Add giant.analysis module for notebook-based model quality diagnostics

Provides stratified marginal comparisons, joint-structure checks (correlation
matrices, physically-coupled pairwise plots, direction alignment), and
physical-constraint validation (unit-norm directions, non-negative raw
targets) for a trained model's generated samples, building on the aggregate
marginal/KL check already in giant.validate.

Supports two entry points: live sampling against a checkpoint + val data
(load_model_bundle/collect_samples), or loading a precomputed
`giant predict --coord local` parquet directly (load_predicted_local) without
needing the checkpoint at all. Predict output is now tagged with parquet
schema metadata so the loader can verify a file's format and reject
coord=global or untagged files with a clear error instead of guessing from
column names.

Also extends the config git-hash mismatch warning (added for --config
loading) to checkpoint loading: both `giant predict` and
analysis.load_model_bundle now look for a config.toml next to the checkpoint
and warn (without failing) if it was generated from a different git commit.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

giant/analysis.py

@@ -0,0 +1,565 @@
+"""Notebook-friendly diagnostics for a trained model's sample quality.
+
+Typical use from a Jupyter notebook::
+
+    from giant.analysis import load_model_bundle, make_val_loader, collect_samples
+    from giant.analysis import plot_marginals, plot_correlation_matrices, plot_pairwise
+    from giant.analysis import plot_direction_alignment, plot_constraint_violations
+
+    bundle = load_model_bundle("runs/my_run/best.pt")
+    val_loader = make_val_loader(bundle, "path/to/steps.parquet")
+    samples = collect_samples(bundle, val_loader)
+
+    plot_marginals(samples, group_by="energy")
+    plot_correlation_matrices(samples)
+    plot_pairwise(samples)
+    plot_direction_alignment(samples)
+    plot_constraint_violations(samples)
+
+If predictions were already generated offline via `giant predict --coord local`,
+skip the checkpoint/model entirely and load the parquet directly::
+
+    from giant.analysis import load_predicted_local
+    samples = load_predicted_local("path/to/steps_predicted_local.parquet")
+
+(`--coord global` output isn't supported here — it has no ground-truth columns
+to compare against.)
+
+Three tiers of checks, building on the aggregate marginal/KL check in
+`giant.validate.validate_marginals`:
+
+1. stratified marginals  — per-dimension real-vs-generated comparison, sliced by
+   pdg / material / energy so failures hidden by the aggregate don't go unnoticed.
+2. joint structure        — correlation matrices, physically-coupled pairwise
+   plots, and post/travel direction alignment, since marginals can match while
+   the model decorrelates targets that are coupled by the underlying physics.
+3. physical constraints   — unit-norm direction vectors and non-negative raw
+   step_length/delta_e/edep, checked in denormalized physical units; nothing in
+   the unconstrained MLP output enforces these, so violations are a pure
+   generation artifact.
+
+`collect_samples` takes a `steps` argument (forwarded to the flow ODE
+integrator) so a later sampler-step-count ablation can sweep it by calling this
+function repeatedly without any new plumbing.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+from torch.utils.data import DataLoader
+
+import pyarrow.parquet as pq
+
+from giant.config import warn_if_checkpoint_config_mismatch
+from giant.constants import (
+    LOCAL_TARGET_NAMES,
+    PREDICT_COORD_METADATA_KEY,
+    PREDICT_SCHEMA_VERSION,
+    PREDICT_SCHEMA_VERSION_KEY,
+)
+from giant.data.dataset import StepsDataset, train_val_split
+from giant.data.loader import find_parquet_files, load_steps
+from giant.data.transforms import Normalizer, build_features, inv_log_transform
+from giant.model.network import DenoisingMLP
+from giant.model.schedule import CosineSchedule
+from giant.sample import sample_ddim, sample_ddpm, sample_flow
+from giant.validate import _histogram_kl
+
+_N_LOG_DIMS = 3  # log_step_length, log_delta_e, log_edep are the first 3 target dims
+RAW_TARGET_NAMES = [n.removeprefix("log_") for n in LOCAL_TARGET_NAMES]
+
+
+def _hist_edges(*arrays: np.ndarray, bins: int) -> np.ndarray:
+    """Bin edges that don't blow up on near-constant data (e.g. a tight unit-norm cluster).
+
+    Plain `np.linspace(lo, hi, bins+1)` raises when `hi - lo` is too small relative
+    to float precision to support `bins` distinct edges, which is a real failure
+    mode here (a well-trained model can push direction norms to within float32
+    epsilon of 1.0), not just a test artifact.
+    """
+    lo = min(a.min() for a in arrays)
+    hi = max(a.max() for a in arrays)
+    if not (hi - lo > 1e-6 * max(abs(hi), 1.0)):
+        lo, hi = lo - 0.5, hi + 0.5
+    return np.linspace(lo, hi, bins + 1)
+
+
+@dataclass
+class ModelBundle:
+    model: torch.nn.Module
+    cond_normalizer: Normalizer
+    target_normalizer: Normalizer
+    pdg_map: dict[int, int]
+    mat_map: dict[str, int]
+    model_config: dict
+    mode: str
+    schedule: CosineSchedule | None
+    device: torch.device
+
+    @property
+    def idx_to_pdg(self) -> dict[int, int]:
+        return {v: k for k, v in self.pdg_map.items()}
+
+    @property
+    def idx_to_mat(self) -> dict[int, str]:
+        return {v: k for k, v in self.mat_map.items()}
+
+
+@dataclass
+class SampleCollection:
+    cond_cont_raw: np.ndarray  # (N, 9) denormalized conditioning (pre_E delogged)
+    pdg: np.ndarray  # (N,) raw PDG codes
+    material: np.ndarray  # (N,) raw material names
+    real_raw: np.ndarray  # (N, 9) denormalized + delogged real targets
+    gen_raw: np.ndarray  # (N, 9) denormalized + delogged generated targets
+    # Normalized-space (model's native training space) targets — only available
+    # when collected live with the normalizer (collect_samples). A predict
+    # parquet has already been denormalized on disk with no normalizer
+    # attached, so loaders built from one (e.g. load_predicted_local) leave
+    # these as None rather than fabricate a value.
