def __init__(self, fs, svn, alpha, fd_range, dump_bins=False):
gr.hier_block2.__init__(self, "acquisition",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(3, 3, gr.sizeof_float))
fft_size = int(1e-3 * fs)
doppler_range = self.get_doppler_range(fd_range)
agc = gr.agc_cc(1.0 / fs, 1.0, 1.0, 1.0)
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, fft_size)
fft = gr.fft_vcc(fft_size, True, [])
argmax = gr.argmax_fs(fft_size)
max = gr.max_ff(fft_size)
self.connect(self, s2v, fft)
self.connect((argmax, 0), gr.short_to_float(), (self, 0))
self.connect((argmax, 1), gr.short_to_float(),
gr.add_const_ff(-fd_range), gr.multiply_const_ff(1e3),
(self, 1))
self.connect(max, (self, 2))
# Connect the individual channels to the input and the output.
self.correlators = [
single_channel_correlator(fs, fd, svn, alpha, dump_bins)
for fd in doppler_range
]
for (correlator, i) in zip(self.correlators,
range(len(self.correlators))):
self.connect(fft, correlator)
self.connect(correlator, (argmax, i))
self.connect(correlator, (max, i))
def __init__(self, fs, svn, alpha, fd_range, dump_bins=False):
gr.hier_block2.__init__(self,
"acquisition",
gr.io_signature(1,1, gr.sizeof_gr_complex),
gr.io_signature(3,3, gr.sizeof_float))
fft_size = int( 1e-3*fs)
doppler_range = self.get_doppler_range(fd_range)
agc = gr.agc_cc( 1.0/fs, 1.0, 1.0, 1.0)
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, fft_size)
fft = gr.fft_vcc(fft_size, True, [])
argmax = gr.argmax_fs(fft_size)
max = gr.max_ff(fft_size)
self.connect( self, s2v, fft)
self.connect( (argmax, 0),
gr.short_to_float(),
(self, 0))
self.connect( (argmax,1),
gr.short_to_float(),
gr.add_const_ff(-fd_range),
gr.multiply_const_ff(1e3),
(self,1))
self.connect( max, (self, 2))
# Connect the individual channels to the input and the output.
self.correlators = [ single_channel_correlator( fs, fd, svn, alpha, dump_bins) for fd in doppler_range ]
for (correlator, i) in zip( self.correlators, range(len(self.correlators))):
self.connect( fft, correlator )
self.connect( correlator, (argmax, i) )
self.connect( correlator, (max, i) )
def __init__(self,
N_id_2,
decim=16,
avg_halfframes=2 * 8,
freq_corr=0,
dump=None):
gr.hier_block2.__init__(
self,
"PSS correlator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float),
)
vec_half_frame = 30720 * 5 / decim
self.taps = []
for i in range(0, 3):
self.taps.append(
gen_pss_td(i, N_re=2048 / decim,
freq_corr=freq_corr).get_data_conj_rev())
self.corr = filter.fir_filter_ccc(1, self.taps[N_id_2])
self.mag = gr.complex_to_mag_squared()
self.vec = gr.stream_to_vector(gr.sizeof_float * 1, vec_half_frame)
self.deint = gr.deinterleave(gr.sizeof_float * vec_half_frame)
self.add = gr.add_vff(vec_half_frame)
self.argmax = gr.argmax_fs(vec_half_frame)
self.null = gr.null_sink(gr.sizeof_short * 1)
self.max = gr.max_ff(vec_half_frame)
self.to_float = gr.short_to_float(1, 1. / decim)
self.interleave = gr.interleave(gr.sizeof_float)
#self.framestart = gr.add_const_ii(-160-144*5-2048*6+30720*5)
self.connect(self, self.corr, self.mag, self.vec)
self.connect((self.argmax, 1), self.null)
#self.connect(self.argmax, self.to_float, self.to_int, self.framestart, self)
