pycbc.inference.jump package

Submodules

pycbc.inference.jump.angular module

Jump proposals that use cyclic boundaries on [0, 2pi).

class pycbc.inference.jump.angular.EpsieATAdaptiveAngular(parameters, adaptation_duration, componentwise=False, start_step=1, target_rate=None, jump_interval=1)[source]

Bases: epsie.proposals.angular.ATAdaptiveAngular

Adds from_config method to epsie’s adaptive angular proposal.

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

Loads a proposal from a config file.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveBoundedNormal and with_boundaries set to False (since the boundaries for the angular proposals are always \([0, 2\pi)\)). See that function for details on options that can be read.

Example:

[jump_proposal-ra]
name = adaptive_angular_proposal
adaptation-duration = 1000
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 angular proposal for use with epsie samplers.

Return type

epsie.proposals.AdaptiveAngularProposal

class pycbc.inference.jump.angular.EpsieAdaptiveAngular(parameters, adaptation_duration, jump_interval=1, start_step=1, **kwargs)[source]

Bases: epsie.proposals.angular.AdaptiveAngular

Adds from_config method to epsie’s adaptive angular proposal.

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

Loads a proposal from a config file.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveBoundedNormal and with_boundaries set to False (since the boundaries for the angular proposals are always \([0, 2\pi)\)). See that function for details on options that can be read.

Example:

[jump_proposal-ra]
name = adaptive_angular
adaptation-duration = 1000
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 angular proposal for use with epsie samplers.

Return type

epsie.proposals.AdaptiveAngular

class pycbc.inference.jump.angular.EpsieAngular(parameters, cov=None, jump_interval=1, jump_interval_duration=None)[source]

Bases: epsie.proposals.angular.Angular

Adds from_config method to epsie’s angular proposal.

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

Loads a proposal from a config file.

This calls epsie_from_config() with cls set to epsie.proposals.Angular and with_boundaries set to False. See that function for details on options that can be read.

Example:

[jump_proposal-ra]
name = angular
var-ra = 0.01
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 angular proposal for use with epsie samplers.

Return type

epsie.proposals.Angular

pycbc.inference.jump.bounded_normal module

Jump proposals that use a bounded normal distribution.

class pycbc.inference.jump.bounded_normal.EpsieATAdaptiveBoundedNormal(parameters, boundaries, adaptation_duration, componentwise=False, start_step=1, target_rate=None, jump_interval=1)[source]

Bases: epsie.proposals.bounded_normal.ATAdaptiveBoundedNormal

Adds from_config method to epsie’s adaptive bounded proposal.

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

Loads a proposal from a config file.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveBoundedProposal. See that function for details on options that can be read.

Example:

[jump_proposal-q]
name = adaptive_bounded_proposal
min-q = 1
max-q = 8
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 bounded proposal for use with epsie samplers.

Return type

epsie.proposals.AdaptiveBoundedProposal

class pycbc.inference.jump.bounded_normal.EpsieAdaptiveBoundedNormal(parameters, boundaries, adaptation_duration, start_step=1, jump_interval=1, **kwargs)[source]

Bases: epsie.proposals.bounded_normal.AdaptiveBoundedNormal

Adds from_config method to epsie’s adaptive normal proposal.

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

Loads a proposal from a config file.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveBoundedNormal. See that function for details on options that can be read.

Example:

[jump_proposal-q]
name = adaptive_bounded_normal
adaptation-duration = 1000
min-q = 1
max-q = 8
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.AdaptiveBoundedNormal

class pycbc.inference.jump.bounded_normal.EpsieBoundedNormal(parameters, boundaries, cov=None, jump_interval=1, jump_interval_duration=None)[source]

Bases: epsie.proposals.bounded_normal.BoundedNormal

Adds from_config method to epsie’s bounded normal proposal.

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

Loads a proposal from a config file.

This calls epsie_from_config() with cls set to epsie.proposals.BoundedNormal and with_boundaries set to True. See that function for details on options that can be read.

Example:

[jump_proposal-mchrip+q]
name = bounded_normal
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

A bounded normal proposal for use with epsie samplers.

Return type

epsie.proposals.BoundedNormal

pycbc.inference.jump.discrete module

Jump proposals that use a bounded normal distribution.

class pycbc.inference.jump.discrete.EpsieAdaptiveBoundedDiscrete(parameters, boundaries, adaptation_duration, successive=None, jump_interval=1, **kwargs)[source]

Bases: epsie.proposals.discrete.AdaptiveBoundedDiscrete

Adds from_config method to epsie’s adaptive bounded discrete proposal.

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

Loads a proposal from a config file.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveBoundedDiscrete. See that function for details on options that can be read.

