def wgnasreference():
branches = 3
sampling_rate = 48000.0
length = 2**18
start_freq = 100.0
stop_freq = 20000.0
fade_out = 0.00
fade_in = 0.00
filter_taps = 2**11
# input generator
sine_g = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
cos_g = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
# real world reference system, load input and output noise and sweeps
load_noise = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/Noise18.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sine = sumpf.modules.SignalFile(
filename="C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sine.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_cosine = sumpf.modules.SignalFile(
filename="C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/cos.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
output_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[1]).GetOutput()
input_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[0]).GetOutput()
output_sine = sumpf.modules.SplitSignal(data=load_sine.GetSignal(),
channels=[1]).GetOutput()
output_cos = sumpf.modules.SplitSignal(data=load_cosine.GetSignal(),
channels=[1]).GetOutput()
impulse = sumpf.modules.ImpulseGenerator(
length=filter_taps, samplingrate=sampling_rate).GetSignal()
# initialize hgm
iden_nlsystem = nlsp.HammersteinGroupModel_up(
max_harmonics=range(1, branches + 1),
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=[
impulse,
] * branches)
# only sine based system identification
found_filter_spec_sine, nl_function_sine = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sine_g, output_sine, branches)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_sine)
iden_nlsystem.SetFilterIRS(found_filter_spec_sine)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified power noise",
show=False)
print "SNR between Reference and Identified output Sine: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# only cosine based system identification
found_filter_spec_cos, nl_function_cos = nlsp.nonlinearconvolution_chebyshev_temporalreversal(
cos_g, output_cos, branches)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_cos)
iden_nlsystem.SetFilterIRS(found_filter_spec_cos)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified cheby noise",
show=False)
print "SNR between Reference and Identified output Cos: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# only noise based system identification
found_filter_spec_adapt, nl_function_adapt = nlsp.adaptive_identification(
input_generator=input_noise,
outputs=output_noise,
iterations=1,
branches=branches,
step_size=0.1,
filtertaps=filter_taps,
algorithm=nlsp.multichannel_nlms,
Plot=False)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_adapt)
iden_nlsystem.SetFilterIRS(found_filter_spec_adapt)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Adapt noise",
show=False)
print "SNR between Reference and Identified output Adapt: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# sine based as init coeff for noise based system identification
found_filter_spec_sine_reducedlength = nlsp.change_length_filterkernels(
found_filter_spec_sine, length=filter_taps)
found_filter_spec_sineadapt, nl_function_sineadapt = nlsp.adaptive_identification(
input_generator=input_noise,
outputs=output_noise,
iterations=1,
branches=branches,
step_size=0.1,
filtertaps=filter_taps,
algorithm=nlsp.multichannel_nlms,
Plot=False,
init_coeffs=found_filter_spec_sine_reducedlength)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_sineadapt)
iden_nlsystem.SetFilterIRS(found_filter_spec_sineadapt)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified power Adapt noise",
show=False)
print "SNR between Reference and Identified output Sine Adapt: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# cos based as init coeff for noise based system identification
found_filter_spec_cos_reducedlength = nlsp.change_length_filterkernels(
found_filter_spec_cos, length=filter_taps)
found_filter_spec_cosadapt, nl_function_cosadapt = nlsp.adaptive_identification(
input_generator=input_noise,
outputs=output_noise,
iterations=1,
branches=branches,
step_size=0.1,
filtertaps=filter_taps,
algorithm=nlsp.multichannel_nlms,
Plot=False,
init_coeffs=found_filter_spec_cos_reducedlength,
nonlinear_func=nlsp.function_factory.chebyshev1_polynomial)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_cosadapt)