self.connect(self.argmax, self.to_float, self.interleave, self)
self.connect(self.max, (self.interleave, 1))
if avg_halfframes == 1:
self.connect(self.vec, self.argmax)
self.connect(self.vec, self.max)
else:
self.connect(self.vec, self.deint)
self.connect(self.add, self.argmax)
self.connect(self.add, self.max)
for i in range(0, avg_halfframes):
self.connect((self.deint, i), (self.add, i))
if dump != None:
self.connect(
self.mag,
gr.file_sink(gr.sizeof_float, dump + "_pss_corr_f.cfile"))
self.connect(
self.add,
gr.file_sink(gr.sizeof_float * vec_half_frame,
dump + "_pss_corr_add_f.cfile"))
def test_002(self): src_data=(-100,-99,-98,-97,-96,-1) expected_result = (float(max(src_data)), ) src = gr.vector_source_f(src_data) s2v = gr.stream_to_vector(gr.sizeof_float, len(src_data)) op = gr.max_ff( len(src_data) ) dst = gr.vector_sink_f() self.fg.connect(src, s2v, op, dst) self.fg.run() result_data = dst.data() self.assertEqual(expected_result, result_data)
def test_002(self): src_data=(-100,-99,-98,-97,-96,-1) expected_result = (float(max(src_data)), ) src = gr.vector_source_f(src_data) s2v = gr.stream_to_vector(gr.sizeof_float, len(src_data)) op = gr.max_ff( len(src_data) ) dst = gr.vector_sink_f() self.tb.connect(src, s2v, op, dst) self.tb.run() result_data = dst.data() self.assertEqual(expected_result, result_data)
def test_001(self):
src_data = (0,0.2,-0.3,0,12,0)
expected_result = (float(max(src_data)), )
src = gr.vector_source_f(src_data)
s2v = gr.stream_to_vector(gr.sizeof_float, len(src_data))
op = gr.max_ff( len(src_data) )
dst = gr.vector_sink_f()
self.tb.connect(src, s2v, op, dst)
self.tb.run()
result_data = dst.data()
self.assertEqual(expected_result, result_data)
def __init__(self, N_id_2, decim=16, avg_halfframes=2*8, freq_corr=0, dump=None):
gr.hier_block2.__init__(
self, "PSS correlator",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(1, 1, gr.sizeof_float),
)
vec_half_frame = 30720*5/decim
self.taps = []
for i in range(0,3):
self.taps.append(gen_pss_td(i, N_re=2048/decim, freq_corr=freq_corr).get_data_conj_rev())
self.corr = filter.fir_filter_ccc(1, self.taps[N_id_2])
self.mag = gr.complex_to_mag_squared()
self.vec = gr.stream_to_vector(gr.sizeof_float*1, vec_half_frame)
self.deint = gr.deinterleave(gr.sizeof_float*vec_half_frame)
self.add = gr.add_vff(vec_half_frame)
self.argmax = gr.argmax_fs(vec_half_frame)
self.null = gr.null_sink(gr.sizeof_short*1)
self.max = gr.max_ff(vec_half_frame)
self.to_float = gr.short_to_float(1, 1./decim)
self.interleave = gr.interleave(gr.sizeof_float)
#self.framestart = gr.add_const_ii(-160-144*5-2048*6+30720*5)
self.connect(self, self.corr, self.mag, self.vec)
self.connect((self.argmax,1), self.null)
#self.connect(self.argmax, self.to_float, self.to_int, self.framestart, self)
self.connect(self.argmax, self.to_float, self.interleave, self)
self.connect(self.max, (self.interleave,1))
if avg_halfframes == 1:
self.connect(self.vec, self.argmax)
self.connect(self.vec, self.max)
else:
self.connect(self.vec, self.deint)
self.connect(self.add, self.argmax)
self.connect(self.add, self.max)
for i in range(0, avg_halfframes):
