Improve adduct/isotope generation [WIP]

[joewandy]

WIP -- need human review

Motivation

• Improve realism of simulated isotope envelopes by moving from a C-only/binomial shortcut to multi-element isotope convolution so tests better reflect real elemental patterns.
• Provide configurable adduct priors and named adduct profile presets plus per-compound/profile selection so adduct mixtures can match different ESI regimes.
• Add a small, intensity-aware deisotoping implementation to validate the new generator while leaving UnknownChemical behavior unchanged so ROI-derived workflows are not altered.

Description

• Added multi-element isotope data and adduct prior presets in vimms/Common.py (NATURAL_ISOTOPES, ADDUCT_PRIOR_POS, ADDUCT_PRIOR_NEG, ADDUCT_PROFILE_PRESETS).
• Reworked Isotopes in vimms/Chemicals.py to compute isotope envelopes by element-wise convolution with exposed parameters (min_prob, max_peaks, max_states, mass_precision) for a fidelity/performance tradeoff.
• Enhanced Adducts in vimms/Chemicals.py to accept adduct_profile and adduct_concentration (Dirichlet), support callable/ per-formula priors, apply cutoff + sum normalization, and propagated profile settings through ChemicalMixtureCreator.
• Added vimms/Deisotoping.py implementing Deisotoper with multi-element isotope mass-diff candidates and intensity-aware clustering rules to support heavier-isotope patterns (e.g., Cl/Br M+2).
• Added tests in tests/test_deisotoping.py covering multi-element isotope generation, a chlorine M+2 check, and end-to-end deisotoping scenarios (including an M+2-only case).

Testing

• Ran pytest -q tests/test_deisotoping.py which attempted to exercise the new isotope and deisotoper tests but failed with ModuleNotFoundError: No module named 'numpy' in this environment and so the automated tests did not complete successfully.
• No other automated test suites were executed in this environment after the changes were applied.

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[chatgpt-codex-connector]

Preserve monoisotope when pruning isotope distribution

The new filtering step drops any peaks below min_prob (and peaks may already be dropped by max_states), but nothing guarantees the zero-shift monoisotopic peak survives. For large formulas or tighter min_prob/max_states, the monoisotope can be pruned, so get_isotopes returns a list where index 0 is no longer monoisotopic. Downstream code treats isotopes[0] as the monoisotopic reference (e.g., get_mz_msn in vimms/MassSpecUtils.py subtracts isotopes[0][0]), so this can shift all m/z offsets and intensities. Consider explicitly retaining the zero-shift peak (or re-inserting it) before normalization.

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