:py:mod:`gwsnr.utils`
=====================

.. py:module:: gwsnr.utils


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   multiprocessing_routine/index.rst
   utils/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   gwsnr.utils.NumpyEncoder



Functions
~~~~~~~~~

.. autoapisummary::

   gwsnr.utils.save_json
   gwsnr.utils.load_json
   gwsnr.utils.save_pickle
   gwsnr.utils.load_pickle
   gwsnr.utils.load_ann_h5
   gwsnr.utils.append_json
   gwsnr.utils.add_dictionaries_together
   gwsnr.utils.get_param_from_json
   gwsnr.utils.load_ann_h5_from_module
   gwsnr.utils.load_json_from_module
   gwsnr.utils.load_pickle_from_module
   gwsnr.utils.dealing_with_psds
   gwsnr.utils.power_spectral_density_pycbc
   gwsnr.utils.interpolator_check
   gwsnr.utils.interpolator_pickle_path
   gwsnr.utils.get_gw_parameters
   gwsnr.utils.noise_weighted_inner_prod_h_inner_h
   gwsnr.utils.noise_weighted_inner_prod_d_inner_h
   gwsnr.utils.noise_weighted_inner_prod_ripple



.. py:class:: NumpyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)


   Bases: :py:obj:`json.JSONEncoder`

   
   Custom JSON encoder for numpy data types. It converts numpy.ndarray objects (and any nested-list composition
   that includes ndarray objects) into regular lists for JSON serialization. This is particularly useful when
   serializing data structures that include numpy arrays.
















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   .. py:method:: default(obj)

      
      Implement this method in a subclass such that it returns
      a serializable object for ``o``, or calls the base implementation
      (to raise a ``TypeError``).

      For example, to support arbitrary iterators, you could
      implement default like this::

          def default(self, o):
              try:
                  iterable = iter(o)
              except TypeError:
                  pass
              else:
                  return list(iterable)
              # Let the base class default method raise the TypeError
              return super().default(o)















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.. py:function:: save_json(file_name, param)

   
   Save a dictionary as a json file.


   :Parameters:

       **file_name** : `str`
           json file name for storing the parameters.

       **param** : `dict`
           dictionary to be saved as a json file.














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.. py:function:: load_json(file_name)

   
   Load a json file.


   :Parameters:

       **file_name** : `str`
           json file name for storing the parameters.

   :Returns:

       **param** : `dict`
           ..













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.. py:function:: save_pickle(file_name, param)

   
   Save a dictionary as a pickle file.


   :Parameters:

       **file_name** : `str`
           pickle file name for storing the parameters.

       **param** : `dict`
           dictionary to be saved as a pickle file.














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.. py:function:: load_pickle(file_name)

   
   Load a pickle file.


   :Parameters:

       **file_name** : `str`
           pickle file name for storing the parameters.

   :Returns:

       **param** : `dict`
           ..













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.. py:function:: load_ann_h5(filename)

   
   Function to load a specific dataset from an .h5 file


   :Parameters:

       **filename** : str
           name of the .h5 file

   :Returns:

       **model** : `keras.models.Model`
           Keras model loaded from the .h5 file













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.. py:function:: append_json(file_name, new_dictionary, old_dictionary=None, replace=False)

   
   Append (values with corresponding keys) and update a json file with a dictionary. There are four options:

   1. If old_dictionary is provided, the values of the new dictionary will be appended to the old dictionary and save in the 'file_name' json file.
   2. If replace is True, replace the json file (with the 'file_name') content with the new_dictionary.
   3. If the file does not exist, create a new one with the new_dictionary.
   4. If none of the above, append the new dictionary to the content of the json file.

   :Parameters:

       **file_name** : `str`
           json file name for storing the parameters.

       **new_dictionary** : `dict`
           dictionary to be appended to the json file.

       **old_dictionary** : `dict`, optional
           If provided the values of the new dictionary will be appended to the old dictionary and save in the 'file_name' json file.
           Default is None.