iden_nlsystem.SetFilterIRS(found_filter_spec_cosadapt)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified chebyshev Adapt noise",
show=False)
print "SNR between Reference and Identified output Cos Adapt: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
plot.relabelandplotphase(
sumpf.modules.FourierTransform(output_noise).GetSpectrum(),
"Reference Noise",
show=True)
def loudspeaker_model_sweep(Plot=True):
sampling_rate = 48000.0
start_freq = 100.0
stop_freq = 20000.0
length = 2**18
fade_out = 0.00
fade_in = 0.00
branches = 3
sine = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
op_sine = sumpf.modules.SignalFile(
filename="C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sine.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
op_sine = sumpf.modules.SplitSignal(data=op_sine.GetSignal(),
channels=[1]).GetOutput()
found_filter_spec, nl_functions = nlsp.nonlinearconvolution_chebyshev_adaptive(
sine, op_sine, branches)
iden_nlsystem_sine = nlsp.HammersteinGroupModel_up(
input_signal=sine.GetOutput(),
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
linear_op = nlsp.linear_identification_temporalreversal(
sine, op_sine, sine.GetOutput())
if Plot is True:
plot.relabelandplot(
sumpf.modules.FourierTransform(op_sine).GetSpectrum(),
"Reference System",
show=False,
line='b-')
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_sine.GetOutput()).GetSpectrum(),
"NL Identified System",
show=False,
line='r-')
plot.relabelandplot(
sumpf.modules.FourierTransform(linear_op).GetSpectrum(),
"Linear Identified System",
show=True,
line='g-')
print "SNR between Reference and Identified output, nonlinear: %r" % nlsp.snr(
op_sine, iden_nlsystem_sine.GetOutput())
print "SNR between Reference and Identified output, linear: %r" % nlsp.snr(
op_sine, linear_op)
load_sample = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/Speech1.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sample = nlsp.append_zeros(load_sample.GetSignal())
ref_sample = sumpf.modules.SplitSignal(data=load_sample,
channels=[1]).GetOutput()
ip_sample = sumpf.modules.SplitSignal(data=load_sample,
channels=[0]).GetOutput()
iden_nlsystem_sine.SetInput(ip_sample)
linear_op = nlsp.linear_identification_temporalreversal(
sine, op_sine, ip_sample)
# save the output to the directory
iden = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sim/identified",
signal=iden_nlsystem_sine.GetOutput(),
format=sumpf.modules.SignalFile.WAV_FLOAT)
ref = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sim/reference",
signal=ref_sample,
format=sumpf.modules.SignalFile.WAV_FLOAT)
inp = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sim/input",
signal=ip_sample,
format=sumpf.modules.SignalFile.WAV_FLOAT)
linear = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sim/linear",
signal=linear_op,
format=sumpf.modules.SignalFile.WAV_FLOAT)
print "Distortion box, SNR between Reference and Identified output Sample,nl: %r" % nlsp.snr(
ref_sample, iden_nlsystem_sine.GetOutput())
print "Distortion box, SNR between Reference and Identified output Sample,l: %r" % nlsp.snr(
ref_sample, linear_op)
def loudspeaker_evaluation_all(branches=3, Plot=True):
branches = branches
sampling_rate = 48000.0
length = 2**18
start_freq = 100.0
stop_freq = 20000.0
fade_out = 0.02
fade_in = 0.02
filter_taps = 2**10
iterations = 5
source_dir = "C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_db/"
# input generator
sine_g = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
cos_g = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
# real world reference system, load input and output noise and sweeps
load_noise = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "Noise18.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sine = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "sine_f.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_cosine = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "cos_f.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sample = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "Music1.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
output_sample = sumpf.modules.SplitSignal(data=load_sample.GetSignal(),
channels=[1]).GetOutput()
input_sample = sumpf.modules.SplitSignal(data=load_sample.GetSignal(),
