Skip to content
Snippets Groups Projects
Commit 502cc09c authored by Colm Talbot's avatar Colm Talbot
Browse files

Merge branch 'Simplify_wg_redundant_code' into 'master' 

Simplify wg redundant code

See merge request Monash/tupak!124
parents 36793a8c 0e1cc4e5
No related branches found
No related tags found
1 merge request!124Simplify wg redundant code
Pipeline #27342 passed with warnings
Stage: test
Stage: deploy
Hide whitespace changes
Inline Side-by-side
test/waveform_generator_tests.py
+ 67
− 41
View file @ 502cc09c
...@@ -6,21 +6,13 @@ import mock ...@@ -6,21 +6,13 @@ import mock
from mock import MagicMock from mock import MagicMock
def gaussian_frequency_domain_strain(frequency_array, amplitude, mu, sigma, ra, dec, geocent_time, psi, **kwargs): def dummy_func_array_return_value(frequency_array, amplitude, mu, sigma, ra, dec, geocent_time, psi, **kwargs):
ht = {'plus': amplitude * np.exp(-(mu - frequency_array) ** 2 / sigma ** 2 / 2), return amplitude + mu + frequency_array + sigma + ra + dec + geocent_time + psi
'cross': amplitude * np.exp(-(mu - frequency_array) ** 2 / sigma ** 2 / 2)}
return ht
def gaussian_frequency_domain_strain_2(frequency_array, a, m, s, ra, dec, geocent_time, psi, **kwargs):
ht = {'plus': a * np.exp(-(m - frequency_array) ** 2 / s ** 2 / 2),
'cross': a * np.exp(-(m - frequency_array) ** 2 / s ** 2 / 2)}
return ht
def gaussian_time_domain_strain_2(time_array, a, m, s, ra, dec, geocent_time, psi, **kwargs): def dummy_func_dict_return_value(frequency_array, amplitude, mu, sigma, ra, dec, geocent_time, psi, **kwargs):
ht = {'plus': a * np.exp(-(m - time_array) ** 2 / s ** 2 / 2), ht = {'plus': amplitude + mu + frequency_array + sigma + ra + dec + geocent_time + psi,
'cross': a * np.exp(-(m - time_array) ** 2 / s ** 2 / 2)} 'cross': amplitude + mu + frequency_array + sigma + ra + dec + geocent_time + psi}
return ht return ht
...@@ -29,7 +21,7 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC ...@@ -29,7 +21,7 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
def setUp(self): def setUp(self):
self.waveform_generator = \ self.waveform_generator = \
tupak.gw.waveform_generator.WaveformGenerator(1, 4096, tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
frequency_domain_source_model=gaussian_frequency_domain_strain) frequency_domain_source_model=dummy_func_dict_return_value)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1, self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
ra=1.375, ra=1.375,
dec=-1.2108, dec=-1.2108,
...@@ -47,7 +39,7 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC ...@@ -47,7 +39,7 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
self.assertEqual(self.waveform_generator.sampling_frequency, 4096) self.assertEqual(self.waveform_generator.sampling_frequency, 4096)
def test_source_model(self): def test_source_model(self):
self.assertEqual(self.waveform_generator.frequency_domain_source_model, gaussian_frequency_domain_strain) self.assertEqual(self.waveform_generator.frequency_domain_source_model, dummy_func_dict_return_value)
def test_frequency_array_type(self): def test_frequency_array_type(self):
self.assertIsInstance(self.waveform_generator.frequency_array, np.ndarray) self.assertIsInstance(self.waveform_generator.frequency_array, np.ndarray)
...@@ -64,7 +56,7 @@ class TestWaveformArgumentsSetting(unittest.TestCase): ...@@ -64,7 +56,7 @@ class TestWaveformArgumentsSetting(unittest.TestCase):
def setUp(self): def setUp(self):
self.waveform_generator = \ self.waveform_generator = \
tupak.gw.waveform_generator.WaveformGenerator(1, 4096, tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
frequency_domain_source_model=gaussian_frequency_domain_strain, frequency_domain_source_model=dummy_func_dict_return_value,
waveform_arguments=dict(test='test', arguments='arguments')) waveform_arguments=dict(test='test', arguments='arguments'))
def tearDown(self): def tearDown(self):
...@@ -80,7 +72,7 @@ class TestSetters(unittest.TestCase): ...@@ -80,7 +72,7 @@ class TestSetters(unittest.TestCase):
