate, #samp_rate
"Before and After Down Conversion", #name
2 #number of inputs
)
self.qtgui_time_sink_x_1_0_1.set_update_time(0.10)
self.qtgui_time_sink_x_1_0_1.set_y_axis(-1, 1)
self.qtgui_time_sink_x_1_0_1.set_y_label('Amplitude', "")
self.qtgui_time_sink_x_1_0_1.enable_tags(-1, True)
self.qtgui_time_sink_x_1_0_1.set_trigger_mode(qtgui.TRIG_MODE_FREE,
qtgui.TRIG_SLOPE_POS,
0.0, 0, 0, "")
self.qtgui_time_sink_x_1_0_1.enable_autoscale(False)
self.qtgui_time_sink_x_1_0_1.enable_grid(False)
self.qtgui_time_sink_x_1_0_1.enable_axis_labels(True)
self.qtgui_time_sink_x_1_0_1.enable_control_panel(False)
self.qtgui_time_sink_x_1_0_1.enable_stem_plot(False)
if not True:
self.qtgui_time_sink_x_1_0_1.disable_legend()
labels = ['', '', '', '', '', '', '', '', '', '']
widths = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
colors = [
"blue", "red", "green", "black", "cyan", "magenta", "yellow",
"dark red", "dark green", "blue"
]
styles = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
markers = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
alphas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
for i in xrange(2):
if len(labels[i]) == 0:
self.qtgui_time_sink_x_1_0_1.set_line_label(
i, "Data {0}".format(i))
else:
self.qtgui_time_sink_x_1_0_1.set_line_label(i, labels[i])
self.qtgui_time_sink_x_1_0_1.set_line_width(i, widths[i])
self.qtgui_time_sink_x_1_0_1.set_line_color(i, colors[i])
self.qtgui_time_sink_x_1_0_1.set_line_style(i, styles[i])
self.qtgui_time_sink_x_1_0_1.set_line_marker(i, markers[i])
self.qtgui_time_sink_x_1_0_1.set_line_alpha(i, alphas[i])
self._qtgui_time_sink_x_1_0_1_win = sip.wrapinstance(
self.qtgui_time_sink_x_1_0_1.pyqwidget(), Qt.QWidget)
self.top_layout.addWidget(self._qtgui_time_sink_x_1_0_1_win)
self.qtgui_time_sink_x_1_0_0 = qtgui.time_sink_f(
1024, #size
samp_rate, #samp_rate
"Imaginary", #name
1 #number of inputs
)
self.qtgui_time_sink_x_1_0_0.set_update_time(0.10)
self.qtgui_time_sink_x_1_0_0.set_y_axis(-1, 1)
self.qtgui_time_sink_x_1_0_0.set_y_label('Amplitude', "")
self.qtgui_time_sink_x_1_0_0.enable_tags(-1, True)
self.qtgui_time_sink_x_1_0_0.set_trigger_mode(qtgui.TRIG_MODE_FREE,
qtgui.TRIG_SLOPE_POS,
0.0, 0, 0, "")
self.qtgui_time_sink_x_1_0_0.enable_autoscale(False)
self.qtgui_time_sink_x_1_0_0.enable_grid(False)
self.qtgui_time_sink_x_1_0_0.enable_axis_labels(True)
self.qtgui_time_sink_x_1_0_0.enable_control_panel(False)
self.qtgui_time_sink_x_1_0_0.enable_stem_plot(False)
if not True:
self.qtgui_time_sink_x_1_0_0.disable_legend()
labels = ['', '', '', '', '', '', '', '', '', '']
widths = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
colors = [
"blue", "red", "green", "black", "cyan", "magenta", "yellow",
"dark red", "dark green", "blue"
]
styles = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
markers = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
alphas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
for i in xrange(1):
if len(labels[i]) == 0:
self.qtgui_time_sink_x_1_0_0.set_line_label(
i, "Data {0}".format(i))
else:
self.qtgui_time_sink_x_1_0_0.set_line_label(i, labels[i])
self.qtgui_time_sink_x_1_0_0.set_line_width(i, widths[i])
self.qtgui_time_sink_x_1_0_0.set_line_color(i, colors[i])
self.qtgui_time_sink_x_1_0_0.set_line_style(i, styles[i])
self.qtgui_time_sink_x_1_0_0.set_line_marker(i, markers[i])
self.qtgui_time_sink_x_1_0_0.set_line_alpha(i, alphas[i])
self._qtgui_time_sink_x_1_0_0_win = sip.wrapinstance(