+    real_norm: np.ndarray | None = None
+    gen_norm: np.ndarray | None = None
+
+
+def load_model_bundle(
+    ckpt_path: str | Path,
+    mode: str = "flow",
+    device: torch.device | None = None,
+) -> ModelBundle:
+    """Reconstruct a trained model and its normalizers/vocab from a training checkpoint."""
+    warn_if_checkpoint_config_mismatch(ckpt_path)
+    device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)
+
+    pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()}
+    mat_map = dict(ckpt["mat_map"])
+
+    model = DenoisingMLP(**ckpt["model_config"])
+    model.load_state_dict(ckpt["model"])
+    model.to(device).eval()
+
+    cond_normalizer = Normalizer.from_dict(ckpt["normalizer"]["cond"])
+    target_normalizer = Normalizer.from_dict(ckpt["normalizer"]["target"])
+    schedule = CosineSchedule().to(device) if mode != "flow" else None
+
+    return ModelBundle(
+        model=model, cond_normalizer=cond_normalizer, target_normalizer=target_normalizer,
+        pdg_map=pdg_map, mat_map=mat_map, model_config=ckpt["model_config"],
+        mode=mode, schedule=schedule, device=device,
+    )
+
+
+def make_val_loader(
+    bundle: ModelBundle,
+    data: str | Path,
+    val_fraction: float = 0.1,
+    seed: int = 42,
+    batch_size: int = 4096,
+) -> DataLoader:
+    """Build a DataLoader over the val split, normalized with the bundle's fitted stats.
+
+    Loads the whole file into memory — fine for typical validation-set sizes; for
+    very large datasets, build a StreamingStepsDataset directly (see giant.pipeline).
+    """
+    files = find_parquet_files(data)
+    chunks = [load_steps(f) for f in files]
+    full = {k: np.concatenate([c[k] for c in chunks]) for k in chunks[0]}
+
+    cond_cont, cond_cat, target, _, _ = build_features(
+        full, bundle.pdg_map, bundle.mat_map,
+        cond_normalizer=bundle.cond_normalizer, target_normalizer=bundle.target_normalizer,
+    )
+    _, val_ds = train_val_split(full, cond_cont, cond_cat, target, val_fraction=val_fraction, seed=seed)
+    return DataLoader(val_ds, batch_size=batch_size, shuffle=False)
+
+
+def _to_raw_targets(target_norm: np.ndarray, normalizer: Normalizer) -> np.ndarray:
+    raw = normalizer.inverse_transform(target_norm)
+    raw[:, :_N_LOG_DIMS] = inv_log_transform(raw[:, :_N_LOG_DIMS])
+    return raw
+
+
+@torch.no_grad()
+def collect_samples(
+    bundle: ModelBundle,
+    val_loader: DataLoader,
+    n_batches: int | None = None,
+    steps: int = 10,
+) -> SampleCollection:
+    """Run the sampler over `val_loader`, pairing generations with real targets + conditioning."""
+    model = bundle.model
+    device = bundle.device
+
+    cond_list, real_list, gen_list = [], [], []
+    for i, (cond_cont, cond_cat, x1) in enumerate(val_loader):
+        if n_batches is not None and i >= n_batches:
+            break
+        cond_cont = cond_cont.to(device)
+        cond_cat = cond_cat.to(device)
+
+        if bundle.mode == "flow":
+            gen = sample_flow(model, cond_cont, cond_cat, steps=steps)
+        elif bundle.mode == "ddpm":
+            gen = sample_ddpm(model, cond_cont, cond_cat, bundle.schedule)
+        else:
+            gen = sample_ddim(model, cond_cont, cond_cat, bundle.schedule)
+
+        cond_full = torch.cat([cond_cont.cpu(), cond_cat.cpu().float()], dim=-1)
+        cond_list.append(cond_full.numpy())
+        real_list.append(x1.numpy())
+        gen_list.append(gen.cpu().numpy())
+
+    cond_all = np.concatenate(cond_list, axis=0)
+    real_norm = np.concatenate(real_list, axis=0)
+    gen_norm = np.concatenate(gen_list, axis=0)
+
+    cond_cont_norm, cond_cat_arr = cond_all[:, :-2], cond_all[:, -2:]
+    cond_cont_raw = bundle.cond_normalizer.inverse_transform(cond_cont_norm)
+    cond_cont_raw[:, 3] = inv_log_transform(cond_cont_raw[:, 3])  # log(pre_E) -> pre_E
+
+    idx_to_pdg, idx_to_mat = bundle.idx_to_pdg, bundle.idx_to_mat
+    pdg = np.array([idx_to_pdg[i] for i in cond_cat_arr[:, 0].astype(np.int64)])
+    material = np.array([idx_to_mat[i] for i in cond_cat_arr[:, 1].astype(np.int64)])
+
+    return SampleCollection(
+        cond_cont_raw=cond_cont_raw, pdg=pdg, material=material,
+        real_raw=_to_raw_targets(real_norm, bundle.target_normalizer),
+        gen_raw=_to_raw_targets(gen_norm, bundle.target_normalizer),
+        real_norm=real_norm, gen_norm=gen_norm,
+    )
+
+
+def _check_predict_metadata(path: Path) -> None:
+    """Verify a parquet file's giant-predict tag before trusting its column layout.
+
+    Raises rather than warns: a wrong or missing tag means the column-layout
+    assumptions below don't hold, so silently proceeding could mix up which
+    columns are predictions vs. ground truth.
+    """
+    metadata = pq.read_schema(path).metadata or {}
+    coord = metadata.get(PREDICT_COORD_METADATA_KEY.encode())
+    if coord is None:
+        raise ValueError(
+            f"{path} has no '{PREDICT_COORD_METADATA_KEY}' parquet metadata — it wasn't "
+            "written by `giant predict` (or predates schema tagging), so its column "
+            "layout can't be verified"
+        )
+    if coord.decode() != "local":
+        raise ValueError(
+            f"{path} was written with --coord {coord.decode()!r}, not 'local' — "
+            "load_predicted_local only supports coord=local predict output, which "
+            "is the only mode that also writes ground-truth columns"
+        )
+    version = metadata.get(PREDICT_SCHEMA_VERSION_KEY.encode())
+    if version is not None and version.decode() != PREDICT_SCHEMA_VERSION:
+        raise ValueError(
+            f"{path} has predict schema version {version.decode()!r}, but "
+            f"giant.analysis expects {PREDICT_SCHEMA_VERSION!r} — column layout may "
+            "have changed; update load_predicted_local to match"
+        )
+
+
+def load_predicted_local(path: str | Path) -> SampleCollection:
+    """Build a SampleCollection from a `giant predict --coord local` parquet file.
+
+    Reads the `pred_*`/`true_*` columns directly — no checkpoint or model needed,
