refactor(model_util): replace simulator_start_here_model_from with direct random_galaxy_for_simulation_from#438
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…rect random_galaxy_for_simulation_from The old helper built an af.Model around lp_snr.Sersic and exposed signal_to_noise_ratio as a UniformPrior — making model.info print look like the reader was fitting SNR (they were not, the model was only used for .random_instance()). Replace with random_galaxy_for_simulation_from, which returns a concrete Galaxy by sampling each parameter directly from a numpy.random.Generator and constructing lp_snr.Sersic as a profile *instance*. SNR is a property of the data, not a fit parameter; this removes the misleading prior surface. Also exports SIMULATOR_RANDOM_GALAXY_SUMMARY as a one-line replacement for the verbose model.info print used by the start_here scripts. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
simulator_start_here_model_from()— it built anaf.Model(lp_snr.Sersic)withsignal_to_noise_ratio = af.UniformPrior(20, 60), makingmodel.infoprint look like the user was fitting SNR.random_galaxy_for_simulation_from(rng=None)— returns a concreteag.Galaxywhose bulge is anlp_snr.Sersicinstance sampled directly from anumpy.random.Generator. SNR appears only as a profile attribute on the instance, never as a fitting parameter.SIMULATOR_RANDOM_GALAXY_SUMMARY(one-line replacement for the verbosemodel.infoprint used by start_here scripts).Test plan
pytest test_autogalaxy/ -x -q— 908 passedsignal_to_noise_ratio.*Priorreturns zero hits acrossautogalaxy/python -c "import autogalaxy as ag; assert not hasattr(ag.model_util, 'simulator_start_here_model_from')"passesCoordinated PRs
feature/fix-snr-model-smell-and-ci(sibling change foral.model_util)feature/fix-snr-model-smell-and-ci(callers + CI fix)feature/fix-snr-model-smell-and-ci(callers)Library-first merge gate per CLAUDE.md: this PR + the PyAutoLens PR should merge before the workspace PRs.
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