channels=[0]).GetOutput()
output_sample = nlsp.change_length_signal(output_sample, 2**18)
input_sample = nlsp.change_length_signal(input_sample, 2**18)
output_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[1]).GetOutput()
input_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[0]).GetOutput()
output_sine = sumpf.modules.SplitSignal(data=load_sine.GetSignal(),
channels=[1]).GetOutput()
output_cos = sumpf.modules.SplitSignal(data=load_cosine.GetSignal(),
channels=[1]).GetOutput()
impulse = sumpf.modules.ImpulseGenerator(
length=filter_taps, samplingrate=sampling_rate).GetSignal()
# linear identification
kernel_linear, nlfunc = nlsp.linear_identification(sine_g, output_sine,
branches)
iden_nlsystem_linear = nlsp.HammersteinGroupModel_up(
filter_irs=kernel_linear, nonlinear_functions=nlfunc)
# linear identification
kernel_linear_hgm, nlfunc_hgm = nlsp.linear_identification_powerhgm(
sine_g, output_sine, branches)
iden_nlsystem_linear_hgm = nlsp.HammersteinGroupModel_up(
filter_irs=kernel_linear_hgm, nonlinear_functions=nlfunc_hgm)
# only sine based system identification
# found_filter_spec_sine, nl_function_sine = nlsp.systemidentification("LS",nlsp.nonlinearconvolution_powerseries_temporalreversal,
# branches,sine_g,output_sine)
found_filter_spec_sine, nl_function_sine = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sine_g, output_sine, branches)
iden_nlsystem_sine = nlsp.HammersteinGroupModel_up(
max_harmonics=range(1, branches + 1),
nonlinear_functions=nl_function_sine,
filter_irs=found_filter_spec_sine)
# only cosine based system identification
# found_filter_spec_cos, nl_function_cos = nlsp.systemidentification("LS",nlsp.nonlinearconvolution_chebyshev_temporalreversal,
# branches,cos_g,output_cos)
found_filter_spec_cos, nl_function_cos = nlsp.nonlinearconvolution_chebyshev_temporalreversal(
cos_g, output_cos, branches)
iden_nlsystem_cos = nlsp.HammersteinGroupModel_up(
max_harmonics=range(1, branches + 1),
nonlinear_functions=nl_function_cos,
filter_irs=found_filter_spec_cos)
# only noise based system identification
# found_filter_spec_adapt, nl_function_adapt = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,iterations=iterations,branches=branches,
# step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False)
# iden_nlsystem_adapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt,
# filter_irs=found_filter_spec_adapt)
# sine based as init coeff for noise based system identification
# found_filter_spec_sine_reducedlength = nlsp.change_length_filterkernels(found_filter_spec_sine,length=filter_taps)
# found_filter_spec_sineadapt, nl_function_sineadapt = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,iterations=iterations,branches=branches,
# step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_sine_reducedlength)
# iden_nlsystem_sineadapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sineadapt,
# filter_irs=found_filter_spec_sineadapt)
# cos based as init coeff for noise based system identification
# found_filter_spec_cos_reducedlength = nlsp.change_length_filterkernels(found_filter_spec_cos,length=filter_taps)
# found_filter_spec_cosadapt, nl_function_cosadapt = nlsp.adaptive_identification_powerseries(input_generator=input_noise,outputs=output_noise,iterations=iterations,branches=branches,
# step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_cos_reducedlength,
# nonlinear_func=nlsp.function_factory.chebyshev1_polynomial)
# iden_nlsystem_cosadapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cosadapt,
# filter_irs=found_filter_spec_cosadapt)
# set excitation as input
iden_nlsystem_linear.SetInput(sine_g.GetOutput())
iden_nlsystem_linear_hgm.SetInput(sine_g.GetOutput())
iden_nlsystem_sine.SetInput(sine_g.GetOutput())
iden_nlsystem_cos.SetInput(cos_g.GetOutput())
# iden_nlsystem_adapt.SetInput(input_noise)
# iden_nlsystem_sineadapt.SetInput(input_noise)
# iden_nlsystem_cosadapt.SetInput(input_noise)
plot.relabelandplot(
sumpf.modules.FourierTransform(output_sine).GetSpectrum(),
"Reference system",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_sine.GetOutput()).GetSpectrum(),
"Sweep based system identification",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_linear.GetOutput()).GetSpectrum(),
"Linear identified system",
show=True)
if Plot is True:
