diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml
index c2c0765..e69fc65 100644
--- a/.gitea/workflows/ci.yml
+++ b/.gitea/workflows/ci.yml
@@ -3,76 +3,111 @@ name: CI
 "on":
   push:
     branches: ["**"]
+    tags: ["**"]
   pull_request:
     branches: [master]
 
+env:
+  UV_CACHE_DIR: /uv-cache
+
 jobs:
   ruff-check:
     name: Lint (ruff check)
     runs-on: ubuntu-latest
+    container:
+      image: docker.gitea.com/runner-images:ubuntu-latest
+      volumes:
+        - /srv/act-runner-cache/uv:/uv-cache
     steps:
       - uses: actions/checkout@v4
       - uses: astral-sh/setup-uv@v5
         with:
-          enable-cache: true
+          enable-cache: false
+      - run: |
+          echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV"
+          echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run ruff check .
 
   ruff-format:
     name: Format (ruff format)
     runs-on: ubuntu-latest
+    container:
+      image: docker.gitea.com/runner-images:ubuntu-latest
+      volumes:
+        - /srv/act-runner-cache/uv:/uv-cache
     steps:
       - uses: actions/checkout@v4
       - uses: astral-sh/setup-uv@v5
         with:
-          enable-cache: true
+          enable-cache: false
+      - run: |
+          echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV"
+          echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run ruff format --check .
 
   type-check:
     name: Type check (ty)
     runs-on: ubuntu-latest
+    container:
+      image: docker.gitea.com/runner-images:ubuntu-latest
+      volumes:
+        - /srv/act-runner-cache/uv:/uv-cache
     steps:
       - uses: actions/checkout@v4
       - uses: astral-sh/setup-uv@v5
         with:
-          enable-cache: true
+          enable-cache: false
+      - run: |
+          echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV"
+          echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run ty check .
 
   test:
     name: Tests
+    needs: [ruff-check, type-check]
     runs-on: ubuntu-latest
+    container:
+      image: docker.gitea.com/runner-images:ubuntu-latest
+      volumes:
+        - /srv/act-runner-cache/uv:/uv-cache
     steps:
       - uses: actions/checkout@v4
       - uses: astral-sh/setup-uv@v5
         with:
-          enable-cache: true
+          enable-cache: false
+      - run: |
+          echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV"
+          echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV"
       - run: uv sync --extra cpu --extra dev
       - run: uv run pytest
 
-  build:
-    name: Bump version, build & publish wheel
-    needs: [ruff-check, ruff-format, type-check, test]
-    if: github.event_name == 'push' && github.ref == 'refs/heads/master'
+  sync-version-on-tag:
+    name: Sync project version with tag
+    if: startsWith(github.ref, 'refs/tags/')
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
         with:
           token: ${{ secrets.CI_TOKEN }}
       - uses: astral-sh/setup-uv@v5
-      - name: Bump patch version
+      - name: Check tag against project version, update if they differ
         run: |
-          git config user.name "gitea-actions"
-          git config user.email "actions@git.larsbogner.de"
-          uv version --bump patch --no-sync
-          NEW_VERSION=$(uv version --short)
-          git add pyproject.toml uv.lock
-          git commit -m "chore: bump version to ${NEW_VERSION} [skip ci]"
-          git push
-      - run: uv build
-      - name: Publish to Gitea package registry
-        env:
-          TWINE_USERNAME: ${{ secrets.PACKAGE_USERNAME }}
-          TWINE_PASSWORD: ${{ secrets.CI_TOKEN }}
-        run: uvx twine upload --repository-url https://git.larsbogner.de/api/packages/lars/pypi dist/*
+          TAG_VERSION="${GITHUB_REF_NAME#v}"
+          CURRENT_VERSION=$(uv version --short)
+          if [ "$TAG_VERSION" != "$CURRENT_VERSION" ]; then
+            echo "Tag version ($TAG_VERSION) != project version ($CURRENT_VERSION); updating pyproject.toml"
+            uv version "$TAG_VERSION" --no-sync
+            git config user.name "gitea-actions"
+            git config user.email "actions@git.larsbogner.de"
+            git add pyproject.toml uv.lock
+            git commit -m "chore: sync project version to tag ${GITHUB_REF_NAME} [skip ci]"
+            git push origin HEAD:master
+            git push origin ":refs/tags/${GITHUB_REF_NAME}"
+            git tag -f "${GITHUB_REF_NAME}" HEAD
+            git push origin "refs/tags/${GITHUB_REF_NAME}"
+          else
+            echo "Tag version matches project version ($CURRENT_VERSION)"
+          fi
diff --git a/.gitignore b/.gitignore
index 377b0b7..95aeaf8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,3 +17,6 @@ checkpoints/
 
 # Scratch working directory
 /scratchpad/
+
+# giant analyze run directories (shared.json, reduced/, plots/, condor logs)
+/analysis_runs/
diff --git a/CLAUDE.md b/CLAUDE.md
index 726a99d..27c19c2 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -12,8 +12,12 @@ uv sync --extra cpu --extra geometry  # add scikit-learn for the geometry oracle
 pytest               # run tests
 giant train path/to/steps.parquet --mode flow   # train (flow matching)
 giant train path/to/steps.parquet --mode ddpm   # train (DDPM baseline)
+giant train path/to/steps.parquet --mode wgan   # train (WGAN-GP, single-pass eval; implemented, not yet tested)
+giant train path/to/steps.parquet --router --router-type energy  # MoE routing trunk (implemented; first rollout benchmark failed with lambda_balance=0, retrain needed — see Roadmap)
 giant predict path/to/steps.parquet --checkpoint ckpt/best.pt   # per-step predictions
 giant rollout path/to/steps.parquet --checkpoint ckpt/best.pt --geometry oracle.pkl  # full showers
+giant analyze submit rollout.yaml --accounting-group cms   # parallel rollout-vs-reference analysis on HTCondor
+giant analyze render <run_dir> --gallery                   # render PDFs + HTML gallery (run_dir from prep/submit)
 dwarf --help                                     # dataset/tooling CLI: convert, migrate, bump-gen,
                                                   # bump-schema, status, update-manifest, create-manifest,
                                                   # make-root, build-geometry-oracle, hparam-scan
@@ -32,6 +36,14 @@ uv run ty check .       # type check
 
 Part of the `dev` extra. Run these periodically (not just at commit time) to catch drift early.
 
+## Compute environment
+
+Work on this repo happens across three kinds of machine:
+
+- **Local dev machines** (laptop + desktop, identical): repo at `~/Programming/giant`, no access to `/ceph` — datasets, training results, and models aren't reachable here.
+- **Portal machines** (`portal1`, `deepthought`, `deepthought2`, `bms1`, `bms2`, `bms3`): repo lives under `/work`, and `/ceph` holds ROOT/parquet files and trained models. **These are shared with other users** — stay strictly within `/work/lbogner` and `/ceph/lbogner`, and keep resource usage to roughly a quarter of CPU/RAM and a single GPU so as not to disturb other users' jobs.
+- **HTCondor worker nodes**: never run or SSH onto these directly — the only sanctioned path is submitting jobs through condor (`giant analyze submit`, and the in-progress remote-GPU train/rollout submission on `condor-gpu-train-rollout`). `/ceph` is available there; `/work` is only sometimes mounted, depending on the node.
+
 ## 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 — now including the variable-length list of secondary particles the step produces (Phase 2, see Roadmap).
@@ -54,7 +66,13 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep
 
 **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. `giant/analysis.py` is a fully-streaming (lazy polars) diagnostics module, sized for predict/rollout files larger than RAM, with no in-memory `SampleCollection` and no full-array materialization. It covers one-step-ahead `giant predict --coord local` output (`compute_event_observables_pl` + `plot_total_energy`/`plot_longitudinal_profile`/etc. for shower-level observables, plus the marginal/correlation/constraint tiers) and, via the `RolloutVsTruth` source type, a full autoregressive `giant rollout` shower compared against held-out truth data (`compute_rollout_vs_truth_observables_pl` for shower-level observables, reusing the same plot functions) — see the module docstring.
+**WGAN-GP mode (`--mode wgan`, implemented, not yet tested):** a throwaway fast-eval alternative to the flow/DDPM samplers above — single forward pass instead of ~10 ODE steps. Dedicated noise-conditioned generators (`WGANGenerator`/`WGANSecondaryGenerator`, `giant/model/network.py`) stand in for `DenoisingMLP`/`SecondaryDecoder`, trained against `Critic`/`SecondaryCritic` discriminators with the gradient-penalty loss in `giant/model/wgan.py` (Gulrajani et al. 2017); `sample_wgan` (`giant/sample.py`) does the single-pass draw at inference. Not yet validated against the flow-matching baseline.
+
+**MoE routing trunk (`--router`, implemented; first rollout benchmark shows the experts don't specialize — see Roadmap):** an alternative to `DenoisingMLP`'s monolithic `ResBlock` trunk — a `Router` (`giant/model/network.py`, `ROUTER_REGISTRY`/`build_router`) gates between small per-expert `ResBlock` stacks (`Expert`), soft-mixed over all experts at train time but **top-1 dispatched at eval time** (each row runs exactly one small expert), which is the actual inference-speed win. Router types gate on different conditioning axes: `EnergyRouter`/`PdgRouter` read a quantity already known at inference time, `ProcessRouter` runs its own small classifier over pre-step conditioning (since process isn't known upfront); `ComposedRouter` gates jointly over multiple axes (outer-product expert cells) via repeated `--router-axis "type:key=val,..."` flags. Config lives under `model.router` (`giant/config.py`), deep-merged one level so `router.enabled` alone doesn't drop the rest of the defaults.
+
+**Validation** (`giant/validate.py`): step-level marginal comparisons.
+
+**Analysis** (`giant/analysis/`, `giant analyze` CLI): a lean, streaming rollout-vs-reference plotting pipeline that compares one autoregressive `giant rollout` (for a given checkpoint) against a held-out miniCaloSim reference steps file, and produces publication-styled PDFs assembled into an HTML gallery. It exploits the fact that rollout output and a raw reference file share a world-frame physical column subset under identical names (`pre_*`/`post_*`/`edep`/`step_length`/`pdg`/`material`/`event_id`), so no ALR/local-frame decode is needed — everything is world-frame mm/MeV. Structure: `sources.py` (canonical LazyFrames + synthetic-termination-row filtering + the secondary view, which is `generation>0 & step_no==0` rollout tracks vs exploded `sec_*_list` reference columns), `reduce.py` (the streaming primitives — a single `hist1d` `group_by([group,bin]).len()` pass, per-event scalars, edep-weighted depth/transverse profiles, species share, leakage), `grouping.py`/`context.py` (fixed bin edges + energy-quantile/pdg/material group sets resolved once by `prep` into `shared.json`, so every compute job is one pass with no range scan), `catalog.py` (the declarative `PlotSpec` registry — marginals × {overall,energy,pdg,material}, per-event totals, shower profiles, species/leakage, secondaries), and `render.py` (the only module importing ETPlot's `plotstyle`/LaTeX; dispatches on `Reduced.kind`, writes PDFs + `metadata.yaml`). **Input is a `giant rollout` YAML sidecar** (`condor.py:load_rollout_yaml`): its `output`/`dataset` keys name the rollout parquet and the seed file (= the reference truth), and the rest of the YAML (checkpoint, geometry oracle, cutoffs) flows into each plot's gallery metadata. `prep` derives its own **run directory** next to the rollout parquet (`<...>/analysis_<id>/`) holding `shared.json`, `run_meta.json`, `reduced_partial/`, `reduced/`, `plots/`. **Compute/merge/render split:** `giant analyze submit rollout.yaml --chunks N` runs `prep` (recording the run's chunk count `N` in `run_meta.json`) then submits one HTCondor job per (plot, chunk) pair (`compute-one --id --chunk --run-dir`, polars/numpy only — no LaTeX on workers), each streaming over an `event_id`-disjoint slice (`event_id % N == chunk`) and writing a small `reduced_partial/<id>__<chunk>.json`; every `PlotSpec` (`catalog.py`) splits into a `compute_partial`/`finalize` pair so a plot's chunks can be summed/concatenated back together correctly (`chunkable=False` specs — the router diagnostics, already bounded/subsampled — always run as a single chunk regardless of `N`). The local `giant analyze render <run_dir>` first joins every plot's chunk partials into `reduced/<id>.json` (`merge_all`, a no-op join when `N=1`), then turns those into the styled PDF/gallery tree. See `giant/analysis/__init__.py`.
 
 **Shower rollout** (`giant/rollout.py`, `giant rollout` CLI): autoregressively steps the two-stage model into a full shower — each primary post-step becomes the next pre-step, secondaries are pushed as new tracks, and per-step `material`/`layer_id` come from a `GeometryOracle` (`giant/geometry.py`, built via `dwarf build-geometry-oracle`) that learns position → (material, layer_id) from data and flags detector escape by nearest-neighbour distance. Tracks terminate on energy cutoff, per-track max steps, escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction.
 
@@ -66,4 +84,10 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep
 
 **Physical-property conditioning (implemented):** `model.conditioning = "physical" | "embedding"` (see above) replaces the learned PDG/material embeddings with a small MLP over particle mass/charge and material Z_eff/A_eff/density/X0/λ_int, and Stage 2 predicts a secondary's mass/charge directly instead of a snapped species embedding. `"embedding"` stays available as the generalization-comparison baseline. `giant/materials.py`'s table is already filled with real values for every material the geometry produces. **Not yet done:** the actual held-out-material/species generalization comparison against the `"embedding"` baseline is unrun — the 34GB multi-material dataset at the repo root (6 materials, 237 PDG codes including nuclear/ion codes) is the natural dataset for that experiment.
 
-**Next directions** (parallel, not yet built): faster-eval architectures measured against a ~10× native-Geant4 budget — a Wasserstein-GAN throwaway (single-pass eval) and a mixture-of-experts / routing tree of small nets selected per call (pdg / energy / process), with soft/differentiable gating on continuous routing axes; a sampling-calorimeter (multi-material) dataset. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`).
+**Faster-eval architectures (implemented, validation in progress):** both tracks below target a ~10× native-Geant4 eval budget and are now wired into `giant train`/`giant/model/network.py`, but neither has a validated result yet — treat both as unproven until the corresponding analysis run says otherwise:
+- **WGAN-GP** (`--mode wgan`, see Architecture above): implemented, **not yet tested** — no rollout-vs-reference analysis run against it yet.
+- **MoE routing trunk** (`--router`, see Architecture above): implemented, **first rollout benchmark done (2026-07-22), result: needs retraining with a different router config, not abandoned.** A 10-expert `EnergyRouter` run (`n_experts=10`, `temperature=0.5`, `learn_centers=true`, **`lambda_balance=0.0`**, only 20 fine-tuning epochs resumed from a non-routed checkpoint) diverged badly from Geant4 on step granularity, secondary species, and shower shape, despite roughly matching bulk total deposited energy. The `router_gating` diagnostic plot points at the likely cause: the ten experts overlap heavily across ~5 decades of pre-step energy instead of partitioning it — even the top-energy expert only reaches ~60–65% gate weight at the highest energies plotted — so eval-time top-1 (Voronoi) dispatch is choosing among near-ties rather than real specialists. Two contributors were identified: the missing load-balancing loss (`lambda_balance=0.0`), and `EnergyRouter`'s center init (`torch.linspace(-2, 2, n_experts)`) assuming a roughly uniform z-normalized energy distribution, which real energy spectra don't match. **Fixed (2026-07-27):** `EnergyRouter` now accepts an optional `centers_init` (backward compatible — omitting it keeps the old linspace), and `giant train` auto-populates it from real data quantiles via a reservoir sample collected during the existing normalizer-fitting pass in `giant/pipeline.py` (no extra file scan), for `--router-type energy` only. The routing *strategy* itself may still be sound, but the specific benchmarked config wasn't. **Next step before further evaluation: retrain with `lambda_balance > 0` and the new quantile-seeded centers (and consider more epochs / a from-scratch run rather than a short fine-tune), then re-check whether `router_gating` sharpens up.** Full writeup: `/home/lars/knowledge-base/experiments/giant-router-energy-rollout-validation.md`.
+
+A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`).
+
+**Condor-submitted GPU training/rollout (in progress, `condor-gpu-train-rollout` branch, not yet merged):** moves `giant train`/`giant rollout` off the shared portal GPU dev machines (see Compute environment) onto remote-GPU HTCondor submission on TOpAS/NEMO2 (`giant/condor.py`). Partway between "needs major features" and feature-complete — not ready to merge yet.
diff --git a/analysis/rollout_validation.ipynb b/analysis/rollout_validation.ipynb
deleted file mode 100644
index e16259f..0000000
--- a/analysis/rollout_validation.ipynb
+++ /dev/null
@@ -1,544 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f91460f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Auto-reload edited modules (e.g. giant.analysis) without restarting the kernel.\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f10da93",
-   "metadata": {},
-   "source": [
-    "# GIANT rollout-vs-truth validation notebook\n",
-    "\n",
-    "Diagnostics for a full autoregressive `giant rollout` shower, compared against a held-out ground-truth steps file (the same schema `giant train` consumes — see `giant.data.loader.load_steps`) rather than one-step-ahead `giant predict` output.\n",
-    "\n",
-    "This is the sibling of `validation.ipynb`: that notebook checks whether one-step generation (conditioned on the *real* preceding state, every row) reproduces real marginals/correlations/shower observables. This one checks the thing that actually matters for deployment — whether a shower **rolled out autoregressively from the model's own outputs** still looks physical, which is where covariate shift (small per-step errors compounding across a track) would show up.\n",
-    "\n",
-    "Built on `RolloutVsTruth`, which treats the rollout file as \"generated\" and the truth file as \"real\". Unlike the paired predict-parquet `source` (`pred_*`/`true_*` columns of the same row), the two files here are **independent, unpaired datasets** — a rollout doesn't replay real events row-for-row, so real/generated may have different lengths and there's no per-row correspondence. Everything below only ever compares real-vs-generated *distributions*, never individual paired rows, and every check still streams (no `SampleCollection`, no full-file materialization) — see `giant.analysis`'s module docstring for the `RolloutVsTruth` mechanics.\n",
-    "\n",
-    "Same four tiers as `validation.ipynb`, all built on the same functions — pass a `RolloutVsTruth` in place of the predict-parquet path/LazyFrame everywhere:\n",
-    "\n",
-    "1. **stratified marginals** — per-dimension real-vs-generated, sliced by pdg/material/energy\n",
-    "2. **joint structure** — correlation matrices, physically-coupled pairwise plots, direction alignment\n",
-    "3. **physical constraints** — unit-norm directions, non-negative step_length/delta_e/edep (checked on the rollout's own output — with autoregression, a constraint violation early in a track can compound into later steps, unlike one-step-ahead validation)\n",
-    "4. **event-level (shower) observables** — total/mean/median energy and length per event, longitudinal/transverse profiles, shower-max depth, computed directly from the rollout shower against the truth file's own events (`compute_rollout_vs_truth_observables_pl`, the Tier 4 counterpart to `RolloutVsTruth`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "251dc1f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from giant.analysis import RolloutVsTruth, plot_kl_bars_pl\n",
-    "\n",
-    "# `giant rollout` output for the shower(s) under test.\n",
-    "ROLLOUT_FILE = \"/ceph/lbogner/geant_steps/predictions/563f5ee3-507c-4f07-98ec-7b25bff7dfb2.parquet\"\n",
-    "# Any held-out file sharing giant train's input schema (real miniCaloSim\n",
-    "# steps) — e.g. the val split the rollout's seed events were drawn from.\n",
-    "TRUTH_FILE = \"/ceph/lbogner/geant_steps/processed/steps/gen3/schema2/pbwo4_50gev/shard-023.parquet\"\n",
-    "\n",
-    "# sample_frac subsamples each side of the Tier 1-3 checks independently\n",
-    "# (kept memory-bounded for large files); defaults to every row. Tier 4\n",
-    "# (compute_rollout_vs_truth_observables_pl, below) always streams every row\n",
-    "# regardless — per-event sums would be silently corrupted by row subsampling.\n",
-    "SOURCE = RolloutVsTruth(rollout=ROLLOUT_FILE, truth=TRUTH_FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df4bf24b",
-   "metadata": {},
-   "source": [
-    "## Tier 1: stratified marginals\n",
-    "\n",
-    "KL(real || generated) per target dimension, streamed straight from both files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13cd0838",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for grouping in [None, \"energy\", \"pdg\", \"material\"]:\n",
-    "    fig = plot_kl_bars_pl(SOURCE, group_by=grouping)\n",
-    "    fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aba92bf9",
-   "metadata": {},
-   "source": [
-    "## Detailed marginals (Tier 1, overlaid histograms)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7bab2e35",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 3150x280 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from giant.analysis import plot_marginals, plot_correlation_matrices, plot_pairwise\n",
-    "from giant.analysis import plot_direction_alignment, plot_constraint_violations\n",
-    "\n",
-    "_ = plot_marginals(SOURCE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6c2958e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 3150x1120 with 36 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_marginals(SOURCE, group_by=\"energy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0fe70835",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 3150x1680 with 54 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_marginals(SOURCE, group_by=\"pdg\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "038dda3b",
-   "metadata": {},
-   "source": [
-    "## Tier 2: joint structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3c463d50",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Real vs. generated Pearson correlation matrices (+ their difference) over\n",
-    "# the 9 raw target dims — catches a model that decorrelates targets that are\n",
-    "# physically coupled even when every individual marginal looks clean.\n",
-    "_ = plot_correlation_matrices(SOURCE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "393c6845",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Scatter for physically-coupled pairs (step_length/delta_e/edep) — the\n",
-    "# joint-structure check correlation matrices alone can't fully capture.\n",
-    "_ = plot_pairwise(SOURCE, n_sample=10000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1dab5305",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cos(angle) between post_dir and travel_dir — coupled through the\n",
-    "# scattering physics, so this is another joint-structure check.\n",
-    "_ = plot_direction_alignment(SOURCE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7232d3b4",
-   "metadata": {},
-   "source": [
-    "## Tier 3: physical constraints\n",
-    "\n",
-    "Unit-norm direction vectors, non-negative step_length/delta_e/edep. `constraint_report_pl`/`plot_constraint_violations` only ever check the *generated* side (here the rollout output) — under autoregression a violation isn't just a one-off artifact, it can feed the next step's conditioning, so this is worth watching more closely here than in one-step-ahead validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "86be25fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_ = plot_constraint_violations(SOURCE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed4d3037",
-   "metadata": {},
-   "source": [
-    "## Tier 4: event-level (shower) observables\n",
-    "\n",
-    "Built on `compute_rollout_vs_truth_observables_pl`, not `compute_event_observables_pl` — the rollout file carries its own `track_id`/`termination_reason` columns the event-level aggregation needs, and the shower here already *is* a full autoregressive rollout rather than one-step generations re-aggregated by event. Entry axis/point and per-event totals are computed separately per side (rollout and truth events are unrelated), but depth/transverse bin edges are shared across both so the profiles below overlay on one binning.\n",
-    "\n",
-    "Returns the same `EventObservables` `compute_event_observables_pl` does, so every plot function from `validation.ipynb` works unchanged here too."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9175876a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from giant.analysis import compute_rollout_vs_truth_observables_pl\n",
-    "from giant.analysis import plot_total_energy, plot_total_length\n",
-    "from giant.analysis import plot_mean_energy_per_step, plot_mean_length_per_step\n",
-    "from giant.analysis import plot_longitudinal_profile, plot_transverse_profile\n",
-    "from giant.analysis import plot_shower_max_depth\n",
-    "\n",
-    "obs = compute_rollout_vs_truth_observables_pl(ROLLOUT_FILE, TRUTH_FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "83e6dd0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_total_energy(obs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1e35a387",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_total_length(obs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9221e682",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_mean_energy_per_step(obs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "4536acb0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_mean_length_per_step(obs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3a03c04f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_longitudinal_profile(obs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "eae0536e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_transverse_profile(obs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "98c221e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_shower_max_depth(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "208ca6e4",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "For the dataset-wide breakdown of which particle species contributed how much of the total energy/length (`pdg_contribution_table_pl`), see `validation.ipynb` — it needs the paired predict schema, which this rollout-vs-truth comparison doesn't have."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7fb27b941602401d91542211134fc71a",
-   "metadata": {},
-   "source": [
-    "## Router gating showcase (MoE)\n",
-    "\n",
-    "Every other section above is file-only — it reads `ROLLOUT_FILE` and never touches\n",
-    "a checkpoint (see `giant.analysis`'s module docstring). This section is the one\n",
-    "deliberate exception: soft gate weights only exist inside the trained `Router`,\n",
-    "not in the rollout parquet, so this loads the checkpoint that produced\n",
-    "`ROLLOUT_FILE` and calls `model.router.gate(...)` directly on that shower's\n",
-    "pre-step conditioning.\n",
-    "\n",
-    "`model.router` is Stage 1's router; Stage 2 (`sec_decoder.router`) is a separate,\n",
-    "independently trained `Router` instance over the same axis (see\n",
-    "`giant.model.network.build_models`) and isn't shown here.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "acae54e37e7d407bbb7b55eff062a284",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import polars as pl\n",
-    "import torch\n",
-    "\n",
-    "from giant.analysis import plot_router_gating\n",
-    "from giant.data.transforms import Normalizer, build_cond_features\n",
-    "from giant.model.network import build_models\n",
-    "\n",
-    "# Checkpoint that produced ROLLOUT_FILE (needs `model.router` enabled at\n",
-    "# train time, i.e. trained with `--router` / `model.router.enabled = true`).\n",
-    "CHECKPOINT = \"/ceph/lbogner/geant_steps/checkpoints/REPLACE_ME/best.pt\"\n",
-    "\n",
-    "ckpt = torch.load(CHECKPOINT, map_location=\"cpu\", weights_only=False)\n",
-    "model_cfg = ckpt[\"model_config\"]\n",
-    "conditioning = model_cfg.get(\"conditioning\", \"embedding\")\n",
-    "pdg_map = {int(k): v for k, v in ckpt[\"pdg_map\"].items()}\n",
-    "mat_map = {str(k): v for k, v in ckpt[\"mat_map\"].items()}\n",
-    "cond_norm = Normalizer.from_dict(ckpt[\"normalizer\"][\"cond\"])\n",
-    "\n",
-    "model, _sec_decoder = build_models(model_cfg)\n",
-    "model.load_state_dict(ckpt[\"model\"])\n",
-    "model.eval()\n",
-    "\n",
-    "if not hasattr(model, \"router\"):\n",
-    "    raise RuntimeError(\n",
-    "        f\"{CHECKPOINT} has no router — it was trained with model.router.enabled=False\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Pre-step conditioning for every row of the rollout shower, reconstructed\n",
-    "# the same way `giant predict`/`giant rollout` do (giant.data.transforms).\n",
-    "cols = [\n",
-    "    \"pdg\",\n",
-    "    \"pre_x\",\n",
-    "    \"pre_y\",\n",
-    "    \"pre_z\",\n",
-    "    \"pre_E\",\n",
-    "    \"pre_dx\",\n",
-    "    \"pre_dy\",\n",
-    "    \"pre_dz\",\n",
-    "    \"material\",\n",
-    "    \"layer_id\",\n",
-    "]\n",
-    "df = pl.read_parquet(ROLLOUT_FILE, columns=cols)\n",
-    "\n",
-    "# Rows whose pdg/material fell outside the training vocab can't be encoded\n",
-    "# (mirrors the pdg_mask filtering in `giant predict`'s CLI path).\n",
-    "known = df[\"pdg\"].map_elements(\n",
-    "    lambda p: int(p) in pdg_map, return_dtype=pl.Boolean\n",
-    ") & df[\"material\"].map_elements(lambda m: str(m) in mat_map, return_dtype=pl.Boolean)\n",
-    "n_dropped = (~known).sum()\n",
-    "if n_dropped:\n",
-    "    print(f\"dropping {n_dropped}/{len(df)} rows with unknown pdg/material\")\n",
-    "df = df.filter(known)\n",
-    "\n",
-    "data = {\n",
-    "    \"pre_pos\": df.select(\"pre_x\", \"pre_y\", \"pre_z\").to_numpy().astype(np.float32),\n",
-    "    \"pre_E\": df[\"pre_E\"].to_numpy().astype(np.float32),\n",
-    "    \"pre_dir\": df.select(\"pre_dx\", \"pre_dy\", \"pre_dz\").to_numpy().astype(np.float32),\n",
-    "    \"layer_id\": df[\"layer_id\"].to_numpy(),\n",
-    "    \"pdg\": df[\"pdg\"].to_numpy(),\n",
-    "    \"material\": df[\"material\"].to_numpy(),\n",
-    "}\n",
-    "cond_cont, cond_cat = build_cond_features(\n",
-    "    data, pdg_map, mat_map, cond_norm, conditioning=conditioning\n",
-    ")\n",
-    "cc = torch.from_numpy(cond_cont).float()\n",
-    "ck = torch.from_numpy(cond_cat).long()\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    gate_weights = model.router.gate(cc, ck).numpy()  # (N, n_experts), rows sum to 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8dd0d8092fe74a7c96281538738b07e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# EnergyRouter gates on pre-step energy, so that's the natural x-axis here —\n",
-    "# swap for a categorical plot if this checkpoint used a different router type.\n",
-    "_ = plot_router_gating(data[\"pre_E\"], gate_weights, x_label=\"pre_E\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "giant (3.12.13.final.0)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/analysis/validation.ipynb b/analysis/validation.ipynb
deleted file mode 100644
index 70eecea..0000000
--- a/analysis/validation.ipynb
+++ /dev/null
@@ -1,706 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "bb22f42a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Auto-reload edited modules (e.g. giant.analysis) without restarting the kernel.\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f65c3ad9",
-   "metadata": {},
-   "source": [
-    "# GIANT validation notebook\n",
-    "\n",