def setUp(self): def setUp(self):
self.waveform_generator = \ self.waveform_generator = \
tupak.gw.waveform_generator.WaveformGenerator(1, 4096, tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
frequency_domain_source_model=gaussian_frequency_domain_strain) frequency_domain_source_model=dummy_func_dict_return_value)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1, self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
ra=1.375, ra=1.375,
dec=-1.2108, dec=-1.2108,
...@@ -112,12 +104,12 @@ class TestSetters(unittest.TestCase): ...@@ -112,12 +104,12 @@ class TestSetters(unittest.TestCase):
self.assertTrue(np.array_equal(new_time_array, self.waveform_generator.time_array)) self.assertTrue(np.array_equal(new_time_array, self.waveform_generator.time_array))
def test_parameters_set_from_frequency_domain_source_model(self): def test_parameters_set_from_frequency_domain_source_model(self):
self.waveform_generator.frequency_domain_source_model = gaussian_frequency_domain_strain_2 self.waveform_generator.frequency_domain_source_model = dummy_func_dict_return_value
self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())), self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
sorted(list(self.simulation_parameters.keys()))) sorted(list(self.simulation_parameters.keys())))
def test_parameters_set_from_time_domain_source_model(self): def test_parameters_set_from_time_domain_source_model(self):
self.waveform_generator.time_domain_source_model = gaussian_time_domain_strain_2 self.waveform_generator.time_domain_source_model = dummy_func_dict_return_value
self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())), self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
sorted(list(self.simulation_parameters.keys()))) sorted(list(self.simulation_parameters.keys())))
...@@ -126,9 +118,9 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase): ...@@ -126,9 +118,9 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase):
def setUp(self): def setUp(self):
self.waveform_generator = \ self.waveform_generator = \
tupak.gw.waveform_generator.WaveformGenerator(1, 4096, tupak.gw.waveform_generator.WaveformGenerator(duration=1, sampling_frequency=4096,
frequency_domain_source_model=gaussian_frequency_domain_strain) frequency_domain_source_model=dummy_func_dict_return_value)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1, self.simulation_parameters = dict(amplitude=1e-2, mu=100, sigma=1,
ra=1.375, ra=1.375,
dec=-1.2108, dec=-1.2108,
geocent_time=1126259642.413, geocent_time=1126259642.413,
...@@ -144,30 +136,47 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase): ...@@ -144,30 +136,47 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase):
self.waveform_generator.frequency_domain_strain() self.waveform_generator.frequency_domain_strain()
def test_frequency_domain_source_model_call(self): def test_frequency_domain_source_model_call(self):
self.waveform_generator.frequency_domain_source_model = MagicMock(return_value=3) self.waveform_generator.parameters = self.simulation_parameters
self.assertEqual(3, self.waveform_generator.frequency_domain_strain()) expected = self.waveform_generator.frequency_domain_source_model(self.waveform_generator.frequency_array,
self.simulation_parameters['amplitude'],
self.simulation_parameters['mu'],
self.simulation_parameters['sigma'],
self.simulation_parameters['ra'],
self.simulation_parameters['dec'],
self.simulation_parameters['geocent_time'],
self.simulation_parameters['psi'])
actual = self.waveform_generator.frequency_domain_strain()
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
def test_time_domain_source_model_call_with_ndarray(self): def test_time_domain_source_model_call_with_ndarray(self):
self.waveform_generator.frequency_domain_source_model = None self.waveform_generator.frequency_domain_source_model = None
self.waveform_generator.time_domain_source_model = MagicMock(return_value=np.array([1, 2, 3])) self.waveform_generator.time_domain_source_model = dummy_func_array_return_value