self.connect((self.deint, i), (self.add, i))
if dump != None:
self.connect(self.mag, gr.file_sink(gr.sizeof_float, dump + "_pss_corr_f.cfile"))
self.connect(self.add, gr.file_sink(gr.sizeof_float*vec_half_frame, dump + "_pss_corr_add_f.cfile"))
def __init__(self, vlen):
gr.hier_block2.__init__(self, "vector_equalizer",
gr.io_signature(1,1,gr.sizeof_gr_complex*vlen),
gr.io_signature(1,1,gr.sizeof_gr_complex*vlen))
self.input=gr.add_const_vcc([0.0]*vlen)
self.connect(self, self.input)
c2mag = gr.complex_to_mag(vlen)
max_v = gr.max_ff(vlen)
interpolator = gr.interp_fir_filter_fff(vlen,[1.0]*vlen)
f2c = gr.float_to_complex()
v2s = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
normalizer = gr.divide_cc()
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, vlen)
self.connect(self.input, v2s, (normalizer,0))
self.connect(self.input, c2mag, max_v, interpolator, f2c, (normalizer,1))
self.connect(normalizer, s2v)
self.connect(s2v, self)
def __init__(self, vlen):
gr.hier_block2.__init__(
self, "vector_equalizer",
gr.io_signature(1, 1, gr.sizeof_gr_complex * vlen),
gr.io_signature(1, 1, gr.sizeof_gr_complex * vlen))
self.input = gr.add_const_vcc([0.0] * vlen)
self.connect(self, self.input)
c2mag = gr.complex_to_mag(vlen)
max_v = gr.max_ff(vlen)
interpolator = gr.interp_fir_filter_fff(vlen, [1.0] * vlen)
f2c = gr.float_to_complex()
v2s = gr.vector_to_stream(gr.sizeof_gr_complex, vlen)
normalizer = gr.divide_cc()
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, vlen)
self.connect(self.input, v2s, (normalizer, 0))
self.connect(self.input, c2mag, max_v, interpolator, f2c,
(normalizer, 1))
self.connect(normalizer, s2v)
self.connect(s2v, self)
def __init__(self, fft_length, block_length, block_header, range, options):
gr.hier_block2.__init__(self, "integer_fo_estimator",
gr.io_signature3(3,3,gr.sizeof_gr_complex,gr.sizeof_float,gr.sizeof_char),
gr.io_signature2(3,3,gr.sizeof_float,gr.sizeof_char))
raise NotImplementedError,"Obsolete class"
self._range = range
# threshold after integer part frequency offset estimation
# if peak value below threshold, assume false triggering
self._thr_lo = 0.4 #0.19 # empirically found threshold. see ioe_metric.float
self._thr_hi = 0.4 #0.2
# stuff to be removed after bugfix for hierblock2s
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.time_sync = gr.kludge_copy(gr.sizeof_char)
self.epsilon = (self,1)
self.connect((self,0),self.input)
self.connect((self,2),self.time_sync)
delay(gr.sizeof_char,
block_header.schmidl_fine_sync[0]*block_length)
# sample ofdm symbol (preamble 1 and 2)
sampler_symbol1 = vector_sampler(gr.sizeof_gr_complex,fft_length)
sampler_symbol2 = vector_sampler(gr.sizeof_gr_complex,fft_length)
time_delay1 = delay(gr.sizeof_char,block_length*block_header.schmidl_fine_sync[1])
self.connect(self.input, (sampler_symbol1,0))
self.connect(self.input, (sampler_symbol2,0))
if block_header.schmidl_fine_sync[0] > 0:
time_delay0 = delay(gr.sizeof_char,block_length*block_header.schmidl_fine_sync[0])
self.connect(self.time_sync, time_delay0, (sampler_symbol1,1))
else:
self.connect(self.time_sync, (sampler_symbol1,1))
