diff --git a/tupak/gw/waveform_generator.py b/tupak/gw/waveform_generator.py index cd18651b269db5782adc41bbb2ae66135c83dac9..48c1d58a9bca0ec56331f54a3241bc4c065e0681 100644 --- a/tupak/gw/waveform_generator.py +++ b/tupak/gw/waveform_generator.py @@ -83,12 +83,19 @@ class WaveformGenerator(object): .format(self.duration, self.sampling_frequency, self.start_time, fdsm_name, tdsm_name, param_conv_name, self.waveform_arguments) - def frequency_domain_strain(self, parameters): + def frequency_domain_strain(self, parameters=None): """ Rapper to source_model. Converts self.parameters with self.parameter_conversion before handing it off to the source model. Automatically refers to the time_domain_source model via NFFT if no frequency_domain_source_model is given. + Parameters + ---------- + parameters: dict, optional + Parameters to evaluate the waveform for, this overwrites + `self.parameters`. + If not provided will fall back to `self.parameters`. + Returns ------- array_like: The frequency domain strain for the given set of parameters @@ -105,13 +112,20 @@ class WaveformGenerator(object): transformed_model=self.time_domain_source_model, transformed_model_data_points=self.time_array) - def time_domain_strain(self, parameters): + def time_domain_strain(self, parameters=None): """ Rapper to source_model. Converts self.parameters with self.parameter_conversion before handing it off to the source model. Automatically refers to the frequency_domain_source model via INFFT if no frequency_domain_source_model is given. + Parameters + ---------- + parameters: dict, optional + Parameters to evaluate the waveform for, this overwrites + `self.parameters`. + If not provided will fall back to `self.parameters`. + Returns ------- array_like: The time domain strain for the given set of parameters @@ -130,7 +144,8 @@ class WaveformGenerator(object): def _calculate_strain(self, model, model_data_points, transformation_function, transformed_model, transformed_model_data_points, parameters): - self.parameters = parameters.copy() + if parameters is not None: + self.parameters = parameters.copy() if model is not None: model_strain = self._strain_from_model(model_data_points, model) elif transformed_model is not None: