lars
80b198c1a5
load_predicted_local now reads predict parquet via a lazy polars scan with column projection pushed into the reader, instead of materializing the whole file as a pandas DataFrame. Also adds marginal_table_pl and constraint_report_pl, polars-native duplicates that read straight from a predict parquet path/LazyFrame and stay lazy per (group, dim) pair, so peak memory is one column slice rather than the whole SampleCollection. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
38 lines
643 B
TOML
38 lines
643 B
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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"torch>=2.3",
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"numpy>=1.26",
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"pandas>=2.2",
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"pyarrow>=16",
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"typer>=0.12",
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]
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[project.optional-dependencies]
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dev = [
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"pytest>=8",
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]
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convert = [
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"uproot>=5.3",
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"awkward>=2.6",
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"polars>=1.0",
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]
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analysis = [
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"matplotlib>=3.8",
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"polars>=1.0",
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]
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[project.scripts]
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giant = "giant.cli: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"]
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