       **replace** : `bool`, optional
           If True, replace the json file with the dictionary. Default is False.














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.. py:function:: add_dictionaries_together(dictionary1, dictionary2)

   
   Adds two dictionaries with the same keys together.


   :Parameters:

       **dictionary1** : `dict`
           dictionary to be added.

       **dictionary2** : `dict`
           dictionary to be added.

   :Returns:

       **dictionary** : `dict`
           dictionary with added values.













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.. py:function:: get_param_from_json(json_file)

   
   Function to get the parameters from json file.


   :Parameters:

       **json_file** : `str`
           json file name for storing the parameters.

   :Returns:

       **param** : `dict`
           ..













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.. py:function:: load_ann_h5_from_module(package, directory, filename)

   
   Function to load a specific dataset from an .h5 file within the package


   :Parameters:

       **package** : str
           name of the package

       **directory** : str
           name of the directory within the package

       **filename** : str
           name of the .h5 file

   :Returns:

       **model** : `keras.models.Model`
           Keras model loaded from the .h5 file













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.. py:function:: load_json_from_module(package, directory, filename)

   
   Function to load a specific dataset from a .json file within the package


   :Parameters:

       **package** : str
           name of the package

       **directory** : str
           name of the directory within the package

       **filename** : str
           name of the .json file

   :Returns:

       **data** : `dict`
           Dictionary loaded from the .json file













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.. py:function:: load_pickle_from_module(package, directory, filename)

   
   Function to load a specific dataset from a .pkl file within the package


   :Parameters:

       **package** : str
           name of the package

       **directory** : str
           name of the directory within the package

       **filename** : str
           name of the .pkl file

   :Returns:

       **data** : `dict`
           Dictionary loaded from the .pkl file













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.. py:function:: dealing_with_psds(psds=None, ifos=None, f_min=20.0, sampling_frequency=2048.0)

   
   Function to deal with psds inputs and for creating bilby.gw.detector.PowerSpectralDensity objects.


   :Parameters:

       **psds** : dict
           dictionary of psds. psds.keys()=detector names, psds.values()=psds file names or pycbc psd names

       **ifos** : `list` or `None`
           List of interferometer objects or interferometer name list. Default is None. If None, bilby's default interferometer objects will be used.

       **f_min** : `float`
           Minimum frequency of the psds. Default is 20.

       **sampling_frequency** : `float`
           Sampling frequency of the psds. Default is 2048.

   :Returns:

       **psds_list** : `list`
           list of bilby.gw.detector.PowerSpectralDensity objects

       **detector_tensor_list** : `list`
           list of detector tensors

       **detector_list** : `list`
           list of detector names













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.. py:function:: power_spectral_density_pycbc(psd, f_min=20.0, sampling_frequency=2048.0)

   
   psd array finder from pycbc


   :Parameters:

       **psd** : str
           name of the psd
           e.g. 'aLIGOaLIGODesignSensitivityT1800044'

       **f_min** : float
           minimum frequency of the psd
           default: 20.

       **sampling_frequency** : float
           sampling frequency of the psd
           default: 2048.

   :Returns:

       **psd_array** : bilby.gw.detector.psd.PowerSpectralDensity object
           ..













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.. py:function:: interpolator_check(param_dict_given, interpolator_dir, create_new)

   
   Function for interpolator (snr_partialsacaled) check and generation if not exists.


