ler.utils

Submodules

• ler.utils.plots
• ler.utils.utils

Package Contents

Classes

NumpyEncoder

Class for storing a numpy.ndarray or any nested-list composition as JSON file. This is required for dealing np.nan and np.inf.

Functions

load_json(file_name)	Load a json file.
append_json(file_name, new_dictionary[, ...])	Append and update a json file with a dictionary.
add_dict_values(dict1, dict2)	Adds the values of two dictionaries together.
get_param_from_json(json_file)	Function to get the parameters from json file.
rejection_sample(pdf, xmin, xmax[, size, chunk_size])	Helper function for rejection sampling from a pdf with maximum and minimum arguments.
rejection_sample2d(pdf, xmin, xmax, ymin, ymax[, ...])	Helper function for rejection sampling from a 2D pdf with maximum and minimum arguments.
add_dictionaries_together(dictionary1, dictionary2)	Adds two dictionaries with the same keys together.
trim_dictionary(dictionary, size)	Filters an event dictionary to only contain the size.
create_func_pdf_invcdf(x, y[, category])	Function to create a interpolated function, inverse function or inverse cdf from the input x and y.
create_conditioned_pdf_invcdf(x, conditioned_y, ...)	pdf_func is the function to calculate the pdf of x given y
create_func(x, y)	Function to create a spline interpolated function from the input x and y.
create_func_inv(x, y)	Function to create a spline interpolated inverse function from the input x and y.
create_pdf(x, y)	Function to create a spline interpolated normalized pdf from the input x and y.
create_inv_cdf_array(x, y)	Function to create a spline interpolated inverse cdf from the input x and y.
create_conditioned_pdf(x, conditioned_y, pdf_func)	Function to create a conditioned pdf from the input x and y.
create_conditioned_inv_cdf_array(x, conditioned_y, ...)	Function to create a conditioned inv_cdf from the input x and y.
interpolator_from_pickle(param_dict_given, directory, ...)	Function to decide which interpolator to use.
interpolator_pickle_path(param_dict_given, directory, ...)	Function to create the interpolator pickle file path.
interpolator_pdf_conditioned(x, conditioned_y, ...)	Function to find the pdf interpolator coefficients from the conditioned y.
interpolator_sampler_conditioned(conditioned_y, ...[, ...])	Function to find sampler interpolator coefficients from the conditioned y.
cubic_spline_interpolator(xnew, coefficients, x)	Function to interpolate using cubic spline.
inverse_transform_sampler(size, cdf, x)	Function to sample from the inverse transform method.
batch_handler(size, batch_size, sampling_routine, ...)	Function to run the sampling in batches.
get_param_from_json(json_file)	Function to get the parameters from json file.
param_plot([param_name, param_dict, plot_label, ...])	Function to plot the distribution of the GW source parameters.
relative_mu_dt_unlensed(param[, size])	Function to generate relative magnification vs time delay difference for unlensed samples.
relative_mu_dt_lensed(lensed_param[, snr_threshold])	Function to classify the lensed images wrt to the morse phase difference.
mu_vs_dt_plot(x_array, y_array[, savefig, ax, colors, ...])	Function to generate 2D KDE and plot the relative magnification vs time delay difference for lensed samples.

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

Bases: json.JSONEncoder

Class for storing a numpy.ndarray or any nested-list composition as JSON file. This is required for dealing np.nan and np.inf.

Parameters:

json.JSONEncoderclass

class for encoding JSON file

Returns:

json.JSONEncoder.defaultfunction

function for encoding JSON file

default(obj)

function for encoding JSON file

ler.utils.load_json(file_name)

Load a json file.

Parameters:

file_namestr

json file name for storing the parameters.

Returns:

paramdict

ler.utils.append_json(file_name, new_dictionary, old_dictionary=None, replace=False)

Append and update a json file with a dictionary.

Parameters:

file_namestr

json file name for storing the parameters.

new_dictionarydict

dictionary to be appended to the json file.

replacebool, optional

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

ler.utils.add_dict_values(dict1, dict2)

Adds the values of two dictionaries together.

Parameters:

dict1dict

dictionary to be added.

dict2dict

dictionary to be added.

Returns:

dict1dict

dictionary with added values.

ler.utils.get_param_from_json(json_file)

Function to get the parameters from json file.