+    since the predict CLI already denormalized them into the same log-scaled,
+    local-frame space `collect_samples` produces internally before raw conversion.
+    Requires the file to carry the `giant predict` metadata tag (see
+    `_check_predict_metadata`); raises if it's missing or from --coord global,
+    rather than guessing from column names.
+    """
+    path = Path(path)
+    _check_predict_metadata(path)
+    df = pd.read_parquet(path)
+
+    gen_log_local = df[[f"pred_{name}" for name in LOCAL_TARGET_NAMES]].to_numpy(dtype=np.float32)
+    real_log_local = df[[f"true_{name}" for name in LOCAL_TARGET_NAMES]].to_numpy(dtype=np.float32)
+
+    def to_raw(log_local: np.ndarray) -> np.ndarray:
+        raw = log_local.copy()
+        raw[:, :_N_LOG_DIMS] = inv_log_transform(raw[:, :_N_LOG_DIMS])
+        return raw
+
+    cond_cont_raw = np.column_stack([
+        df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32),
+        df["pre_E"].to_numpy(dtype=np.float32),
+        df[["pre_dx", "pre_dy", "pre_dz"]].to_numpy(dtype=np.float32),
+        df["layer_id"].to_numpy(dtype=np.float32),
+        df["n_sec"].to_numpy(dtype=np.float32),
+    ]).astype(np.float32)
+
+    return SampleCollection(
+        cond_cont_raw=cond_cont_raw,
+        pdg=df["pdg"].to_numpy(),
+        material=df["material"].to_numpy(),
+        real_raw=to_raw(real_log_local),
+        gen_raw=to_raw(gen_log_local),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Tier 1: stratified marginals
+# ---------------------------------------------------------------------------
+
+def _group_labels(
+    collection: SampleCollection,
+    group_by: str | None,
+    n_energy_bins: int,
+) -> list[tuple[str, np.ndarray]]:
+    n = len(collection.pdg)
+    if group_by is None:
+        return [("all", np.ones(n, dtype=bool))]
+    if group_by == "pdg":
+        return [(f"pdg={v}", collection.pdg == v) for v in np.unique(collection.pdg)]
+    if group_by == "material":
+        return [(f"material={v}", collection.material == v) for v in np.unique(collection.material)]
+    if group_by == "energy":
+        pre_E = collection.cond_cont_raw[:, 3]
+        edges = np.quantile(pre_E, np.linspace(0, 1, n_energy_bins + 1))
+        edges[-1] += 1e-6
+        bin_idx = np.digitize(pre_E, edges[1:-1])
+        return [
+            (f"E∈[{edges[i]:.3g},{edges[i+1]:.3g})", bin_idx == i)
+            for i in range(n_energy_bins)
+        ]
+    raise ValueError(f"unknown group_by={group_by!r}")
+
+
+def marginal_table(
+    collection: SampleCollection,
+    group_by: str | None = None,
+    n_energy_bins: int = 4,
+    bins: int = 50,
+) -> pd.DataFrame:
+    """Per-dimension real-vs-generated summary stats + KL(real||gen), in raw units.
+
+    `group_by`: None for an aggregate table, or one of "pdg", "material", "energy"
+    to stratify rows by that conditioning variable. Sorted worst-KL first, so
+    failure modes hidden by the aggregate surface at the top.
+    """
+    rows = []
+    for label, mask in _group_labels(collection, group_by, n_energy_bins):
+        if mask.sum() < 2:
+            continue
+        real, gen = collection.real_raw[mask], collection.gen_raw[mask]
+        for j, name in enumerate(RAW_TARGET_NAMES):
+            rows.append({
+                "group": label, "dim": name, "n": int(mask.sum()),
+                "real_mean": real[:, j].mean(), "gen_mean": gen[:, j].mean(),
+                "real_std": real[:, j].std(), "gen_std": gen[:, j].std(),
+                "kl_real_gen": _histogram_kl(real[:, j], gen[:, j], bins=bins),
+            })
+    return pd.DataFrame(rows).sort_values("kl_real_gen", ascending=False).reset_index(drop=True)
+
+
+def plot_marginals(
+    collection: SampleCollection,
+    dims: list[str] | None = None,
+    group_by: str | None = None,
+    n_energy_bins: int = 4,
+    bins: int = 50,
+    max_groups: int = 6,
+    figsize_per_axis: tuple[float, float] = (3.5, 2.8),
+):
+    """Overlaid real-vs-generated histograms: one row per group, one column per dim.
+
+    Without `group_by`, a single row over the whole val set. With "pdg",
+    "material", or "energy", one row per stratum, worst-KL groups first
+    (capped at `max_groups`), so failures hidden by the aggregate are visible.
+    """
+    dims = dims or RAW_TARGET_NAMES
+    dim_idx = [RAW_TARGET_NAMES.index(d) for d in dims]
+    groups = _group_labels(collection, group_by, n_energy_bins)
+
+    if group_by is not None:
+        table = marginal_table(collection, group_by=group_by, n_energy_bins=n_energy_bins, bins=bins)
+        worst_first = table.groupby("group")["kl_real_gen"].max().sort_values(ascending=False)
+        order = {label: rank for rank, label in enumerate(worst_first.index)}
+        groups = sorted(groups, key=lambda g: order[g[0]])[:max_groups]
+
+    n_rows, n_cols = len(groups), len(dims)
+    fig, axes = plt.subplots(
+        n_rows, n_cols, squeeze=False,
+        figsize=(figsize_per_axis[0] * n_cols, figsize_per_axis[1] * n_rows),
+    )
+    for row, (label, mask) in enumerate(groups):
+        real, gen = collection.real_raw[mask], collection.gen_raw[mask]
+        for col, j in enumerate(dim_idx):
+            ax = axes[row][col]
+            edges = _hist_edges(real[:, j], gen[:, j], bins=bins)
+            ax.hist(real[:, j], bins=edges, density=True, alpha=0.5, label="real")
+            ax.hist(gen[:, j], bins=edges, density=True, alpha=0.5, label="generated")
+            if row == 0:
+                ax.set_title(dims[col], fontsize=9)
+            if col == 0:
+                ax.set_ylabel(label, fontsize=8)
+            if row == 0 and col == n_cols - 1:
+                ax.legend(fontsize=7)
+    fig.tight_layout()
+    return fig
+
+
+# ---------------------------------------------------------------------------
+# Tier 2: joint structure
+# ---------------------------------------------------------------------------
+
+def correlation_matrices(collection: SampleCollection) -> tuple[np.ndarray, np.ndarray]:
+    """Pearson correlation matrices of the raw targets, real vs generated."""
+    return (
+        np.corrcoef(collection.real_raw, rowvar=False),