-    "Diagnostics for a trained checkpoint's sample quality, run against `giant predict --coord local` output (`pred_*`/`true_*` columns, denormalized but still local-frame/log-scaled — see `giant.analysis`'s module docstring). Four tiers, each building on the last:\n",
-    "\n",
-    "1. **stratified marginals** — per-dimension real-vs-generated, sliced by pdg/material/energy\n",
-    "2. **joint structure** — correlation matrices, physically-coupled pairwise plots, direction alignment\n",
-    "3. **physical constraints** — unit-norm directions, non-negative step_length/delta_e/edep\n",
-    "4. **event-level (shower) observables** — total energy, longitudinal/transverse profiles, shower-max depth, in world-frame physical units (mm, MeV)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1b65ca80",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from giant.analysis import plot_kl_bars_pl\n",
-    "\n",
-    "# Predict parquet produced by `giant predict --coord local --checkpoint ...`,\n",
-    "# carrying both pred_*/true_* columns so real vs. generated can be compared.\n",
-    "FILE = \"/home/lars/Programming/giant/0932fb02-f2ce-43ca-a4ef-60a2b1221bbc.parquet\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82142e6b",
-   "metadata": {},
-   "source": [
-    "## Tier 1: stratified marginals\n",
-    "\n",
-    "KL(real || generated) per target dimension, computed lazily straight from the parquet over every row in the file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f2752229",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_1223195/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
-      "  fig.show()\n",
-      "/tmp/ipykernel_1223195/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
-      "  fig.show()\n",
-      "/tmp/ipykernel_1223195/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
-      "  fig.show()\n",
-      "/tmp/ipykernel_1223195/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
-      "  fig.show()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# KL(real || generated) per target dimension, read lazily straight from the\n",
-    "# parquet (no SampleCollection, no row subsampling) so this covers every row\n",
-    "# in the file regardless of size. \"energy\"/\"pdg\"/\"material\" stratify the\n",
-    "# aggregate check so a failure hidden by the overall KL doesn't go unnoticed.\n",
-    "for grouping in [None, \"energy\", \"pdg\", \"material\"]:\n",
-    "    fig = plot_kl_bars_pl(FILE, group_by=grouping)\n",
-    "    fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22c67dc4",
-   "metadata": {},
-   "source": [
-    "## Detailed marginals, correlation & constraints (Tiers 1–3, in-memory sample)\n",
-    "\n",
-    "The richer per-row diagnostics below (overlaid histograms, correlation matrices, pairwise scatter, direction alignment, constraint violations) need `real_raw`/`gen_raw` materialized as numpy arrays, so they run on a `SampleCollection` built from a 50% row sample rather than the lazy, full-file path used above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6f0903ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tiers 1-3 detailed plots. Every one streams straight from the predict\n",
-    "# parquet path (bounded memory, no in-memory SampleCollection) — pass FILE\n",
-    "# directly, exactly like the lazy KL bars above and the event-level checks below.\n",
-    "from giant.analysis import plot_marginals, plot_correlation_matrices, plot_pairwise\n",
-    "from giant.analysis import plot_direction_alignment, plot_constraint_violations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6d292c9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAADEQAAAEOCAYAAAA6vSPLAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdYFNcaBvB3WZal96IUAcFegrEE0WhsUaPEHitqYuwdK5YYEqMmaqKxXJPYosZujDUWjJrExBaDvWELRZDqsgLLAnP/EEcXFlwUdinv73nyXObMmdlvuPLt7Oz5zpEIgiCAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIioDDEydABERERERERERERERERERERERERERERERERFxYIIIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqc1gQQUREREREREREREREREREREREREREREREZQ4LIoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqMxhQQQREREREREREREREREREREREREREREREZU5LIggIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqIyhwURRERERERERERERERERERERERERERERERU5rAggoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyhwWRBARERERERERERERERERERERERERERERUZnDggiiEhAWFoZKlSoZNIb9+/fDy8vLoDEQERXG1NQU58+f16lvnz59MGvWrBKPiYiIntu6dSvq1q1r6DCIiOgl/vzzT9ja2ho6DCKicm3w4MGYPHmyocMgIirXPv30U/Ts2dPQYRARlSkJCQmQSCSIjY19peNXr14Nf3//Yo+LiKg8WbBgATp37vzKx/v7+2P16tXFGhMRUXnzumMTOK6MwIIIIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqi1gQQRXCoUOH4O7ubugwSkx5vz4iorx69uyJTz/91NBhEBERERG9NltbW/z555+GDoOIqNzauXMnatasaegwiIjKtfPnz8PU1NTQYRAR6U1sbCwkEgkSEhIMHQoRUbk1d+5cdO3a1dBhEBGVa5s2bYKfn5+hwyAqFiyIICIiIiIiIiIiIiIiIiIiIiIiIiIiIiKiMocFEVRu/Pfff+jYsSNsbGzg5uaGkJAQZGZmIiIiAh07dkR0dDQkEgkkEgn2798PADh58iSaNWsGa2tr+Pr6YtmyZeL59u/fD09PT3z22Wfw9PSEk5MTRo8ejYyMjFeKT5fXWrBgAapWrQobGxv0798f6enpYp8ffvgBVatWhYODA3r16oV+/fphxIgRhV6fIAiFnpOISF8UCgWCgoJgZ2cHX19frFixQmO/Wq1GaGgoqlWrBisrK7zzzju4dOmS1nNNnz4du3btQmhoKCQSCSpVqgQAGDNmjJgH3d3dMWHCBJ1zdlFen4iorLp79y66du0KJycnuLq6YuzYsUhLSxP3//3333jzzTdhbW2N5s2b4+rVqzofn5KSAolEgm+//Ra1a9eGtbU1OnXqhOjoaL1fJxFRSbC0tMSXX36JBg0awNraGi1btsTNmzfF/REREejUqRPs7Ozg4eGBSZMmifeigiBg+vTpcHV1FfPjvXv3AACNGjXC48eP8fbbb0MikWDAgAEvjeX69et4++23YWVlhYYNG+LUqVPivvDwcFhZWYkrTuTk5KB169b46KOPSuC3QkRUfLZu3YpatWphxowZ8PDwgIuLC4KDg6FWq8U+GzZsQJ06dWBpaYkGDRrgwIED4r6IiAi0a9cO1tbWqFKlCj799FNkZWUhPDwcvXr1ws2bN8VnBidOnHhpPCtWrICXlxecnJwQFBQEhUIBAMjMzISLi4vGayN3JcsxY8YU6++EiKi4nT59GpaWlliyZAl8fHxgb2+PQYMGITU1Vexz6NAhNGzYEJaWlqhduzbWrVsn7ktISEC3bt1gZ2eHSpUqic8FUlJS0LhxY6hUKjHXrl+//qXx7Nq1CzVq1ICdnR0CAwPx8OFDcV/Xrl3Fcz37z9fXtwR+K0REr+ZZTnJycoJEIsHcuXMRFRUFiUSCVatWoU6dOpDJZFAqlWIek8vl8PPzw+HDh8XzdOjQAbNnz9Y497Jly1CvXj1x++bNm+jUqRMcHBzg7u6OyZMnQ6VSvVLcJ06cwBtvvAEbGxu0atUKt2/fFvf98ssvsLe3F59ZKBQKVKtWDV999dUrvRYR0evYunUrZs+ejT179oh5NDY2Fp9++inee+89DB06FK6urujbty9Wr14t9nF0dMQHH3yAR48eAbljyYyNjcXc9oyfnx++/fZbcXv9+vWoV68eLC0t4efnh19//fWV4s7OzkZISAgqVaoENzc3TJ48GVlZWQCA6OhomJiYICIiQuOYunXrYvny5a/0ekREr+P06dMICgrCxYsXxTx6+vRprF+/Hn5+fpg+fTo8PT3RtGlThIWFiX1sbGzQoUMH3LlzBwCQlpYGGxsbnDx5UuP8Xbp0QXBwsLh94MABNG7cGFZWVqhZsyY2bNjwyrF/88038PT0hLOzMz788EMolUpxX82aNfM9U+jcufMrvxaVIQJROREUFCT07NlTSEhIEOLi4oSvvvpK+P333wVBEIRff/1VcHNz0+h/6dIlwdzcXNi8ebOQmpoq/PPPP0KVKlWE7du3C4IgCPv27RMACAMGDBASEhKEa9euCXXq1BGmTJny0liOHj0quLi4FPm1goODhaSkJOHOnTuCj4+PsHTpUkEQBOG3334TLCwshF9//VVQKBTCunXrBADC8OHDC7y+l52TiEifBgwYIDRv3ly4f/++EBMTI7z33nsCAOHcuXOCIAjCxIkThUaNGgmXL18WUlNThSVLlgiurq5CamqqIAiC0Lt3b2HmzJni+Xr06CHMmTNH62vl5OQIN2/eFN566y3hiy++0Cm+l70+EVFZp1QqhSpVqgjTp08XEhMThcjISKF169bCuHHjBEEQhISEBMHW1lZYsGCB8PjxY+HEiROCvb29UKdOHZ2OT05OFgAIb775pnD79m0hLi5O6NGjhxAQEGDQ6yYiKi4WFhZC1apVhfDwcCEpKUkYNWqU4OPjI2RmZgoZGRmCj4+PMGzYMCExMVG4evWqULt2bTFHHjhwQHB2dhauXr0qPHnyRDh06JAwb9488dw2NjbCH3/8oVMcGRkZgpeXlzBx4kQhOTlZ+OeffwQvLy/BxsZG7PPdd98Jbm5uwqNHj4RZs2YJdevWFZ48eVICvxUiouKzZcsWAYCYSy9evCj4+voKoaGhgpD7/NPMzEzYu3evoFAohA0bNggymUz4999/BUEQhG7dugkDBw4UkpKShIcPHwqff/65cP78eUEQBGHHjh1CjRo1dI5l//79grW1tXD06FHh8ePHwrfffisAECZNmiQIgiCEhIQInTt3Fvs/fPhQIxYiotLq77//FgAIgYGBwsOHD4W7d+8Kb731lvDhhx8KgiAIly9fFmQymbB27VpBoVAIBw4cECwsLIS9e/cKQu4z1LZt2wqxsbFCYmKisHLlSnHfuXPnBLlcrnMsly5dEkxMTIStW7cKCoVC2L59u2BsbCz06NEjX9/k5GShTp06wtdff11svwsiotf18OFDAYAQHx8vtkVGRgoAhLZt2woPHjzId0xaWpqwbds2wdraWoiJiREEQRC2bdsmeHh4CNnZ2WK/N998U8x5ycnJgouLi/DZZ58JycnJwr1794SAgABhxowZgiAIQnx8vABAePjw4UtjjomJEczNzYVly5YJCoVCOHLkiGBtbS289dZbYp9n35dlZGQIH3zwgdCpUychJyfnNX9bRESv5vPPPxe6dOmi0TZnzhwBgLBixQpBqVTmO+bhw4dC3759hV69eolt7dq10xhbcOHCBcHExERISEgQBEEQfvzxR8HV1VU4efKk8OTJE2Hfvn2ClZWVcOPGDUEQBGH+/PlCp06ddIp50aJFgpeXl3DhwgUhKSlJmDBhggBA+OGHHwQh9/nF5MmTxf5//PGHYGpqKiQnJxf590NEVBw2btwovPHGGxptz8amhoSECCkpKfmOSUpKEoKDg4XGjRuLbUOHDhUGDRokbsfFxQkymUy4dOmSIAiCcPLkScHKykrYu3evoFQqhT///FNwdHQUjh07Jgi5z4efjU14mZ07dwp2dnbC8ePHhcePHwsLFy4UAGiMK3smIiJCqFy5svj8gso3FkRQuTF06FBh4MCBQkZGRr592goGhg8fLj7kfeabb74R3n//fUHILSiQSqXC48ePxf07d+4UnJycXhpL3oIIXV7L0tJS40FHcHCwMHjwYEEQBGHQoEFi8cMzrVq1emlBRGHnJCLSF5VKJUilUuHMmTNiW0REhFgQkZWVJcjlcuHUqVMax9WpU0e8IS1KQcQze/bs0XiIWxBdXp+IqKzbuHGjUL16dY22c+fOCfb29oIgCMLq1auFWrVqaeyfNWuW+NDhZcc/K4g4fPiwuD82NlYAINy8ebPErouISF8sLCyENWvWiNvp6emCpaWlcOzYMeHw4cOCpaWlkJ6eLu7fvXu3YGFhIQiCIBw7dkxwd3cX/vvvP63nLkpBxKFDhwR7e3shMzNTbFu1apVGQYSQW5Bcv359wdraWvzijoioNNuyZYtgbm4upKWliW3r168XvL29BUEQhF69euV7PtqpUyex+Kxv377CiBEjBJVKle/cRS2I6NmzpzBx4kSNtkaNGokFEQ8ePBBkMpk4yO3zzz/X+PKPiKi0elYQ8eIg3RMnTggymUxQq9XClClThPbt22scM3r0aPG7rJCQECEwMFDrwLOiFkRMnjxZ6Natm0Zbz5498xVEZGRkCC1bthQmTJig87mJiPShsIKI8PDwQo9t166dsHHjRkHIzXN2dnbCkSNHBEEQhIsXLwoymUx49OiRIAiC8L///U9o0KCBxvHHjx8XqlSpIghFLIhYsmRJvvvWCRMmaHyXlpmZKTRt2lTw8/MTqlSpIiQmJurw2yAiKhkFFUQ0atSo0OMePHggmJqaigVdmzdvFry8vMTtsWPHCj179hT7N2zYUFixYoXGOfr37y989tlnglDEgoi6desK3333nbidmZkp2NvbiwURR48eFRwdHcWxbf379xeCgoJ0OjcRUUkoqCDC2dlZY9xpXmq1WpDJZOJ96F9//SVYWFiIE88uXrxYI1/36NEj30TkM2fOFD766CNBKGJBROfOnYWQkBCNtnr16uUriIiPjxeqVasmrFq1SqfzUtlnZOgVKoiKy5dffgknJyc0b94crVu3xjfffIPMzMwC+9+7dw/r1q3TWBpn4sSJuH//vtjH0dER1tbW4raPjw/i4+OLvASlLq/l4OAAI6Pnf5JmZmZIT08HcpdN8/b21jhn1apVX/q6hZ2TiEhfHj58iOzsbI285e3tDYlEAgCIiYmBSqVCs2bNNPLk1atXNfLky/z2229o1qwZbGxsIJFI0KVLF0RFRb30uOJ6fSKi0uzevXu4deuWRp5r3LgxkpKSkJqaiqioqHz3lz4+Pjof/8yL53BxcYGFhYVOuZiIqCx4MceZmprCzc0NUVFRiIyMhJubG0xNTcX9vr6+ePLkCZKSktC6dWuEhoYiKCgIb731FsaOHYsHDx68UgxRUVHw8PCATCYT217M18+MGzcOly5dQs+ePVGjRo1Xei0iIn2rXLkyzMzMxG0fHx/xXjIyMhK+vr4a/X19fREZGQkA+Pbbb2FiYoKAgAC0bdsWy5cvR1ZW1ivF8bJ74ypVqqBDhw744YcfkJOTg9WrV2Po0KGv9FpERPpmbGyMKlWqiNs+Pj5Qq9V49OjRS3PtzJkzUb9+fbRu3RotWrTA/PnzkZaW9kpxvCzXAoAgCBg0aBCcnJywePHiV3odIiJDeDHPZmdnY8aMGfD29oaJiQkkEgmOHj0q3ufK5XL069cP69atAwCsW7cOgYGBcHJyAnKfy/77778az2VbtWqFyMhI5OTkFCkuXXKvTCbDyJEjER4ejjFjxsDe3v6Vfw9ERCXlxTwLADdv3kSnTp3g6OgIIyMjeHp6IiMjA4mJiQCAbt26ISUlBcePH0dmZiY2b96Mjz76SDz+3r17GD16tEau/emnn15prEDeXCuTyeDh4SFut2nTBg4ODti5cycSExOxc+dOPlMgolLJ3d1dY9xpbGws+vTpAxcXF0ilUshkMqjVavG+tmnTpvDw8MD27dsBAOvXr8+XaxcuXKiRa7/44otiybXQMpY2PT0dgYGB6NWrF4YPH17k16CyiQURVG7Y2dlh0aJFOHfuHLZu3Ypdu3Zh/vz5AKCRnJ/x9PTEqFGjkLtSivjfxYsXxT4JCQkaA7zu3r0LJycnyOXyIsWmy2sVxs3NDffu3dNou3v3rviztusjIiotKleuDKlUqpHH7t+/D0EQxP0mJia4cOFCvjw5duxYrefMm/eys7PRrVs39OvXD/fv30d2djZ2796t0+CHV3l9IqKyxtPTE/Xr18+X5wRBgJWVFdzd3Qu933zZ8c+8eI5Hjx7hyZMncHd319NVEhGVrBdznEqlQkxMDNzd3eHh4SEW2T4TEREBCwsLceDARx99hBMnTuDPP/+Es7MzOnToIPYtymd6d3d3REZGatznvpivASAtLQ2DBw/GRx99hB07duD48eOvfM1ERPr08OFDZGRkiNt3794V7yU9PDzy5buIiAhxUIGjoyOWLl2K8+fPY8OGDVi/fj2WLl0KvMKz05fdGwPAqFGjsGbNGuzduxeJiYno27dvEa+WiMgwsrKyxAIH5OY3mUwGZ2fnl+ZaCwsLzJ07F2fOnMGePXvw559/Ytq0aUAJ5dopU6YgOjoaGzdu5PdgRFTqFJaXnk0IBgAbN27E9u3bsXfvXigUCgiCgDZt2mh8rv/www/xyy+/ICEhAT/99BM+/PBDcZ+npycCAgLyPZPNyckpkdwbFRWF4OBgjBo1Cl988QUiIiKK9BpERMWpoDz3Yp4FgCFDhqBq1aq4evUq1Gq1mOue5VpTU1P06dMH69evx759+2Bqaop3331XPN7T0xNr167Nl2vXrFlT5Jjz5lq1Wq1x/y2RSDBixAisWrUK69atg5eXF95+++0ivw4RUXHRNddOnjwZWVlZOH/+PDIyMqBSqSCTyTTuawcPHoz169fjn3/+we3btzWemXp6euKTTz7Jl2uPHTtW5Jhfdl+bk5ODvn37olq1avjiiy+KfH4qu/j0iMqN4cOH4+DBg1AoFMjKykJOTg6USiWQO9g1ISEB//33n9h/5MiR2LhxIzZv3gyFQoGHDx/ihx9+wMKFC8U+2dnZGDt2LBITE3Hjxg3MmTMHgwYNKnJsurxWYQYOHIhNmzbh8OHDUCqVWL9+PU6cOCHu13Z9RESlhYmJCfr06YPJkycjMjISsbGxGD9+vLjf2NgYI0eOxKhRoxAeHo709HRcvnwZH3/8MW7duqX1nJUrV8aVK1fElYCysrKQkZEBa2trmJub4+rVqwgNDdUpvld5fSKisqZr165ISUnBrFmzEBcXh8ePH+PgwYPibAhdu3ZFdHQ0Fi1ahNTUVPzxxx9YuXKlzsc/M2PGDNy5cwfx8fEYM2YM/P39Ub16db1fLxFRSZg3bx4uXbqElJQUTJ48WVylsmXLlnB0dMTEiRORlJSEa9euYdasWeLMN+vWrcPXX3+NyMhIqNVqjecVyL23DQ8P12lmx3feeQdWVlaYPn06UlJSEB4ejgULFmj0GTlyJFxdXfHDDz/g22+/Rb9+/RAXF1cCvxEiouKVlpaG4OBgJCcn48qVK5g7d674LHbw4MHYsGEDDh48CKVSiY0bN+Lw4cMYPHgwkDuI7MiRI1AqlcjOzkZ2drbGs+GYmBjExsbqFMegQYOwZs0aHDt2DKmpqVi5ciXOnTun0ad9+/YwNzfH8OHD0adPH1haWhb774OIqKSMHTsWcXFxuH//PqZPn47+/fvD2NgYQUFBCAsLw4YNG6BUKvHrr79i/fr1GDJkCAAgODgYP//8M5KTk/N9D1epUiWoVCpcu3ZNpxiCgoKwf/9+7NixA0qlErt27cLu3bvF/cuWLcOBAwewZ88ejZXYiIhKCwcHB5iYmODff/8VJwDTRqlUwtjYGDY2NsjOzsbatWvzTVzQsGFD+Pr6YuDAgTA2NkbHjh3Ffb1798adO3fwxRdfID4+HikpKdizZ88rTejVq1cvXLlyBStXrkRqaiqOHTumMdg3KysLffr0wQcffIAVK1Zg+PDh+OCDDzQmgCAi0qfKlSsjIiICCoWi0H5KpRLm5uawsrJCdHQ0JkyYkK/Phx9+iF27dmHZsmUYOHAgpFKpuG/cuHGYM2cOjh8/jrS0NNy5cwehoaHYu3dvkWMeNGgQ5s+fj/DwcKSkpGD69OlISkrS6DN48GBcuHABCxcu5OoQRGRwlStXRmRkJOLj4wvtp1QqYWpqChsbGyQnJ2PcuHFQq9UafQYOHIi///4bs2fPRrdu3WBrayvuGzt2LL799lvs2bMHSqUSUVFR+Pbbb/Hdd98VOeZBgwZh1apVOHnyJFJTU/HNN9/g8uXLGq/15MmTVypso7KNBRFUbowZMwarVq2Ch4cH3njjDfj4+GDWrFkAgHr16mHQoEF44403IJFIsH//fjRo0AD79+/H999/D09PTzRq1Aj//POP+CUacpdZ8/b2RoMGDdC8eXM0a9YMn332WZFj0+W1CtO6dWssWrQII0aMQJUqVbB//35069YNJiYmBV4fEVFpsmLFCri7u6NevXpo1qwZ2rdvr7HazldffYX3338fffr0gaOjI4KCghAQEIBq1appPd+oUaNw7949WFtbo1KlSpDL5Vi9ejVmzZoFKysr9O/fH4GBgTrHV9TXJyIqa6ytrXH8+HHcunULfn5+8Pb2xqpVqzBmzBgg9wu8AwcO4KeffoKrqytCQkIwYsQInY9/ZsCAAQgMDISPjw9SU1OxY8cOvV8rEVFJ+fjjjzFo0CB4eHggPDwc+/btg4mJCeRyOQ4dOoR79+6hatWqePfdd9G2bVt8+eWXAIAePXogLi4OzZo1g7OzM44cOYJt27aJ5/3ss8+waNEiyGQyDBgwoNAY5HI59u/fj7///hseHh4YMmSIRnHamjVrcPToUfz0008wMjLC4MGD0b59e/Tv31+nggsiIkOqWbMmbGxsUK9ePbRu3RrvvfceQkJCAADvvfceli1bhsmTJ8PFxQWLFy/Grl270KBBAyD32fA333wDV1dXNGrUCA0bNsTkyZMBAP7+/ujSpQtq1KgBiUSiMdGMNp07d8Znn32Gjz76CN7e3vjrr7/QrVs3jT7PZnR89OgRhg0bVmK/EyKi4mZhYYF33nkHTZs2RYMGDeDj4yOuqFOvXj3s3r0b33zzDVxcXDBx4kQsWbIE77//PpBbeLt161b4+PigZs2asLCwEItzXV1dMXXqVLRo0QISiQTr168vNI769etj48aNmDlzJtzd3bFu3TqxoBi5z5Nv3LgBBwcHSCQSSCQS+Pr6lujvhoioKKRSKebPn4+goCBIpVLMnTtXa7+PPvoI9evXR506deDq6opjx46hbdu2+fp9+OGH+PXXX/MN0nVwcMDJkydx/vx51K1bFz4+PtiwYUO+57K6cHV1xd69e7Fq1Sq4ubnh888/13gGHBISgvT0dHz99dcAgC+++ALm5uZaBxYTEelDjx494OrqCldXV0gkkgInOli2bBkOHjwIOzs7tGzZEi1atMjXp0mTJvD29sbJkyc1VuJBboHCF198geDgYHF1X6lUqrGKhK4mTJiAXr16oUOHDqhduzaQW/j2IltbW/Tt2xcpKSmvNCkvEVFxatGiBdq1awcfHx9IJBKcPn1aa7958+YhIiICzs7O8PPzg4eHB2xsbDT6VK5cGe+++y5+/fVXjc/4ANCqVSts3rwZX375Jdzc3NCsWTPcv38fvXv3LnLMPXv2REhICAYMGICqVavi4sWL6NSpk7h/5cqVCAsLg4mJifhMoXPnzkV+HSp7JEJh5epEFdj+/fsxZswY3L9/39ChaBUQEIAePXpg0qRJhg6FiIiIiCq4lJQU2NnZITIyEu7u7oYOh4io2FlaWiIsLAz+/v6GDoWIqFzaunUr5s6diytXrhg6FJ19/vnn2LlzJy5evGjoUIiIdHL69Gm0bdtWY7UyIiIiIiLSv/79+yM7Oxtbt241dChERETlBleIICojJkyYgNu3byMlJQVff/01Lly4gO7duxs6LCIiIiIiIiIiIqIKJS4uDt999x3Gjx9v6FCIiIiIiIiIqAy5evUqdu3ahXHjxhk6FCIionKFBRFEr+Djjz8Wl9PJ+19ERESJvGazZs3QrVs3eHh4YOvWrTh06BC8vb1L5LWIiMqbgnL2O++8Y+jQiIiIiIhEYWFhBd67zp0719DhERGVC3369Ckw18bGxr70+E8//RQeHh5o2rQpBg0apJeYiYjKmunTpxeYa0+fPm3o8IiIyqXly5cXmHt37txp6PCIiMoFPz8/rXnW1tZWp+P79OmDxo0bY9y4cQgICCjxeImIyqKuXbsWeF+bkpJi6PCoFJMIgiAYOggiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqKi4AoRRERERERERERERERERERERERERERERERU5rAggoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyhwWRBARERERlVFpaWmGDoGIiIiIiIiIiIiIiIiIiIiIiMhgjA0dQFmSk5ODmJgYWFlZQSKRGDocIipnBEFAamoqXF1dYWRUcevVmGuJqCSVl1x79epVdOvWDVFRUahduzb27NkDNzc3nY5lniWiklRe8uzrYq4lopLEXMs8S0Qlj7mWuZaIShbz7FPMtURUkphrmWeJqGQxzz7FXEtEJakouVYiCIKgt8jKuKioKHh4eBg6DCIq5yIjI+Hu7m7oMAyGuZaI9KGs59o2bdqgV69eGDFiBGbPno3o6GisXbtWp2OZZ4lIH8p6nn1dzLVEpA8VOdcyzxKRvjDXMtcSUcmqyHkWzLVEpCcVOdcyzxKRPlTkPAvmWiLSE11yLVeIKAIrKysg9xdrbW1t6HCIqJxRKBTw8PAQc01FxVxLRCWptOTatLQ0HDp0CKampnjvvfe09rl37x4uXLgAOzs7vP3225DJZEBu9fPff/+NQ4cOAQCGDx+OFi1a6PzazLNEVJJKS541NOZaIipJzLXMs0RU8phrmWuJqGQxzz7FXEtEJYm5lnmWiEoW8+xTzLVEVJKKkmtZEFEEz5b0sba2ZvImohJT0ZcPY64lIn0wVK4VBAETJkzAjh07YGJiAltbW60FEV999RVCQ0PRvHlz3LlzB8bGxjh27Bjc3NygVCohk8nEAgl7e3skJSXpHAPzLBHpA+9pmWuJqORV5FzLPEtE+sJcy1xLRCWrIudZMNcSkZ5U5FzLPEtE+lCR8yyYa4lIT3TJtUZ6iYSIiIiIiCAIAqpWrYpr166hZ8+eWvuEh4dj+vTp2Lp1Kw4fPoyrV6/CxsYG48aNA3JnWMjOzkZqaiqQuwRlpUqV9HodREREREREREREREREREREREREpQFXiCAiIiIi0hMjIyOMHz++0D5btmyBt7c3AgMDAQByuRwjR47E0KFDkZqaCisrK7z33nuYPXs2Ro0ahU8//RTvv/9+gedTqVRQqVTitkKhKMYrIiIiIiIiIiIiIiIiIiIiItKUnZ0NtVpt6DDKBZlMBqlUaugwiEo1FkQQEREREZUily9fRt26dTXa6tWrh6ysLNy4cQONGzfG8uXLMXr0aHTu3BnNmzfHJ598UuD55s+fj9DQUD1ETkRERERERESlyaZNmxAbGwtTU1OMGTPG0OEQERERERERUQWhVCoRFRUFQRAMHUq5IJFI4O7uDktLS0OHQlRqsSCCiIiIiKgUefz4MapVq6bRZm9vL+4DAGdnZ+zYsUOn84WEhCA4OFjcVigU8PDwKNaYiYjKo6ioKACAmZkZHBwcDB0OEVG58+LsYCYmJjAyMjJ0SERE5U5iYiKio6Oxbt06FkQQERERERERkV5kZ2cjKioK5ubmcHJygkQiMXRIZZogCIiPj0dUVBSqVavGlSKICsCCCCIiIiKiUsTMzAxKpVKjLTU1VdxXVHK5HHK5vNjiIyKqCFQqFfz9/aFSqfDWW29h//79hg6JiKjc+eGHHzBhwgSo1Wrs2rULXbt2NXRIRETlzvjx45GRkYF169YZOhQiIiIiIiIiqiDUajUEQYCTk9MrjXGg/JycnHD//n2o1WoWRBAVgNNuERERERGVIlWrVsWDBw802p5tV61a1UBRERFVLHK5HFFRUdiyZYuhQyEiKrdGjBiBjIwM9OrVy9ChEBGVSkqlEt999x06d+6MBQsWaO0TFxeH6dOno0uXLhg9ejSuXbum9ziJiCqya9euwcvLC15eXpg3b56hwyEiIiIiKlW4MkTx4e+S6OW4QoQexP53G8rkOI02SzsXVKpSzWAxERGVJ8yzRFSedOrUCatXr8adO3fg4+MDANi6dSsaNGiAypUrGywu5loiKmtu3LiBM2fO4K233kLNmjXz7c/OzsYff/yB2NhY1K1bF3Xr1jVInETg+yyVYefOnUNcXBw6duyodVaq9PR0nD17FlKpFI0bN+bKZVQimEOpPLp37x6aNWuGTp064eHDhwgPD8/XJzk5Gf7+/qhevToGDx6Mo0ePokmTJjh9+jTvbYlKCN9zKC9fX1+cOHECGzZsQFJSkqHDoQqAeYiIqHhFp6Qj+UmmRpudhQncbDmjPVFFtnLlSgDAqFGjDB0KUalUWj+XsCCihMX+dxvWa5qhkkSl0Z4myBE75JTB/wEQEZV1zLNEVNYcPHgQSUlJuH79OpKTk7Fp0yYAQO/evSGTyfD+++/j3XffRceOHTFy5Ehcv34dO3fuxJEjRwwWM3MtEZUlFy9exMSJExETE4O7d+/i66+/zlcQkZKSgnbt2iE+Ph7169fHiRMnMHDgQCxfvtxgcVPFxfdZKot++uknfPnll0hKSkJ0dDRSU1NhaWmp0ef3339Hjx494OLiArVajdTUVOzbtw8NGzY0WNxU/jCHUnnl4uKCGzduwNraGp07d9baZ9myZcjIyMDevXshl8vRt29fREREYM6cOdi1a5feYyYq7/ieU36lpaUhJiYGbm5uMDPTPvhRoVAgNTUVrq6uGjOzmpiYwMvLC/b29lAoFHqMmioi5iEiouIVnZKOtotPIl2drdFuJpMibFJLFkUQVWBpaWmGDoGo1CrNn0tYEFHClMlxqCRR4fybX8LW8+mMPCkPrqDRhWmISY4D+KGUiOi1MM8SUVlz6tQpPHjwAHZ2dnj77bdx6NAhAED37t0hk8kgkUiwb98+rF27FufOnYOdnR3++ecfg87uyFxLRGVJeno6Zs2ahdatW8PW1lZrn1mzZkGhUODy5cuwsrLC+fPn8dZbb6Fjx47o1KmT3mOmio3vs1QWJScnY+PGjbh37x66deuWb39GRgb69OmDfv36YenSpQCAAQMGoE+fPrh58yaMjIwMEDWVR8yhVF6Zm5u/tM+hQ4fQoUMHjdV3unfvjtmzZ0MQBEgkEuzfvx9XrlyBSqXCokWL0LhxY7Rs2VLr+VQqFVSq519kcmAvkSa+55Q/ERERWLp0KbZt24b4+HgcPXoUbdu21eijVCoxaNAg7Nu3D+bm5rC1tcWPP/5YYC4lKknMQ0RExSv5SSbs1HFY2d4VHvZPix8ik9Ix83AMkp9ksiCCiIhIi9L8uYQFEXpi61kXvm80BwBEAMAFQ0dERFS+MM8SUVnxxRdfvLSPTCbD8OHDMXz4cL3EpCvmWiIqC/z9/V/aZ8uWLZgyZQqsrKwAAI0aNUJAQAA2b94sFkTExsYiISEBGRkZiIqKgqOjI0xNTbWej4PHqDjwfZbKkjFjxgAA7t27p3X/0aNHERsbi6lTp4pt06ZNQ/369fHXX3+hefPmyMnJQWZmJnJycqBWq6FSqTQG9b6IeZZehjmUKqL79+/nG7jr7u4OpVKJhIQEODk54fHjx0hISMDo0aMRGxuL1NTUAs83f/58hIaG6iFyorKN7znlx+HDh1GtWjX8/vvvqFWrltY+48ePx5UrVxAZGQknJyfMmDEDXbp0QUREBBwdHfUeMxGYh4iIio1MGY0w+RSYn3z+zMkXQJhcjkhlYwA2Bo2PiF7d77//jjVr1iA7OxsXLlzAmTNnMHXqVBw5cgQ2NjYIDQ1FYGAgNm3ahAkTJsDExAQNGjTA//73P1SpUsXQ4ROVCaXxcwkLIoiIiIiIiIiowoiJiUFSUhLq1Kmj0V63bl389ddf4nZgYCAePnwI5BZZrFmzBu3bt9d6Tg4eIyLSdPHiRTg4OMDNzU1sq1u3LoyNjXHx4kU0b94cx44dQ2BgIABg7969cHR0RFRUlNbzMc8SEeWXmZkJMzPNGUufrSyRmZkJAOjfvz/69++v0/lCQkIQHBwsbisUCnh4eBRrzEREpcno0aOB3AkRtElNTcWmTZuwcuVKuLi4AADmzJmD5cuXY/PmzRg3bpxe4yUiIqLiJc1IgrlEhchWS+FRzQ8AEHk7HB7Hx0OakWTo8IjKlfTMbNyJVxbrOX2cLGFmItW6LzMzE/v27cPu3btRt25dzJw5E1ZWVjh+/DgePXqEAQMG4O2330avXr3QoUMHZGVlYd++fZg5cyY2btxYrHESkf5UyIKIzMxMpKWlwdbW1tChEBEREREREZEePX78GADyPROwt7cX9wHAuXPndD4nB48REWlKSUmBvb29RptEIoGdnR1SUlIAAO3atUNGRoZO52OeJSLKz9bWFklJmoN0EhMTxX1FJZfLC1yph4ioIrp06RIyMzPRtGlTsc3MzAwNGjTA+fPnAQBpaWmoXbs2UlNTkZWVhZ07d+LYsWPw8fHRek6ufEZERFT6qGx9AdenBRGqYh6wTURP3YlXovOyP4v1nPvHNkddt4JXcmnbti1atmwJAPjrr79w9+5drF27Fsgtfr558yYyMzMREhKCu3fvQqVSwdPTs1hjJCL9qlAFEYIgYN68eVi0aBFycnJQr1497NmzBw4ODoYOjYiIiIiIiIj04Nksuk+ePNFoT01NzTfDrq44eIyISJOJiQnS0tLytaelpcHExKTI52OeJSLKz8/PD//++69G27///gtfX19YWFgYLC4iovIiISEBAPKNJXB0dBT3mZmZ4cSJExr7X1wlLS+ufEZEREREFZGPkyX2j21e7OcszIvPoWvWrIk+ffpg0KBBkEgkQO5kEu3bt8eHH36I9957DydPnsTcuXOLNUYi0q8KVRChVquRkZGB6OhoyGQydO/eHRs2bMDEiRMNHRoRERERERER6YG7uzvkcjnu37+v0X7//v0CZ3AkIqKi8fb2Rnx8PFQqlVjIkJycjCdPnsDb29vQ4RERlQuDBg1C9+7dcfr0afj7+yMqKgqbNm3CuHHjDB0aEVG5YGRkBOSOMXhRZmYmpFIpkLsKmpeXl87n5MpnRERERFQRmZlIC13NoaQtXLgQAwcORGhoqPi8Oi4uDh988AHGjBkDKysrdOvWzWDxEVHxKHUFEf/99x/CwsJQq1YtjeUnX/TXX3/hzp078Pb2RrNmzcSqLQCIjIxEdnZ2vmOcnJxgYWGBzz//XGyztLTkF3BEREREREREFYixsTHat2+P7du3Y/jw4ZBIJIiPj0dYWBi+/vprQ4dHRFQuvPvuu1Cr1Th48KD4RdLOnTthYmKCVq1aGTo8IqIy4f3330dmZiYuXLgAqVSKDh06wN7eHps3bwYABAYGYvLkyWjVqhXq16+P69evo02bNpg6daqhQyciKheeFSrExsbC3d1dbI+NjUWTJk1e6Zxc+YyIiIiIqOS1bNkS/v7+4nblypVx9OhRqNVqPH78GAAglUoxfPhwfPTRR5BIJJBKpeK+0aNHGyx2Inp1paYgIioqCqNGjcKlS5egVCrRp0+ffAURarUa3bt3x7lz59CsWTP89ddfeOONN7Bnzx7xwUH37t0RHx+f7/zLli1DYGCguL18+XJYWFiga9euerg6IiIiIiIiItKHxMRE7Nu3D8idtfHMmTOwtLSEl5cX3nnnHQDA/PnzERAQgJ49e6JZs2b48ccfUadOHXz00UcGjp6IqGy4evUq7t27h/PnzwMADh06BFNTUzRp0gTOzs7w9PTE+PHjMWzYMERHR0OtVuOTTz7BzJkz4eDgYOjwiYjKhLFjx+abACzvINr58+djzJgxuHXrFtzc3FC9enU9R0lEVH7VrVsXDg4OOHz4MBo1agQAePjwIcLDw1l8RkRERERUislkMshkMq3tjo6O+dqesbW1BQCYmZnpIUoiKm6lpiBCpVJhyJAh6Ny5M5o1a6a1z//+9z/8+eefuHTpEjw8PBAdHY369evj22+/xZQpUwAA586de+lrzZ49GykpKfjhhx+K/TqIiIiIiIiIyHBSU1Nx4sQJAMAHH3wAADhx4gQaN24sFkTUrl0bFy9exNq1a3Hz5k18/PHH+Pjjj2FiYmLQ2ImIyoo//vgD+/fvBwB06tQJ69evBwC4uLjA2dkZALB48WI0aNAABw8ehJGREdasWSPmZSIierl27drp1M/NzQ1ubm4lHg8RUXmjVCoRGxuLhIQEAEBMTAwiIiJgb28Pe3t7GBsbY/bs2Zg5cya8vLzg7e2NGTNmoE6dOujevbuhwyciIiIiIiKiF5SagggfHx/4+PgU2mfLli3o2rWruDylm5sbevTogS1btogFEYXJzs4WZ3sMDQ3FgwcPYGNjAzs7O639VSoVVCqVuK1QKIp4VURERERERESkT15eXuLA3MJ4enoiNDRULzEREZU3I0aMwIgRIwrtI5FIEBQUhKCgIL3FRURERESkq2PHjmHSpElA7liFzz77DAAwevRoTJw4EQAwfvx4mJmZYeXKlVAoFGjatCnmzp2rdbZZIiIiIiIiIjKcUlMQoYsrV67km22hTp06+PHHH3U6XqFQ4OTJkwAgzgo5bNgwzJgxQ2v/+fPnc3AEERERERERERERERERERFROdKlSxd06dLlpf2GDRuGYcOG6SUmIiIiKh3kKRFAjOXzBnMHwNbDkCERERHRS5SZgghBEKBUKvOt5mBvb4/MzExkZGTA1NS00HPY2dnh/v37Or9mSEgIgoODxW2FQiGuTkFEREREREREREREREREREREREREZV+2qT3SBDk8jo8Hjr+wQ2YOjD7LoggiIqJSrMwUREgkEsjlcqSmpmq0p6amwsjICHK5vNhfUy6Xl8h5iYjKu9u3b2Pv3r2ws7PDBx98AEtLSx2OIiIiIiIiIiIiIiIiIiIiIiLSP7WlG9qqFmJDXx/4OuWOc0m4Bfw8FEhLZEEEERFRKWZk6ACKwtfXN98KD/fu3UPVqlUhkUgMFhcRET13+PBh9OzZEw8fPsTWrVvRrFkzCIJg6LCIiIiIiIiIiIiIiIiIiIiIiAoUA0dkONYDXP2e/udY3dAhEVEFs3jxYoSHhxs6DKIyp0wVRAQGBmLPnj1IT08HAGRkZODnn39GYGCgoUMjIqJcPj4+OHv2LBYtWoRDhw7hwYMHSElJMXRYRERERERERERERERERERERERERCXu0qVLWLBgQZGPu3z5MhISEkokJqLyzNjQATyTlZWF9evXAwDi4+Nx7do1rF69Gvb29ujevTsAYMqUKdixYwfatWuHLl26YP/+/cjOzsb06dMNHD0RUdnxxx9/YNWqVbhz5w7Wr1+PmjVr5uuzadMmbN26Fenp6WjVqhUmTZoEMzMzAMDNmzexcePGfMfI5XLMnj0bvr6+Yttvv/0Gf39/2NnZlfBVERERERERERERERERERERERERERleUlISV3og0qNSUxCRnZ2N06dPAwBatWoFADh9+jTc3NzEggh7e3ucP38ea9aswZ07d9C5c2cMGTIE9vb2Bo2diKismDBhAs6fP48OHTpg8+bNUCqV+fosXLgQoaGhWLx4MRwdHTFz5kycPn0a+/fvBwBIpVKYmprmO87ExERj+8iRI1i4cCF27NhRgldERERERERERERERERERERERERE9NT69etx8OBBNG7cGBKJBG3btoWfnx+OHDmCn376CVlZWRg0aBDeffddhIeH48iRI5DL5fjzzz/RuXNnDBo0CMgd/1ZQfxMTE5w7dw7z5s3D8OHDYWRkhKpVq2LatGnw8PDAggULcPHiRXTo0AH9+vXDwIEDtZ4PADZs2IADBw6gUaNGEATBwL89orKp1BREyOVyrF69+qX9bG1tMWnSJL3ERERU3oSGhsLGxgY3btzA7Nmz8+1PT0/HZ599hrlz52L48OEAAE9PTzRu3BinTp1Cs2bN4Ovri1mzZhX6Ohs2bMD27dvxyy+/wMLCosSuh4iIiIiIiIiIiIiIiIiIiIiIiEqpzDQg4VbxntOxOmBirnXXnj17MH/+fISGhuLhw4f47LPP4OfnhxMnTmDKlCmYOnUqjIyMEBwcjG3btiEhIQELFizA7Nmz0aNHD0yZMgUBAQGIjo4usP+8efMwe/ZsfPjhh3BwcMCECROQk5ODa9euoV+/fvjjjz/QsWNHZGRkYMKECfDx8Snw9e/evYu5c+ciNDQU0dHR+OWXXxAUFFS8vy+iCqDUFEQQEVHJs7GxKXT/mTNnoFQqERgYKLY1atQIrq6uCAsLQ7NmzV76Gtu3b8fIkSMxevRozJ8/H8hdmcLR0VFrf5VKBZVKJW4rFIoiXBERERERERERERERERERERERERGVSgm3gO9bFu85h50EXP207goLC8PMmTPRp08fAMAff/wBAPjll1+QnZ2NjRs3Arlj1k6ePInq1aujVatWmDhxIgDgxIkTuHnzJsLCwgrs37JlS3Fi97S0NJw5cwYXL15EWloaLl68CLVajTfeeAN///03OnToAABYsWKF1vNdv34dM2fORN++fQEAp06dKt7fFVEFwYIIIiISPXjwAADg6uqq0e7q6iruexl3d3eEhIRotEkkkgL7P6vIJSIiIiIiIiIiIiIiIiIiIiIionLEsfrTAobiPmcB7OzscPv2bQBAdnY27t69CwBwcHCAn58fBgwYIPatWbMmIiIiYGZmJrYZGxsjKyur0P5WVlZi2+LFi3H9+nUMHDgQRkZG+Ouvv6BSqcTzPFPQ+eLi4sR4s7KycOfOnWL4BRFVPCyIICIikVqtBgCYmJhotJuZmYn7XiYgIAABAQE6v2ZISAiCg4PFbYVCAQ8PD52PJyIiIiIiIiIiIiIiIiIiIiIiolLIxLzA1RxKwrBhw9C0aVMcPXoUGRkZEAQBcrkcY8aMQbt27TBz5kw4OTkBucUMBdG1f40aNbB8+XLExcUhOzsbFhYWYvvx48fRrl07BAUFFXi+4cOHo2nTpggLC0N6ejpkMlkJ/WaIyjcWRBARkcje3h4AkJycDEdHR7E9MTERb775Zom8plwuh1wuL5FzExERERERERERERERERERUdl17949/Pvvv/Dz80PVqlUNHQ4REZVy7u7uuH79Oi5dugQvLy+0adMGVatWhZ2dHc6ePYsrV67g4cOHEAQBbm5uqFSpEpydncXjR48eDQcHB537f/DBB3jzzTeRkJAAPz8/nD9/HmZmZrC0tMTVq1dx9epVVKlSpcDz2dra4urVq7h06RJq1qyJ6OhouLq6Gui3R1R2sSCCiIhEz4oezp07h44dOwK5xRG3b9/G1KlTDRwdERERERERERERERERERERVRSHDh3CoEGDEBAQgFGjRmH9+vXo0KGDocMiIip3zp07B4VCgYCAAJiZmRk6nNcWFBSEJ0+e4OrVq+jatSvc3NwAAEZGRqhfvz7q16+v0d/BwUH8uVatWuLPuvQHAF9fX/j6+gIAmjdvLrZXqlQJlSpVeun5LC0tERAQALwwoTERFQ0LIoiISOTl5YXWrVtjwYIFaN26NeRyOebNmwcrKyt07drV0OERERERERERERERERERERFRBREaGop169bhvffew9GjRzFr1iwWRBARFbOhQ4fi999/h4uLCxQKBf766y+Ym5sbOqzXMnr0aOTk5MDT0xM1atQwdDhEpAcsiCAiqkB27tyJRYsWISMjAwDw4YcfwsLCAuPGjUO/fv0AAD/++CO6dOmCSpUqwcrKCpmZmdi5cydsbGwMHD0REREREREREREREZEepEQCaYnipjwlwqDhEBERlVV//fUXNm3aBGNjY3z77bf59ufk5GDjxo04deoUrKys0K9fPzRs2FDcf+XKFbRq1QoA0KpVK1y9elWv8RMRlXe3b9/Gr7/+itu3b8PMzAy9e/fGpk2bMGzYMEOH9lratm1r6BCISM9YEEFEVIG8/fbbcHd3z9fu6ekp/uzu7o5//vkHt2/fRnp6OmrVqgWZTKbnSImIiIiIiIiIiIiIiAwgJRJY0QRQp4lNHgDSBDmyTe0NGhoREVFZ0rRpUwCAra0tLl++rLUgom/fvjh9+jTGjRuHBw8ewN/fH7t370bnzp0BAGq1GsbGT4e3SaVS5OTkQBAESCQSPV8NVSQRj5Tiz6YJSvgaNBqiwqnVavz88884ceIEOnXqJObPF8XHx+PHH39EZGQkatasiUGDBokrQFy+fBkBAQEwMzMDALz77ru4cOGC3q+DiOh1sSCCiKgCcXFxgYuLi059q1WrVuLxEBERERERERERERERlSppiYA6DZGtlkJl+3T4W2RSOmYejsH3lm6Gjo6IiKjMWLduHWrWrIkFCxbg8uXL+fb//vvv2L59O/755x+8+eabAIDs7GxMmDBBHNDr6emJa9eu4Y033sCNGzfg5ubGYggqMXYWJjCTSTFhW7jYVkdyDwfkwCOlCs4GjY4ovzNnzqBnz57w9/fHiRMn4OLikq8gIjIyEv7+/qhVqxbatGmD77//Ht9//z1OnToFc3NzqFQqyOVysb9cLkdGRkaxxCcIQrGch/i7JNIFCyKIiIiIiIiIiIiIiIiIiIjwfLDbiENKXBUei+1mMhfYWZgYNDYiIqKypGbNmoXu379/P3x9fcViCADo06cPVq5cievXr6NWrVoYNmwYBg8ejA8//BAbNmzAiBEjCjyfSqWCSqUStxUKRTFdCVUUbrZmCJvUEslPMsW2+FsmwElAka5mQQSVOm5ubjh//jxcXFzg66t9LZNPP/0Uzs7OOHToEIyNjTFixAhUrVoVK1euxOTJk8XCs2euXr0KLy+v14pLJpNBIpEgPj4eTk5OLGR7TYIgID4+HhKJBDKZzNDhUAUT+99tKJPjxO2UB1cMGk9hWBBBRERERERERERERERERESE54PdJr9bA07Vm4jtdhYmcLM1M2hsRERE5cndu3fh6emp0fZs++7du6hVqxYmTZoEFxcXnDlzBuPGjcPAgQMLPN/8+fMRGhpa4nFT+eZma6ZxzxeRwPs/Kr3c3d1f2mfv3r2YOHEijI2fDhW2s7NDp06dsHfvXkyePBn+/v5IT0/HuHHj4OXlhdWrV+Ps2bMFnk+X4jOpVAp3d3dERUXh/v37r3x99JxEIoG7uzukUqmhQ6EKJPa/27Be0wyVJCqN9jRBDks7F4PFVRAWRBARERERERERERERERERUYVU0GyHHvZm8HWzMWBkRERE5Vt6ejosLS012qysrMR9zwwYMAADBgx46flCQkIQHBwsbisUCnh4eBRrzEREZUlKSgoSEhLg7e2t0e7t7Y3ffvsNAGBkZITjx49j6dKluHv3Lg4ePJiv/4t0LT6ztLREtWrVoFari+FKSCaTsRiC9E6ZHIdKEhXOv/klbD3riu2Wdi6oVKWaQWPThgURRERERERERERERERUrqQ8uIKIF7ZL65c0RERkWGVttkMiIqLyxMbGBtHR0RptSUlJ4r6iksvlkMvlxRYfEVFZ96y47Fmx2TPW1tZIS0sTt11cXDBv3jydzlmU4jOpVMpB/ETlgK1nXfi+0dzQYbwUCyKIiIiIiIiIiIiIiKhcsLRzQZogR6ML04ALz9vTBDlih5xiUQQREWkoa7MdElHFUdDqNUTlSb169XD06FFkZ2eLA2YvX74MAKhbt+5LjiYiopextrYGcleKeFFycrK4r6hYfEZEpRULIoiIiIiIypjIyEjMnj0bV65cQZMmTTBv3jzY2toaOiwiIiIiIiKDq1SlGmKHnEJMnsFjjS5Me9rGwa1ERBVW3sHFeGGAcVmZ7ZCIKgauXkMVxQcffIBPPvkEmzdvRlBQELKzs7Fs2TK0atUKlStXNnR4RERlnoWFBTw9PXHt2jWN9mvXrqFOnToGi4uISqeyXpTNgggiIiIiojJmwoQJ6NKlC8aMGYPPPvsMISEh+N///mfosIiIiEgXKZFAWqK4KU+JKLjrgyvIu5cz1RJRRabrFzKVqlTTKHyIAIALuTk3xvJ5R3MHwNajRGMmIqLSoaDBxeAAYyIqhbh6DZUXCxYswJUrV3D16lUkJydjwIABAIDFixfDxcUFPj4++PbbbzFixAhs2LAB0dHRSEtLQ1hYmKFDJyIqN/r06YPNmzdj6tSpsLW1xe3bt3H48GGsWrXK0KERUSlSHoqyWRBBRERERFQCEhMTkZ2dDWdn50L7WFhYwNTUtEjn3rZtG4yNn97K9+3bF4cPH37teImIiEgPUiKRs7wxjLLSxSaP3IeJ2ab2YpulnQvSBDkaXZgGXNA8RZogR+yQUxwAQUQVzut8IZNtao80QQ6P4+OB48/bc4zNYDTmHIsiiIjKIW1FdI20DC4GBxgTUSnG1WuorGvcuDHc3d3RoUMHjXYLCwvx55EjR6Jz5844e/YsrKys0KJFiyJ/b0ZEVFElJiZi5syZAID4+Hjs378fsbGxqF69OoKDgwEAM2fOxIkTJ9CgQQM0adIEx48fR2BgIIKCggwcPREZVJ4J3NT3wmFexouyWRBBRERERFSMdu/ejf/97384ceIEateujfDw8Hx9jh8/jo8//hixsbHIyspC//79sWrVKpiYmAAAxo8fr7XIYcCAAZg1a5ZYDPHo0SMsW7YM69at08OVERERUZHleZiY9N8V2GelY3zmKEQIbmJ7urEtNrp4i9uVqlRD7JBTiHlhABeeDeK6MA2J904Bxk+e7+AM50RUDuk6kFWXL2QsXbzROedrmGWliG2+kmgsxUok3TgJ+yovDIxlTiUiKvMKK6Jz92tTZr7IJyIiKuvatGmjUz8PDw94ePBzGBFRUZmYmMDPzw8AxP8FAFdXV/FnKysrnDp1Cr/99huioqIwfvx4BAQEGCReIiolCpnAzapGC/jWqG3Q8F4VCyKIiIiIiIpJdnY2NmzYgEmTJqFevXo4duxYvj4PHz7E+++/j4kTJ+KTTz7BgwcP0LJlS8yYMQOLFi0CAEybNg0jR47Md6ydnZ3487179zBw4ECsXLkSNWrUKOErIyIioiLT8jDRPvdh4nudusPNq7rYbmdhAjdbM43DK1WpBuQZqHXT1B5p/3CGcyIqX/IWPgBAWnIcfI+PLLaBrG62Ztg4qQeSn2SKbdH3byHtyBrYHxqt0TfH2AxGfTYB5o7PG1kkQURUqhVnER0RkUHkmVBBnhJRpMPlKRFAjOXzBt6/EhERVQhWVlYYMWLES/tJpVK0a9dOLzERUen36FEMnHWYwK2sYUEEEREREVExkUql2L17NwDg6NGjWvv8+OOPMDExwSeffAJjY2P4+Phg/PjxmDt3LubNmwcTExO4urpqzNqQ1/nz5zF69Ghs3LgR1atXL7AfAKhUKqhUzwcRKRSKV74+IiIiKljeQVjq2BuoVdDDxDp18xVA6KKwGc6vnzkMWaWaz/tysBcRlUJ5c2VBhQ8AkAY5Lr2zFuZ2LmLb6+Q2N1szjdxrZ1EXnQ9p5lQHiQKrhCUw39RD41gWSRARlV5cDYKIypw8xQ9IS0DO1gFaZ2fNNrUv9FTZpvZIEwqYOCHv/St4D0tERERERIAiXQ1nAF3btYZT9SZiu7YJ3MoSFkQQEREREenR2bNn0aRJExgbP78Vf/vtt6FQKHDz5k3Uq1fvpecYMGAAUlJS8P777wMAGjZsiJ9++klr3/nz5yM0NLQYr4CIiIh0HdCbJsjRt1cfWDp7iW2v8zBR2wznykf3kfbLGtT6e1K+177U6n/FNpCYiKiodM6VWgofkJuz6pdgztKWUxOfZKLzxio6F0n81/Z7ZJlpDlJjriUiKllcDYKIypK8Ocs4PQlVwoZpFD8AQIYgxwj1NCQK1mKbLrOzaps4oaD7VxRwD8t8SURERERUMXnYm8HXzcbQYRQbFkQQEREREelRfHw8vLy8NNocHR3Ffbo4ePAgMjOfD9oxNzcvsG9ISAiCg4PFbYVCAQ8PzgBFRESkq9cZ0Gtp5wL/Yh5UkHeGc7i9gViHU4jREmP9Ex9pxsgiCSIqIa+bK0uy8KEw+XIqUKQiCa9DQfnOyVxLpcn27dtx6dIlAMCMGTMKfX5AVBpFp6TnKwau/0s7rgZBRIaXd5UHAI+UKijS1eJ2YZMnaCt+mDO4PRwsTMQ2XSZU0LXIF4Xcw2q7f7U2k8HZUq75YlxdgojKujy5W54SUaTD5SkRQIzl8wbmRSIiKgPyPrtH7uQS5RELIoiIiIiI9MjIyAhZWVkabWr10y9JpFKpTueoWrWqzq8nl8shl8t16ElERERldUBvpSrVgDyvHVu1PoskiOi1afuyJK+ykit1pUuRBABEKrtCmpGk0cZcS6WNTCaDqakpFi5ciAkTJrAggkq1vMUPiU8yEbrxsMaAXl9JNPxNVLjedDFklWqK7cypRKRXKZHIWd443yoPzrn/vUjbPXG2qT2mWrpp9Hvd1SR1uX+FlnvYgu5ftckxNoPRmHMc/EtEpZ+WojWkJSBn6wCN3O2R+3k929Q+/zlekG1qjzRBDo/j44Hjz9tzjM1g1GcTYO5YeDwsnCAiIgOJ/e82rNc0y/fsHrnvgZZ5Vm0u61gQQURERESkR+7u7oiJidFoi4t7OsDIzc2tgKOIiIiouJXV4gddsUiCSruzZ89i1KhRAICxY8di0KBBhg6pwtM1L2pTVnOlrrQNMgO0LyX+OrlWG+Zfeh3dunVDt27dsGrVKkOHQqRBl+IHB4kC+2VLYC7XfB/KMTZDrbfac1AZEemFtgJhdewN1MpKx/jMUYgQnj/TNzU2woxOtWBjJhPbDHVPrP3+FVrvYfPevz5OV2PegevIyMoR23wl0ViKlbh+5jAL0oioVMmbp43Tk1AlbFi+ojUAyChghZ6NLt6Fvoalizc653ytdeVI8009Xhqj1sIJFkkQEZEeKJPjUEmiwvk3v4StZ12NfeXxXp4FEUREREREetSiRQtMmDABqampsLKyAgAcOnQIbm5u8PHxMXR4RESU69KlS8jMzISZmRnq1Klj6HBIR3kHVgGATBld4Aze5aX4QVfFXSShTXl8gEolo1atWli1ahW+//57PHz40NDhlGu65Mai5EVtylOufF2vk2u1YZFa+RUfH4+1a9di3759aNWqFT7//PN8fR48eID58+fj+vXrcHd3x/jx49GkSRNx/7Jly8RJFl4UGBiIt956q8SvgUgXr1v8gD67NAaPGXHwGJUiT548gYvL0/fowMBAbNmyxdAh0WvIm6+Uj+6j/i/tCpxNtW+vPrB09hLbXmeVB0PSdv/6bU3/fL+LtF/WoNbfkzT68V6ViEqMllUeHilVUKSrxe0Cn2VoKXxAbvHDnMHt4WBhIrbpkrvdbM3yrbyT+CQTnTdW0bin1aagwgkWSRARUUnIWyiY8uAKAMDWsy5832huwMj0gwURRERERETF6NGjR8jMzIRSqYRarUZUVBSQu/qDRCJBUFAQvvzySwwYMACffvoprl+/jqVLl+Kbb76BRCIxdPhERJQrJCQEkZGRyMzMxI0bNwwdDmmR96GethkMHSQKrJItgbm2wQvlvPhBV/oYuAsOiCizMjMzkZaWBltb2wL7qNVqSCQSGBsX7VGzlZUVGjVqhP379xdDpIQCCh8KGnSqLTcyL5YcXXKtNkUpUss2tYfaUnPVwbI6KK8iuHPnDlq2bIm+fftCrVbj9u3b+fo8evQITZs2RdOmTTF9+nQcOXIELVq0wJ9//olGjRoBAORyOUxNTfMdK5VK9XIdRHmx+IEqGgsLC8TGxiIsLAzr1683dDj0GqJT0hG0eJdGvvKVRMPfRIXrTRdrrIqA3Ptk/3J8n5xvhQm3NxDrcErnCRVih5ziMwAienUpkchZ3jjfKg/Ouf+9SNuzjGxTe0zN8/kYr/kZWdvKO3mLJLTRVjhRaJHEmHO8/yUiolcS+99tWK9pprVQ0PIlEx6VFyyIICIiIiIqRkFBQbh69aq47e/vDwC4du0arK2tYW5ujuPHj2P69Ono1asX7OzssHTpUgwbNsyAURMRUV4HDhzA/fv30aFDB0OHQlqKHwqa/WuXFECe8X85xma433YjsszsNdo5yLdgxT1wF5w1ssy5cuUKVq5ciZ9++gkKhQKpqamwtLTU6BMVFYUhQ4bgt99+g0QiQceOHbF69Wo4OTkBADZt2oQlS5bkO3ft2rWxYcMGvV1LeaDLCg/aisLwkkGneXMj86J+acu12hRlJZ8R6gkaM2CmG9tiTpDm7JcFYfGEfrm5ueHOnTuQy+Xo3Lmz1j5Lly6FVCrFtm3bYGxsjI4dO+Ly5csIDQ3Fvn37AIDPEshgCirCG7HxH6Srs8U2VyQgTD6FxQ9kUImJibh37x5q1KghrtibV1RUFB4/fozq1atDJpMV6fyWlpZai9OodMv7nOFRbDT2GwVrzVe13mrP/KTjs4KUB1fQ6MI0XAs/pvH75ed/IipM3pysjr2BWlnpGJ85ChHC88IGU2MjzOhUCzZmz9+rDfksQ1uRhDa6rC7hK4nGUqzE9TOHtRbhMYcSEVFeeZ/NxN+5j1YSFc6/+SVsPeuK7RXpfYQFEURERERExejw4cMv7ePp6Vlulk+PTEpHRvRjcZuDaIhIX06dOoXTp0+jTZs28PPzy7c/PT0dBw4cQGxsLOrWrYt33nlH3JednY1///1X63nr1KkDMzPmMX3RZZBvgUufa5n9y9pMBmdLuUY/I3MHeHHgwmt71YG74AznZdLSpUvh5+eHZcuWYdCgQfn2C4KArl27ws7ODnFxcVCr1Xj//ffRp08fHDt2DADQrl071KxZM9+xFhYWermGskBbDsyrKCs8aCsKQyGDTpkbywZdBp4ZpyehStgwbJB8qdEvTZBjxHrNIomCaCueYP4tOboMnD127Bg6dOigsQJPly5dMG3aNOTk5MDIyOil5wgLC8OJEyegUCgwf/58NGzYEP369dPaV6VSQaV6nlcUCoXO10Plx6u+Nz1T29gIM7o+H6QmT4mH+XEV0P0HwLG62I/FD6QPly9fxldffYVDhw4hISEBR48eRdu2bTX6JCcno2fPnjh9+jTs7e2RkZGB9evXo1OnTgCAc+fOoVWrVlrPn5iYCLlcrnUflS66TLLgm/ucIaHrFjg6u4rtzFeFy3uvGmvngrR/5Gh0YRpw4Xk/TpJARM/kvd9UPrqP+r+00zqbdd9efWDp7CW2ldXPqLqsLqF8dB9pv6xBrb8n5TueK+8QEVFeBa1w18oEqFKzAZyrv2XQ+AyFBRFERERERFRk1rlfbC86chNXDz9/YGcmkyJsUssy+UCSiMqGM2fOYOjQobCwsMC///4LuVyeryAiLi4OLVq0gFwuR6NGjfD555+jdevW2Lx5MyQSCdLT0zFixAit59+8eTOqV6+udR/prrgH+WorfuBM5qVTQYUT+pjhvKx+KVoa/fDDDwCAX375Rev+P//8E//88w8uXboEe/unKwwsWLAArVu3xpUrV1C3bl24uLjAxaViLMOsi7x5Udss2sidSdtOkipuF2WFB21FYeAgrnJJa66t2QBIS3y+nZYA060D8hVJFERb8QTzr2E9ePAA7du312hzdXVFeno6EhIS4Ozs/NJzyGQymJqaYurUqeJ2QebPn4/Q0NBiiJxKI13vz1/nvUl0KM+2zByo0pTvRaR3586dQ7t27fD555/D29tba5/Ro0cjISEBDx8+hLW1NebNm4fevXvj9u3bqFy5Mho2bIjY2Fitx7IYonTSdYXJgp4zcLDp66lUpRpih5zS+fM/iySIKpaCBm/6m6hwvelijZURLO1c4F+O80G+Igm3NxDrcCrfRDNceYeIiFDEFe6cXyjwrmgqbEGEIAjIycmBVKplyiwiIiIiIirUs4FWS/v4IcOxHgAg4pESE7aFI/lJJgfDEFGJkcvl2LRpE+rXrw9bW1utfUJCQmBqaoozZ87A1NQU169fR/369dGjRw/07NkTlpaWOH/+vN5jL6/0MciXxQ9lnz5mOOfs5vpz5swZ2NjYoF69emLb22+/DSMjI5w5cwZ169Yt9HgAePjwIQIDAxETEwMjIyPs3LkTx44dg42NTb6+pXnW8teZSTvvLNpP/wamwSgrXaMfV3ggndh65BtsbDTmnGaRREEKKJ54nfyrDXNy0ajV6nyDbZ+tZKZWq3U6R8uWLdGyZUud+oaEhCA4OFjcVigU8PBgnimLdL0/16aqLBkLu7q90ntTgViYRwby0UdPB18XVNDw+PFj7NixA2vWrIG19dP3ukmTJuHLL7/Eli1bEBwcDCMjI1haWhZbTKX5vrYs0nmWcU6yoFe6fP4vSpEEOPCXqMwqyuDNWm+1r/D3jFrzJ1feISKqcHT5nMMV7rSrsAURAwcOxO7du6FUKg0dChERERFRmeUriQEkT78UNDVSwhUJhg6JiMq5vKtB5CUIAnbt2oVPPvkEpqamAIBatWqhRYsW2L59O3r27KnT69y8eRO3bt1CRkYGzp8/Dw8PjwJnOi+PAxp0GeALDvKl11TcM5xzdnP9efToERwdNQdAGhsbw87ODo8ePdLpHA4ODli1apVGm4WFhda++p61vCg58LVn0tY2i/aA/HmxIn+JQa9BS5FEQfIVT7xm/k0WrBADzTxhJpNiVVBD5mQd2dvbIykpSaMtMTEREokEdnZ2xf56crmcs52XIrq+F+VV0HtT3kIHbZ7es0+G0SHNe3a+N1F5denSJWRlZaFx48Zim1wuxxtvvIELFy4UeuyLfHx8EBMTA7VaDUtLS+zcuRMdOnTQ2per8RSfos4