Example:

[jump_proposal-index]
name = adaptive_bounded_discrete
adaptation-duration = 1000
min-index = 0
max-index = 42
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.AdaptiveBoundedDiscrete

class pycbc.inference.jump.discrete.EpsieAdaptiveNormalDiscrete(parameters, prior_widths, adaptation_duration, successive=None, jump_interval=1, **kwargs)[source]

Bases: epsie.proposals.discrete.AdaptiveNormalDiscrete

Adds from_config method to epsie’s adaptive bounded discrete proposal.

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

Loads a proposal from a config file.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveNormalDiscrete. See that function for details on options that can be read.

Example:

[jump_proposal-index]
name = adaptive_normal_discrete
adaptation-duration = 1000
min-index = 0
max-index = 42
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.AdaptiveBoundedDiscrete

class pycbc.inference.jump.discrete.EpsieBoundedDiscrete(parameters, boundaries, cov=None, successive=None, jump_interval=1, jump_interval_duration=None)[source]

Bases: epsie.proposals.discrete.BoundedDiscrete

Adds from_config method to epsie’s bounded discrete proposal.

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

Loads a proposal from a config file.

This calls epsie_from_config() with cls set to epsie.proposals.BoundedDiscrete and with_boundaries set to True. See that function for details on options that can be read.

Example:

[jump_proposal-index]
name = bounded_discrete
min-index = 0
max-index = 19
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 bounded discrete proposal for use with epsie samplers.

Return type

epsie.proposals.BoundedDiscrete

class pycbc.inference.jump.discrete.EpsieNormalDiscrete(parameters, cov=None, successive=None, jump_interval=1, jump_interval_duration=None)[source]

Bases: epsie.proposals.discrete.NormalDiscrete

Adds from_config method to epsie’s normal discrete proposal.

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

Loads a proposal from a config file.

This calls epsie_from_config() with cls set to epsie.proposals.NormalDiscrete and with_boundaries set to False. See that function for details on options that can be read.

Example:

[jump_proposal-index]
name = discrete
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 bounded discrete proposal for use with epsie samplers.

Return type

epsie.proposals.BoundedDiscrete

pycbc.inference.jump.normal module

Jump proposals that use a normal distribution.

class pycbc.inference.jump.normal.EpsieATAdaptiveNormal(parameters, adaptation_duration, diagonal=False, componentwise=False, start_step=1, target_rate=None, jump_interval=1)[source]

Bases: epsie.proposals.normal.ATAdaptiveNormal

Adds from_config method to epsie’s ATAdaptiveProposal.

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

Loads a proposal from a config file.

This calls epsie_from_config() with cls set to epsie.proposals.AdaptiveProposal and with_boundaries set to False. See that function for details on options that can be read.

Example:

[jump_proposal-mchrip+q]
name = adaptive_proposal
diagonal =
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 proposal for use with epsie samplers.

Return type

epsie.proposals.AdaptiveProposal

class pycbc.inference.jump.normal.EpsieAdaptiveNormal(parameters, prior_widths, adaptation_duration, start_step=1, jump_interval=1, **kwargs)[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.

This calls epsie_adaptive_from_config() with cls set to epsie.proposals.AdaptiveNormal. See that function for details on options that can be read.

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, jump_interval=1, jump_interval_duration=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.

This calls epsie_from_config() with cls set to epsie.proposals.Normal and with_boundaries set to False. See that function for details on options that can be read.

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

pycbc.inference.jump.normal.epsie_adaptive_from_config(cls, cp, section, tag, with_boundaries=True, boundary_arg_name='boundaries')[source]

Generic function for loading adaptive epsie proposals 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:

  • namestr

    Required. Must match the name of the proposal.

  • adaptation-durationint

    Required. Sets the adaptation_duration.

  • min-{param} : float

  • max-{param}float

    The bounds on each parameter. Required if with_boundaries is set to True, in which case bounds must be provided for every parameter.

  • var-{param}float

    Optional. Initial variance to use. If not provided, will use a default based on the bounds (see epsie.proposals.AdaptiveSupport for details).

  • adaptation-decayint

    Optional. Sets the adaptation_decay. If not provided, will use the class’s default.

  • start-iterationint

    Optional. Sets the start_iteration.If not provided, will use the class’s default.

  • target-ratefloat

    Optional. Sets the target_rate. If not provided, will use the class’s default.

Parameters

  • cls (epsie.proposals.BaseProposal) – The epsie proposal class to initialize. The class should have epsie.proposals.normal.AdaptiveSupport.

  • 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.

  • with_boundaries (bool, optional) – Try to load boundaries from the section and pass a boundaries argument to the class’s initialization. Default is True.

  • boundary_arg_name (str, optional) – The name of the argument for the boundaries (only used if with_boundaries is True). Provided because some adaptive proposals that only need the boundary widths call this prior_widths. Default is 'boundaries'.

Returns

The class initialized with the options read from the config file.

Return type

cls

pycbc.inference.jump.normal.epsie_at_adaptive_from_config(cls, cp, section, tag, with_boundaries=False)[source]

Generic function for loading AT Adaptive Normal proposals 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:

  • namestr

    Required. Must match the name of the proposal.