+        np.corrcoef(collection.gen_raw, rowvar=False),
+    )
+
+
+def plot_correlation_matrices(collection: SampleCollection):
+    """Side-by-side real/generated correlation heatmaps, plus their difference."""
+    real_corr, gen_corr = correlation_matrices(collection)
+    diff = gen_corr - real_corr
+
+    fig, axes = plt.subplots(1, 3, figsize=(13, 4))
+    for ax, mat, title, cmap, vlim in [
+        (axes[0], real_corr, "real", "coolwarm", (-1, 1)),
+        (axes[1], gen_corr, "generated", "coolwarm", (-1, 1)),
+        (axes[2], diff, "generated − real", "PuOr", (-0.5, 0.5)),
+    ]:
+        im = ax.imshow(mat, vmin=vlim[0], vmax=vlim[1], cmap=cmap)
+        ax.set_xticks(range(len(RAW_TARGET_NAMES)))
+        ax.set_xticklabels(RAW_TARGET_NAMES, rotation=90, fontsize=7)
+        ax.set_yticks(range(len(RAW_TARGET_NAMES)))
+        ax.set_yticklabels(RAW_TARGET_NAMES, fontsize=7)
+        ax.set_title(title)
+        fig.colorbar(im, ax=ax, fraction=0.046)
+    fig.tight_layout()
+    return fig
+
+
+_DEFAULT_PAIRS = [
+    ("step_length", "delta_e"),
+    ("delta_e", "edep"),
+    ("step_length", "edep"),
+]
+
+
+def plot_pairwise(
+    collection: SampleCollection,
+    pairs: list[tuple[str, str]] | None = None,
+    n_sample: int = 3000,
+    seed: int = 0,
+):
+    """Real-vs-generated scatter for physically coupled target pairs.
+
+    Marginals matching doesn't imply the joint does — these pairs are coupled by
+    the underlying physics (energy loss tracks distance, edep is part of
+    delta_e), so a model that decorrelates them shows up here even with clean
+    per-dimension marginals.
+    """
+    pairs = pairs or _DEFAULT_PAIRS
+    rng = np.random.default_rng(seed)
+    n = len(collection.pdg)
+    idx = rng.choice(n, size=min(n_sample, n), replace=False)
+
+    fig, axes = plt.subplots(2, len(pairs), squeeze=False, figsize=(4 * len(pairs), 7))
+    for col, (a, b) in enumerate(pairs):
+        ia, ib = RAW_TARGET_NAMES.index(a), RAW_TARGET_NAMES.index(b)
+        for row, (data, title) in enumerate([
+            (collection.real_raw, "real"), (collection.gen_raw, "generated")
+        ]):
+            ax = axes[row][col]
+            ax.scatter(data[idx, ia], data[idx, ib], s=3, alpha=0.3)
+            ax.set_xlabel(a)
+            ax.set_ylabel(b)
+            if col == 0:
+                ax.set_title(title, loc="left", fontsize=9)
+    fig.tight_layout()
+    return fig
+
+
+def direction_alignment(collection: SampleCollection) -> tuple[np.ndarray, np.ndarray]:
+    """cos(angle) between post_dir_local and travel_dir_local, real vs generated.
+
+    These two unit vectors are coupled through the scattering physics, so their
+    joint alignment is a check the per-dimension marginals can't see.
+    """
+    def cos_angle(raw: np.ndarray) -> np.ndarray:
+        post, travel = raw[:, 3:6], raw[:, 6:9]
+        return np.sum(post * travel, axis=1) / (
+            np.linalg.norm(post, axis=1) * np.linalg.norm(travel, axis=1) + 1e-8
+        )
+    return cos_angle(collection.real_raw), cos_angle(collection.gen_raw)
+
+
+def plot_direction_alignment(collection: SampleCollection, bins: int = 50):
+    real_cos, gen_cos = direction_alignment(collection)
+    fig, ax = plt.subplots(figsize=(5, 4))
+    edges = np.linspace(-1, 1, bins + 1)
+    ax.hist(real_cos, bins=edges, density=True, alpha=0.5, label="real")
+    ax.hist(gen_cos, bins=edges, density=True, alpha=0.5, label="generated")
+    ax.set_xlabel("cos(angle) between post_dir and travel_dir")
+    ax.legend()
+    fig.tight_layout()
+    return fig
+
+
+# ---------------------------------------------------------------------------
+# Tier 3: physical constraints
+# ---------------------------------------------------------------------------
+
+def constraint_report(collection: SampleCollection, norm_tol: float = 0.05) -> pd.DataFrame:
+    """Rate of physical-constraint violations in the generated raw-space samples.
+
+    The model is an unconstrained MLP, so nothing forces post_dir_local /
+    travel_dir_local to stay unit-norm or step_length/delta_e/edep to stay
+    non-negative — both hold by construction in the real data, so any
+    violation rate here is purely a generation artifact.
+    """
+    gen = collection.gen_raw
+    post_norm = np.linalg.norm(gen[:, 3:6], axis=1)
+    travel_norm = np.linalg.norm(gen[:, 6:9], axis=1)
+
+    rows = [
+        {
+            "check": "post_dir unit norm",
+            "violation_rate": float(np.mean(np.abs(post_norm - 1) > norm_tol)),
+            "mean_abs_error": float(np.mean(np.abs(post_norm - 1))),
+        },
+        {
+            "check": "travel_dir unit norm",
+            "violation_rate": float(np.mean(np.abs(travel_norm - 1) > norm_tol)),
+            "mean_abs_error": float(np.mean(np.abs(travel_norm - 1))),
+        },
+    ]
+    for j, name in enumerate(RAW_TARGET_NAMES[:_N_LOG_DIMS]):
+        rows.append({
+            "check": f"{name} >= 0",
+            "violation_rate": float(np.mean(gen[:, j] < 0)),
+            "mean_abs_error": float(np.mean(np.clip(-gen[:, j], 0, None))),
+        })
+    return pd.DataFrame(rows)
+
+
+def plot_constraint_violations(collection: SampleCollection):
+    """Histograms backing `constraint_report`: direction norms and sign of the log-dims."""
+    gen = collection.gen_raw
+    post_norm = np.linalg.norm(gen[:, 3:6], axis=1)
+    travel_norm = np.linalg.norm(gen[:, 6:9], axis=1)
+
+    n_panels = 2 + _N_LOG_DIMS
+    fig, axes = plt.subplots(1, n_panels, figsize=(4 * n_panels, 3.5))
+    for ax, norm, title in [(axes[0], post_norm, "||post_dir||"), (axes[1], travel_norm, "||travel_dir||")]:
+        ax.hist(norm, bins=_hist_edges(norm, bins=50))
+        ax.axvline(1.0, color="k", linestyle="--", linewidth=1)
+        ax.set_title(title)
+    for k, name in enumerate(RAW_TARGET_NAMES[:_N_LOG_DIMS]):
+        ax = axes[2 + k]
+        ax.hist(gen[:, k], bins=_hist_edges(gen[:, k], bins=50))
+        ax.axvline(0.0, color="k", linestyle="--", linewidth=1)
+        ax.set_title(f"generated {name}")
+    fig.tight_layout()
+    return fig