yzuIHw3qdIgnk3m/GDjnFgb5EpZguK/Rw8GbRceUdIqLyTdukFnm/dy3scw5zvKYKWRCxatUqtGjRArt37zZ0KEREREREZZO5w9MvxH8eKjb5AgiTyxGpbAwg/8y+RET6EB0dDYVCgRo1ami016xZE7///rvO5/n666/xzz//wNHRESNGjMCECRMwYMAArX3L0oAGXWcy12WALzjIl0rCq85wXoTZzV9nkC44UFeUk5OTry0rKwsSiUSn401MTNCoUSOd+upz1vLolHS0XXxS6yza2vLg6xSAacW8SIZSzPk3x9gM/7X9Xlzx6XG6GlMORGPQ2rMvDcVMJkXYpJYVPte++eabOHfunEbbmTNnUKNGDZibmxssLtLNqxY0oIgrOmijfZUHLYUO2mi5Z+d7E5VXiYlP3+McHBw02h0dHcV9urh8+bLGvfGz1Xy04Wo8ry5vXo2+f4uzjJdxuhRJAEDKgytodGEaroUf0xhszQFgRIbzuiv08G+3aF535Z3TXY/C0tlLbOOzTSKikveqK0sX9L0rP+foptQVRKSmpuLvv/+Gh4cHatWqpbXPgwcPcPfuXXh7e8PLy0tjX3Z2NgRByHeMVCqFRCLBlStXcP36dXz55ZeYOHFiiV0HEREREVG5ZusBjD6rMTgm8nY4PI6PhzQjqdBDiYhKUmrq04Gqtra2Gu22trbiPl189913OvctrQMatM0qouvAKu2DqPIP8AUH+ZI+6DjDua6zm2sbpDvvwHV8tS4i3zl1LZ6oaF8kurm5IT4+HoIgiAUQKpUKCoUCrq6uLz2+qPQ5a3nyk0zYqeOwsr0rPOyf/39aWB5kARiVW6+Rf422BcHrUJBGvzC5Gf7r9Dz/ahOZlI6Zh2OQ/CSzQuVVbYYMGYIOHTrg2LFjaNOmDSIiIrB582bMmDHD0KGVSq9TgFDcXregATqu6KBNUVZ50IrvV1SByGRP/75eXPERANLT08V9uihKkRpX49GNrrOktjdRIanDCthXqSu28767bNM66NfOBWn/yNHowjTghcVbOMiXqORpu8cu6szVXKGnZOhSJKGOvYFaf0/Clh1bESG4ie3aVtRl/iQienWv+n2sKxIQJp+itfgh7/eu/Jyjm1JTEBEfH485c+Zg9+7dePLkCQYOHIjly5dr9BEEAaNGjcKPP/6IevXq4cqVK+jduzdWr14NIyMjAICnpydiY2PznX/z5s0IDAzE3LlzsXr1amRnP/3Hlp2dDalUqqerJCIiIiIqR/IMjlHFKw0aDhERXhiIoFAoNNofP34MCwuLEnnN0jigoaAZznUZWFXUQVR8CEelhi6zmxcwSHeXFICWR4S6znBe0WYzb968OZRKJc6cOQN/f38AQFhYGARBQPPmzQ0d3muRKaOffglxUsvKNxxMSqSdtsKJPAX0BeXfvCrSyoMtW7aESqXCzZs3IZVK4e/vDwcHBxw4cAAA0K5dO8ybNw+dO3dGlSpV8ODBA/Tt27dcT/YV+99tjZmfdfXs/fmu2q5E4nqRttWCtMm7glBRFGlFB224ygORTqpUqQIAiImJ0ZjUICYmBm+//bYBI6tYdCl+KGyWVPuaLZnfyrlKVaohdsgpDvIl0rPolHQELd6lkY/BmatLtXxFEp6eyDk3C0uxUqOfthV1041tsXFSD+ZLIqJCFFQoqK34wUwmxY8fNdG4L5UpozUmF5WnxMP8uAro/gPgWF1s5/eur65UFUTUqVMHCxYswLvvvqu1z/r167Fx40acO3cOderUwY0bN9C4cWO89dZbGD58OAAgKiqqwNfYvXs3du7ciZ07dwK5xRAWFhbIyMgooasiIiIiIiIiIn1yd3eHubk57ty5o9F+584dVKtWcWaj0jbDeZEGVnEQFZUXugzSLYiOM5yXx9nM09PToVKp8OTJEyC3qCwrKwuWlpYwNjZGw4YN8e6772LkyJFYt24d1Go1Jk6ciN69e6Nq1aqGDv+1SDOSYC5RIbLVUnhU89PcyTxIpLtXzL8VaeXBhQsXIicnR6PNxMREY3vatGkYMWIE7t69C1dXV7i4uOg5Sv2J/e82rNc0QyWJloI0Hew3kiOiw/9gbldyv6NCVwvSJu8KQkWhaxGeNny/ItJJnTp1UKlSJRw4cABvvfUWAODBgwe4fPkyPv30U0OHV+7oOniIs6SSNq87yJdFEkQvlzdPR9+/hf1GwfnyMZiTyw5bD51X1E0T5Pjjag0kez0fkMtcSUQVWVFWfdBW/OCY/QiVjKOfd0pLAHYGAeo0zYNl5kCVpnwPLSalpiCidu3aqF27dqF9fvzxR3Tu3Bl16tQBANSsWRPdunXD+vXrxYKIwnTr1g1ZWVkAgIyMDDg6OkKpLHgWW5VKpbFEZt7ZJYmIiIiIiIiodJFKpejcuTM2b96MUaNGQSqVIjIyEr/99hu+//57Q4enNwXOcM7ZzYm0D9ItiA4znJfH2cznzJkj5kwbGxvxeey2bdvQvn178edp06YhMDAQRkZG6NKlC+bPn2/QuIuTytYXcPXToScR6UyH/FuRVh5s0qSJTv1sbGzQoEGDEo/H0JTJcagkUeH8m1/C1rNukY59WqgwDPVPfFRi8Ylep1ChKHg/TvTakpKScPfuXSQmPr2fv3XrFmxtbeHq6gpXV1cYGRnh888/x5gxY+Di4gJvb2/MmTMHTZo0wfvvv2/o8Mu0ogweyruSJWdJJZ0UcZBv27ULEYPn791mMilWBTVkkQRRLm2rQfhKotHeRIWkDitgX0Xz/pw5uQzRYUXdpP+uwP7QaBw88HO+VXe4agQRVUTRKelou/ikTqs+oKDih20FFD9wMroSVWoKInQRHh6OkJAQjbYGDRqIKz4UhUQigbFx4Zc/f/58hIaGFvncRERERERERFQy4uLisHHjRiB3IoNjx44hIyMDNWrUQGBgIABgwYIFCAgIQLt27eDv749t27bh7bffxoABAwwcvf4UOMM5H6wRFY0OM5yXx9nMv/rqK3z11VeF9rG1tcV3332nt5iIiKhisPWsC983mhf9wJoNdFsB6nXxfpqozDhz5gxmz54NAGjYsCHWrl2LtWvXYsiQIRg5ciQA4OOPP4a1tTV+/PFHKBQKtGrVCjNmzIBUKjVw9GWHrsUPeQsfUNhKlpwllXShwyBfJNyC+c9Dsa2jAJXt0wkMHqerMeVANAatPatxLIskqCLRdTWIHGMz2NdsyXxc3uTJn/bmDsgJM9O66g5XjSCiiij5SSbS1dlY0tsPvs6WYrvWHJgSCaxoweKHUqLMFEQIgoDHjx/D3t5eo93BwUFcwl0ul+t8PrlcjpSUlEL7hISEIDg4WNxWKBTw8OA/SCIiIiIiIiJDUavViI2NBQCMHj0aABAbGwtnZ2exj7e3Ny5fvowtW7YgNjYWc+fORa9evSrkgAbOcE5UAvJ8aViRZjMnIiIqtYqyAhQRVQgdO3ZEx44dX9rvgw8+wAcffKCXmMqbgmZOzVv8UGDhAzhQiIpZ3vsBcwdAZg6P4+M1uoXJzfBfp++RZfZ0/BGLJKg801a4FrrxsE6rQXAliApCy6o7ha0aMSeoPfMiEZUred8rIx49/c7H19kSdd1eWBU8JRKIyTMZR8Ktp8UQeVa442cawygzBRESiQQymQzp6ZofktPS0sR9xU0ulxepyIKIiIiIiIiISpa7uzsWLVr00n6Ojo4YO3asXmIiIiIiIiIiIirvtA0UyjtzqkwZjWo7hmhf9SFv4QM4UIhKmK1HvlUekZYAo21B8DoUpNE1TG6G2wN+g9ry6cDfZ6udaCuSCJvUkoN/qVTSpfjBQaLAftkSrgZBmoqwasSI9ROQKFiLbdqKJMBCCSIqIwoq8jaTSWH3Yl5LiQRWNMm/EgS4wl1pUmYKIgDAy8sLkZGRGm1RUVHw8PCAkZGRweIiIiIiIiIiIiIiorIn72CB+KR0+Bo0IiIiIiIiw9M2qHbExn+0rgYRYBEFZ0nuRJOqW0BWOmdIpdJD2ypSeYskEm7B6OehqKG6Aljl/ru3BI4P9UGC9PmqtBGPlJiwLRzn7iUhObcICBz0SwaiS552RQLC5FO0Fj+gj2aRGleDIA1aVo1AWgJMtw7ABsmXGl3TBDnarl2IGGgWPXKVHSIqC5KfZOYr8oa2fJWWqH0lCPCzTmlSpgoiOnTogH379mHBggWQSqXIycnBL7/8gg4dOhg6NCIiIiIiIiIiIiIqQ7TN/lRHcg+t5IC1WfGvSExEREREVBYUNkvqjx81EQc2iqtBbNayGgRnSKXSLG+RhLnD03+3Pw/V6FZJZo5KvTeKg8adLVSoKkvGhG3hGv24agSVtKIUqS3s6gab3Gca8pR4mB9X5Ru8yeIH0omWgrJ8RRIJt2D+81Bs6yhAZWsjNj9OV2PKgWitq+ywSIKISiNfZ0vUdXuex5ASCcRo5jsAT99PXf30HyDppNQURGRnZ+Po0aMAgMePH+O///7DoUOHYGVlhWbNmgEApk2bhm3btqFXr17o1asXdu/ejYcPHyIkJMTA0RMRERERERERERFRWaJt9ifTBBtgN+BsKTd0eEREREREBlHQLKmO2Y9QyTj6eUeuBkHlha1H/lUj0hKAbUHAph5ikzOAMLkZbg/4DWpLN+CFVSOSn2RyQC8Vi1ctfjBOT0KVsMkwOsQiNSpBBRSUeRwfn69rmNwM/3X6Hllm9gCLJIioLEmJBFY0eboixItk5k/zHpVapaYgQq1WY8mSJQAAT09PZGZmYsmSJfDy8hILItzc3HD27FksWbIEW7Zsgbe3N86fPw8vLy8DR/9qIpPSkRH9WNzmmzkRERERERERERGR/rgiAXWN7sFXkjvQSxJj6JCIiIiIiEoFjVlSUyKBFS20DwriQFsqD7TMhJ6vSCLhFox+HooaVpmAq41G14hHynyn5BggKqoir9CjrfhhwC5xVROARWpUwrQVlOFpUZnRtiB4HQrSaM5bVPas4EdbkQRX3iEig0lLfPq5h4XfZU6pKYgwNTXFoUOHXtrP09MT33zzjV5iKinPlltfdOQmrh5+XtXLN3MiIiIiIiIiIiIi/ZApoxEmnwLz3ao8OzjTExERERFVHHlnI494pIQrEmCacBl4VjiccIuDgqji0VYkgdy/h1zOT1SoKkvGhG3h+bpxDBC9jLb8yxV6qMwpKFcWVFSmugJY5f67twSOD/VBgtRZ7PZs5Z1z95KQ/MLfAYvMiEjvHKsDrn6GjoKKoNQURFQkz5ZbX9rHDxmO9QAuo0dERERERERERESkV9KMJJhLVIhstRQe1V74YoMDCIiIiIiogtA2G7krEgouHOZqEFSRmTs8/Tv4eajY5KxlxnNwDBDpoKDVIKrKkhFgEQVnydOxZUhLALYFcYUeKnvyFkpoyaEAUElmjkq9N4ormzhbaC80Y5EZERG9DAsiDMhXEiPOqGBq9HSWBSIiIiIiIiIiIiLSH5WtL2d6IiIiIqIKKflJZr7ZyE0TLj8thuDM40SabD0KnvHcKhNwtTFkdFSK5V0JAgWsBiFTRqPajiEw2pyueQKZOTBglzhgHGBOpjJIWw59VvCzqYfYpK3QjKtGEFFx0Xl1PCqTWBBhCFoqHn0BhMnliFQ2BsAPSURERERERERERERERERERFTyfJ0tUdctd5zCs4FAjtVZOEyUV94Zz4leoqCVIJA7431jb/vnA7pj7gFZ6SxIo/JLWw7VodDMzsIEZjIpV40gotdS5NXxzB30HyS9FhZEGIKWisfI2+HwOD4e0owkg4ZGRERERPS6IpPSkRH9WNzm7BxERERERERERERERFRu5ZlJ2DTh6WzDRNpW4nnGMfsRKqXdBNJyG579O2JBGlUkBRWavZBX3QAcH+qDBKmz2PZs1YjkJ5n8HpqIdMLV8co/FkQYSp43c1W80qDhEBERERG9LmszGQBg0ZGbuHr4+TKDnJ2DiIiIiIiIioKF9kRERPrjigSYJlx+vjJEnoHdRFQIc4enMwj/PFSj2RdAmFyOSGVjADYGC49KD42VeAAgJRJY0QJQp2l25IzUVNEVkFcrycxRafRZDlAmotfG1fHKLxZEEBERERFRsXC2lAMAlvbxQ4ZjPYCzcxAREVEFwsG7RESvj4X2RERE+iVTRiNMPuXprKgaOzggl0gnth7A6LNAWqJGc+TtcHgcHw9pRpLBQqNSLi3xaTEEZ6Qm0qQtrybcelogkZbIvw8iIioQCyKIiIiIiKhY+TpZAq6c8YiIiIgqBg7eJSIqPiy0JyIi0i9pRhLMJSpEtloKj2ovzIjKAblEurP1yPf3oopXGiwcKn3yrcSDF1bj4YzURPlpyauA5ipWpglKuCJBv3EREVGpxoIIIiIqMZcuXYKVlRW8vb0NHQoRERERERFRiXg2eHdVB0uobJ8WhUYmpWPm4RgO3iUiekW+khhxsJCpEQc5EBERlTSVrS8H5BIRlYACV+IBV+Mh0pm5w9O/l5+Hik2+AMLkckQqGwPgRH1ERMSCCCIiKiF//PEH+vfvj+7du2PJkiWGDoeIiIiIqFyLTklH8pPnM9PHJ6XD16AREVUguV/IeRwfLzbxCzkiolfEQQ5ERERERPSCxMREzJkzB+fPn0f9+vUxd+5cODs7GzosnRW4Eg+4Gg+Rzmw9gNFngbREsSnydjg8jo+HNCPJoKGVJ8nJycjIyEDlypUNHQoR0SthQQQRERW7pKQkLF26FBMmTMB///1n6HCIiEjfuFwpEZFeRaeko+3ik0hXZ4ttdST30EoOWJvJDBobUYXAL+SIiIoPcyoRERERlSORSenIiH4sbttZmHAlySKaOHEi/P39ERQUhCVLlmDs2LHYtm2bocMqUEET13AlHqLXZOuhUUCkilcCzLPFIjMzE+PHj8eOHTsAAHXq1MH+/fthZWVl6NCIiIqEBRFERBVMRkYGduzYgTt37mDYsGFwdXXN1+e///7D3r17kZ6ejnfeeQeNGzcW9yUnJ+Pq1av5jpFKpWjatCkAYMqUKVi8eDH27dtXwldDRESlCmfyJCIyiOQnmUhXZ2NJbz/4OlsCAEwTbIDdgLOl3NDhEVUMBXwhR0REr4A5tULgCmdERERUnj2bpGTRkZu4evj5PY+ZTIqwSS3L3WBdhUIBlUoFJyenQvuYmprCxMSkSOdes2YNZLKnv88hQ4bgm2++ee14SwonriHSn4qWZ0tSYmIiGjRogBUrViA7Oxtt2rTB3r170b9/f0OHRkRUJCyIICKqQFavXo05c+agbt26OHLkCDp37pyvIOK3335D586d0b59ezg6OiI0NBQzZ85ESEgIAOD69euYPn16vnObm5vjyJEjWLFiBZydnREZGYm7d+8iJiYGd+7cgY+Pj96uk4iIDIQzeRIRGYwrElDX6B58JU8LIiCJMXRIRERERERacaAYEZHhsCCNSD+eTVKytI8fMhzrAQAiHikxYVs4kp9klpuBumFhYVi5ciUOHDgAJycnREVF5etz7tw5fPTRR7h9+zYEQcAHH3yA7777Dubm5gCAOXPmYNeuXfmOCwwMxPz588ViiNTUVHzxxRf48ssv9XBlr4YT1xDpT0XJs8+Eh4fj5MmTeOutt+Dv759vv1qtxuHDhxEZGYmaNWvinXfegUQiEfdfvXoVgiDkO87b2xuVK1fGsGHDAABGRkYQBAE1atQo4SsiMgxXJMA04TLw7PvUhFuGDomKEQsiiIgqkJo1a+LSpUuIj49HrVq18u0XBAFDhw7F4MGDsXLlSgBAq1atMHDgQPTu3RtVq1ZFQEAA/vzzzwJf4969ezh9+jT++OMPPHz4EBkZGdi9ezcmT55cotdGRESlBGfyJCLSO5kyGmHyKTDfrcqzw/zp6j1ERERERKUIB4oRERkGC9KI9M/XyRJwLb+rZ69atQr9+/fHm2++iVWrVuXb//jxY3Tq1Am9e/fG+fPnERsbi1atWmHChAn4/vvvAQDDhw9Hr1698h1ra2sr/vzo0SP06dMHn376KZo0aVLCV/X6fJ0tUdct9//3ZwMuiahElPc8e+3aNXz88cdQKpV48OABJk6cmK8g4vHjx2jdujVSU1PRtGlTfP7552jcuDF+/vlnSKVSAED//v2RlZWV7/yrV68WzycIAoKDg9GxY0c0atRIT1dIpD/8PrX8Y0EEEVEF0rx5cwBAfHy81v3//vsv7t69i48++khs69WrF0aNGoU9e/Zg4sSJL32NRYsWiT8vX74cERERhRZDqFQqqFTPbzQUCoXO10NERERERIA0IwnmEhUiWy2FRzW/5zvMHTSK1IiIiIiISguucEZEpH8sSCOi4rZz504AwIIFC7Tu37p1K1JTUzF//nzI5XJ4enpi2rRpGD9+PBYvXgwrKyu4urrC1dW1wNe4desWgoKCsGLFipcO0OXYAyIqbyQSCRYtWoSAgAD4+mpf1+vzzz9HcnIywsPDYW1tjXv37qFOnTpYv349hgwZAuSuMFEYtVqNIUOGoEGDBjqNDSMqC/KtjhcTjRr8PrVcY0EEERGJbt68CQCoVq2a2CaTyeDp6SnuKwpXV1fk5OQU2mf+/PkIDQ19hWhfLuXBFUTkabO0c0GlKtUKOIKIqGz5/fffMW/ePHz33Xfw9PQ0dDhERGRgKltfwNVPh55ERERERIbDGfmIiAyLM5cT6VHCLfFH0wQlXJFg0HD07ezZs/Dz84Ol5fNc06JFC6hUKly8eFGc0LEwQ4YMwf379zF48GAAQNWqVbF3716tfUty7AERkSHUqlXrpX22b9+OgQMHwtraGgDg7e2NDh06YPv27WJBRGHS0tIQGBgIPz8/tGvXDleuXEHlypXh4KD98zmLz6gsKGx1PHnlWvw+tZxiQQQREYmUSiUAiDfJz9ja2or7iqJ79+4v7RMSEoLg4GBxW6FQwMPj9aouLe1ckCbI0ejCNOCC5r40QY7YIadYFEFEZV5iYiKWLl2KyMhIpKamGjocIiIiIiIiIiKdcIUzIiIiKvfMHZ4We/48VGzyBRAmlyNS2RiAjUHD05f4+Hg4OjpqtDk5OQEAHj16pNM5fvzxR6SlpYnbcnnBK9qUxNiDonJFAkwTLj8vOHuhKIaISkAFLzxTKpWIjIxEzZo1Ndpr1qyJjRs36nSOmJgYxMXF4fDhwzh8+DAAYPLkyWIhWl4sPqOygKvjVUwsiCAiItGzmRkUCgVsbJ4/hElJSdGp6vhVyOXyQh9avIpKVaohdsgpxCTHabSnPLiCRhemPW1nQQQRlXHBwcH4+uuv0aVLF0OHQkRERERERERUZFzhjIhI/zhQl0hPbD2A0WeBtESxKfJ2ODyOj4c0I8mgoemTRCKBWq3WaMvKygIASKVSnc5RtWpVnV+vJMYeFAVXQiPSIxaeAYA4caKtra1Gu52dnc6TKvr6+uLKlSs6v2ZpKD4j0hVXx6tYWBBBRESiGjVqAABu376NRo0aAQDUajUePHhQYOVvaVWpSrV8RQ8RQL4VI4iIituFCxewatUqbN26FdWrV8f58+fz9bly5QrGjRuHc+fOwc7ODh9//DFmz54NiUQCAPjiiy/wxx9/5Dvu/fffx6hRo/Dtt9+iV69e8PT01Ms1FYfIpHRkRD8Wt+0sTOBma2bQmIiIiIiIiIiIiIgqCg7UJdIzWw+N1a9U8UqDhmMIbm5uOH36tEZbXNzTSQ1dXV0NFFXJ4UpoRHrEwjMAgLm5OfBCYcQzCoVC3FfcDF18RqRNdEo6kp9kitsRjyrefRexIIKIiF7QoEEDVK1aFWvXrhULInbs2AGlUskZyImIdJCdnY2PP/4Yw4cPh5GRUb6HvMhddadt27bo1KkTfvrpJ1y7dg09evSAiYkJpk+fDgDo1KkTGjZsmO9YT09PJCUl4fPPP0fDhg2xfPly3L17FyNGjMC6detQrVrpW/3G2kwGAFh05CauHn7+AdRMJkXYpJYsiiAiIiIiIiIiIiLSAw7UJSJ9a9GiBb777js8evQIzs7OAIDDhw/DxsYG9evXN3R4JYYroRHpCQvPYGNjA2dnZ9y5c0ej/c6dO6Vy7ABRSYhOSUfbxSeRrs7WaDeTSWFnYWKwuEj/WBBBRFSBnD17FgcPHkRCQgIA4Pvvv8f+/fvx7rvvIiAgABKJBN9//z0CAwMRGxsLR0dHbN68GZ999lmRlqIkIqqopFIpLlx4uhTN5MmTtfbZsGEDnjx5ghUrVsDU1BSVK1fGxIkT8fXXX2PKlCmQSqXw8yv4IalKpcLGjRvF7du3byMoKAguLi4lcEWvz9ny6ewQS/v4IcOxHpBbjT9hWziSn2SyIIKIiIiIiIiIiMo0eUoEEGP5vIGDy6mU40BdIiouycnJUKlUUCqVyMnJQWxsLADAyckJUqkU3bt3R40aNRAUFISvvvoKDx48wLx58zBlyhTOLk5EVEy6dOmCbdu2Ydq0aZDJZEhISMDBgwcxZ84cQ4dGpBfJTzKRrs7Gkt5+8HV+/tnczsKE41EqGBZElDKRSenIiH4sbvOPkohKgqOjY4E3vm3atMH169exd+9epKen4/jx42jcuLHeYyQiKq9OnToFf39/mJqaim1t2rTBp59+itu3b6NmzZqFHi+Xy9GhQwdx28rKCs2aNYO1tbXW/iqVCirV8yXQFQpFsVxHUfk6WQKuNgZ5bSIiIiIiIiIiouKWbWqPNEEOj+PjgeMv7JCZA6PPsiiCiIjKveHDh+P3338Xt59N+HX69Gl4eXnBxMQEYWFhmDp1Krp27QorKyvMmjULkyZNMmDURERlh0KhwNq1awEAjx8/xunTp7FkyRK4u7ujZ8+eAIBPP/0U/v7+aNOmDVq1aoVdu3ahWrVqGDFihIGjJ9IvX2dL1HV7YUxKSiQQk/h8O+GWQeIi/WFBRClhbSYDACw6chNXD2eK7WYyKcImtWRRBBEViyZNmqBJkyYv7efp6YmxY8fqJSYioorm4cOH8PDQ/DLUyckJABAbG/vSgoi8Vq5cCS8vrwL3z58/H6Ghoa8YLREREREREREREWmjtnRDW9VCbOjr83QyEOQOsPh5KJCWyIIIIiIq97Zv3/7SPq6urti0aZNe4iEiKm+ysrJw//59AED//v0BAPfv34ex8fNhv66urrh48SI2bdqEqKgoBAcHo3///lyJh0qt6JR0JD/JzNderJPHp0QCK5oA6jTNdpn501UdqVxiQUQp4Wz59A1oVQdLqGyfVilFJqVj5uEYJD/JZEEEERERUTlmZGQEABAEocjHBgQEFLo/JCQEwcHB4rZCochXkEFERERERERE5dNff/2F69evIyAgALVq1TJ0OETlTgwckeFYjyujEhERERFRsbO3t8eSJUte2s/Ozo4T31KplLf4IfFJJkZs/Afp6ux8fXWdPD7vOSMeKfN3Skt8WgzR/QfAsfrzdnMHTl5QjrEgorQwdwBk5k+XVM3lCyBMLkeksjEAPkQjIiIiKg9cXFwQHx+v0fbo0SNxX3GTy+Wc/YGIiIiIiIioApoyZQrOnTsHX19fTJs2DWvXrsX7779v6LCIiIiIiIiIiKgMK2iVhxcVVPxgJpPix4+awMHCRGyLeKTEhG3hOHcvCcnOlq90TrsXzidyrA64+ul+YVSmsSCitLD1AEaffVqZlCvydjg8jo+HNCPJoKERERERUfEJCAjAnDlzoFKpxEKF3377DQ4ODqhevfpLjyciIiIiKi/i4uJw9OhRODs7o23btuLKaUREVDz69euHhQsXAgAaNmyIgwcPsiCCiIiIiIiIiIi00rXQIXTjYZhlpbz0fLWNjTCjay3YmMnENmszGZwtozX6OVuo0FD2AD9sv/fq50y7CaTlNiTceul5qPzRuSCiZ8+eL+2zc+fO142nYrP10FiORRWvZSkXIiIiIirTBg0ahHnz5mHChAmYN28erl+/jiVLlmD8+PEwNi7H9covfOA0TVDCFQkGDYeIiIiIDOvcuXP48MMP0ahRI1y6dAne3t7YtWuXocMiItKbtLQ0bNmyBfv27UNAQACmTp2ar098fDyWLl2K69evw93dHaNGjUKNGjXE/Zs2bUJCQv7P161atcIbb7yBBg0a4Oeff8aNGzdw4MABsTiCiIiKz7Zt2/DNN99AIpFg7Nix6Nevn6FDIiIiIiIi0lCchQ4OEgX2y5bAXK7S7cUPvbyLM4BdUgBS3U6pyzkhMwfMHXQ8IZUHOo+46tOnT8lGQkREpCcpD64g4oVtSzsXVKpSzYAREVF50qhRI4SHhyMnJweCIIhFDomJibCxsYG9vT0OHz6M0aNHw9nZGba2thg+fDhmzZpl6NBLhrnD0w+aPw8Vm3wBhMnliFQ2BmBj0PCIiIiIyDCsrKxw5swZWFhYICMjA7a2thAEARKJxNChERGVuPv37yMgIADt27fHgwcPYGpqmq9PSkoK/P394e3tjUGDBuHo0aNo3LgxTp8+jdq1awMAoqOj8fDhw3zHpqamij/HxcXh/v37SE9PR1ISVyQnIipOt2/fxuHDh7F8+XIkJyejX79+8Pf3R9WqVQ0dGhERERERlVG6FC8URqaMhjTj+TOgx+lqzDtwHRlZOYUeV5RChxxjM6DPLsDc8ZXjLHHmDhoT1FP5V6wrRBAREZVmlnYuSBPkaHRhGnDheXuaIEfskFMsiiCiYnHmzBkIgpCv/cXVH9588038/fffeo7MQGw9gNFngbREsSnydjg8jo/X+BBORFQapaWlISMjA/b29oYOhYhI786ePYs1a9YgLi4OW7du1TpYd8uWLTh48CCkUim6dOmCbt26ifvCw8Px559/5jvG2dkZH3zwAWrWrInbt2/j119/xdmzZzFy5EgWQxBRheHk5IRr167B1tYWnTt31tpn2bJlePLkCfbt2wczMzMEBQWhefPm+OSTT8QV26dNm1bo60RFRWHkyJEAgJMnT2L27NkFvh4RUXmVlpaGmJgYuLm5wczMTGsfhUKB1NRUuLq6Fume1MfHB2vXrgUAPHnyBPb29lqfDRMRUfmUd8BqfFI6fA0aEREBQGRSOjKiH4vbdhYmcLPVfh9I5Ye2IgJd/79/3QIEXeUtVNBG1+KFgjhIFFglWwJziWZRg66rL+ha6GDEYgMqhYq1IOLZA1giIqLSqFKVaogdcgoxyXFiW8qDK2h0YdrTNhZEEFExkEp1XcOvArH10PgwrIpXGjQcIiJdhIaGYtmyZcjKykKTJk2we/duWFhYGDosIiK96N27N+7evYu6detiz549yMrKytdn6tSpWLNmDWbPno3MzEwEBQVhxowZmDFjBpC7QtqNGzfyHZeeni7+nJqaihs3biAmJgbm5uYlfFVERKWHLveVhw4dQseOHTUG7/bo0QOffPKJzivq9OnTB02bNoWNjQ127NiBHj16FNhXpVJBpXr+ZblCodDpWoiISquIiAgsXboU27ZtQ3x8PI4ePYq2bdtq9FEqlRg0aBD27dsHc3Nz2Nra4scff0TLli0BAP/++69G0e+Lbt68CblcjqioKDRv3hzx8fGYMmUKfHx89HJ9RERkWNEp6Wi7+CTS1dliWx3JPbSSA9ZmMoPGRlRRPfvbW3TkJq4efj643UwmRdikliyKKMe05WQAqCpLxsJObrApJC+/bgGCrgoqVNBG1+KFguQYm+F+243IMns+4Zu1mQzOlvKXHstCByrLdC6I6NOnT8lGQkREpAeVqlTTKHyIADRWiyAiIiIiAgBTU1M8fPgQEokEHTp0wP79+9G7d29Dh0VEpBcLFy5ElSpV8Msvv2D9+vX59v/333/4+uuvsW3bNnFwrY2NDcaPH4+RI0fCzs4Obdq0QZs2bQp8jejoaLz55ptYuXIlAKBatWq4desWqlevXoJXRkRUdty7dw+tW7fWaPPw8IBSqURCQgKcnJxeeo4DBw5g3bp1SEpKwoIFC9ChQ4cC+86fPx+hoaHFEjsRUWlw+PBhVKtWDb///jtq1aqltc/48eNx5coVREZGwsnJCTNmzECXLl0QEREBR0dH1K5dGydOnNB6rImJCQCgUqVKOHHiBCIjIzFixAi0b98eTZs2LdFrIyIiw0t+kol0dTaW9PaDr7MlAMA0wQbYDZ0GnBJR8Xv2t7e0jx8yHOsBACIeKTFhWziSn2SyIKIcS36SCTt1HFa2d4WH/dP/n9OS4+B7PBjmh0q+AEFX2goVtNG1eKEgRuYO8GJRA1VAxbpCRFlx4sQJ7NmzB/Hx8ejXrx/ee+89Q4dEREREREREREUQHh6OM2fOoHnz5qhTp06+/Wq1GseOHUNsbCzq1q2LRo0aiftycnJw9+5dreetUqUKTExMMG3aNNy9exdxcXFISEhAjRo1SvR6iIhKkypVqhS6/8iRI5BKpejcubPY1qtXL4wYMQK//fZboTOQP7Nx40ZcvnwZb775Jq5evQpBEODp6am1L2ctJ6KKKDMzM9/qOc+2MzMzCzhKk42NDSZMmKBT35CQEAQHB4vbCoUCHh788pyIyq7Ro0cDAGJjY7XuT01NxaZNm7By5Uq4uLgAAObMmYPly5dj8+bNGDduHORyOby8vAp8jbCwMNjY2KBx48aoVKkSXFxcEBkZWWBBBO9riYjKH19nS9R1s3m6IbE0dDhEBMDXyRJwtTF0GKRHMmU0wuRTYH5Ss/ghR6afAgRdsVCBqGTpXBCR14ULF3D9+nWo1WqxbfDgwcUVV4nZuHEjJk6ciKlTp6Jhw4bw9vY2dEhERERERBVSZFI6MqIfa7TZWZhwdg4iKtSFCxcwZswYKJVK3Lx5E4sXL85XEJGYmIjWrVsjPT0dfn5+CA4ORvfu3bF69WoAwJMnTwqcHXffvn3izJGBgYF4+PAhWrVqxRnLiYhecO/ePTg7O0Muf/4lkb29PczNzXHv3j2dzjF9+nT88ssv+Ouvv9CgQQMsXLhQ43wv4qzlRFQR2draIikpSaMtMTERyC10KG5yubzAPExEVB5dunQJmZmZGsULZmZmaNCgAc6fP6/TOerUqYN+/frh6tWrSEtLw/vvv48uXboU2J/3tURERERExU+akQRziQqRrZbCo5qf2M4CBKKK5ZUKIubOnYvdu3fjwoUL6NWrF/bv34+2bduWiYKIWbNmYfXq1ejatauhQyEiIiIiqpCszWQAgEVHbuLqYc1ZLc1kUoRNasmiCCIqUHZ2NhYtWoSAgADY2tpq7TNz5kyo1Wr8+++/sLCwQHh4OBo2bIjAwEB06dIFVlZWiIiIKPA1cnJyYGRkJM5YPmrUKCxZsgQzZswowSsjIio7VCpVvlnLAcDS0hIZGRk6n6dr1646PaflrOVEVBG98cYbuHjxokZbeHg4fHx8YGnJmWeJiF5XQkICAMDBwUGj3dHRUdz3MpUrV8bx48eRlJQECwuLlxaW8b6WiKh8cUUCTBMuP18ZIuGWoUMiImj+LZomKOEK3e7tqOxT2foCrn469CSi8uiVCiJWrFiBc+fOwcPDA9u2bcPRo0exZMmS1w7m7Nmz+N///ofDhw9j8ODBmDdvXr4+J0+eRGhoKO7cuQNvb2/MmjULbdu2FfePGTMGKSkp+Y4bM2YMateujZiYGFhZWWHYsGHw9fXFyJEjYWVl9dqxExERERGRbp4tN7m0jx8yHOuJ7RGPlJiwLRzJTzJZEEFEBWrcuHGh+wVBwLZt2xASEgILCwsAgJ+fH95++21s3bq10Jkan7lw4QI2bdqEoKAgqFQqXLt2DfXr1y+wv0qlgkr1fBlehUJRpGsiIiprbGxskJycnK89OTkZdnZ2xf56nLWciCqigQMHomfPnjh79iyaNGmC6OhobNq0CaNHjzZ0aERE5YKRkREAQK1Wa7RnZmZCKpUW6Vz29vY69eN9LRFBy+rZXDm7bJIpoxEmnwLz3ao8O8wBc4eCDiOikmTu8PRv8OehYpMvgDC5HJHKxgCKf7VFIiIqPV6pICI2Nhbu7u6wsrJCSkoK3n77bfTq1eu1Avnjjz8QHByMESNGIDw8XOvggYsXL6J9+/aYOnUqVqxYgZ07d+K9997DX3/9hUaNGgEAWrVqhfT0/7N353FRVf0fwD+sw7DJJiAIKIu444Ybue+aS2pZaVmZufbT9CkzM9Me03bNXB7NrKyAzKXcFSUXXFMx3MUFUURA2ffl/P5Aro4MMODsfN6vV6+cc8+98z0zw5k7957vObnl9q1Xrx7Mzc1hbm6OX3/9FSEhIdixYwdOnDiBP/7446li1zRZWiyQ8NhsP9bOAJfyISJSq7S4c3h8jmBbRze4ewfoMCIiIuPnX9cW8OCFJyJSr4SEBKSlpaFp06YK5U2bNsXhw4dVOka7du1w9uxZTJ48Gaamphg8eDAmTJhQYf1FixZh/vz5Tx07EZGhCAoKQkpKChISEuDh4QEAOHfuHAoLCytNICMiokeGDx+OgoICnDx5Eubm5nj22Wfh6OiI9evXAwCGDh2K6dOno3v37mjdujUuXLiALl26YNasWboOnYjIKJStzFA29qFMYmIi2rdvr8PIiMhYVbR6NlfONkxmeQ9gbZKP+B5L4RXw2GzkHNNFpDsOXsCUE0DOfako/mo0vCKnwSzvgU5DIyIizatRQkSZzp074+OPP4aVlRUaNmz4VIGEhITg5MmTAIA1a9YorfPFF1+gdevWWLBgAQBg7ty52Lt3Lz777DNs2LABADBixIhKn6d+/fr46KOP0KBBA4SEhGDQoEFPFbcmFVs5IUfI4BU5DYh8VF5iLofp1JM8gSYiUgNbRzfkCBnanZ4FnH5UniNkSBwXxaQIIiIiIgNTNsGCg4ODQrmjo2O1Vm4YN24cxo0bp1Ld2bNnY8aMGQoxlA2sICIyRn369IGbmxu+/PJLfP3118DDa7d+fn7o3LmzrsMjIjII48ePR3FxMSZOnCiVWVlZKdT54osv8Pbbb+Py5cvw9PQsl/RLREQ117x5czg7O2P37t3S5It3795FdHQ03nvvPV2HR0RGSNnq2Vw52/DlO/gDHq1UqElEWuHgpTCmMj85S6fhEBGR9tQoIWLv3r0AgLVr12LhwoXIyMhAeHj4UwVStiRlZQ4ePIjXXntNoaxfv3749ttvVX6eTz/9FH369EHr1q1x/PhxTJo0qcK6+fn5yM9/tLRZdQZOqIOtW0M8W/I15EVpUpm/yR0sxQokJSXAlQkRRERPzd07AInjopCQek8qS4s7h3anZ5WWMSGCiIiIyKDI5aU3DjMzMxXKMzIyYG1trZHnlMlkkMlkGjk2EZEurFmzBtu3b8fdu3cBAC+99BLMzMzw4Ycfol27dpDL5fjtt98wcuRI7N+/H4WFhbh//z7++usvmJmZ6Tp8IiKDMGDAAJXqeXt7w9vbW+PxEBEZm6ysLCQmJiIlJQV4uKJkbGwsnJyc4OTkBHNzc8ydOxdz5sxBgwYN0LBhQ3zwwQdo1qwZhg8fruvwiciIcfVsIiIiIiL1q1FCRO/evQEAnp6eWLFihbpjqlBCQgLc3NwUytzc3JCUlISioiKYm1fdnOeffx5t27bF2bNnMW/ePDRr1qzCuosWLcL8+fPVEntNeDrIsX7mCKRmP1oqL/nKCeDACmTkFsJVZ5ERERkXd+8AhcSHWEBhtQgiIiIiMhz169eHlZUVbty4oVB+48YN+Pv76ywuIiJD0qFDB9StW7dc+eOr3/To0QNxcXE4fvw4TE1N0bFjRykpjYiIiIhI1/bt24eZM2cCAPz8/LBgwQIAwJQpU/DOO+8AAKZNmwa5XI4VK1YgIyMDnTp1wn//+19YWFjoNHYiIiIiIiIiqp4aJUToihCiXNJD2eOSkhKVj+Pr6wtfX98q682ePRszZsyQHmdkZCjc9NMGTwe5wrJ4sSm8qUhERERERERUEXNzcwwcOBBhYWGYNGkSTExMkJiYiH379mHZsmW6Do+IyCC0bNkSLVu2rLKera0tevXqpZWYiIiIiIiqY+jQoRg6dGiV9d566y289dZbWomJiIiIiIie3p20XMVJxh/kglOiEZFBJUS4uLhIS1qWSU5ORp06dWBpaan255PJZJDJZGo/LhERERERERHVTHJyMjZu3AgAKCgowOHDh2Fubg4/Pz/06dMHeLjiY6dOnTB48GB07twZv/zyC9q0aYOxY8fqOHoiIiIiIiIiIiIiIiIiqok7abno/dUB5BYWS2XNTG6ghwywl3OlN6LazKASItq3b4/Dhw8rlB08eBDt27fXWUxERGS80uLOIfaxx7aObnD3DtBhRERERESUm5uL6OhoAMCrr74KAIiOjoapqalUp1GjRoiJicFPP/2ExMREvPPOO3j11VdhYcELoURERERERERERERERESGKDW7ALmFxVgyqhX8XW0BAFYpdYDNgKstJz8nqs0MKiFi6tSpePbZZ7F582YMGzYM27Ztw65du6SZIYmIiNTB1tENOUKGdqdnAacflecIGRLHRTEpgoiIiEiHvL29sWrVqirreXh4YPbs2VqJiYiIiIiIiIiIiIiIiIi0w9/VFs0965Q+MLHVdThEpAf0JiEiJycHvr6+AIAHDx4gJiYGf/zxBxo3boy///4bANCvXz98++23GD9+PF5++WXI5XJ8+eWXGDJkiI6jJyIiY+LuHYDEcVFISL0nlaXFnUO707NKy5gQQURERERERERERERERERE1ZVyRfqnVUoWPJCi03CIiIiIiIyB3iREyOVyREdHlyu3sLBQeDxp0iRMmDABaWlpcHBwgKmpqRaj1B+ytFgg4bHMNmtnwMFLlyERERkVd+8AhcSHWEBhtQgiIiIiIiIiIiIiIiIiIiKVWDsDFtbApvFSkT+ACJkM8VnBAOroNDwiIiIiIkOmNwkRJiYmcHd3V6muqakpnJycNB6TPiq2ckKOkMErchoQ+dgGC2tgygkmRRARERGR4XhsFiQ8NhNSbFKWQrmjjSU8HeRaDo6IiIiIiIiIiIiIiEhNHLxKx/Xk3JeK4q9GwytyGszyHug0NCIiIiIiQ6c3CRGkmkJbT/TO/wIL+3nAy6l0UJgsLbY0QSLnPhMiiIg0LC3uXOlqEQ/ZOrqVriZBRESqUzILEh6bCal3+BdIgItULrcwQ8TMbkyKICIiIiIiIiIiIiIiw+XgpTCuJz85q9LqRERERESkGiZEGBhHG0ukWrjh9d0FAAoAAM1MsrBdBiRl5cNV1wESERkpW0c35AgZ2p2eBZx+VJ4jZEgcF8WkCCKi6lAyCxJQumKE9abx+PklP+S5tAAAxCZlYXp4NFKzC5gQQURERERERERERERERERERERECpgQYWA8HeSImNkNqdkFUlnyFUvgAJCRW8iECCIiDXH3DkDiuCgkpN6TytLizqHd6VmlZUyIICKqnidmQXqcf11bwKOO1kMiIiIiIiIiIiIiMhZ30nIVxxU8yIW/TiMiIiIiIiIi0gwmRBggTwe5wuy4sSmcKZeISBvcvQMUEh9iAYXVIoiIiIiIiIiIiIiIiIh07U5aLnp/dQC5hcVSWTOTG+ghA+zlFjqNjYiIiIiIiEjdmBBBRET0lNLizpUmRzxk6+hWmjxBREREREREREREREREpGWp2QXILSzGklGt4O9qCwCwSqkDbAZcbWW6Do+IiIiIiIhIrZgQQUREVEO2jm7IETK0Oz1LYaWIHCFD4rgoJkUQERERERERERERERGRzvi72qK5Z53SBya2ug6HiIiIiIiISCOYEGFEZGmxQMJjFzGsnQEHL12GRERk1Ny9A5A4LgoJqfeksrS4c2h3elZpGRMiiIiIiIiIiIiIiIiIiIiIiIiIiIg0hgkRRqDYygk5QgavyGlA5KPyEnM5TKeeZFIEEZEGuXsHKCQ+xALA6dLEiNjH6tk6unHFCCIiIiIiIiIiIiIiIiIiIiIiIiIiNWJChBGwdWuIZ0u+hrwoTSrzN7mDpViBpKQEuDIhgohIa2wd3ZAjZGh3ehZw+lF5jpAhcVwUkyKIiIiIiIiIiIiIiIiIiIiIiIiIyGjI0mKBBNtHBdbOWp3QnwkRRsDTQY71M0cgNbtAKku+cgI4sAIZuYVw1Wl0RES1i7t3ABLHRSEh9Z5UlhZ3Du1OzyotY0IEERERERERERERERERERERERERERm4Yisn5AgZvCKnAZGPbbCwBqac0FpSBBMijISngxyeDnLpcWyKvNL6RESkOe7eAQqJD7EAcLo0MSL2sXq2jm5cMYKIiIiIiIiIiIiIiIiIiIiIiIiIDE6hrSd653+Bn1/yg3/dhytEpFwBNo0Hcu4zIYKIiMhY2Dq6IUfI0O70LOD0o/IcIUPiuCgmRRAREREREREREREREREREVUgLS0Nv/zyC5577jl4enrqOhwiIqOUnp6O9PR0eHt76zoUIjIwCXBBnksLwKOOzmJgQgQREZGGuXsHIHFcFBJS70llaXHn0O70rNIyJkQQEVUpNilL4bGjjaXCCmlERERERERERERERERknKZPn47Dhw+jefPmTIggItKAvLw8DBkyBHfu3EFsbKyuwyEiqjYmRBi5+Ae5yLuTrlDGwWNERNrn7h2gkPgQCwCnSxMjHv8ZYevoxhUjiEglRUVFiI6ORlxcHDw9PdGxY0ddh6QRjjaWkFuYYXp4tEK53MIMETO78byWiIiIiIiIiIiIiIjIiK1atQp9+/bF7du3dR0KEZHRmjVrFt5++228//77ug6FiKhGmBBhpOzlFgCALXv3I3bPZYVtueYOWD9zBAePERHpkK2jG3KEDO1OzwJOPyrPETIkjotiUgQRVSo1NRUDBgxAWloamjdvjk6dOhltQoSngxwRM7shNbtAKotNysL08GikZhfwnJaIiIiIiIiIiIiIiEgHhBDYu3cvfvnlF1haWuL7778vV6ewsBCrV69GVFQU7OzsMGbMGHTp0kXaHhkZifPnz5fbLzAwEH369MG5c+dw+fJlfPPNN/jhhx803iYiIn106dIlHDhwAG3btkW7du3KbS8uLkZkZCTi4+PRuHFjdOrUSWH7tWvXIIQot5+npyfkcjk2btyIwMBApcfWRx5IgVVKDGBiW1qQckXXIRGRHmBChJFydfVAibkcS7Gi3LYcIUP8vTaAQ1OdxEZERKUrRiSOi0JC6j2pLC3uHNqdnoUL0fuQ9Vg5uHIEET3h008/hZOTE6KiomBmZqbrcDTO00HOxAciIiIiIiIiIiKiauBAMSLStNatW6Nu3bqwtrbGqVOnlNYZMWIELl68iP/85z+Ii4tDjx49EBYWhpEjRwIA7t69i0uXLpXbz97eHiUlJRg7dixeeuklfPfdd7h9+zY2b94MX19feHt7a7x9RES6dvnyZUyaNAkJCQlISEjAjBkzyiUtZGZmom/fvkhMTET79u0xa9Ys9OjRA6GhoTA1NQUADBo0CEVFReWO//PPP8PT0xPr16/Hl19+ibi4OBQWFuLmzZto0KCB1tpZHRZZdxAhexfWm/Of2GANWDvrKiwi0gNMiDBWDl4wnXoSyLmvUBx/NRpekdNglvdAZ6EREVEpd+8A4LEkh0RHN+ScKr9qBLhyBJHBOXHiBMLCwuDs7Iw5c+aU215SUoKwsDCcPHkSjo6OGD16NPz8/KTtx44dU7rsr7+/P1q1aoXIyEjMmzcP+/fvR506ddC+fXuNt4mIiIiIiIiIiIiIDAMHihGRNvz111/w9vbG4sWLlSZE7Nu3D1u3bkVMTAyaN28OAMjNzcXMmTMxYsQImJiY4OWXX8bLL7+s9PhFRUXo1KkTbt68CQDIzs7GrVu3kJ2dreGWERHph8LCQnz44Yfo0aMHAgKUjxdauHAh7t69izNnzsDR0RFXr15Fy5YtsX79eowdOxZ4uMJERX755RecO3cO/fv3R1FREe7evYtXXnkFhw4d0li7noZZ3gNYm+QjvsdSeAW0erTB2hlw8NJlaESkY0yIMGYOXuU6+fzkLABA/INc5N1Jl8odbSw56y4RkY4pWzUCj60ccfzaTaSYuUrl7LuJ9E9JSQmCg4NhamoKS0tLZGdnK02IGDlyJE6fPo1x48bhwoULaNGiBfbv34+OHTsCAKKionD06NFy+w0YMACtWrVCZmYmlixZAisrK1y/fh1NmjTBpk2btNJGIiIiIiIiIiIiItJvHChGRNpQ1SoNO3fuRGBgoJQMAQDPP/88lixZggsXLqBZs2aV7m9ubo7vvvtOenzp0iVMmzYNTZo0UVo/Pz8f+fmPEsEyMjKq0RoiIv3zeP9ZkbCwMLz88stwdHQEAAQEBKBfv34ICwuTEiIqM2bMGIwZMwYAcPPmTfTu3bvSZAh96WvzHfwBj1Yq1CSi2oIJEbWMvdwCAPDlnss4v7tAKpdbmCFiZjcOrCUi0rEnV40AgCS5BXAa2LJ3P2L3XJbKc80dsH7mCPbdRHrExMQEa9asQZs2bfCf//wHERER5ers3LkTmzdvxoULF6QLtiNHjsT06dNx7NgxAMDMmTMrfZ4GDRpg7NixePnll1FYWAh3d3ckJyejbt26GmoZERERERERERERERkaDhQjIl26ceMGvLwUk7DKHt+4caPKhIgnDRs2DJ6enhVuX7RoEebPn1/DaImIDE92djbi4uLQtGlThfKmTZvil19+qfbxLCws4OPjU2kd9rVEpK+YEFHLuNrKAABLX2yFPJcWAIDYpCxMD49GanYBB9USEekhV1cPlJjLsRQrFMpzhAzx99oADk0r3JeItMvExARt2rSptM5ff/2F1q1bK8xeM2bMGDz33HO4d+8e3Nzcqnye8ePH49tvv0VJSQkuXboEe3t7ODk5Ka2rLzM0EBERERERERERERERUe2Rn58Pa2trhTJbW1sAQF5eXrWPN3Xq1Eq3z549GzNmzJAeZ2RklEvIICIyJmX3/h0cHBTKHR0dazQuwNPTE/v27au0DvtaItJXTIiopfzr2gIedXQdBhEZqezsbCxZsgSnT59Gfn4+wsPDYWNjo+uwDJeDF0ynngRy7ktF8Vej4RU5DZmXDyI274FUbuvoVrrKBBHpratXr6Jhw4YKZWWPr127plJCxMiRI2Fqaort27fDyckJf//9N8zMzJTW5QwNREREREREREREREREpG116tRBfHy8Qtn9+6X3vB0dHdX+fDKZDDKZTO3HJSLSV3J56eTXmZmZCuUZGRnlEtLUhX0tEekrJkTUVilXpH9apWTBAymITcpSqOJoY8kVI4io2kpKStCjRw+4u7vjlVdegVwu54mwOjh4lf73kEWRDXL2y9Du9Czg9KNqOUKGf3ushLXjowHVTJIg0i+5ubmoX7++Qpm9vT0AICcnR+XjDB8+HMOHD6+ynkHO0PDYuSoAwNpZoQ8kIiIiIiIiIiIiIiIi/RYUFIRdu3ahqKgI5ualQ9Sio6NhYmKC5s2b6zo8IiKD5+DgABcXF9y4cUOh/MaNG/D399dZXEREusCEiNrG2hmwsAY2jZeK/AFEyGToHf4FEuAilcstzBAxsxuTIoioWnbt2oWkpCQcPXq0wtnK6em5ewcgcVwUElLvSWU5qffgHzkJLf9+Q6FujpAhcVwUkyKI9IS9vT3S0tIUylJTU6Vt6mZQMzQoOVcFUFo25QSTIoiIiIiIiIiq4dtvv8WJEyekx5988km5VSuJiIiIiDRl1KhRmDt3Ln744Qe89dZbKCwsxNKlS9G3b1+VVkwnIqKqDRkyBBs2bMB7770Hc3NzpKamYvv27ZgzZ46uQyMi0iomRNQ2Dl6lg8ly7j8qS7kC603j8fNLfshzaQEAiE3KwvTwaKRmFzAhgsiIpKen4+eff8aqVatw7do1HDlyBG3atFGoU1BQgA8++ABhYWHIzc1Fjx49sHTpUnh6egIATp48ifnz55c7tlwux4YNG3DhwgX07NkTc+fOxc2bNzFw4ECMGTNGa22sTdy9A4AnkhwSfVsqJEmkxZ1Du9OzSsuYEEGkF5o1a4a//vpLoezChQswMzNDYGCgzuLSCxWcq2LT+NIyJkQQERERERERqezgwYNo2rQpGjVqBACoU6eOrkMiIiIiIiMyb948nD17FlevXsX9+/cxbNgwAMCqVavg7u4OHx8frF69GpMnT8a6detw9+5dWFlZ4ZdfftF16EREBiEzMxO//vorACAjIwP//PMPVq1aBQ8PDwwZMgQA8PHHH6NDhw7o378/evXqhd9//x0+Pj6YPHmyjqMnItIuJkTURg5eSgeT+de1BTx4MZzImM2bNw9FRUVYsGABRo4ciZKSknJ1pk+fjm3btmHjxo1wcXHBxIkTMWDAAJw5cwZmZmbw8fHBxIkTy+1XtsSllZUVIiIiMGfOHDRt2hSffPIJ7OzsMHToUK20sbZ7MkkiFgBOlyZGxD5Wz9bRjStGEOnIqFGj8NVXX2Hv3r3o06cPioqKsGrVKgwYMIADE1DxuSoRERERGZekpCQcOXJEetywYUMEBQXpNCYiImN08eJFpKamYsiQIXByctJ1OERERkUIgfv3H03uYm5uDgcHB53GRESkTf369UPr1q3LldvZ2Un/Hjt2LAYNGoRTp07Bzs4O7du3l8YWEBFR5QoKChAdHQ0AGD58OAAgOjoaOTk5Uh0vLy+cPXsWP/74I27fvo3x48fjtddeg7W1tc7iJiLSBZ5hEhHVIkuWLAEAXLp0Sen2Bw8eYM2aNfjhhx/QoUMHAMDq1avh6+uLHTt2YPDgwXB1dcWzzz5b4XO0adMGvr6+mDBhAgDgn3/+wcWLF5kQoSO2jm7IETK0Oz0LOP2oPEfI8G+PlbB2dFOoyyQJoqe3cOFCxMfH49ixY7h9+7aURPbNN99ALpcjODgYc+fOxfDhw9G3b19cvXoVGRkZWL9+va5DJyIiIiLSmtOnT+P//u//pJUr+/fvz4QIIqpVzp8/j1WrVmHr1q0YNGgQli9fXq7OmTNnMHfuXFy8eBH169fHu+++q3Btdvbs2YiPjy+339ixY9GnTx9MmzYNcXFxuHv3Ll599VV89913GDFihMbbRkQPVz19nLUzJwExQunp6XB3d5eSIFq0aIHIyEhdh0VEpDWdO3dWqZ6Liwv69eun8XiIiIyNs7MzVq1aVWW9unXr4t1339VKTERE+ooJEVSp2KQshceONpbwdJDrLB4i0qzjx4+jqKgIPXr0kMoaNmwIX19fREVFYfDgwVUeo3PnzvDw8EDr1q1hY2ODW7du4eDBgxXWz8/PR35+vvQ4IyNDDS2hMu7eAUgcF4WE1HtSWU7qPfhHTkLLv99QqJsjZEh57ke4uHo8KuRNGqJqCwwMhLOzM1q1aqVQbmZmJv17wYIFGDVqFE6ePAlHR0f06dOHMzQQERERUa3TqlUrTJo0CY0bN4aPj4+uwyEi0ppLly7hhRdewFtvvYUGDRoozC5eJi4uDt27d8crr7yCRYsWYc+ePRg2bBh2796NXr16AQBCQkKQlpZWbt+yPrVLly7o0qUL8HDGyPDwcCZEEGmatTNgYQ1sGq9YbmENTDnB6+1GyN/fH6dPn+b1XSIiIiIiIiIdYkIEPfLYTCWu2fnwtUjF9PBohSpyCzNEzOzGpAgiI3X37l0AgKurq0K5q6srEhMTVT7Or7/+ipiYGKSnp6Ndu3aQyyvuMxYtWoT58+c/RdRUFXfvAOCJlR8SfVsqJEkkJd5BqyNvw2XLSwr1SszlMJ16kjdpiKph5MiRKtVr1qwZmjVrpvF4iIiIiIhqIj4+Hj/++CPu3buHr7/+GpaWluXqHDp0CDt27ICZmRmGDBmC9u3bS9tiY2Nx7ty5cvs4OjqiW7ducHNzg6mpKb755hucOHECH374IWbMmKHxdhER6YOAgACcP38eALB3716ldZYuXQpXV1d89913wMNZxw8dOoSFCxdKCRGVreT7pFu3bsHW1lYt8RNRJRy8ShMfch5LdEq5UpogkXOf19p14OzZs7h48SJ69OgBNze3ctsLCgpw5MgRpKenIzg4GB4ejyaNKioqUpp4hoeznZuamiI1NRX169dHSUkJPvnkE7z99tsabQ8RERERERERlceECFI6U4krgAiZHFfH7EehrSfwcLWI6eHRSM0uYEIEkZEzMTFReGxqagohRLX2b9mypUp1Z8+erTDgISMjA15evCGgaU8mSch9cvHs4ULIix5d2Pc3uYOlWIGLx3fDwr2xVG7r6Fa6PxERERERERmlt99+G1u3bkWLFi2wbds2LF68uFxCxFdffYWPPvoIkyZNQnZ2Nrp06YLly5fjzTffBACcP38eP/74Y7lj+/v7o1u3bmjdujW2bNkCPJwFvXnz5pg6darSxAsiImPz+CqSFTlw4AB69+6tUNa/f39Mnz4dxcXFKh1j7NixKC4uxt27dxETE4PIyMgK63IlXyI1cvBi4oMe2L9/P+bMmYP79+/j6tWr2Lt3b7mEiJs3b6JPnz4AgPr16+P48eP4+uuvMXHiROBhMkW/fv2UHv/OnTuwt7fHvXulk0+VJV307dsXgYGBGm8fERERERERET1S6xIihBA4ePAgbt++jdatW6Np06a6Dkn3KpipxHTTeATaFQAedXQZHRFpUdmF4JSUFNSrV08qT0pKQkhIiEaeUyaTQSaTaeTYpDpPBznWzxyB1OwCqSwr6SZytqxFk6MzFermCBmODdsLW9cGUpmjjSWT5YhI62KTshQesy8iInXauHEjcnNzgYcD1l566aUq9yEiMhZjx47FkiVLsHXrVmzbtq3c9nv37mHOnDlYsWIF3njjDeDhALIZM2bgxRdfhK2tLYYOHYqhQ4eq9Hz169eHpaUlcnJymBBBRPTQ7du3MWTIEIUyNzc35OfnIyUlReks50/q168fhBBwdnZG586dYW9vX2FdruRLRMYmKysLX331FXx9fRXueT1u4sSJqF+/Pvbu3Qtzc3P8+OOPGD9+PHr37g1/f3+0bdsWKSkpKj1fkyZN0KFDB1y9epUJEURERERERERaVusSIt544w38888/CAoKwjvvvIOvv/4aY8aM0XVYuleNmUo48IzIeLVv3x6mpqY4cOAAXnzxReDhjbdr166hU6dOug6PNMzTQa7Yn3sGIdE5Cgmp96SiwsRLaHJ0JkI3hCFWeErlueYOWD9zBL8PiEhzUq5I/3TNzoevRSqmh0crVJFbmCFiZjf2RUSkFlOmTJFm5LW0tGRCBBHVKu3atat0++7du1FSUoIXXnhBKnvllVcwa9YsREZGYvDgwVU+x9mzZ3Hjxg3k5+cjPDwcQUFBcHBwUFqXs5YTUW1UUlICc3PF23gWFhYAgOLiYpWO8fLLL6v8fFzJl4iMTVlSWWJiotLtSUlJ2LNnD/744w+pv33llVfw3nvvITw8HHPmzKnyOfLz85GZmYni4mIcOXIEUVFR+O677yqtz/NaIiIiIiIiIvWrdQkRf/31Fy5fvgwXFxeEhobizz//ZEKEihxtLCG3MOPAMyIjVrduXYwZMwYfffQR2rRpA2dnZ7z99tvw9/fHoEGDdB0e6YC7dwDgHfCowMcHJSc/xFKsUKiXI2SIPm6BXHdPhXJbR7fSYxAR1ZS1M2BhDWwaLxW5AoiQyXF1zH4U2pb2O7FJWZgeHo3U7AKelxKR2owaNQo2Njbo0qWLrkMhItIrV69eRd26dWFrayuV1atXD3K5HFevXlXpGEePHsWuXbtgZWWF4OBgTJkypcK6nLWciGqjunXrlpuVPCUlBaampnByclL783ElXyKqbc6fPw8hBJo3by6VmZmZoWnTpoiJiVHpGJs3b8bUqVNhbm4OX19f/Pzzz5Umk/G8loiIiIiIiEgz9CohIi8vD+Hh4di9ezf69OmD119/vVyd5ORkrFy5EteuXUPDhg0xceJEuLu7S9t/++035OTklNuvZ8+e8PX1xcSJEzFjxgy0b98eGzduxKxZszTeLmPh6SBHxMxuSM0ukMo48IzIsKxatQrTp0+HEAIAEBISAhMTEyxevBjTp08HAKxcuRJvv/02WrdujYKCAnTt2hU7d+6EpaWljqMnveDgBdOpJ4Gc+1JRSlICrDe/hs5H3ypXPUfI8G+PlbB2dJPKmCRBRNXi4AVMOaHQ7yDlCkw3jUegXQHgUUeX0RGRjmRlZeHXX3/FsWPHMGbMGPTq1atcnbi4OKxbtw6JiYlo3rw5xo0bB7m89HdrYWEhwsPDlR570KBBcHR0xMiRIxEWFoYLFy5ACIGoqCjY2NhovG1ERIYgNzcXdnZ25crt7OyUXptVZuLEiZg4caJKdTlrORHVRu3bt0dUVJRC2aFDh9CyZUtYWVnpLC4iImORnp4OAOWSzJydnZGWlqbSMV588UVpxXVV8LyWiIiIiIiISDP0JiEiJiYG/fv3R8+ePXH8+HE4OTmVS4hISkpCu3btEBgYiOHDh+PPP//E6tWrcerUKdSrVw8AcOrUKenixeOCgoLg6+sLc3Nz3Lp1C0II5Obmaq19BivlisJDT2tneHryogyRoRo/fjxee+21cuVlS60DgLW1NdauXYu1a9eipKQEpqamWo6S9J6DV+l/D7l4tEKiUxQSUu8pVMtJvQf/yElo+fcbiuVMkiCi6nqi3yGi2m3nzp0YN24cBg0ahPDwcLRt27ZcQsTFixfRqVMn9OrVCyEhIVizZg1+/vlnHD58GJaWligsLMSuXbuUHr9r165wdHTEd999J5UNHToU4eHheOONN5TuQ0RU29ja2iodJJaWlgZ7e3u1Px9nLSei2mjSpEkICQnB+vXrMWbMGBw5cgRhYWFYunSprkMjIjIKZeeXWVlZcHFxkcqzsrKkCRU08Zw8ryUiIiIiIiJSP71JiPD09ERMTAycnJzQsWNHpXU+//xzWFhYYPv27bC0tMSbb76J5s2b49NPP8WyZcsAAF999VWFz5GUlITly5cjKSkJpqamOHXqFMaPH4/+/ftrrF0Gy9oZsLAGNo1XLLewLp2hlwPSiAySmZkZzMzMVK7PZAhSlbt3AKAkoSHRt6VCokRlSRIpz/0IF1ePR4XWzvy+ISIionKaNWuGy5cvw87ODhs2bFBa5/3330ebNm3wxx9/wMTEBK+88gp8fHywbt06TJgwAdbW1vjll19Ufk4vLy9kZWWpsRVERIatWbNmSE5ORkpKijR47Nq1aygoKEDTpk11HR4RkUHw9/dHXl4e7t+/DxMTE9SvXx+urq44ffo0AKBDhw5Yt24dZs6ciUmTJgEAZsyYgTfffFPHkRMRGQc/Pz8AwK1bt9CgQQOpPC4uDgMHDtRhZERERERERERUXXqTEPHkUpTK7NixA0OHDoWlpSXwcEbz5557Dr///ruUEFHVc8jlcrz99tto2rQpNm/ejFatWlVYPz8/H/n5+dLjjIwMldtj8By8ShMfcu4/Kku5UpogkXO/3ADV2CTFgSGONpbwdNDMzBlERGQ4lCVKPJkkkZR4B62OvA2XLS8p1Csxl+Pq8/tRaOsplfH7hYgUPLaamVVKFjyQotNwiEg7vL29K91eVFSE3bt3Y8mSJTAxMQEA1K1bF71798bWrVsxYcKEKp8jJSUFu3btghACFy5cwG+//YaTJ09WWL9WXz8golqpX79+sLOzw//+9z/MmTMHALB8+XLUq1cPXbt21XV4REQG4cCBAxBCKJQ9OZnNK6+8gtGjR+P+/ftwcHBQWOmXiIieTqNGjeDv748NGzZI57Bnz57F5cuXsXz5cl2HR0RERERERETVoDcJEaq4du1auYEPPj4+uHnzJoQQ0kCHipibmyMqKgo//vgjLl68iOHDh+P111+vsP6iRYswf/58tcVvcBy8qpyZ29HGEnILM0wPj1Yol1uYIWJmNw5aJSKicp5MkpD75OLZw4WQF6VJZf4md7AUK7Dip/WIFY8SInLNHbB+5gh+vxDVdkpWM/MHECGTIT4rGEAdnYZHRLp1+/Zt5OfnK8zuCAANGjTA3r17VTpGeno6du3aBVNTU3h6euLYsWPSzJHK1PrrB0RkdDZs2IADBw7gxo0bAICZM2fCwsICkyZNQrNmzWBvb481a9Zg7NixOHr0KAoLCxEVFYU//vhDmsyGiIgq5+npqUKt0lV869atq/F4iIiMTVxcHI4ePYr09HQAwN9//42UlBQ0a9YMLVq0AAB88803eO6552Bubo6GDRtiyZIlGDp0KHr16qXj6ImIiIiIiIioOgwmIUIIgYKCAtja2iqU29raQgiB/Px8WFlZVXkcb29vfPTRRyo95+zZszFjxgzpcUZGBry8Kk8QqBUem4nXE0DkeD+kmLlKZbFJWZgeHo3U7AIOWCUioip5OsixfuYIpGYXSGUWWXdQsmEdlmKFQt0cIUP0cQvkuj+6YWzr6FaaZEFEtYeS1czir0bDK3IazPIe6DQ0ItK93NxcAICNjY1CuZ2dnbStKn5+fvjll19Ufk5ePyAiY+Pm5obGjRujcePGGDBggFT+eN/6/PPPo2PHjti/fz9MTU3x008/wd3dXUcRExEREREpio+Px5YtWwAAo0aNQmxsLGJjY2FqaiolRDz77LM4cuQIfvnlF5w5cwbvv/8+3njjDR1HTkRERERERETVZTAJESYmJrCzs8ODB4oDnO7fvw+ZTKZSMkR1yWQyyGQytR/XYCmZiRcA3C2s4T7lRJWrSRAREVXE00H+RBJdHWDqSYXBzilJCbDe/Bo6H31LYd8cIcO/PVbC2tFNKiu2ckKhreIse442lkzUIzImT6xmlp+cpdNwiEh/2NvbAwDS0tIUyh88eIA6dTSzggyvHxCRsenatSu6du1aZT0vLy+MHTtWKzEREREREVXHM888g2eeeabKesHBwQgODtZKTERERERERESkGQaTEAEALVu2xLlz5xTKYmJipBkcSMOUzMSLlCulCRI595kQQURE6vXEYGcXj1ZIdIpCQuo9qSwn9R78Iyeh5d+KMzblCBkmFk7HfWEvleWaO2D9zBFMiiAiIjJyHh4ecHJywvnz5zF48GCp/Ny5c2jevLlOYyMiIiIiIiIiIiIiIiIiIiL1MqiEiNGjR+P999/H9evX4evri5s3b2LTpk34+OOPdR1a7fHE4FQiIiJtcvcOALwDFMoSfVsqJEmY5z6Ad8Rb+NnkM4V6OUKG6OMWyHV/tHKEraNb6TGJiIjIaJiYmODFF1/EunXrMGXKFNjZ2eGff/7BkSNH8P777+s6PCIiIiIiIiIiIiIiIiIiIlIjvUmIyM/Pl5ZXj42NxYMHD/Diiy/Cy8sLX3zxBQDgrbfewoEDB9CuXTsEBwfjn3/+Qffu3TF58mQdR09ERES6oixJAo1bK6xolJKUAOvNr6Hz0bcUquUIGf7tsRLWjm4K5UyUIDJcsrRYIMH2UYG1MxN6iYzMzZs3pYkRcnJy8Msvv+Cff/5BcHAwpkyZAgD473//i+PHj6NFixZo2bIlDhw4gEmTJmHQoEE6jp6IiIiIiIiIiIiIiIiIiIjUSW8SIszMzDBs2DAAkP4PAA4ODgp1wsLCcPbsWVy7dg2LFy9G69atdRIvVS02KUvhsaONJTwd5DqLh4iIapEnVjRy8WiFRKcohZUkclLvwT9yElr+/Ua53ZUlSjBJgki/FVs5IUfI4BU5DYh8VF5iLofp1JNMiiAyInZ2dujevTsASP8HgAYNGkj/dnR0xPHjx3Hw4EEkJiZi4cKFaNGihU7iJSIiIiIiIiIiIiIiIiIiIs3Rm4QIc3NzvPjiiyrVDQoKQlBQkMZjoppxtLGE3MIM08OjFcrlFmaImNmNSRFERKQTylaSSPRtqZAkgUoSJZgkQaTfbN0a4tmSryEvSpPK/E3uYClWICkpAa5MiCAyGs7OznjttdeqrGdmZoYePXpoJSYiIiIiIiIiIiIiIiIiIiLSDb1JiCDj4ekgR8TMbkjNLpDKYpOyMD08GqnZBUyIICIivaEsSQJKEiWYJEGk/zwd5Fg/c4TCOWjylRPAgRXIyC2Eq06jIyIiIiIiIqrF0uKBnPvSQ1larE7DIdKZlCuKj62duaopEREREREREZEaMCGC1OOJC3ie1s7w9OQFPCIiMkyqrCbBJAki/ePpIFdIvo1NKf23LC0WSLB9VJE3m4mIiIiIiIi0Iy0eWN4eKMyRirweXkMrtnLSaWhEWmPtDFhYA5vGK5ZbWANTTvA6FRERERERERHRU2JCBD0dXsAjIqJaQt1JEsowcYJIvYqtnJAjZPCKnAZEPraB56pERERERERE2pFzvzQZYvgawKURACA2OQuvhl7DaltPXUdHpB0OXqXXoh5bKQUpV0rvr+bc5zUqIiIiQ8BVz4iIiIj0GhMi6OlUdgHv1lGp3ColCx5I0V2cREREGvA0SRLK5AgZjg3bC1vXBlKZo42lwoz3RKS6QltP9M7/Aj+/5Af/ug9XiODNZiIiIqLai4MXiIh0x6UR4NEKAJAn0pGAdF1HRKRdDl68FkVERGSouOoZERERkd5jQgQ9vScv4ClZNcIfQIRMhkM3mwJoJJVzkCcRERkbVZIklClMvIQmR2cidEMYYsWj2fFyzR2wfuYIfl8S1VACXJDn0gLwqKPrUIiIiIhIlzh4gYiIiIiIiIhqgqueEREREek9JkSQ+ilZNeLBrXNw2jUF3247jvMiSSqXW5ghYmY3DvIkIiKjpixJohwfH5Sc/BBLsUKhOEfIEH3cArnujy6m2Tq6lR6TiGou5YriY2tnztJHREREZMw4eIGIiIiIiIiIngZXPSMiIiLSW0yIIM14YtWIsjnWlr7YqnSGXgCxSVmYHh6N1OwCJkQQERE5eMF06kmFhMKUpARYb34NnY++pVA1R8jwb4+VsHZ0q/KwxVZOKHxicA9XaKJaTclqZgBKy6acYFIEERERkbHj4AUiIiIiIiLSM7K0WCDB9lEBJ3EiIiIiIqoWJkSQVvnXtQU86ug6DCIiIv30REKhi0crJDpFISH1nlSWk3oP/pGT0PLvN1Q6ZI6QYWLhdNwX9lJZrrkD1s8cwaQIqp2UrGaGlCulCRI593mDgYiIiIiIiIiIiIiItKLYygk5QgavyGlA5GMbOIkTEREREVG1MCGCtCvlivRPq5QseCBFp+