self.connect(self.time_sync, time_delay1, (sampler_symbol2,1))
# negative fractional frequency offset estimate
epsilon = gr.multiply_const_ff(-1.0)
self.connect(self.epsilon, epsilon)
# compensate for fractional frequency offset on per symbol base
# freq_shift: vector length, modulator sensitivity
# freq_shift third input: reset phase accumulator
# symbol/preamble 1
freq_shift_sym1 = frequency_shift_vcc(fft_length, 1.0/fft_length)
self.connect(sampler_symbol1, (freq_shift_sym1,0))
self.connect(epsilon, (freq_shift_sym1,1))
self.connect(gr.vector_source_b([1], True), (freq_shift_sym1,2))
# symbol/preamble 2
freq_shift_sym2 = frequency_shift_vcc(fft_length, 1.0/fft_length)
self.connect(sampler_symbol2, (freq_shift_sym2,0))
self.connect(epsilon, (freq_shift_sym2,1))
self.connect(gr.vector_source_b([1], True), (freq_shift_sym2,2))
# fourier transfrom on both preambles
fft_sym1 = gr.fft_vcc(fft_length, True, [], True) # Forward + Blockshift
fft_sym2 = gr.fft_vcc(fft_length, True, [], True) # Forward + Blockshift
# calculate schmidl's metric for estimation of freq. offset's integer part
assert(hasattr(block_header, "schmidl_fine_sync"))
pre1 = block_header.pilotsym_fd[block_header.schmidl_fine_sync[0]]
pre2 = block_header.pilotsym_fd[block_header.schmidl_fine_sync[1]]
diff_pn = concatenate([[conjugate(math.sqrt(2)*pre2[2*i]/pre1[2*i]),0.0j] for i in arange(len(pre1)/2)])
cfo_estimator = schmidl_cfo_estimator(fft_length, len(pre1),
self._range, diff_pn)
self.connect(freq_shift_sym1, fft_sym1, (cfo_estimator,0)) # preamble 1
self.connect(freq_shift_sym2, fft_sym2, (cfo_estimator,1)) # preamble 2
# search for maximum and its argument in interval [-range .. +range]
#arg_max = arg_max_vff(2*self._range + 1)
arg_max_s = gr.argmax_fs(2*self._range+1)
arg_max = gr.short_to_float()
ifo_max = gr.max_ff(2*self._range + 1) # vlen
ifo_estimate = gr.add_const_ff(-self._range)
self.connect(cfo_estimator, arg_max_s, arg_max, ifo_estimate)
self.connect(cfo_estimator, ifo_max)
self.connect((arg_max_s,1),gr.null_sink(gr.sizeof_short))
# threshold maximal value
ifo_threshold = gr.threshold_ff(self._thr_lo, self._thr_hi, 0.0)
ifo_thr_f2b = gr.float_to_char()
self.connect(ifo_max, ifo_threshold, ifo_thr_f2b)
# gating the streams ifo_estimate (integer part) and epsilon (frac. part)
# if the metric's peak value was above the chosen threshold, assume to have
# found a new burst. peak value below threshold results in blocking the
# streams
self.gate = gate_ff()
self.connect(ifo_thr_f2b, (self.gate,0)) # threshold stream
self.connect(ifo_estimate, (self.gate,1))
self.connect(epsilon, (self.gate,2))
# peak filtering
# resynchronize and suppress peaks that didn't match a preamble
filtered_time_sync = peak_resync_bb(True) # replace
self.connect(self.time_sync, (filtered_time_sync,0))
self.connect(ifo_thr_f2b, (filtered_time_sync,1))
# find complete estimation for frequency offset
# add together fractional and integer part
freq_offset = gr.add_ff()
self.connect((self.gate,1), gr.multiply_const_ff(-1.0), (freq_offset,0)) # integer offset
self.connect((self.gate,2), (freq_offset,1)) # frac offset
# output connections
self.connect(freq_offset, (self,0))
