lars
68fb99bed8
Adds model.conditioning = "physical" | "embedding": physical mode routes particle mass/charge and material Z_eff/A_eff/density/X0/lambda_int through small MLPs to replace the learned PDG/material embedding tables, so the surrogate generalizes to PDG codes/materials outside the training vocab instead of memorizing it. "embedding" stays available as the comparison baseline (old checkpoints without the key default to it). Stage 2 now regresses a secondary's mass/charge directly against a fixed physics-derived target instead of a learned/snapped embedding, and uses no snapping at inference — the model's raw predicted (mass, charge) is the secondary's physical identity, including for its own further rollout steps. A separate reporting-only nearest-known-PDG lookup (never fed back into the model) populates output pdg columns / the embedding-mode rollout fallback. giant/materials.py's table is populated with Geant4's own built-in NIST constants (Z_eff, A_eff, density, X0, lambda_int), extracted directly from the Geant4 11.4.1 build vendored in minicalosim via G4NistManager rather than hand-typed literature values. G4_LYSO is left unfilled: confirmed (both by runtime lookup and by searching minicalosim's history) that it's never actually a constructed Geant4 material there, only documentation/UI color-map text. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
78 lines
1.4 KiB
TOML
78 lines
1.4 KiB
TOML
[project]
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name = "giant"
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version = "0.1.0"
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description = "Geant4 step-function surrogate via conditional flow matching"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"numpy>=1.26,<3",
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"pandas>=2.2,<4",
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"pyarrow>=16,<25",
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"tqdm>=4.60,<5",
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"typer>=0.12,<1",
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"pyyaml>=6,<7",
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"particle>=1.0,<2",
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]
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[project.optional-dependencies]
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cpu = [
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"torch>=2.3,<2.4",
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]
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cuda = [
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"torch>=2.3,<2.4",
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]
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dev = [
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"pytest>=8,<10",
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"ruff>=0.15,<1",
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"ty>=0.0.50,<0.1",
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"giant[convert,analysis,geometry]",
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]
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geometry = [
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"scikit-learn>=1.4,<2",
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]
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convert = [
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"uproot>=5.3,<6",
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"awkward>=2.6,<3",
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"polars>=1.0,<2",
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]
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analysis = [
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"matplotlib>=3.8,<4",
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"polars>=1.0,<2",
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"ipykernel>=7.3.0",
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]
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[project.scripts]
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giant = "giant.cli:app"
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dwarf = "scripts.dwarf:app"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["giant", "scripts"]
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[tool.uv]
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conflicts = [
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[
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{ extra = "cpu" },
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{ extra = "cuda" },
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],
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]
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[tool.uv.sources]
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torch = [
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{ index = "pytorch-cpu", extra = "cpu" },
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{ index = "pytorch-cu118", extra = "cuda" },
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]
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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[[tool.uv.index]]
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name = "pytorch-cu118"
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url = "https://download.pytorch.org/whl/cu118"
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explicit = true
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