self.qtgui_time_sink_x_1_0_0.pyqwidget(), Qt.QWidget)
self.top_layout.addWidget(self._qtgui_time_sink_x_1_0_0_win)
self.qtgui_time_sink_x_1_0 = qtgui.time_sink_f(
1024, #size
samp_rate, #samp_rate
"Real", #name
1 #number of inputs
)
self.qtgui_time_sink_x_1_0.set_update_time(0.10)
self.qtgui_time_sink_x_1_0.set_y_axis(-1, 1)
self.qtgui_time_sink_x_1_0.set_y_label('Amplitude', "")
self.qtgui_time_sink_x_1_0.enable_tags(-1, True)
self.qtgui_time_sink_x_1_0.set_trigger_mode(qtgui.TRIG_MODE_FREE,
qtgui.TRIG_SLOPE_POS, 0.0,
0, 0, "")
self.qtgui_time_sink_x_1_0.enable_autoscale(False)
self.qtgui_time_sink_x_1_0.enable_grid(False)
self.qtgui_time_sink_x_1_0.enable_axis_labels(True)
self.qtgui_time_sink_x_1_0.enable_control_panel(False)
self.qtgui_time_sink_x_1_0.enable_stem_plot(False)
if not True:
self.qtgui_time_sink_x_1_0.disable_legend()
labels = ['', '', '', '', '', '', '', '', '', '']
widths = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
colors = [
"blue", "red", "green", "black", "cyan", "magenta", "yellow",
"dark red", "dark green", "blue"
]
styles = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
markers = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
alphas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
for i in xrange(1):
if len(labels[i]) == 0:
self.qtgui_time_sink_x_1_0.set_line_label(
i, "Data {0}".format(i))
else:
self.qtgui_time_sink_x_1_0.set_line_label(i, labels[i])
self.qtgui_time_sink_x_1_0.set_line_width(i, widths[i])
self.qtgui_time_sink_x_1_0.set_line_color(i, colors[i])
self.qtgui_time_sink_x_1_0.set_line_style(i, styles[i])
self.qtgui_time_sink_x_1_0.set_line_marker(i, markers[i])
self.qtgui_time_sink_x_1_0.set_line_alpha(i, alphas[i])
self._qtgui_time_sink_x_1_0_win = sip.wrapinstance(
self.qtgui_time_sink_x_1_0.pyqwidget(), Qt.QWidget)
self.top_layout.addWidget(self._qtgui_time_sink_x_1_0_win)
self.filter_delay_fc_0 = filter.filter_delay_fc((rrc_taps))
self.digital_constellation_modulator_0 = digital.generic_mod(
constellation=constel,
differential=False,
samples_per_symbol=sps,
pre_diff_code=True,
excess_bw=0.35,
verbose=False,
log=False,
)
self.blocks_vector_source_x_0 = blocks.vector_source_b((255, 127, 0),
True, 1, [])
self.blocks_multiply_xx_0_0_2 = blocks.multiply_vff(1)
self.blocks_multiply_xx_0_0_1 = blocks.multiply_vff(1)
self.blocks_multiply_xx_0_0_0 = blocks.multiply_vff(1)
self.blocks_multiply_xx_0_0 = blocks.multiply_vff(1)
self.blocks_complex_to_float_0_0 = blocks.complex_to_float(1)
self.blocks_complex_to_float_0 = blocks.complex_to_float(1)
self.blocks_add_xx_0 = blocks.add_vff(1)
self.analog_sig_source_x_0_1 = analog.sig_source_f(
samp_rate, analog.GR_COS_WAVE, 10e4, 1, 0)
self.analog_sig_source_x_0_0_0 = analog.sig_source_f(
samp_rate, analog.GR_SIN_WAVE, 10e4, -1, 0)
self.analog_sig_source_x_0_0 = analog.sig_source_f(
samp_rate, analog.GR_SIN_WAVE, 10e4, -1, 0)
self.analog_sig_source_x_0 = analog.sig_source_f(
samp_rate, analog.GR_COS_WAVE, 10e4, 1, 0)
##################################################
# Connections
##################################################
self.connect((self.analog_sig_source_x_0, 0),
(self.blocks_multiply_xx_0_0, 0))
self.connect((self.analog_sig_source_x_0_0, 0),
(self.blocks_multiply_xx_0_0_0, 0))
self.connect((self.analog_sig_source_x_0_0_0, 0),
(self.blocks_multiply_xx_0_0_2, 1))
self.connect((self.analog_sig_source_x_0_1, 0),
(self.blocks_multiply_xx_0_0_1, 0))