self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2): def side_effect(value, value2):
return value return value
with mock.patch('tupak.core.utils.nfft') as m: with mock.patch('tupak.core.utils.nfft') as m:
m.side_effect = side_effect m.side_effect = side_effect
self.assertTrue(np.array_equal(np.array([1, 2, 3]), self.waveform_generator.frequency_domain_strain())) expected = self.waveform_generator.time_domain_strain()
actual = self.waveform_generator.frequency_domain_strain()
self.assertTrue(np.array_equal(expected, actual))
def test_time_domain_source_model_call_with_dict(self): def test_time_domain_source_model_call_with_dict(self):
self.waveform_generator.frequency_domain_source_model = None self.waveform_generator.frequency_domain_source_model = None
self.waveform_generator.time_domain_source_model = MagicMock(return_value=dict(plus=1, cross=2)) self.waveform_generator.time_domain_source_model = dummy_func_dict_return_value
self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2): def side_effect(value, value2):
return value, value2 return value, self.waveform_generator.frequency_array
with mock.patch('tupak.core.utils.nfft') as m: with mock.patch('tupak.core.utils.nfft') as m:
m.side_effect = side_effect m.side_effect = side_effect
self.assertDictEqual(dict(plus=1, cross=2), self.waveform_generator.frequency_domain_strain()) expected = self.waveform_generator.time_domain_strain()
actual = self.waveform_generator.frequency_domain_strain()
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
def test_no_source_model_given(self): def test_no_source_model_given(self):
self.waveform_generator.time_domain_source_model = None self.waveform_generator.time_domain_source_model = None
...@@ -194,7 +203,7 @@ class TestTimeDomainStrainMethod(unittest.TestCase): ...@@ -194,7 +203,7 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
def setUp(self): def setUp(self):
self.waveform_generator = \ self.waveform_generator = \
tupak.gw.waveform_generator.WaveformGenerator(1, 4096, tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
time_domain_source_model=gaussian_time_domain_strain_2) time_domain_source_model=dummy_func_dict_return_value)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1, self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
ra=1.375, ra=1.375,
dec=-1.2108, dec=-1.2108,
...@@ -211,32 +220,47 @@ class TestTimeDomainStrainMethod(unittest.TestCase): ...@@ -211,32 +220,47 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
self.waveform_generator.time_domain_strain() self.waveform_generator.time_domain_strain()
def test_time_domain_source_model_call(self): def test_time_domain_source_model_call(self):
self.waveform_generator.time_domain_source_model = MagicMock(return_value=3) self.waveform_generator.parameters = self.simulation_parameters
self.assertEqual(3, self.waveform_generator.time_domain_strain()) expected = self.waveform_generator.time_domain_source_model(self.waveform_generator.time_array,
self.simulation_parameters['amplitude'],
self.simulation_parameters['mu'],
self.simulation_parameters['sigma'],
self.simulation_parameters['ra'],
self.simulation_parameters['dec'],
self.simulation_parameters['geocent_time'],
self.simulation_parameters['psi'])
actual = self.waveform_generator.time_domain_strain()
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
def test_frequency_domain_source_model_call_with_ndarray(self): def test_frequency_domain_source_model_call_with_ndarray(self):
self.waveform_generator.time_domain_source_model = None self.waveform_generator.time_domain_source_model = None
self.waveform_generator.frequency_domain_source_model = MagicMock(return_value=np.array([1, 2, 3])) self.waveform_generator.frequency_domain_source_model = dummy_func_array_return_value
self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2): def side_effect(value, value2):
return value return value
with mock.patch('tupak.core.utils.infft') as m: with mock.patch('tupak.core.utils.infft') as m:
m.side_effect = side_effect m.side_effect = side_effect