giant/cli.py

@@ -11,7 +11,12 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 
 from giant import config as gconfig
-from giant.constants import LOCAL_TARGET_NAMES
+from giant.constants import (
+    LOCAL_TARGET_NAMES,
+    PREDICT_COORD_METADATA_KEY,
+    PREDICT_SCHEMA_VERSION,
+    PREDICT_SCHEMA_VERSION_KEY,
+)
 from giant.data.loader import (
     find_parquet_files,
     iter_file_chunks,
@@ -135,6 +140,7 @@ def predict(
     model.load_state_dict(ckpt["model"])
     model.to(_device).eval()
     typer.echo(f"loaded checkpoint: {checkpoint}")
+    gconfig.warn_if_checkpoint_config_mismatch(checkpoint)
 
     # --- Output path ---
     if out is None:
@@ -234,6 +240,11 @@ def predict(
                     "post_z":       post_pos_world[:, 2],
                 })
 
+            table = table.replace_schema_metadata({
+                PREDICT_COORD_METADATA_KEY: coord.value,
+                PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
+            })
+
             if writer is None:
                 writer = pq.ParquetWriter(out, table.schema)
             writer.write_table(table)

giant/config.py

@@ -41,6 +41,41 @@ def load_toml(path: Path) -> dict:
         return tomllib.load(f)
 
