def test_methods_savemodel():
"""
Test the methods of the SaveHGMModel class.
"""
branches = 5
location1 = "C:\Users\diplomand.8\Desktop\some1.npz"
location2 = "C:\Users\diplomand.8\Desktop\some2.npz"
ref_filters = nlsp.helper_functions.create_arrayof_bpfilter(
branches=branches)
ref_nl = [
nlsp.nonlinear_function.Power(degree=i + 1) for i in range(branches)
]
ref_aliasing = nlsp.aliasing_compensation.ReducedUpsamplingAliasingCompensation(
)
model1 = nlsp.HammersteinGroupModel()
model2 = nlsp.HammersteinGroupModel(nonlinear_functions=ref_nl,
filter_impulseresponses=ref_filters,
aliasing_compensation=ref_aliasing)
save_model = nlsp.SaveHGMModel(filename=location1, model=model1)
save_model.SetFilename(location2)
save_model.SetModel(model=model1)
model1_retrieved = nlsp.RetrieveHGMModel(filename=location1).GetModel()
model2_retrieved = nlsp.RetrieveHGMModel(filename=location2).GetModel()
sample = sumpf.modules.NoiseGenerator().GetSignal()
model1.SetInput(sample)
model1_retrieved.SetInput(sample)
evaluation = nlsp.evaluations.CompareWithReference(
reference_signal=model1.GetOutput(),
signal_to_be_evaluated=model1_retrieved.GetOutput())
assert evaluation.GetSignaltoErrorRatio() > 500
evaluation.SetReferenceOutput(model2.GetOutput())
evaluation.SetIdentifiedOutput(model2_retrieved.GetOutput())
assert evaluation.GetSignaltoErrorRatio() > 500
os.remove(location1)
os.remove(location2)
def test_savemodel_withclippingfunction():
branches = 2
artificial_location_adaptive = "O:\\Diplomanden\\Logeshwaran.Thamilselvan\\Loudspeaker nonlinearity\\models\\artificialadaptive.npz"
method = 'Nelder-Mead'
ref_hgm = nlsp.HammersteinGroupModel(
nonlinear_functions=[
nlsp.nonlinear_function.Power(degree=i) for i in range(branches)
],
filter_impulseresponses=nlsp.helper_functions.
create_arrayof_simplefilter(branches=branches),
aliasing_compensation=nlsp.aliasing_compensation.
ReducedUpsamplingAliasingCompensation())
adaptive_identification = nlsp.system_identification.ClippingAdaptive(
select_branches=range(1, branches + 1),
excitation_length=2**15,
thresholds=[[-1.0, 1.0], [-0.9, 0.9]])
excitation = adaptive_identification.GetExcitation()
ref_hgm.SetInput(excitation)
adaptive_identification.SetResponse(response=ref_hgm.GetOutput())
output_model_adaptive = adaptive_identification.GetOutputModel()
save_adaptive = nlsp.SaveHGMModel(filename=artificial_location_adaptive,
model=output_model_adaptive)
iden_hgm = nlsp.RetrieveHGMModel(
filename=artificial_location_adaptive).GetModel()
os.remove(artificial_location_adaptive)
def test_saveandretreive_clippingHGM():
"""
test save and retreive model class for HGM with clipping functions as nonlinear functions.
"""
branches = 5
thresholds = [
[-1.0, 1.0],
] * branches
location = "C:\Users\diplomand.8\Desktop\some1.npz"
model = nlsp.HammersteinGroupModel(nonlinear_functions=[
nlsp.nonlinear_function.HardClip(clipping_threshold=threshold)
for threshold in thresholds
])
save = nlsp.SaveHGMModel(filename=location, model=model)
retrieve = nlsp.RetrieveHGMModel(filename=location)
model_retrieved = retrieve.GetModel()
sample = sumpf.modules.SweepGenerator().GetSignal()
model.SetInput(sample)
model_retrieved.SetInput(sample)
evaluation = nlsp.evaluations.CompareWithReference(
reference_signal=model.GetOutput(),
signal_to_be_evaluated=model_retrieved.GetOutput())
assert evaluation.GetSignaltoErrorRatio() > 500
def test_methods_retrievemodel():
"""
Test the methods of the Retrieve model class.
"""
branches = 5
location = "C:\Users\diplomand.8\Desktop\some1.npz"
ref_filters = nlsp.helper_functions.create_arrayof_bpfilter(
branches=branches)
ref_nl = [
nlsp.nonlinear_function.Power(degree=i + 1) for i in range(branches)
]
ref_aliasing = nlsp.aliasing_compensation.ReducedUpsamplingAliasingCompensation(
)
model = nlsp.HammersteinGroupModel(nonlinear_functions=ref_nl,
filter_impulseresponses=ref_filters,
aliasing_compensation=ref_aliasing)
save_model = nlsp.SaveHGMModel(filename=location, model=model)
retrieve_model = nlsp.RetrieveHGMModel()
try:
retrieve_model.GetModel()
except Exception as e:
assert str(e) == "Please enter the filename"
retrieve_model.SetFilename(location)
retrieve_model.GetModel()
def test_saveandretrieve_accuracy():
branches = 5
location = "C:\Users\diplomand.8\Desktop\some.npz"
ref_filters = nlsp.helper_functions.create_arrayof_bpfilter(
branches=branches)
ref_nl = [
nlsp.nonlinear_function.Power(degree=i + 1) for i in range(branches)
]
ref_aliasing = nlsp.aliasing_compensation.ReducedUpsamplingAliasingCompensation(
)
model = nlsp.HammersteinGroupModel(nonlinear_functions=ref_nl,
filter_impulseresponses=ref_filters,
aliasing_compensation=ref_aliasing)
nlsp.SaveHGMModel(filename=location, model=model)
model_retrieved = nlsp.RetrieveHGMModel(filename=location)
model_retrieved = model_retrieved.GetModel()
sample = sumpf.modules.SweepGenerator().GetSignal()
model.SetInput(sample)
model_retrieved.SetInput(sample)
evaluation = nlsp.evaluations.CompareWithReference(
reference_signal=model.GetOutput(),
signal_to_be_evaluated=model_retrieved.GetOutput())
assert evaluation.GetSignaltoErrorRatio() > 500
os.remove(location)