self.assertTrue(np.array_equal(np.array([1, 2, 3]), self.waveform_generator.time_domain_strain())) expected = self.waveform_generator.frequency_domain_strain()
actual = self.waveform_generator.time_domain_strain()
self.assertTrue(np.array_equal(expected, actual))
def test_frequency_domain_source_model_call_with_dict(self): def test_frequency_domain_source_model_call_with_dict(self):
self.waveform_generator.time_domain_source_model = None self.waveform_generator.time_domain_source_model = None
self.waveform_generator.frequency_domain_source_model = MagicMock(return_value=dict(plus=1, cross=2)) self.waveform_generator.frequency_domain_source_model = dummy_func_dict_return_value
self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2): def side_effect(value, value2):
return value, value2 return value
with mock.patch('tupak.core.utils.infft') as m: with mock.patch('tupak.core.utils.infft') as m:
m.side_effect = side_effect m.side_effect = side_effect
self.assertDictEqual(dict(plus=(1, self.waveform_generator.sampling_frequency), expected = self.waveform_generator.frequency_domain_strain()
cross=(2, self.waveform_generator.sampling_frequency)), actual = self.waveform_generator.time_domain_strain()
self.waveform_generator.time_domain_strain()) self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
def test_no_source_model_given(self): def test_no_source_model_given(self):
self.waveform_generator.time_domain_source_model = None self.waveform_generator.time_domain_source_model = None
...@@ -245,7 +269,9 @@ class TestTimeDomainStrainMethod(unittest.TestCase): ...@@ -245,7 +269,9 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
self.waveform_generator.time_domain_strain() self.waveform_generator.time_domain_strain()
def test_key_popping(self): def test_key_popping(self):
self.waveform_generator.parameter_conversion = MagicMock(return_value=(dict(a=1e-21, m=100, s=1, self.waveform_generator.parameter_conversion = MagicMock(return_value=(dict(amplitude=1e-2,
mu=100,
sigma=1,
ra=1.375, dec=-1.2108, ra=1.375, dec=-1.2108,
geocent_time=1126259642.413, geocent_time=1126259642.413,
psi=2.659, c=None, d=None), psi=2.659, c=None, d=None),
...@@ -255,7 +281,7 @@ class TestTimeDomainStrainMethod(unittest.TestCase): ...@@ -255,7 +281,7 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
except RuntimeError: except RuntimeError:
pass pass
self.assertListEqual(sorted(self.waveform_generator.parameters.keys()), self.assertListEqual(sorted(self.waveform_generator.parameters.keys()),
sorted(['a', 'm', 's', 'ra', 'dec', 'geocent_time', 'psi'])) sorted(['amplitude', 'mu', 'sigma', 'ra', 'dec', 'geocent_time', 'psi']))
if __name__ == '__main__': if __name__ == '__main__':
......
tupak/gw/waveform_generator.py
+ 60
− 55
View file @ 502cc09c
...@@ -7,7 +7,8 @@ import numpy as np ...@@ -7,7 +7,8 @@ import numpy as np
class WaveformGenerator(object): class WaveformGenerator(object):
def __init__(self, duration=None, sampling_frequency=None, start_time=0, frequency_domain_source_model=None, def __init__(self, duration=None, sampling_frequency=None, start_time=0, frequency_domain_source_model=None,
time_domain_source_model=None, parameters=None, parameter_conversion=None, time_domain_source_model=None, parameters=None,
parameter_conversion=lambda parameters, search_keys: (parameters, []),
non_standard_sampling_parameter_keys=None, non_standard_sampling_parameter_keys=None,
waveform_arguments=None): waveform_arguments=None):
""" A waveform generator """ A waveform generator
...@@ -32,7 +33,8 @@ class WaveformGenerator(object): ...@@ -32,7 +33,8 @@ class WaveformGenerator(object):
Initial values for the parameters Initial values for the parameters
parameter_conversion: func, optional parameter_conversion: func, optional
Function to convert from sampled parameters to parameters of the Function to convert from sampled parameters to parameters of the
waveform generator waveform generator. Default value is the identity, i.e. it leaves
the parameters unaffected.