self.connect((self.blocks_add_xx_0, 0),
(self.blocks_multiply_xx_0_0_1, 1))
self.connect((self.blocks_add_xx_0, 0),
(self.blocks_multiply_xx_0_0_2, 0))
self.connect((self.blocks_add_xx_0, 0),
(self.qtgui_time_sink_x_1_0_1, 0))
self.connect((self.blocks_complex_to_float_0, 0),
(self.blocks_multiply_xx_0_0, 1))
self.connect((self.blocks_complex_to_float_0, 1),
(self.blocks_multiply_xx_0_0_0, 1))
self.connect((self.blocks_complex_to_float_0, 0),
(self.qtgui_time_sink_x_1_0, 0))
self.connect((self.blocks_complex_to_float_0, 1),
(self.qtgui_time_sink_x_1_0_0, 0))
self.connect((self.blocks_complex_to_float_0_0, 0),
(self.qtgui_time_sink_x_1_0_1, 1))
self.connect((self.blocks_complex_to_float_0_0, 1),
(self.qtgui_time_sink_x_1_0_1_0, 1))
self.connect((self.blocks_complex_to_float_0_0, 0),
(self.qtgui_time_sink_x_1_0_1_0, 0))
self.connect((self.blocks_multiply_xx_0_0, 0),
(self.blocks_add_xx_0, 0))
self.connect((self.blocks_multiply_xx_0_0_0, 0),
(self.blocks_add_xx_0, 1))
self.connect((self.blocks_multiply_xx_0_0_1, 0),
(self.filter_delay_fc_0, 0))
self.connect((self.blocks_multiply_xx_0_0_2, 0),
(self.filter_delay_fc_0, 1))
self.connect((self.blocks_vector_source_x_0, 0),
(self.digital_constellation_modulator_0, 0))
self.connect((self.digital_constellation_modulator_0, 0),
(self.blocks_complex_to_float_0, 0))
self.connect((self.filter_delay_fc_0, 0),
(self.blocks_complex_to_float_0_0, 0))
def test_001_filter_delay_one_input(self):
# expected result
expected_result = ( -1.4678005338941702e-11j,
-0.0011950774351134896j,
-0.0019336787518113852j,
-0.0034673355985432863j,
-0.0036765895783901215j,
-0.004916108213365078j,
-0.0042778430506587029j,
-0.006028641015291214j,
-0.005476709920912981j,
-0.0092810001224279404j,
-0.0095402700826525688j,
-0.016060983762145042j,
-0.016446959227323532j,
-0.02523401565849781j,
-0.024382550269365311j,
-0.035477779805660248j,
-0.033021725714206696j,
-0.048487484455108643j,
-0.04543270543217659j,
-0.069477587938308716j,
-0.066984444856643677j,
-0.10703597217798233j,
-0.10620346665382385j,
-0.1852707713842392j,
-0.19357112050056458j,
(7.2191945754696007e-09 -0.50004088878631592j),
(0.58778399229049683 -0.6155126690864563j),
(0.95105588436126709 -0.12377222627401352j),
(0.95105588436126709 +0.41524654626846313j),
(0.5877838134765625 +0.91611981391906738j),
(5.8516356205018383e-09 +1.0670661926269531j),
(-0.5877840518951416 +0.87856143712997437j),
(-0.95105588436126709 +0.35447561740875244j),
(-0.95105588436126709 -0.26055556535720825j),
(-0.5877838134765625 -0.77606213092803955j),
(-8.7774534307527574e-09 -0.96460390090942383j),
(0.58778399229049683 -0.78470128774642944j),
(0.95105588436126709 -0.28380891680717468j),
(0.95105588436126709 +0.32548999786376953j),
(0.5877838134765625 +0.82514488697052002j),
(1.4629089051254596e-08 +1.0096219778060913j),
(-0.5877840518951416 +0.81836479902267456j),
(-0.95105588436126709 +0.31451958417892456j),
(-0.95105588436126709 -0.3030143678188324j),
(-0.5877838134765625 -0.80480599403381348j),
(-1.7554906861505515e-08 -0.99516552686691284j),
(0.58778399229049683 -0.80540722608566284j),
(0.95105582475662231 -0.30557557940483093j),
(0.95105588436126709 +0.31097668409347534j),
(0.5877838134765625 +0.81027895212173462j),
(2.3406542482007353e-08 +1.0000816583633423j),
(-0.5877840518951416 +0.80908381938934326j),
(-0.95105588436126709 +0.30904293060302734j),
(-0.95105588436126709 -0.30904296040534973j),
(-0.5877838134765625 -0.80908387899398804j),
