# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Commands

```bash
uv sync              # install dependencies (including dev extras: uv sync --extra dev)
pytest               # run tests
python scripts/train.py --data path/to/steps.parquet --mode flow   # train (flow matching)
python scripts/train.py --data path/to/steps.parquet --mode ddpm   # train (DDPM baseline)
```

## Architecture

GIANT is a conditional generative surrogate for the Geant4 step function. It replaces the stochastic physics engine: given a pre-step particle state (conditioning), it samples a post-step outcome.

**Data pipeline** (`giant/data/`): parquet files from miniCaloSim are loaded into numpy arrays (`loader.py`), then log-transformed and rotated into a local coordinate frame where `pre_dir = ẑ` (`transforms.py`), before being wrapped in a PyTorch `Dataset` (`dataset.py`). Train/val split is by `event_id` to avoid leaking correlated steps from the same shower.

**Output space (6D):** `step_length` (log), `ΔE` (log), `edep` (log), and `post_dir` as a unit vector in the local frame.

**Conditioning vector:** PDG code (embedding), pre-step position, log(pre-energy), pre-step direction, material (embedding), layer ID, number of secondaries (Phase 1 only — see Roadmap below).

**Model** (`giant/model/`): `DenoisingMLP` built from `ResBlock`s, with a `SinusoidalEmbedding` for the diffusion/flow time variable and a `ConditionEncoder` that fuses all conditioning inputs. `schedule.py` provides both a `CosineSchedule` for DDPM and the flow matching loss utilities (Lipman et al. 2022 conditional flow matching).

**Samplers** (`giant/sample.py`): DDPM, DDIM, and flow matching (ODE integration, ~10 steps). Flow matching is the primary mode.

**Validation** (`giant/validate.py`): step-level marginal comparisons; shower-level rollout validation is planned.

## Roadmap

Phase 1 (current): number of secondaries is a conditioning input — model predicts only 6D post-step kinematics.

Phase 2 (target): model must jointly predict the number of secondaries and all their properties (energy, direction, species), requiring an extended output space and likely a set-based or autoregressive generation scheme for the variable-length secondary list.