EQERHpO3fvAMA7QKEs0belQpJERcxzH8A74i38bPKZQnmOkOHQ+UCkNmhU6f5cSYKM1hPJR5LHzlUBzsBERERERERERERERESaU2jrid75X+Dnl/xKJxgFJ3EiItIErsRDRGT8mBBB2mHtXJrBvmm8VOQPIEImQ3xWMACuGkFERKQqZUkSFWrcWmEm/Ae3zsFp1xTs2L4JscKz0l25kgTVGkrOVQHOwERERERERERERERERJqVABfkubQAPDhuhohI3bgSDxFR7cGECNIOB6/Sk4jHBmTGX42GV+Q0mOU90GloRERERu2JmfCdrJ1REiHHUqyoctccIUPSJXvA2/tRIWdKIAMUm5Sl8Ljc6idKzlWlGZhuHVUs598AEREREREREREREREREZHe40o8RES1BxMiSHueGJCZn5xVaXUiIiLSAAcvmE49qTjAW4mbt27BdeebaLDrFYXyEnM5bvVejSK5k1Rm6+hWumoFkZ5xtLGE3MIM08OjFcrlFmZY9UpbONtYPlbXBZ4ej13w4qoRRKTP0uIVvstlabE6DYeIiIiIiIiIdIzXCoiIiIiU4ko8RES1AxMiSOfiH+Qi70669LjcjL1ERESkXk8kKSpjYR2IZ7d/DXlRmlTmbJKBVWJJuSSJHCHDsWF7YevaQCrj9znpA08HOSJmdkNqdoFUdj+7ABPXn8LYH04o1JVbmCFiZrdHn9vKVo3gbCFEpEtp8cDy9kBhjlTk9fD7uNjKqdJdiYiIiIiIiMgI8VoBERERERkrJv4SkYqYEEE6Yy+3AAB8uecyzu9+NEit3GA0IiIi0jpPBznWzxyhMJAcAOKzhsEs74H0uDDxEpocnYnQDWGIFZ5Sea65A9bPHMHvcw3Lzs6GjY2NrsPQa54O8nKfwyeTJGKTsjA9PBqp2QWKdVVIHiIi0rqc+6UDHIavAVwaAQBik7Pwaug1rLb1rHJ3ItIuWVoskGD7qMDamecXRERERESkXrxWQERERETGiIm/RFQNTIggnXG1lQEAVvW3Rb5D6ZJU8Q9yMWd3QvnBaERERKR1ygaSA08sI+njg5KTH2IpVigU5wgZ4u+1ARyaaj7QWujEiRN46aWXkJSUBLlcjhUrVmDkyJG6DstgKP9slyZGPI4rnRCRXnNpBHi0AgDkiXQkIL3KXYhIe4qtnJAjZPCKnAZEPrbBwrp0FSomRRARERERHq5I+jgm0NLT4LUCIiIiIjImTPwlompgQgTpjrUzYGFdemP4IX8AETIZ4rOCyw+4JCIiIv3j4AXTqScVliiMvxoNr8hpyLx8ELGPrSZh6+gGd+8AHQVqXD799FO88847mDp1Kvbv348JEyYwIeIpONpYQm5hhunh0QrlXLmMiIiIaqrQ1hO987/Azy/5wb/uwxUiUq4Am8aXnjtzkBsRUbWVW3UHHDhMRAbs4X1SbBqvWM4EWiIiIiIiIkVM/CUiFTAhgnTHwav0gp6SAZQJCXdQ+EQWH2foJSIi0lMOXgo36CyKbJCzX4Z2p2cBpx9VyxEyHBu2F7auDRR2N8bv+Ly8POzZswcymQz9+vVTWic+Ph6nT5+Go6MjOnfuDHPzR6fmmZmZyM/PL7ePXC6HjY0NGjdujFu3buHOnTuIjY1FkyZNNNoeY+fpIEfEzG5IzS6QymKTsjA9PFr1lcvS4hXOawEOzCEiIqrtEuCCPJcWgAcn/SAiehoVrroDDhwmIgOm5D4pE2iJiIiUO3PmDJYvX47r16+jZcuWWLJkia5DIiIyKoWFhfj000+xa9cupKam4osvvsDgwYN1HRYRUbUwIYJ064kBlLKs0oF/X+65jPO7CxSqcoZeIiIiw+DuHYDEcVFISL0nlRUmXkKTozMRuiEMsUIx6THX3AHrZ44wiu94IQT+85//ICwsDGZmZnByclKaEPHVV19h7ty56NSpE65fvw5ra2vs3bsXHh4eAIBp06bhr7/+Krffm2++icWLF+PNN99E3759sXLlSlhaWuLPP//USvuMmaeDvOafwbR4YHn70uU6H8eBOURERERERE9N6ao74MBhvfXkhAEpV3QZDZF+e+I+KREREZV37tw5dO3aFR988AFGjx4NR0dHXYdERGR0pk6dinPnzuHzzz9H3bp1pXELRESGhAkRpFdcbWUAgFX9bZHv8Gj2vPgHufhyz2XkxtkCOY/d8OCsu0RERHrJ3TsA8A54VODjg5KTH2IpVpSrmyNkOHQ+EKkNGkllhrpqhBACHh4eiImJwaeffoqIiIhydf7991+8++672LRpE4YNG4a8vDx06dIF06ZNw4YNGwAAP/zwQ6XP8+KLL+K7777DwIEDceXKFXTr1g3x8fEKq0yQ5sRfjUZ+cpb0WJYWC6/CHGD4mtLlOvHYwJxbRxUHgvD8lYiIiIiIqNq46o6BqGzCAGtnXUVFRERERBpWUlICIQTMzMzUfuxvvvkG48ePx+zZs9V+bCIiQyGEQEJCAuzs7GBvb6+0TlFREVJTU+Hs7AxTU1OVj52dnY1169bh0qVL8PX1VWPURETaxRFTpF+snQEL69Klrx/jD6CHDMDmJ+pz1l0iIiLD4OAF06knFQeGA3hw6xycdk3Bt9uO47xIksrlFmZY9UpbONtYSmWGkCRhamqKmTNnVlrnt99+g4+PD4YNGwYAsLKywuTJkzFhwgRkZmbCzs6uyucpKipCbGwsEhIScPnyZeTm5sLExERp3fz8fOTn50uPMzIyqt0uKpVYZAN7ISt3roqHiT0ZDq3h7vEwEejheS02jVesyPNXIiIiIiIiMlY590uTIR6fMACcHICIiIjIWJ05cwYrV67Eb7/9BgcHB9y+fbtcnYsXL2Ly5MmIioqCnZ0dXn31VXz++eewsLAAAHzxxRfYvn17uf369OmDOXPm4NKlS3j++ecxYsQIWFpa4v/+7//QqVMnrbSPiEjXMjIy8P333+N///sfYmNjMXfuXHz88cfl6s2fPx9ffvklhBCQy+X48ssvMXbsWGl78+bNUVRUVG6/devWoU6dOqhXrx5CQ0OxceNGNG/eHJ999hnq1aun8fYREakTEyJIvzh4lQ4Qe2KwZGxyFqaFRWPpi60eLYnN5bCJiIgMi5Il4J0e/n/pi61KZ3oEcD+7ABPXn8LYH04o1JVbmCFiZje9T4qoSkxMDFq0aKFQ1qJFCxQWFuLy5cto165dlcdYs2YN3n33XXz++efw8PDAb7/9VuGsO4sWLcL8+fPVFn9tlmLmiuH5X2BhPw94OT36HMY/yMWc3QlYbeYK97JCZee1PH8lIiIiIiKi2sClEeDRStdREBEREZGGzZw5E6NGjYKrqyt+/PHHctuzs7PRr18/dO3aFQkJCbh16xaeffZZmJqa4quvvgIADBkyBMHBweX2dXNzAwBYW1tj165dePvtt3H79m0MGTIEV65cgaOjoxZaSESkW8ePH8ft27fx119/YdCgQUrrrFu3Dp9//jl2796NkJAQ/Prrrxg7diwCAgLQuXNnAMDGjRshhCi3r7e3Nx48eICEhAS4uLhg3bp1+P777/F///d/2LBhg8bbR0SkTkyIIP2jZLBknkjHeZHOJbGJiIiMlL9JAmDyMOnRFogc74cUM1dpe2xSFqaHRyM1u8DgEyLS09Ph7++vUObs7AwASEtLU+kYwcHB+Pvvv1WqO3v2bMyYMUN6nJGRAS8vDsavqQS4oG6j9vD3fHROmncnHQm7D5evrOS8loiIiIiIiIiIiIiIyBjs378fALB48WKl23///XckJiZi+fLlqFOnDlxcXDB79my8//77+OSTT2BtbY3AwEAEBgZW+Bzt27eHo6MjBg0ahKKiIixevBhJSUlMiCCiWqFPnz7o06dPpXWWL1+OUaNG4ZlnngEAjBkzBsuWLcPKlSulhIjK+lkrKys0aNAAAwYMgLe3N7p164bvvvuuwvr5+fnIz8+XHmdkZNSgZURE6seECKq90uLLrUTBZZuJiIi0zNoZsLAunTX/Me4W1nCfcsIov5dlMhmysrIUysoeW1lZaeT5ZDKZ2o9bW8QmZSn9d1V1AcDRxlJ5Ak/KFcXHPAclIiIiIiIiIiIiIiIjc+TIEbRq1Qp16jyaZKpnz57IyclBdHS0NFC3MtOmTUPv3r2l5IqQkBA0atRIaV0O0iWi2qagoADR0dGYNGmSQnmXLl2wdetWlY5hamqKFStWoH379pDL5cjLy0NYWFiF9RctWoT58+c/dexEROrGhAiqndLigeXtgcIcxXILa8BIB18SERHpJQev0u/ex5MUU66UJkjk3DfK72Q/Pz9ER0crlMXFxQEAfH19dRQVPcnRxhJyCzNMD1d8r+QWZnC0sVS5bsTMbo+SIipIAOI5KBEREREREWnCq6++qjAIrGXLlliwYIFOYyIiIiKi2uPevXuoW7euQpmrq6u0TRWurq44deoULly4ABsbm3KrsD+Og3SJqLZJTU1FcXExXFxcFMpdXFyQnJys8nH69OmDO3fuICEhAfXq1YO5ecXDimfPno0ZM2ZIjzMyMuDlxfvcRKR7TIig2innfmkyxPA1gMvDzHF9HHzJVSyIiKg2cPBS/t322Cz6VilZ8ECKduPSkIEDB+L777/HjRs30LBhQwBAeHg4WrVqBQ8PD12HRw95OsgRMbMbUrMLFMqVrfqgrG5sUhamh0cjNbvgUf3KEoBuHVUs5zkfERERERERPaWXXnpJmiF37dq1cHBw0HVIRERERFTLlZSUAABMTExU3sfCwgJBQUFV1uMgXSKqbUxNTQEARUVFCuWFhYUwMzOr1rHMzMxU6jNlMhlkMlk1IyUi0jwmRFDt5tII8Gil6yiU4yoWRERUWymZRd8fQIRMhvisYAB1Kt1d13bv3o3U1FRcvnwZaWlp0nKSI0aMgIWFBYYOHYpevXphwIABmDJlCi5evIjw8HDs2rVL16HTEzwd5OWSH5667pMJQFw1goiIiGqpnJwc5OQoXvdydHSs9o06IiKq2IABA4CHAyNmzJiBH3/8UdchEREZlezsbNy9e1ehzM3NDXZ2djqLiYhIn9SrVw+nT59WKCubsdzd3V3tz8dBukRU2zg7O8PKygqJiYkK5ffu3YOnp6fO4iIi0gUmRBDpK0NZxYKIiEjdlMyiH381Gl6R02CW90CnoakiMjISN2/ehI2NDTp27IgtW7YAAIYMGQILCwuYmJhg+/btWLNmDU6ePAlHR0ecPHlSpZltyAhVtmoEz/mIiIjIiH311VdYunSp9Dg9PR13794tt7w7EZGxOnLkCFauXImtW7di2LBhSpMVDh8+jPfffx8XL15E/fr18d5772H06NHS9mnTpiEuLq7cfm+99RYGDhwoPd60aRM6d+4MZ2dnDbaIiKj2OXr0KCZOnCg9vnXrFnbs2IHevXvrNC4iIn0REhKCdevWITU1FY6OjgCAffv2wcbGRr/vi6XFl79vQ0Skh0xNTfHMM89g7969mDJlilS+e/duaZIEIqLaggkRRPpOn1exICIi0pQnZtHPT87SaTjVsXjx4irrWFpaKlyQoFruyVUjiIiIiGqBuXPnYu7cuQCAPXv2YNWqVUyGIKJa4/z585g5cyYmTpyIu3fvIi8vr1ydq1evom/fvnjnnXcQGhqKPXv2YOzYsXBwcMCgQYMAAEOHDkVGRka5fZs0aaLweMWKFfjkk0802CIiotqpd+/eiI2NBQDcvXsX3bt3R69evXQdFhGR1pSUlEj/4eHKZABgbl46HO3555/HvHnzMGHCBHzzzTeIi4vDp59+iqlTp0IuV22Fbq1LiweWty+dwPRxFtalq34TEWlRcXGxtCJZcXExMjIycPv2bchkMtStWxcAMGfOHPTp0wdffvkl+vXrh9WrV+PevXt45513dBw9EZF2MSGCDF9NMrENPXv7yfitnTmIjoiIaoX4B7nIu5MuPXa0sYSng55eMCVS4k5aLlKzCxTKKvocxyZnIU/U4PP+5MxFqPh8sTrxEBERkZoYyHUdIQT+/vtv3Lt3DyNHjpQGMzwuLS0NUVFRMDMzwzPPPANbW1tpW25uLrKzs8vtY2FhgTp16iiULV++HJMnT9ZQS4jI6BlIv/q4Zs2a4ejRowCADRs2KK2zdOlSNGjQAAsXLgQAjBs3Drt378bixYulhIiePXtW+Vznz59HcnIyunTpotY2EFEtVo1rT/rgwIEDuHjxIgYPHgxPT89y27OyshAREYH09HR07NgRgYGB0rb8/HzEx8crPa6fnx9MTEykx6tXr8Zrr72mUKYRyl5/Q7/3TUQGa8CAAdi3b5/02MrKCgBw+fJl+Pn5QS6XY8+ePXj77bfRuHFj2NnZ4fXXX8d///tfHUZdhZz7pckQw9eUTmBaRo+/64jIeN29excdO3aUHv/+++/4/fff0alTJ+l6Qvfu3bFlyxZ8/vnnWLlyJQIDA7F//340bNhQh5ETET2kxWu3RpsQcffuXemGm7+/f7ntSUlJqFu3ruYvSJDmWDuXZmBvGl+z/Q0xe7uiNltYA1NO8McXEREZLXu5BQDgyz2XcX73o8HbcgszRMzsxsHbZBDupOWi91cHkFtYrFD+5Oc4KSsfrgCmhUXj/GMJESp93iubueiJ80VV4yEiIiI1MaDrOt9//z0+++wzCCFw7do1PPvsswrJDni4qsMLL7yAxo0bo6CgALdu3cKff/6JkJAQAMC6devw0UcflTt2q1atEBERIT2+desWLly4gL59+2qhZURkVAyoX62Jw4cPl0t46NOnD6ZMmYLCwkJYWFiodJzly5fjrbfeqrJefn4+8vPzpcfKVp4gIqrOtSdd2759O959911YW1vj1KlT8Pf3L5cQcfnyZfTs2RPu7u7w9vbG5MmTMW/ePLz33nsAgIsXL2LkyJFKj3/hwgVYWloCD2dE//HHH3Hs2DHNNqqi1x8Geu+biAze7t27q6wTEBCAXbt2aSUetXJpBHi00nUURFTL1a9fH7dv366y3qBBg6TJEwzak8m/TPwlMlw6uHZrtAkRc+bMwcGDB3H9+nUUFBRIM5jFxsZi4MCBSE1NhVwux/bt29GiRQtdh0s14eBV+ofx5AwYqjLE7G1lbU65Utpp5Nw3vPYQERGpyNVWBgBY+mIr5LmUnrvFJmVheng0UrMLOHCbDEJqdgFyC4uxZFQr+LuWDihU9jnOyC2EK4D/9A1E3UbtK6ynlLKZiyo4X1Q1HiIiIlITA7quY2Jigp07d+LcuXN47rnnym3PycnBmDFjMHHiRCxevBgA8Oabb2LMmDG4evUqzM3NMXnyZJVWfVi1ahXeeOMNTlxDRNVnQP1qTSQkJMDV1VWhzNXVFYWFhbh//z7c3d1VOs7w4cPRoUOHKustWrQI8+fPr3G8RFRLVOPak65ZWlrijz/+gJOTE+rVq6e0zoQJE9CyZUts374dpqam2LBhA1588UUMHjwYTZo0QatWrRAbG1vlc23evBnBwcFwc3PTQEseU9Gs5TDQe99ERERERGUqS75m4i+R4dHBtVujTYj44YcfAKDczGUffPAB3nzzTbz33ntYt24dZsyYgb179+ooSnpqDl6178JObWwzERHRQ/51bQGPOroOg+ip+Lvaorln1Z9jLyc5/FWop1Q1Zi5SNR4i0oInZ74BBzQQGR0Dua4zbtw4AMC5c+eUbt+zZw9SUlIwffp0qWzmzJlYu3YtoqKi0K1bN5Wep6CgAD///DNOnTpVaT3OWk5EFTKQfrWmTE1NlT4WQqh8jN69e6tUb/bs2ZgxY4b0OCMjA15exvvaEtFTMoBZs/v06QMASExMVLr97t27OHDgAP7880+pfx05ciTc3Nzw+++/Y968eSo/14oVK/Dhhx9WWU9t57UG8PoTEREREVVLRcm/vE9GZLi0fO1WZwkRN27cwN69e9G0aVM888wz5bYLIXDo0CFcu3YNDRs2RNeuXRUu/N68eRNFRUXl9nN3dy+XBPG4qKgofPvttwCAMWPG4O2331Zbm4iIiIiIiIjI8KWmpuKnn35SKAsODkZISIjmnrSymW80tGwoEVFNxcTEwMXFRWF28iZNmsDCwgIxMTEqJ0Ts2LEDvXr1qnImXc5aTkS1kaurK5KTkxXKkpOTYWZmBmdn9c+MKJPJIJPJ1H5cIiJ9deHCBQBA06ZNpTITExM0btwY58+fV/k48fHxKC4uRs+ePausy/NaIiIiIqIqMPmXiGpI6wkR8fHxmDBhAi5duoTMzEyMGjWqXEJEQUEBhg0bhujoaHTp0gVRUVFo3Lgxtm3bBisrKwDASy+9VO5CMAAsXboUgwYNqvD509PTUadO6eynFhYWMDU1RV5ennRcIiJ6evn5+Th58iTMzMzQrl07WFhY6DokIiIiIiKVFRYW4ubNm9LjjRs3YtmyZZp9UmUz32h42VAioppKT0+Hk5NTuXJHR0ekpaWpfJxhw4Zh2LBhVdbjrOVEVBt16tQJBw8eVCiLjIxE69atYWlpqbO4iIiMRdnqDA4ODgrljo6O1Vq5wcvLq1x/XRGe1xIRERERERFphtYTIgoLCzFlyhQMGDAAnTt3VlpnxYoVOHbsGGJiYuDp6Ym7d++iRYsWWLp0KWbNmgUAOHr0aI2e393dHbdu3UJgYCBSU1Nhbm7OZAgiIjW6f/8+OnToAGdnZxQVFSEvLw/Hjh2DnZ2drkMjIiIiIlKJq6srlixZAgBISUnB1q1b8eyzz2rnyTnzDREZAJlMhqysrHLlWVlZGrnWylnLiag2mjJlCoKDg7Fs2TK89dZb2LdvHzZs2IC1a9fqOjQiIqMgl8sBAJmZmXBxcZHKMzIyNHZPi+e1RERERERERJqh9YQIX19f+Pr6VlonLCwMw4YNg6enJwCgXr16GDlyJMLCwqSEiKqkpKQgLS0NQghcu3YNjo6OcHV1xbBhwzBnzhx88MEHWLFiBZ577rkKj5Gfn4/8/HzpcXVmgiDNiE1SvNHqaGMJTwe5ep8k5UrN9rN2Vj5jZ1p86WyeVdV9sl5N46jseZWpKG51P7cqbdZUPNpgTG15SocOHUJgYCC2b98OAOjatStOnDiBXr166To0IjIGj30/WqVkwQMpOg2HSF0eP89NfpALfwCytFggwRZ4+HlvZnIDyVcsEZvy6PzXXm4BV9vHbqI+/BuJTc5CnkiX9vUHEH81GvnJis9T0d+QSufdBnD+cyctF6nZBQpl6v4NkXjrKrJS7ymU2Tq6wd07QG3PoRFK3r/EIhukmLmWq6qR311PKTExEWvXrsWxY8cwceJEpStFnjt3DqtWrUJiYiKaN2+OadOmwdHREXj4e3/lypVKjz169GjUrVtXerx27VqMHj0a5uZav4TyyOO/D5/2tyIRkRr4+fkhOTlZYeXdlJQU5OTkVHntl4hIK548Z9Kz3yoA4OLigry8POTl5cHExAS2trZwc3PDtWvXAACtWrXChg0bMHPmTEyfPh3Ozs5YuHAhRo8erevQiUjT1N2HPc01HHXfv9QjAQGl125u3LiBhg0bSuU3b96sdAwBEZHWGMA5LRGRQVN2bsu+VnuM+LcGEemGDu/mV+z8+fMYOXKkQlnTpk2xbt06lY+xfPlyrF+/HvXq1cOgQYMwYsQIfPbZZ1iwYAHee+89vPnmm2jdujW++eabCo+xaNEizJ8//6naQurhaGMJuYUZpodHK5TLLcwQMbObegbnWDsDFtbApvE129/CGphyQvGkKC0eWN4eKMypvG5l9aydqx9LRcdTNe6noY42qzMebTCwtqSlpWH9+vW4du0aZs6cqXQp3gsXLmDTpk3Izc1Fjx490Lt3b2lbUlISTpw4UW4fc3Nz9O/fHx06dMD8+fPx448/orCwEFlZWWjTpo3G20VERk7J97Q/gAiZDPFZwQDq6DQ8oppSdp7rgRREyGTwipwGRJaW+QPYLgNwoOpj5ggZXg29hgSklz/eY8r+hjKKO0t/QyqfdxvA+c+dtFz0/uoAcguLFcrV+Rsi8dZV2K8NgbtJvkJ5jpAhcVyU/iZFVPD+2QsZhud/gQS4KJSr9XeXGmzevBn/93//hzFjxiAyMhL9+vUrV+fUqVPo0qULxowZgyFDhmD16tX4/fff8c8//8Da2holJSW4efOm0uMXFDxKoikpKcGaNWuwf/9+jbapQhX9Tq3pb0UiIjXp27cvSkpK8Oeff2LUqFEAgPDwcMjlcvTs2VPX4RFRbVbZ+ZOe/FYpExcXByGEQpmpqanC42HDhmHYsGEoLCyEhYWFliMkIq3TRB/2NNdw1H3/Us/4+fmhadOm+O2336Rz2GPHjiE2NhZDhgzRdXhEVJsZ0DktEZFBqmyMIPta7TDy3xpEpBt6lxAhhEBWVhYcHBwUyh0dHVFQUKAw61hl5s2bh3nz5pUrt7a2xnfffadSLLNnz8aMGTOkxxkZGUoHDpPmeTrIETGzm8LsrrFJWZgeHo3U7AL1DMxx8Co9oVFlVYUnpVwpPUnKua94QpRzv/SLe/gawKVRxXWV1cNTZJ1WdDxV434aT9NmTcSjDQbUlm+//RaLFy9GSEgI/vjjD4wZM6Zcv7Z161aMHDkSL7/8MlxcXDBy5EhMmjQJixYtAh7eqFu1alW5Y8vlcvTv3x9WVlZo2LAh1qxZg+LiYjRp0oTL/xLR01PyPR1/NRpekdNglvdAp6ERPQ1l57kAkFHcGdbm2QplSVn5yMgtlB7HP8jFl3suY+mLreBft3QlidjkLLwaeg3vjeoNf1fbR3Wzgsv9rcjSYuEVOU3heVQ+7zaA85/U7ALkFhZjyahW0muh7t8QWan34G6Sj3/afAYHn+YAgLS4c2h3ehYSUu8B+poQoeT9K+tTF/bzQN1G7aWqav/dpQYhISG4fv06LCwsKlzl4YMPPkCPHj2wevVq4OFAsvr162P16tWYPn065HI5lixZUuVz7dixA40bN4a3t7fa26GSin6ncoYiItKw06dP48qVK/jnn38AABs3boRMJkPXrl3h4eGB+vXr47333sOECRNw48YNFBYWYtGiRViwYEG567pERFql7PxJz36rlLGxsVG5LpMhiGoJTfRhT3MNR933L7Xs6tWr2LdvHzIyMgAA27ZtQ2xsLNq2bYvg4GAAwLJlyzBgwAAUFxejYcOGWLVqFcaMGYNnnnlGx9ETUa1mQOe0REQGqaJ7L+xrtcfAf2sQkX7Su4QIExMTyGQyZGZmKpRnZGTA1NRUq4NqZTIZB/HqEU8HueYH4Dh4aeZL1aUR4NFKffXU/byaoKs265IBtOWZZ55BbGwsbt26hT/++KPc9pKSEkyePBlvv/02vvzyS+DhgLMRI0bgtddeQ2BgIIKDg7Ft27YKn2P16tXw9vbGpk2bAADjxo3DL7/8grfeekuDLSOiWuGJ7+n85CydhkOkLsrPc8uveuL68L8yeXfScX53AfJcWgAepfXzRDoSkA5/V1s096xT6fGQYCutQFF1PBUwgPOf8q+F+jn4NId/UOmN8lgAOK3Rp1Ofx96/sj7Vy0kOfw2/Xk/L1dW10u0FBQWIjIxUSJawt7dHnz59sGvXLkyfPl3l51q+fDkmT55cZb38/Hzk5z9aKaRssIVaaOp3KhFRJc6ePYvdu3cDAEaNGoWdO3cCAAIDA+Hh4QEAWLhwIdq1a4cdO3bA1NQUGzduxIABA3QaNxERwPMnIjJwur5Xqe59dSg1NRXR0aWroE6YMAF5eXmIjo6WzmcBoGfPnjh79ixCQ0ORmpqKJUuW4Pnnn9dh1ERED/GclohIs9jP6gcD/a1BRPpJ7xIiACAgIAA3btxQKLtx4wb8/PxgYmKis7iIiAxdmzZtKt1+6tQp3L59G2PGjJHKBg8ejDp16uCvv/7Cu+++W+VzNGjQAD/88APatm2L4uJi7Nu3Dy+++GKF9TU6eIyIiIiIap3bt2+jsLAQ9evXVyj38vKSBvSqIj8/Hy1btsTAgQOrrLto0SLMnz+/RvESEemj119/Ha+//nqV9Z577jk899xzWomJiIiIiKg62rdvj/bt21dZr3HjxvxNT0RERERERGTgTHUdgDJDhgzBn3/+iZycHABAbm4uNm7ciCFDhug6NCIio3blyhUAgJ+fn1RmZmYGHx8fXL16VaVjjBo1Cu+//z527dqFiIgIfP755+jTp0+F9RctWoQ6depI/3l5MQObiIiIiGquoKAAAGBtba1Qbm1tLW1ThUwmw2effQYzM7Mq686ePRvp6enSf/Hx8TWInIiIiIiIiIiIiIiIiIiIiKpL6ytEFBUV4fvvvwcAJCUl4fz581i1ahWcnJzwwgsvAAD+85//YMOGDejVqxeGDBmC7du3w8TEBLNmzdJ2uEREtUpZIpqtra1Cub29vbRNFarOJImHg8dmzJghPc7IyGBSBBERERHVmIODAwDgwYMHCuX379+XtqmbTCaDTCbTyLGJiIiIiIiIiIiIiIiIiIioYlpfIaK4uBjR0dGIjo5G3759ERgYiOjoaFy+fFmq4+joiH/++Qcvvvgi7t69ixEjRuD06dOoW7eutsMlIqpV7OzsAADp6ekK5ampqdI2dZPJZLC3t1f4j4iIiIioptzd3eHq6oqzZ88qlEdHRyMoKEhncREREREREREREREREREREZH6aX2FCJlMhlWrVlVZz97eHtOmTdNKTEREVKpp06YAgEuXLqFjx44AgPz8fNy4cQPjx4/XcXRERERERKp59dVX8cMPP2Dy5MlwcXFBZGQkTpw4gUWLFuk6NCIiIiIiIiIiIiIiIiIiIlIjrSdEGDIhBAAgIyND5X0ys7KRkS9K/1+N/ahqWZkZKMnPQVZmBjIyTHQbTGYWkC9K///4+6ysXNWyp3luVY/3NM/7tPGo+3XQJTW1payPKOtrdKFly5Zo3LgxVq1aJSVErF+/HgUFBRg2bJhWYqhuX8t+lqj2qsnfvz70tbrGc1rtUHauqu7z16d+DhXPV5Qe0wDO5bTxHij72zCIvxcl71VFcVf3NdNGPxsbG4vp06cDALKzs7Fq1Srs2rULzzzzDN5//30AwMcff4zo6GgEBgYiMDAQZ86cwbx589CzZ0+NxfW4mvS1T/M3pFe/j4kIqM7fpYFeP9A1dfWz7D+JDMNT/62yr60RTZ7Tsv8lY6GVz/LTXm/Rxn1E9rM1Vu2+thqvNftaIsPA6weaxXNaItLEvdMy7GdL8Z4YEenLOa2JqO09cjXcvn0bXl5eug6DiIxcfHw86tevr5FjHz58GH/88QdSU1Px888/46WXXoKrqyuGDh2KHj16AACOHj2KAQMGoE2bNnBxccG2bdvw+eefY+rUqRqJ6Unsa4lIGzTZ1+o79rNEpA2a7GfT09Nx6NChcuX16tVD27ZtFcpiYmKQmJiIpk2bwtPTUyPxKMO+loi0gee07GeJSPPY17KvJSLNqs39LNjXEpGW1Oa+lv0sEWlDbe5nwb6WiLRElb6WCRHVUFJSgoSEBNjZ2cHERLXssoyMDHh5eSE+Ph729vYaj9HY8fVUL76e6qOO11IIgczMTHh4eMDU1FTtMQLA2bNnERkZWa68a9euaNOmjfQ4OTkZu3btQm5uLrp27YrGjRtrJB5lqtvXGtvn2Jjaw7bop9reFm30tfqO57TVx/az/Wy/6u1nP1uqJn2tOhn759aY28e2GS5tto99rWb6WWP/jIJtNBpso3awr9X+Oa0+vO/GiK+r5vC1fTrsZ0uxr1UvY28fakEb2T71Yl+rvX7W2D+7qCVtRC1pZ21oI7TUTvazpXR5T6w2fJ6NvY3G3j7UgjZqun3V6WvN1f7sRszU1LTG2Xz29vZG+WHWFb6e6sXXU32e9rWsU6eOWuN5UlBQEIKCgqqsV7duXbzyyisajaUiNe1rje1zbEztYVv0U21ui6b7Wn3Hc9qaY/vZfrZftfbX9n4WT9nXqpOxf26NuX1sm+HSVvtqe1+ryX7W2D+jYBuNBtuoeexrdXNOq+v33VjxddUcvrY1V9v7WbCv1Rhjbx9qQRvZPvWp7X2ttvtZY//sopa0EbWknbWhjdBCO2t7Pws9uSdWGz7Pxt5GY28fakEbNdk+Vfva2puaRkREREREREREREREREREREREREREREREBosJEUREREREREREREREREREREREREREREREZHCYEKFhMpkM8+bNg0wm03UoRoGvp3rx9VQfvpa6Y2yvvTG1h23RT2wL1URtf63Zfraf7a+97TdUxv6+GXP72DbDZeztqw1qw3vINhoHtpGMFd93zeDrqjl8bckQGfvn1tjbh1rQRraPDFVteG9rQxtRS9pZG9qIWtTO2q42vM/G3kZjbx9qQRv1qX0mQgih6yCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiqgytEEBERERERERERERERERERERERERERERGRwWFCBBERERERERERERERERERERERERERERERGRwmRBARERERERERERERERERERERERERERERkcEx13UAxuzatWuIiYmBq6srOnbsCFNT5p88KS4uDkePHi1XPmLECFhYWCiUXbx4EZcuXYKXlxfatm0LExOTcvupUsfYFBQUYO/evSgpKcHgwYOV1klLS0NUVBTMzMzwzDPPwNbWVmN1DF12djb27t0LOzs79OrVS2FbQUEBNm3aVG6fzp07w9vbW6EsKSkJx44dg42NDUJCQmBlZVVuP1XqUOWKi4tx7NgxJCcnIygoCA0bNtR1SEplZmbi9OnTyM/PR8uWLeHu7q603pkzZ3Dz5k34+/ujRYsWNa6jDSUlJdi4cSOsra0xaNCgctuvXr2K8+fPw93dHe3bt1f6HahKHU0TQuD06dNISEhA27Zt4eHhUa5OQkICTp48iTp16qBz586wtLSsUR1Ni4+Px7lz52BqaooWLVoobYu+9vWJiYk4ePAgGjVqhFatWimto67PlD587gzN/fv3cfToUchkMoSEhMDa2lrXIWlMamoqTp8+DRMTEwQFBcHZ2blcHSEETpw4gbt376Jp06Zo1KiRTmLVpISEBBw8eBCBgYFo3bq1wraSkhIcP34c9+7dQ/PmzeHv76+zODUhNTUVx44dg62tLTp27FjuN0lJSQmOHTuGpKQktGjRAn5+fjqLVd2ysrJw6tQppKeno0GDBmjZsmW5OoZy7lXbpaWlYd++fXB3d0dISIhK+9y+fRunTp2Cg4MDOnfuXO6zry/y8vIQFRWF7OxsdOzYEa6urpXW37NnDx48eKBQ1rBhQ3To0EHDkVYuJiYGsbGxaNCgQbl+Vp376EJ1zhtKSkrw+++/lytv3749fH19NRxpzZw8eRLXrl1Dv3794OjoWGV99puGIzMzE3v37oWzszO6deum0j53797FyZMnYWdnh5CQEJ38DqyO6vT16enp2LlzZ7nynj17Vtn3asPNmzcRHR0NFxcXdOrUCWZmZhrZR5eqc2/h3r17iIyMLFc+aNAg2NnZaTjSmrt79y4OHTqExo0bKz33VObKlSu4cOEC6tWrh/bt29eK6/+1XWxsLP755x+9Pj/QZ0II/PPPP7h9+zaaNGmCxo0b6zoko3LmzBlcvnxZb74fiSpTWFiIiIgIFBQUYOjQoSrtk5GRgaioKABASEgI7O3tNRzl0zl79iyuX78OX19fBAUFVVr3ypUrOH36tEKZqakpXnjhBQ1HWbX09HRERUXBxMQEzzzzjErnczXZR5eqc6/z4sWLOHv2rEKZhYUFRowYoeEoay4/Px979+6FiYmJ0nuoypTdmzM3N0dISIhRjsMwVkVFRYiIiEBOTg6GDx+u0j5ZWVk4fPgwiouLERISAgcHB43H+bSqc3+17B7TkwYPHgwbGxsNR1q1oqIiHDlyBKmpqWjTpg28vLw0so+uVWc8UnXGQumbsu/0Dh06qHztVV/G3NDTS0pKwt9//w1/f3+0adNGpX0MaTxvfn4+oqKikJWVhfbt21c41qzMvn37kJycrFDm4+ODTp06aTjSql2/fh3//vsvXF1d0aFDB5WuzdZkH10pKChAVFQUMjMzERwcjHr16lVaf//+/UhKSlIo8/LyUvm+ri6kpKQgMjISDRo0QHBwsEr73LhxA2fPnkXdunXRsWNH7byHgjRizpw5wtraWvTu3Vt4enqK4OBg8eDBA12HpXfWr18vrKysxKhRoxT+y87OluqUlJSI8ePHCzs7O9G3b19Rt25d0adPH5GTk1OtOsboww8/FJ6ensLHx0f4+PgorbN7925Rp04d0aFDB9G6dWvh7OwsDh8+rJE6hqygoEC8/fbbol69esLT01OEhISUq5OcnCwAiH79+il8Xo8cOaJQ75dffhE2NjaiS5cuokmTJsLLy0tcuHCh2nWocsnJyaJVq1bC29tb9OrVS1hbW4sFCxboOqxyPv74Y+Hp6Sm6desmevXqJeRyufjkk08U6uTn54shQ4YIZ2dn0bdvX1GnTh0xevRoUVxcXK062vTxxx8LS0tL4efnV27bzJkzhY2NjejTp4+oV6+e6Ny5s0hPT692HU27ceOGaN26tfDy8hLDhg0TjRs3FitXrlSos3LlSmFtbS26d+8u/P39hb+/v7hx40a162jajBkzhFwuF/369RO9evUSVlZW4uOPP1aoo499fXx8vHjhhReEp6ensLe3FzNnzlRaT12fKX343BmajRs3CltbWxESEiKaN28u6tWrJ86cOaPrsDTi7bffFh4eHqJ3796iS5cuwsbGplyfkJGRIbp06SLq1asn+vTpI2xsbMT06dN1FrMmFBYWik6dOglLS0sxbdo0hW1paWmiY8eO0utkbW0tZs2apbNY1W3JkiXCxsZGdOvWTfTr108EBweLhIQEafuDBw9EcHCw8PT0lNo/Z84cncasLjt37hSOjo6iTZs2YsiQIcLFxUV07dpVZGRkSHUM5dyrNktLSxPjxo0T9erVE66urmLEiBEq7bdkyRIhl8tFjx49hK+vr2jcuLG4ffu2xuOtrgsXLggvLy/RpEkT0aVLF2FtbS1++eWXSvdp27ataNmypcLvtxUrVmgt5icVFRWJ0aNHCwcHB9G3b1/h7OwshgwZIvLz89W6j65U97whNzdXABC9evVSeI/279+v1bhVsW3bNtGmTRsREBAgAIiTJ09WuQ/7TcOQnZ0tJk6cKNzd3YW7u7vo16+fSvutXr1a+h0YEBAgfH19xbVr1zQeb01Vt6+PiYkRAMTQoUMV/j7Pnz+v1biVWbhwobC2tha9evUS3t7eomXLluLevXtq30eXqntvYe/evQJAuWvsd+7c0WrcqoqLixMjR44U9evXF3Z2dir/ppg+fbqwtbUVffr0Ee7u7qJLly4K56tkXM6cOSP69u0r/Pz8hKmpqVizZo2uQzI42dnZomfPnsLNzU307dtX2NnZiUmTJuk6LKOwd+9e0aFDB+ncMDIyUtchEVVqwYIFwsvLSzRo0EC4ubmptE9kZKRwdHQU7dq1E8HBwcLR0VFvP+uFhYVi5MiRwtHRUfTt21c4OjqKkSNHisLCwgr3+eabb0SdOnUUzp1efvllrcatTEREhHBwcBDt27cXbdu2FU5OTuLAgQNq30dXanKvc9GiRcLR0VHhvRo7dqxW466O2bNnC09PT+Ht7a30HqoyO3bsEPb29qJjx46iVatWom7duuLo0aMaj5We3uLFi4W3t7do2LChqFOnjkr7HD58WDg7O4s2bdqIDh06iDp16ojdu3drPNanUd37q1u3bhWmpqblfqMmJSVpNW5l7ty5I5o2bSoaNmwoevbsKeRyufjyyy/Vvo+uVXc8kqpjofTJqVOnRO/evUVAQIAwMTER69atq3IffRtzQzWXkJAgXnrpJeHh4SEcHBzElClTVNrPkMbzXr16VTRo0EAEBgaKrl27Cmtra7F27dpK9wkJCRHNmjVT+DteunSp1mKuyEcffSS97vXr1xdt27YVKSkpat9HV65duyZ8fX1FQECA6N69u7C2tharV6+udJ9u3bqJpk2bKrxX33zzjdZiro6kpCTxyiuvCA8PD+Hk5CTGjRun0n4LFiyQrsl7eXmJ1q1bi+TkZI3Hy4QIDYiMjBQAxMGDB4UQQqSnp4tGjRqJiRMn6jo0vbN+/foqL7yEhoYKmUwmYmJihBBC3L17V7i7uysM9FSljjH6+uuvRWJiovjkk0+UJkRkZ2eLunXrKtxUGjdunGjQoIF0EUhddQxddna2WLp0qUhNTRUTJkyoNCGisgEdCQkJQi6Xi2XLlgkhhCguLhYDBw4UHTt2rFYdqtrYsWNFy5YtRVZWlhBCiF27dgkAeneBaM2aNSIzM1N6vGPHDgFAREVFSWVffPGFcHFxEfHx8UIIIS5fviysra3F999/X6062nLw4EHRsGFDMXXq1HIX83bu3ClMTU3FsWPHhHg4eLRhw4YKA4ZVqaNpBQUFolmzZuLZZ5+VBpMVFhaKHTt2SHWuXLkizM3Nxfr166V9unTpIvr371+tOpr277//CgAKsa9fv14AELdu3RJCj/v6c+fOibCwMJGfny+CgoKUJkSo6zOlD587Q3P//n1hb28vFi1aJJWNHDlSNG/eXKdxacrKlStFXl6e9PiHH34Qpqam4sqVK1LZzJkzRcOGDcX9+/eFEEIcP35cmJqaim3btukkZk2YPXu2GD16tAgKCiqXEDFlyhTRqFEjkZaWJoQQ4tChQwKAiIiI0FG06rNp0yZhZmYm9uzZI5XFxMQoDGycMGGCaNy4sTToqux3399//62TmNWpUaNG4o033pAeJyUlCTs7O/H1119LZYZy7lWbJSQkiDVr1oisrCwxdOhQlRIizp07J0xNTcWGDRuEEELk5eWJ9u3bi2HDhmkh4urp0KGDGDx4sHRz4ptvvhFyuVwhcelJbdu2Vfge07U1a9YIW1tb6bslLi5OODk5VXoDrSb76EJNzhvKEiIOHTqkpShr7vfffxf//POPuHjxosoJEew3DcODBw/EihUrREZGhhg9erRKCRHXrl0TFhYW0o3WwsJC0b17d9G7d28tRFx9NenryxIi7t69q8VIq3bixAkBQOzcuVOIh79jW7ZsKcaMGaPWfXSpJvcWyhIiDMW///4rwsPDpWszqiREbN26VZiZmUn9b0pKivD29hb/+c9/tBAx6cLff/8tdu3aJUpKSoRMJmNCRA3MmTNH1K9fXxp4dvr0aWFubi7++OMPXYdm8DZv3iyOHDki4uPjmRBBBmHJkiUiISFBfPHFFyolROTl5QkPDw+Fa4NTpkwRnp6eepmcv2zZMuHg4CBNVHXt2jVhb28vvvvuuwr3+eabb0SzZs20GGXVcnJyhLu7u8J9kgkTJggvLy9RUFCgtn10qSb3OhctWiTatm2rxSifzldffSXu3bsn5s2bp1JCRGZmpnB2dlaYeOe1114Tfn5+HKBrAJYtWyZu374tli1bplJCRGFhofDx8RETJkyQymbOnClcXV0VJo3VJzW5v7p161Yhk8m0GKXqRowYIdq3by/dD9ywYYMwMTER//77r1r30aWajEdSZSyUvtm/f7/YvXu3KCkpEWZmZiolROjTmBt6OpcvXxa//PKLyMvLEx06dFApIcLQxvN269ZN9O3bVxQVFQnxcCyDpaWliIuLq3CfkJAQMW/ePC1GWbWy8QRlE2FlZGSIxo0bizfffFOt++hSr169RK9evaRxVWvWrBEWFhbi+vXrFe7TrVs3g5l48dq1a+Knn34Subm5olu3biolRBw5ckQAEHv37hVCCJGVlSWaNWsmXnvtNY3HazhXyQ3IuHHjRHBwsELZ559/Luzt7fmj5Qnr168Xzs7OYteuXWLXrl1KZyYbNGiQGDx4sELZjBkzhL+/f7XqGLOKEiI2b94sTExMFG5eXrhwQWHglrrqGJOqEiLWrFkj/vzzT6Wz4i1btkzY2toqDKjcs2ePACCuXr2qch2qXH5+vpDL5WL58uUK5U2bNhVTp07VWVyqejL2oKCgcrNzvfDCC6J79+7VqqMN9+/fFz4+PuLAgQNizpw55S7mjR49WnTp0kWhbMGCBcLFxaVadTRtw4YNAkClM3h+8sknwtXVVeG7+/fffxcmJibSjURV6mjayZMnBQCFWR2ioqIU+hRD6OsrSohQ12dKHz53hmbdunXC0tJSYcbNsh8u0dHROo1NG9LT0wUAsXHjRqnMzc1NzJ8/X6Fe165dxYsvvqiDCNUvIiJCNGzYUKSlpZVLiCgpKREODg7is88+U9inffv2Wvnhqmlt27YVL7zwQoXbi4uLhb29vfjqq68Uytu0aaO3F1+qw8fHR2HW8pKSEuHp6SkWL14shBGce9VGqiZEzJkzR3h5eSmU/fzzz8LMzExKftIHV69eFQDEvn37pLKcnBxhY2NT6QCHtm3bikmTJolNmzaJ48ePi9zcXC1FrFy3bt3ESy+9pFD21ltviVatWql1H12oyXlDWULEsmXLxJYtW8S///4rSkpKtBh19amaEMF+0zCpmhCxaNEi4eLiIt2MEg+TK01MTCpN0tKVmvT1ZQkRGzZsEFu3blVIEtaladOmiSZNmiiUrVixQlhZWVXYx9dkH12qyb2FsoSIiIgIsX37dq2vWvk0VE2IGDVqlOjRo4dC2UcffSTc3d01GB3pCyZE1IyPj4+YPXu2Qlnfvn31MvnZUDEhggyNqgkRO3fuFADEzZs3pbLr168LAGLXrl0ajrL6OnToIF5//XWFsldffbXSgZ/ffPON8PPzE9u3bxd79uwRiYmJWoi0clu3bhUApEGa4uGkXJVNSFOTfXSpJvc6Fy1aJJo2bSq2bdsm9u7dq9crvT1O1YSIDRs2CFNTU4V7imUToWlyBXdSL1UTIsoG416+fFkqu337tjAxMRFbtmzRcJQ1U5P7q1u3bhWWlpZiz549YseOHZUO3tWmjIwMYW5uLn766SeFch8fH/H++++rbR9dq8l4JFXGQukzVRMi9GXMDamXqgkRhjSet+y35vbt26WygoIC4eDgUOlkWSEhIWLcuHFi06ZN4ujRoyInJ0dLEVdswoQJonXr1gplX3/9tbC1ta1wYtaa7KMrCQkJAoD4888/pbLCwkLh5OQk3WdXplu3buK1116T3it9TYx8kqoJEVOmTBEtWrRQKPv222+FtbW1xpPsTUFqFxMTg+bNmyuUtWjRAhkZGbh165bO4tJXOTk5+Oyzz7Bw4UL4+vpi+vTpEEJI2yt6PWNjY5Gbm6tyndooJiYGLi4ucHd3l8qaNGkCCwsLxMTEqLVObWFiYoKVK1di5cqVCAkJQY8ePXDv3j1pe0xMDAICAiCTyaSyFi1aAADOnTunch2q3LVr15Cbm6v0717fP5NHjhxRiL2kpATnz5+vtC2q1NGWN954Ay+//DK6du2qdHtF/XFKSgoSExNVrqNpBw8ehL+/Pzw9PREREYE9e/Yo/C2XxdmsWTOYmj46XWrRogWEEDh//rzKdTStXbt2mDp1Kl599VX873//w4oVKzBx4kTMnTsX/v7+UpyG2ter6zOlD587QxMTEwMfHx/Y2dlJZWXfV/re16pDREQEAKBZs2YAgOTkZNy7d08v+mJNSEpKwtixY/HTTz+hTp065bbfvn0baWlpRtn+rKwsnD59Gn379sX169exZcsWnDx5EsXFxVKduLg4ZGRkGGX7AWDZsmX44YcfsHDhQqxbtw7PP/88/P39MWHCBMDAz72ochV9PxYXF+PixYs6i+tJZZ+zx2OVy+Xw9/ev8jO4Y8cOrF27FqNGjUJgYCAOHTqk8XgrUtHrff78eYXrIE+7jy48zXnD999/j//973/o3r07nnnmGdy+fVvj8Woa+03jFhMTgyZNmsDMzEwq0/bvwOqoaV9vZmaGL7/8Et9++y1at26NoUOHIjMzUwsRV6yituTl5SE2NlZt++hSTe8tWFhYYP78+fjyyy/RuHFjvPrqqygsLNRCxNpR0euSmJiIlJQUncVFpK8yMzMRFxfHcxEiqpGYmBjY2dnBx8dHKmvYsCFsbGz0sg+p6Dyhqljv3buHJUuWYN68efD29sbHH3+s4UgrFxMTA0dHR9SvX18qCwgIgJWVVYVtqck+uvI09zrj4+Px7bff4sMPP4S3tzcWLVqk4Wi1JyYmBm5ubqhbt65UVnbfUd/eQ3p6MTExsLS0RKNGjaQyT09PODk56e37XdP7qyYmJvjvf/+Lzz//HAEBAXjzzTcV7rnowqVLl1BUVFSuPc2bN6/w9a/JPrpW0/FIVY2FMnT6NOaGdMOQxvMquydmYWGBwMDAKj+ve/bswdq1azF69GgEBARg//79Go+3MhW97llZWbh586ba9tGVsn718XjNzc3RpEmTKt+riIgIrF27FmPGjEFAQIA0PsUYVPQe5uTk4Pr16xp9bnONHr2WSk9Ph5OTk0KZs7MzACAtLU1HUemnsqQFDw8P4OFA4e7du6NZs2YYP348UMXrmZ6eDrlcrlKd2kjZ6wIAjo6O0mdRXXVqAysrKxw4cABdunQBHg6M7NKlCyZPnoyNGzcCKv79s494eunp6QCg9HW8dOmSjqKqWmpqKl577TUMHDhQSijIyclBUVGR0raUfR5UqaMNy5Ytw507d7Bhw4YK61T1+XZ3d1epjqYlJSXB0tISHTp0gIuLC4qKinDixAl8/vnnmDp1qkptUbWONgQHB2PHjh3YuHEjioqKkJeXh1atWknbDbmvV9dnSh8+d4ZG2Wtma2sLS0tLo/++un37NqZMmYI33ngDgYGBQBXfPYb+egghMHbsWIwdO1Y6z3mSMbc/JSUFQgjs3LkT//3vf9GiRQucOXMGzs7O2L59Ozw9PY26/QDg6+sLLy8vbNiwAV5eXjh79ixefvll2NjYAAZ87mXoUlNTsXv37krrBAYGonXr1jV+jvT0dIWb5tDiucyuXbsqfQ5LS0sMHz5cihM1+Bv873//i379+sHExARFRUV488038cILL+DKlSsKA/e1paLzkcLCQmRnZ8PW1lYt++hCTc4bzMzMsHfvXvTu3Rt4+Jnv2bMnxo0bV+VnX9+x39SdzMxMbN++vdI6fn5+CA4OrvFz6Pp3YHW/H2rS1zs7O+PUqVMICgoCANy6dQsdO3bE+++/j+XLl6upJdWXnp6OgIAAhbKq2lKTfXSpJp8vHx8fXLhwQZoU4cKFC+jYsSMCAgIwd+5cLUSteVW9Li4uLjqKjFR14cIF/Pvvv5XW6d69O6/PqImx/4Ylqu02bNhQ6aBSJycn9O3bt8bHr+hegbb6kIsXL+Ls2bOV1unatSs8PDxQVFSEnJwcpf1ddnY2ioqKYG5efkhM586dcfPmTel8YseOHXj22WcRFBSE5557Ts0tUk1Fr7uTk1Ol57rV3UdXanqvs3v37pg4cSIcHBwAAJs3b8bw4cPRqlUrDBgwQONxa5qy99DU1BQODg569x7WBps2bUJBQUGF2x0cHNC/f/8aHz89PR2Ojo7lyrV5jnb79m0cPny40jpt2rSRkjZqcn/Vz88Ply5dQoMGDQAAZ8+eRefOnREYGIh3331Xja2pnsrOka9du6a2fXStJtcVVBkLZej0ZcwNKZeQkICDBw9WWicoKAhNmjSp8XPo+prunj178ODBgwq3m5mZ4fnnnwee4jf93Llz0adPH5iamqK4uBiTJ0/GqFGjcOXKFaXfP9pQk9dd1+9VddT0vfrggw/Qu3dvmJqaoqSkBFOnTpXeq7K2GrL09HQpGa+Mtt5DJkRogEwmQ1ZWlkJZ2WMrKysdRaWfym6qlencuTMGDBiArVu3SgkRqryefM2VU/a64OFrU9lrV5M6tYGtra3CIMG6deti2rRpmDFjBoqLi2FmZsbPq5aUZbMrex319TXMyMjAgAED4ODggNDQUKlclbboQ3tTUlLw7rvv4oMPPpB+9F64cAFZWVkICwtDly5d4OnpaTB/A1ZWVrhw4QI2btwoDbT74Ycf8NZbb2HgwIHw9fU1mLYcPHgQY8eOxcGDB6U+auvWrRg2bBjOnDmDli1bGnRfr673QR/eK0Oj7DUrLCxEQUGBUb9m9+7dQ58+fdCyZUusWLFCKteHvlhTwsPDcfToUbz88ssICwsDHv4QvXLlCsLCwjBq1Cijbn9Z/FevXsXFixdhZWWF3NxcdOzYEf/5z38QGhpq1O0vKChA//79MWLECCxZsgQAcP/+fbRs2RJyuRzz5s0z6vbrs9TUVGzZsqXSOgMHDnyqhAhdfj/u27cP8fHxFW63sbGRztMe/wyW3QAve+zl5VXhMR6/QWlubo558+bhp59+wsmTJ9GzZ081tUR1NXm9DeUcpibnDRYWFlIyBB4m4s6YMQOvvfYacnNzDXpyC/abupOZmVll39mzZ8+nSoiQyWTSjY4y2vy7rO73Q036kXr16qFevXrSY29vb4wfPx7r1q3TaUKEMfejZWoS75MJH02bNsVLL72ErVu3Gk1ChKG9j1Te5cuXq+y7mjRpwoQINeG5CJFx27p1a6UDdv38/J4qIULX9wquXr1a5XdGYGAgPDw8YG5uDjMzM6X9nbm5udJkCABo3769wuOBAweiU6dO2Lp1q84SImryuuv6vaqOmn43dezYUeHxc889hzZt2mDr1q1GkRBR0XuYnZ2td+9hbbB9+3ZkZ2dXuN3Ly+upEiL04W/2zp07VfaxderUkRIiavJb7MkBy0FBQRg5ciS2bt2q04SImvRDhnheXZP3TJWxUIbOEN/L2uTevXtV9k3W1tZPlRCh62tLBw4cqDSRytLSUkqIePzz+vikWFlZWQrXbJ/Ur18/6d9mZmb4+OOPsXr1ahw9ehQDBw5UU0uqx9iv51Z2/7KyJJTHf6+ZmppiwYIFWLlyJaKiojBkyBANR615unwPmRChAX5+fuWW0omLi4OZmZnC0pKknL29vcJy5RW9no6OjlJHokqd2sjPzw/JycnIy8uTOpOUlBTk5OTA19dXrXVqK3t7e+Tl5SErKwt16tSBn58f9u7dq1AnLi4OeDjzLh6+nlXVoco1bNgQJiYm0gyFZeLi4vTyNczMzET//v1RVFSEiIgI2NvbS9ssLCzg5eWltA8ra4sqdTRNCIFhw4bhwoULuHDhAvDwRmZ2dja2bNkCf39/eHp6VtgfW1paSjNSqlJH0/z8/GBmZoZhw4ZJZSNGjMC4ceMQHR0NX19f+Pn5YdeuXeXixBN/z1XV0bQDBw7A3d1d4SLF4MGDYWlpiYMHD6Jly5YG3der6zOlD587Q+Pn54fffvtN4UKXsX9fJSUloWfPnqhfvz62bNmisJysh4cHrKysdNoXa4qTkxP69++vMJtyeno6rl27hi1btuCFF16At7c3zLjMO6AAABZGSURBVM3NjbL9bm5usLW1xcCBA6X+Ty6XY+DAgVISoI+PD8zMzIyy/deuXcPt27eli2x4OENDjx49EBkZiXnz5hncuZex8PX1lZKUNMXPzw/Hjh1TKNNWX//FF1+oXNfPzw94OEP547/xb926hUGDBql8nLLz8OTk5GrFqi4VnY94eXlVOEijJvvogrrOG+zt7VFSUoIHDx7A09NTY/FqGvtN3fHw8NBK3/nkDTptnidX9/tBXX29vb29zvrPMn5+fuWWRa+qLTXZR5fUdW9BH94vdarodbGysqr0RjDpj+eee05nA0xrIxcXF9jb2xvlb1giAn7++WeNHt/Pzw+pqanIzMyUVlfMyMhAWlqaVvqQIUOGVGsQkK+vr9L+rmHDhtV6Xl2fP/n5+eH+/fvIycmBtbU18HDSmoyMjErPdau7j66o816nrt8rdfLz88O9e/eQn58v3Y9ITExEfn6+3r2HtcHatWs1enw/Pz9kZ2fjwYMH0kzSubm5SE5O1tr73aFDh2pfU1DH/VV9+Lt9/Bpz8+bNpfK4uDi0adNGbfvomrrGIz05FsrQ6cOYG6pY69attXJNV5fjeRcuXKhy3cf7nscnjrh16xZCQkJUPk7Zubyuz3GVve6mpqbSSkLq2EdXHn+vHv9ejIuLQ9u2bVU+jo2NDUxMTHT+XakuFb2HJiYm1f6dVl2mGj16LTVw4EBERkYiJSVFKgsPD0ePHj0MenY7Tbh7967C44yMDOzbt09hlraBAwdix44dyMnJAQCUlJTgjz/+UBj0oEqd2qhv374oKSnBn3/+KZWFh4dDLpdLs2Gqq05t8OTnFQ+XTQwICJB+AAwcOBB3795FVFSUVCc8PBzu7u7SF50qdahyDg4O6Ny5MzZs2CCV3blzB1FRUXr3d5+VlYX+/fujoKAAe/fuVZqkNXDgQGzevBlFRUUAgLy8PPz111/l+rmq6mhS3bp1ERYWpvDfc889Bzc3N4SFhaFdu3ZSnHv37lVY4ur3339H3759pQFbqtTRtMGDB6O4uBhXr16Vyi5evAg8nN2jLM6LFy/i3LlzUp3w8HD4+/tLs2KoUkfTvLy8cP/+fYUT4xs3biAvL0864Tbkvl5dnyl9+NwZmgEDBiA9PR0RERFSWXh4uNQHG5vk5GT07NkTHh4e+Ouvv8qdt5uZmaFfv34K3z1paWnYs2eP3n33VFffvn3L9fE+Pj4YMGAAwsLCYGpqCplMhl69eim0PyUlBfv37zf49puYmGDQoEG4dOmSQvnFixel7wRra2t0795dof1JSUn4+++/Db79np6eMDExkb4H8TAR8tKlS9L3iCGde1Hl0tLSEBYWhqSkJODh92N0dLTCOVF4eDiaN28Ob29vHUaqqE2bNnBzc1P4DB44cACJiYkKn8Hdu3fj+PHjwMO25uXlKRxn06ZNMDExkc5dtW3gwIH466+/kJ+fDwAoKirCpk2bFNpw9epVhIWFQQih8j76QJXzhqKiIoSFheH69evAwxv8T9q0aRM8PT3h4eGhxejV4+jRo1L72W8al7KVERMSEoCHf5dXr15FdHS0VCc8PBwNGzZ8qlnKNEWVvv7J74cnr4EJIbBly5anWllDHQYOHIijR4/izp07Ull4eDg6duwoDSZJSUlBWFgYUlNTVd5Hn6hybyExMRFhYWHIzMwElLxfBQUF2LZtm87fr6dx+/ZthIWFITc3F3j4uuzZs0danUUIgd9//x39+/c3ipkqidTNxMQEAwYMwB9//CGdV2ZmZmLnzp08FyEipTZu3ChNhtW7d2+Ym5tj06ZN0vYNGzbA3NxcYZU/fTFw4EBs2bIFhYWFwMNzoS1btij0d5cvX8bvv/8uPX7y/CkxMRFHjx7V6flTnz59YGJigs2bN0tl4eHh0nXZMhs2bJCu46m6j75Q5V5n2eruZZ58r+Lj43Hy5EmDPtcNCwvDlStXgIczORcWFmLbtm3S9vDwcOl6NBm+zZs3IyYmBgDQo0cPWFlZKVwv2rRpE4QQCrN66xNV7q/euXMHYWFh0uoaT/7d5uXlYefOnTr/u/Xw8EBQUJDC6x8bG4vTp08r9EOHDh3C33//Xa199Ikq45Hy8vIQFhYmDVZVZSyUIXryO0XXY25Iu568fmZI43lbtGgBLy8vhb7n+PHjuHnzpsLnNSIiAkePHgUejrktGzdbpuwcUVf3xPDwdT948KB03RkPX/euXbtKq1/cu3cPYWFh0nU/VfbRF02aNEGDBg0U3qtTp07h2rVrCu/Vvn37pH45MzOz3IpUW7ZsgRBC59+VNZWUlISwsDDpfGHgwIE4fPiwwvdLeHg4QkJCNP+9Ikjt8vPzRXBwsGjVqpX47rvvxCuvvCKsra3FP//8o+vQ9M6IESPEK6+8IlasWCG+/vprERgYKAICAsTdu3elOunp6SIgIEA888wzYsWKFWLYsGHCyclJxMbGVquOMdq3b58IDQ0VL7zwgnBxcRGhoaEiNDRUZGVlSXU++OADUadOHbFo0SKxYMECIZfLxRdffKFwHHXVMXTbtm0ToaGhonfv3iIwMFB6PUtKSoQQQqxYsUJ0795dfP7552LVqlVi0KBBwtbWVuzatUvhOK+++qrw9PQUX3/9tZg1a5YwNzcXv/76a7XrUOWOHDkiZDKZGDdunPjuu+9E8+bNRUhIiCgqKtJ1aAp69OghbGxsxMqVK6XPVGhoqPj333+lOvHx8cLNzU0MHDhQrFixQvTo0UP4+PiI5OTkatXRtjlz5gg/Pz+FstzcXBEUFCTatWsnli9fLl566SVha2ur0F5V6mjDhAkTREBAgFi2bJn45ptvhLe3txgxYoRCnaFDh4qGDRuKpUuXimnTpglzc3Oxbdu2atfRpKysLNG4cWPRokULsXz5cvHtt9+KRo0aibZt24r8/Hypnj729fn5+dLfhI+Pj3j22WdFaGio2L17t1RHXZ8pffncGZrJkyeLunXrii+++EJ8+OGHwsLCQqxevVrXYaldYWGhaNmypXB2dhY//PCDQn995coVqd65c+eEnZ2dePHFF8Xy5ctFcHCwaN68ucjJydFp/JoQFBQkpk2bplB25swZYWNjI8aMGSOWL18uWrduLdq0aSPy8vJ0Fqe6xMbGChcXF/Hmm2+KtWvXijfffFPIZDJx6NAhqc4///wjrK2txauvviq+++47ERQUJIKDgxX6WkP1zjvvCHt7ezFv3jzx/fffi2HDhgm5XC6io6OlOoZy7lXb/fHHHyI0NFS0a9dOdOjQQYSGhopNmzZJ28+cOSMAiMjISCGEECUlJaJ///4iICBAfPvtt2LKlCnC3Nxc7NmzR4etUO7XX38V5ubmYtasWeLrr78WHh4eYuzYsQp1goKCpLJz586JZs2aiblz54q1a9eKqVOnCplMJubMmaOjFgiRnJwsfHx8RM+ePcWKFSvEgAEDhJubm4iPj5fqLFu2TAAQhYWFKu+jL6o6b8jMzBQAxJo1a4QQQvz0008iJCRELF68WKxevVo899xzQi6Xi82bN+uwFcpduXJFhIaGiq+++koAEAsXLhShoaHi0qVLUp1Ro0aJDh06SI/ZbxqOLVu2iNDQUPHMM8+IoKAgERoaKn7//Xdp+9WrVwUAsXXrVqlsxIgRokGDBmLJkiXinXfeEWZmZmLLli06akHlVOnrn/x+mDdvnhg4cKD4+uuvxfLly0W3bt2Ek5OTOHHihA5bIkRRUZHo2rWraNasmVi2bJl48803haWlpTh8+LBU59ChQwKAOHnypMr76BNV7i3s3LlTABAXL14UQggxceJEMXLkSLFs2TLx7bffirZt2woPDw+F3zL6JDc3V/q9Vb9+fTFkyBARGhoq9u7dK9XZsGGDACB932VnZ4sWLVqI9u3bixUrVohRo0YJOzs7cf78eR22hDQpOTlZ+pxYWFiIt956S4SGhoqjR4/qOjSDcfnyZeHg4CBGjBghVqxYITp16iQaN24sMjIydB2awbt+/boIDQ0Vy5cvFwDE3LlzRWhoqIiJidF1aERK/f333yI0NFSMHj1a1KlTR+pfH+8PbGxsxCeffCI9XrBggbC1tRULFy4UCxcuFLa2tgrb9cndu3eFp6en6Nu3r1ixYoXo06eP8PT0VBhv8MUXXwgzMzPpcc+ePcX48ePF//73P/HZZ58JHx8f0bp1a5GWlqajVpT66KOPhL29vfj000/FJ598IqytrcWiRYsU6shkMoUyVfbRF6rc6/zkk0+EjY2N9DgkJERMmDBB/O9//xOLFi0S9evXF8HBwSIzM1NHrahcRESECA0NFSNGjBBubm7S39vj9xAAiG+++UZ6/N577wlHR0exePFi8fHHHwsrKyuF7aS/Dh06JEJDQ8Xrr78urK2tpfc7NTVVquPs7KxwPfLzzz8X1tbWYsGCBWLRokXC3t5ep9crq6LK/dXNmzcLAOLGjRtCCCFef/11MWrUKPHdd9+JJUuWiKCgIOHt7S2uX7+uw5aU2vf/7d1/SNXXH8fxl+LSGpNGszRysjIqtR8SFZtJxCKYhauwJqHeq2V6C8pFUNDawo2tbW3JFl1tZvbzRrVpkRbpKKFf9gOVfpCGgwYt7AdtGNYyPd8/ph92uzVttN17930+/rvncw6cD5/7OZ/z+Zwf7x9/NC+99JJxOBzWmPq0adOsOUHGGDNjxgzz9ttvP1cZX9PTfKSbN28aScblchnzHHOhfElLS4t1zwUGBprc3FzjcrnMmTNnrDxPPlN8cc4N/p729nbr+kdHR5vp06cbl8tlDh8+bOV58vuZv83n3b9/vwkKCjIrVqwwGzZsMJGRkea9995zyzNp0iQrrampycTExJgPPvjAbNmyxeTl5ZmQkBCzYsUKL53BHx49emQmTZpkxowZYzZu3Gjsdrvp27evqa2ttfJUVVUZSdY7dW/K+JLy8nITFBRkli9fbgoKCkxUVJTH3LOEhAQrrbm52YwaNcqsXr3abNmyxbz//vsmJCTE5OXleekM/lpnZ6d1v40aNcpMnTrVuFwut3lxx44dM5JMXV2dMV336FtvvWXi4uLMxo0bTVZWlgkODv5XvusFmO6tQfBCtbW1qbCwUA0NDRo4cKAWLlyoESNGeLtaPqezs1Pff/+9ampqFBAQoLFjxyojI0N9+vRxy3fv3j05nU41NjZqyJAhysnJ8ditsjd5/mvy8/Ot3Tr+7Ntvv1VYWJj1u6ysTJWVlQoMDNSsWbP0zjvveJR5UXn8WV5e3lN3qNy9e7cCA/8IqHPu3Dn98MMPunPnjqKjo2Wz2dzCU6nrf71jxw7V1NSoX79+Sk1N1eTJk587D3p2+fJllZSU6O7du4qPj1dOTo5CQkK8XS03drvdY2daSZozZ47mzZtn/f7ll19UWFio69eva9iwYVq8eLFee+01tzK9yfNv2rt3r06dOqWCggK39Pv378vpdOrSpUsKDw9Xdna2oqOjnzvPP617N8GqqiqFhIQoISFBqampCggIsPI8fvxYJSUlOn36tEJDQ5WWluaxIrc3ef5pbW1t2rp1qy5evKjAwECNGzdONpvNCq/bzdfa+tbWVmVnZ3ukDxs2zC1k4Iv6T/nC/87fGGO0Z88eVVdXKzg4WHPnztXUqVO9Xa0X7vfff5fNZnvqsaysLE2fPt363dzcrO+++043b95UbGysHA6HFW7yv2TVqlWKiYlRRkaGW3pTU5OKi4vV0tKi0aNHy+Fw6OWXX/ZaPV+kGzduqKioSD///LNef/112e12jzC5jY2NKi4u1q1btzR27Fjl5uZa4ej9XWVlpaqrq/Xrr7/qjTfekN1utyJkdPOHvtf/u6f1PUNDQ7V582apKxzpypUr9eGHHyomJkaS1N7eruLiYtXW1qp///6y2WyKj4/3Sv17cuLECe3Zs0dtbW2aMmWK0tPTrXc1dbVdUVFRcjgcUtfOVtu2bdO1a9c0ePBgJScne313lTt37mjTpk1qbm626hoREWEdP3r0qEpKStzeQ3sq4yt66jd0P29zcnKs9Pr6eu3bt08tLS0aOnSo0tPTPdoeX9B9XZ6UlpammTNnSpK++eYb3b59Wx9//LF1nHbTP+Tm5rrtdChJffr00fbt26WuHaqWLVumlStXWu1jR0eHtm7dqpMnT+qVV15RWlqaJk6c6JX690ZPbf3Tng/Hjx/XoUOH1NraqhEjRigzM1OvvvqqF8/iDw8fPtTmzZt14cIFDRgwQFlZWYqLi7OONzY26qOPPtKnn35q9eV6KuNrehpbaGho0Geffaavv/7aiqhTUVGhqqoqPXr0SLGxsbLb7T7bT//tt9+Uk5PjkT58+HCrDa2trdWGDRu0adMmK5JHa2urnE6nLl++rIiICC1atMijv47/ju57+UmJiYlasmSJV+rkj65fv66ioiLduHFDI0eO1OLFi/16h1dfUVNTI6fT6ZGekpKilJQUr9QJ+Cvr1q1zi27W7c99CbvdrnfffVezZ8+2jh88eNDatX7mzJlKTk7+F2v9fFpaWuR0OvXTTz9p6NChcjgcGjRokHX80KFDcrlc2rVrl9Q1ruNyuXTq1CkFBwdr/Pjxmj9/vk9EniovL1dFRYUCAgKUnJxsvXN2y8jIUEpKitv16KmML+lprLOsrEwHDhxQaWmp1PUus3v3bp0+fVp9+/bVhAkTlJqa6vY9yJesXbvWIxKxJLd+bWpqqmw2m9v42/79+3XkyBEFBQVp9uzZPhstAO6++uornTt3ziP9888/V1RUlCRp0aJFmjZtmtu8gMrKSh04cECdnZ1KSkpya3t9UU/jq+fPn9f69evd5ikdPHhQ1dXV6ujoUFxcnOx2u8/swF5fX69t27bp3r17mjBhgrKzs93mqH3xxRdqb2/X6tWre13G1/Q0H6n7vXzZsmV68803pV7OhfIlV65cUX5+vkf6lClTrPGBJ58p8sE5N/h7njW2HxkZqS+//FJ6xvczf5vPe+bMGe3atUv3799XYmKibDabW391zZo1CgsL09KlS6WuPnFpaamampoUERGhGTNmWPe4Nz148ECFhYWqr69XWFiYFixY4BZp+dKlS/rkk0+0fv16DRkypFdlfM3Zs2e1c+dOtba2KiEhQZmZmW7Xau3aterfv7/y8vIkSbdv31ZpaamuXr2q8PBwJSUlKSEhwYtn8GydnZ2aP3++R3p4eLg1X7C7Tf5zH+jhw4cqKipSXV2dBgwYoAULFlhjD/8kFkQAAAAAAAAAAAAAAAAAAAAAAAC/45vLpgEAAAAAAAAAAAAAAAAAAAAAAP4CCyIAAAAAAAAAAAAAAAAAAAAAAIDfYUEEAAAAAAAAAAAAAAAAAAAAAADwOyyIAAAAAAAAAAAAAAAAAAAAAAAAfocFEQAAAAAAAAAAAAAAAAAAAAAAwO+wIAIAAAAAAAAAAAAAAAAAAAAAAPgdFkQAAAAAAAAAAAAAAAAAAAAAAAC/w4IIAAAAAAAAAAAAAAAAAAAAAADgd1gQAQAAAAAAAAAAAAAAAAAAAAAA/A4LIgAAAAAAAAAAAAAAAAAAAAAAgN9hQQQAAAAAAAAAAAAAAAAAAAAAAPA7LIgAAAAAAAAAAAAAAAAAAAAAAAB+539xDvcAOTb6zwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 3150x280 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_marginals(FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "79e066b1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 3150x1120 with 36 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_marginals(FILE, group_by=\"energy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d5a0bdb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 3150x1680 with 54 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_marginals(FILE, group_by=\"pdg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "87ed1985",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 3150x1680 with 54 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_marginals(FILE, group_by=\"material\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3585ead",