   :Parameters:

       **param_dict_given** : dict
           dictionary of parameters based on which the existence of interpolator will be checked

       **interpolator_dir** : str
           path to the interpolator pickle file

       **create_new** : bool
           if True, new interpolator will be generated even if the interpolator exists
           if False, existing interpolator will be used if exists, otherwise new interpolator will be generated

   :Returns:

       **psds_list_** : list
           list of psd objects

       **detector_tensor_list_** : list
           list of detector tensors

       **detector_list_** : list
           list of detector names













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.. py:function:: interpolator_pickle_path(param_dict_given, path='./interpolator_pickle')

   
   Function for storing or getting interpolator (snr_partialsacaled) pickle path


   :Parameters:

       **param_dict_given** : dict
           dictionary of parameters based on which the existence of interpolator will be checked

       **path** : str
           path to the directory where the interpolator pickle file will be stored

   :Returns:

       **path_interpolator** : str
           path to the interpolator pickle file
           e.g. './interpolator_pickle/L1/partialSNR_dict_0.pickle'

       it_exist: bool
           True if the interpolator exists
           False if the interpolator does not exists













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.. py:function:: get_gw_parameters(gw_param_dict)


.. py:function:: noise_weighted_inner_prod_h_inner_h(params)

   
   Probaility of detection of GW for the given sensitivity of the detectors


   :Parameters:

       **params** : list
           list of parameters for the inner product calculation
           List contains:

           params[0] : float
               mass_1
           params[1] : float
               mass_2
           params[2] : float
               luminosity_distance
           params[3] : float
               theta_jn
           params[4] : float
               psi
           params[5] : float
               phase
           params[6] : float
               ra
           params[7] : float
               dec
           params[8] : float
               geocent_time
           params[9] : float
               a_1
           params[10] : float
               a_2
           params[11] : float
               tilt_1
           params[12] : float
               tilt_2
           params[13] : float
               phi_12
           params[14] : float
               phi_jl
           params[15] : float
               lambda_1
           params[16] : float
               lambda_2
           params[17] : float
               eccentricity
           params[18] : float
               approximant
           params[19] : float
               f_min
           params[20] : float
               f_ref
           params[21] : float
               duration
           params[22] : float
               sampling_frequency
           params[23] : int
               index tracker
           params[24] : list
               list of psds for each detector
           params[25] : str
               frequency_domain_source_model name

   :Returns:

       **SNRs_list** : list
           contains opt_snr for each detector and net_opt_snr

       **params[22]** : int
           index tracker













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.. py:function:: noise_weighted_inner_prod_d_inner_h(params)

   
   Probaility of detection of GW for the given sensitivity of the detectors


   :Parameters:

       **params** : list
           list of parameters for the inner product calculation
           List contains:

           params[0] : float
               mass_1
           params[1] : float
               mass_2
           params[2] : float
               luminosity_distance
           params[3] : float
               theta_jn
           params[4] : float
               psi
           params[5] : float
               phase
           params[6] : float
               ra
           params[7] : float
               dec
           params[8] : float
               geocent_time
           params[9] : float
               a_1
           params[10] : float
               a_2
           params[11] : float
               tilt_1
           params[12] : float
               tilt_2
           params[13] : float
               phi_12
           params[14] : float
               phi_jl
           params[15] : float
               lambda_1
           params[16] : float
               lambda_2
           params[17] : float
               eccentricity
           params[18] : float
               approximant
           params[19] : float
               f_min
           params[20] : float
               f_ref
           params[21] : float
               duration
           params[22] : float
               sampling_frequency
           params[23] : int
               index tracker
           params[24] : list
               list of psds for each detector
           params[25] : str
               frequency_domain_source_model name
           params[26] : list or None
               noise realization. If None, then PSD as noise realization

   :Returns:

       **SNRs_list** : list
           contains opt_snr for each detector and net_opt_snr

       **params[22]** : int
           index tracker













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.. py:function:: noise_weighted_inner_prod_ripple(params)

   
   Probaility of detection of GW for the given sensitivity of the detectors


   :Parameters:

       **params** : list
           list of parameters for the inner product calculation
           List contains:

           params[0] : `numpy.ndarray`
               plus polarization
           params[1] : `numpy.ndarray`
               cross polarization
           params[2] : `numpy.ndarray`
               frequency array
           params[3] : `float`
               cutt-off size of given arrays
           params[4] : `float`
               minimum frequency
           params[5] : `float`
               duration
           params[6] : `int`
               index
           params[7] : `list`
               psd objects of given detectors

   :Returns:

       **SNRs_list** : list
           contains opt_snr for each detector and net_opt_snr

       **params[22]** : int
           index tracker













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