self.connect(filtered_time_sync, (self,1))
self.connect((self.gate,0), (self,2)) # used for frame trigger
#########################################
# debugging
if options.log:
self.epsilon2_sink = gr.vector_sink_f()
self.connect(epsilon, self.epsilon2_sink)
self.connect(cfo_estimator, gr.file_sink(gr.sizeof_float*(self._range*2+1), "data/ioe_metric.float"))
# output joint stream
preamble_stream = gr.streams_to_vector(fft_length * gr.sizeof_gr_complex, 2)
self.connect(fft_sym1, (preamble_stream,0))
self.connect(fft_sym2, (preamble_stream,1))
self.connect(preamble_stream, gr.file_sink(gr.sizeof_gr_complex * 2 * fft_length, "data/preambles.compl"))
# output, preambles before and after correction, magnitude and complex spectrum
self.connect(sampler_symbol1, gr.fft_vcc(fft_length, True, [], True), gr.file_sink(gr.sizeof_gr_complex * fft_length, "data/pre1_bef.compl"))
self.connect(sampler_symbol1, gr.fft_vcc(fft_length, True, [], True), gr.complex_to_mag(fft_length), gr.file_sink(gr.sizeof_float * fft_length, "data/pre1_bef.float"))
self.connect(sampler_symbol2, gr.fft_vcc(fft_length, True, [], True), gr.file_sink(gr.sizeof_gr_complex * fft_length, "data/pre2_bef.compl"))
self.connect(sampler_symbol2, gr.fft_vcc(fft_length, True, [], True), gr.complex_to_mag(fft_length), gr.file_sink(gr.sizeof_float * fft_length, "data/pre2_bef.float"))
self.connect(freq_shift_sym1, gr.fft_vcc(fft_length, True, [], True), gr.file_sink(gr.sizeof_gr_complex * fft_length,"data/pre1.compl"))
self.connect(freq_shift_sym1, gr.fft_vcc(fft_length, True, [], True), gr.complex_to_mag(fft_length), gr.file_sink(gr.sizeof_float * fft_length,"data/pre1.float"))
self.connect(freq_shift_sym2, gr.fft_vcc(fft_length, True, [], True), gr.file_sink(gr.sizeof_gr_complex * fft_length,"data/pre2.compl"))
self.connect(freq_shift_sym2, gr.fft_vcc(fft_length, True, [], True), gr.complex_to_mag(fft_length), gr.file_sink(gr.sizeof_float * fft_length,"data/pre2.float"))
# calculate epsilon from corrected source to check function
test_cp = cyclic_prefixer(fft_length, block_length)
test_eps = foe(fft_length)
self.connect(freq_shift_sym1, test_cp, test_eps, gr.file_sink(gr.sizeof_float, "data/eps_after.float"))
try:
gr.hier_block.update_var_names(self, "ifo_estimator", vars())
gr.hier_block.update_var_names(self, "ifo_estimator", vars(self))
except:
pass
def __init__(self, fft_length, block_length, block_header, range, options):
gr.hier_block2.__init__(
self, "integer_fo_estimator",
gr.io_signature3(3, 3, gr.sizeof_gr_complex, gr.sizeof_float,
gr.sizeof_char),
gr.io_signature2(3, 3, gr.sizeof_float, gr.sizeof_char))
raise NotImplementedError, "Obsolete class"
self._range = range
# threshold after integer part frequency offset estimation
# if peak value below threshold, assume false triggering
self._thr_lo = 0.4 #0.19 # empirically found threshold. see ioe_metric.float
self._thr_hi = 0.4 #0.2
# stuff to be removed after bugfix for hierblock2s
self.input = gr.kludge_copy(gr.sizeof_gr_complex)
self.time_sync = gr.kludge_copy(gr.sizeof_char)
self.epsilon = (self, 1)
self.connect((self, 0), self.input)
self.connect((self, 2), self.time_sync)
