gravelamps.prior.discrete
Discrete Priors
Implements priors for discrete distributions as needed for the Morse phase which may take one of three distinct values, or the millilensing number of images which must obviously be discrete
- Written by Mick Wright
Ania Liu Justin Janquart
Classes
Discrete Uniform Prior for handling the number of millilensing image signals. |
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Discrete Uniform Prior for handling the Morse phase. |
Module Contents
- class gravelamps.prior.discrete.ImageNumber(ncategories, name='num_images', latex_label='$n_{\\mathrm{signals}}$', unit=None)
Bases:
bilby.core.prior.CategoricalDiscrete Uniform Prior for handling the number of millilensing image signals.
Lightly modified such that the minimum must be 1
Attributes
- ncategoriesint
Number of potential images
- namestr
Name of the parameter used, defaults to ‘num_images’
- latex_labelstr
The latex compatible output to be used on plots, etc. Defaults to ‘$n_{mathrm{signals}}$’.
- unitstr
Unit of the parameter
- rescale(val)
Rescale a sample from the unit line element to one of the categories
- Parameters:
- valUnion[float, int, array_like]
Uniform probability between 0 and 1
- Returns:
- Union[float, array_like]
- cdf(val)
Calculate CDF for sample values
- Parameters:
- valUnion[float, int, array_like]
Sample values
- Returns:
- float
- class gravelamps.prior.discrete.UniformMorse(name='morse_phase', latex_label='$n$', unit=None)
Bases:
bilby.core.prior.CategoricalDiscrete Uniform Prior for handling the Morse phase.
This is a restricted subset of the Categorical prior to the cases of 0, 0.5, 1
- Attributes:
- namestr
Name of the parameter used, defaults to ‘morse_phase’
- latex_labelstr
The latex compatible output to be used on plots, etc. Defaults to ‘$n$’
- unitstr
Unit of the parameter
Methods
rescale
Maps the continuous distribution 0 to 1 to discrete distribution of 0, 0.5, 1
- rescale(val)
Rescale a sample from the unit line element to one of the categories
- Parameters:
- valUnion[float, int, array_like]
Uniform proabability between 0 and 1
- Returns:
- Union[float, array_like]
- cdf(val)
Calculate CDF for sample values
- Parameters:
- valUnion[float, int, array_like]
Sample values
- Returns:
- float