:py:mod:`gwsnr.ann.ann_model_generator`
=======================================

.. py:module:: gwsnr.ann.ann_model_generator

.. autoapi-nested-parse::

   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.

   ..
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Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   gwsnr.ann.ann_model_generator.ANNModelGenerator




.. py:class:: 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:

       **npool** : `int`
           Number of processors to use for parallel processing.
           Default is 4.

       **gwsnr_verbose** : `bool`
           If True, print the progress of the GWSNR calculation.
           Default is True.

       **snr_th** : `float`
           SNR threshold for the error calculation.
           Default is 8.0.

       **waveform_approximant** : `str`
           Waveform approximant to be used for the GWSNR calculation and the ANN model.
           Default is "IMRPhenomXPHM".

       **\*\*kwargs** : `dict`
           Keyword arguments for the GWSNR class.
           To see the list of available arguments,
           >>> from gwsnr import GWSNR
           >>> help(GWSNR)











   .. rubric:: 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



   ..
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   .. py:attribute:: directory
      :value: "'./gwsnr_data'"

      

   .. py:attribute:: ann_model

      

   .. py:attribute:: ann
      :value: 'None'

      

   .. py:attribute:: scaler
      :value: 'None'

      

   .. py:attribute:: gwsnr_args

      

   .. py:attribute:: gwsnr

      

   .. py:method:: get_input_output_data(params=None, randomize=True)


   .. py:method:: 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]















      ..
          !! processed by numpydoc !!

   .. py:method:: standard_scaling_initialization(X_train)


   .. py:method:: ann_model_4layers(num_nodes_list, activation_fn_list, optimizer, loss, metrics)


   .. py:method:: get_parameters(gw_param_dict)


   .. py:method:: get_scaled_data(gw_param_dict, randomize=True, test_size=0.1, random_state=42)


   .. py:method:: 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')


   .. py:method:: pdet_error(gw_param_dict=None, randomize=True, error_adjustment=True)


   .. py:method:: 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')


   .. py:method:: pdet_confusion_matrix(gw_param_dict=None, randomize=True, snr_threshold=8.0)


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


   .. py:method:: helper_error_adjustment(y_pred, y_test, snr_range=[4, 10])


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


   .. py:method:: predict_snr(gw_param_dict, error_adjustment=True)


   .. py:method:: predict_pdet(gw_param_dict, snr_threshold=8.0, error_adjustment=True)