delay(gr.sizeof_char, block_header.schmidl_fine_sync[0] * block_length)
# sample ofdm symbol (preamble 1 and 2)
sampler_symbol1 = vector_sampler(gr.sizeof_gr_complex, fft_length)
sampler_symbol2 = vector_sampler(gr.sizeof_gr_complex, fft_length)
time_delay1 = delay(gr.sizeof_char,
block_length * block_header.schmidl_fine_sync[1])
self.connect(self.input, (sampler_symbol1, 0))
self.connect(self.input, (sampler_symbol2, 0))
if block_header.schmidl_fine_sync[0] > 0:
time_delay0 = delay(
gr.sizeof_char,
block_length * block_header.schmidl_fine_sync[0])
self.connect(self.time_sync, time_delay0, (sampler_symbol1, 1))
else:
self.connect(self.time_sync, (sampler_symbol1, 1))
self.connect(self.time_sync, time_delay1, (sampler_symbol2, 1))
# negative fractional frequency offset estimate
epsilon = gr.multiply_const_ff(-1.0)
self.connect(self.epsilon, epsilon)
# compensate for fractional frequency offset on per symbol base
# freq_shift: vector length, modulator sensitivity
# freq_shift third input: reset phase accumulator
# symbol/preamble 1
freq_shift_sym1 = frequency_shift_vcc(fft_length, 1.0 / fft_length)
self.connect(sampler_symbol1, (freq_shift_sym1, 0))
self.connect(epsilon, (freq_shift_sym1, 1))
self.connect(gr.vector_source_b([1], True), (freq_shift_sym1, 2))
# symbol/preamble 2
freq_shift_sym2 = frequency_shift_vcc(fft_length, 1.0 / fft_length)
self.connect(sampler_symbol2, (freq_shift_sym2, 0))
self.connect(epsilon, (freq_shift_sym2, 1))
self.connect(gr.vector_source_b([1], True), (freq_shift_sym2, 2))
# fourier transfrom on both preambles
fft_sym1 = gr.fft_vcc(fft_length, True, [],
True) # Forward + Blockshift
fft_sym2 = gr.fft_vcc(fft_length, True, [],
True) # Forward + Blockshift
# calculate schmidl's metric for estimation of freq. offset's integer part
assert (hasattr(block_header, "schmidl_fine_sync"))
pre1 = block_header.pilotsym_fd[block_header.schmidl_fine_sync[0]]
pre2 = block_header.pilotsym_fd[block_header.schmidl_fine_sync[1]]
diff_pn = concatenate(
[[conjugate(math.sqrt(2) * pre2[2 * i] / pre1[2 * i]), 0.0j]
for i in arange(len(pre1) / 2)])
cfo_estimator = schmidl_cfo_estimator(fft_length, len(pre1),
self._range, diff_pn)
self.connect(freq_shift_sym1, fft_sym1,
(cfo_estimator, 0)) # preamble 1
self.connect(freq_shift_sym2, fft_sym2,
(cfo_estimator, 1)) # preamble 2
# search for maximum and its argument in interval [-range .. +range]
#arg_max = arg_max_vff(2*self._range + 1)
arg_max_s = gr.argmax_fs(2 * self._range + 1)
arg_max = gr.short_to_float()
ifo_max = gr.max_ff(2 * self._range + 1) # vlen
ifo_estimate = gr.add_const_ff(-self._range)
self.connect(cfo_estimator, arg_max_s, arg_max, ifo_estimate)
self.connect(cfo_estimator, ifo_max)
self.connect((arg_max_s, 1), gr.null_sink(gr.sizeof_short))
# threshold maximal value
ifo_threshold = gr.threshold_ff(self._thr_lo, self._thr_hi, 0.0)
ifo_thr_f2b = gr.float_to_char()
self.connect(ifo_max, ifo_threshold, ifo_thr_f2b)
# gating the streams ifo_estimate (integer part) and epsilon (frac. part)
# if the metric's peak value was above the chosen threshold, assume to have
# found a new burst. peak value below threshold results in blocking the
# streams
self.gate = gate_ff()
