gwsnr.ann.ann_model_generator

This module contains the ANNModelGenerator class which is used to generate the ANN (Artificial Neural Network) model that can be used to predict the SNR of the GW events.

Module Contents

Classes

ANNModelGenerator

ANNModelGenerator class is used to generate the ANN model that can be used to predict the SNR of the GW events.

class gwsnr.ann.ann_model_generator.ANNModelGenerator(directory='./gwsnr_data', npool=4, gwsnr_verbose=True, snr_th=8.0, snr_method='interpolation_aligned_spins', waveform_approximant='IMRPhenomXPHM', **kwargs)

ANNModelGenerator class is used to generate the ANN model that can be used to predict the SNR of the GW events.

Parameters:

npoolint

Number of processors to use for parallel processing. Default is 4.

gwsnr_verbosebool

If True, print the progress of the GWSNR calculation. Default is True.

snr_thfloat

SNR threshold for the error calculation. Default is 8.0.

waveform_approximantstr

Waveform approximant to be used for the GWSNR calculation and the ANN model. Default is “IMRPhenomXPHM”.

**kwargsdict

Keyword arguments for the GWSNR class. To see the list of available arguments, >>> from gwsnr import GWSNR >>> help(GWSNR)

Examples

>>> from gwsnr import ANNModelGenerator
>>> amg = ANNModelGenerator()
>>> amg.ann_model_training(gw_param_dict='gw_param_dict.json') # training the ANN model with pre-generated parameter points

directory = "'./gwsnr_data'"

ann_model

ann = 'None'

scaler = 'None'

gwsnr_args

gwsnr

get_input_output_data(params=None, randomize=True)

get_input_data(params)

Function to generate input and output data for the neural network

Parameters: idx: index of the parameter points params: dictionary of parameter points

params.keys() = [‘mass_1’, ‘mass_2’, ‘luminosity_distance’, ‘theta_jn’, ‘psi’, ‘geocent_time’, ‘ra’, ‘dec’, ‘a_1’, ‘a_2’, ‘tilt_1’, ‘tilt_2’, ‘L1’]

Returns: X: input data, [snr_partial_[0], amp0[0], eta, chi_eff, theta_jn] y: output data, [L1]

standard_scaling_initialization(X_train)

ann_model_4layers(num_nodes_list, activation_fn_list, optimizer, loss, metrics)

get_parameters(gw_param_dict)

get_scaled_data(gw_param_dict, randomize=True, test_size=0.1, random_state=42)

ann_model_training(gw_param_dict, randomize=True, test_size=0.1, random_state=42, num_nodes_list=[5, 32, 32, 1], activation_fn_list=['relu', 'relu', 'sigmoid', 'linear'], optimizer='adam', loss='mean_squared_error', metrics=['accuracy'], batch_size=32, epochs=100, error_adjustment_snr_range=[4, 10], ann_file_name='ann_model.h5', scaler_file_name='scaler.pkl', error_adjustment_file_name='error_adjustment.json', ann_path_dict_file_name='ann_path_dict.json')

pdet_error(gw_param_dict=None, randomize=True, error_adjustment=True)

save_ann_path_dict(ann_file_name='ann_model.h5', scaler_file_name='scaler.pkl', error_adjustment_file_name='error_adjustment.json', ann_path_dict_file_name='ann_path_dict.json')

pdet_confusion_matrix(gw_param_dict=None, randomize=True, snr_threshold=8.0)

load_model_scaler_error(ann_file_name='ann_model.h5', scaler_file_name='scaler.pkl', error_adjustment_file_name=False)

helper_error_adjustment(y_pred, y_test, snr_range=[4, 10])

snr_error_adjustment(gw_param_dict=None, randomize=True, snr_threshold=8.0, snr_range=[4, 10], error_adjustment_file_name='error_adjustment.json')

predict_snr(gw_param_dict, error_adjustment=True)

predict_pdet(gw_param_dict, snr_threshold=8.0, error_adjustment=True)