  • adaptation-durationint

    Sets the adaptation_duration. If not provided will use the class’s default.

  • diagonalbool, optional

    Determines whether only to adapt the variance. If True will only train the diagonal elements.

  • componentwisebool, optional

    Whether to include a componentwise scaling of the parameters. By default set to False. Componentwise scaling ndim times more expensive than global scaling.

  • min-{param} : float

  • max-{param}float

    The bounds on each parameter. Required if with_boundaries is set to True, in which case bounds must be provided for every parameter.

  • start-iterationint

    Optional. Sets the start_iteration. If not provided, will use the class’s default.

  • target-ratefloat

    Optional. Sets the target_rate. If not provided, will use the class’s default.

Parameters

  • cls (epsie.proposals.BaseProposal) – The epsie proposal class to initialize. The class should have epsie.proposals.normal.AdaptiveSupport.

  • 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.

  • with_boundaries (bool, optional) – Try to load boundaries from the section and pass a boundaries argument to the class’s initialization. Default is True.

Returns

The class initialized with the options read from the config file.

Return type

cls

pycbc.inference.jump.normal.epsie_from_config(cls, cp, section, tag, with_boundaries=False)[source]

Generic function for loading epsie proposals from a config file.

This should be used for proposals that are not adaptive.

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:

  • namestr

    Required. Must match the name of the proposal.

  • var-{param}float

    Optional. Variance to use for parameter {param}. If with_boundaries is True, then any parameter not specified will use a default variance of \((\Delta p/10)^2\), where \(\Delta p\) is the boundary width for that parameter. If with_boundaries is False, will use a default value of 1.

  • min-{param} : float

  • max-{param}float

    The bounds on each parameter. Required if with_boundaries is set to True, in which case bounds must be provided for every parameter.

Parameters

  • cls (epsie.proposals.BaseProposal) – The epsie proposal class to initialize.

  • 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.

  • with_boundaries (bool, optional) – Try to load boundaries from the section and pass a boundaries argument to the class’s initialization. This should be set to true for bounded proposals. Default is False.

Returns

The class initialized with the options read from the config file.

Return type

cls

pycbc.inference.jump.normal.get_epsie_adaptation_settings(opts, name=None)[source]

Get settings for Epsie adaptive proposals from a config file.

This requires that adaptation_duration is in the given dictionary. It will also look for adaptation_decay, start_iteration, and target_rate, but these are optional. Arguments will be popped from the given dictionary.

Parameters

  • opts (dict) – Dictionary of option -> value that was loaded from a config file section.

  • name (str (optional)) – Proposal name

Returns

Dictionary of argument name -> values.

Return type

dict

pycbc.inference.jump.normal.get_epsie_discrete_successive_settings(params, opts)[source]

Get settings for Epsie successive discrete proposal successive jumps from a config file.

If successive is not defined for a parameter then assumes successive jumps are not allowed (i.e. jumps from an integer to the same integer). Arguments will be popped from the given dictionary.

Example::

[jump_proposal-k+n] name = discrete successive-k =

This example sets successive jumps for k but does not do so for n.

Parameters

  • params (list of str) – List of parameter names to get the successive option for.

  • opts (dict) – Dictionary of option -> value that was loaded from a config file section.

Returns

Dictionary of parameter names -> bools

Return type

dict

pycbc.inference.jump.normal.get_param_boundaries(params, opts)[source]

Gets parameter boundaries for jump proposals.

The syntax for the options should be (min|max)_{param} = value. Both a minimum and maximum should be provided for every parameter in params. If the opts are created using load_opts, then the options can be formatted as (min|max)-{param}, since that function will turn all - to _ in option names.

Arguments will be popped from the given opts dictionary.

Parameters

  • params (list of str) – List of parameter names to get boundaries for.

  • opts (dict) – Dictionary of option -> value that was loaded from a config file section.

Returns

Dictionary of parameter names -> epsie.proposals.Boundaries

Return type

dict

pycbc.inference.jump.normal.get_variance(params, opts, default=1.0)[source]

Gets variance for jump proposals from the dictionary of options.

This looks for var_{param} for every parameter listed in params. If found, the argument is popped from the given opts dictionary. If not found, default will be used.

Parameters

  • params (list of str) – List of parameter names to look for.

  • opts (dict) – Dictionary of option -> value that was loaded from a config file section.

  • default (float, optional) – Default value to use for parameters that do not have variances provided. Default is 1.

Returns

Array of variances to use. Order is the same as the parameter names given in params.

Return type

numpy.array

pycbc.inference.jump.normal.load_opts(cp, section, tag, skip=None)[source]

Loads config options for jump proposals.

All - in option names are converted to _ before returning.

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.

  • skip (list, optional) – List of option names to skip loading.

Returns

  • params (list) – List of parameter names the jump proposal is for.

  • opts (dict) – Dictionary of option names -> values, where all values are strings.

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

List of the proposal instances.

Return type

list