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… marginalization Implements Thrane & Talbot (2019) Eq. 79: phase is marginalized first analytically via Bessel function, then distance is marginalized numerically. Inherits all distance grid setup from DistanceMarginalizedLikelihoodFD. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@src/jimgw/core/single_event/likelihood.py`:
- Line 537: The method _likelihood currently has an unused parameter named data
which triggers Ruff ARG002; rename the parameter to _data in the _likelihood
method signature and update any internal references (if any) to use _data so the
linter recognizes the parameter as intentionally unused — adjust the definition
of def _likelihood(self, params: dict[str, Float], data: dict) -> Float to use
_data instead and run tests/lint to confirm the warning is resolved.
- Around line 529-535: The evaluate method currently applies
self.fixed_parameters then unconditionally sets params["phase_c"] = 0.0 which
silently ignores any user-supplied phase_c; modify evaluate (method evaluate,
symbol fixed_parameters, and call to _likelihood) to detect if "phase_c" is
present in self.fixed_parameters and raise a clear ValueError (fail fast)
explaining that phase_c cannot be fixed when phase is marginalised, before
overwriting params["phase_c"]; keep the remaining assignments (d_L,
trigger_time, gmst) and then call self._likelihood(params, data).
Comment on lines
+529
to
+535
| def evaluate(self, params: dict[str, Float], data: dict) -> Float: | ||
| params.update(self.fixed_parameters) | ||
| params["d_L"] = self.ref_dist | ||
| params["phase_c"] = 0.0 | ||
| params["trigger_time"] = self.trigger_time | ||
| params["gmst"] = self.gmst | ||
| return self._likelihood(params, data) |
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Reject fixed phase_c when phase is marginalised.
Line 530 applies fixed_parameters, but Line 532 always overrides phase_c = 0.0. If phase_c is supplied in fixed parameters, it is silently ignored. Please fail fast to avoid hidden misconfiguration.
🔧 Proposed fix
def evaluate(self, params: dict[str, Float], data: dict) -> Float:
+ if "phase_c" in self.fixed_parameters:
+ raise ValueError("Cannot have phase_c fixed while marginalising over phase_c")
params.update(self.fixed_parameters)
params["d_L"] = self.ref_dist
params["phase_c"] = 0.0
params["trigger_time"] = self.trigger_time
params["gmst"] = self.gmst
return self._likelihood(params, data)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def evaluate(self, params: dict[str, Float], data: dict) -> Float: | |
| params.update(self.fixed_parameters) | |
| params["d_L"] = self.ref_dist | |
| params["phase_c"] = 0.0 | |
| params["trigger_time"] = self.trigger_time | |
| params["gmst"] = self.gmst | |
| return self._likelihood(params, data) | |
| def evaluate(self, params: dict[str, Float], data: dict) -> Float: | |
| if "phase_c" in self.fixed_parameters: | |
| raise ValueError("Cannot have phase_c fixed while marginalising over phase_c") | |
| params.update(self.fixed_parameters) | |
| params["d_L"] = self.ref_dist | |
| params["phase_c"] = 0.0 | |
| params["trigger_time"] = self.trigger_time | |
| params["gmst"] = self.gmst | |
| return self._likelihood(params, data) |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/jimgw/core/single_event/likelihood.py` around lines 529 - 535, The
evaluate method currently applies self.fixed_parameters then unconditionally
sets params["phase_c"] = 0.0 which silently ignores any user-supplied phase_c;
modify evaluate (method evaluate, symbol fixed_parameters, and call to
_likelihood) to detect if "phase_c" is present in self.fixed_parameters and
raise a clear ValueError (fail fast) explaining that phase_c cannot be fixed
when phase is marginalised, before overwriting params["phase_c"]; keep the
remaining assignments (d_L, trigger_time, gmst) and then call
self._likelihood(params, data).
| params["gmst"] = self.gmst | ||
| return self._likelihood(params, data) | ||
|
|
||
| def _likelihood(self, params: dict[str, Float], data: dict) -> Float: |
There was a problem hiding this comment.
Address Ruff ARG002 for unused _likelihood argument.
data is unused in this override; rename it to _data to make intent explicit and clear the warning.
🧹 Proposed fix
- def _likelihood(self, params: dict[str, Float], data: dict) -> Float:
+ def _likelihood(self, params: dict[str, Float], _data: dict) -> Float:📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def _likelihood(self, params: dict[str, Float], data: dict) -> Float: | |
| def _likelihood(self, params: dict[str, Float], _data: dict) -> Float: |
🧰 Tools
🪛 Ruff (0.15.2)
[warning] 537-537: Unused method argument: data
(ARG002)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/jimgw/core/single_event/likelihood.py` at line 537, The method
_likelihood currently has an unused parameter named data which triggers Ruff
ARG002; rename the parameter to _data in the _likelihood method signature and
update any internal references (if any) to use _data so the linter recognizes
the parameter as intentionally unused — adjust the definition of def
_likelihood(self, params: dict[str, Float], data: dict) -> Float to use _data
instead and run tests/lint to confirm the warning is resolved.
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Summary
PhaseDistanceMarginalizedLikelihoodFDclass implementing combined phase + distance marginalization following Thrane & Talbot (2019), Section C.5 (Eq. 79)I_0, then distance is marginalized numerically over a pre-computed grid vialogsumexpDistanceMarginalizedLikelihoodFD(merged in PR Add distance-marginalized likelihood #84)phase_cnord_Lare sampling parameters — reduces dimensionality by 2Implementation
The integrand at each distance grid point i is:
where
kappa²_C_refis the complex matched-filter inner product at the reference distance (computed viacomplex_inner_product),s_i = D_0/D_iis the scaling factor, andlog_w_iare the normalized log prior weights.Verification
Tested against bilby's
GravitationalWaveTransientwithphase_marginalization=True, distance_marginalization=Trueusing shared 4s injection data (IMRPhenomD, M_c=35, q=0.9, 3-detector H1/L1/V1):The 0.0026 difference is due to bilby's 2D lookup table with bicubic spline interpolation vs Jim's direct quadrature (same as distance-only marginalization in PR #84).
Test plan
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