non_standard_sampling_parameter_keys: list, optional non_standard_sampling_parameter_keys: list, optional
List of parameter name for *non-standard* sampling parameters. List of parameter name for *non-standard* sampling parameters.
waveform_arguments: dict, optional waveform_arguments: dict, optional
...@@ -62,6 +64,8 @@ class WaveformGenerator(object): ...@@ -62,6 +64,8 @@ class WaveformGenerator(object):
self.__time_array_updated = False self.__time_array_updated = False
self.__full_source_model_keyword_arguments = {} self.__full_source_model_keyword_arguments = {}
self.__full_source_model_keyword_arguments.update(self.waveform_arguments) self.__full_source_model_keyword_arguments.update(self.waveform_arguments)
self.__full_source_model_keyword_arguments.update(self.parameters)
self.__added_keys = []
def frequency_domain_strain(self): def frequency_domain_strain(self):
""" Rapper to source_model. """ Rapper to source_model.
...@@ -78,32 +82,11 @@ class WaveformGenerator(object): ...@@ -78,32 +82,11 @@ class WaveformGenerator(object):
RuntimeError: If no source model is given RuntimeError: If no source model is given
""" """
added_keys = [] return self._calculate_strain(model=self.frequency_domain_source_model,
if self.parameter_conversion is not None: model_data_points=self.frequency_array,
self.parameters, added_keys = self.parameter_conversion(self.parameters, transformation_function=utils.nfft,
self.non_standard_sampling_parameter_keys) transformed_model=self.time_domain_source_model,
transformed_model_data_points=self.time_array)
if self.frequency_domain_source_model is not None:
self.__full_source_model_keyword_arguments.update(self.parameters)
model_frequency_strain = self.frequency_domain_source_model(
self.frequency_array,
**self.__full_source_model_keyword_arguments)
elif self.time_domain_source_model is not None:
model_frequency_strain = dict()
self.__full_source_model_keyword_arguments.update(self.parameters)
time_domain_strain = self.time_domain_source_model(
self.time_array, **self.__full_source_model_keyword_arguments)
if isinstance(time_domain_strain, np.ndarray):
return utils.nfft(time_domain_strain, self.sampling_frequency)
for key in time_domain_strain:
model_frequency_strain[key], self.frequency_array = utils.nfft(time_domain_strain[key],
self.sampling_frequency)
else:
raise RuntimeError("No source model given")
for key in added_keys:
self.parameters.pop(key)
return model_frequency_strain
def time_domain_strain(self): def time_domain_strain(self):
""" Rapper to source_model. """ Rapper to source_model.
