gwsnr.ann

Submodules

• gwsnr.ann.ann_model_generator

Package 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.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)