def differentlength_evaluation(input_generator,
branches,
Plot,
reference=None):
length_ref = [2**15, 2**16, 2**17]
length_iden = [2**15, 2**16, 2**17]
input_generator_ref = input_generator
input_generator_iden = input_generator
for signal_length, ref_length in zip(length_iden, length_ref):
input_generator_ref.SetLength(ref_length)
input_ref = input_generator_ref.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_ref)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_ref,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_ref.GetSamplingRate(), length=len(input_ref))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_ref)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator_ref,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
input_generator_iden.SetLength(signal_length)
input_iden = input_generator_iden.GetOutput()
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_iden,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_ref))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output", False)
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Reference(length:%r) and Identified output(length:%r) : %r" % (
len(input_ref), len(input_iden),
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def robustness_excitation_evaluation(input_generator,
branches,
Plot,
reference=None):
excitation_signal_amp = [0.5, 1.0]
sample_signal_amp = [0.5, 1.0, 2.0]
input = input_generator.GetOutput()
for excitation_amp, sample_amp in itertools.product(
excitation_signal_amp, sample_signal_amp):
input_signal = sumpf.modules.AmplifySignal(
input=input, factor=excitation_amp).GetOutput()
sample_signal = nlsp.WhiteGaussianGenerator(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal),
distribution=sumpf.modules.NoiseGenerator.UniformDistribution(
minimum=-sample_amp, maximum=sample_amp))
sample_signal = sample_signal.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=sample_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
ref_nlsystem.SetInput(sample_signal)
if Plot is True:
nlsp.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output Scaled", False)
nlsp.relabelandplot(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Scaled Identified with(amp:%r) and Tested with(amp:%r) output: %r" % (
excitation_amp, sample_amp,
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def differentlength_evaluation(input_generator,
branches,
iden_method,
Plot,
reference=None):
"""
Evaluation of System Identification method by hgm virtual nl system
nonlinear system - virtual hammerstein group model with power series polynomials as nl function and bandpass filters
as linear functions
inputsignal - signal signal
plot - the original filter spectrum and the identified filter spectrum, the reference output and identified output
expectation - utmost similarity between the two spectrums
"""
length_ref = [2**15, 2**16, 2**17]
length_iden = [2**15, 2**16, 2**17]
input_generator_ref = input_generator
input_generator_iden = input_generator
for signal_length, ref_length in zip(length_iden, length_ref):
input_generator_ref.SetLength(ref_length)
input_ref = input_generator_ref.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_ref)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_ref,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
found_filter_spec, nl_functions = iden_method(input_generator,
ref_nlsystem.GetOutput(),
branches)
input_generator_iden.SetLength(signal_length)
input_iden = input_generator_iden.GetOutput()
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_iden,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_ref))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output", False)
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Reference(length:%r) and Identified output(length:%r) : %r" % (
len(input_ref), len(input_iden),
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def hgmwithalphafilter_evaluation(input_generator,
branches,
nlfunction,
Plot,
label=None,
reference=None):
input_signal = input_generator.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlfunction, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output",
show=True)
print "SNR between Reference and Identified output with weighted filtering: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
def differentbranches_evaluation(input_generator,
branches,
iden_method,
Plot,
reference=None):
ref_branches = 3
for branches in range(1, branches):
input_signal = input_generator.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=ref_branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, ref_branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, ref_branches + 1))
found_filter_spec, nl_functions = iden_method(input_generator,
ref_nlsystem.GetOutput(),
branches)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output", False)
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Reference and Identified output : %r, with number of ref_branches: %r and iden_branches: %r" % (
nlsp.snr(ref_nlsystem.GetOutput(),
iden_nlsystem.GetOutput()), ref_branches, branches)
def hgmwithalphafilter_evaluation(input_generator,
branches,
nlfunction,
iden_method,
Plot,
label=None,
reference=None):
input_signal = input_generator.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlfunction, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
found_filter_spec, nl_functions = iden_method(input_generator,
ref_nlsystem.GetOutput(),
branches)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output",
show=True)
print "SNR between Reference and Identified output with weighted filtering: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
def adaptive_polynomial_evaluation_virtual():
for input_generator, nlfunc in itertools.product(excitation, nl_functions):
print input_generator
print nlfunc
input_signal = input_generator.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
found_filter_spec, nl_func = nlsp.adaptive_identification(
input_generator,
ref_nlsystem.GetOutput(),
branches,
nonlinear_func=nlfunc)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_func,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
ref_nlsystem.SetInput(wgn_pink.GetOutput())
iden_nlsystem.SetInput(wgn_pink.GetOutput())
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output",
show=True)
print "SNR between Reference and Identified output without overlapping filters: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
print
print
def robustness_differentexcitation_evaluation(input_generator, branches,
iden_method, Plot):
input_signal = input_generator.GetOutput()
sampling_rate = input_signal.GetSamplingRate()
start_freq = input_generator.GetStartFrequency()
stop_freq = input_generator.GetStopFrequency()
length = len(input_signal)
normal = sumpf.modules.NoiseGenerator.GaussianDistribution(
mean=0.0, standard_deviation=1.0)
uniform = sumpf.modules.NoiseGenerator.UniformDistribution()
pink = sumpf.modules.NoiseGenerator.PinkNoise()
laplace = sumpf.modules.NoiseGenerator.LaplaceDistribution(mean=0.0,
scale=0.3)
sine = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq)
cos = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq)
wgn_normal = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=normal)
wgn_uniform = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=uniform)
wgn_pink = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=pink)
wgn_laplace = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=laplace)
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
found_filter_spec, nl_functions = iden_method(input_generator,
ref_nlsystem.GetOutput(),
branches)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
inputs = [sine, cos, wgn_normal, wgn_uniform, wgn_pink, wgn_laplace]
print "SNR between reference and identified output: %r" % (nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
for input in inputs:
ref_nlsystem.SetInput(input.GetOutput())
iden_nlsystem.SetInput(input.GetOutput())
if Plot is True:
nlsp.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output Scaled", False)
nlsp.relabelandplot(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between reference and identified output: %r for inputsignal: %r" % (
nlsp.snr(ref_nlsystem.GetOutput(),
iden_nlsystem.GetOutput()), str(input))
print
print