...@@ -121,29 +104,51 @@ class WaveformGenerator(object): ...@@ -121,29 +104,51 @@ class WaveformGenerator(object):
RuntimeError: If no source model is given RuntimeError: If no source model is given
""" """
added_keys = [] return self._calculate_strain(model=self.time_domain_source_model,
if self.parameter_conversion is not None: model_data_points=self.time_array,
self.parameters, added_keys = self.parameter_conversion(self.parameters, transformation_function=utils.infft,
self.non_standard_sampling_parameter_keys) transformed_model=self.frequency_domain_source_model,
if self.time_domain_source_model is not None: transformed_model_data_points=self.frequency_array)
self.__full_source_model_keyword_arguments.update(self.parameters)
model_time_series = self.time_domain_source_model( def _calculate_strain(self, model, model_data_points, transformation_function, transformed_model,
self.time_array, **self.__full_source_model_keyword_arguments) transformed_model_data_points):
elif self.frequency_domain_source_model is not None: self._apply_parameter_conversion()
model_time_series = dict() if model is not None:
self.__full_source_model_keyword_arguments.update(self.parameters) model_strain = self._strain_from_model(model_data_points, model)
frequency_domain_strain = self.frequency_domain_source_model( elif transformed_model is not None:
self.frequency_array, **self.__full_source_model_keyword_arguments) model_strain = self._strain_from_transformed_model(transformed_model_data_points, transformed_model,
if isinstance(frequency_domain_strain, np.ndarray): transformation_function)
return utils.infft(frequency_domain_strain, self.sampling_frequency)
for key in frequency_domain_strain:
model_time_series[key] = utils.infft(frequency_domain_strain[key], self.sampling_frequency)
else: else:
raise RuntimeError("No source model given") raise RuntimeError("No source model given")
self._remove_added_keys()
for key in added_keys: return model_strain
def _apply_parameter_conversion(self):
self.parameters, self.__added_keys = self.parameter_conversion(self.parameters,
self.non_standard_sampling_parameter_keys)
self.__full_source_model_keyword_arguments.update(self.parameters)
def _strain_from_model(self, model_data_points, model):
return model(model_data_points, **self.__full_source_model_keyword_arguments)
def _strain_from_transformed_model(self, transformed_model_data_points, transformed_model, transformation_function):
transformed_model_strain = self._strain_from_model(transformed_model_data_points, transformed_model)
if isinstance(transformed_model_strain, np.ndarray):
return transformation_function(transformed_model_strain, self.sampling_frequency)
model_strain = dict()
for key in transformed_model_strain:
if transformation_function == utils.nfft:
model_strain[key], self.frequency_array = \
transformation_function(transformed_model_strain[key], self.sampling_frequency)
else:
model_strain[key] = transformation_function(transformed_model_strain[key], self.sampling_frequency)
return model_strain
def _remove_added_keys(self):
for key in self.__added_keys:
self.parameters.pop(key) self.parameters.pop(key)
return model_time_series
@property @property
def frequency_array(self): def frequency_array(self):
...@@ -155,8 +160,8 @@ class WaveformGenerator(object): ...@@ -155,8 +160,8 @@ class WaveformGenerator(object):
""" """
if self.__frequency_array_updated is False: if self.__frequency_array_updated is False:
self.frequency_array = utils.create_frequency_series( self.frequency_array = utils.create_frequency_series(
self.sampling_frequency, self.sampling_frequency,
self.duration) self.duration)
return self.__frequency_array return self.__frequency_array
@frequency_array.setter @frequency_array.setter
...@@ -175,9 +180,9 @@ class WaveformGenerator(object): ...@@ -175,9 +180,9 @@ class WaveformGenerator(object):
if self.__time_array_updated is False: if self.__time_array_updated is False:
self.__time_array = utils.create_time_series( self.__time_array = utils.create_time_series(
self.sampling_frequency, self.sampling_frequency,
self.duration, self.duration,
self.start_time) self.start_time)
self.__time_array_updated = True self.__time_array_updated = True
return self.__time_array return self.__time_array
...@@ -191,8 +196,6 @@ class WaveformGenerator(object): ...@@ -191,8 +196,6 @@ class WaveformGenerator(object):
def parameters(self): def parameters(self):
""" The dictionary of parameters for source model. """ The dictionary of parameters for source model.
Does some introspection into the source_model to figure out the parameters if none are given.
Returns Returns
------- -------
dict: The dictionary of parameter key-value pairs dict: The dictionary of parameter key-value pairs
...@@ -202,6 +205,8 @@ class WaveformGenerator(object): ...@@ -202,6 +205,8 @@ class WaveformGenerator(object):
@parameters.setter @parameters.setter
def parameters(self, parameters): def parameters(self, parameters):
""" Does some introspection into the source_model to figure out the parameters if none are given.
"""
self.__parameters_from_source_model() self.__parameters_from_source_model()
if isinstance(parameters, dict): if isinstance(parameters, dict):
for key in parameters.keys(): for key in parameters.keys():
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment