add acceleration option to JointPrimaryMarginalizedModel likelihood #4688
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@ahnitz As you suggested, I have made this PR a general one. The specific LISA multiband one will be in another PR. |
| @@ -723,24 +733,43 @@ def total_loglr(self): | |||
| # not using self.primary_model.current_params, because others_model | |||
| # may have its own static parameters | |||
| current_params_other = other_model.current_params.copy() | |||
| for i in range(nums): | |||
| if not self.accelerate_loglr: | |||
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Can you choose a more descriptive name to what the option does (e.g. how does it make the likelihood faster than than just the fact that it does)
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Is self.static_margin_params_in_other_models better?
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@ahnitz In this PR, we don't apply any amplitude and phase correction (as general as possible), so I will not include these in the option name.
| @@ -640,6 +626,11 @@ def __init__(self, variable_params, submodels, **kwargs): | |||
| self.other_models.pop(kwargs['primary_lbl'][0]) | |||
| self.other_models = list(self.other_models.values()) | |||
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| # determine whether to accelerate total_loglr | |||
| from pycbc.inference.models.tools import str_to_bool | |||
| self.static_margin_params_in_other_models = \ | |||
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This still has the old name in the config file. Also, why not just do
self.static_margin_params = 'static_margin_params' in kwargs
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@ahnitz OK, I have updated.
| sh_others = sh_others[0] | ||
| hh_others = hh_others[0] | ||
| sh_total = sh_primary + sh_others | ||
| hh_total = hh_primary + hh_others | ||
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| # calculate marginalize_vector_weights | ||
| self.primary_model.marginalize_vector_weights = \ |
@ahnitz This PR adds an acceleration option to JointPrimaryMarginalizedModel likelihood by assuming all extrinsic parameters can be fixed.