Parameters:

json_filestr

json file name for storing the parameters.

Returns:

paramdict

ler.utils.rejection_sample(pdf, xmin, xmax, size=100, chunk_size=10000)

Helper function for rejection sampling from a pdf with maximum and minimum arguments.

Parameters:

pdffunction

pdf function.

xminfloat

minimum value of the pdf.

xmaxfloat

maximum value of the pdf.

sizeint, optional

number of samples. Default is 100.

chunk_sizeint, optional

chunk size for sampling. Default is 10000.

Returns:

x_samplenumpy.ndarray

samples from the pdf.

ler.utils.rejection_sample2d(pdf, xmin, xmax, ymin, ymax, size=100, chunk_size=10000)

Helper function for rejection sampling from a 2D pdf with maximum and minimum arguments.

Parameters:

pdffunction

2D pdf function.

xminfloat

minimum value of the pdf in the x-axis.

xmaxfloat

maximum value of the pdf in the x-axis.

yminfloat

minimum value of the pdf in the y-axis.

ymaxfloat

maximum value of the pdf in the y-axis.

sizeint, optional

number of samples. Default is 100.

chunk_sizeint, optional

chunk size for sampling. Default is 10000.

Returns:

x_samplenumpy.ndarray

samples from the pdf in the x-axis.

ler.utils.add_dictionaries_together(dictionary1, dictionary2)

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.

ler.utils.trim_dictionary(dictionary, size)

Filters an event dictionary to only contain the size.

Parameters:

dictionarydict

dictionary to be trimmed.

sizeint

size to trim the dictionary to.

Returns:

dictionarydict

trimmed dictionary.

ler.utils.create_func_pdf_invcdf(x, y, category='function')

Function to create a interpolated function, inverse function or inverse cdf from the input x and y.

Parameters:

xnumpy.ndarray

x values.

ynumpy.ndarray

y values.

categorystr, optional

category of the function. Default is “function”. Other options are “function_inverse”, “pdf” and “inv_cdf”.

Returns:

pdfpdf function

interpolated pdf function.

inv_pdffunction inverse

interpolated inverse pdf function.

inv_cdffunction

interpolated inverse cdf.

ler.utils.create_conditioned_pdf_invcdf(x, conditioned_y, pdf_func, category)

pdf_func is the function to calculate the pdf of x given y x is an array and the output of pdf_func is an array y is the condition we consider parameter plane of x and y

Parameters:

xnumpy.ndarray

x values.

conditioned_ynumpy.ndarray

conditioned y values.

pdf_funcfunction

function to calculate the pdf of x given y.

categorystr, optional

category of the function. Default is “function”. Other options are “function_inverse”, “pdf” and “inv_cdf”.

ler.utils.create_func(x, y)

Function to create a spline interpolated function from the input x and y.

Parameters:

xnumpy.ndarray

x values.

ynumpy.ndarray

y values.

Returns:

cnumpy.ndarray

spline coefficients.

ler.utils.create_func_inv(x, y)

Function to create a spline interpolated inverse function from the input x and y.

Parameters:

xnumpy.ndarray

x values.

ynumpy.ndarray

y values.

Returns:

cnumpy.ndarray

spline coefficients.

ler.utils.create_pdf(x, y)

Function to create a spline interpolated normalized pdf from the input x and y.

Parameters:

xnumpy.ndarray

x values.

ynumpy.ndarray

y values.

Returns:

cnumpy.ndarray

spline coefficients.

ler.utils.create_inv_cdf_array(x, y)

Function to create a spline interpolated inverse cdf from the input x and y.

Parameters:

xnumpy.ndarray

x values.

ynumpy.ndarray

y values.

Returns:

cnumpy.ndarray

spline coefficients.

ler.utils.create_conditioned_pdf(x, conditioned_y, pdf_func)

Function to create a conditioned pdf from the input x and y.

Parameters:

xnumpy.ndarray

x values.

conditioned_ynumpy.ndarray

conditioned y values.

pdf_funcfunction

function to calculate the pdf of x given y.

Returns:

list_list

list of pdfs.

ler.utils.create_conditioned_inv_cdf_array(x, conditioned_y, pdf_func)

Function to create a conditioned inv_cdf from the input x and y.