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sine.GetOutput()).GetSpectrum(),"Identified power",show=False)
plot.relabelandplot(
sumpf.modules.FourierTransform(output_sine).GetSpectrum(),
"Reference system",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_linear.GetOutput()).GetSpectrum(),
"Linear identified system",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_sineadapt.GetOutput()).GetSpectrum(),
"sweep-adaptive output",
show=True)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cos.GetOutput()).GetSpectrum(),"Identified cheby",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(output_cos).GetSpectrum(),"Reference cheby",show=True)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_adapt.GetOutput()).GetSpectrum(),"Identified Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sineadapt.GetOutput()).GetSpectrum(),"Identified power Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cosadapt.GetOutput()).GetSpectrum(),"Identified chebyshev Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(output_noise).GetSpectrum(),"Reference Adapt noise",show=True)
print "SNR between Reference and Identified output linear: %r" % nlsp.snr(
output_sine, iden_nlsystem_linear.GetOutput())
print "SNR between Reference and Identified output linear hgm: %r" % nlsp.snr(
output_sine, iden_nlsystem_linear_hgm.GetOutput())
print "SNR between Reference and Identified output Powerseries NL convolution: %r" % nlsp.snr(
output_sine, iden_nlsystem_sine.GetOutput())
print "SNR between Reference and Identified output Chebyshev NL convolution: %r" % nlsp.snr(
output_cos, iden_nlsystem_cos.GetOutput())
print "SNR between Reference and Identified output Adaptive: %r" % nlsp.snr(
output_noise, iden_nlsystem_adapt.GetOutput())
print "SNR between Reference and Identified output Powerseries Adaptive: %r" % nlsp.snr(
output_noise, iden_nlsystem_sineadapt.GetOutput())
# print "SNR between Reference and Identified output Chebyshev Adaptive: %r" %nlsp.snr(output_noise, iden_nlsystem_cosadapt.GetOutput())
# set sample as input
iden_nlsystem_linear.SetInput(input_sample)
iden_nlsystem_linear_hgm.SetInput(input_sample)
iden_nlsystem_sine.SetInput(input_sample)
iden_nlsystem_cos.SetInput(input_sample)
iden_nlsystem_adapt.SetInput(input_sample)
iden_nlsystem_sineadapt.SetInput(input_sample)
# iden_nlsystem_cosadapt.SetInput(input_sample)
# if Plot is True:
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sine.GetOutput()).GetSpectrum(),"Identified power",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cos.GetOutput()).GetSpectrum(),"Identified cheby",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_adapt.GetOutput()).GetSpectrum(),"Identified Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sineadapt.GetOutput()).GetSpectrum(),"Identified power Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cosadapt.GetOutput()).GetSpectrum(),"Identified chebyshev Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(output_sample).GetSpectrum(),"Reference Adapt noise",show=True)
print "SNR between Reference and Identified output linear: %r" % nlsp.snr(
output_sample, iden_nlsystem_linear.GetOutput())
print "SNR between Reference and Identified output linear hgm: %r" % nlsp.snr(
output_sample, iden_nlsystem_linear_hgm.GetOutput())
print "SNR between Reference and Identified output Powerseries NL convolution: %r" % nlsp.snr(
output_sample, iden_nlsystem_sine.GetOutput())
print "SNR between Reference and Identified output Chebyshev NL convolution: %r" % nlsp.snr(
output_sample, iden_nlsystem_cos.GetOutput())
print "SNR between Reference and Identified output Adaptive: %r" % nlsp.snr(
output_sample, iden_nlsystem_adapt.GetOutput())
print "SNR between Reference and Identified output Powerseries Adaptive: %r" % nlsp.snr(
output_sample, iden_nlsystem_sineadapt.GetOutput())
stop_freq = 20000.0
length = 2**16
fade_out = 0.00
fade_in = 0.00
branches = 3
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=0.2)
Plot = False
Save = False
sine = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= fade_out,fade_in=fade_in)
cos = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= fade_out,fade_in=fade_in)
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)
excitation = [sine,
sine,
sine,
cos,
wgn_uniform,
wgn_normal,
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