 
+def warn_if_git_hash_mismatch(file_cfg: dict, config_path: Path) -> None:
+    """Warn (don't fail) if a config.toml's [meta].git_hash predates the current checkout.
+
+    A config saved by a previous run may have been produced by code that has
+    since changed, so its hyperparameters might not mean what they used to —
+    surface that as a heads-up rather than blocking the rerun.
+    """
+    file_hash = file_cfg.get("meta", {}).get("git_hash")
+    current_hash = git_hash()
+    if not file_hash or file_hash == "unknown" or current_hash == "unknown":
+        return
+    if file_hash != current_hash:
+        print(
+            f"warning: {config_path} was generated at git commit {file_hash}, "
+            f"but the current checkout is at {current_hash} — hyperparameters "
+            "may not match the code that originally produced this config",
+            file=sys.stderr,
+        )
+
+
+def warn_if_checkpoint_config_mismatch(ckpt_path: str | Path) -> None:
+    """Look for a config.toml next to a checkpoint and warn on a git_hash mismatch.
+
+    Training writes config.toml into the same out_dir as its checkpoints, so a
+    checkpoint loaded later (for `predict` or `giant.analysis`) can be
+    cross-checked the same way `--config` loading is, without the caller having
+    to pass the toml path explicitly. Silently does nothing if no config.toml
+    is found alongside the checkpoint.
+    """
+    config_path = Path(ckpt_path).parent / "config.toml"
+    if not config_path.exists():
+        return
+    warn_if_git_hash_mismatch(load_toml(config_path), config_path)
+
+
 def merge_cli_overrides(
     defaults: dict,
     config_path: Path | None,
@@ -53,6 +88,7 @@ def merge_cli_overrides(
         file_cfg = load_toml(config_path)
         for section in ("train", "model"):
             cfg[section].update(file_cfg.get(section, {}))
+        warn_if_git_hash_mismatch(file_cfg, config_path)
     cfg["train"].update(train_overrides)
     cfg["model"].update(model_overrides)
     return cfg

giant/constants.py

@@ -11,3 +11,10 @@ LOCAL_TARGET_NAMES = [
     "travel_dy",
     "travel_dz",
 ]
+
+# Parquet schema metadata written by `giant predict` and checked by
+# giant.analysis loaders, so a file's format can be verified without
+# guessing from its column names.
+PREDICT_COORD_METADATA_KEY = "giant.predict.coord"
+PREDICT_SCHEMA_VERSION_KEY = "giant.predict.schema_version"
+PREDICT_SCHEMA_VERSION = "1"

pyproject.toml

@@ -21,6 +21,9 @@ convert = [
     "awkward>=2.6",
     "polars>=1.0",
 ]
+analysis = [
+    "matplotlib>=3.8",
+]
 
 [project.scripts]
 giant = "giant.cli:app"

