lars
f4c2545e8b
Replace the monolithic giant/analysis.py (predict-local + RolloutVsTruth
diagnostics) with a lean giant/analysis/ package that compares one
autoregressive `giant rollout` for a checkpoint against a held-out
miniCaloSim reference file, and generates publication-styled plots in
parallel on HTCondor.
Rollout output and a raw reference file share a world-frame physical
column subset under identical names, so the old ALR/local-frame decode
machinery is gone — everything is world-frame mm/MeV.
- sources.py: canonical LazyFrames, synthetic-termination-row filtering,
the secondary view (rollout generation>0 tracks vs reference sec_*_list).
- reduce.py: streaming primitives — a single hist1d group_by pass, per-event
scalars, edep-weighted depth/transverse profiles, species share, leakage.
- context.py/grouping.py: prep resolves fixed bin edges + energy/pdg/material
group sets once into shared.json, so each compute job is one pass, no range
scan (histogram efficiency).
- catalog.py: declarative PlotSpec registry — marginals x {overall,energy,pdg,
material}, per-event totals, shower profiles, species/leakage, secondaries.
- render.py: the only plotstyle/LaTeX importer; PDFs + gallery metadata.
- condor.py + `giant analyze` CLI (prep/compute-one/list/render/submit):
one job per plot, compute/render split (workers polars-only, no LaTeX).
Styling via ETPlot's plotstyle (added to the analysis extra). New tests cover
the reduce primitives, catalog id uniqueness + compute, condor submit, and a
guarded render smoke test. Delete the two predict-diagnostics notebooks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
85 lines
1.8 KiB
TOML
85 lines
1.8 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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# ETPlot's plotstyle (KIT matplotlib theme) + gallery CLI. Only the local
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# `giant analyze render` step imports it; compute workers never do.
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"gallery[plotting]",
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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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# ETPlot ships the `gallery` distribution (which provides the `plotstyle`
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# package under its `plotting` extra). Local checkout next to this repo; swap
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# for `{ git = "https://git.larsbogner.de/lars/ETPlot" }` off-machine.
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gallery = { path = "../ETPlot", editable = true }
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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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