gwsnr.utils

Submodules

Package Contents

Classes

NumpyEncoder

Custom JSON encoder for numpy data types. It converts numpy.ndarray objects (and any nested-list composition

Functions

save_json(file_name, param)

Save a dictionary as a json file.

load_json(file_name)

Load a json file.

save_pickle(file_name, param)

Save a dictionary as a pickle file.

load_pickle(file_name)

Load a pickle file.

load_ann_h5(filename)

Function to load a specific dataset from an .h5 file

append_json(file_name, new_dictionary[, ...])

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

add_dictionaries_together(dictionary1, dictionary2)

Adds two dictionaries with the same keys together.

get_param_from_json(json_file)

Function to get the parameters from json file.

load_ann_h5_from_module(package, directory, filename)

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

load_json_from_module(package, directory, filename)

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

load_pickle_from_module(package, directory, filename)

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

dealing_with_psds([psds, ifos, f_min, sampling_frequency])

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

power_spectral_density_pycbc(psd[, f_min, ...])

psd array finder from pycbc

interpolator_check(param_dict_given, interpolator_dir, ...)

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

interpolator_pickle_path(param_dict_given[, path])

Function for storing or getting interpolator (snr_partialsacaled) pickle path

get_gw_parameters(gw_param_dict)

noise_weighted_inner_prod_h_inner_h(params)

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

noise_weighted_inner_prod_d_inner_h(params)

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

noise_weighted_inner_prod_ripple(params)

Probaility of detection of GW for the given sensitivity of the detectors
class gwsnr.utils.NumpyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]

Bases: 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.

default(obj)[source]

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)
gwsnr.utils.save_json(file_name, param)[source]

Save a dictionary as a json file.

Parameters:
file_namestr

json file name for storing the parameters.

paramdict

dictionary to be saved as a json file.

gwsnr.utils.load_json(file_name)[source]

Load a json file.

Parameters:
file_namestr

json file name for storing the parameters.

Returns:
paramdict
gwsnr.utils.save_pickle(file_name, param)[source]

Save a dictionary as a pickle file.

Parameters:
file_namestr

pickle file name for storing the parameters.

paramdict

dictionary to be saved as a pickle file.

gwsnr.utils.load_pickle(file_name)[source]

Load a pickle file.

Parameters:
file_namestr

pickle file name for storing the parameters.

Returns:
paramdict
gwsnr.utils.load_ann_h5(filename)[source]

Function to load a specific dataset from an .h5 file

Parameters:
filenamestr

name of the .h5 file

Returns:
modelkeras.models.Model

Keras model loaded from the .h5 file

gwsnr.utils.append_json(file_name, new_dictionary, old_dictionary=None, replace=False)[source]

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_namestr

json file name for storing the parameters.

new_dictionarydict

dictionary to be appended to the json file.

old_dictionarydict, 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.

replacebool, optional

If True, replace the json file with the dictionary. Default is False.

gwsnr.utils.add_dictionaries_together(dictionary1, dictionary2)[source]

Adds two dictionaries with the same keys together.

Parameters:
dictionary1dict

dictionary to be added.

dictionary2dict

dictionary to be added.

Returns:
dictionarydict

dictionary with added values.

gwsnr.utils.get_param_from_json(json_file)[source]

Function to get the parameters from json file.

Parameters:
json_filestr

json file name for storing the parameters.

Returns:
paramdict
gwsnr.utils.load_ann_h5_from_module(package, directory, filename)[source]

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

Parameters:
packagestr

name of the package

directorystr

name of the directory within the package

filenamestr

name of the .h5 file

Returns:
modelkeras.models.Model

Keras model loaded from the .h5 file

gwsnr.utils.load_json_from_module(package, directory, filename)[source]

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

Parameters:
packagestr

name of the package

directorystr

name of the directory within the package

filenamestr

name of the .json file

Returns:
datadict

Dictionary loaded from the .json file

gwsnr.utils.load_pickle_from_module(package, directory, filename)[source]

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

Parameters:
packagestr

name of the package

directorystr

name of the directory within the package

filenamestr

name of the .pkl file

Returns:
datadict

Dictionary loaded from the .pkl file

gwsnr.utils.dealing_with_psds(psds=None, ifos=None, f_min=20.0, sampling_frequency=2048.0)[source]

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

Parameters:
psdsdict

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

ifoslist or None

List of interferometer objects or interferometer name list. Default is None. If None, bilby’s default interferometer objects will be used.

f_minfloat

Minimum frequency of the psds. Default is 20.

sampling_frequencyfloat

Sampling frequency of the psds. Default is 2048.

Returns:
psds_listlist

list of bilby.gw.detector.PowerSpectralDensity objects

detector_tensor_listlist

list of detector tensors

detector_listlist

list of detector names

gwsnr.utils.power_spectral_density_pycbc(psd, f_min=20.0, sampling_frequency=2048.0)[source]

psd array finder from pycbc

Parameters:
psdstr

name of the psd e.g. ‘aLIGOaLIGODesignSensitivityT1800044’

f_minfloat

minimum frequency of the psd default: 20.

sampling_frequencyfloat

sampling frequency of the psd default: 2048.

Returns:
psd_arraybilby.gw.detector.psd.PowerSpectralDensity object
gwsnr.utils.interpolator_check(param_dict_given, interpolator_dir, create_new)[source]

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

Parameters:
param_dict_givendict

dictionary of parameters based on which the existence of interpolator will be checked

interpolator_dirstr

path to the interpolator pickle file

create_newbool

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

gwsnr.utils.interpolator_pickle_path(param_dict_given, path='./interpolator_pickle')[source]

Function for storing or getting interpolator (snr_partialsacaled) pickle path

Parameters:
param_dict_givendict

dictionary of parameters based on which the existence of interpolator will be checked

pathstr

path to the directory where the interpolator pickle file will be stored

Returns:
path_interpolatorstr

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

gwsnr.utils.get_gw_parameters(gw_param_dict)[source]
gwsnr.utils.noise_weighted_inner_prod_h_inner_h(params)[source]

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

Parameters:
paramslist

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_listlist

contains opt_snr for each detector and net_opt_snr

params[22]int

index tracker

gwsnr.utils.noise_weighted_inner_prod_d_inner_h(params)[source]

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

Parameters:
paramslist

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_listlist

contains opt_snr for each detector and net_opt_snr

params[22]int

index tracker

gwsnr.utils.noise_weighted_inner_prod_ripple(params)[source]

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

Parameters:
paramslist

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_listlist

contains opt_snr for each detector and net_opt_snr

params[22]int

index tracker