tests/test_analysis.py

@@ -0,0 +1,243 @@
+import matplotlib
+
+matplotlib.use("Agg")  # no display needed for plot smoke tests
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pytest
+
+from giant.analysis import (
+    RAW_TARGET_NAMES,
+    SampleCollection,
+    constraint_report,
+    correlation_matrices,
+    direction_alignment,
+    load_predicted_local,
+    marginal_table,
+    plot_constraint_violations,
+    plot_correlation_matrices,
+    plot_direction_alignment,
+    plot_marginals,
+    plot_pairwise,
+)
+from giant.constants import (
+    LOCAL_TARGET_NAMES,
+    PREDICT_COORD_METADATA_KEY,
+    PREDICT_SCHEMA_VERSION,
+    PREDICT_SCHEMA_VERSION_KEY,
+)
+from giant.data.transforms import log_transform
+
+
+def _unit_vectors(rng, n):
+    v = rng.standard_normal((n, 3)).astype(np.float32)
+    return v / np.linalg.norm(v, axis=1, keepdims=True)
+
+
+def _make_collection(n=200, seed=0, gen_offset=0.0) -> SampleCollection:
+    rng = np.random.default_rng(seed)
+    real = np.column_stack([
+        rng.uniform(0.1, 5.0, n),  # step_length
+        rng.uniform(0.1, 5.0, n),  # delta_e
+        rng.uniform(0.1, 5.0, n),  # edep
+        _unit_vectors(rng, n),  # post_dir
+        _unit_vectors(rng, n),  # travel_dir
+    ]).astype(np.float32)
+    gen = real + gen_offset
+
+    pre_E = rng.uniform(1.0, 100.0, n).astype(np.float32)
+    cond_cont_raw = np.column_stack([
+        rng.standard_normal((n, 3)), pre_E, rng.standard_normal((n, 3)),
+        rng.integers(0, 5, n), rng.integers(0, 3, n),
+    ]).astype(np.float32)
+
+    return SampleCollection(
+        cond_cont_raw=cond_cont_raw,
+        pdg=rng.choice([11, -11, 22], size=n),
+        material=rng.choice(["W", "Pb"], size=n),
+        real_raw=real, gen_raw=gen,
+        real_norm=real, gen_norm=gen,
+    )
+
+
+def test_marginal_table_aggregate_has_all_dims():
+    table = marginal_table(_make_collection())
+    assert set(table["dim"]) == set(RAW_TARGET_NAMES)
+    assert (table["group"] == "all").all()
+
+
+@pytest.mark.parametrize("group_by", ["pdg", "material", "energy"])
+def test_marginal_table_grouped_covers_all_rows(group_by):
+    collection = _make_collection()
+    table = marginal_table(collection, group_by=group_by)
+    assert table["n"].groupby(table["group"]).first().sum() == len(collection.pdg)
+
+
+def test_marginal_table_identical_distributions_have_zero_kl():
+    collection = _make_collection(gen_offset=0.0)
+    table = marginal_table(collection)
+    np.testing.assert_allclose(table["kl_real_gen"], 0.0, atol=1e-6)
+
+
+def test_marginal_table_shifted_distribution_has_positive_kl():
+    collection = _make_collection(gen_offset=3.0)
+    table = marginal_table(collection)
+    assert (table["kl_real_gen"] > 0).all()
+
+
+def test_correlation_matrices_are_symmetric_unit_diagonal():
+    real_corr, gen_corr = correlation_matrices(_make_collection())
+    for corr in (real_corr, gen_corr):
+        np.testing.assert_allclose(np.diag(corr), 1.0, atol=1e-5)
+        np.testing.assert_allclose(corr, corr.T, atol=1e-5)
+
+
+def test_direction_alignment_real_data_is_unit_norm_dot_product():
+    real_cos, gen_cos = direction_alignment(_make_collection())
+    assert np.all(real_cos >= -1.0 - 1e-5) and np.all(real_cos <= 1.0 + 1e-5)
+    assert np.all(gen_cos >= -1.0 - 1e-5) and np.all(gen_cos <= 1.0 + 1e-5)
+
+
+def test_constraint_report_clean_data_has_no_violations():
+    report = constraint_report(_make_collection(gen_offset=0.0))
+    assert (report["violation_rate"] == 0.0).all()
+
+
+def test_constraint_report_flags_negative_log_dims_and_bad_norms():
+    collection = _make_collection(gen_offset=0.0)
+    collection.gen_raw[:, 0] = -1.0  # negative step_length
+    collection.gen_raw[:, 3:6] *= 2.0  # post_dir no longer unit norm
+    report = constraint_report(collection)
+    violations = dict(zip(report["check"], report["violation_rate"]))
+    assert violations["step_length >= 0"] == 1.0
+    assert violations["post_dir unit norm"] == 1.0
+
+
+def test_plot_marginals_runs_without_error():
+    fig = plot_marginals(_make_collection())
+    assert fig is not None
+
+
+def test_plot_marginals_grouped_runs_without_error():
+    fig = plot_marginals(_make_collection(), group_by="material")
+    assert fig is not None
+
+
+def test_plot_correlation_matrices_runs_without_error():
+    fig = plot_correlation_matrices(_make_collection())
+    assert fig is not None
+
+
+def test_plot_pairwise_runs_without_error():
+    fig = plot_pairwise(_make_collection())
+    assert fig is not None
+
+
+def test_plot_direction_alignment_runs_without_error():
+    fig = plot_direction_alignment(_make_collection())
+    assert fig is not None
+
+
+def test_plot_constraint_violations_runs_without_error():
+    fig = plot_constraint_violations(_make_collection())
+    assert fig is not None
+
+
+def _write_predicted_local_parquet(path, n=50, metadata=None, rng=None):
+    """Mimic `giant predict --coord local`'s output schema for the loader tests."""
+    rng = rng or np.random.default_rng(0)
+    true_log_local = rng.standard_normal((n, 9)).astype(np.float32)
+    true_log_local[:, :3] = log_transform(rng.uniform(0.1, 5.0, (n, 3)).astype(np.float32))
+    pred_log_local = true_log_local + rng.normal(0, 0.01, (n, 9)).astype(np.float32)
+
+    table = pa.table({
+        "event_id": rng.integers(0, 10, n),
+        "pdg": rng.choice([11, -11, 22], n),
+        "pre_x": rng.standard_normal(n).astype(np.float32),
+        "pre_y": rng.standard_normal(n).astype(np.float32),
+        "pre_z": rng.standard_normal(n).astype(np.float32),
+        "pre_E": rng.uniform(1.0, 100.0, n).astype(np.float32),
+        "pre_dx": rng.standard_normal(n).astype(np.float32),
+        "pre_dy": rng.standard_normal(n).astype(np.float32),
+        "pre_dz": rng.standard_normal(n).astype(np.float32),
+        "material": rng.choice(["W", "Pb"], n),
+        "layer_id": rng.integers(0, 10, n).astype(np.int32),
+        "n_sec": rng.integers(0, 3, n).astype(np.int32),
+        **{f"pred_{name}": pred_log_local[:, j] for j, name in enumerate(LOCAL_TARGET_NAMES)},
+        **{f"true_{name}": true_log_local[:, j] for j, name in enumerate(LOCAL_TARGET_NAMES)},
+    })
+    if metadata is not None:
+        table = table.replace_schema_metadata(metadata)
+    pq.write_table(table, path)
+    return true_log_local, pred_log_local
+
+
+def test_load_predicted_local_round_trips_values(tmp_path):
+    path = tmp_path / "predicted_local.parquet"
+    true_log_local, pred_log_local = _write_predicted_local_parquet(
+        path,
+        metadata={
+            PREDICT_COORD_METADATA_KEY: "local",
+            PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
+        },
+    )
+
+    collection = load_predicted_local(path)
+
+    expected_real = true_log_local.copy()
+    expected_real[:, :3] = np.exp(expected_real[:, :3]) - 1e-8
+    expected_gen = pred_log_local.copy()
+    expected_gen[:, :3] = np.exp(expected_gen[:, :3]) - 1e-8
+    np.testing.assert_allclose(collection.real_raw, expected_real, atol=1e-4)
+    np.testing.assert_allclose(collection.gen_raw, expected_gen, atol=1e-4)
+    assert collection.real_norm is None
+    assert collection.gen_norm is None
+
+
+def test_load_predicted_local_usable_by_downstream_plots(tmp_path):
+    path = tmp_path / "predicted_local.parquet"
+    _write_predicted_local_parquet(
+        path,
+        metadata={
+            PREDICT_COORD_METADATA_KEY: "local",
+            PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
+        },
+    )
+    collection = load_predicted_local(path)
+    assert marginal_table(collection) is not None
+    assert plot_marginals(collection) is not None
+
+
+def test_load_predicted_local_rejects_missing_metadata(tmp_path):
+    path = tmp_path / "no_metadata.parquet"
+    _write_predicted_local_parquet(path, metadata=None)
+    with pytest.raises(ValueError, match="no '.*' parquet metadata"):
+        load_predicted_local(path)
+
+
+def test_load_predicted_local_rejects_global_coord(tmp_path):
+    path = tmp_path / "global.parquet"
+    _write_predicted_local_parquet(
+        path,
+        metadata={
+            PREDICT_COORD_METADATA_KEY: "global",
+            PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
+        },
+    )
+    with pytest.raises(ValueError, match="coord=local"):
+        load_predicted_local(path)
+
+
+def test_load_predicted_local_rejects_mismatched_schema_version(tmp_path):
+    path = tmp_path / "old_version.parquet"
+    _write_predicted_local_parquet(
+        path,
+        metadata={
+            PREDICT_COORD_METADATA_KEY: "local",
+            PREDICT_SCHEMA_VERSION_KEY: "999",
+        },
+    )
+    with pytest.raises(ValueError, match="schema version"):
+        load_predicted_local(path)