self.connect(ifo_thr_f2b, (self.gate, 0)) # threshold stream
self.connect(ifo_estimate, (self.gate, 1))
self.connect(epsilon, (self.gate, 2))
# peak filtering
# resynchronize and suppress peaks that didn't match a preamble
filtered_time_sync = peak_resync_bb(True) # replace
self.connect(self.time_sync, (filtered_time_sync, 0))
self.connect(ifo_thr_f2b, (filtered_time_sync, 1))
# find complete estimation for frequency offset
# add together fractional and integer part
freq_offset = gr.add_ff()
self.connect((self.gate, 1), gr.multiply_const_ff(-1.0),
(freq_offset, 0)) # integer offset
self.connect((self.gate, 2), (freq_offset, 1)) # frac offset
# output connections
self.connect(freq_offset, (self, 0))
self.connect(filtered_time_sync, (self, 1))
self.connect((self.gate, 0), (self, 2)) # used for frame trigger
#########################################
# debugging
if options.log:
self.epsilon2_sink = gr.vector_sink_f()
self.connect(epsilon, self.epsilon2_sink)
self.connect(
cfo_estimator,
gr.file_sink(gr.sizeof_float * (self._range * 2 + 1),
"data/ioe_metric.float"))
# output joint stream
preamble_stream = gr.streams_to_vector(
fft_length * gr.sizeof_gr_complex, 2)
self.connect(fft_sym1, (preamble_stream, 0))
self.connect(fft_sym2, (preamble_stream, 1))
self.connect(
preamble_stream,
gr.file_sink(gr.sizeof_gr_complex * 2 * fft_length,
"data/preambles.compl"))
# output, preambles before and after correction, magnitude and complex spectrum
self.connect(
sampler_symbol1, gr.fft_vcc(fft_length, True, [], True),
gr.file_sink(gr.sizeof_gr_complex * fft_length,
"data/pre1_bef.compl"))
self.connect(
sampler_symbol1, gr.fft_vcc(fft_length, True, [], True),
gr.complex_to_mag(fft_length),
gr.file_sink(gr.sizeof_float * fft_length,
"data/pre1_bef.float"))
self.connect(
sampler_symbol2, gr.fft_vcc(fft_length, True, [], True),
gr.file_sink(gr.sizeof_gr_complex * fft_length,
"data/pre2_bef.compl"))
self.connect(
sampler_symbol2, gr.fft_vcc(fft_length, True, [], True),
gr.complex_to_mag(fft_length),
gr.file_sink(gr.sizeof_float * fft_length,
"data/pre2_bef.float"))
self.connect(
freq_shift_sym1, gr.fft_vcc(fft_length, True, [], True),
gr.file_sink(gr.sizeof_gr_complex * fft_length,
"data/pre1.compl"))
self.connect(
freq_shift_sym1, gr.fft_vcc(fft_length, True, [], True),
gr.complex_to_mag(fft_length),
gr.file_sink(gr.sizeof_float * fft_length, "data/pre1.float"))
self.connect(
freq_shift_sym2, gr.fft_vcc(fft_length, True, [], True),
gr.file_sink(gr.sizeof_gr_complex * fft_length,
"data/pre2.compl"))
self.connect(
freq_shift_sym2, gr.fft_vcc(fft_length, True, [], True),
gr.complex_to_mag(fft_length),
gr.file_sink(gr.sizeof_float * fft_length, "data/pre2.float"))
# calculate epsilon from corrected source to check function
test_cp = cyclic_prefixer(fft_length, block_length)
test_eps = foe(fft_length)
self.connect(freq_shift_sym1, test_cp, test_eps,
gr.file_sink(gr.sizeof_float, "data/eps_after.float"))
try:
gr.hier_block.update_var_names(self, "ifo_estimator", vars())
gr.hier_block.update_var_names(self, "ifo_estimator", vars(self))
except:
pass
