sage.data.waveform.distributions.powerlaw

Filename : powerlaw.py Description : Short description of the file

Created on 2026-02-16 10:56:50

__author__ = Narenraju Nagarajan __copyright__ = Copyright 2026, ProjectName __license__ = MIT Licence __version__ = 0.0.1 __maintainer__ = Narenraju Nagarajan __affiliation__ = N/A __email__ = N/A __status__ = [‘inProgress’, ‘Archived’, ‘inUsage’, ‘Debugging’]

GitHub Repository: NULL

Documentation: NULL

Classes

UniformPowerLaw	GPU-friendly power-law sampler for 1D parameters.
UniformRadius	Uniform in volume (3D sphere) radius sampler.

Module Contents

class UniformPowerLaw(low, high, dim=3)

GPU-friendly power-law sampler for 1D parameters.

Draws samples from the distribution whose PDF scales as r^(dim-1) over the interval [low, high]. For dim=3 this is uniform in volume (the standard astrophysical distance prior assuming a uniform spatial number density).

Parameters:

• low (float) – Lower bound of the distribution.

• high (float) – Upper bound of the distribution.

• dim (int) – Dimensionality exponent; 3 gives uniform-in-volume (default).

name = 'uniform_power_law'

low

high

dim = 3

sample(shape, device=None, dtype=torch.float32, generator=None)

Sample a batch from the power-law distribution on GPU.

class UniformRadius(low=0.0, high=1.0)

Bases: UniformPowerLaw

Uniform in volume (3D sphere) radius sampler.

name = 'uniform_radius'