tests/test_config.py

@@ -0,0 +1,102 @@
+from giant import config as gconfig
+
+
+def _write_config(path, git_hash):
+    path.write_text(
+        f"""
+[train]
+epochs = 5
+
+[model]
+hidden_dim = 64
+
+[meta]
+git_hash = "{git_hash}"
+"""
+    )
+
+
+def test_merge_cli_overrides_applies_file_then_cli(tmp_path, monkeypatch):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "abc123")
+    path = tmp_path / "config.toml"
+    _write_config(path, "abc123")
+
+    cfg = gconfig.merge_cli_overrides(
+        gconfig.DEFAULT_CONFIG, path, train_overrides={}, model_overrides={"hidden_dim": 128},
+    )
+    assert cfg["train"]["epochs"] == 5  # from file
+    assert cfg["model"]["hidden_dim"] == 128  # CLI override wins over file
+
+
+def test_merge_cli_overrides_warns_on_git_hash_mismatch(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "current999")
+    path = tmp_path / "config.toml"
+    _write_config(path, "old111")
+
+    cfg = gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {})
+
+    assert cfg["train"]["epochs"] == 5  # does not fail, config still applied
+    captured = capsys.readouterr()
+    assert "warning" in captured.err
+    assert "old111" in captured.err
+    assert "current999" in captured.err
+
+
+def test_merge_cli_overrides_no_warning_on_matching_git_hash(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "same123")
+    path = tmp_path / "config.toml"
+    _write_config(path, "same123")
+
+    gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {})
+    assert capsys.readouterr().err == ""
+
+
+def test_merge_cli_overrides_no_warning_when_git_hash_unknown(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "unknown")
+    path = tmp_path / "config.toml"
+    _write_config(path, "abc123")
+
+    gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {})
+    assert capsys.readouterr().err == ""
+
+
+def test_merge_cli_overrides_no_warning_when_meta_section_absent(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "current999")
+    path = tmp_path / "config.toml"
+    path.write_text("[train]\nepochs = 5\n")
+
+    gconfig.merge_cli_overrides(gconfig.DEFAULT_CONFIG, path, {}, {})
+    assert capsys.readouterr().err == ""
+
+
+def test_warn_if_checkpoint_config_mismatch_finds_sibling_toml(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "current999")
+    ckpt_path = tmp_path / "best.pt"
+    ckpt_path.write_bytes(b"")  # contents irrelevant, only its directory is used
+    _write_config(tmp_path / "config.toml", "old111")
+
+    gconfig.warn_if_checkpoint_config_mismatch(ckpt_path)
+
+    captured = capsys.readouterr()
+    assert "warning" in captured.err
+    assert "old111" in captured.err
+    assert "current999" in captured.err
+
+
+def test_warn_if_checkpoint_config_mismatch_no_warning_when_toml_absent(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "current999")
+    ckpt_path = tmp_path / "best.pt"
+    ckpt_path.write_bytes(b"")
+
+    gconfig.warn_if_checkpoint_config_mismatch(ckpt_path)
+    assert capsys.readouterr().err == ""
+
+
+def test_warn_if_checkpoint_config_mismatch_no_warning_when_hashes_match(tmp_path, monkeypatch, capsys):
+    monkeypatch.setattr(gconfig, "git_hash", lambda: "same123")
+    ckpt_path = tmp_path / "best.pt"
+    ckpt_path.write_bytes(b"")
+    _write_config(tmp_path / "config.toml", "same123")
+
+    gconfig.warn_if_checkpoint_config_mismatch(ckpt_path)
+    assert capsys.readouterr().err == ""

uv.lock

@@ -90,6 +90,72 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419, upload-time = "2025-07-26T12:01:21.16Z" },
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@@ -255,6 +371,9 @@ dependencies = [
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 [package.optional-dependencies]
+analysis = [
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+]
 convert = [
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 [package.metadata]
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+    { name = "matplotlib", marker = "extra == 'analysis'", specifier = ">=3.8" },
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+
 [[package]]
 name = "pytest"
 version = "9.1.0"