pycbc.inference.jump package

Submodules

pycbc.inference.jump.normal module

Jump proposals that use a normal distribution.

class pycbc.inference.jump.normal.EpsieAdaptiveNormal(parameters, prior_widths, adaptation_duration, adaptation_decay=None, start_iteration=1, target_rate=0.234, initial_std=None)[source]

Bases: epsie.proposals.normal.AdaptiveNormal

Adds from_config method to epsie’s adaptive normal proposal.

classmethod from_config(cp, section, tag)[source]

Loads a proposal from a config file.

The section that is read should have the format [{section}-{tag}], where {tag} is a pycbc.VARARGS_DELIM separated list of the parameters to create the jump proposal for.

Options that are read:

  • name : str
    Required. Must match the name of the proposal.
  • adaptation-duration : int
    Required. Sets the adaptation_duration.
  • min-{param} : float
  • max-{param} : float
    Required. Bounds must be provided for every parameter. These are used to determine the prior widths.
  • adaptation-decay : int
    Optional. Sets the adaptation_decay. If not provided, will use the class’s default.
  • start-iteration : int
    Optional. Sets the start_iteration.If not provided, will use the class’s default.
  • target-rate : float
    Optional. Sets the target_rate. If not provided, will use the class’s default.

Note

The min and max parameter bounds are only used for setting the width of the covariance of the proposal; they are not used as bounds on the proposal itself. In other words, it is possible to get proposals outside of the given min and max values.

Example:

[jump_proposal-mchirp+q]
name = adaptive_normal
adaptation-duration = 1000
min-q = 1
max-q = 8
min-mchirp = 20
max-mchirp = 80
Parameters:
  • cp (WorkflowConfigParser instance) – Config file to read from.
  • section (str) – The name of the section to look in.
  • tag (str) – pycbc.VARARGS_DELIM separated list of parameter names to create proposals for.
Returns:

An adaptive normal proposal for use with epsie samplers.
Return type:

epsie.proposals.AdaptiveNormal
class pycbc.inference.jump.normal.EpsieNormal(parameters, cov=None)[source]

Bases: epsie.proposals.normal.Normal

Adds from_config method to epsie’s normal proposal.

classmethod from_config(cp, section, tag)[source]

Loads a proposal from a config file.

The section that is read should have the format [{section}-{tag}], where {tag} is a pycbc.VARARGS_DELIM separated list of the parameters to create the jump proposal for.

Variances for each parameter may also be specified, by giving options var-{param} = val. Any parameter not specified will use a default variance of 1.

Example:

[jump_proposal-mchrip+q]
name = normal
var-q = 0.1
Parameters:
  • cp (WorkflowConfigParser instance) – Config file to read from.
  • section (str) – The name of the section to look in.
  • tag (str) – pycbc.VARARGS_DELIM separated list of parameter names to create proposals for.
Returns:

A normal proposal for use with epsie samplers.
Return type:

epsie.proposals.Normal

Module contents

Provides custom jump proposals for samplers.

pycbc.inference.jump.epsie_proposals_from_config(cp, section='jump_proposal')[source]

Loads epsie jump proposals from the given config file.

This loads jump proposals from sub-sections starting with section (default is ‘jump_proposal’). The tag part of the sub-sections’ headers should list the parameters the proposal is to be used for.

Example:

[jump_proposal-mtotal+q]
name = adaptive_normal
adaptation-duration = 1000
min-q = 1
max-q = 8
min-mtotal = 20
max-mtotal = 160

[jump_proposal-spin1_a]
name = normal
Parameters:
  • cp (WorkflowConfigParser instance) – The config file to read.
  • section (str, optional) – The section name to read jump proposals from. Default is 'jump_proposal'.
Returns:

Dictionary mapping parameter names to proposal instances.
Return type:

dict