(-2.6332360292258272e-08 -1.0000815391540527j),
(0.58778399229049683 -0.80908381938934326j),
(0.95105582475662231 -0.30904299020767212j),
(0.95105588436126709 +0.30904293060302734j),
(0.5877838134765625 +0.80908381938934326j),
(3.218399768911695e-08 +1.0000815391540527j))
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data)
dst2 = blocks.vector_sink_c()
# calculate taps
taps = filter.firdes_hilbert(ntaps)
hd = filter.filter_delay_fc(taps)
tb.connect(src1, hd)
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_003_filter_delay_two_inputs(self):
# give two different inputs
# expected result
expected_result = ( -0.0020331963896751404j,
-0.0016448829555884004j,
-0.0032375147566199303j,
-0.0014826074475422502j,
-0.0033034090884029865j,
-0.00051144487224519253j,
-0.0043686260469257832j,
-0.0010198024101555347j,
-0.0082517862319946289j,
-0.003456643782556057j,
-0.014193611219525337j,
-0.005875137634575367j,
-0.020293503999710083j,
-0.0067503536120057106j,
-0.026798896491527557j,
-0.0073488112539052963j,
-0.037041611969470978j,
-0.010557252913713455j,
-0.055669989436864853j,
-0.018332764506340027j,
-0.089904911816120148j,
-0.033361352980136871j,
-0.16902604699134827j,
-0.074318811297416687j,
-0.58429563045501709j,
(7.2191945754696007e-09 -0.35892376303672791j),
(0.58778399229049683 +0.63660913705825806j),
(0.95105588436126709 +0.87681591510772705j),
(0.95105588436126709 +0.98705857992172241j),
(0.5877838134765625 +0.55447429418563843j),
(5.8516356205018383e-09 +0.026006083935499191j),
(-0.5877840518951416 -0.60616838932037354j),
(-0.95105588436126709 -0.9311758279800415j),
(-0.95105588436126709 -0.96169203519821167j),
(-0.5877838134765625 -0.57292771339416504j),
(-8.7774534307527574e-09 -0.0073488391935825348j),
(0.58778399229049683 +0.59720659255981445j),
(0.95105588436126709 +0.94438445568084717j),
(0.95105588436126709 +0.95582199096679688j),
(0.5877838134765625 +0.58196049928665161j),
(1.4629089051254596e-08 +0.0026587247848510742j),
(-0.5877840518951416 -0.59129220247268677j),
(-0.95105588436126709 -0.94841635227203369j),
(-0.95105588436126709 -0.95215457677841187j),
(-0.5877838134765625 -0.58535969257354736j),
(-1.7554906861505515e-08 -0.00051158666610717773j),
(0.58778399229049683 +0.58867418766021729j),
(0.95105582475662231 +0.94965213537216187j),
(0.95105588436126709 +0.95050644874572754j),
(0.5877838134765625 +0.58619076013565063j),
(2.3406542482007353e-08 +1.1920928955078125e-07j),
(-0.5877840518951416 -0.58783555030822754j),
(-0.95105588436126709 -0.95113480091094971j),
(-0.95105588436126709 -0.95113474130630493j),
(-0.5877838134765625 -0.58783555030822754j),
(-2.6332360292258272e-08 -8.1956386566162109e-08j),
(0.58778399229049683 +0.58783555030822754j),
(0.95105582475662231 +0.95113474130630493j),
(0.95105588436126709 +0.95113474130630493j),
(0.5877838134765625 +0.58783560991287231j),
(3.218399768911695e-08 +1.1920928955078125e-07j))
tb = self.tb
sampling_freq = 100
ntaps = 51
N = int(ntaps + sampling_freq * 0.10)
data1 = sin_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
data2 = cos_source_f(sampling_freq, sampling_freq * 0.10, 1.0, N)
src1 = blocks.vector_source_f(data1)
src2 = blocks.vector_source_f(data2)
taps = filter.firdes_hilbert(ntaps)
hd = filter.filter_delay_fc(taps)
dst2 = blocks.vector_sink_c()
tb.connect(src1, (hd,0))
tb.connect(src2, (hd,1))
tb.connect(hd, dst2)
tb.run()
# get output
result_data = dst2.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
