def test02(self):
# Test float/float version
omega = 2
gain_omega = 0.01
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital_swig.clock_recovery_mm_ff(omega, gain_omega, mu, gain_mu, omega_rel_lim)
data = 100 * [1]
self.src = gr.vector_source_f(data, False)
self.snk = gr.vector_sink_f()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 100 * [0.99972] # doesn't quite get to 1.0
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 30
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp :]
dst_data = dst_data[len_d - Ncmp :]
# print expected_result
# print dst_data
self.assertFloatTuplesAlmostEqual(expected_result, dst_data, 5)
def test02(self):
# Test float/float version
omega = 2
gain_omega = 0.01
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital.clock_recovery_mm_ff(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 100*[1,]
self.src = blocks.vector_source_f(data, False)
self.snk = blocks.vector_sink_f()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 100*[0.99972, ] # doesn't quite get to 1.0
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 30
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#print expected_result
#print dst_data
self.assertFloatTuplesAlmostEqual(expected_result, dst_data, 5)
def test04(self):
# Test float/float version
omega = 2
gain_omega = 0.01
mu = 0.25
gain_mu = 0.1
omega_rel_lim = 0.001
self.test = digital.clock_recovery_mm_ff(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 1000*[1, 1, -1, -1]
self.src = blocks.vector_source_f(data, False)
self.snk = blocks.vector_sink_f()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 1000*[-1.31, 1.31]
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 100
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#print expected_result
#print dst_data
self.assertFloatTuplesAlmostEqual(expected_result, dst_data, 1)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
gain_mu=_def_gain_mu,
mu=_def_mu,
omega_relative_limit=_def_omega_relative_limit,
freq_error=_def_freq_error,
verbose=_def_verbose,
log=_def_log):
gr.hier_block2.__init__(
self,
"gmsk_demod",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_char)) # Output signature
self._samples_per_symbol = samples_per_symbol
self._gain_mu = gain_mu
self._mu = mu
self._omega_relative_limit = omega_relative_limit
self._freq_error = freq_error
self._differential = False
if samples_per_symbol < 2:
raise TypeError, "samples_per_symbol >= 2, is %f" % samples_per_symbol
self._omega = samples_per_symbol * (1 + self._freq_error)
if not self._gain_mu:
self._gain_mu = 0.175
self._gain_omega = .25 * self._gain_mu * self._gain_mu # critically damped
# Demodulate FM
sensitivity = (pi / 2) / samples_per_symbol
self.fmdemod = analog.quadrature_demod_cf(1.0 / sensitivity)
# the clock recovery block tracks the symbol clock and resamples as needed.
# the output of the block is a stream of soft symbols (float)
self.clock_recovery = digital.clock_recovery_mm_ff(
self._omega, self._gain_omega, self._mu, self._gain_mu,
self._omega_relative_limit)
# slice the floats at 0, outputting 1 bit (the LSB of the output byte) per sample
self.slicer = digital.binary_slicer_fb()
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect & Initialize base class
self.connect(self, self.fmdemod, self.clock_recovery, self.slicer,
self)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
gain_mu=_def_gain_mu,
mu=_def_mu,
omega_relative_limit=_def_omega_relative_limit,
freq_error=_def_freq_error,
verbose=_def_verbose,
log=_def_log):
gr.hier_block2.__init__(self, "gmsk_demod",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_char)) # Output signature
self._samples_per_symbol = samples_per_symbol
self._gain_mu = gain_mu
self._mu = mu
self._omega_relative_limit = omega_relative_limit
self._freq_error = freq_error
self._differential = False
if samples_per_symbol < 2:
raise TypeError, "samples_per_symbol >= 2, is %f" % samples_per_symbol
self._omega = samples_per_symbol*(1+self._freq_error)
if not self._gain_mu:
self._gain_mu = 0.175
self._gain_omega = .25 * self._gain_mu * self._gain_mu # critically damped
# Demodulate FM
sensitivity = (pi / 2) / samples_per_symbol
self.fmdemod = analog.quadrature_demod_cf(1.0 / sensitivity)
# the clock recovery block tracks the symbol clock and resamples as needed.
# the output of the block is a stream of soft symbols (float)
self.clock_recovery = digital.clock_recovery_mm_ff(self._omega, self._gain_omega,
self._mu, self._gain_mu,
self._omega_relative_limit)
# slice the floats at 0, outputting 1 bit (the LSB of the output byte) per sample
self.slicer = digital.binary_slicer_fb()
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect & Initialize base class
self.connect(self, self.fmdemod, self.clock_recovery, self.slicer, self)
def __init__(self,
samples_per_symbol=_def_samples_per_symbol,
sensitivity=_def_sensitivity,
gain_mu=_def_gain_mu,
mu=_def_mu,
omega_relative_limit=_def_omega_relative_limit,
freq_error=_def_freq_error,
verbose=_def_verbose,
log=_def_log):
"""
Hierarchical block for Gaussian Minimum Shift Key (GFSK)
demodulation.
The input is the complex modulated signal at baseband.
The output is a stream of bits packed 1 bit per byte (the LSB)
@param samples_per_symbol: samples per baud
@type samples_per_symbol: integer
@param verbose: Print information about modulator?
@type verbose: bool
@param log: Print modualtion data to files?
@type log: bool
Clock recovery parameters. These all have reasonble defaults.
@param gain_mu: controls rate of mu adjustment
@type gain_mu: float
@param mu: fractional delay [0.0, 1.0]
@type mu: float
@param omega_relative_limit: sets max variation in omega
@type omega_relative_limit: float, typically 0.000200 (200 ppm)
@param freq_error: bit rate error as a fraction
@param float
"""
gr.hier_block2.__init__(self, "gfsk_demod",
gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature
gr.io_signature(1, 1, gr.sizeof_char)) # Output signature
self._samples_per_symbol = samples_per_symbol
self._gain_mu = gain_mu
self._mu = mu
self._omega_relative_limit = omega_relative_limit
self._freq_error = freq_error
self._differential = False
if samples_per_symbol < 2:
raise TypeError, "samples_per_symbol >= 2, is %f" % samples_per_symbol
self._omega = samples_per_symbol*(1+self._freq_error)
if not self._gain_mu:
self._gain_mu = 0.175
self._gain_omega = .25 * self._gain_mu * self._gain_mu # critically damped
# Demodulate FM
#sensitivity = (pi / 2) / samples_per_symbol
self.fmdemod = gr.quadrature_demod_cf(1.0 / sensitivity)
# the clock recovery block tracks the symbol clock and resamples as needed.
# the output of the block is a stream of soft symbols (float)
self.clock_recovery = digital.clock_recovery_mm_ff(self._omega, self._gain_omega,
self._mu, self._gain_mu,
self._omega_relative_limit)
# slice the floats at 0, outputting 1 bit (the LSB of the output byte) per sample
self.slicer = digital.binary_slicer_fb()
if verbose:
self._print_verbage()
if log:
self._setup_logging()
# Connect & Initialize base class
self.connect(self, self.fmdemod, self.clock_recovery, self.slicer, self)