Parameters:

xnumpy.ndarray

x values.

conditioned_ynumpy.ndarray

conditioned y values.

pdf_funcfunction

function to calculate the pdf of x given y.

Returns:

list_list

list of inv_cdfs.

ler.utils.interpolator_from_pickle(param_dict_given, directory, sub_directory, name, x, pdf_func=None, y=None, conditioned_y=None, dimension=1, category='pdf', create_new=False)

Function to decide which interpolator to use.

Parameters:

param_dict_givendict

dictionary of parameters.

directorystr

directory to store the interpolator.

sub_directorystr

sub-directory to store the interpolator.

namestr

name of the interpolator.

xnumpy.ndarray

x values.

pdf_funcfunction

function to calculate the pdf of x given y.

ynumpy.ndarray

y values.

conditioned_ynumpy.ndarray

conditioned y values.

dimensionint

dimension of the interpolator. Default is 1.

categorystr

category of the function. Default is “pdf”.

create_newbool

if True, create a new interpolator. Default is False.

Returns:

interpolatorfunction

interpolator function.

ler.utils.interpolator_pickle_path(param_dict_given, directory, sub_directory, interpolator_name)

Function to create the interpolator pickle file path.

Parameters:

param_dict_givendict

dictionary of parameters.

directorystr

directory to store the interpolator.

sub_directorystr

sub-directory to store the interpolator.

interpolator_namestr

name of the interpolator.

Returns:

path_inv_cdfstr

path of the interpolator pickle file.

it_existbool

if True, the interpolator exists.

ler.utils.interpolator_pdf_conditioned(x, conditioned_y, y_array, interpolator_list)

Function to find the pdf interpolator coefficients from the conditioned y.

Parameters:

xnumpy.ndarray

x values.

conditioned_yfloat

conditioned y value.

y_arraynumpy.ndarray

y values.

interpolator_listlist

list of interpolators.

Returns:

interpolator_list[idx](x)numpy.ndarray

samples from the interpolator.

ler.utils.interpolator_sampler_conditioned(conditioned_y, y_array, interpolator_list, size=1000)

Function to find sampler interpolator coefficients from the conditioned y.

Parameters:

conditioned_yfloat

conditioned y value.

y_arraynumpy.ndarray

y values.

interpolator_listlist

list of interpolators.

sizeint

number of samples.

Returns:

ler.utils.cubic_spline_interpolator(xnew, coefficients, x)

Function to interpolate using cubic spline.

Parameters:

xnewnumpy.ndarray

new x values.

coefficientsnumpy.ndarray

coefficients of the cubic spline.

xnumpy.ndarray

x values.

Returns:

resultnumpy.ndarray

interpolated values.

ler.utils.inverse_transform_sampler(size, cdf, x)

Function to sample from the inverse transform method.

Parameters:

sizeint

number of samples.

cdfnumpy.ndarray

cdf values.

xnumpy.ndarray

x values.

Returns:

samplesnumpy.ndarray

samples from the cdf.

ler.utils.batch_handler(size, batch_size, sampling_routine, output_jsonfile, save_batch=True, resume=False)

Function to run the sampling in batches.

Parameters:

sizeint

number of samples.

batch_sizeint

batch size.

sampling_routinefunction

function to sample the parameters. e.g. unlensed_sampling_routine() or lensed_sampling_routine()

output_jsonfilestr

name of the json file to store the parameters.

resumebool

if True, it will resume the sampling from the last batch. default resume = False.

ler.utils.get_param_from_json(json_file)

Function to get the parameters from json file.

Parameters:

json_filestr

json file name for storing the parameters.

Returns:

paramdict

ler.utils.param_plot(param_name='zs', param_dict='./gw_params.json', plot_label='zs', param_min=None, param_max=None, kde=True, kde_bandwidth=0.2, histogram=True, histogram_bins=30)

Function to plot the distribution of the GW source parameters.

Parameters:

param_namestr

name of the parameter to plot. default param_name = ‘zs’.

param_dictdict or str

dictionary of GW source parameters or json file name. default param_dict = ‘./gw_params.json’.

param_xlabelstr

x-axis label. default param_xlabel = ‘source redshift’.

param_ylabelstr

y-axis label. default param_ylabel = ‘probability density’.

param_minfloat

minimum value of the parameter. default param_min = None.

param_maxfloat

maximum value of the parameter. default param_max = None.

figsizetuple

figure size. default figsize = (4, 4).

kdebool

if True, kde will be plotted. default kde = True.

kde_bandwidthfloat

bandwidth for kde. default kde_bandwidth = 0.2.

histogrambool

if True, histogram will be plotted. default histogram = True.

histogram_binsint

number of bins for histogram. default histogram_bins = 30.

Examples

>>> import matplotlib.pyplot as plt
>>> from ler.rates import LeR
>>> ler = LeR()
>>> param = ler.unlensed_cbc_statistics();
>>> rate, param_detectable = ler.unlensed_rate()
>>> plt.figure(figsize=(6, 4))
>>> ler.param_plot(param_name='zs', param_dict=param, plot_label='all events')
>>> ler.param_plot(param_name='zs', param_dict=param_detectable, plot_label='detectable events')
>>> plt.xlabel('source redshift')
>>> plt.ylabel('probability density')
>>> plt.title('source redshift distribution')
>>> plt.grid(alpha=0.5)
>>> plt.savefig('source_redshift_distribution.png')

ler.utils.relative_mu_dt_unlensed(param, size=100)

Function to generate relative magnification vs time delay difference for unlensed samples.

Parameters:

paramdict

dictionary of unlensed GW source parameters. unlensed_param.keys() = [‘m1’, ‘m2’, ‘z’, ‘snr’, ‘theta_jn’, ‘ra’, ‘dec’, ‘psi’, ‘phase’, ‘geocent_time’]

Returns:

dmufloat.array

relative magnification.

dtfloat.array

relative time delay.

ler.utils.relative_mu_dt_lensed(lensed_param, snr_threshold=[8.0, 8.0])

Function to classify the lensed images wrt to the morse phase difference.

Parameters:

lensed_paramdict

dictionary of lensed GW source parameters, lens galaxy parameters and image paramters. lensed_param.keys() = [‘zl’, ‘zs’, ‘sigma’, ‘q’, ‘e1’, ‘e2’, ‘gamma1’, ‘gamma2’, ‘Dl’, ‘Ds’, ‘Dls’, ‘theta_E’, ‘gamma’, ‘mass_1’, ‘mass_2’, ‘mass_1_source’, ‘mass_2_source’, ‘luminosity_distance’, ‘theta_jn’, ‘psi’, ‘phase’, ‘geocent_time’, ‘ra’, ‘dec’, ‘n_images’, ‘x0_image_positions’, ‘x1_image_positions’, ‘magnifications’, ‘time_delays’, ‘traces’, ‘determinants’, ‘image_type’, ‘weights’, ‘optimal_snr_net’, ‘L1’, ‘H1’, ‘V1’]

snr_thresholdfloat

threshold for detection signal to noise ratio. e.g. snr_threshold = [8.,8.] or [8.,6.] for subthreshold

Returns:

mu_rel0float.array

relative magnification for 0 degree phase difference.

dt_rel0float.array

relative time delay for 0 degree phase difference.

mu_rel90float.array

relative magnification for 90 degree phase difference.

dt_rel90float.array

relative time delay for 90 degree phase difference.

ler.utils.mu_vs_dt_plot(x_array, y_array, savefig=False, ax=None, colors='blue', linestyles='-', origin='upper', alpha=0.6, extent=[0.01, 500.0, 0.01, 100.0], contour_levels=[0.1, 0.4, 0.68, 0.95])

Function to generate 2D KDE and plot the relative magnification vs time delay difference for lensed samples.

Parameters:

x_arrayfloat.array

x array.

y_arrayfloat.array

y array.

xlabelstr

x label.

ylabelstr

y label.

titlestr

title.

savefigbool

if True, it will save the figure. default savefig = False.

axmatplotlib.axes

matplotlib axes. default ax = None.

colorsstr

color of the plot. default colors = ‘blue’.

linestylesstr

linestyle of the plot. default linestyles = ‘-‘.

originstr

origin of the plot. default origin = ‘upper’.

alphafloat

alpha of the plot. default alpha = 0.6.

extentlist

extent of the plot. default extent = [1e-2,5e2,1e-2,1e2].

contour_levelslist

contour levels of the plot. default contour_levels = [0.10,0.40,0.68,0.95] which corresponds to 1,2,3,4 sigma.

Returns:

None