-   "metadata": {},
-   "source": [
-    "## Tier 2: joint structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8cdb9c2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1300x400 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Real vs. generated Pearson correlation matrices (+ their difference) over\n",
-    "# the 9 raw target dims — catches a model that decorrelates targets that are\n",
-    "# physically coupled even when every individual marginal looks clean.\n",
-    "_ = plot_correlation_matrices(FILE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e88c8bd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x700 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Scatter for physically-coupled pairs (step_length/delta_e/edep) — the\n",
-    "# joint-structure check correlation matrices alone can't fully capture.\n",
-    "_ = plot_pairwise(FILE, n_sample=10000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8c6e5d81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGGCAYAAAC0W8IbAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlcelbwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQgBJREFUeJzt3XlcFVXjP/AP+74jIkhggApu8BSaaYmau5ZmVpaWqZlFWVGmldpiZlZPWYqaZVp+Ncvd3LcwN9wAH8sFVEREZJF9387vj2R+Xbn3crlcYO7web9ez+vpzpw5c2buyOfOzJkzJkIIASIiIpIl0+ZuABEREWnGoCYiIpIxBjUREZGMMaiJiIhkjEFNREQkYwxqIiIiGWNQExERyRiDmoiISMbMm7sBhlZdXY2bN2/CwcEBJiYmzd0cIiKiWoQQKCgogJeXF0xNtZ8zKy6ob968CR8fn+ZuBhERUZ1SUlLQtm1brWUUF9QODg7AnY13dHRs7uYQERHVkp+fDx8fHymztFFcUNdc7nZ0dGRQExGRrOlyi5adyYiIiGSMQU1ERCRjDGoiIiIZU9w9al1UV1ejvLy8uZtBjcjCwgJmZmbN3QwiogZrcUFdXl6OpKQkVFdXN3dTqJE5OzvD09OTz9MTkVFrUUEthEBaWhrMzMzg4+NT50PmZJyEECguLkZGRgYAoE2bNs3dJCIivbWooK6srERxcTG8vLxga2vb3M2hRmRjYwMAyMjIgIeHBy+DE5HRalGnlFVVVQAAS0vL5m4KNYGaH2MVFRXN3RQiIr21qKCuwXuWLQO/ZyJSghYZ1KTqq6++Qnx8fHM3g4iI1FBMUEdFRSE4OBhhYWHN3RSjw6AmIpIvxXQmi4iIQEREBPLz8+Hk5NTczSEiImOUmwIU39Y839YNcG7aNzQqJqiVbMuWLaiurka3bt2wY8cOmJiY4LXXXgPuvNbz999/R0FBATp16oTBgwer3Jtdvnw5bt68CRMTE3h5eaFv374ICAhoxq0hIpKp3BRULw6DaWWJxiLV5jYwffVUk4Y1g9oIbNmyBYcPH4aZmRmGDh2Ke++9FwCwfft2jBs3DsOGDYOXlxdWrVqFr776Crt27YK5uepXW11djejoaERGRuKnn37C448/3kxbQ0QkTxkZN+FRWYLXy1/BZeFda36ASSq+wZJ/yjGo6W7p6elISEiAl5cXcOddpuPGjcPq1asxYsQIAMDcuXPRuXNnrFq1CpMnTwYATJkyRaWe5cuXY/r06QxqIqK75JdUwAPAyAH90Kp991rzMxNOAoeWSOWaSosP6pLyKlzJLGzy9fq3soeNpe6DcPTu3VsKaQDYv38/CgoKEB8fj7Nnz0IIASEE7OzscOLECSmohRA4cOAA/v77b+Tm5iI9PR1Xr15FYWEh7O3tG2XbiIiMmY+rDQK8a/d1upxl0yztafFBfSWzEMMXHWny9W5/rTc6qzkQNHF3d1f5nJaWBmtra2kQlxqjRo1CcHAwcGcktkGDBiEpKQmDBw+Gm5ubNGxqbm4ug5qIyAi0+KD2b2WP7a/1bpb1NoSHhweKi4vx+uuvw8XFRW2Zo0eP4siRI0hPT4ezszMA4ODBg4iKimrQuomIqOm0+KC2sTSr15mtXDzyyCNwdHTEvHnz8OWXX0rTs7KykJ2djfbt26O4uBhmZmawsLAA7lwGX7ZsWTO2moiI6qvFB7WxcnFxwZo1azB27FicPn0aYWFhSElJwZkzZ/Dzzz8DAPr06QMfHx/06dMH/fr1w/Hjx5GWltbcTScionpgUBuBkSNHqn1/9vDhw3H16lVs374d6enp6N69O77//ns4ODgAd15Kcfr0aaxfvx7p6el488038eCDD2LZsmVwdHSU6omMjERISEiTbhMREemGQW0ERo4cqXFeq1at8MILL2ic7+DggIkTJ6pM+/DDD1U+R0ZGGqCVRETUGBQz1jcREZESMaiJiIhkjEFNREQkYwxqIiIiGWNQExERyRiDmoiISMYY1ERERDLGoCYiIpIxBjUZrZSUFFy8eLG5m0FE1KgY1NSkkpKSkJiYaJC6oqKi8MYbbxikLiIiueIQotSkFixYgKysLGzYsKG5m0JEZBQY1EYkISEB5ubmaNeuHW7cuIGioiJ07NhRpUxOTg6Sk5Ph4+MDNzc3lXlXrlwBAPj7+yM1NRWFhYUICAiAmZlZrXXpUs+9996LxMRElJSUoFu3bkhNTUVKSgoAwMnJCf7+/rC0tJSWu3HjBjIyMpCbm4uYmBgAQHBwsPSCEG3rrJGYmAgzMzO0a9dOr31IRGRsGNRGIDs7G8OHD8e5c+fQtm1bVFRUIDAwEEII7N69GwBQUVGBadOmYc2aNfDz80NKSgr69u2LVatWSUE4d+5cXLlyBWVlZcjPz0d2djbc3d2xd+9etG3btt71FBUVobi4GF26dMH69euxb98+6X3X2dnZyMjIwOLFizFu3DgAwI4dO3D06FFUVlZKl6wXLVqEkJCQOteZm5uLxx57DGfOnJH2Qfv27Zvh2yAialqyu0d9+/ZtdO7cGZ07d8a7777b3M2RhdmzZ6OgoADJycm4cOECfvjhB+zdu1elzAcffIC4uDgkJSXhf//7H27cuIGcnBy89957KuWOHz+Ozz//HBcvXsT169dhZ2eHzz77rN71HD16FJ9++ikuXryI9evXAwAmTJiAmJgYxMTEICEhAWvWrMFLL72EmzdvAgBeeukljBo1Cn379pXKhYWF6bTODz74ANnZ2UhOTsbFixexfPly7Nmzp1H2NxGRnMjujNrJyQnr1q3Dvn37cObMmcZfYXkxkJXQ+Ou5m3t7wNJWp6I///wzFi9eDFdXVwBAeHg4BgwYIM2vrq7G0qVL8c477yApKQlXr16FEAJ9+/bFjz/+iMWLF0tl+/bti/DwcACAtbU1hgwZgkOHDtW7np49e2Lw4MG12lpdXY2UlBRkZGTAzc0N9vb2OHHiBEaNGqV223Rd58qVK/Htt99Kl8T79++PRx55RKf9R0RkzBolqKuqqpCZmQlnZ2dYW1urLVNZWYnCwkI4OzurNsjcHJ07d8bFixebJqizEoDlfRp/PXebcgjwCqmzWGZmJgoLC2td5u3QoQMuXboEAEhPT0dubi7Wrl2LrVu3qpSruaRdo3Xr1iqfbW1tUVRUVO96fH19a7X18OHDmDBhAvLz89G2bVtYWVkhPz8faWlpGrdPl3VmZmaioKAAHTp0UJnfsWNHJCQ0w48sIqImZNCgzsjIwHfffYcVK1YgOTkZq1evlu5P1qiqqsKbb76JH374AUIItGnTBsuWLcPAgQMN2RTdubf/JzSbY706sLe3h4mJiRSmNQoLC6X/rvkx9PHHH2s8c9VFfeoxNa1912TChAkYO3Ys5s6dCxMTEwBAmzZtUF1d3aB1Ojg4qN0Hd38mIlIigwb1jh07UFZWhj///FPtGRcAzJs3D+vWrcOJEycQFBSE+fPnY+TIkbhw4YLGZRqVpa1OZ7bNxcbGBkFBQThw4IB0qbe6uhqHDh1CQEAAAMDFxQVdu3bF2rVra4VdUVER7OzsdFpXQ+qpqqpCcnIy+vfvL4V0fHw8bt26VWt7Kioq6rVOa2trBAcHY//+/dI+qKqqwsGDB2udZRMRKY1Bg/qFF17QOl8IgaioKERERKBLly4AgPfffx9RUVFYsWIFPv74Y0M2RzHmzJmD559/Hm5ubujSpQt+/PFHpKamIjAwUCrzzTffYMiQIXj++efx1FNPobS0FAcOHEBJSQl+/PFHndelbz1mZmZ46KGHMHv2bMyZMweZmZmYNWsWLCwsVMp17twZv/zyC3bu3AlXV1cEBwfrtM6PPvoIzzzzDFxdXdGtWzcsX74ct27dYlATkeI1aWeya9euISMjA71795ammZqaolevXjh58qQ0rXv37rh16xby8/PRuXNnrFy5EmFhYWrrLCsrQ1lZmfQ5Pz+/kbei6T311FMAgFWrVuHPP/9EeHg4nJ2dkZWVJZUJDw/HmTNnsHjxYnz++edwd3fHgAEDMHHiRKmMv78/qqqqVOr29vaWfjQ1pB4A+PXXXzF37lx8+umncHNzw7fffos1a9bA09NTKjN+/Hhcv34dCxcuRH5+PhYtWqTTOkePHo01a9ZgxYoVOHLkCMLDwxEeHo6rV68aZB8TEcmViRBCNErFJia17lGfPHkSPXr0QHx8PLp16yZNnzp1Kk6ePInY2FgAwIULF1SCoF27dhovu3744Yf46KOPak3Py8uTnsGtUVpaiqSkJLRr105jJzc5Ki8vVxk4pLq6GiEhIRg1apTabad/GOv3TUTN4/LZIwjYPAyXR+1AQLfe9Z5fH/n5+XByclKbVXdr0jPqmnuXlZWVKtMrKytVRscKCgrSuc53330XkZGR0uf8/Hz4+PgYpL1yceXKFcyYMQMTJkyAlZUVVq1ahRs3bmDy5MnN3TQiImpkTRrU3t7ewJ1Hcv4tPT0dXl5eetVpZWUFKysrg7RProKCgjB16lSsXr0at2/fRlBQEP73v//VemSKiIiUp0mD2svLC/7+/ti3bx+GDh0K3Lk8eejQIXzwwQdN2RSjM3ToUGmfERFRy2HQoC4vL0d2drb0OS8vD7du3YKtra10DX7WrFmYOnUq7rvvPoSGhuKTTz6Bvb09Jk2a1KB1R0VFISoqSm0nJyIiImNl0LG+T5w4gZCQEISEhKB169aYO3cuQkJCVDo8TZgwAVFRUfj6668xbNgwlJaWIjo6utYIZfUVERGB8+fP49SpUwbYEiIiInkw6Bn1Qw89VGuAC3UmTZrU4DPohmikju4kM/yeiUgJZPf2rMZU07O8vLy8uZtCTaC4uBgAag26QkRkTGT39qzGZG5uDltbW2RmZsLCwkLteNVk/IQQKC4uRkZGBpydnVUe/SMiMjYtKqhNTEzQpk0bJCUlITk5ubmbQ43M2dlZZVQ0IiJjpJig1rXXt6WlJQIDA3n5W+EsLCx4Jk1EiqCYoI6IiEBERIQ0LJs2pqamHFKSiIiMAm/SEhERyRiDmoiISMYY1ERERDLGoCYiIpIxxQR1VFQUgoODERYW1txNISIiMhjFBDXH+iYiIiVSTFATEREpEYOaiIhIxhjUREREMsagJiIikjEGNRERkYwpJqj5eBYRESmRYoKaj2cREZESKSaoiYiIlIhBTUREJGMMaiIiIhljUBMREckYg5qIiEjGGNREREQyppig5nPURESkRIoJaj5HTURESqSYoCYiIlIiBjUREZGMMaiJiIhkjEFNREQkYwxqIiIiGWNQExERyRiDmoiISMYY1ERERDKmmKDmyGRERKREiglqjkxGRERKpJigJiIiUiIGNRERkYwxqImIiGSMQU1ERCRjDGoiIiIZY1ATERHJGIOaiIhIxhjUREREMsagJiIikjEGNRERkYwxqImIiGRMMUHNl3IQEZESKSao+VIOIiJSIsUENRERkRIxqImIiGSMQU1ERCRjDGoiIiIZY1ATERHJGIOaiIhIxhjUREREMsagJiIikjEGNRERkYwxqImIiGSMQU1ERCRjDGoiIiIZY1ATERHJGIOaiIhIxhjUREREMsagJiIikjEGNRERkYwpJqijoqIQHByMsLCw5m4KERGRwSgmqCMiInD+/HmcOnWquZtCRERkMIoJaiIiIiViUBMREckYg5qIiEjGGNREREQyxqAmIiKSMQY1ERGRjDGoiYiIZIxBTUREJGMMaiIiIhljUBMREckYg5qIiEjGGNREREQyxqAmIiKSMQY1ERGRjDGoiYiIZIxBTUREJGMMaiIiIhljUBMREckYg5qIiEjGGNREREQyxqAmIiKSMQY1ERGRjMk2qC9fvozExMTmbgYREVGzMm/uBqjz1FNPISYmBgDQp08f/Pzzz83dJCIiomYhu6COjo7GxYsXkZiYCCEE7rvvPhw7dgwPPvhgczeNiIiMXW4KUHxb7Syr3MtN3hxdGDyoKysrsW3bNsTGxmLMmDHo1q1brTJZWVnYuHEjMjMzERoaimHDhknzTp8+jcGDB8PS0hIAMHToUJw+fZpBTUREDZObgurFYTCtLFE72wdAsbBClbVrkzdNG4MG9a5duzB16lSEhoZi69at6NixY62gTkhIQO/evdG5c2d07doVL774Ih5++GGsW7cOAFBUVAQ7OzupvJ2dHQoKCgzZTCIiaoEyMm7Co7IEr5e/gsvCW22ZEnNnrG7drsnbpo1Bg9rb2xvHjh2Dt7c3TExM1JZ588030aVLF+zbtw+mpqaYPHkyunbtinHjxmH48OFo27YtDh06JJVPTEzEwIEDDdlMIiJqgfJLKuABYOSAfmjVvrvaMi52lvB2tmnytmlj0KDu2rWr1vlFRUXYu3cvVqxYAVPTfzqcd+7cGWFhYdi0aROGDx+Oxx57DDNmzMB3330HIQT27t2LxYsXa6yzrKwMZWVl0uf8/HwDbhERESmNj6sNArydmrsZOmvSx7OuXLmCyspKBAQEqEwPDAzEpUuXAADu7u7YsWMHDh06hMOHD2PXrl1wdnbWWOf8+fPh5OQk/c/Hx6fRt4OIiKipNGmv76KiIgCAo6OjynQnJydpHgD06NEDa9eu1anOd999F5GRkdLn/Px8hjURESlGkwa1vb09ACAvL09lem5urjSvvqysrGBlZWWQ9hEREclNk1769vf3h7m5ORISElSmJyQkoGPHjk3ZFCIiIqPQpEFta2uLIUOGYNWqVaiqqgIAxMXF4fTp03jiiSeasilERERGwaCXvq9cuYKVK1dKnzds2ICLFy/i/vvvx8iRIwEAX3/9NR566CE8/PDD6Nq1KzZu3IjnnnsOgwcPbtC6o6KiEBUVJf0AICIiUgKDBrWZmRmsra0BAHPnzpWmW1hYSP/t7++P8+fPY/Pmzbh9+zZ++eUX9O/fv8HrjoiIQEREBPLz8+HkZDzd7omIiLQxaFD7+flh1qxZdZZzdnbGCy+8YMhVExERKZJsX3NJREREDGoiIiJZU0xQR0VFITg4GGFhYc3dFCIiIoNRTFBHRETg/PnzOHXqVHM3hYiIyGAUE9RERERKxKAmIiKSMQY1ERGRjDGoiYiIZEwxQc1e30REpERN+prLxsQhRImIKDW3BDlF5WrnZWaXIKDJW9RwiglqIiJq2VJzSzD+vxthU5mrdn6ASSr6WgKONhZq58sVg5qIiBShMD0J200jYWtVprFMtbkNPDy8mrRdDcWgJiIiRTArzYatSRlS+n4Dn8AQtWVMbd0AZ58mb1tDMKiJiEhRypwDAC/1QW2MFNPrm4iISIkUE9R8PIuIiJRIMUHNl3IQEZESKSaoiYiIlIhBTUREJGMMaiIiIhljUBMREckYg5qIiEjGGNREREQyppiRyaKiohAVFYWqqqrmbgoRETUiTW/IMta3Y9VFMUHN11wSESlfam4JHvnvIZRU1D4p62SShL5Wxvd2rLooJqiJiEj5corKUVJRhYVPhSDAw15lnnWWE7AZ8LC3arb2NQYGNRERGRUvZKGzaRICTFSDGiY3m6tJjYpBTURERsOiMBX7rabDdrOGd05b2AK2bk3drEbFoCYiIqNR5zunjfB903VhUBMRkdFR2junteFz1ERERDLGoCYiIpIxBjUREZGMMaiJiIhkTDGdyTiEKBGRMmgaIhQKHiZUG8UENYcQJSIyftqGCIWChwnVRjFBTURExk/bEKFQ8DCh2jCoiYhIdgI87NHZW83V0buHDW0BGNRERCQrXsiCddY59aGcldAcTWpWDGoiIpKNOsfyhjLH89aGQU1ERLJR51jeUOZ43towqImISHZa0ljedeGAJ0RERDLGM2oiImpSHNCkfhjURETUZDigSf0xqImIqMlwQJP6Y1ATEVGT44AmulNMZ7KoqCgEBwcjLCysuZtCRERkMIo5o+ZLOYiIjANHHqsfxQQ1ERHJH0ceqz8GNRERNRmOPFZ/DGoiIjIoXZ6T5shjumNQExGRwfA5acNjUBMRkcHwOWnDY1ATEZHB8Tlpw1HMc9RERERKxKAmIiKSMV76JiIig+KAJobFoCYionrR9vhV6rUEDmhiYAxqIiLSmS6PXw2yKkP24Ci43tNZfSUc0KReGNRERKQzXR+/cr2nMwc0MRAGNRER1Rsfv2o67PVNREQkYwxqIiIiGeOlbyIiqkVTz+7LGYV8/KqJKSaoo6KiEBUVhaoq9T0RiYhIN9p6dnshi49fNTHFBHVERAQiIiKQn58PJyc1HRyIiEgn2np2W2ed+yekH/8ecG+vvgI+fmVQiglqIiIyLLU9u2sud7u35+NXTYSdyYiIiGSMQU1ERCRjvPRNRNQCaRuv+3JGYZO3hzRjUBMRtTB1jdcNAPda5MCj6CJw00p1Bh+/anIMaiKiFqau8botClMRuH4STNeWqK+Aj181KQY1EVELpXG87ptJQGWJ5kew+PhVk2JQExGRenwESxYY1ERECsTOYsrBoCYiUhhdOovZWJjBxc6ySdtF+mFQExEpTF2dxQDAvSoDnsWXgGI1M9mzW1YY1ERECqWxs1huChD1MFChLqXvYM9u2WBQExEZoQbdgy6+/U9I88UaRoFBTURkZAx2D5q9uo0Cg5qIyMjocg/axc4S3s42Td42MjwGNRGRkdJ4D5oUhUFNRKREuSn/3ItWh726jQqDmohIpjR1GKuzs1huChDVnb26FYJBTUQkQ3V1GNPaWYy9uhWFQU1EJEN1dRjTqbMYe3UrAoOaiEjGtA5acpP3oFsCBjURUTPRe9AS3oNuURjURETNoEGDlvAedIsiy6D28/NDYWEh7r//fuzevbu5m0NEZHAGGbSE96BbBFkG9ZkzZ3Du3DnMnDmzuZtCRNSoOGgJ1UWvoC4sLMTly5fh6+sLFxcXtWUyMzNx+/ZttGvXDlZWVvWq383NDc7Ozvo0jYhINhr04gxoGbSEncValHoFdVJSEr788kts2rQJt27dwurVqzFu3DiVMqWlpXjuueewdetWtGrVCkVFRViyZAnGjh0LAEhJSUFoaKja+uPi4uDjw3sqRGT8GvzijLo6jLGzWItRr6A+deoUgoKCcOnSJTg5qb9UM2vWLJw4cQJJSUnw8vLC8uXL8dxzzyE0NBQdO3aEt7c3Ll68qHZZV1dX/baCiEhmGnwPuq4OY+ws1mLUK6iffPJJrfOrqqqwYsUKzJw5E15eXgCAKVOmYN68eVi5ciUWLFgAU1NTuLu7N6zV/1JWVoaysjLpc35+vsHqJiLSRpdL2w2+B80OYy2eQTuTXb16Fbm5uejevbvK9B49eiA2Nlbnevr27Yv4+HgUFhbC3d0dCxcurHWJvcb8+fPx0UcfNbjtRET1YZB3QvPFGaQDgwb17dv/HHBubqr3Tdzc3JCYmKhzPZs3b0ZlZaX02cHBQWPZd999F5GRkdLn/Px83ucmokbX4EvbHLSEdGTQoLawsADuXI7+t7KyMmmeLurT49vKyqrevcqJiHRV1xus9L60zUFLSEcGDeqaM9m0tDSV6Tdv3uRZLhEZnQa9wUpXvAdNdTBoUHt4eKBTp07YuXMnHn30UQBAQUEB/vzzT3z55ZeGXBURUaNr8BuseA+aDKBeQV0z0EmN69evIz4+Hm5ubtIZ8yeffIIxY8YgICAAoaGh+Pzzz+Ht7Y0JEyYYvvX/EhUVhaioKFRVae7YQUR0t0bruc170GQgJkIIoWvhEydO4KWXXqo1/fHHH8ecOXOkz9u3b8fSpUtx+/ZthIaGYs6cOWjTpo3hWq1Ffn4+nJyckJeXB0dHxyZZJxEZJ117bu9/q0/d736+2814YHkf3oNWkMtnjyBg8zBcHrUDAd16N6iu+mRVvc6oe/Togfj4+DrLDR8+HMOHD69P1URETc4gL8aoC+9BUwPJ8qUcRESG0qiDkvAeNDUBBjURKZZBBiXRhPegqYkwqIlIsRr10jafg6YmopigZq9vopap0cfbrutVk7wHTY1MMUEdERGBiIgIqScdESlfo17aBl81SfKgmKAmImWq64y5UXtt81WTJAMMaiKSLV3PmMPaueofxrr03OblbWpGDGoianbaXnzRqGfM7LlNRoBBTUTNSpcXXzTqGTN7bpPMKSao2eubyDg1+MUX2uh6xnxPT4YxyZZigpq9vonkq9EfodKEzzqTAigmqIlInprkESp2BiMFY1ATUYM12yNU7AxGLQCDmojqpC2IbxeVY+rqM433CBU7g1ELx6AmIq10vXT908TucNNw+brRz5jZGYwUjEFNREBzPsuMOsbT5hkztXCKCWo+nkWkv0Z/llkbXcbT5hkztWCKCWo+nkWkXUM6fDXaGTN0OGvmGTO1cIoJaiLSrEnGzNaE95mJGoRBTaQQzfqWKfbMJmo0DGoiBeAZM5FyMaiJjATPmIlaJgY1kRFo1vcyF2cBv47nGTNRM2FQE8mIbN/LbGELjNsI2Lqrn88zZqJGo5ig5nPUZAwaMhRns76XmUFM1GwUE9R8jprkrqFDcdZ5xqwtiHn5mshoKSaoieRA9m+R4uVrIqPDoCYykEbv8MWe10QtEoOaqB4a/YyZPa+J6C4MaiIdNckZM3teE9FdGNRE/9JsZ8xgz2siUo9BTS2OpjCu6/EoNPYZM3j5mohqY1BTi6LLe5c1PR6Fxj5jBs+aiag2BjUpTqO+dzk3BbjJZ5WJqOkoJqg5MhnBEB2+DBHE7PBFRAakmKDmyGQtR6N1+OKgIUQkQ4oJalKOhoyHDV06fGk6a+Y9ZCKSIQY1NYuG9rzW1uHLvSoDnsWXAHUnxnVdvuY9ZCKSGQY1NbmG9rx2r8qAp3mq+sobeh+ZZ8xEJDMMamoUDbmP3KhBDIYxERkXBjXppaH3ke+1yMGDdjfgYWKlOoNBTESkgkFNahkiiL8Y6Q0nG4ta88xLsnHP/rdhurZE/cIMYiIiCYO6BWtIhy6dgni3hiAG7xMTEemKQa1gDTkrbtQgBsOYiEhXDGoj1liXpxnERETywaBuKG0vYahLHWFWVxB/tHoPbCpzNS4fbG6K90YG6XdWzCAmIpIFBnVD6DLkpBbV5ja4/shyVNq41pqXV1KBT3dcQGlltdpl3Uzysd1iIWytyrSvZLeWebxPTEQke4oJ6mZ5KUfxbaCiGCl9v0GZc0D9Fs1JR8AfL8Nv93iNZTaaATDTXEe1uQ3wtJaz3rowjImIZE8xQd1YL+XQdvm5LCUH9wGYursQf4u8etZsjXstvsIXw9R32AIARxsLeNhbqZ0HAKYMWiIixVNMUDeGuoa67GSShB1WwMePdYKVz3/qXX+d7z4mIqIWj0GtRU5ROVwq0rFkkBd8XGsHqlWuPfAHcN89LoAXX61JRESGx6DWwqIwFfutpsP2kJYOWxa2/9zrJSIiagQMai3MSrNha1KGlL7fwCcwRH0h3icmIqJGxKDWQZlzAOClIaiJiIgakWlzN4CIiIg0Y1ATERHJGIOaiIhIxhjUREREMsagJiIikjEGNRERkYwxqImIiGSMQU1ERCRjDGoiIiIZY1ATERHJmOKGEBVCAADy8/MbXFdBYRHyy8Q//2+A+oiIyHgZMhNqlq/JLG1MhC6ljMiNGzfg48OXZBARkfylpKSgbdu2WssoLqirq6tx8+ZNODg4wMTEpEF15efnw8fHBykpKXB0dDRYG5uSsW+DsbcfCtgGY28/FLANxt5+cBtqEUKgoKAAXl5eMDXVfhdacZe+TU1N6/x1Ul+Ojo5Ge2DVMPZtMPb2QwHbYOzthwK2wdjbD26DCicnJ53KsTMZERGRjDGoiYiIZIxBrYWVlRU++OADWFlZNXdT9Gbs22Ds7YcCtsHY2w8FbIOxtx/chgZRXGcyIiIiJeEZNRERkYwxqImIiGSMQU1ERCRjinuOWl8XL15EVlYWevfurVN5IQQuXryIsrIydOrUCRYWFnqVMZS8vDwkJiaidevWdY7MlpCQgIyMjFrTLSws0KNHDwBAbm4u/vrrr1pl7r//flhbWxuw5f/f5cuXkZeXh+DgYNjY2Ggt+9dffyE3N1dlmpubG4KCghpUb0MUFxfjwoULcHZ2hr+/v07LZGZmIjU1FX5+fnB2dq5VX2xsbK1lunTpovPzl5okJycjMzMTHTp0gIODg8GW0adefZSXl+Pvv/+GjY0NOnbsqNMyOTk5uHbtGnx8fODu7q4yr6qqCsePH6+1TIcOHdCqVSuDtfvf0tLScOPGDfj7+8PV1VVr2evXr+P69esq0ywtLdG9e/cG1dsQVVVV+Pvvv2FiYoJOnTppHbQjOzsb58+fVzvv38fz8ePHUVVVpTLfz8/P4GNj1MjPz8e5c+fQrl07eHl56bRMVlYWkpKScM8996B169Z6l6kX0cL99ttvomfPnsLFxUXoujuuXLkiOnfuLNzd3YWvr6/w9PQUhw4dqncZQ/n222+FjY2NCAoKEjY2NuLxxx8XpaWlGst//PHHolevXir/s7e3F0FBQVKZXbt2CQC1yt24ccPg7c/MzBQPPvigcHJyEgEBAcLJyUls2rRJ6zL9+/cXbdu2VWnbu+++2+B69bVu3Trh6OgoAgMDhYODg+jTp4/Izs7WWP7YsWOid+/ewsPDQ4SEhAgbGxsxefJkUVFRIZU5d+6cACC6d++usp2xsbF6t7OoqEgMHz5c2Nraio4dOwpbW1vx3XffNXgZferV1/79+0WrVq1Eu3bthJubm+jWrZu4fv26xvJ//fWXGDJkiHB1dRWhoaHCzs5OjB49WhQUFEhlcnJyBADRrVs3lX29Z88eg7e/srJSTJgwQVhbW4vg4GBhZWUlPvroI63LvP/++8LR0VGlbY8++miD69VXbGys8PX1Fd7e3sLT01P4+/uLc+fOaSz/559/1vpb4uvrKwCIv//+WypnZ2cngoKCVMr93//9n8Hbf+3aNTFlyhTh6ekpzM3Nxddff63TcjNmzBBWVlbS/n3llVdEdXV1vcvUV4sP6g8//FAcOXJErF69Wueg7tmzpxg4cKD0R/X1118XHh4eKv/wdSljCDExMcLExERs27ZNCCFEamqq8PLyqhVa2mRnZwtra2uxYMECadquXbuEmZmZQduqyRNPPCH+85//iMLCQiGEEF988YWwsbHR+qOgf//+YsaMGQavVx9Xr14VlpaWYsmSJUIIIfLy8kRwcLAYP368xmV++uknceTIEenzpUuXhIuLi5g3b540rSao09LSDNbWN954Q/j5+Yn09HQhhBC//PKLMDExEXFxcQ1aRp969ZGTkyNcXFzEe++9J4QQoqysTDz00EOib9++GpfZvHmz2Llzp/T55s2bws/PT7z88ssq9QIQp06dMmh71fnyyy+Fq6uruHz5shBCiOjoaGFmZiZ27NihcZn3339f9OnTx+D16qO8vFzce++94vnnnxdCCFFdXS3GjBkjOnbsKKqqqnSuZ+jQoSIsLExlmp2dndi8ebNB26vOvn37xNKlS0VBQYFwc3PTKajXrVsnrKysxMmTJ4W48+/Tzs5O5QepLmX00eKDuoauQX3+/HkBQERHR0vTMjIyhJmZmfjll190LmMoU6ZMESEhISrTZs2aJVq3bq1zHYsWLRIWFhbi1q1b0rSaoL5w4YI4e/asKC4uNmi7a2RnZwszMzOVX81lZWXCyclJfPHFFxqX69+/v3jllVfEqVOnxPXr12v9YtW3Xn18/PHHwsPDQ+WP1LJly4SVlZX0I0EXTzzxhBgyZIj0uSaojx49KuLi4hr8I6+qqkq4uLiIzz77TGV6YGCgeP311/VeRp969fXjjz8KS0tLkZeXJ03bvn27ACCSkpJ0rueNN94QXbp0kT7XBPWmTZvE6dOnRU5OjkHb/W/BwcHi1VdfVZkWHh4uRo8erXGZ999/Xzz44IMiLi5OXLp0SeXKS0Pq1cfevXsFAOkHgRBCxMfHCwDi8OHDOtVx48YNYWZmJpYvX64y3c7OTixbtkycOnVKZGZmGrTdmuga1AMHDhQjR45UmTZu3DjRo0ePepXRBzuT1VNcXBwA4L777pOmtWrVCr6+vtI8XcoYsj3/Xg8AdO/eHenp6UhLS9OpjhUrVuDRRx+tdS+lqqoKw4YNw+jRo+Hi4oLZs2fr9Eq2+jh37hyqqqpUtsHS0hLdunWrc1/9+OOPmDx5Mrp27YouXbrg1KlTBqm3vuLi4hAaGqpyj6579+4oKyvTeF/ubpWVlYiPj0dAQECteU8++SSeeeYZuLq6IiIiAuXl5Xq189q1a8jJyal1vISFhWncJ7oso0+9+oqLi0NgYKDKOMs192nrs67Tp0+r3dcvvfQSJkyYAA8PDzzzzDPIy8szUMv/UVpaigsXLqj9N1tX+2NiYjBu3DiEh4fDy8sLv/76q0Hqra+4uDg4OTmp9MPo1q0bLC0tdV7XqlWrYGNjg6effrrWvJkzZ2LSpEnw8fHBkCFDcPPmTYO2X1+a/tbGx8dLfxd1KaMPxXUmO336NEpLSzXOt7KyQlhYmN71Z2dnw9LSEvb29irT3dzckJ2drXMZbfXX9ce9Xbt28Pb2lsq7ubnVWk/NvDZt2mitKzY2FvHx8fjss89Uprdp0wbHjx/HAw88AAA4cOAAhg4dCi8vL7z88sta6zx37lydf+BqOu3V7A9126BtX02cOBFbtmyBvb09SkpKMHHiRIwcORLnz5+Hk5OT3vUCQGFhIeLj47WW8fb2Rrt27aRtqPk+/r2ef29fXWbNmoX09HS88cYb0jRHR0fs2rULgwcPBu78EejXrx+cnZ0xb948ner9N2375H//+5/ey+hTr77UHe81HaZ03deLFy9GTEwMjh49Kk2zsLDAmjVr8MwzzwB3OiD2798f06ZNw08//WSw9ufm5kIIUe/j8sEHH0RycjLatm0LIQQWLFiAcePGoX379ggNDdW7Xn2o+w7qsy4hBFauXImnn366VofDhQsXYuLEiTA1NUVaWhqGDBmC8ePH48CBAwbdBn1o+ltbVlaG4uJi2NnZ6VRGH4oL6gULFmg9k/Tw8MCmTZv0rt/CwgIVFRWoqqqCmZmZNL2kpASWlpY6l9Hk77//xrvvvqu1zKuvvir9ErWwsKj1w6SkpAS4cwZZlxUrVsDX1xcDBgxQmd6tWzeVz/3798eYMWOwbt26OoN66dKldf6BPnLkiNR+3DkjuHsbtLW/5g8qANjY2GDhwoXw9PTEn3/+iREjRuhdLwCkpqZi5syZWss8+eSTmDZtmrQNDfkOvvnmGyxcuBBbtmzBvffeK02/5557cM8990ifQ0ND8dJLL2HdunV6BbU++0SXZRqyr+tL3b6u+azLun777TdERkbihx9+UOkxbWdnp3JMBQQEYPr06XjnnXewcuXKOl9DWJ/2Q499NXToUOm/TUxMMHPmTHz33XfYsGEDQkNDm/07qM+6oqOjceXKFaxdu7bWvMmTJ0v/3aZNG3z44YcYNWoUMjMzG633va50+Xfe0L8FmiguqNevX9+o9fv6+kIIgbS0NOmRgZrPNX9UdSmjyUMPPSSFmK7tSU1NVZmWmpqq0+s+S0tLsXbtWkRGRur0h6h169aIiYmps9ySJUt0aPk/fH19pTb/+7Gy1NRU9OnTR+d63NzcYGZmJu2LhtTboUOHen8Hdz/KVtOOur7vxYsXY8aMGdi4caN05qxN69ata33f9Wnnv9v277Zqaqcuy+hTr758fX2xb9++WuuBDvt6w4YNGD9+PJYtW4bnn3++znW1bt0aJSUlyM7OrvU4l75cXV3h4OBgkH3l4eEh1WPIeuvi6+uLrKwslJeXS+FTVFSEvLw8nda1YsUKdO3aVe2jZXeruR2Xmpra7EGt6W+tp6en9ENJlzL64D1qHZw5cwZXr14FAPTq1Qs2NjbYtm2bNP/IkSO4ffu2dFaqSxlDGTBgAPbv34/i4mJp2tatW6U24M7zo0eOHKn1S2/Dhg0oKCjACy+8UKveoqIilc9VVVU4ePAgOnfubND2BwcHw8vLS2VfXb16FefOnVPZV+fOncOFCxcAAGVlZbWetTxw4ACqqqqk9ularyEMGDAAp0+fVrmSs3XrVrRr1066j1dUVIQjR46o3BKIiorC22+/jQ0bNmDYsGG16r37OwCAffv26f0dODs74/7771fZJ3l5eYiOjlbZJwkJCTh79qzOy+haryEMGDAAqampKs+Xb926FY6OjtIYAJWVlThy5AgyMzOlMhs3bsSzzz6LJUuWYOLEibXqVbev9+7dC09PT7WXefVlYmKC/v37q+yriooK7Ny5U2VfJScn4+TJkxrbl5aWhr/++ks6FnSt1xD69++PiooK7N69W5q2bds2mJqaol+/ftK0Y8eO4caNGyrL5ubmYuPGjXjxxRdr1avpO7C2tlbbn6CxpaWlqfxgHzBgAHbs2IHq6mpp2rZt21T2ry5l9NKgrmgKkJCQIA4fPixmz54t9Vo8fPiwyM3Nlcr4+/uLiIgI6fO8efOEg4ODWLZsmfjll19Eu3btxJNPPqlSry5lDCE/P1/ce++9YuDAgWLr1q1ixowZwtzcXOWZ7d9//10AEImJiSrLhoeHi+HDh6utd9y4cSIyMlJs3rxZbNy4UQwePFg4Ojoa/HEbcedRJQsLC/Hll1+KjRs3ipCQENG7d2+VXtR9+vQRjz32mBBCiMuXL4v//Oc/YsmSJWLPnj3i66+/Fm5ubmLUqFH1rtcQKisrRVhYmAgLCxObNm0S8+fPF+bm5uLXX3+VysTFxQkA4o8//hBCCLFq1SphYmIiZs6cKR1zhw8fVtm/b7/9tpgyZYr47bffxNatW8XYsWOFpaWl2Lt3r95t3bt3rzA3NxezZ88WW7ZsEX369BEdOnRQ6dX/7LPPivvuu69ey+hSxlBGjBgh2rdvL3799VcRFRUlbGxsxFdffSXNz8zMFADE6tWrhRBC7N69W1hYWIhJkyap7OuYmBhpmf/+97/i6aefFmvWrBE7duwQL7/8sjA3Nxc///yzwdsfFxcnbGxsREREhNi2bZsYOXKk8PT0lB5tE3eexf33kxshISFi/vz5YufOnWLVqlUiKChIdOzYUeXvlC71GsrLL78sPD09xc8//yxWrlwp3NzcRGRkpEoZKysrMX/+fJVpixcvFjY2Nmp71a9bt04MHTpUrFq1SuzatUvMmDFDWFpaqjw2aiiFhYXSceDk5CSmTZsmDh8+LM6fPy+VWbRokQAg9bC/fv26cHNzE2PHjhXbtm0TEydOFA4ODuLixYvSMrqU0UeLf3vWp59+ip07d9aavmjRIoSGhgIAnn76afTo0QNvvvmmNH/16tVYv349ysrK8Mgjj+D111+vdQ9ClzKGcOvWLXz22Wc4d+4cWrdujYiICPTq1Uuaf+zYMbzzzjv49ddfVTqhjRw5ErNnz1b7a6+8vBwrVqzA/v37UVlZiU6dOmHatGnw9PQ0ePtx51fnTz/9hPz8fPTs2RPTp09X6Wjy2muvwcHBAZ9++ilw56xvyZIluHDhAjw9PTFkyBA89dRTMDExqVe9hpKXl4fPP/8cJ06cgLOzMyZNmoQhQ4ZI8y9fvowJEyZIx9WHH36I/fv316onMDAQK1euBABUV1dj7dq12L59OwoKCtChQwe8+uqrKvex9XHo0CEsXboUmZmZCAkJwcyZM1UuK86dOxfJycn44YcfdF5G1zKGUFpaiq+//hp//PGH1HN47Nix0vy8vDwMGzYMc+bMwcCBAxEVFYVffvmlVj3Ozs7Yvn279Hnr1q3YsGEDsrKy4O/vjylTpqBr164Gbz/udAxcuHAhbty4gQ4dOmDGjBnSLQQAWLZsGXbt2oWtW7cCAG7fvo1Fixbh1KlTsLe3xwMPPICXX3651iiBddVrKFVVVVi6dCl27NgBExMTPProo5gyZYrKLbT+/ftj4sSJePbZZ6Vpr776KpycnDT2sYiOjsbq1atx48YN+Pn54bnnnlP5W2YoNf8e7/bwww9Lf2M2bdqEr776CocOHZL6Gl2+fBmff/45rly5Al9fX7z11lvo1KlTrbrrKlNfLT6oiYiI5Iz3qImIiGSMQU1ERCRjDGoiIiIZY1ATERHJGIOaiIhIxhjUREREMsagJiIikjEGNelFCIH169dLA843pd27dyMhIaFey2zevLnON3r9/vvv0lCxJG/79++XhpSFnsdEY7u7jcagofvRGL4XY8SgJr38+OOPiIqKksYTb0pvv/222tHktDl48CBmz56ttcxrr72GgwcPNrB1wM6dO5GYmNjgepTKEPtn1qxZ0qhd0POYaGx3t/FucjxOGrofjeF7MUYMaqq3iooKzJ49G++//35zN0Vn77zzDr777jtcv3690dcVGRmJPXv2NPp6jFVj7J8hQ4agQ4cOBq2zsbWE48QYvxc5UtxrLukflZWVOHXqFLKysnDffffBy8tLZX5RURGOHj2KkpIShIWF1ZoPAGfPnkVycjL8/f1VxqrduHEjzMzM0L9/f2ladHQ0bt26BRMTE7Ru3RqhoaFwcnJSqe/3339Hp06d4ODggLi4ONjY2OCBBx6o9fq3zMxMHD9+HK1bt0ZISAgOHz4Mb29vBAUFad3emJgYZGdno2PHjmjfvr3KfB8fH/Tu3RvLly/HJ598onXf3bp1C2fPnoWNjQ169eql8k7xutZ14MABFBQUIDY2FuvWrQMAdOrUCVlZWejbty9w521me/bswQMPPAA/Pz8AQGxsLEpKSqRxjevaHl3K6Lq/1S1ja2urdR/oe/yo2z/qxmi/2+3bt3H06FHpmLhb//79VcZAr9kOa2trnD59Gm3atEFYWFit5Qx53NbVxn/TtB+2b9+utt11tfP48eMQQuDBBx9UWc/Ro0eBO2/0g47HVX001vdCqhjUCnT+/HmMHDkS5eXl6NSpEy5cuICZM2diypQpwJ1/vCNHjoS3tzdcXV0RExODTz75BJGRkcCdf8yPPfYY4uPjERYWhuTkZHh5eWHLli2wsLDA9u3bER4erjIA/9GjR3Hu3DkIIZCcnIzExESsW7dO5YUfr732GgIDA3H16lUEBwcjPj4enp6eOHLkCKysrAAAe/bswejRoxEUFAQ7Ozvcvn0bhYWFeOmllzQG9YULF/Doo4/CxsYGfn5+OHHiBAYNGoRVq1aptLFfv35Yv3691qBet24dPvjgA3Tr1g2xsbEICAjAnj17YGdnp9O6Dh8+jIKCApw9e1Z69agQAm+88QbS09OBOy8KmTBhAqZPn47PP/8cAPDWW2/h4YcfRq9evXTaHl3K6LK/7/baa68hICAAFy5c0LgPGnL8qNs/Tz75pNagPnjwIEaOHIkOHTrAzs4OWVlZtV6J+Pbbb2Py5MlS8NRsx+XLlxESEoKhQ4eqDQRDHbe6tPHfNO0HTe2uq52xsbH49NNPkZKSIn3/1dXVGDNmDGbOnKnzcVUfjfm90F0a9O4tkp3KykoRGBgoxowZI8rLy4UQQpSXl4udO3dK/x0YGCimTp0qLbNhwwZhZmYm/v77byGEENHR0cLGxkblFXq7d+8WRUVFQgghgoKCxKeffqq1Hd98843w8/NTmebr6yu6dOkiCgoKhBBC5OTkCBcXF+lVguXl5cLPz0+89dZb0jLff/+9AKDyurxOnTqJr7/+WgghRFVVlQgKChJz586V5ufk5Ag/Pz/x/fffq6x/69atwsTERJSVlalts6+vr/D09BRpaWlCCCGysrKEr6+v+Pjjj+u1rg4dOohFixZJn2/duiUASPt3woQJ4v777xdhYWFCCCFKS0uFtbW1OHjwoE7r0LUdde1vffaBIY6fu/ePNhUVFcLf31/lFYpLly7VekzUbEdAQIDa1ylqo89xq2sb76ZuP+ja7rvbmZmZKczNzcX+/fulafv27RPm5uYiPT1d52Pm7v2oSVN/Ly0d71ErzLFjx5CYmIj58+dLl+YsLCykVy7GxsYiMTER7733nrTM6NGjERgYiA0bNgAAbGxsUFlZifPnz0tlBg0aBFtbWwBAVlYWXFxcaq07NTUVe/fuxa+//goTExNcu3YNmZmZKmXGjRsHe3t74M5rBrt164ZLly4BAE6fPo1r165h+vTpUvmJEyfC1dVV4/bGxMTgwoULaNu2LTZs2ID169dj7969CAgIwB9//KFS1sXFBUII3L59W2N948aNk17l6ebmhkmTJuG3336r97r+rXXr1ujYsaNUJjo6GnPmzEF8fDzy8/MRExMDIQR69uyp0zrq0w5t+1uffWCI46c+Tp8+jStXruCdd96Rpr344otqj7+7jR8/Hs7OznWWM8Rxq28b69Nube10d3fHoEGDsGbNGqn8mjVrMGDAAHh4eOh97GrSFN8L/X+89K0w169fh7m5ucZ3FicnJ8Pc3Bw+Pj4q0/39/ZGcnAwA6N69O2bNmoVhw4bB1dUV/fr1w4svvihdorK3t691iWvmzJlYtGgRwsLC0KpVK1RUVAAAMjIyVN5JfHfoWllZobS0FACQkpICa2trtG7dWppvamqKe+65R+P2Xrt2Daampti9e7fKdDc3NwQHB6tMq2mztvdR19wzrtGuXTtpv9RnXXcLDw/HH3/8gREjRiA9PR2DBg1Cp06d8Oeff+LMmTPo0aMHrK2tdVpHfdqhbX/rsw8McfzUx/Xr12sdE2ZmZlqPiRpt2rSps4whjtuGtFHXduvSzmeffRZTp07FkiVLgDvvU166dCnQwGNXncb+XkgVg1phnJ2dUVlZiby8PLW/Wt3d3VFZWYmCggKVwMrOzlbpMDZnzhy89957iIuLw2+//YaePXvi+PHjCAsLQ/v27ZGUlCSVTUhIwIIFC3D27Fl07doVAPDXX39h69atqM/rzl1dXVFaWorS0lJYW1tL03NycjQu4+joiOrqanzzzTcqfzTUSUpKgpeXl3RmpM7d68rJyYG7u3u913W38PBwvPrqqzh48CB69eoFS0tLhIeHIzo6GmfOnEF4eLjO62hIO3ShbR8Y4vipDzc3N5SWlqKkpETlUUBtx0SNujqoGeq4bUgbdWm3ru187LHHMGXKFGzfvh1CCFRXV2PkyJFAIxwzjfm9UG289K0wvXr1gq2tLX7++WeV6TWXyEJCQmBvb49NmzZJ85KTk3Hq1Cn07t0bAJCeno7KykqYm5sjLCwMX3zxBby9vXHq1CngTk/Omt6kuNNL2tTUFIGBgdK0msug9REaGgobGxts27ZNmlbTc1iT3r17w87ODt99953K9Orqaty6dUtl2tGjR/HII49obcPdf/w2bdok9ZjVdV329va1zlrDw8ORlZWFqKgoKZTDw8OxZ88exMTESNN0WUd9tlkf2vaBIY4fdftHk9DQUNjZ2ak8m3vmzBmDPGZnyONWnzbquh90baetrS1GjRqFNWvWYM2aNRg1apR0u8HQx0xjfi9UG8+oFcbZ2RnffvstXn75ZSQkJCAkJAQnT56Eubk5lixZAldXV3z00Ud45ZVXcO3aNbi6uuLbb79Fv379MGLECABAXFwc3n77bYwZMwZ+fn44duwYcnNzMXDgQADA888/j9mzZ+PixYvo2LGj9PjXE088gdGjR6s8clIfbm5ueOedd/Diiy/i8uXLsLOzw7fffgtHR0eNv8KdnZ2xdOlSTJo0CUlJSejVqxfS0tKwefNmzJw5E08++SQAoKSkBL///ju2b9+utQ3Xrl3Do48+ihEjRmDfvn04c+YMli9fXq913X///Vi9ejVatWoFKysrPPXUU9J96tOnT+Obb74BAPTp0wfnz5+HhYUFevbsqfM6dG2HvrTtA0McP+r2j6bv19XVFTNnzsSLL76IK1euwM7ODgsXLoSjo2ODthGAwY5bfduobj80tJ3jxo2Tvofff/9dmm7oY6YxvxeqjWfUCjRp0iQcO3YMVlZWiImJQY8ePbBo0SJpfmRkJDZt2oT09HTExsZi+vTpKv+oBw8ejM2bN0MIgejoaLRp0wZnz55FQEAAAMDDwwNTpkyRAsfOzg7Hjx9H165dER0djVatWuHIkSN46qmnVC6/jxgxAv7+/iptDQ8PR2hoqPT5gw8+wNKlS5GYmIi0tDT89ttv8PDwULnMevcgCuPHj0dsbCzatGmDw4cPo7KyEqtXr1b54/PTTz8hNDQUDz30kMb9NmLECKxbtw6PP/44YmNj0bZtW5w8eVLlTEaXdS1YsABjx47FH3/8gS1btkhnp9OmTcPYsWOly7/Ozs54/fXXERkZqXKpX5d16FJGl/2tzrx587Tug4YeP5r2jyazZs3C999/j8uXL+PWrVvYuHEjpk2bpnJv9e5jQt22382Qx60ubbybuv2gbl26thN3rnaNHTsWY8eOVRnnADoeM/UZoKSxvheqzUTU52YM0R05OTl48803sXTpUoMOI5qdna3ScefSpUvo1KkTjh49ih49euhd71tvvYUJEyagS5cuBmqpMvn5+WHWrFmYPHlyczeFiO7gpW/Si4uLC1atWmXwenfs2IFNmzZh0KBByMvLw6JFizBs2LAGhTQA/Pe//zVYG8lwbt26hejoaI3zn3766SZtD/2D34u8MKhJVsaPHw8XFxfs3bsXQggsWLAAzzzzTHM3q8Vo6kuT6enp2LJli8b5DITmwe9FXnjpm4iISMbYmYyIiEjGGNREREQyxqAmIiKSMQY1ERGRjDGoiYiIZIxBTUREJGMMaiIiIhljUBMREckYg5qIiEjG/h9hZ0kXp3xeTgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# cos(angle) between post_dir and travel_dir — coupled through the\n",
-    "# scattering physics, so this is another joint-structure check.\n",
-    "_ = plot_direction_alignment(FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6aed1234",
-   "metadata": {},
-   "source": [
-    "## Tier 3: physical constraints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2fb6c511",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 2000x350 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Unit-norm direction vectors, non-negative step_length/delta_e/edep — the\n",
-    "# unconstrained MLP has nothing enforcing these, so any violation here is a\n",
-    "# pure generation artifact rather than a real-data property.\n",
-    "_ = plot_constraint_violations(FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a0446d6",
-   "metadata": {},
-   "source": [
-    "## Tier 4: event-level (shower) observables\n",
-    "\n",
-    "Everything above is a **step-level** check: one row in, one row out, compared in the local frame (`pre_dir = ẑ`). This section aggregates those same rows **per `event_id`**, reconstructed into world-frame physical units (mm, MeV), to check the shower-level quantities that actually matter physically: total deposited energy, longitudinal/transverse shower profiles, and shower-max depth (see `diffusion-model-tutorial.md` §7.2).\n",
-    "\n",
-    "**Caveat:** this re-aggregates one-step-ahead generations — each row is generated conditioned on the *real* preceding state, then grouped by event — not a full autoregressive shower rollout. It won't surface covariate-shift failures that only appear under true rollout, only how well one-step generation reconstructs aggregate shower structure when fed real conditioning throughout.\n",
-    "\n",
-    "`compute_event_observables_pl` streams the full file directly (two polars passes, no `SampleCollection`) rather than reusing `samples` above — per-event sums would be silently corrupted by `sample_frac`-style row subsampling, since a partially-sampled event no longer sums to the true per-event total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d9a6c47b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from giant.analysis import compute_event_observables_pl\n",
-    "from giant.analysis import plot_total_energy, plot_total_length\n",
-    "from giant.analysis import plot_mean_energy_per_step, plot_mean_length_per_step\n",
-    "from giant.analysis import plot_longitudinal_profile\n",
-    "from giant.analysis import plot_transverse_profile, plot_shower_max_depth\n",
-    "\n",
-    "# Full file, not `samples` — see the markdown cell above for why.\n",
-    "obs = compute_event_observables_pl(FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "078d4844",
-   "metadata": {},
-   "source": [
-    "### Total deposited energy per event\n",
-    "\n",
-    "`sum(edep)` grouped by `event_id`, real vs. generated, with the resolution (σ/μ) for each annotated in the legend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0e5d3f74",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_total_energy(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c63c999",
-   "metadata": {},
-   "source": [
-    "### Total length traveled per event\n",
-    "\n",
-    "`sum(step_length)` grouped by `event_id` — total path length traveled by every track in the shower, real vs. generated (not the same as the depth of any single point, since tracks scatter)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d6561b73",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_total_length(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dab935fd",
-   "metadata": {},
-   "source": [
-    "### Mean deposited energy per step, per event\n",
-    "\n",
-    "`total_edep / n_steps` grouped by `event_id` — the per-event mean, as opposed to the per-event total above, so a difference here points at the typical step's energy deposit rather than at how many steps the event happened to have."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "e761bdc2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_mean_energy_per_step(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6b6ae02",
-   "metadata": {},
-   "source": [
-    "### Mean step length per step, per event\n",
-    "\n",
-    "`total_length / n_steps` grouped by `event_id` — the per-event mean step length, as opposed to the per-event total above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "79b08c5a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_mean_length_per_step(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf3ea615",
-   "metadata": {},
-   "source": [
-    "### Longitudinal profile\n",
-    "\n",
-    "Mean deposited energy per event, binned by depth along the shower axis (the `pre_dir` of each event's highest-`pre_E` row), with the event-to-event RMS as error bars — the classic `E_dep(depth)` profile."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "a94c6034",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_longitudinal_profile(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adbbd0ef",
-   "metadata": {},
-   "source": [
-    "### Transverse profile\n",
-    "\n",
-    "Same idea, binned by perpendicular distance from the shower axis instead of depth — a Molière-radius-style lateral containment check."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f3652b29",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_transverse_profile(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de4713d9",
-   "metadata": {},
-   "source": [
-    "### Shower-maximum depth\n",
-    "\n",
-    "Per event, the depth bin where that event's longitudinal profile peaks — compares the real vs. generated distribution of shower-max depth across events, rather than the pooled profile above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "78bf19d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_shower_max_depth(obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8dabe1c",
-   "metadata": {},
-   "source": [
-    "## Particle-species (pdg) contribution shares\n",
-    "\n",
-    "Dataset-wide (not per-event) breakdown of which pdg species contributed how much of the total deposited energy / total length traveled, real vs. generated. Pure lazy `group_by(\"pdg\")` over the whole file — no post_pos reconstruction needed since these are scalar sums."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "d4ef0559",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from giant.analysis import pdg_contribution_table_pl\n",
-    "from giant.analysis import plot_pdg_energy_share, plot_pdg_length_share\n",
-    "\n",
-    "pdg_table = pdg_contribution_table_pl(FILE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44aa3ad3",
-   "metadata": {},
-   "source": [
-    "### Energy share by particle type\n",
-    "\n",
-    "Two pies side by side — real and generated — so the breakdown of which species deposits the energy can be compared directly. Small contributors are lumped into \"other\" (see `max_slices`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "06952b61",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 900x450 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_pdg_energy_share(pdg_table)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b1ee5f0",
-   "metadata": {},
-   "source": [
-    "### Length-traveled share by particle type\n",
-    "\n",
-    "Same breakdown, but by total path length traveled (`sum(step_length)`) rather than deposited energy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d46f1d96",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 900x450 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_pdg_length_share(pdg_table)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c61eeb68",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "giant",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/phase2_plan.md b/docs/phase2_plan.md
deleted file mode 100644
index 596edec..0000000
--- a/docs/phase2_plan.md
+++ /dev/null
@@ -1,172 +0,0 @@
-# Phase 2: Secondary Particle Prediction
-
-## Context
-
-Phase 1 takes `n_sec` (secondary count) and `e_sec` (total secondary energy) as **conditioning inputs**. Phase 2 must instead **predict** them, making the surrogate self-contained for shower rollout. Per Jan's 2026-06-29 decision: hard discrete `n_sec` integer head; escalation to Gumbel-Softmax only if empirically needed.
-
-Two-stage factorization:
-- **Stage 1**: existing 9D flow model (reduced conditioning: drop `n_sec` + `log(e_sec)`) + a new discrete `n_sec` classification head
-- **Stage 2**: non-AR flow matching over `K_MAX` secondary slots simultaneously, each slot predicting `(stick_break_logit, dir_local_3D, type_emb)` — conditioned on pre-step state + Stage 1 output; padded slots masked from loss
-
-Training: joint, combined loss `L = L_flow_s1 + λ_nsec * L_nsec + λ_s2 * L_flow_s2`.
-
----
-
-## Prerequisite: Determine K_MAX
-
-Before implementing, run a quick analysis over existing parquet files to find `max(n_sec)` and the 99th percentile. Expected to be 5–20 for EM shower steps. Set `K_MAX` as a constant in `giant/constants.py` (suggest 15 as a starting point, revise from data).
-
----
-
-## New Branch
-
-```bash
-git checkout -b phase2-secondary-prediction master
-```
-
----
-
-## Part A — Data Pipeline
-
-### A1. `scripts/steps_to_parquet.py`
-
-Extend `_add_secondary_energy` to also collect per-secondary attributes from the spawning tree join:
-- For each `child_track_id`, look up the child's first step → get `pdg`, `pre_E`, `pre_dx/dy/dz`
-- Emit list columns in the parquet: `sec_pdg_list`, `sec_E_list`, `sec_dx_list`, `sec_dy_list`, `sec_dz_list`
-- Lists are sorted **descending by energy** at write time
-- Truncate to `K_MAX` entries if needed (flag if any row truncated)
-
-Re-run ROOT→parquet conversion after this change.
-
-### A2. `giant/data/loader.py`
-
-In `_df_to_dict`: read the five new list columns. Pad each to length `K_MAX` with zeros (energy) / sentinel values (pdg → 0, dir → (0,0,1)). Return as fixed-shape arrays `(N, K_MAX)` / `(N, K_MAX, 3)`.
-
-Also return a boolean validity mask `sec_valid` of shape `(N, K_MAX)`: `True` for slots `i < n_sec`.
-
-### A3. `giant/data/transforms.py`
-
-Add `encode_secondaries(sec_pdg_list, sec_E_list, sec_dir_list, sec_valid, e_sec, pdg_emb_weight, pre_dir, K_MAX)`:
-1. **Direction**: call existing `local_frame_rotation` per slot
-2. **Energy (stick-breaking)**:
-   - Slot 0: `f_0 = E_0 / e_sec` → logit `log(f_0/(1-f_0))` (clamped)
-   - Slot i: `f_i = E_i / (e_sec - sum(E_0..E_{i-1}))` → logit
-   - Last valid slot: logit = large positive constant (takes all remaining budget)
-   - Padding slots (beyond `n_sec`): set logit = 0, masked out of loss anyway
-3. **Type embedding**: index into `pdg_emb_weight` (the PDG embedding table weights) to get the target embedding vector for each secondary's `pdg`. Shape `(K_MAX, emb_dim)`.
-
-Returns `sec_targets: (K_MAX, 1 + 3 + emb_dim)` and `sec_valid: (K_MAX,)`.
-
-Inverse (`decode_secondaries`): sigmoid stick-breaking fractions → energies, inv local frame rotation → world dirs, nearest-neighbor lookup in PDG embedding table → pdg code.
-
-### A4. `giant/data/dataset.py`
-
-Update `build_features` and `StreamingStepsDataset.__iter__` to also yield `sec_targets` and `sec_valid` alongside the existing `(cond_cont, cond_cat, x1)` batch items.
-
----
-
-## Part B — Constants (`giant/constants.py`)
-
-- `COND_DIM`: 10 → **8** (remove `n_sec` and `log(e_sec)`)
-- Add `K_MAX: int` (set after data analysis, e.g. 15)
-- Add `SEC_SLOT_DIM: int` (= 4 + `emb_dim` = 20 for default emb_dim=16; 1 stick + 3 dir + 16 type)
-- Add `SEC_DIM: int = K_MAX * SEC_SLOT_DIM` (flattened Stage 2 target dimension)
-- Update `LOCAL_TARGET_NAMES` (Stage 1 only, still 9D)
-
----
-
-## Part C — Model (`giant/model/network.py`)
-
-### C1. `DenoisingMLP` — Stage 1 (minimal changes)
-
-- `ConditionEncoder.cont_dim` drops from 10 to 8 (COND_DIM change propagates automatically)
-- Add `n_sec_head = nn.Sequential(Linear(cond_out_dim, hidden_dim//2), SiLU(), Linear(hidden_dim//2, K_MAX + 1))` applied to `c_emb` (the condition encoding, not the diffused latent)
-- Add method `predict_n_sec(cond_cont, cond_cat) -> Tensor[B, K_MAX+1]` — no diffusion, just encode conditioning and run the head
-
-### C2. `SecondaryDecoder` — Stage 2 (new class)
-
-Architecture mirrors `DenoisingMLP` but:
-- **Input**: `x_t` of shape `(B, SEC_DIM)` (flattened K_MAX secondary slots)
-- **Conditioning**: pre-step state (8D cont + 2 cat → same ConditionEncoder as Stage 1) concatenated with Stage 1 output (9D normalized target, detached from Stage 1 loss for stability initially). Total cond dim to the ResBlocks: `time_dim + cond_s1_out_dim + 9`
-- **Output**: vector field of shape `(B, SEC_DIM)`
-- Uses same `ResBlock` / `SinusoidalEmbedding` / `ConditionEncoder` building blocks
-
-A `SecondaryConditionEncoder` wraps the base `ConditionEncoder` and concatenates the Stage 1 output:
-```python
-class SecondaryConditionEncoder(nn.Module):
-    # base: ConditionEncoder(pdg_vocab, mat_vocab, 8, emb_dim, cond_out_dim)
-    # stage1_proj: Linear(X_DIM, stage1_cond_dim)
-    # mlp: fuses both
-```
-
----
-
-## Part D — Loss / Training
-
-### `giant/model/schedule.py`
-
-Add `flow_matching_loss_masked(model, x1, cond_cont, cond_cat, mask)`:
-- Same as `flow_matching_loss` but divides by `mask.sum()` instead of `B * SEC_DIM`, zeroing out padded slots before averaging. `mask` shape: `(B, K_MAX)`, broadcast over slot dims.
-
-### `giant/train.py`
-
-Batch now unpacks as `(cond_cont, cond_cat, x1_s1, n_sec_target, x1_s2, sec_mask)`.
-
-Combined loss per batch:
-```
-L_s1   = flow_matching_loss(stage1_model, x1_s1, cond_cont, cond_cat)
-L_nsec = cross_entropy(stage1_model.predict_n_sec(cond_cont, cond_cat), n_sec_target)
-L_s2   = flow_matching_loss_masked(sec_decoder, x1_s2, cond_cont, cond_cat, stage1_detached, sec_mask)
-L      = L_s1 + lambda_nsec * L_nsec + lambda_s2 * L_s2
-```
-
-Config adds `lambda_nsec` (suggest 0.1) and `lambda_s2` (suggest 1.0) under `[train]`.
-
-Both `stage1_model` and `sec_decoder` share a single `optimizer` (AdamW over all parameters).
-
-Checkpoint saves both `stage1_model.state_dict()` and `sec_decoder.state_dict()`, plus `K_MAX` and `SEC_SLOT_DIM` in `model_config`.
-
-### `giant/pipeline.py`
-
-- Compute `K_MAX` from data (max `n_sec` over training events) before constructing models
-- Build both `DenoisingMLP` and `SecondaryDecoder`, pass both to `run_training`
-
----
-
-## Part E — Sampling (`giant/sample.py`)
-
-```python
-def sample_stage1(model, cond_cont, cond_cat, steps=10):
-    # Euler ODE → primary sample (9D), + argmax n_sec head
-    ...
-
-def sample_secondaries(sec_decoder, cond_cont, cond_cat, stage1_out, n_sec, steps=10):
-    # Euler ODE on SEC_DIM → decode stick-breaking → energies
-    # inv_local_frame_rotation → world-frame dirs
-    # nearest-neighbor in pdg_emb_weight → pdg codes
-    # mask slots >= n_sec
-    ...
-```
-
----
-
-## Part F — Wiring
-
-- **`giant/validate.py`**: add secondary-specific marginals (n_sec distribution, species distribution, energy fraction per slot)
-- **`giant/cli.py`**: `predict` command loads both checkpoints, calls both samplers, appends secondary columns to output parquet
-
----
-
-## Type embedding design note
-
-The type embedding target at training is `pdg_emb.weight[sec_pdg_idx]` (the Stage 1 PDG embedding table rows). Gradients flow into the embedding table from both the conditioning path (input PDG) and the secondary type loss — this is intentional; the shared embedding space is the bridge. At inference, snap: `argmin_k ||pred_emb - pdg_emb.weight[k]||`.
-
----
-
-## Verification
-
-1. `uv run pytest` — existing tests pass (Stage 1 shape/interface unchanged beyond COND_DIM)
-2. Unit tests for `encode_secondaries` / `decode_secondaries` (round-trip: energies sum to `e_sec`, directions are unit vectors)
-3. Unit test for `flow_matching_loss_masked`: verify padded slots contribute zero gradient
-4. Short training run (1–2 epochs): confirm all three loss components decrease
-5. Sampling smoke test: verify `sum(sec_E) ≈ e_sec` per sample, all directions unit-normed
diff --git a/giant/analysis.py b/giant/analysis.py
deleted file mode 100644
index ec2fe40..0000000
--- a/giant/analysis.py
+++ /dev/null
@@ -1,2359 +0,0 @@
-"""Notebook diagnostics for a trained model's sample quality, fully streaming.
-
-Every check here reads directly from a `giant predict --coord local` parquet
-file (`pred_*`/`true_*` columns, denormalized but still local-frame/log-scaled)
-and is computed with **lazy, streaming polars** so peak memory stays bounded no
-matter how large the file is (production predict output runs to tens of GB).
-There is no in-memory `SampleCollection`, no full-array materialization, and no
-on-the-fly (checkpoint + live sampler) path: generate predictions once via the
-CLI, then run every diagnostic below against that file.
-
-Each public function takes `source: str | Path | pl.LazyFrame` — a path to the
-predict parquet, or a pre-built `LazyFrame` with the same columns (for tests) —
-and returns either a matplotlib figure or a small reduced table/dataclass.
-
-Four tiers of checks::
-
-    from giant.analysis import plot_kl_bars_pl, plot_marginals
-    from giant.analysis import plot_correlation_matrices, plot_pairwise
-    from giant.analysis import plot_direction_alignment, plot_constraint_violations
-
-    FILE = "path/to/steps_predicted_local.parquet"
-
-    # Tier 1 — stratified marginals (per-dimension real-vs-generated, sliced by
-    #          pdg / material / energy so failures hidden by the aggregate show)
-    for grouping in (None, "energy", "pdg", "material"):
-        plot_kl_bars_pl(FILE, group_by=grouping)
-    plot_marginals(FILE)
-    plot_marginals(FILE, group_by="energy")
-
-    # Tier 2 — joint structure (correlations + physically-coupled pairs + the
-    #          post/travel direction alignment marginals can't see)
-    plot_correlation_matrices(FILE)
-    plot_pairwise(FILE)
-    plot_direction_alignment(FILE)
-
-    # Tier 3 — physical constraints (unit-norm directions, non-negative scalars;
-    #          any violation is a pure generation artifact of the unconstrained MLP)
-    plot_constraint_violations(FILE)
-
-For event-level (shower) observables — total deposited energy, total length,
-longitudinal/transverse profiles, shower-max depth — aggregated per `event_id`
-in the world frame with physical units (mm, MeV)::
-
-    from giant.analysis import compute_event_observables_pl
-    from giant.analysis import plot_total_energy, plot_total_length
-    from giant.analysis import plot_mean_energy_per_step, plot_mean_length_per_step
-    from giant.analysis import plot_longitudinal_profile, plot_transverse_profile
-    from giant.analysis import plot_shower_max_depth
-
-    obs = compute_event_observables_pl(FILE)
-    plot_total_energy(obs)
-    plot_total_length(obs)
-    plot_longitudinal_profile(obs)
-    plot_transverse_profile(obs)
-    plot_shower_max_depth(obs)
-
-This re-aggregates one-step-ahead generations (each row generated conditioned on
-the *real* preceding state) grouped by event — not a full autoregressive shower
-rollout — so it won't surface covariate-shift failures that only appear under
-true rollout, only how well one-step generation reconstructs aggregate shower
-structure when fed real conditioning throughout.
-
-For the dataset-wide breakdown of which particle species (pdg) contributed how
-much of the total energy/length::
-
-    from giant.analysis import pdg_contribution_table_pl
-    from giant.analysis import plot_pdg_energy_share, plot_pdg_length_share
-
-    table = pdg_contribution_table_pl(FILE)
-    plot_pdg_energy_share(table)
-    plot_pdg_length_share(table)
-
-To compare a full autoregressive `giant rollout` shower against held-out truth
-data instead of one-step-ahead `giant predict` output, pass a `RolloutVsTruth`
-in place of `source` everywhere above (Tiers 1-3), and use
-`compute_rollout_vs_truth_observables_pl` in place of `compute_event_observables_pl`
-for Tier 4::
-
-    from giant.analysis import RolloutVsTruth, compute_rollout_vs_truth_observables_pl
-
-    source = RolloutVsTruth(rollout="path/to/rollout.parquet", truth="path/to/val.parquet")
-    plot_marginals(source)
-    obs = compute_rollout_vs_truth_observables_pl(source.rollout, source.truth)
-    plot_total_energy(obs)
-
-`rollout` and `truth` are independent, unpaired files (a rollout doesn't replay
-real events row-for-row) — unlike the paired `pred_*`/`true_*` predict-parquet
-`source`, they may have different row/event counts. Everything still streams:
-each side is scanned and decoded into `RAW_TARGET_NAMES` space with polars
-expressions (the forward local-frame rotation, mirroring
-`giant.data.transforms.local_frame_rotation`/`travel_direction`), never
-materialized as a whole.
-"""
-
-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 polars as pl
-
-import pyarrow.parquet as pq
-
-from giant.constants import (
-    LOCAL_TARGET_NAMES,
-    PREDICT_COORD_METADATA_KEY,
-    PREDICT_SCHEMA_VERSION,
-    PREDICT_SCHEMA_VERSION_KEY,
-    ROLLOUT_COORD_VALUE,
-    TERM_ENERGY_CUTOFF,
-    TERM_ESCAPED,
-    TERM_MAX_STEPS,
-    TERM_UNKNOWN_PDG,
-)
-
-# The first 3 target dims are the scalar (non-direction) outputs. In raw/physical
-# space they are step_length, delta_e, edep; in the model's native target space the
-# first is log_step_length and the next two are the deposit/secondary ALR energy
-# logits (see giant.constants.LOCAL_TARGET_NAMES and energy_simplex_encode).
-_N_SCALAR_DIMS = 3
-RAW_TARGET_NAMES = [
-    "step_length",
-    "delta_e",
-    "edep",
-    "post_dx",
-    "post_dy",
-    "post_dz",
-    "travel_dx",
-    "travel_dy",
-    "travel_dz",
-]
-_LOG_EPS = (
-    1e-8  # mirrors giant.data.transforms._EPS, duplicated for use in polars exprs
-)
-
-
-# ---------------------------------------------------------------------------
-# Predict-parquet scanning, metadata verification, and physical-value exprs
-# ---------------------------------------------------------------------------
-
-
-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' — "
-            "giant.analysis 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 giant.analysis to match"
-        )
-
-
-def _scan_predicted_local(source: str | Path | pl.LazyFrame) -> pl.LazyFrame:
-    if isinstance(source, pl.LazyFrame):
-        return source
-    path = Path(source)
-    _check_predict_metadata(path)
-    return pl.scan_parquet(path)
-
-
-def _edep_pl(prefix: str) -> pl.Expr:
-    """Physical edep from a `{prefix}_edep_logit`/`{prefix}_sec_logit` pair + `pre_E`.
-
-    Polars equivalent of `energy_simplex_decode(...)[0]` (the deposit component):
-    a softmax over `[z_edep, z_sec, 0]` times pre_E.
-    """
-    z1, z2 = pl.col(f"{prefix}_edep_logit"), pl.col(f"{prefix}_sec_logit")
-    m = pl.max_horizontal(z1, z2, pl.lit(0.0))
-    e1, e2, e3 = (z1 - m).exp(), (z2 - m).exp(), (pl.lit(0.0) - m).exp()
-    return (e1 / (e1 + e2 + e3)) * pl.col("pre_E")
-
-
-def _delta_e_pl(prefix: str) -> pl.Expr:
-    """Physical delta_e (= edep + e_sec = pre_E - post_E) from the ALR logits + pre_E."""
-    z1, z2 = pl.col(f"{prefix}_edep_logit"), pl.col(f"{prefix}_sec_logit")
-    m = pl.max_horizontal(z1, z2, pl.lit(0.0))
-    e1, e2, e3 = (z1 - m).exp(), (z2 - m).exp(), (pl.lit(0.0) - m).exp()
-    return ((e1 + e2) / (e1 + e2 + e3)) * pl.col("pre_E")
-
-
-def _raw_dim_expr(prefix: str, j: int) -> pl.Expr:
-    """Physical raw value of target dim j (`RAW_TARGET_NAMES[j]`) from a predict parquet.
-
-    Dim 0 is de-logged step_length; dims 1–2 are the physical `delta_e`/`edep`
-    decoded from the deposit/secondary ALR logits against `pre_E`; dims ≥3 are
-    direction components, used as-is. `prefix` is "true" or "pred".
-    """
-    if j == 0:
-        return pl.col(f"{prefix}_log_step_length").exp() - _LOG_EPS
-    if j == 1:
-        return _delta_e_pl(prefix)
-    if j == 2:
-        return _edep_pl(prefix)
-    return pl.col(f"{prefix}_{LOCAL_TARGET_NAMES[j]}")
-
-
-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. Used only on small per-event
-    reduced arrays, never on raw file rows.
-    """
-    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)
-
-
-# ---------------------------------------------------------------------------
-# Streaming histogram / KL primitives
-# ---------------------------------------------------------------------------
-
-
-def _kl_from_counts(
-    real_counts: np.ndarray, gen_counts: np.ndarray, eps: float = 1e-8
-) -> float:
-    """KL(real || gen) from two aligned histogram bin-count arrays.
-
-    Add-eps smoothing then normalize both to distributions, as is standard for a
-    histogram-estimated KL; the counts here come from a polars `group_by`
-    aggregation rather than `np.histogram`.
-    """
-    p = real_counts.astype(np.float64) + eps
-    q = gen_counts.astype(np.float64) + eps
-    p /= p.sum()
-    q /= q.sum()
-    return float(np.sum(p * np.log(p / q)))
-
-
-def _resolve_hist_range(
-    lf: pl.LazyFrame, expr: pl.Expr, lo: float | None, hi: float | None
-) -> tuple[float, float]:
-    """Fill in any missing (lo, hi) from a streaming min/max pass over `expr`.
-
-    Mirrors `_hist_edges`'s degenerate-range widening (±0.5 when the span is too
-    tight to support distinct edges) so fixed-edge and data-ranged histograms
-    agree on edge cases. Only runs the extra streaming pass when a bound is
-    actually unknown.
-    """
-    if lo is not None and hi is not None:
-        return lo, hi
-    row = (
-        lf.select(expr.min().alias("lo"), expr.max().alias("hi"))
-        .collect(engine="streaming")
-        .row(0, named=True)
-    )
-    data_lo, data_hi = row["lo"], row["hi"]
-    if not (data_hi - data_lo > 1e-6 * max(abs(data_hi), 1.0)):
-        data_lo, data_hi = data_lo - 0.5, data_hi + 0.5
-    return (data_lo if lo is None else lo, data_hi if hi is None else hi)
-
-
-def _streaming_hist1d(
-    lf: pl.LazyFrame,
-    expr: pl.Expr,
-    bins: int,
-    lo: float | None = None,
-    hi: float | None = None,
-) -> tuple[np.ndarray, np.ndarray]:
-    """Bounded-memory 1D histogram of `expr` over `lf`, returning (counts, edges).
-
-    A single streaming `group_by(bin).len()` pass — a proper single hash-pass
-    histogram — after (optionally) a streaming min/max pass to fix the range
-    when `lo`/`hi` aren't supplied. Never materializes the underlying column.
-    """
-    lo, hi = _resolve_hist_range(lf, expr, lo, hi)
-    width = hi - lo
-    bin_expr = ((expr - lo) / width * bins).floor().cast(pl.Int64).clip(0, bins - 1)
-    hist = (
-        lf.select(bin_expr.alias("bin"))
-        .group_by("bin")
-        .agg(pl.len().alias("count"))
-        .collect(engine="streaming")
-    )
-    counts = np.zeros(bins, dtype=np.int64)
-    for bin_idx, count in hist.iter_rows():
-        counts[bin_idx] = count
-    return counts, np.linspace(lo, hi, bins + 1)
-
-
-def _step_density(counts: np.ndarray, edges: np.ndarray) -> np.ndarray:
-    """Density-normalized bin heights (integrate to 1), matching `hist(density=True)`."""
-    total = counts.sum()
-    if total == 0:
-        return counts.astype(np.float64)
-    width = edges[1] - edges[0]
-    return counts.astype(np.float64) / (total * width)
-
-
-# ---------------------------------------------------------------------------
-# Rollout vs. held-out truth: a second, unpaired `source` every Tier 1-3
-# function below accepts in place of a `giant predict` path/LazyFrame.
-#
-# `giant rollout` output and a training-input-schema truth file (see
-# `giant.data.loader.load_steps`) both carry pre_*/post_*/edep/step_length in
-# raw world-frame units (mm, MeV) — no ALR/log-step decode needed, just the
-# same forward local-frame rotation `giant.data.transforms.build_features`
-# uses to build `target_s1` (`local_frame_rotation`/`travel_direction`),
-# expressed in polars so the decode streams. Unlike the paired predict-parquet
-# `source`, `rollout`/`truth` are independent files — a rollout doesn't replay
-# real events row-for-row — so every Tier 1-3 function funnels both cases
-# through `_real_gen_lazyframes`, which returns two canonical LazyFrames (one
-# per side, columns = `RAW_TARGET_NAMES` + pdg/material/pre_E) regardless of
-# which `source` variant it was given.
-# ---------------------------------------------------------------------------
-
-
-@dataclass(frozen=True)
-class RolloutVsTruth:
-    """Compare a `giant rollout` shower (generated) against held-out truth data (real).
-
-    `truth` — any file sharing `giant train`'s input schema (real miniCaloSim
-    steps, e.g. a held-out/val parquet) — is "real"; `rollout` (`giant rollout`
-    output) is "generated". Pass this in place of `source` to any Tier 1-3
-    function (`plot_marginals`, `marginal_table_pl`, `plot_kl_bars_pl`,
-    `correlation_matrices_pl`, `plot_correlation_matrices`, `plot_pairwise`,
-    `plot_direction_alignment`, `constraint_report_pl`,
-    `plot_constraint_violations`). The two files are independent rather than
-    row-for-row paired, so they may have different lengths.
-    """
-
-    rollout: str | Path | pl.LazyFrame
-    truth: str | Path | pl.LazyFrame
-    sample_frac: float = 1.0
-    seed: int = 0
-
-
-def _check_rollout_metadata(path: Path) -> None:
-    """Raise if `path` carries coord metadata that isn't `ROLLOUT_COORD_VALUE`.
-
-    A missing tag (older rollout output, predating tagging) is let through
-    silently, matching `giant predict`/`giant rollout`'s own leniency; a tag
-    that's present but wrong is a real mismatch.
-    """
-    metadata = pq.read_schema(path).metadata or {}
-    coord = metadata.get(PREDICT_COORD_METADATA_KEY.encode())
-    if coord is not None and coord.decode() != ROLLOUT_COORD_VALUE:
-        raise ValueError(
-            f"{path} is not a rollout file (coord={coord.decode()!r}); "
-            "expected a `giant rollout` output"
-        )
-
-
-# `rollout.py`'s `_terminal_rows` writes one synthetic bookkeeping row per track
-# for these termination reasons (escaped/unknown_pdg/energy_cutoff/max_steps):
-# step_length=0, post_pos=pre_pos, and — for every reason but escaped — the
-# track's *entire remaining pre_E* dumped into `edep` in one row, so the shower's
-# total energy still conserves. These aren't steps in any physical sense (truth
-# data has no equivalent), so mixing them into a per-step real-vs-generated
-# comparison would inject a spurious step_length=0 spike and roughly double the
-# apparent mean edep purely from bookkeeping, not model behavior. Real generated
-# steps carry "" (continuing) or `TERM_NATURAL_END` (the track's last real step,
-# which does have genuine step_length/edep) and are kept.
-_SYNTHETIC_ROLLOUT_TERMINATION_REASONS = frozenset(
-    {TERM_ESCAPED, TERM_UNKNOWN_PDG, TERM_ENERGY_CUTOFF, TERM_MAX_STEPS}
-)
-
-
-def _world_frame_local_exprs() -> list[pl.Expr]:
-    """`RAW_TARGET_NAMES` + pdg/material/pre_E expressions for a world-frame steps frame.
-
-    Both `giant rollout` output and a raw truth-schema steps file carry
-    pre_*/post_*/edep/step_length directly in world-frame units — this decodes
-    them into the same `RAW_TARGET_NAMES` space `_raw_dim_expr` produces from a
-    predict parquet, via the *forward* Rodrigues rotation (post_dir/travel_dir
-    into the local frame where pre_dir maps to ẑ). Mirrors
-    `giant.data.transforms.local_frame_rotation`/`travel_direction`; a near
-    mirror-image of `_geometry_exprs` below (that one applies the *inverse*
-    rotation to reconstruct world-frame post_pos from a local-frame target) —
-    same axis/cos_t/sin_t construction, `+kxv*sin_t` instead of `-kxv*sin_t`.
-    """
-    norm = (
-        pl.col("pre_dx") ** 2 + pl.col("pre_dy") ** 2 + pl.col("pre_dz") ** 2
-    ).sqrt()
-    ux, uy, uz = (
-        pl.col("pre_dx") / norm,
-        pl.col("pre_dy") / norm,
-        pl.col("pre_dz") / norm,
-    )
-    cos_t = uz.clip(-1.0, 1.0)
-    sin_t = (1.0 - cos_t**2).clip(lower_bound=0.0).sqrt()
-
-    axis_norm = (uy**2 + ux**2).sqrt()
-    degenerate = axis_norm < 1e-7
-    ax = pl.when(degenerate).then(pl.lit(1.0)).otherwise(uy / axis_norm)
-    ay = pl.when(degenerate).then(pl.lit(0.0)).otherwise(-ux / axis_norm)
-    omc = 1.0 - cos_t
-
-    def _rotate(
-        vx: pl.Expr, vy: pl.Expr, vz: pl.Expr
-    ) -> tuple[pl.Expr, pl.Expr, pl.Expr]:
-        kxv_x, kxv_y, kxv_z = ay * vz, -(ax * vz), ax * vy - ay * vx
-        kdv = ax * vx + ay * vy
-        return (
-            vx * cos_t + kxv_x * sin_t + ax * kdv * omc,
-            vy * cos_t + kxv_y * sin_t + ay * kdv * omc,
-            vz * cos_t + kxv_z * sin_t,
-        )
-
-    post_lx, post_ly, post_lz = _rotate(
-        pl.col("post_dx"), pl.col("post_dy"), pl.col("post_dz")
-    )
-
-    ddx = pl.col("post_x") - pl.col("pre_x")
-    ddy = pl.col("post_y") - pl.col("pre_y")
-    ddz = pl.col("post_z") - pl.col("pre_z")
-    dnorm = (ddx**2 + ddy**2 + ddz**2).sqrt()
-    degenerate_d = dnorm < 1e-7
-    tvx = pl.when(degenerate_d).then(pl.lit(0.0)).otherwise(ddx / dnorm)
-    tvy = pl.when(degenerate_d).then(pl.lit(0.0)).otherwise(ddy / dnorm)
-    tvz = pl.when(degenerate_d).then(pl.lit(1.0)).otherwise(ddz / dnorm)
-    trav_lx, trav_ly, trav_lz = _rotate(tvx, tvy, tvz)
-
-    return [
-        pl.col("step_length").alias(RAW_TARGET_NAMES[0]),
-        (pl.col("pre_E") - pl.col("post_E")).alias(RAW_TARGET_NAMES[1]),
-        pl.col("edep").alias(RAW_TARGET_NAMES[2]),
-        post_lx.alias(RAW_TARGET_NAMES[3]),
-        post_ly.alias(RAW_TARGET_NAMES[4]),
-        post_lz.alias(RAW_TARGET_NAMES[5]),
-        trav_lx.alias(RAW_TARGET_NAMES[6]),
-        trav_ly.alias(RAW_TARGET_NAMES[7]),
-        trav_lz.alias(RAW_TARGET_NAMES[8]),
-        pl.col("pdg"),
-        pl.col("material"),
-        pl.col("pre_E"),
-    ]
-
-
-def _scan_world_frame_side(
-    source: str | Path | pl.LazyFrame,
-    *,
-    is_rollout: bool,
-    sample_frac: float,
-    seed: int,
-) -> pl.LazyFrame:
-    """Canonical (`RAW_TARGET_NAMES` + pdg/material/pre_E) LazyFrame for one `RolloutVsTruth` side.
-
-    `is_rollout` selects the coord-metadata check and the synthetic
-    termination-row filter (see `_SYNTHETIC_ROLLOUT_TERMINATION_REASONS`) —
-    both only apply to the `giant rollout` side, never the truth side.
-    `sample_frac` < 1 subsamples via a hash filter before decoding, same
-    hash-threshold trick `plot_pairwise` uses elsewhere in this module.
-    """
-    if isinstance(source, pl.LazyFrame):
-        lf = source
-    else:
-        path = Path(source)
-        if is_rollout:
-            _check_rollout_metadata(path)
-        lf = pl.scan_parquet(path)
-    if is_rollout:
-        lf = lf.filter(
-            ~pl.col("termination_reason").is_in(
-                list(_SYNTHETIC_ROLLOUT_TERMINATION_REASONS)
-            )
-        )
-    if sample_frac < 1.0:
-        threshold = int(sample_frac * 2**32)
-        lf = (
-            lf.with_row_index("_ri")
-            .filter((pl.col("_ri").hash(seed=seed) % 2**32) < threshold)
-            .drop("_ri")
-        )
-    return lf.select(*_world_frame_local_exprs())
-
-
-def _real_gen_lazyframes(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-) -> tuple[pl.LazyFrame, pl.LazyFrame]:
-    """Canonical (real_lf, gen_lf) pair: `RAW_TARGET_NAMES` + pdg/material/pre_E columns.
-
-    For a plain predict-parquet `source`, `real_lf`/`gen_lf` are the
-    `true_*`/`pred_*` projections of the same underlying rows (paired,
-    row-for-row). For a `RolloutVsTruth`, they're two independently-decoded
-    world-frame files (unpaired, possibly different lengths). Every Tier 1-3
-    function funnels through this so the same downstream aggregation code
-    serves both `source` variants.
-    """
-    if isinstance(source, RolloutVsTruth):
-        real_lf = _scan_world_frame_side(
-            source.truth,
-            is_rollout=False,
-            sample_frac=source.sample_frac,
-            seed=source.seed,
-        )
-        gen_lf = _scan_world_frame_side(
-            source.rollout,
-            is_rollout=True,
-            sample_frac=source.sample_frac,
-            seed=source.seed,
-        )
-        return real_lf, gen_lf
-    lf = _scan_predicted_local(source)
-    real_lf = lf.select(
-        *[
-            _raw_dim_expr("true", j).alias(RAW_TARGET_NAMES[j])
-            for j in range(len(RAW_TARGET_NAMES))
-        ],
-        "pdg",
-        "material",
-        "pre_E",
-    )
-    gen_lf = lf.select(
-        *[
-            _raw_dim_expr("pred", j).alias(RAW_TARGET_NAMES[j])
-            for j in range(len(RAW_TARGET_NAMES))
-        ],
-        "pdg",
-        "material",
-        "pre_E",
-    )
-    return real_lf, gen_lf
-
-
-# ---------------------------------------------------------------------------
-# Tier 1: stratified marginals
-# ---------------------------------------------------------------------------
-#
-# One native `group_by` per dim (covering every group's mean/std/n/histogram
-# range at once) plus one pass for per-bin counts — runtime independent of group
-# cardinality. Filtering and re-collecting once per (group, dim) pair instead
-# would make runtime scale with the number of groups: on a 114M-row file with
-# 138 distinct pdg codes that extrapolated to ~an hour vs ~1 minute here.
-
-
-def _add_group_labels(
-    real_lf: pl.LazyFrame,
-    gen_lf: pl.LazyFrame,
-    group_by: str | None,
-    n_energy_bins: int,
-) -> tuple[pl.LazyFrame, pl.LazyFrame]:
-    """Add a `_group` string column to each side, with labels consistent across both.
-
-    For "pdg"/"material" each side's `_group` is an independent per-row string
-    expr — `group_by("_group")` downstream discovers the distinct values
-    itself. "energy" needs one streaming pass over *both* sides' `pre_E`
-    (combined) to fix shared quantile bin edges before either side can be
-    labeled, so pdg/material/energy strata line up between real and gen even
-    when the two come from independent files with different distributions
-    (`RolloutVsTruth`); for the paired predict-file case real/gen share the
-    same `pre_E` per row, so this is a no-op relative to using either side alone.
-    """
-    if group_by is None:
-        lit = pl.lit("all").alias("_group")
-        return real_lf.with_columns(lit), gen_lf.with_columns(lit)
-    if group_by == "pdg":
-        expr = (pl.lit("pdg=") + pl.col("pdg").cast(pl.Int64).cast(pl.Utf8)).alias(
-            "_group"
-        )
-        return real_lf.with_columns(expr), gen_lf.with_columns(expr)
-    if group_by == "material":
-        expr = (pl.lit("material=") + pl.col("material")).alias("_group")
-        return real_lf.with_columns(expr), gen_lf.with_columns(expr)
-    if group_by == "energy":
-        real_E = real_lf.select("pre_E").collect(engine="streaming").to_series()
-        gen_E = gen_lf.select("pre_E").collect(engine="streaming").to_series()
-        edges = np.quantile(
-            np.concatenate([real_E.to_numpy(), gen_E.to_numpy()]),
-            np.linspace(0, 1, n_energy_bins + 1),
-        )
-        edges[-1] += 1e-6
-        labels = [
-            f"E∈[{edges[i]:.3g},{edges[i + 1]:.3g})" for i in range(n_energy_bins)
-        ]
-
-        def _label_expr() -> pl.Expr:
-            expr = pl.when(pl.col("pre_E") < edges[1]).then(pl.lit(labels[0]))
-            for i in range(1, n_energy_bins - 1):
-                expr = expr.when(pl.col("pre_E") < edges[i + 1]).then(pl.lit(labels[i]))
-            return expr.otherwise(pl.lit(labels[-1])).alias("_group")
-
-        return real_lf.with_columns(_label_expr()), gen_lf.with_columns(_label_expr())
-    raise ValueError(f"unknown group_by={group_by!r}")
-
-
-# On a file larger than RAM (never page-cached) the binding constraint is *peak
-# memory*, not scan count: each streaming scan mmaps the columns it reads, so a
-# scan that touches only ~3 columns keeps resident memory low, while one that
-# reads all 9 dims' ~19 source columns at once mmaps almost the whole 32GB file
-# and OOMs. So every scan here is kept *narrow* — stats and histogram are
-# computed one dim at a time over just that dim's real/gen columns. Real and gen
-# are always two independent LazyFrames (see `_real_gen_lazyframes`) rather than
-# two columns of one row, so each dim needs one scan per side rather than one
-# combined pass — more scans than a single-source join would allow, but each is
-# cheap in memory and works identically whether real/gen share a file or not,
-# which is what actually matters at this file size.
-
-
-def _pad_range(lo: float, hi: float) -> tuple[float, float]:
-    """Widen (lo, hi) by ±0.5 when too tight to support `bins` distinct edges.
-
-    Scalar duplicate of `_hist_edges`'s degenerate-range handling, applied per
-    (group, dim).
-    """
-    if not (hi - lo > 1e-6 * max(abs(hi), 1.0)):
-        return lo - 0.5, hi + 0.5
-    return lo, hi
-
-
-def _side_group_stats(lf: pl.LazyFrame, name: str) -> dict[str, dict[str, float]]:
-    """One narrow streaming pass: per-group n/mean/std/min/max for column `name`.
-
-    `n` (= `pl.len()`) is the group size; ddof=0 matches numpy's population std.
-    Returns a `{group: row}` dict (small — one row per stratum, not per file row)
-    for cheap merging with the other side in `_dim_stats`.
-    """
-    rows = (
-        lf.select("_group", pl.col(name).alias("val"))
-        .group_by("_group")
-        .agg(
-            pl.len().alias("n"),
-            pl.col("val").mean().alias("mean"),
-            pl.col("val").std(ddof=0).alias("std"),
-            pl.col("val").min().alias("lo"),
-            pl.col("val").max().alias("hi"),
-        )
-        .collect(engine="streaming")
-        .iter_rows(named=True)
-    )
-    return {row["_group"]: row for row in rows}
-
-
-def _dim_stats(real_lf: pl.LazyFrame, gen_lf: pl.LazyFrame, j: int) -> pl.DataFrame:
-    """Per-group n/mean/std/range for dim `j`, merged across the real & gen sides.
-
-    Real and gen may have different strata present (e.g. a pdg code only the
-    rollout side ever produces) — `groups` is the union, and a group missing
-    from one side gets `n=0` there (filtered out by the `n >= 2` gate downstream).
-    """
-    name = RAW_TARGET_NAMES[j]
-    real = _side_group_stats(real_lf, name)
-    gen = _side_group_stats(gen_lf, name)
-    groups = sorted(set(real) | set(gen))
-    rows = []
-    for group in groups:
-        r, g = real.get(group), gen.get(group)
-        los = [v["lo"] for v in (r, g) if v is not None]
-        his = [v["hi"] for v in (r, g) if v is not None]
-        rows.append(
-            {
-                "_group": group,
-                "n": r["n"] if r is not None else 0,
-                "n_gen": g["n"] if g is not None else 0,
-                "real_mean": r["mean"] if r is not None else float("nan"),
-                "gen_mean": g["mean"] if g is not None else float("nan"),
-                "real_std": r["std"] if r is not None else float("nan"),
-                "gen_std": g["std"] if g is not None else float("nan"),
-                "lo": min(los),
-                "hi": max(his),
-            }
-        )
-    return pl.DataFrame(
-        rows,
-        schema={
-            "_group": pl.Utf8,
-            "n": pl.Int64,
-            "n_gen": pl.Int64,
-            "real_mean": pl.Float64,
-            "gen_mean": pl.Float64,
-            "real_std": pl.Float64,
-            "gen_std": pl.Float64,
-            "lo": pl.Float64,
-            "hi": pl.Float64,
-        },
-    )
-
-
-def _side_hist_counts(
-    lf: pl.LazyFrame, name: str, lo_hi: pl.DataFrame, bins: int
-) -> dict[str, np.ndarray]:
-    """One streaming pass: per-group histogram bin counts for column `name`.
-
-    Joins each row to its group's padded `(lo, hi)` range (small — one row per
-    stratum — so this join never buffers file-sized data) and bins with the
-    same floor/clip formula the original single-source path used (so counts
-    are bit-identical).
-    """
-    width = pl.col("hi") - pl.col("lo")
-    bin_expr = (
-        ((pl.col(name) - pl.col("lo")) / width * bins)
-        .floor()
-        .cast(pl.Int64)
-        .clip(0, bins - 1)
-    )
-    hist = (
-        lf.select("_group", name)
-        .join(lo_hi.lazy(), on="_group")
-        .select("_group", bin_expr.alias("bin"))
-        .group_by(["_group", "bin"])
-        .agg(pl.len().alias("count"))
-        .collect(engine="streaming")
-    )
-    by: dict[str, np.ndarray] = {}
-    for group, bin_idx, count in hist.iter_rows():
-        by.setdefault(group, np.zeros(bins, dtype=np.int64))[bin_idx] = count
-    return by
-
-
-def _dim_hist_counts(
-    real_lf: pl.LazyFrame, gen_lf: pl.LazyFrame, j: int, lo_hi: pl.DataFrame, bins: int
-) -> tuple[dict[str, np.ndarray], dict[str, np.ndarray]]:
-    """Real & gen histogram bin counts for dim `j`, by group (one pass per side)."""
-    name = RAW_TARGET_NAMES[j]
-    return (
-        _side_hist_counts(real_lf, name, lo_hi, bins),
-        _side_hist_counts(gen_lf, name, lo_hi, bins),
-    )
-
-
-# Per (group, dim) histogram payload: (real_counts, gen_counts, lo, hi).
-_DimHists = dict[str, dict[str, tuple[np.ndarray, np.ndarray, float, float]]]
-
-
-def _marginal_histograms(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-    group_by: str | None,
-    n_energy_bins: int,
-    bins: int,
-) -> tuple[pl.DataFrame, _DimHists]:
-    """Stratified per-dim marginal stats table + the per-(group, dim) histograms.
-
-    The single source of truth for both `marginal_table_pl` (which wants only the
-    table) and `plot_marginals` (which also wants the bin counts).
-    """
-    real_lf, gen_lf = _real_gen_lazyframes(source)
-    real_lf, gen_lf = _add_group_labels(real_lf, gen_lf, group_by, n_energy_bins)
-
-    empty = np.zeros(bins, dtype=np.int64)
-    rows = []
-    hists: _DimHists = {name: {} for name in RAW_TARGET_NAMES}
-    for j, name in enumerate(RAW_TARGET_NAMES):
-        stats = _dim_stats(real_lf, gen_lf, j).filter(
-            (pl.col("n") >= 2) & (pl.col("n_gen") >= 2)
-        )
-        range_by = {
-            row["_group"]: _pad_range(row["lo"], row["hi"])
-            for row in stats.iter_rows(named=True)
-        }
-        lo_hi = pl.DataFrame(
-            [{"_group": g, "lo": lo, "hi": hi} for g, (lo, hi) in range_by.items()],
-            schema={"_group": pl.Utf8, "lo": pl.Float64, "hi": pl.Float64},
-        )
-        real_by, gen_by = _dim_hist_counts(real_lf, gen_lf, j, lo_hi, bins)
-        for row in stats.iter_rows(named=True):
-            group = row["_group"]
-            real_counts = real_by.get(group, empty)
-            gen_counts = gen_by.get(group, empty)
-            lo, hi = range_by[group]
-            hists[name][group] = (real_counts, gen_counts, lo, hi)
-            rows.append(
-                {
-                    "group": group,
-                    "dim": name,
-                    "n": row["n"],
-                    "n_gen": row["n_gen"],
-                    "real_mean": row["real_mean"],
-                    "gen_mean": row["gen_mean"],
-                    "real_std": row["real_std"],
-                    "gen_std": row["gen_std"],
-                    "kl_real_gen": _kl_from_counts(real_counts, gen_counts),
-                }
-            )
-
-    table = pl.DataFrame(rows).sort("kl_real_gen", descending=True)
-    return table, hists
-
-
-def marginal_table_pl(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-    group_by: str | None = None,
-    n_energy_bins: int = 4,
-    bins: int = 50,
-) -> pl.DataFrame:
-    """Per-dimension real-vs-generated summary stats + KL(real||gen), in raw units.
-
-    `source` is a path to a `giant predict --coord local` parquet file, an
-    already-built LazyFrame with the same `pred_*`/`true_*`/`pdg`/`material`/
-    `pre_E` columns (e.g. for testing), or a `RolloutVsTruth` (a `giant rollout`
-    output compared against held-out truth data — see that class). `group_by`:
-    None for an aggregate table, or one of "pdg", "material", "energy" to
-    stratify. Sorted worst-KL first, so failure modes hidden by the aggregate
-    surface at the top. `n`/`n_gen` are the real/gen group sizes — equal for
-    the paired predict-file case, independent for `RolloutVsTruth`.
-    """
-    table, _ = _marginal_histograms(source, group_by, n_energy_bins, bins)
-    return table
-
-
-def _limit_groups_by_kl_n(table: pd.DataFrame, max_groups: int) -> pd.DataFrame:
-    """Keep only the `max_groups` groups with the largest max(kl) * n.
-
-    Ranks by how badly wrong *and* how common a stratum is, rather than by KL
-    alone, so a rare pdg/material with a noisy, high-variance KL estimate from a
-    handful of samples doesn't crowd out groups that actually matter.
-    """
-    by_group = table.groupby("group").agg(
-        kl_max=("kl_real_gen", "max"), n=("n", "first")
-    )
-    worst_first = (by_group["kl_max"] * by_group["n"]).sort_values(ascending=False)
-    keep = set(worst_first.index[:max_groups])
-    return table[table["group"].isin(keep)]
-
-
-def _worst_first_groups(table: pd.DataFrame, max_groups: int) -> list[str]:
-    """Group labels ranked by max(kl) * n (worst first), capped at `max_groups`."""
-    by_group = table.groupby("group").agg(
-        kl_max=("kl_real_gen", "max"), n=("n", "first")
-    )
-    worst_first = (by_group["kl_max"] * by_group["n"]).sort_values(ascending=False)
-    return list(worst_first.index[:max_groups])
-
-
-def plot_marginals(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-    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 file. With "pdg", "material",
-    or "energy", one row per stratum, ranked by max(kl) * n (capped at
-    `max_groups`) so groups that are both badly wrong and common surface first,
-    rather than rare groups with a noisy, high-variance KL estimate. Every
-    histogram is computed with streaming bin counts (`_marginal_histograms`) — no
-    row is ever materialized.
-    """
-    dims = dims or RAW_TARGET_NAMES
-    table, hists = _marginal_histograms(source, group_by, n_energy_bins, bins)
-
-    if group_by is None:
-        groups = ["all"]
-    else:
-        groups = _worst_first_groups(table.to_pandas(), 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 in enumerate(groups):
-        for col, name in enumerate(dims):
-            ax = axes[row][col]
-            entry = hists[name].get(label)
-            if entry is not None:
-                real_counts, gen_counts, lo, hi = entry
-                edges = np.linspace(lo, hi, bins + 1)
-                ax.stairs(_step_density(real_counts, edges), edges, label="real")
-                ax.stairs(_step_density(gen_counts, edges), edges, label="generated")
-                ax.set_yscale("log")
-            if row == 0:
-                ax.set_title(name, 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
-
-
-def _plot_kl_bars(table: pd.DataFrame, figsize: tuple[float, float]):
-    """Shared bar-plot body for `plot_kl_bars_pl`.
-
-    `table` is a `marginal_table_pl` result converted to pandas with
-    `group`/`dim`/`kl_real_gen` columns.
-    """
-    pivot = table.pivot(index="dim", columns="group", values="kl_real_gen")
-    pivot = pivot.reindex(RAW_TARGET_NAMES)
-    groups = sorted(pivot.columns)
-    pivot = pivot[groups]
-
-    n_dims, n_groups = len(RAW_TARGET_NAMES), len(groups)
-    x = np.arange(n_dims)
-    width = 0.8 / n_groups
-
-    fig, ax = plt.subplots(figsize=figsize)
-    for i, group in enumerate(groups):
-        offset = (i - (n_groups - 1) / 2) * width
-        ax.bar(x + offset, pivot[group].to_numpy(), width=width, label=group)
-    ax.set_xticks(x)
-    ax.set_xticklabels(RAW_TARGET_NAMES, rotation=45, ha="right")
-    ax.set_ylabel("KL(real || gen)")
-    if n_groups > 1:
-        ax.legend(fontsize=7)
-    fig.tight_layout()
-    return fig
-
-
-def plot_kl_bars_pl(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-    group_by: str | None = None,
-    n_energy_bins: int = 4,
-    bins: int = 50,
-    max_groups: int = 6,
-    figsize: tuple[float, float] = (8, 4),
-):
-    """Bar plot of KL(real||gen) per target dimension, optionally stratified.
-
-    One bar cluster per dimension; with `group_by` set, one bar per stratum
-    within each cluster, so which dimension/stratum combination drives a KL
-    regression is visible at a glance. With "pdg" or "material" — open-ended
-    vocabularies — groups are capped at `max_groups`, ranked by max(kl) * n;
-    "energy" is already bounded by `n_energy_bins` and isn't capped.
-    """
-    table = marginal_table_pl(
-        source, group_by=group_by, n_energy_bins=n_energy_bins, bins=bins
-    ).to_pandas()
-    if group_by in ("pdg", "material"):
-        table = _limit_groups_by_kl_n(table, max_groups)
-    return _plot_kl_bars(table, figsize=figsize)
-
-
-# ---------------------------------------------------------------------------
-# Tier 2: joint structure
-# ---------------------------------------------------------------------------
-
-
-def _corr_matrix(lf: pl.LazyFrame) -> np.ndarray:
-    """9×9 Pearson correlation matrix of `RAW_TARGET_NAMES`, from streaming sufficient stats.
-
-    One pass accumulating per-dim sums and all i≤j cross-products (in
-    float64) — rather than loading the target arrays and calling
-    `np.corrcoef`. `cov_ij = E[x_i x_j] - E[x_i]E[x_j]` (population/N
-    normalization, matching `np.corrcoef`), then `corr = cov / sqrt(cov_ii cov_jj)`.
-    """
-    n_dims = len(RAW_TARGET_NAMES)
-    narrow = lf.select(
-        *[
-            pl.col(RAW_TARGET_NAMES[j]).cast(pl.Float64).alias(f"x{j}")
-            for j in range(n_dims)
-        ]
-    )
-    aggs: list[pl.Expr] = [pl.len().alias("n")]
-    for j in range(n_dims):
-        aggs.append(pl.col(f"x{j}").sum().alias(f"s{j}"))
-    for i in range(n_dims):
-        for j in range(i, n_dims):
-            aggs.append((pl.col(f"x{i}") * pl.col(f"x{j}")).sum().alias(f"p{i}_{j}"))
-    row = narrow.select(aggs).collect(engine="streaming").row(0, named=True)
-    n = row["n"]
-
-    mean = np.array([row[f"s{j}"] for j in range(n_dims)]) / n
-    cov = np.empty((n_dims, n_dims), dtype=np.float64)
-    for i in range(n_dims):
-        for j in range(i, n_dims):
-            c = row[f"p{i}_{j}"] / n - mean[i] * mean[j]
-            cov[i, j] = cov[j, i] = c
-    std = np.sqrt(np.diag(cov))
-    return cov / np.outer(std, std)
-
-
-def correlation_matrices_pl(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-) -> tuple[np.ndarray, np.ndarray]:
-    """9×9 Pearson correlation matrices of the raw targets, (real, generated).
-
-    Real and gen correlations are independent per-side computations (never a
-    cross real/gen quantity), so this works identically whether both sides
-    come from one paired predict file or two independent `RolloutVsTruth` files.
-    """
-    real_lf, gen_lf = _real_gen_lazyframes(source)
-    return _corr_matrix(real_lf), _corr_matrix(gen_lf)
-
-
-def plot_correlation_matrices(source: str | Path | pl.LazyFrame | RolloutVsTruth):
-    """Side-by-side real/generated correlation heatmaps, plus their difference."""
-    real_corr, gen_corr = correlation_matrices_pl(source)
-    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 _sample_lf(
-    lf: pl.LazyFrame, columns: list[str], n_sample: int, seed: int
-) -> pl.DataFrame:
-    """~`n_sample` rows of `columns`, via a streaming count + hash-subsample.
-
-    Only the requested columns are ever materialized, and only for the kept
-    rows — a cheap streaming count fixes the hash-subsample fraction first.
-    """
-    n_total = lf.select(pl.len()).collect(engine="streaming").item()
-    if n_total == 0:
-        raise ValueError("no rows in source")
-    frac = min(1.0, n_sample / n_total)
-    threshold = int(frac * 2**32)
-    sampled = (
-        lf.select(*columns)
-        .with_row_index("_ri")
-        .filter((pl.col("_ri").hash(seed=seed) % 2**32) < threshold)
-        .drop("_ri")
-        .collect(engine="streaming")
-    )
-    if sampled.height > n_sample:
-        idx = np.random.default_rng(seed).choice(
-            sampled.height, size=n_sample, replace=False
-        )
-        sampled = sampled[idx]
-    return sampled
-
-
-def plot_pairwise(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth,
-    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. Real and gen are sampled independently (~`n_sample`
-    rows each), since with a `RolloutVsTruth` `source` they come from unrelated
-    files with unrelated row counts.
-    """
-    pairs = pairs or _DEFAULT_PAIRS
-    names = sorted({name for pair in pairs for name in pair})
-    real_lf, gen_lf = _real_gen_lazyframes(source)
-
-    real_sampled = _sample_lf(real_lf, names, n_sample, seed)
-    gen_sampled = _sample_lf(gen_lf, names, n_sample, seed)
-
-    fig, axes = plt.subplots(2, len(pairs), squeeze=False, figsize=(4 * len(pairs), 7))
-    for col, (a, b) in enumerate(pairs):
-        for row, (data, title) in enumerate(
-            [(real_sampled, "real"), (gen_sampled, "generated")]
-        ):
-            ax = axes[row][col]
-            ax.scatter(data[a].to_numpy(), data[b].to_numpy(), 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 _cos_alignment_expr() -> pl.Expr:
-    """cos(angle) between post_dir and travel_dir, per row (canonical column names)."""
-    post = [pl.col(n) for n in ("post_dx", "post_dy", "post_dz")]
-    travel = [pl.col(n) for n in ("travel_dx", "travel_dy", "travel_dz")]
-    dot = sum(p * t for p, t in zip(post, travel))
-    post_norm = pl.sum_horizontal([p**2 for p in post]).sqrt()
-    travel_norm = pl.sum_horizontal([t**2 for t in travel]).sqrt()
-    return dot / (post_norm * travel_norm + 1e-8)
-
-
-def plot_direction_alignment(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth, bins: int = 50
-):
-    """cos(angle) between post_dir and travel_dir, 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. Fixed
-    `[-1, 1]` edges, one streaming bin-count pass per side — no materialization.
-    """
-    real_lf, gen_lf = _real_gen_lazyframes(source)
-    real_counts, edges = _streaming_hist1d(
-        real_lf, _cos_alignment_expr(), bins, lo=-1.0, hi=1.0
-    )
-    gen_counts, _ = _streaming_hist1d(
-        gen_lf, _cos_alignment_expr(), bins, lo=-1.0, hi=1.0
-    )
-
-    fig, ax = plt.subplots(figsize=(5, 4))
-    ax.stairs(_step_density(real_counts, edges), edges, label="real")
-    ax.stairs(_step_density(gen_counts, edges), edges, label="generated")
-    ax.set_yscale("log")
-    ax.set_xlabel("cos(angle) between post_dir and travel_dir")
-    ax.legend()
-    fig.tight_layout()
-    return fig
-
-
-# ---------------------------------------------------------------------------
-# Tier 3: physical constraints
-# ---------------------------------------------------------------------------
-
-
-def _norm_expr(names: list[str]) -> pl.Expr:
-    """||v|| for the 3 canonical columns in `names`."""
-    return pl.sum_horizontal([pl.col(n) ** 2 for n in names]).sqrt()
-
-
-def constraint_report_pl(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth, norm_tol: float = 0.05
-) -> pl.DataFrame:
-    """Rate of physical-constraint violations in the generated raw-space samples.
-
-    The model is an unconstrained MLP, so nothing forces post_dir / travel_dir to
-    stay unit-norm or step_length/delta_e/edep to stay non-negative — all hold by
-    construction in the real data, so any violation rate here is purely a
-    generation artifact (real is never checked). A single streaming aggregation
-    over the gen side; never materializes the target arrays.
-    """
-    _, gen_lf = _real_gen_lazyframes(source)
-    post_norm = _norm_expr(["post_dx", "post_dy", "post_dz"])
-    travel_norm = _norm_expr(["travel_dx", "travel_dy", "travel_dz"])
-    raw_scalar_dims = [pl.col(RAW_TARGET_NAMES[j]) for j in range(_N_SCALAR_DIMS)]
-
-    agg = (
-        gen_lf.select(
-            [
-                ((post_norm - 1).abs() > norm_tol)
-                .mean()
-                .alias("post_dir_violation_rate"),
-                (post_norm - 1).abs().mean().alias("post_dir_mean_abs_error"),
-                ((travel_norm - 1).abs() > norm_tol)
-                .mean()
-                .alias("travel_dir_violation_rate"),
-                (travel_norm - 1).abs().mean().alias("travel_dir_mean_abs_error"),
-                *[
-                    (raw < 0).mean().alias(f"{name}_violation_rate")
-                    for raw, name in zip(
-                        raw_scalar_dims, RAW_TARGET_NAMES[:_N_SCALAR_DIMS]
-                    )
-                ],
-                *[
-                    raw.clip(upper_bound=0).abs().mean().alias(f"{name}_mean_abs_error")
-                    for raw, name in zip(
-                        raw_scalar_dims, RAW_TARGET_NAMES[:_N_SCALAR_DIMS]
-                    )
-                ],
-            ]
-        )
-        .collect(engine="streaming")
-        .row(0, named=True)
-    )
-
-    rows = [
-        {
-            "check": "post_dir unit norm",
-            "violation_rate": agg["post_dir_violation_rate"],
-            "mean_abs_error": agg["post_dir_mean_abs_error"],
-        },
-        {
-            "check": "travel_dir unit norm",
-            "violation_rate": agg["travel_dir_violation_rate"],
-            "mean_abs_error": agg["travel_dir_mean_abs_error"],
-        },
-    ]
-    for name in RAW_TARGET_NAMES[:_N_SCALAR_DIMS]:
-        rows.append(
-            {
-                "check": f"{name} >= 0",
-                "violation_rate": agg[f"{name}_violation_rate"],
-                "mean_abs_error": agg[f"{name}_mean_abs_error"],
-            }
-        )
-    return pl.DataFrame(rows)
-
-
-def plot_constraint_violations(
-    source: str | Path | pl.LazyFrame | RolloutVsTruth, bins: int = 50
-):
-    """Histograms backing `constraint_report_pl`: direction norms and sign of the scalars.
-
-    Each panel is a streaming histogram (`_streaming_hist1d`) over the generated
-    side — the direction norms and the three physical scalar dims — so nothing
-    is materialized. The dashed line marks the constraint boundary (1.0 for norms,
-    0.0 for the non-negative scalars).
-    """
-    _, gen_lf = _real_gen_lazyframes(source)
-
-    panels: list[tuple[pl.Expr, str, float]] = [
-        (_norm_expr(["post_dx", "post_dy", "post_dz"]), "||post_dir||", 1.0),
-        (_norm_expr(["travel_dx", "travel_dy", "travel_dz"]), "||travel_dir||", 1.0),
-    ]
-    for name in RAW_TARGET_NAMES[:_N_SCALAR_DIMS]:
-        panels.append((pl.col(name), f"generated {name}", 0.0))
-
-    fig, axes = plt.subplots(1, len(panels), figsize=(4 * len(panels), 3.5))
-    for ax, (expr, title, boundary) in zip(axes, panels):
-        counts, edges = _streaming_hist1d(gen_lf, expr, bins)
-        ax.stairs(counts, edges)
-        ax.set_yscale("log")
-        ax.axvline(boundary, color="k", linestyle="--", linewidth=1)
-        ax.set_title(title)
-    fig.tight_layout()
-    return fig
-
-
-# ---------------------------------------------------------------------------
-# Tier 4: event-level (shower) observables
-#
-# Built directly on `giant predict --coord local` parquet output, streamed in
-# two passes rather than materialized: per-event sums (total deposited energy,
-# etc.) run into the tens of millions of rows.
-# ---------------------------------------------------------------------------
-
-_PRE_COLS = ["pre_x", "pre_y", "pre_z", "pre_dx", "pre_dy", "pre_dz", "pre_E"]
-
-
-@dataclass
-class EventObservables:
-    """Real/generated event-level shower observables.
-
-    `real_table`/`gen_table` are independent `pl.DataFrame`s (one row per
-    event, unprefixed columns: `total_edep`, `total_length`, `mean_edep`,
-    `mean_length`, `median_edep`, `median_length`, `centroid_depth`,
-    `transverse_rms`, `max_depth`, `n_steps`) — deliberately not one shared
-    frame, since the two sides may have different event counts
-    (`compute_rollout_vs_truth_observables_pl`'s rollout vs. truth events are
-    unrelated; `compute_event_observables_pl`'s paired real/gen rows share an
-    `event_id` space, so its two tables happen to have equal length). Every
-    `plot_*` function below only ever reads one named column from one side at
-    a time, so this never needs cross-table alignment. `gen_table` may carry
-    extra columns the real side doesn't have (e.g. `n_tracks`/`leaked_E` from a
-    genuine `giant rollout`, which has no truth-file counterpart).
-    """
-
-    real_table: pl.DataFrame
-    gen_table: pl.DataFrame
-    depth_edges: np.ndarray  # (depth_bins+1,) mm, along shower axis
-    transverse_edges: np.ndarray  # (transverse_bins+1,) mm, perpendicular to axis
-    real_depth_profile: np.ndarray  # (depth_bins,) mean edep/event/bin, MeV
-    gen_depth_profile: np.ndarray
-    real_depth_profile_std: np.ndarray  # event-to-event RMS per bin
-    gen_depth_profile_std: np.ndarray
-    real_transverse_profile: np.ndarray
-    gen_transverse_profile: np.ndarray
-    real_transverse_profile_std: np.ndarray
-    gen_transverse_profile_std: np.ndarray
-
-
-# Every per-row use of the per-event entry point / shower axis attaches those
-# six values with `replace_strict` (a 10⁴-entry hash map applied as an
-# expression) rather than a `.join`: polars 1.4's streaming join buffers the
-# whole 323M-row left side and OOMs on a file larger than RAM, whereas
-# `replace_strict` streams in bounded memory.
-
-
-def _entry_axis_exprs(entry_df: pl.DataFrame) -> list[pl.Expr]:
-    """`replace_strict` expressions mapping event_id → each entry_*/axis_* value."""
-    event_ids = entry_df["event_id"].to_numpy()
-    return [
-        pl.col("event_id")
-        .replace_strict(event_ids, entry_df[col].to_numpy(), return_dtype=pl.Float64)
-        .alias(col)
-        for col in ("entry_x", "entry_y", "entry_z", "axis_x", "axis_y", "axis_z")
-    ]
-
-
-def _entry_axis(lf: pl.LazyFrame) -> pl.DataFrame:
-    """Per-event shower axis/entry point (highest-pre_E row), a bounded `group_by`."""
-    return (
-        lf.select(["event_id", *_PRE_COLS])
-        .group_by("event_id")
-        .agg(
-            pl.col("pre_x").get(pl.col("pre_E").arg_max()).alias("entry_x"),
-            pl.col("pre_y").get(pl.col("pre_E").arg_max()).alias("entry_y"),
-            pl.col("pre_z").get(pl.col("pre_E").arg_max()).alias("entry_z"),
-            pl.col("pre_dx").get(pl.col("pre_E").arg_max()).alias("axis_x"),
-            pl.col("pre_dy").get(pl.col("pre_E").arg_max()).alias("axis_y"),
-            pl.col("pre_dz").get(pl.col("pre_E").arg_max()).alias("axis_z"),
-        )
-        .collect(engine="streaming")
-        .sort("event_id")
-    )
-
-
-def _depth_transverse_proxy(
-    lf: pl.LazyFrame,
-    entry_df: pl.DataFrame,
-    sample_rows: int,
-    seed: int,
-) -> tuple[np.ndarray, np.ndarray]:
-    """Hash-subsampled (depth, transverse) proxy from `pre_pos` alone, for bin-edge sizing.
-
-    Estimated on a hash-subsample of ~`sample_rows` rows rather than the whole
-    file, because an exact `quantile` is holistic (materializes every row and
-    OOMs on a >RAM file) and outlier-clipping bin bounds don't need more than
-    a sample. No `post_pos` reconstruction needed — `pre_pos` alone is a
-    perfectly good proxy for where bin edges should fall.
-    """
-    n_total = lf.select(pl.len()).collect(engine="streaming").item()
-    frac = min(1.0, sample_rows / max(n_total, 1))
-    threshold = int(frac * 2**32)
-
-    dx = pl.col("pre_x") - pl.col("entry_x")
-    dy = pl.col("pre_y") - pl.col("entry_y")
-    dz = pl.col("pre_z") - pl.col("entry_z")
-    depth = dx * pl.col("axis_x") + dy * pl.col("axis_y") + dz * pl.col("axis_z")
-    tx = dx - depth * pl.col("axis_x")
-    ty = dy - depth * pl.col("axis_y")
-    tz = dz - depth * pl.col("axis_z")
-    transverse = (tx**2 + ty**2 + tz**2).sqrt()
-
-    sample = (
-        lf.select(["event_id", "pre_x", "pre_y", "pre_z"])
-        .filter((pl.col("pre_x").hash(seed=seed) % 2**32) < threshold)
-        .with_columns(*_entry_axis_exprs(entry_df))
-        .select(depth.alias("depth_proxy"), transverse.alias("transverse_proxy"))
-        .collect(engine="streaming")
-    )
-    return sample["depth_proxy"].to_numpy(), sample["transverse_proxy"].to_numpy()
-
-
-def _bin_edges_from_proxies(
-    depth_proxy: np.ndarray,
-    transverse_proxy: np.ndarray,
-    depth_bins: int,
-    transverse_bins: int,
-) -> tuple[np.ndarray, np.ndarray]:
-    """Robust depth/transverse bin edges from proxy samples (0.1%-99.9% quantiles)."""
-    depth_lo = float(np.quantile(depth_proxy, 0.001))
-    depth_hi = float(np.quantile(depth_proxy, 0.999))
-    if not (depth_hi - depth_lo > 1e-6 * max(abs(depth_hi), 1.0)):
-        depth_lo, depth_hi = depth_lo - 0.5, depth_hi + 0.5
-    depth_edges = np.linspace(depth_lo, depth_hi, depth_bins + 1)
-    transverse_hi = max(float(np.quantile(transverse_proxy, 0.999)), 1e-6)
-    transverse_edges = np.linspace(0.0, transverse_hi, transverse_bins + 1)
-    return depth_edges, transverse_edges
-
-
-def _entry_axis_and_bin_edges(
-    lf: pl.LazyFrame,
-    depth_bins: int,
-    transverse_bins: int,
-    sample_rows: int = 1_000_000,
-    seed: int = 0,
-) -> tuple[pl.DataFrame, np.ndarray, np.ndarray]:
-    """Per-event shower axis (highest-pre_E row) plus depth/transverse bin edges.
-
-    Bin edges are sized from `pre_pos` alone (no `post_pos` reconstruction
-    needed) via robust quantiles of the depth/transverse proxies.
-    """
-    entry_df = _entry_axis(lf)
-    depth_proxy, transverse_proxy = _depth_transverse_proxy(
-        lf, entry_df, sample_rows, seed
-    )
-    depth_edges, transverse_edges = _bin_edges_from_proxies(
-        depth_proxy, transverse_proxy, depth_bins, transverse_bins
-    )
-    return entry_df, depth_edges, transverse_edges
-
-
-# Approximate per-event medians are estimated from fixed log10-spaced value bins
-# (median = the bin the running count crosses half at). Exact medians need a
-# holistic per-group aggregation that can't stream and materializes every row
-# (~18 GB / OOM on a >RAM file), whereas this histogram is a bounded streaming
-# group_by. The range is generous enough to cover physical edep/step values.
-_MED_LOG10_LO = -8.0
-_MED_LOG10_HI = 6.0
-_MED_BINS = 280
-
-
-def _uniform_bin(value: pl.Expr, lo: float, hi: float, nbins: int) -> pl.Expr:
-    """Bin `value` into `[0, nbins)` over uniform edges [lo, hi].
-
-    Matches `np.digitize(value, linspace(lo, hi, nbins+1)[1:-1])` for uniform
-    edges — floor of the scaled offset, clipped to the valid range.
-    """
-    return ((value - lo) / (hi - lo) * nbins).floor().cast(pl.Int64).clip(0, nbins - 1)
-
-
-def _geometry_exprs(src: str, out: str) -> list[pl.Expr]:
-    """Per-row edep / step_length / depth / transverse for one side (`true`/`pred`).
-
-    Reconstructs world-frame post_pos as polars expressions — the inverse
-    local-frame (Rodrigues) rotation of `giant.data.transforms`, closed-form for
-    the constant ẑ axis — then projects `post_pos - entry` onto the per-event
-    shower axis (`depth`) and its perpendicular (`transverse`). Requires the
-    per-event `entry_*`/`axis_*` columns to be joined on already. Doing this in
-    expressions (rather than a numpy loop over pyarrow batches) lets the whole
-    per-row pass parallelize across the streaming engine's cores.
-    """
-    norm = (
-        pl.col("pre_dx") ** 2 + pl.col("pre_dy") ** 2 + pl.col("pre_dz") ** 2
-    ).sqrt()
-    ux, uy, uz = (
-        pl.col("pre_dx") / norm,
-        pl.col("pre_dy") / norm,
-        pl.col("pre_dz") / norm,
-    )
-    cos_t = uz.clip(-1.0, 1.0)
-    sin_t = (1.0 - cos_t**2).clip(lower_bound=0.0).sqrt()
-
-    # Rodrigues axis pre_dir × ẑ = [uy, -ux, 0], normalized; x̂ when pre_dir ∥ ẑ.
-    axis_norm = (uy**2 + ux**2).sqrt()
-    degenerate = axis_norm < 1e-7
-    ax = pl.when(degenerate).then(pl.lit(1.0)).otherwise(uy / axis_norm)
-    ay = pl.when(degenerate).then(pl.lit(0.0)).otherwise(-ux / axis_norm)
-
-    vx, vy, vz = (
-        pl.col(f"{src}_travel_dx"),
-        pl.col(f"{src}_travel_dy"),
-        pl.col(f"{src}_travel_dz"),
-    )
-    # axis × v (axis_z = 0); axis · v; inverse rotation applies R^T (−angle).
-    kxv_x, kxv_y, kxv_z = ay * vz, -(ax * vz), ax * vy - ay * vx
-    kdv = ax * vx + ay * vy
-    omc = 1.0 - cos_t
-    wx = vx * cos_t - kxv_x * sin_t + ax * kdv * omc
-    wy = vy * cos_t - kxv_y * sin_t + ay * kdv * omc
-    wz = vz * cos_t - kxv_z * sin_t  # axis_z * kdv * omc == 0
-
-    step = pl.col(f"{src}_log_step_length").exp() - _LOG_EPS
-    post_x = pl.col("pre_x") + step * wx
-    post_y = pl.col("pre_y") + step * wy
-    post_z = pl.col("pre_z") + step * wz
-
-    dx = post_x - pl.col("entry_x")
-    dy = post_y - pl.col("entry_y")
-    dz = post_z - pl.col("entry_z")
-    depth = dx * pl.col("axis_x") + dy * pl.col("axis_y") + dz * pl.col("axis_z")
-    tx = dx - depth * pl.col("axis_x")
-    ty = dy - depth * pl.col("axis_y")
-    tz = dz - depth * pl.col("axis_z")
-    transverse = (tx**2 + ty**2 + tz**2).sqrt()
-
-    return [
-        _edep_pl(src).alias(f"{out}_edep"),
-        step.alias(f"{out}_step"),
-        depth.alias(f"{out}_depth"),
-        transverse.alias(f"{out}_transverse"),
-    ]
-
-
-def _spatial_grid(
-    geo: pl.LazyFrame,
-    out: str,
-    depth_edges: np.ndarray,
-    transverse_edges: np.ndarray,
-    depth_bins: int,
-    transverse_bins: int,
-) -> pl.DataFrame:
-    """One streaming pass: per-(event, depth-bin, transverse-bin) edep/length sums.
-
-    The reduced grid (at most n_events × depth_bins × transverse_bins rows) carries
-    everything the event-level observables need: summing its cells recovers exact
-    per-event totals and the edep-weighted centroid/RMS numerators (since a sum of
-    per-cell sums is the full per-event sum), and marginalizing one axis gives each
-    longitudinal/transverse profile — so the whole 323M-row reduction happens in a
-    parallel `group_by`, not a serial python loop.
-    """
-    depth_bin = _uniform_bin(
-        pl.col(f"{out}_depth"), depth_edges[0], depth_edges[-1], depth_bins
-    )
-    transverse_bin = _uniform_bin(
-        pl.col(f"{out}_transverse"),
-        transverse_edges[0],
-        transverse_edges[-1],
-        transverse_bins,
-    )
-    edep = pl.col(f"{out}_edep")
-    return (
-        geo.with_columns(depth_bin.alias("db"), transverse_bin.alias("tb"))
-        .group_by(["event_id", "db", "tb"])
-        .agg(
-            pl.len().alias("cnt"),
-            edep.sum().alias("s_edep"),
-            (edep * pl.col(f"{out}_depth")).sum().alias("s_edep_depth"),
-            (edep * pl.col(f"{out}_transverse") ** 2).sum().alias("s_edep_t2"),
-            pl.col(f"{out}_step").sum().alias("s_step"),
-        )
-        .collect(engine="streaming")
-    )
-
-
-def _reduce_spatial_grid(
-    grid: pl.DataFrame,
-    event_ids: np.ndarray,
-    depth_bins: int,
-    transverse_bins: int,
-    depth_edges: np.ndarray,
-) -> dict[str, np.ndarray]:
-    """Collapse a `_spatial_grid` table into per-event arrays (aligned to event_ids).
-
-    Pure numpy over the small reduced grid: `np.bincount` scatter-adds recover the
-    per-event totals, centroid/RMS, dense (event, bin) profile matrices, and the
-    shower-max depth — identical to the previous full-file numpy accumulation, but
-    fed pre-summed grid cells instead of every row.
-    """
-    n_events = len(event_ids)
-    idx = np.searchsorted(event_ids, grid["event_id"].to_numpy())
-    db = grid["db"].to_numpy()
-    tb = grid["tb"].to_numpy()
-    cnt = grid["cnt"].to_numpy().astype(np.float64)
-    s_edep = grid["s_edep"].to_numpy().astype(np.float64)
-    s_edep_depth = grid["s_edep_depth"].to_numpy().astype(np.float64)
-    s_edep_t2 = grid["s_edep_t2"].to_numpy().astype(np.float64)
-    s_step = grid["s_step"].to_numpy().astype(np.float64)
-
-    total_edep = np.bincount(idx, weights=s_edep, minlength=n_events)
-    total_length = np.bincount(idx, weights=s_step, minlength=n_events)
-    n_steps = np.bincount(idx, weights=cnt, minlength=n_events)
-    sum_edep_depth = np.bincount(idx, weights=s_edep_depth, minlength=n_events)
-    sum_edep_t2 = np.bincount(idx, weights=s_edep_t2, minlength=n_events)
-    depth_mat = np.bincount(
-        idx * depth_bins + db, weights=s_edep, minlength=n_events * depth_bins
-    ).reshape(n_events, depth_bins)
-    transverse_mat = np.bincount(
-        idx * transverse_bins + tb, weights=s_edep, minlength=n_events * transverse_bins
-    ).reshape(n_events, transverse_bins)
-
-    safe_total = np.where(total_edep > 0, total_edep, 1.0)
-    safe_n = np.where(n_steps > 0, n_steps, 1)
-    depth_centers = 0.5 * (depth_edges[:-1] + depth_edges[1:])
-    return {
-        "total_edep": total_edep,
-        "total_length": total_length,
-        "n_steps": n_steps.astype(np.int64),
-        "mean_edep": total_edep / safe_n,
-        "mean_length": total_length / safe_n,
-        "centroid_depth": sum_edep_depth / safe_total,
-        "transverse_rms": np.sqrt(sum_edep_t2 / safe_total),
-        "max_depth": depth_centers[np.argmax(depth_mat, axis=1)],
-        "depth_mat": depth_mat,
-        "transverse_mat": transverse_mat,
-    }
-
-
-def _approx_median_columns(
-    geo: pl.LazyFrame, event_ids: np.ndarray, columns: dict[str, str]
-) -> dict[str, np.ndarray]:
-    """Per-event approximate median of each `columns` source column, via a streaming pass.
-
-    `columns` maps output key → source column name in `geo`. Histograms each
-    quantity into fixed log10 bins per event (one streaming `group_by` after
-    unpivoting only the bin-index columns — a bounded expansion), then reads
-    off the bin its running count crosses half at. See the `_MED_*` note for
-    why exact medians are avoided. Shared by `_approx_medians` (paired
-    real/gen, one shared event_id space) and
-    `compute_rollout_vs_truth_observables_pl` (one side/event_id space at a
-    time, since rollout and truth events are unrelated).
-    """
-
-    def _log_bin(col: str) -> pl.Expr:
-        log10 = pl.col(col).clip(lower_bound=1e-30).log10()
-        return (
-            ((log10 - _MED_LOG10_LO) / (_MED_LOG10_HI - _MED_LOG10_LO) * _MED_BINS)
-            .floor()
-            .cast(pl.Int64)
-            .clip(0, _MED_BINS - 1)
-        )
-
-    hist = (
-        geo.select(
-            "event_id", *[_log_bin(col).alias(key) for key, col in columns.items()]
-        )
-        .unpivot(index="event_id", variable_name="q", value_name="bin")
-        .group_by(["q", "event_id", "bin"])
-        .agg(pl.len().alias("c"))
-        .collect(engine="streaming")
-    )
-
-    n_events = len(event_ids)
-    log_width = (_MED_LOG10_HI - _MED_LOG10_LO) / _MED_BINS
-    rows = np.arange(n_events)
-    out: dict[str, np.ndarray] = {}
-    for key in columns:
-        sub = hist.filter(pl.col("q") == key)
-        idx = np.searchsorted(event_ids, sub["event_id"].to_numpy())
-        counts = np.bincount(
-            idx * _MED_BINS + sub["bin"].to_numpy(),
-            weights=sub["c"].to_numpy().astype(np.float64),
-            minlength=n_events * _MED_BINS,
-        ).reshape(n_events, _MED_BINS)
-        cum = np.cumsum(counts, axis=1)
-        total = cum[:, -1]
-        half = total / 2.0
-        median_bin = (cum >= half[:, None]).argmax(axis=1)
-        # Linear interpolation of the log-CDF within the median bin, for sub-bin
-        # resolution (a bare bin center is only ~12% granular).
-        cum_before = np.where(median_bin > 0, cum[rows, median_bin - 1], 0.0)
-        count_in = counts[rows, median_bin]
-        frac = np.where(count_in > 0, (half - cum_before) / count_in, 0.5)
-        log_val = _MED_LOG10_LO + (median_bin + frac) * log_width
-        median = 10.0**log_val
-        median[total == 0] = 0.0
-        out[key] = median
-    return out
-
-
-def _approx_medians(geo: pl.LazyFrame, event_ids: np.ndarray) -> dict[str, np.ndarray]:
-    """Per-event approximate median edep/step for real & gen, via a streaming pass."""
-    return _approx_median_columns(
-        geo,
-        event_ids,
-        {
-            "real_median_edep": "real_edep",
-            "gen_median_edep": "gen_edep",
-            "real_median_length": "real_step",
-            "gen_median_length": "gen_step",
-        },
-    )
-
-
-def _side_event_table(
-    event_ids: np.ndarray,
-    side: dict[str, np.ndarray],
-    median_edep: np.ndarray,
-    median_length: np.ndarray,
-    extra: dict[str, np.ndarray] | None = None,
-) -> pl.DataFrame:
-    """One `EventObservables.real_table`/`gen_table` side, from a `_reduce_spatial_grid` dict."""
-    columns = {
-        "event_id": event_ids,
-        "n_steps": side["n_steps"],
-        "total_edep": side["total_edep"],
-        "total_length": side["total_length"],
-        "mean_edep": side["mean_edep"],
-        "mean_length": side["mean_length"],
-        "median_edep": median_edep,
-        "median_length": median_length,
-        "centroid_depth": side["centroid_depth"],
-        "transverse_rms": side["transverse_rms"],
-        "max_depth": side["max_depth"],
-    }
-    columns.update(extra or {})
-    return pl.DataFrame(columns)
-
-
-def compute_event_observables_pl(
-    source: str | Path | pl.LazyFrame,
-    depth_bins: int = 20,
-    transverse_bins: int = 20,
-) -> EventObservables:
-    """Stream a `giant predict --coord local` parquet file into event-level observables.
-
-    For each event, the highest-`pre_E` row is the primary's entry step
-    (secondaries always carry less energy than their parent), fixing a shower
-    axis/entry point shared by real and generated rows. Every row's `post_pos`/
-    `edep` is reconstructed into the world frame in physical units (mm, MeV) and
-    projected onto depth-along-axis / transverse-distance-from-axis.
-
-    Everything is computed with parallel streaming polars aggregations — no serial
-    python-over-pyarrow-batch loop, and no holistic per-event median (medians are
-    approximated from a streaming log-bin histogram) — so the 323M-row reduction
-    both uses all cores and stays within a bounded memory budget on a file larger
-    than RAM. `real_table`/`gen_table` carry `total_length` (`sum(step_length)`
-    per event) alongside the deposited-energy observables.
-    """
-    lf = _scan_predicted_local(source)
-    entry_df, depth_edges, transverse_edges = _entry_axis_and_bin_edges(
-        lf, depth_bins, transverse_bins
-    )
-    event_ids = entry_df["event_id"].to_numpy()
-
-    # Project to just the geometry inputs *before* the join, and to just the
-    # geometry outputs right after: projection pushdown doesn't reliably prune
-    # across a join in this polars version, so without the explicit narrowing the
-    # join buffers all ~30 columns × 323M rows and OOMs (the same trap the
-    # marginal path documents). Narrowed, only ~9 columns cross the join.
-    geo_inputs = [
-        "event_id",
-        "pre_x",
-        "pre_y",
-        "pre_z",
-        "pre_dx",
-        "pre_dy",
-        "pre_dz",
-        "pre_E",
-        *[
-            f"{prefix}_{name}"
-            for prefix in ("true", "pred")
-            for name in (
-                "log_step_length",
-                "edep_logit",
-                "sec_logit",
-                "travel_dx",
-                "travel_dy",
-                "travel_dz",
-            )
-        ],
-    ]
-    geo = (
-        lf.select(geo_inputs)
-        .join(entry_df.lazy(), on="event_id")
-        .select(
-            "event_id",
-            *_geometry_exprs("true", "real"),
-            *_geometry_exprs("pred", "gen"),
-        )
-    )
-
-    real = _reduce_spatial_grid(
-        _spatial_grid(
-            geo, "real", depth_edges, transverse_edges, depth_bins, transverse_bins
-        ),
-        event_ids,
-        depth_bins,
-        transverse_bins,
-        depth_edges,
-    )
-    gen = _reduce_spatial_grid(
-        _spatial_grid(
-            geo, "gen", depth_edges, transverse_edges, depth_bins, transverse_bins
-        ),
-        event_ids,
-        depth_bins,
-        transverse_bins,
-        depth_edges,
-    )
-    medians = _approx_medians(geo, event_ids)
-
-    real_table = _side_event_table(
-        event_ids, real, medians["real_median_edep"], medians["real_median_length"]
-    )
-    gen_table = _side_event_table(
-        event_ids, gen, medians["gen_median_edep"], medians["gen_median_length"]
-    )
-
-    return EventObservables(
-        real_table=real_table,
-        gen_table=gen_table,
-        depth_edges=depth_edges,
-        transverse_edges=transverse_edges,
-        real_depth_profile=real["depth_mat"].mean(axis=0),
-        gen_depth_profile=gen["depth_mat"].mean(axis=0),
-        real_depth_profile_std=real["depth_mat"].std(axis=0),
-        gen_depth_profile_std=gen["depth_mat"].std(axis=0),
-        real_transverse_profile=real["transverse_mat"].mean(axis=0),
-        gen_transverse_profile=gen["transverse_mat"].mean(axis=0),
-        real_transverse_profile_std=real["transverse_mat"].std(axis=0),
-        gen_transverse_profile_std=gen["transverse_mat"].std(axis=0),
-    )
-
-
-def _world_frame_geometry_exprs(out: str) -> list[pl.Expr]:
-    """Per-row edep/step_length/depth/transverse for a world-frame steps side.
-
-    Unlike `_geometry_exprs` (which reconstructs `post_pos` from an ALR/local-
-    frame predict target), a `giant rollout`/truth-schema file already stores
-    `post_x/y/z` directly — this only needs the entry-relative projection onto
-    the per-event shower axis, no rotation. Requires the per-event
-    `entry_*`/`axis_*` columns to already be joined on.
-    """
-    dx = pl.col("post_x") - pl.col("entry_x")
-    dy = pl.col("post_y") - pl.col("entry_y")
-    dz = pl.col("post_z") - pl.col("entry_z")
-    depth = dx * pl.col("axis_x") + dy * pl.col("axis_y") + dz * pl.col("axis_z")
-    tx = dx - depth * pl.col("axis_x")
-    ty = dy - depth * pl.col("axis_y")
-    tz = dz - depth * pl.col("axis_z")
-    transverse = (tx**2 + ty**2 + tz**2).sqrt()
-    return [
-        pl.col("edep").alias(f"{out}_edep"),
-        pl.col("step_length").alias(f"{out}_step"),
-        depth.alias(f"{out}_depth"),
-        transverse.alias(f"{out}_transverse"),
-    ]
-
-
-def _rollout_extra_table(
-    lf: pl.LazyFrame, event_ids: np.ndarray
-) -> dict[str, np.ndarray]:
-    """`n_tracks`/`leaked_E` per event — rollout-only extras with no truth-file counterpart.
-
-    `leaked_E` sums `pre_E` over each event's escaped-track rows (energy that
-    left the detector rather than being deposited) — needs the raw,
-    unfiltered rollout file (synthetic termination-bookkeeping rows included),
-    unlike `RolloutVsTruth`'s per-step Tier 1-3 comparison which drops them.
-    """
-    agg = (
-        lf.select("event_id", "track_id", "pre_E", "termination_reason")
-        .group_by("event_id")
-        .agg(
-            pl.col("track_id").n_unique().alias("n_tracks"),
-            pl.col("pre_E")
-            .filter(pl.col("termination_reason") == TERM_ESCAPED)
-            .sum()
-            .alias("leaked_E"),
-        )
-        .collect(engine="streaming")
-    )
-    idx = np.searchsorted(event_ids, agg["event_id"].to_numpy())
-    n_tracks = np.zeros(len(event_ids), dtype=np.int64)
-    leaked_E = np.zeros(len(event_ids), dtype=np.float64)
-    n_tracks[idx] = agg["n_tracks"].to_numpy()
-    leaked_E[idx] = agg["leaked_E"].to_numpy().astype(np.float64)
-    return {"n_tracks": n_tracks, "leaked_E": leaked_E}
-
-
-def compute_rollout_vs_truth_observables_pl(
-    rollout_source: str | Path | pl.LazyFrame,
-    truth_source: str | Path | pl.LazyFrame,
-    depth_bins: int = 20,
-    transverse_bins: int = 20,
-    sample_rows: int = 1_000_000,
-    seed: int = 0,
-) -> EventObservables:
-    """Stream a genuine `giant rollout` shower + held-out truth file into event-level observables.
-
-    The Tier 4 counterpart to `RolloutVsTruth`'s Tier 1-3 comparison: unlike
-    `compute_event_observables_pl` (one-step-ahead `giant predict` output
-    re-aggregated by event), this reads a full autoregressive rollout against
-    an independent truth-schema file — two unrelated `event_id` spaces, so
-    entry axis/point and per-event totals are computed separately per side (no
-    shared join key), but depth/transverse bin edges are sized from both sides
-    *combined* so the two profiles share one binning. `rollout_source` must
-    carry `ROLLOUT_COORD_VALUE` coord metadata (same check `RolloutVsTruth`
-    uses).
-
-    Unlike `RolloutVsTruth`'s Tier 1-3 comparison, synthetic termination-
-    bookkeeping rows are *not* dropped here — `rollout.py` dumps a track's
-    remaining energy into one such row's `edep` on every non-escape
-    termination so the shower still conserves energy exactly, so an event's
-    *total* deposited energy needs them; only the per-step marginal comparison
-    (which would see a spurious step_length=0 spike) drops them.
-
-    Returns an `EventObservables` with `gen_table`/`gen_*` = the rollout,
-    `real_table`/`real_*` = the truth file, so every existing
-    `plot_total_energy`/`plot_total_length`/`plot_mean_energy_per_step`/
-    `plot_mean_length_per_step`/`plot_longitudinal_profile`/
-    `plot_transverse_profile`/`plot_shower_max_depth` works unchanged.
-    `gen_table` additionally carries `n_tracks`/`leaked_E`, which have no
-    truth-file counterpart.
-    """
-    if not isinstance(rollout_source, pl.LazyFrame):
-        _check_rollout_metadata(Path(rollout_source))
-    rollout_lf = (
-        rollout_source
-        if isinstance(rollout_source, pl.LazyFrame)
-        else pl.scan_parquet(Path(rollout_source))
-    )
-    truth_lf = (
-        truth_source
-        if isinstance(truth_source, pl.LazyFrame)
-        else pl.scan_parquet(Path(truth_source))
-    )
-
-    rollout_entry = _entry_axis(rollout_lf)
-    truth_entry = _entry_axis(truth_lf)
-    rollout_event_ids = rollout_entry["event_id"].to_numpy()
-    truth_event_ids = truth_entry["event_id"].to_numpy()
-
-    rollout_depth_proxy, rollout_transverse_proxy = _depth_transverse_proxy(
-        rollout_lf, rollout_entry, sample_rows, seed
-    )
-    truth_depth_proxy, truth_transverse_proxy = _depth_transverse_proxy(
-        truth_lf, truth_entry, sample_rows, seed
-    )
-    depth_edges, transverse_edges = _bin_edges_from_proxies(
-        np.concatenate([rollout_depth_proxy, truth_depth_proxy]),
-        np.concatenate([rollout_transverse_proxy, truth_transverse_proxy]),
-        depth_bins,
-        transverse_bins,
-    )
-
-    def _geo(lf: pl.LazyFrame, entry_df: pl.DataFrame, out: str) -> pl.LazyFrame:
-        return (
-            lf.select("event_id", "post_x", "post_y", "post_z", "edep", "step_length")
-            .join(entry_df.lazy(), on="event_id")
-            .select("event_id", *_world_frame_geometry_exprs(out))
-        )
-
-    rollout_geo = _geo(rollout_lf, rollout_entry, "gen")
-    truth_geo = _geo(truth_lf, truth_entry, "real")
-
-    gen = _reduce_spatial_grid(
-        _spatial_grid(
-            rollout_geo,
-            "gen",
-            depth_edges,
-            transverse_edges,
-            depth_bins,
-            transverse_bins,
-        ),
-        rollout_event_ids,
-        depth_bins,
-        transverse_bins,
-        depth_edges,
-    )
-    real = _reduce_spatial_grid(
-        _spatial_grid(
-            truth_geo,
-            "real",
-            depth_edges,
-            transverse_edges,
-            depth_bins,
-            transverse_bins,
-        ),
-        truth_event_ids,
-        depth_bins,
-        transverse_bins,
-        depth_edges,
-    )
-    gen_medians = _approx_median_columns(
-        rollout_geo,
-        rollout_event_ids,
-        {"median_edep": "gen_edep", "median_length": "gen_step"},
-    )
-    real_medians = _approx_median_columns(
-        truth_geo,
-        truth_event_ids,
-        {"median_edep": "real_edep", "median_length": "real_step"},
-    )
-
-    real_table = _side_event_table(
-        truth_event_ids,
-        real,
-        real_medians["median_edep"],
-        real_medians["median_length"],
-    )
-    gen_table = _side_event_table(
-        rollout_event_ids,
-        gen,
-        gen_medians["median_edep"],
-        gen_medians["median_length"],
-        extra=_rollout_extra_table(rollout_lf, rollout_event_ids),
-    )
-
-    return EventObservables(
-        real_table=real_table,
-        gen_table=gen_table,
-        depth_edges=depth_edges,
-        transverse_edges=transverse_edges,
-        real_depth_profile=real["depth_mat"].mean(axis=0),
-        gen_depth_profile=gen["depth_mat"].mean(axis=0),
-        real_depth_profile_std=real["depth_mat"].std(axis=0),
-        gen_depth_profile_std=gen["depth_mat"].std(axis=0),
-        real_transverse_profile=real["transverse_mat"].mean(axis=0),
-        gen_transverse_profile=gen["transverse_mat"].mean(axis=0),
-        real_transverse_profile_std=real["transverse_mat"].std(axis=0),
-        gen_transverse_profile_std=gen["transverse_mat"].std(axis=0),
-    )
-
-
-def plot_total_energy(observables: EventObservables, bins: int = 50):
-    """Real-vs-generated histogram of total deposited energy per event, with resolution."""
-    real = observables.real_table["total_edep"].to_numpy()
-    gen = observables.gen_table["total_edep"].to_numpy()
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    edges = _hist_edges(real, gen, bins=bins).tolist()
-    ax.hist(
-        real,
-        bins=edges,
-        density=True,
-        histtype="step",
-        label=f"real (σ/μ={real.std() / real.mean():.3f})",
-    )
-    ax.hist(
-        gen,
-        bins=edges,
-        density=True,
-        histtype="step",
-        label=f"generated (σ/μ={gen.std() / gen.mean():.3f})",
-    )
-    ax.set_yscale("log")
-    ax.set_xlabel("total deposited energy per event [MeV]")
-    ax.legend(fontsize=8)
-    fig.tight_layout()
-    return fig
-
-
-def plot_total_length(observables: EventObservables, bins: int = 50):
-    """Real-vs-generated histogram of total length traveled per event (sum of step_length)."""
-    real = observables.real_table["total_length"].to_numpy()
-    gen = observables.gen_table["total_length"].to_numpy()
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    edges = _hist_edges(real, gen, bins=bins).tolist()
-    ax.hist(
-        real,
-        bins=edges,
-        density=True,
-        histtype="step",
-        label=f"real (σ/μ={real.std() / real.mean():.3f})",
-    )
-    ax.hist(
-        gen,
-        bins=edges,
-        density=True,
-        histtype="step",
-        label=f"generated (σ/μ={gen.std() / gen.mean():.3f})",
-    )
-    ax.set_yscale("log")
-    ax.set_xlabel("total length traveled per event [mm]")
-    ax.legend(fontsize=8)
-    fig.tight_layout()
-    return fig
-
-
-def _plot_mean_median_per_step(
-    real_mean: np.ndarray,
-    gen_mean: np.ndarray,
-    real_median: np.ndarray,
-    gen_median: np.ndarray,
-    mean_xlabel: str,
-    median_xlabel: str,
-    bins: int,
-    median_bins: int,
-):
-    """Side-by-side real-vs-generated histograms: per-event mean (left), median (right).
-
-    The mean is pulled down by a compressed/under-sampled right tail (rare large
-    values), while the median is robust to that tail. Splitting them into separate
-    panels keeps each comparison legible; if a tail-compression bias explains a
-    low generated mean, the median panel should overlap far more closely. Each
-    panel gets its own bin edges (`median_bins` larger, since the median is far
-    less spread than the mean).
-    """
-    mean_edges = _hist_edges(real_mean, gen_mean, bins=bins)
-    median_edges = _hist_edges(real_median, gen_median, bins=median_bins)
-    prop_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-    real_color, gen_color = prop_colors[0], prop_colors[1]
-
-    fig, (ax_mean, ax_median) = plt.subplots(1, 2, figsize=(12, 4))
-    for ax, real, gen, edges, title, xlabel in [
-        (ax_mean, real_mean, gen_mean, mean_edges, "mean", mean_xlabel),
-        (ax_median, real_median, gen_median, median_edges, "median", median_xlabel),
-    ]:
-        ax.hist(
-            real,
-            bins=edges,
-            density=True,
-            histtype="step",
-            color=real_color,
-            label=f"real (σ/μ={real.std() / real.mean():.3f})",
-        )
-        ax.hist(
-            gen,
-            bins=edges,
-            density=True,
-            histtype="step",
-            color=gen_color,
-            label=f"generated (σ/μ={gen.std() / gen.mean():.3f})",
-        )
-        ax.set_yscale("log")
-        ax.set_title(title)
-        ax.set_xlabel(xlabel)
-        ax.legend(fontsize=8)
-    fig.tight_layout()
-    return fig
-
-
-def plot_mean_energy_per_step(
-    observables: EventObservables, bins: int = 50, median_bins: int = 150
-):
-    """Real-vs-generated histograms of mean/median deposited energy per step, per event.
-
-    Per event: `total_edep / n_steps` (mean) and the per-step median edep —
-    distinct from `plot_total_energy`, which histograms the per-event *total*;
-    these instead ask whether the typical step's energy deposit is right,
-    independent of how many steps the event happened to have.
-    """
-    return _plot_mean_median_per_step(
-        observables.real_table["mean_edep"].to_numpy(),
-        observables.gen_table["mean_edep"].to_numpy(),
-        observables.real_table["median_edep"].to_numpy(),
-        observables.gen_table["median_edep"].to_numpy(),
-        mean_xlabel="mean deposited energy per step, per event [MeV]",
-        median_xlabel="median deposited energy per step, per event [MeV]",
-        bins=bins,
-        median_bins=median_bins,
-    )
-
-
-def plot_mean_length_per_step(
-    observables: EventObservables, bins: int = 50, median_bins: int = 150
-):
-    """Real-vs-generated histograms of mean/median step length per step, per event.
-
-    Per event: `total_length / n_steps` (mean) and the per-step median step length
-    — distinct from `plot_total_length`, which histograms the per-event *total*;
-    these instead ask whether the typical step length is right, independent of how
-    many steps the event happened to have.
-    """
-    return _plot_mean_median_per_step(
-        observables.real_table["mean_length"].to_numpy(),
-        observables.gen_table["mean_length"].to_numpy(),
-        observables.real_table["median_length"].to_numpy(),
-        observables.gen_table["median_length"].to_numpy(),
-        mean_xlabel="mean step length per step, per event [mm]",
-        median_xlabel="median step length per step, per event [mm]",
-        bins=bins,
-        median_bins=median_bins,
-    )
-
-
-def _plot_profile(
-    centers: np.ndarray,
-    real_mean: np.ndarray,
-    gen_mean: np.ndarray,
-    real_std: np.ndarray,
-    gen_std: np.ndarray,
-    xlabel: str,
-):
-    fig, ax = plt.subplots(figsize=(6, 4))
-    ax.errorbar(centers, real_mean, yerr=real_std, fmt="o-", label="real", capsize=2)
-    ax.errorbar(centers, gen_mean, yerr=gen_std, fmt="s-", label="generated", capsize=2)
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel("mean edep per event per bin [MeV]")
-    ax.legend(fontsize=8)
-    fig.tight_layout()
-    return fig
-
-
-def plot_longitudinal_profile(observables: EventObservables):
-    """E_dep(depth) mean ± event-to-event RMS, real vs generated."""
-    centers = 0.5 * (observables.depth_edges[:-1] + observables.depth_edges[1:])
-    return _plot_profile(
-        centers,
-        observables.real_depth_profile,
-        observables.gen_depth_profile,
-        observables.real_depth_profile_std,
-        observables.gen_depth_profile_std,
-        "depth along shower axis [mm]",
-    )
-
-
-def plot_transverse_profile(observables: EventObservables):
-    """E_dep(transverse distance) mean ± event-to-event RMS, real vs generated (Molière-style)."""
-    centers = 0.5 * (
-        observables.transverse_edges[:-1] + observables.transverse_edges[1:]
-    )
-    return _plot_profile(
-        centers,
-        observables.real_transverse_profile,
-        observables.gen_transverse_profile,
-        observables.real_transverse_profile_std,
-        observables.gen_transverse_profile_std,
-        "transverse distance from shower axis [mm]",
-    )
-
-
-def plot_shower_max_depth(observables: EventObservables, bins: int = 30):
-    """Real-vs-generated histogram of per-event shower-maximum depth."""
-    real = observables.real_table["max_depth"].to_numpy()
-    gen = observables.gen_table["max_depth"].to_numpy()
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    edges = _hist_edges(real, gen, bins=bins).tolist()
-    ax.hist(real, bins=edges, density=True, histtype="step", label="real")
-    ax.hist(gen, bins=edges, density=True, histtype="step", label="generated")
-    ax.set_xlabel("depth of shower maximum [mm]")
-    ax.legend(fontsize=8)
-    fig.tight_layout()
-    return fig
-
-
-# ---------------------------------------------------------------------------
-# Particle-species (pdg) contribution shares
-#
-# Dataset-wide (not per-event) breakdown of which pdg species contributed how
-# much of the total deposited energy / total length traveled. Only needs scalar
-# sums — no post_pos reconstruction, no shower axis — so it's a single lazy
-# polars group_by.
-# ---------------------------------------------------------------------------
-
-_PDG_NAMES = {11: "e-", -11: "e+", 22: "gamma", 2112: "n", 2212: "p"}
-
-
-def _pdg_label(pdg: int) -> str:
-    if pdg in _PDG_NAMES:
-        return _PDG_NAMES[pdg]
-    if abs(pdg) > 1_000_000_000:
-        return f"ion{pdg}"
-    return str(pdg)
-
-
-def pdg_contribution_table_pl(source: str | Path | pl.LazyFrame) -> pl.DataFrame:
-    """Total edep / step_length contributed by each pdg species, real vs generated.
-
-    One row per pdg code, sorted by pdg. Pure lazy polars `group_by` over the
-    whole file — `edep`/`step_length` are scalars unaffected by the local-frame
-    rotation. `step_length` is a simple `exp(...) - eps` de-log; `edep` is decoded
-    from the deposit/secondary energy logits against pre_E (`_edep_pl`).
-    """
-    lf = _scan_predicted_local(source)
-
-    def _delog(col: str) -> pl.Expr:
-        return pl.col(col).exp() - _LOG_EPS
-
-    return (
-        lf.group_by("pdg")
-        .agg(
-            _edep_pl("true").sum().alias("real_total_edep"),
-            _edep_pl("pred").sum().alias("gen_total_edep"),
-            _delog("true_log_step_length").sum().alias("real_total_length"),
-            _delog("pred_log_step_length").sum().alias("gen_total_length"),
-        )
-        .collect(engine="streaming")
-        .sort("pdg")
-    )
-
-
-def _pdg_pie_shares(
-    table: pl.DataFrame, real_col: str, gen_col: str, max_slices: int
-) -> tuple[list[str], np.ndarray, np.ndarray]:
-    """Pie-ready (labels, real_values, gen_values), lumping small contributors into 'other'.
-
-    Ranked by combined real+gen contribution so the same species end up in the
-    same slice position in both pies, making them easier to compare.
-    """
-    pdg = table["pdg"].to_numpy()
-    real = table[real_col].to_numpy()
-    gen = table[gen_col].to_numpy()
-
-    order = np.argsort(-(real + gen))
-    pdg, real, gen = pdg[order], real[order], gen[order]
-
-    if len(pdg) > max_slices:
-        keep = max_slices - 1
-        labels = [_pdg_label(int(p)) for p in pdg[:keep]] + ["other"]
-        real = np.append(real[:keep], real[keep:].sum())
-        gen = np.append(gen[:keep], gen[keep:].sum())
-    else:
-        labels = [_pdg_label(int(p)) for p in pdg]
-
-    return labels, real, gen
-
-
-def _plot_pdg_pie(
-    table: pl.DataFrame, real_col: str, gen_col: str, suptitle: str, max_slices: int
-):
-    labels, real_vals, gen_vals = _pdg_pie_shares(table, real_col, gen_col, max_slices)
-
-    fig, axes = plt.subplots(1, 2, figsize=(9, 4.5))
-    for ax, vals, title in [
-        (axes[0], real_vals, "real"),
-        (axes[1], gen_vals, "generated"),
-    ]:
-        ax.pie(vals, labels=labels, autopct="%1.1f%%", startangle=90)
-        ax.set_title(title)
-    fig.suptitle(suptitle)
-    fig.tight_layout()
-    return fig
-
-
-def plot_pdg_energy_share(table: pl.DataFrame, max_slices: int = 6):
-    """Real-vs-generated pies of total deposited energy share by pdg species."""
-    return _plot_pdg_pie(
-        table,
-        "real_total_edep",
-        "gen_total_edep",
-        "deposited energy share by particle type",
-        max_slices,
-    )
-
-
-def plot_pdg_length_share(table: pl.DataFrame, max_slices: int = 6):
-    """Real-vs-generated pies of total length-traveled share by pdg species."""
-    return _plot_pdg_pie(
-        table,
-        "real_total_length",
-        "gen_total_length",
-        "length traveled share by particle type",
-        max_slices,
-    )
-
-
-def plot_router_gating(
-    x: np.ndarray,
-    gate_weights: np.ndarray,
-    x_label: str = "energy",
-    log_x: bool = True,
-    n_bins: int = 40,
-    figsize: tuple[float, float] = (7, 4),
-):
-    """Soft mixture-of-experts gate weight vs. a continuous routing axis.
-
-    Unlike everything else in this module, this doesn't stream from a
-    predict/rollout file — `gate_weights` (N, n_experts, rows already summing
-    to 1, `giant.model.network.Router.gate`'s contract) has to come from a
-    live `Router.gate(cond_cont, cond_cat)` call against a loaded checkpoint,
-    which is a deliberate exception to this module's file-only-diagnostics
-    design (see the module docstring); that on-the-fly step belongs in the
-    calling notebook, not here.
-
-    `x` is binned into `n_bins` equal-population (quantile) bins rather than
-    equal-width ones, since routing axes like energy are usually heavy-tailed
-    and equal-width bins would leave the upper end almost empty. One line per
-    expert, mean gate weight per bin stacked as filled areas — since rows of
-    `gate_weights` are a partition of unity, the stack always fills exactly
-    to 1, and the visible crossover bands are the router's soft decision
-    boundaries (where two experts' means cross ~0.5).
-    """
-    x_arr = np.asarray(x)
-    n_experts = gate_weights.shape[1]
-    order = np.argsort(x_arr)
-    x_sorted = x_arr[order]
-    gw_sorted = gate_weights[order]
-
-    edges = np.quantile(x_sorted, np.linspace(0, 1, n_bins + 1))
-    edges[-1] = np.nextafter(edges[-1], np.inf)  # include the max value
-    bin_idx = np.clip(np.digitize(x_sorted, edges[1:-1]), 0, n_bins - 1)
-
-    centers = np.full(n_bins, np.nan)
-    means = np.full((n_bins, n_experts), np.nan)
-    for b in range(n_bins):
-        mask = bin_idx == b
-        if mask.any():
-            centers[b] = x_sorted[mask].mean()
-            means[b] = gw_sorted[mask].mean(axis=0)
-
-    valid = ~np.isnan(centers)
-    centers, means = centers[valid], means[valid]
-
-    fig, ax = plt.subplots(figsize=figsize)
-    cum = np.zeros(len(centers))
-    for i in range(n_experts):
-        ax.fill_between(centers, cum, cum + means[:, i], alpha=0.7, label=f"expert {i}")
-        cum = cum + means[:, i]
-    if log_x:
-        ax.set_xscale("log")
-    ax.set_xlabel(x_label)
-    ax.set_ylabel("mean gate weight")
-    ax.set_ylim(0, 1)
-    ax.set_title("soft router gating")
-    ax.legend(fontsize=8, ncol=min(n_experts, 4))
-    fig.tight_layout()
-    return fig
diff --git a/giant/analysis/__init__.py b/giant/analysis/__init__.py
new file mode 100644
index 0000000..48c4343
--- /dev/null
+++ b/giant/analysis/__init__.py
@@ -0,0 +1,52 @@
+"""Rollout-vs-reference analysis: streaming compute + plotstyle rendering.
+
+Compares one autoregressive ``giant rollout`` against a held-out miniCaloSim
+reference file, producing publication-styled comparison plots generated in
+parallel on HTCondor (one job per plot x data chunk, compute/merge/render
+split).
+
+Only ``render`` (and the ``render`` CLI path) imports plotstyle/LaTeX; everything
+re-exported here is plotstyle-free so it runs on a compute worker. Import
+``giant.analysis.render`` explicitly for the local render step.
+"""
+
+from giant.analysis.catalog import build_catalog, catalog_ids, get_spec
+from giant.analysis.condor import (
+    RunMeta,
+    SubmitConfig,
+    compute_one,
+    compute_reduced,
+    derive_run_dir,
+    load_rollout_yaml,
+    merge_all,
+    merge_one,
+    prep,
+    write_submit,
+)
+from giant.analysis.context import Context, build_context
+from giant.analysis.reduced import Partial, Reduced
+from giant.analysis.runtime_estimate import RUNTIME_SAFETY_MARGIN, estimate_runtime_s
+from giant.analysis.sources import Side
+
+__all__ = [
+    "build_catalog",
+    "catalog_ids",
+    "get_spec",
+    "RunMeta",
+    "SubmitConfig",
+    "compute_one",
+    "compute_reduced",
+    "derive_run_dir",
+    "load_rollout_yaml",
+    "merge_all",
+    "merge_one",
+    "prep",
+    "write_submit",
+    "Context",
+    "build_context",
+    "Partial",
+    "Reduced",
+    "Side",
+    "RUNTIME_SAFETY_MARGIN",
+    "estimate_runtime_s",
+]
diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py
new file mode 100644
index 0000000..fc665e3
--- /dev/null
+++ b/giant/analysis/catalog.py
@@ -0,0 +1,846 @@
+"""The declarative plot catalog: one ``PlotSpec`` per figure.
+
+Each spec knows its stable ``id`` (used for the reduced-data filename, the PDF
+stem and the condor queue item), its gallery ``family`` (subdirectory), and a
+``compute_partial(bundle) -> dict`` / ``finalize(parts, ctx) -> Reduced`` pair
+that together run the streaming reduction. ``compute_partial`` runs once per
+``(plot, chunk)`` condor job against a ``Bundle`` whose four LazyFrames are
+already filtered to that chunk (see ``Bundle.open``'s ``chunk`` argument); it
+returns a small JSON-safe partial artifact — either a raw sum-mergeable count
+dict (histograms/species sums against fixed edges) or a raw per-event/
+per-secondary array to be concatenated (anything that derives its own edges or
+a mean/std from the full dataset). ``finalize`` merges the per-chunk partials
+(in chunk order) and does the actual histogramming/edge-selection/mean-std
+collapse, once, over the merged data — for ``n_chunks=1`` this reproduces
+exactly what a single unchunked pass would produce. Specs marked
+``chunkable=False`` (the router ones) always run as a single chunk regardless
+of the configured chunk count.
+
+Rendering lives in ``render.py`` and dispatches on ``Reduced.kind`` — the
+catalog itself never imports plotstyle, so ``compute-one`` jobs stay LaTeX-free.
+
+The registry is built by expanding parametric families (marginals over
+variable x grouping, secondaries, ...) into concrete specs.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from typing import Callable
+
+import numpy as np
+import polars as pl
+
+from giant.analysis.context import Context
+from giant.analysis.grouping import (
+    energy_bin_labels,
+    event_energy_bins,
+    material_label,
+    pdg_label,
+)
+from giant.analysis.reduce import (
+    attach_entry_axis,
+    depth_expr,
+    entry_axis,
+    event_scalars,
+    hist1d,
+    leakage_fraction,
+    profile_finalize,
+    profile_partial,
+    species_share,
+    sum_merge,
+    transverse_expr,
+)
+from giant.analysis.reduced import Reduced
+from giant.analysis.router_gating import (
+    compute_router_gating,
+    compute_router_share_by_pdg,
+    compute_router_share_by_process,
+)
+from giant.analysis.sources import Side, open_side, physical_steps, secondaries
+from giant.analysis.variables import RANGED_VARS, cos_scatter_expr
+
+
+@dataclass
+class Bundle:
+    """Everything a compute runs against — built once per ``compute-one`` job."""
+
+    ctx: Context
+    r_all: pl.LazyFrame  # rollout, all rows (incl. synthetic termination rows)
+    t_all: pl.LazyFrame  # reference, all rows
+    r_phys: pl.LazyFrame  # rollout, physical steps only
+    t_phys: pl.LazyFrame  # reference, physical steps only
+    checkpoint: str | None = None  # from the rollout YAML; router_gating only
+
+    @classmethod
+    def open(
+        cls,
+        rollout,
+        reference,
+        ctx: Context,
+        checkpoint=None,
+        chunk: tuple[int, int] | None = None,
+    ) -> "Bundle":
+        """Open both sides, optionally restricted to one event-disjoint chunk.
+
+        ``chunk = (chunk_index, n_chunks)`` filters both sides to
+        ``event_id % n_chunks == chunk_index`` *before* deriving the physical/
+        secondary views, so every downstream reduction (which is either
+        row-local or a ``group_by("event_id")``) sees a self-contained,
+        event-disjoint slice — no cross-chunk lookups are ever needed.
+        """
+        r_all = open_side(rollout, Side.rollout)
+        t_all = open_side(reference, Side.reference)
+        if chunk is not None:
+            idx, n = chunk
+            pred = pl.col("event_id") % n == idx
+            r_all = r_all.filter(pred)
+            t_all = t_all.filter(pred)
+        return cls(
+            ctx=ctx,
+            r_all=r_all,
+            t_all=t_all,
+            r_phys=physical_steps(r_all, Side.rollout),
+            t_phys=physical_steps(t_all, Side.reference),
+            checkpoint=checkpoint,
+        )
+
+
+@dataclass
+class PlotSpec:
+    id: str
+    family: str
+    compute_partial: Callable[[Bundle], dict]
+    finalize: Callable[[list[dict], Context], Reduced]
+    chunkable: bool = True
+
+
+def _unchunkable(
+    compute: Callable[[Bundle], Reduced],
+) -> tuple[Callable[[Bundle], dict], Callable[[list[dict], Context], Reduced]]:
+    """Wrap a whole-dataset ``compute(bundle) -> Reduced`` as a trivial
+    ``(compute_partial, finalize)`` pair, for specs marked ``chunkable=False``
+    (which always run as a single chunk, so ``parts`` is always one element).
+    """
+
+    def partial(b: Bundle) -> dict:
+        return {"reduced": asdict(compute(b))}
+
+    def finalize(parts: list[dict], ctx: Context) -> Reduced:
+        return Reduced(**parts[0]["reduced"])
+
+    return partial, finalize
+
+
+# ---------------------------------------------------------------------------
+# small numpy/hist helpers
+# ---------------------------------------------------------------------------
+
+_ROLL = "rollout"
+_REF = "reference"
+
+
+def _counts(h: dict, key, nbins: int) -> list[int]:
+    return h.get(key, np.zeros(nbins, dtype=np.int64)).astype(np.int64).tolist()
+
+
+def _partial_hist(
+    lf: pl.LazyFrame, value: pl.Expr, edges: np.ndarray, group: pl.Expr | None = None
+) -> dict[str, list[int]]:
+    """One chunk's raw ``hist1d`` result as a JSON-safe, sum-mergeable dict."""
+    nb = len(edges) - 1
+    h = hist1d(lf, value, edges, group=group)
+    return {str(k): _counts(h, k, nb) for k in h}
+
+
+def _finalize_counts(merged: dict[str, list], key, nbins: int) -> list[int]:
+    """One group's merged counts (zero-filled if the group never appeared)."""
+    return list(merged.get(str(key), [0] * nbins))
+
+
+def _np_hist_pair(
+    r: np.ndarray, t: np.ndarray, nbins: int
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Shared-edge histogram of two small per-event arrays (robust range)."""
+    both = np.concatenate([r, t]) if (len(r) or len(t)) else np.array([0.0, 1.0])
+    lo, hi = float(np.quantile(both, 0.001)), float(np.quantile(both, 0.999))
+    if not (hi - lo > 1e-6 * max(abs(hi), 1.0)):
+        lo, hi = lo - 0.5, hi + 0.5
+    edges = np.linspace(lo, hi, nbins + 1)
+    return edges, np.histogram(r, edges)[0], np.histogram(t, edges)[0]
+
+
+# Human-readable figure titles per marginal variable (the axis labels carry units;
+# these read cleanly as a title without them).
+_TITLE_NAMES = {
+    "step_length": "Step length",
+    "edep": "Deposited energy per step",
+    "delta_e": "Energy loss per step",
+    "post_E": "Post-step energy",
+    "cos_scatter": "Scattering cosine",
+}
+
+
+def _var(var: str):
+    """(axis label, value expr) for a marginal variable name."""
+    if var == "cos_scatter":
+        return ("cos of scattering angle", cos_scatter_expr())
+    label, expr = RANGED_VARS[var]
+    return (label, expr)
+
+
+def _marginal_edges(ctx: Context, var: str) -> np.ndarray:
+    if var == "cos_scatter":
+        return np.linspace(-1.0, 1.0, ctx.n_marginal_bins + 1)
+    return ctx.marginal_edges(var)
+
+
+# ---------------------------------------------------------------------------
+# marginals: variable x {overall, energy, pdg, material}
+# ---------------------------------------------------------------------------
+
+
+def _marginal_overall_partial(b: Bundle, var: str) -> dict:
+    _, expr = _var(var)
+    edges = _marginal_edges(b.ctx, var)
+    return {
+        "r": _partial_hist(b.r_phys, expr, edges),
+        "t": _partial_hist(b.t_phys, expr, edges),
+    }
+
+
+def _marginal_overall_finalize(parts: list[dict], ctx: Context, var: str) -> Reduced:
+    label, _ = _var(var)
+    edges = _marginal_edges(ctx, var)
+    nb = len(edges) - 1
+    r = sum_merge([p["r"] for p in parts])
+    t = sum_merge([p["t"] for p in parts])
+    return Reduced(
+        id=f"marginal_{var}",
+        family="marginals",
+        kind="overlay_hist",
+        title=_TITLE_NAMES[var],
+        xlabel=label,
+        payload={
+            "edges": edges.tolist(),
+            _ROLL: _finalize_counts(r, 0, nb),
+            _REF: _finalize_counts(t, 0, nb),
+            "log_y": True,
+        },
+    )
+
+
+def _energy_group_expr(lf: pl.LazyFrame, edges: np.ndarray) -> pl.Expr:
+    ids, bins = event_energy_bins(lf, edges)
+    return pl.col("event_id").replace_strict(
+        ids, bins, default=-1, return_dtype=pl.Int64
+    )
+
+
+def _marginal_grouped_partial(b: Bundle, var: str, axis: str) -> dict:
+    _, expr = _var(var)
+    edges = _marginal_edges(b.ctx, var)
+    if axis == "pdg":
+        r = hist1d(b.r_phys, expr, edges, group=pl.col("pdg"))
+        t = hist1d(b.t_phys, expr, edges, group=pl.col("pdg"))
+    elif axis == "material":
+        r = hist1d(b.r_phys, expr, edges, group=pl.col("material"))
+        t = hist1d(b.t_phys, expr, edges, group=pl.col("material"))
+    else:  # energy
+        e_edges = np.asarray(b.ctx.energy_edges)
+        r = hist1d(b.r_phys, expr, edges, group=_energy_group_expr(b.r_phys, e_edges))
+        t = hist1d(b.t_phys, expr, edges, group=_energy_group_expr(b.t_phys, e_edges))
+    nb = len(edges) - 1
+    return {
+        "r": {str(k): _counts(r, k, nb) for k in r},
+        "t": {str(k): _counts(t, k, nb) for k in t},
+    }
+
+
+def _marginal_grouped_finalize(
+    parts: list[dict], ctx: Context, var: str, axis: str
+) -> Reduced:
+    label, _ = _var(var)
+    edges = _marginal_edges(ctx, var)
+    nb = len(edges) - 1
+    r = sum_merge([p["r"] for p in parts])
+    t = sum_merge([p["t"] for p in parts])
+    groups: dict[str, dict] = {}
+
+    if axis == "pdg":
+        for k in ctx.top_pdgs:
+            groups[pdg_label(k)] = {
+                _ROLL: _finalize_counts(r, k, nb),
+                _REF: _finalize_counts(t, k, nb),
+            }
+    elif axis == "material":
+        for m in ctx.materials:
+            groups[material_label(m)] = {
+                _ROLL: _finalize_counts(r, m, nb),
+                _REF: _finalize_counts(t, m, nb),
+            }
+    else:  # energy
+        e_edges = np.asarray(ctx.energy_edges)
+        for bi, lbl in enumerate(energy_bin_labels(e_edges)):
+            groups[lbl] = {
+                _ROLL: _finalize_counts(r, bi, nb),
+                _REF: _finalize_counts(t, bi, nb),
+            }
+
+    return Reduced(
+        id=f"marginal_{var}_by_{axis}",
+        family="marginals",
+        kind="grouped_hist",
+        title=f"{_TITLE_NAMES[var]} by {axis}",
+        xlabel=label,
+        payload={"edges": edges.tolist(), "groups": groups, "log_y": True},
+    )
+
+
+# ---------------------------------------------------------------------------
+# per-event scalar observables
+# ---------------------------------------------------------------------------
+
+
+def _event_scalar_partial(b: Bundle, col: str, use_all: bool) -> dict:
+    r_lf, t_lf = (b.r_all, b.t_all) if use_all else (b.r_phys, b.t_phys)
+    r = event_scalars(r_lf)[col].to_numpy()
+    t = event_scalars(t_lf)[col].to_numpy()
+    return {"r": r.tolist(), "t": t.tolist()}
+
+
+def _event_scalar_finalize(
+    parts: list[dict], ctx: Context, spec_id: str, title: str, xlabel: str
+) -> Reduced:
+    r = np.concatenate([np.asarray(p["r"], dtype=float) for p in parts])
+    t = np.concatenate([np.asarray(p["t"], dtype=float) for p in parts])
+    edges, rc, tc = _np_hist_pair(r, t, ctx.n_marginal_bins)
+    return Reduced(
+        id=spec_id,
+        family="event",
+        kind="overlay_hist",
+        title=title,
+        xlabel=xlabel,
+        payload={
+            "edges": edges.tolist(),
+            _ROLL: rc.astype(np.int64).tolist(),
+            _REF: tc.astype(np.int64).tolist(),
+            "log_y": False,
+        },
+    )
+
+
+def _event_total_edep_by_energy_partial(b: Bundle) -> dict:
+    r = event_scalars(b.r_all)
+    t = event_scalars(b.t_all)
+    return {
+        "r_incident": r["incident_E"].to_list(),
+        "r_edep": r["total_edep"].to_list(),
+        "t_incident": t["incident_E"].to_list(),
+        "t_edep": t["total_edep"].to_list(),
+    }
+
+
+def _event_total_edep_by_energy_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    e_edges = np.asarray(ctx.energy_edges)
+    r_inc = np.concatenate([np.asarray(p["r_incident"], dtype=float) for p in parts])
+    r_val = np.concatenate([np.asarray(p["r_edep"], dtype=float) for p in parts])
+    t_inc = np.concatenate([np.asarray(p["t_incident"], dtype=float) for p in parts])
+    t_val = np.concatenate([np.asarray(p["t_edep"], dtype=float) for p in parts])
+    r_bin = np.clip(np.digitize(r_inc, e_edges[1:-1]), 0, len(e_edges) - 2)
+    t_bin = np.clip(np.digitize(t_inc, e_edges[1:-1]), 0, len(e_edges) - 2)
+    edges, _, _ = _np_hist_pair(r_val, t_val, ctx.n_marginal_bins)
+    groups: dict[str, dict] = {}
+    for bi, lbl in enumerate(energy_bin_labels(e_edges)):
+        rc = np.histogram(r_val[r_bin == bi], edges)[0]
+        tc = np.histogram(t_val[t_bin == bi], edges)[0]
+        groups[lbl] = {
+            _ROLL: rc.astype(np.int64).tolist(),
+            _REF: tc.astype(np.int64).tolist(),
+        }
+    return Reduced(
+        id="event_total_edep_by_energy",
+        family="event",
+        kind="grouped_hist",
+        title="Total deposited energy per event by incident energy",
+        xlabel="total deposited energy [MeV]",
+        payload={"edges": edges.tolist(), "groups": groups, "log_y": False},
+    )
+
+
+# ---------------------------------------------------------------------------
+# shower shape profiles
+# ---------------------------------------------------------------------------
+
+
+def _profile_partial(b: Bundle, coord_fn, edges_key: str) -> dict:
+    edges = np.asarray(getattr(b.ctx, edges_key))
+    r_lf = attach_entry_axis(b.r_all, entry_axis(b.r_all))
+    t_lf = attach_entry_axis(b.t_all, entry_axis(b.t_all))
+    r_ids, r_mat = profile_partial(r_lf, coord_fn(), edges, pl.col("edep"))
+    t_ids, t_mat = profile_partial(t_lf, coord_fn(), edges, pl.col("edep"))
+    return {
+        "r_ids": r_ids.tolist(),
+        "r_mat": r_mat.tolist(),
+        "t_ids": t_ids.tolist(),
+        "t_mat": t_mat.tolist(),
+    }
+
+
+def _assert_event_disjoint(id_lists: list[list[int]], spec_id: str, side: str) -> None:
+    """Guard the chunking invariant profiles depend on: no event in two chunks.
+
+    A violation would silently double-count that event in the merged mean/RMS
+    with no other symptom, so this is worth a loud failure rather than a
+    quietly-wrong plot.
+    """
+    seen: set[int] = set()
+    for ids in id_lists:
+        overlap = seen & set(ids)
+        if overlap:
+            raise ValueError(
+                f"{spec_id} ({side}): event_id(s) {sorted(overlap)[:5]} appear "
+                "in more than one chunk — chunking must be event-disjoint"
+            )
+        seen.update(ids)
+
+
+def _profile_finalize(
+    parts: list[dict],
+    ctx: Context,
+    spec_id: str,
+    title: str,
+    xlabel: str,
+    edges_key: str,
+) -> Reduced:
+    edges = np.asarray(getattr(ctx, edges_key))
+    nb = len(edges) - 1
+    _assert_event_disjoint([p["r_ids"] for p in parts], spec_id, "rollout")
+    _assert_event_disjoint([p["t_ids"] for p in parts], spec_id, "reference")
+    r_mats = [np.asarray(p["r_mat"], dtype=float).reshape(-1, nb) for p in parts]
+    t_mats = [np.asarray(p["t_mat"], dtype=float).reshape(-1, nb) for p in parts]
+    r_mean, r_std = profile_finalize(r_mats)
+    t_mean, t_std = profile_finalize(t_mats)
+    return Reduced(
+        id=spec_id,
+        family="shower",
+        kind="profile",
+        title=title,
+        xlabel=xlabel,
+        payload={
+            "edges": edges.tolist(),
+            "rollout_mean": r_mean.tolist(),
+            "rollout_std": r_std.tolist(),
+            "reference_mean": t_mean.tolist(),
+            "reference_std": t_std.tolist(),
+            "ylabel": "mean deposited energy per event [MeV]",
+        },
+    )
+
+
+# ---------------------------------------------------------------------------
+# species share + leakage
+# ---------------------------------------------------------------------------
+
+
+def _species_share_partial(b: Bundle) -> dict:
+    r = species_share(b.r_all)
+    t = species_share(b.t_all)
+    return {
+        "r": {str(k): v for k, v in zip(r["pdg"].to_list(), r["total_edep"].to_list())},
+        "t": {str(k): v for k, v in zip(t["pdg"].to_list(), t["total_edep"].to_list())},
+    }
+
+
+def _species_share_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    r_map = sum_merge([p["r"] for p in parts])
+    t_map = sum_merge([p["t"] for p in parts])
+    r_tot = sum(r_map.values()) or 1.0
+    t_tot = sum(t_map.values()) or 1.0
+    labels = [pdg_label(k) for k in ctx.top_pdgs]
+    return Reduced(
+        id="species_edep_share",
+        family="species",
+        kind="bar",
+        title="Deposited-energy share by species",
+        xlabel="species",
+        payload={
+            "labels": labels,
+            _ROLL: [r_map.get(str(k), 0.0) / r_tot for k in ctx.top_pdgs],
+            _REF: [t_map.get(str(k), 0.0) / t_tot for k in ctx.top_pdgs],
+            "ylabel": "fraction of total deposited energy",
+        },
+    )
+
+
+def _leakage_partial(b: Bundle) -> dict:
+    frac = leakage_fraction(b.r_all)
+    return {"frac": frac.tolist()}
+
+
+def _leakage_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    frac = np.concatenate([np.asarray(p["frac"], dtype=float) for p in parts])
+    edges = np.linspace(
+        0.0, max(float(frac.max()) if len(frac) else 1.0, 1e-3), ctx.n_marginal_bins + 1
+    )
+    counts = np.histogram(frac, edges)[0]
+    return Reduced(
+        id="leakage_fraction",
+        family="species",
+        kind="single_hist",
+        title="Escaped (leakage) energy fraction per shower",
+        xlabel="escaped energy fraction",
+        payload={
+            "edges": edges.tolist(),
+            _ROLL: counts.astype(np.int64).tolist(),
+            "log_y": True,
+            "note": "rollout only; the reference has no detector-escape concept",
+        },
+    )
+
+
+# ---------------------------------------------------------------------------
+# secondaries
+# ---------------------------------------------------------------------------
+
+
+def _sec_frames(b: Bundle):
+    return (
+        secondaries(b.r_phys, Side.rollout),
+        secondaries(b.t_all, Side.reference),
+    )
+
+
+def _sec_count_per_event_partial(b: Bundle) -> dict:
+    r_sec, t_sec = _sec_frames(b)
+    r = (
+        r_sec.group_by("event_id")
+        .agg(pl.len().alias("n"))
+        .collect(engine="streaming")["n"]
+        .to_numpy()
+    )
+    t = (
+        t_sec.group_by("event_id")
+        .agg(pl.len().alias("n"))
+        .collect(engine="streaming")["n"]
+        .to_numpy()
+    )
+    return {"r": r.tolist(), "t": t.tolist()}
+
+
+def _sec_count_per_event_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    r = np.concatenate([np.asarray(p["r"], dtype=float) for p in parts])
+    t = np.concatenate([np.asarray(p["t"], dtype=float) for p in parts])
+    edges, rc, tc = _np_hist_pair(r, t, min(ctx.n_marginal_bins, 40))
+    return Reduced(
+        id="sec_count_per_event",
+        family="secondaries",
+        kind="overlay_hist",
+        title="Number of secondaries per event",
+        xlabel="secondaries per event",
+        payload={
+            "edges": edges.tolist(),
+            _ROLL: rc.astype(np.int64).tolist(),
+            _REF: tc.astype(np.int64).tolist(),
+            "log_y": False,
+        },
+    )
+
+
+def _counts_by_pdg(sec_lf: pl.LazyFrame) -> dict[str, int]:
+    df = sec_lf.group_by("pdg").agg(pl.len().alias("n")).collect(engine="streaming")
+    return {str(k): v for k, v in zip(df["pdg"].to_list(), df["n"].to_list())}
+
+
+def _sec_count_per_species_partial(b: Bundle) -> dict:
+    r_sec, t_sec = _sec_frames(b)
+    return {"r": _counts_by_pdg(r_sec), "t": _counts_by_pdg(t_sec)}
+
+
+def _sec_count_per_species_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    r = sum_merge([p["r"] for p in parts])
+    t = sum_merge([p["t"] for p in parts])
+    keys = sorted(set(r) | set(t), key=lambda k: -(r.get(k, 0) + t.get(k, 0)))[
+        : len(ctx.top_pdgs)
+    ]
+    return Reduced(
+        id="sec_count_per_species",
+        family="secondaries",
+        kind="bar",
+        title="Secondary count by species",
+        xlabel="species",
+        payload={
+            "labels": [pdg_label(int(k)) for k in keys],
+            _ROLL: [float(r.get(k, 0)) for k in keys],
+            _REF: [float(t.get(k, 0)) for k in keys],
+            "ylabel": "secondary count",
+        },
+    )
+
+
+def _sec_energy_partial(b: Bundle) -> dict:
+    r_sec, t_sec = _sec_frames(b)
+    edges = np.linspace(*b.ctx.sec_energy_range, b.ctx.n_sec_bins + 1)
+    return {
+        "r": _partial_hist(r_sec, pl.col("energy"), edges),
+        "t": _partial_hist(t_sec, pl.col("energy"), edges),
+    }
+
+
+def _sec_energy_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    edges = np.linspace(*ctx.sec_energy_range, ctx.n_sec_bins + 1)
+    nb = len(edges) - 1
+    r = sum_merge([p["r"] for p in parts])
+    t = sum_merge([p["t"] for p in parts])
+    return Reduced(
+        id="sec_energy",
+        family="secondaries",
+        kind="overlay_hist",
+        title="Secondary birth energy",
+        xlabel="secondary energy [MeV]",
+        payload={
+            "edges": edges.tolist(),
+            _ROLL: _finalize_counts(r, 0, nb),
+            _REF: _finalize_counts(t, 0, nb),
+            "log_y": True,
+        },
+    )
+
+
+def _sec_cos_angle_partial(b: Bundle) -> dict:
+    edges = np.linspace(-1.0, 1.0, b.ctx.n_sec_bins + 1)
+    cos = (
+        pl.col("sdx") * pl.col("axis_x")
+        + pl.col("sdy") * pl.col("axis_y")
+        + pl.col("sdz") * pl.col("axis_z")
+    ).clip(-1.0, 1.0)
+
+    def _side(sec_lf: pl.LazyFrame, steps_lf: pl.LazyFrame) -> dict[str, list[int]]:
+        ea = entry_axis(steps_lf)
+        return _partial_hist(attach_entry_axis(sec_lf, ea), cos, edges)
+
+    r_sec, t_sec = _sec_frames(b)
+    return {"r": _side(r_sec, b.r_phys), "t": _side(t_sec, b.t_all)}
+
+
+def _sec_cos_angle_finalize(parts: list[dict], ctx: Context) -> Reduced:
+    edges = np.linspace(-1.0, 1.0, ctx.n_sec_bins + 1)
+    nb = len(edges) - 1
+    r = sum_merge([p["r"] for p in parts])
+    t = sum_merge([p["t"] for p in parts])
+    return Reduced(
+        id="sec_cos_angle",
+        family="secondaries",
+        kind="overlay_hist",
+        title="Secondary emission angle relative to the shower axis",
+        xlabel="cos of emission angle",
+        payload={
+            "edges": edges.tolist(),
+            _ROLL: _finalize_counts(r, 0, nb),
+            _REF: _finalize_counts(t, 0, nb),
+            "log_y": False,
+        },
+    )
+
+
+# ---------------------------------------------------------------------------
+# router diagnostics (not chunked — already bounded/subsampled)
+# ---------------------------------------------------------------------------
+
+_router_gating_partial, _router_gating_finalize = _unchunkable(
+    lambda b: compute_router_gating(b.checkpoint, b.r_phys, b.t_phys)
+)
+_router_share_pdg_partial, _router_share_pdg_finalize = _unchunkable(
+    lambda b: compute_router_share_by_pdg(
+        b.checkpoint, b.r_phys, b.t_phys, b.ctx.top_pdgs
+    )
+)
+_router_share_process_partial, _router_share_process_finalize = _unchunkable(
+    lambda b: compute_router_share_by_process(b.checkpoint, b.t_phys)
+)
+
+
+# ---------------------------------------------------------------------------
+# registry assembly
+# ---------------------------------------------------------------------------
+
+MARGINAL_VARS = ["step_length", "edep", "delta_e", "post_E", "cos_scatter"]
+GROUPING_AXES = ["energy", "pdg", "material"]
+
+
+def build_catalog() -> list[PlotSpec]:
+    """All concrete plot specs, each with a unique id."""
+    specs: list[PlotSpec] = []
+
+    for var in MARGINAL_VARS:
+        specs.append(
+            PlotSpec(
+                f"marginal_{var}",
+                "marginals",
+                compute_partial=lambda b, v=var: _marginal_overall_partial(b, v),
+                finalize=lambda parts, ctx, v=var: _marginal_overall_finalize(
+                    parts, ctx, v
+                ),
+            )
+        )
+        for axis in GROUPING_AXES:
+            specs.append(
+                PlotSpec(
+                    f"marginal_{var}_by_{axis}",
+                    "marginals",
+                    compute_partial=lambda b, v=var, a=axis: _marginal_grouped_partial(
+                        b, v, a
+                    ),
+                    finalize=lambda parts, ctx, v=var, a=axis: (
+                        _marginal_grouped_finalize(parts, ctx, v, a)
+                    ),
+                )
+            )
+
+    specs += [
+        PlotSpec(
+            "event_total_edep",
+            "event",
+            compute_partial=lambda b: _event_scalar_partial(
+                b, "total_edep", use_all=True
+            ),
+            finalize=lambda parts, ctx: _event_scalar_finalize(
+                parts,
+                ctx,
+                "event_total_edep",
+                "Total deposited energy per event",
+                "total deposited energy [MeV]",
+            ),
+        ),
+        PlotSpec(
+            "event_total_edep_by_energy",
+            "event",
+            compute_partial=_event_total_edep_by_energy_partial,
+            finalize=_event_total_edep_by_energy_finalize,
+        ),
+        PlotSpec(
+            "event_mean_length",
+            "event",
+            compute_partial=lambda b: _event_scalar_partial(
+                b, "mean_length", use_all=False
+            ),
+            finalize=lambda parts, ctx: _event_scalar_finalize(
+                parts,
+                ctx,
+                "event_mean_length",
+                "Mean step length per event",
+                "mean step length [mm]",
+            ),
+        ),
+        PlotSpec(
+            "event_n_steps",
+            "event",
+            compute_partial=lambda b: _event_scalar_partial(
+                b, "n_steps", use_all=False
+            ),
+            finalize=lambda parts, ctx: _event_scalar_finalize(
+                parts,
+                ctx,
+                "event_n_steps",
+                "Number of steps per event",
+                "steps per event",
+            ),
+        ),
+        PlotSpec(
+            "shower_longitudinal",
+            "shower",
+            compute_partial=lambda b: _profile_partial(b, depth_expr, "depth_edges"),
+            finalize=lambda parts, ctx: _profile_finalize(
+                parts,
+                ctx,
+                "shower_longitudinal",
+                "Longitudinal shower profile",
+                "depth along shower axis [mm]",
+                "depth_edges",
+            ),
+        ),
+        PlotSpec(
+            "shower_transverse",
+            "shower",
+            compute_partial=lambda b: _profile_partial(
+                b, transverse_expr, "transverse_edges"
+            ),
+            finalize=lambda parts, ctx: _profile_finalize(
+                parts,
+                ctx,
+                "shower_transverse",
+                "Transverse shower profile",
+                "radius from shower axis [mm]",
+                "transverse_edges",
+            ),
+        ),
+        PlotSpec(
+            "species_edep_share",
+            "species",
+            compute_partial=_species_share_partial,
+            finalize=_species_share_finalize,
+        ),
+        PlotSpec(
+            "leakage_fraction",
+            "species",
+            compute_partial=_leakage_partial,
+            finalize=_leakage_finalize,
+        ),
+        PlotSpec(
+            "sec_count_per_event",
+            "secondaries",
+            compute_partial=_sec_count_per_event_partial,
+            finalize=_sec_count_per_event_finalize,
+        ),
+        PlotSpec(
+            "sec_count_per_species",
+            "secondaries",
+            compute_partial=_sec_count_per_species_partial,
+            finalize=_sec_count_per_species_finalize,
+        ),
+        PlotSpec(
+            "sec_energy",
+            "secondaries",
+            compute_partial=_sec_energy_partial,
+            finalize=_sec_energy_finalize,
+        ),
+        PlotSpec(
+            "sec_cos_angle",
+            "secondaries",
+            compute_partial=_sec_cos_angle_partial,
+            finalize=_sec_cos_angle_finalize,
+        ),
+        PlotSpec(
+            "router_gating",
+            "model",
+            compute_partial=_router_gating_partial,
+            finalize=_router_gating_finalize,
+            chunkable=False,
+        ),
+        PlotSpec(
+            "router_share_by_pdg",
+            "model",
+            compute_partial=_router_share_pdg_partial,
+            finalize=_router_share_pdg_finalize,
+            chunkable=False,
+        ),
+        PlotSpec(
+            "router_share_by_process",
+            "model",
+            compute_partial=_router_share_process_partial,
+            finalize=_router_share_process_finalize,
+            chunkable=False,
+        ),
+    ]
+    return specs
+
+
+def catalog_ids() -> list[str]:
+    return [s.id for s in build_catalog()]
+
+
+def get_spec(spec_id: str) -> PlotSpec:
+    for s in build_catalog():
+        if s.id == spec_id:
+            return s
+    raise KeyError(f"unknown plot id: {spec_id!r}")
diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py
new file mode 100644
index 0000000..60e1424
--- /dev/null
+++ b/giant/analysis/condor.py
@@ -0,0 +1,431 @@
+"""HTCondor orchestration driven by a ``giant rollout`` YAML sidecar.
+
+A rollout writes a YAML sidecar (``giant/cli.py:_write_prediction_ref`` +
+rollout extras) that already names both files we need and carries the run's
+provenance:
+
+* ``output``  — the rollout steps parquet (the *generated* side)
+* ``dataset`` — the file the rollout was seeded from, i.e. the held-out real
+  steps (the *reference* side)
+* ``checkpoint``, ``geometry_oracle``, ``energy_cutoff``, ``steps``, ... —
+  metadata that flows straight into every plot's gallery ``metadata.yaml``.
+
+So the analysis takes that one YAML as input, derives its own **run directory**
+next to the rollout parquet, and lays everything out under it:
+
+    <run_dir>/shared.json     fixed bin edges / group sets (prep)
+    <run_dir>/run_meta.json   resolved rollout/reference paths + plot metadata
+    <run_dir>/reduced_partial/<id>__<chunk>.json   one per (plot, chunk) job
+    <run_dir>/reduced/<id>.json   merged, per plot
+    <run_dir>/plots/<family>/<id>.pdf   rendered locally
+
+Job model (one condor job per (plot, chunk), compute/merge/render split):
+
+1. ``prep`` runs once on the submit node — reads the YAML, resolves the shared
+   context from a subsample, writes ``shared.json`` + ``run_meta.json``
+   (including the run's configured ``n_chunks``).
+2. one job per catalog id x chunk index runs ``giant analyze compute-one
+   --run-dir`` on a worker — a single streaming pass over that
+   ``event_id``-disjoint chunk, writing ``reduced_partial/<id>__<chunk>.json``
+   (polars/numpy only, no LaTeX). Specs marked ``chunkable=False``
+   (``PlotSpec``, ``catalog.py``) always run as a single chunk.
+3. a *local* ``giant analyze render`` first merges every plot's chunk partials
+   (``merge_all`` — sums/concatenates them and re-derives any data-dependent
+   histogram edges or mean/std, per ``PlotSpec.finalize``) into
+   ``reduced/<id>.json``, then renders those into the styled PDF + gallery tree
+   (that step imports plotstyle/LaTeX).
+
+Files on ``/ceph`` or ``/work`` are reached via ``ProvidesETPResources``; no
+HTCondor file transfer of the multi-GB inputs.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import polars as pl
+import yaml
+
+from giant.analysis.catalog import Bundle, catalog_ids, get_spec
+from giant.analysis.context import Context, build_context
+from giant.analysis.reduced import Partial
+from giant.analysis.runtime_estimate import estimate_runtime_s
+from giant.analysis.sources import Side, open_side
+
+# Keys copied verbatim from a rollout YAML into each plot's gallery metadata.
+_PLOT_META_KEYS = (
+    "prediction_id",
+    "checkpoint",
+    "output",
+    "dataset",
+    "geometry_oracle",
+    "energy_cutoff",
+    "max_steps",
+    "steps",
+    "max_tracks_per_event",
+    "escape_threshold",
+    "n_events",
+    "n_seed_events",
+    "timestamp",
+    "comment",
+    "weights",
+    "batch_size",
+    "device",
+    "rollout_seed",
+    "n_rows",
+    "termination_reason_counts",
+    "model_config",
+    "training_epoch",
+    "best_val_loss",
+    "training_config",
+    "training_meta",
+)
+
+
+# ---------------------------------------------------------------------------
+# rollout-YAML → run directory
+# ---------------------------------------------------------------------------
+
+
+def load_rollout_yaml(path: str | Path) -> dict:
+    """Load a ``giant rollout`` YAML sidecar, requiring the two file paths."""
+    d = yaml.safe_load(Path(path).read_text())
+    for key in ("output", "dataset"):
+        if key not in d:
+            raise ValueError(
+                f"{path} is not a rollout YAML (missing {key!r}); expected the "
+                "sidecar `giant rollout` writes next to the checkpoint"
+            )
+    if d.get("kind") not in (None, "rollout"):
+        raise ValueError(f"{path} has kind={d.get('kind')!r}, not a rollout YAML")
+    return d
+
+
+def derive_run_dir(
+    rollout_yaml: dict,
+    run_dir: str | Path | None = None,
+    default_base: str | Path | None = None,
+) -> Path:
+    """Analysis output directory.
+
+    Precedence: an explicit ``run_dir`` always wins. Otherwise
+    ``default_base / analysis_<tag>`` if ``default_base`` is given (the CLI
+    passes the repo's gitignored ``analysis_runs/``, so run directories don't
+    pile up on ``/ceph`` next to the rollout parquet). Falls back to next to
+    the rollout parquet — the original convention — for callers that don't
+    care where the run directory lives.
+    """
+    if run_dir is not None:
+        return Path(run_dir)
+    rollout = Path(rollout_yaml["output"])
+    tag = str(rollout_yaml.get("prediction_id") or rollout.stem)[:8]
+    base = Path(default_base) if default_base is not None else rollout.parent
+    return base / f"analysis_{tag}"
+
+
+def _plot_meta(rollout_yaml: dict) -> dict:
+    return {k: rollout_yaml[k] for k in _PLOT_META_KEYS if k in rollout_yaml}
+
+
+@dataclass
+class RunMeta:
+    """Resolved paths + plot metadata for one analysis run (``run_meta.json``)."""
+
+    rollout: str
+    reference: str
+    run_dir: str
+    title: str
+    plot_meta: dict
+    n_chunks: int = 1
+    # rollout+reference row count of each event_id-disjoint chunk, and the
+    # dataset total — inputs to `runtime_estimate.estimate_runtime_s`. Empty/0
+    # on run directories written before this field existed.
+    rows_per_chunk: list[int] = field(default_factory=list)
+    total_rows: int = 0
+
+    def save(self, path: str | Path) -> None:
+        Path(path).write_text(json.dumps(self.__dict__, indent=2))
+
+    @classmethod
+    def load(cls, path: str | Path) -> "RunMeta":
+        return cls(**json.loads(Path(path).read_text()))
+
+
+def _rows_per_chunk(
+    rollout: str | Path, reference: str | Path, n_chunks: int
+) -> list[int]:
+    """Rollout+reference row count of each ``event_id % n_chunks`` chunk.
+
+    One cheap streaming ``group_by`` per side (just the ``event_id`` column) —
+    the sizing input every job's estimated walltime
+    (``runtime_estimate.estimate_runtime_s``) is computed from.
+    """
+
+    def counts(lf: pl.LazyFrame) -> pl.DataFrame:
+        return (
+            lf.select((pl.col("event_id") % n_chunks).alias("_c"))
+            .group_by("_c")
+            .agg(pl.len().alias("n"))
+            .collect(engine="streaming")
+        )
+
+    out = [0] * n_chunks
+    for lf in (open_side(rollout, Side.rollout), open_side(reference, Side.reference)):
+        df = counts(lf)
+        for c, n in zip(df["_c"].to_list(), df["n"].to_list()):
+            out[c] += n
+    return out
+
+
+def prep(
+    rollout_yaml: str | Path,
+    run_dir: str | Path | None = None,
+    n_chunks: int = 1,
+    default_base: str | Path | None = None,
+    **ctx_kwargs,
+) -> Path:
+    """Read the rollout YAML, build the shared context, and lay out the run dir.
+
+    Writes ``shared.json`` + ``run_meta.json`` and returns the run directory.
+    ``n_chunks`` is the run-level chunk count every ``compute-one``/``merge-one``
+    job reads back out of ``run_meta.json`` (via ``RunMeta.n_chunks``), so it is
+    resolved once here rather than re-passed (and risking disagreement) at every
+    later step. See ``derive_run_dir`` for how ``run_dir``/``default_base``
+    resolve the actual directory.
+    """
+    y = load_rollout_yaml(rollout_yaml)
+    run_path = derive_run_dir(y, run_dir, default_base=default_base)
+    run_path.mkdir(parents=True, exist_ok=True)
+
+    rollout, reference = y["output"], y["dataset"]
+    ctx = build_context(rollout, reference, **ctx_kwargs)
+    ctx.save(run_path / "shared.json")
+
+    rows_per_chunk = _rows_per_chunk(rollout, reference, n_chunks)
+
+    ckpt = Path(y.get("checkpoint", "")).name or "rollout"
+    RunMeta(
+        rollout=str(rollout),
+        reference=str(reference),
+        run_dir=str(run_path),
+        title=f"GIANT rollout analysis — {ckpt}",
+        plot_meta=_plot_meta(y),
+        n_chunks=n_chunks,
+        rows_per_chunk=rows_per_chunk,
+        total_rows=sum(rows_per_chunk),
+    ).save(run_path / "run_meta.json")
+    return run_path
+
+
+# ---------------------------------------------------------------------------
+# per-(plot, chunk) compute (what each condor job runs)
+# ---------------------------------------------------------------------------
+
+
+def compute_reduced(
+    spec_id: str,
+    rollout: str | Path,
+    reference: str | Path,
+    shared: str | Path,
+    out: str | Path,
+    checkpoint: str | None = None,
+    chunk_index: int = 0,
+    n_chunks: int = 1,
+) -> Path:
+    """Core: run one (plot, chunk)'s partial reduction against explicit paths.
+
+    Writes a ``Partial`` JSON — the raw, not-yet-merged output of
+    ``PlotSpec.compute_partial`` — never a finished ``Reduced``; ``merge_one``
+    is what combines every chunk's ``Partial`` for a plot into the final
+    ``Reduced``. Specs with ``chunkable=False`` always run as a single chunk
+    regardless of ``n_chunks``.
+    """
+    ctx = Context.load(shared)
+    spec = get_spec(spec_id)
+    effective_n = n_chunks if spec.chunkable else 1
+    if not (0 <= chunk_index < effective_n):
+        raise ValueError(
+            f"{spec_id}: chunk_index={chunk_index} out of range for "
+            f"n_chunks={effective_n} (chunkable={spec.chunkable})"
+        )
+    bundle = Bundle.open(
+        rollout, reference, ctx, checkpoint=checkpoint, chunk=(chunk_index, effective_n)
+    )
+    partial = Partial(
+        id=spec_id,
+        family=spec.family,
+        chunk=chunk_index,
+        data=spec.compute_partial(bundle),
+    )
+    out = Path(out)
+    partial.save(out)
+    return out
+
+
+def compute_one(spec_id: str, run_dir: str | Path, chunk_index: int = 0) -> Path:
+    """Run one (plot, chunk)'s partial reduction from a prepped run directory."""
+    run_path = Path(run_dir)
+    meta = RunMeta.load(run_path / "run_meta.json")
+    return compute_reduced(
+        spec_id,
+        meta.rollout,
+        meta.reference,
+        run_path / "shared.json",
+        run_path / "reduced_partial" / f"{spec_id}__{chunk_index}.json",
+        checkpoint=meta.plot_meta.get("checkpoint"),
+        chunk_index=chunk_index,
+        n_chunks=meta.n_chunks,
+    )
+
+
+# ---------------------------------------------------------------------------
+# per-plot merge (the join step ``render`` runs before rendering)
+# ---------------------------------------------------------------------------
+
+
+def merge_one(spec_id: str, run_dir: str | Path) -> Path:
+    """Merge every chunk's partial for one plot into the final ``Reduced`` JSON.
+
+    Fails loudly if fewer partials exist than the run's configured chunk count
+    for this plot — that is what catches an incomplete/failed condor job
+    instead of silently rendering a plot from partial data. Idempotent: safe
+    to call again (e.g. from ``render_run``) once all chunks are in.
+    """
+    run_path = Path(run_dir)
+    meta = RunMeta.load(run_path / "run_meta.json")
+    ctx = Context.load(run_path / "shared.json")
+    spec = get_spec(spec_id)
+    effective_n = meta.n_chunks if spec.chunkable else 1
+
+    partial_dir = run_path / "reduced_partial"
+    found = {
+        p.chunk: p
+        for p in (Partial.load(jf) for jf in partial_dir.glob(f"{spec_id}__*.json"))
+    }
+    missing = sorted(set(range(effective_n)) - set(found))
+    if missing:
+        raise FileNotFoundError(
+            f"{spec_id}: missing chunk partial(s) {missing} of {effective_n} "
+            f"under {partial_dir} — did every compute-one job finish?"
+        )
+
+    parts = [found[k].data for k in range(effective_n)]
+    reduced = spec.finalize(parts, ctx)
+    out = run_path / "reduced" / f"{spec_id}.json"
+    reduced.save(out)
+    return out
+
+
+def merge_all(run_dir: str | Path) -> list[Path]:
+    """Merge every catalog plot's chunk partials into ``reduced/<id>.json``."""
+    return [merge_one(spec_id, run_dir) for spec_id in catalog_ids()]
+
+
+# ---------------------------------------------------------------------------
+# submit description
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class SubmitConfig:
+    run_dir: Path
+    accounting_group: str
+    repo_dir: Path
+    docker_image: str = "cverstege/alma9-gridjob"
+    request_memory_mb: int = 8192
+    request_cpus: int = 1
+    remote: bool = False  # +RemoteJob (grid I/O) vs ProvidesETPResources (local files)
+    n_chunks: int = 1  # per-plot data chunks; ignored for chunkable=False specs
+
+
+_WRAPPER = """#!/bin/bash
+set -euo pipefail
+cd {repo_dir}
+exec {repo_dir}/.venv/bin/giant analyze compute-one --id "$1" --chunk "$2" --run-dir {run_dir}
+"""
+
+
+def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> str:
+    reqs_attrs = (
+        "+RemoteJob = True\n"
+        if cfg.remote
+        else "requirements = TARGET.ProvidesETPResources\n"
+    )
+    return (
+        "universe = docker\n"
+        f"docker_image = {cfg.docker_image}\n"
+        f"executable = {wrapper}\n"
+        "arguments = $(plotid) $(chunk)\n"
+        "should_transfer_files = YES\n"
+        "when_to_transfer_output = ON_EXIT\n"
+        f"request_memory = {cfg.request_memory_mb}\n"
+        f"request_cpus = {cfg.request_cpus}\n"
+        "+RequestWalltime = $(walltime)\n"
+        f"accounting_group = {cfg.accounting_group}\n"
+        f"{reqs_attrs}"
+        f"output = {cfg.run_dir}/logs/$(plotid)__$(chunk).out\n"
+        f"error = {cfg.run_dir}/logs/$(plotid)__$(chunk).err\n"
+        f"log = {cfg.run_dir}/logs/condor.log\n"
+        f"queue plotid,chunk,walltime from {jobs_file}\n"
+    )
+
+
+def _job_walltimes(
+    run_dir: Path, ids: list[str], n_chunks: int
+) -> list[tuple[str, int, int]]:
+    """``(spec_id, chunk, walltime_s)`` for every job, sized from ``run_meta.json``.
+
+    Row counts come from ``prep``'s ``RunMeta.rows_per_chunk``/``total_rows``;
+    ``chunkable=False`` specs (router diagnostics) always use the dataset
+    total since they run as a single job regardless of ``n_chunks``.
+    """
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    jobs: list[tuple[str, int, int]] = []
+    for spec_id in ids:
+        chunkable = get_spec(spec_id).chunkable
+        chunks = range(n_chunks) if chunkable else [0]
+        for chunk in chunks:
+            n_rows = meta.rows_per_chunk[chunk] if chunkable else meta.total_rows
+            jobs.append((spec_id, chunk, estimate_runtime_s(spec_id, n_rows)))
+    return jobs
+
+
+def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path:
+    """Write the wrapper script, (plot, chunk) job list, and HTCondor submit
+    description.
+
+    Each catalog id gets ``cfg.n_chunks`` jobs, except ``chunkable=False``
+    specs (the router diagnostics), which always get exactly one regardless of
+    ``cfg.n_chunks``. Every job's ``+RequestWalltime`` is estimated from its
+    chunk's row count (``runtime_estimate.estimate_runtime_s``, requires
+    ``run_meta.json`` from ``prep`` to already carry ``rows_per_chunk``).
+    Returns the submit description path (``<run_dir>/analyze.sub``). Does not
+    submit — call ``condor_submit`` on the returned file.
+    """
+    venv_giant = cfg.repo_dir / ".venv" / "bin" / "giant"
+    if not venv_giant.exists():
+        raise FileNotFoundError(
+            f"{venv_giant} not found — condor jobs run it directly (no `uv` on "
+            f"the worker image), so run `uv sync --extra cpu` in {cfg.repo_dir} "
+            "before submitting."
+        )
+
+    ids = ids or catalog_ids()
+    run_dir = cfg.run_dir
+    (run_dir / "logs").mkdir(parents=True, exist_ok=True)
+    (run_dir / "reduced").mkdir(parents=True, exist_ok=True)
+    (run_dir / "reduced_partial").mkdir(parents=True, exist_ok=True)
+
+    wrapper = run_dir / "run_compute.sh"
+    wrapper.write_text(_WRAPPER.format(repo_dir=cfg.repo_dir, run_dir=run_dir))
+    wrapper.chmod(0o755)
+
+    jobs = _job_walltimes(run_dir, ids, cfg.n_chunks)
+    jobs_file = run_dir / "jobs.txt"
+    jobs_file.write_text("\n".join(f"{i},{k},{w}" for i, k, w in jobs) + "\n")
+
+    sub = run_dir / "analyze.sub"
+    sub.write_text(_submit_description(cfg, wrapper, jobs_file))
+    return sub
diff --git a/giant/analysis/context.py b/giant/analysis/context.py
new file mode 100644
index 0000000..f8cd84b
--- /dev/null
+++ b/giant/analysis/context.py
@@ -0,0 +1,199 @@
+"""Shared analysis context (the ``prep`` step): fixed bin edges + group sets.
+
+Every histogram in the catalog bins against **fixed** edges so each compute job
+is a single streaming pass with no min/max range scan. Those edges — plus the
+energy-bin quantiles, the top PDG species and the material list to stratify by,
+and the shower depth/transverse ranges — are resolved *once* here, on the submit
+node, from a hash-subsample plus a few cheap exact ``group_by`` passes, and shipped
+in ``shared.json``. Tiny and self-describing; no per-event arrays.
+
+plotstyle-free (runs on the submit node, but also importable by workers).
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+
+import numpy as np
+import polars as pl
+
+from giant.analysis.grouping import energy_bin_edges
+from giant.analysis.reduce import (
+    attach_entry_axis,
+    depth_expr,
+    entry_axis,
+    transverse_expr,
+)
+from giant.analysis.sources import Side, open_side, physical_steps, secondaries
+from giant.analysis.variables import RANGED_VARS
+
+
+@dataclass
+class Context:
+    """Resolved bin edges and grouping sets shared by every compute job."""
+
+    n_marginal_bins: int
+    var_ranges: dict[str, tuple[float, float]]  # ranged var -> (lo, hi)
+    energy_edges: list[float]
+    top_pdgs: list[int]
+    materials: list[str]
+    depth_edges: list[float]
+    transverse_edges: list[float]
+    sec_energy_range: tuple[float, float]
+    n_sec_bins: int
+    n_events: dict[str, int] = field(default_factory=dict)
+
+    # -- (de)serialization -------------------------------------------------
+    def save(self, path: str | Path) -> None:
+        Path(path).write_text(json.dumps(asdict(self), indent=2))
+
+    @classmethod
+    def load(cls, path: str | Path) -> "Context":
+        d = json.loads(Path(path).read_text())
+        d["var_ranges"] = {k: tuple(v) for k, v in d["var_ranges"].items()}
+        d["sec_energy_range"] = tuple(d["sec_energy_range"])
+        return cls(**d)
+
+    # -- convenience -------------------------------------------------------
+    def marginal_edges(self, var: str) -> np.ndarray:
+        lo, hi = self.var_ranges[var]
+        return np.linspace(lo, hi, self.n_marginal_bins + 1)
+
+
+_LO_Q, _HI_Q = 0.001, 0.999
+
+
+def _row_subsample(lf: pl.LazyFrame, sample_rows: int, seed: int) -> pl.LazyFrame:
+    """Hash-subsample ~``sample_rows`` rows (for range estimation only)."""
+    n_total = lf.select(pl.len()).collect(engine="streaming").item()
+    if n_total <= sample_rows:
+        return lf
+    threshold = int(sample_rows / n_total * 2**32)
+    return lf.filter((pl.col("pre_E").hash(seed=seed) % 2**32) < threshold)
+
+
+def _combined_quantiles(
+    r_vals: np.ndarray, t_vals: np.ndarray, lo_q: float, hi_q: float
+) -> tuple[float, float]:
+    """Robust (lo_q, hi_q) range over the union of two value samples."""
+    both = np.concatenate([r_vals, t_vals])
+    lo, hi = float(np.quantile(both, lo_q)), float(np.quantile(both, hi_q))
+    if not (hi - lo > 1e-6 * max(abs(hi), 1.0)):
+        lo, hi = lo - 0.5, hi + 0.5
+    return lo, hi
+
+
+def build_context(
+    rollout: str | Path | pl.LazyFrame,
+    reference: str | Path | pl.LazyFrame,
+    *,
+    n_energy_bins: int = 4,
+    n_marginal_bins: int = 50,
+    n_sec_bins: int = 40,
+    top_k_pdg: int = 6,
+    sample_rows: int = 1_000_000,
+    seed: int = 0,
+) -> Context:
+    """Resolve the shared context from the two files (the ``prep`` step)."""
+    r_all = open_side(rollout, Side.rollout)
+    t_all = open_side(reference, Side.reference)
+    r_lf = physical_steps(r_all, Side.rollout)
+    t_lf = physical_steps(t_all, Side.reference)
+
+    # Ranged marginal variables: robust ranges over a shared row subsample.
+    exprs = [e.alias(n) for n, (_, e) in RANGED_VARS.items()]
+    r_s = (
+        _row_subsample(r_lf, sample_rows, seed)
+        .select(exprs)
+        .collect(engine="streaming")
+    )
+    t_s = (
+        _row_subsample(t_lf, sample_rows, seed)
+        .select(exprs)
+        .collect(engine="streaming")
+    )
+    var_ranges = {
+        name: _combined_quantiles(
+            r_s[name].to_numpy(), t_s[name].to_numpy(), _LO_Q, _HI_Q
+        )
+        for name in RANGED_VARS
+    }
+
+    # Energy-bin edges from exact per-event incident energies (cheap group_by).
+    def _incident(lf: pl.LazyFrame) -> np.ndarray:
+        return (
+            lf.group_by("event_id")
+            .agg(pl.col("pre_E").max())
+            .collect(engine="streaming")["pre_E"]
+            .to_numpy()
+        )
+
+    r_inc, t_inc = _incident(r_lf), _incident(t_lf)
+    energy_edges = energy_bin_edges(np.concatenate([r_inc, t_inc]), n_energy_bins)
+
+    # Top PDG species and material list (cheap single-column group_bys).
+    def _counts(lf: pl.LazyFrame, col: str) -> pl.DataFrame:
+        return lf.group_by(col).agg(pl.len().alias("n")).collect(engine="streaming")
+
+    pdg_counts = (
+        pl.concat([_counts(r_lf, "pdg"), _counts(t_lf, "pdg")])
+        .group_by("pdg")
+        .agg(pl.col("n").sum())
+        .sort("n", descending=True)
+    )
+    top_pdgs = [int(x) for x in pdg_counts["pdg"].to_list()[:top_k_pdg]]
+    materials = sorted(
+        set(_counts(r_lf, "material")["material"].to_list())
+        | set(_counts(t_lf, "material")["material"].to_list())
+    )
+
+    # Shower depth / transverse ranges from a subsampled proxy.
+    def _proxy(lf: pl.LazyFrame) -> tuple[np.ndarray, np.ndarray]:
+        ea = entry_axis(lf)
+        sub = (
+            attach_entry_axis(_row_subsample(lf, sample_rows, seed), ea)
+            .select(depth_expr().alias("d"), transverse_expr().alias("t"))
+            .collect(engine="streaming")
+        )
+        return sub["d"].to_numpy(), sub["t"].to_numpy()
+
+    r_d, r_t = _proxy(r_lf)
+    t_d, t_t = _proxy(t_lf)
+    d_lo, d_hi = _combined_quantiles(r_d, t_d, _LO_Q, _HI_Q)
+    depth_edges = np.linspace(d_lo, d_hi, n_marginal_bins + 1)
+    t_hi = max(float(np.quantile(np.concatenate([r_t, t_t]), _HI_Q)), 1e-6)
+    transverse_edges = np.linspace(0.0, t_hi, n_marginal_bins + 1)
+
+    # Secondary energy range.
+    r_se = secondaries(r_lf, Side.rollout).select("energy")
+    t_se = secondaries(t_all, Side.reference).select("energy")
+    r_se = _row_sample_col(r_se, sample_rows, seed)
+    t_se = _row_sample_col(t_se, sample_rows, seed)
+    sec_energy_range = _combined_quantiles(r_se, t_se, _LO_Q, _HI_Q)
+
+    return Context(
+        n_marginal_bins=n_marginal_bins,
+        var_ranges=var_ranges,
+        energy_edges=[float(x) for x in energy_edges],
+        top_pdgs=top_pdgs,
+        materials=materials,
+        depth_edges=[float(x) for x in depth_edges],
+        transverse_edges=[float(x) for x in transverse_edges],
+        sec_energy_range=sec_energy_range,
+        n_sec_bins=n_sec_bins,
+        n_events={
+            "rollout": len(r_inc),
+            "reference": len(t_inc),
+        },
+    )
+
+
+def _row_sample_col(lf: pl.LazyFrame, sample_rows: int, seed: int) -> np.ndarray:
+    """Collect a subsample of a single-column ``energy`` LazyFrame to numpy."""
+    vals = lf.collect(engine="streaming")["energy"].to_numpy()
+    if len(vals) > sample_rows:
+        rng = np.random.default_rng(seed)
+        vals = vals[rng.choice(len(vals), size=sample_rows, replace=False)]
+    return vals
diff --git a/giant/analysis/grouping.py b/giant/analysis/grouping.py
new file mode 100644
index 0000000..e5ec095
--- /dev/null
+++ b/giant/analysis/grouping.py
@@ -0,0 +1,108 @@
+"""Grouping axes (overall / energy / pdg / material) and their labels.
+
+Energy grouping is by the event's **incident (primary) energy** — the largest
+``pre_E`` in the event — so every step of a shower lands in one bin, the physically
+meaningful stratification for a calorimeter surrogate. The quantile bin *edges* are
+sized once in ``prep`` (from a subsample) and shipped in ``shared.json``; a compute
+job that needs them re-derives the small per-event ``event_id -> bin`` map itself
+(one bounded streaming ``group_by`` over ``pre_E``), so ``shared.json`` stays tiny.
+
+Pure/plotstyle-free so it can run on the compute workers.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import polars as pl
+
+# Common electromagnetic/hadronic species; anything else falls back to its code.
+PDG_NAMES: dict[int, str] = {
+    11: "e-",
+    -11: "e+",
+    22: "gamma",
+    2112: "n",
+    2212: "p",
+    -2212: "pbar",
+    111: "pi0",
+    211: "pi+",
+    -211: "pi-",
+    13: "mu-",
+    -13: "mu+",
+    321: "K+",
+    -321: "K-",
+    130: "K0L",
+}
+
+
+def pdg_label(code: int) -> str:
+    """Human-readable species label for a PDG code (falls back to the code)."""
+    code = int(code)
+    if code in PDG_NAMES:
+        return PDG_NAMES[code]
+    if abs(code) > 1_000_000_000:
+        return f"ion {code}"
+    return str(code)
+
+
+def material_label(name: str) -> str:
+    """Display label for a Geant4 material, dropping the ``G4_`` prefix."""
+    return name[3:] if name.startswith("G4_") else name
+
+
+def energy_bin_edges(incident_E: np.ndarray, n_bins: int = 4) -> np.ndarray:
+    """Equal-population (quantile) bin edges over per-event incident energies.
+
+    Returns ``n_bins + 1`` monotonically non-decreasing edges. The top edge is
+    nudged up so the largest value falls inside the last bin under a
+    right-open convention. Degenerate (single-value) input widens by +/-0.5.
+    """
+    incident_E = np.asarray(incident_E, dtype=np.float64)
+    edges = np.quantile(incident_E, np.linspace(0.0, 1.0, n_bins + 1))
+    edges = np.unique(edges)
+    if edges.size < 2:
+        v = edges[0] if edges.size else 0.0
+        edges = np.array([v - 0.5, v + 0.5])
+    edges[-1] = np.nextafter(edges[-1], np.inf)
+    return edges
+
+
+def energy_bin_labels(edges: np.ndarray) -> list[str]:
+    """``E in [lo, hi)`` labels for each bin defined by ``edges`` (MeV)."""
+    return [
+        f"E in [{edges[i]:.3g}, {edges[i + 1]:.3g}) MeV" for i in range(len(edges) - 1)
+    ]
+
+
+def digitize_expr(value: pl.Expr, edges: np.ndarray) -> pl.Expr:
+    """Bin index of ``value`` under arbitrary (possibly non-uniform) ``edges``.
+
+    ``bin = (#interior edges <= value)``, clipped to ``[0, n_bins-1]`` — matches
+    ``np.digitize(value, edges[1:-1])`` and works for the quantile energy edges.
+    Vectorized as a sum of boolean comparisons; no per-row Python.
+    """
+    interior = [float(e) for e in edges[1:-1]]
+    n_bins = len(edges) - 1
+    idx = pl.lit(0, dtype=pl.Int32)
+    for e in interior:
+        idx = idx + (value >= e).cast(pl.Int32)
+    return idx.clip(0, n_bins - 1)
+
+
+def event_energy_bins(
+    lf: pl.LazyFrame, edges: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
+    """Per-event incident-energy bin: ``(event_ids, bin_idx)`` numpy arrays.
+
+    Incident energy is ``max(pre_E)`` per event (the primary). One bounded
+    streaming ``group_by``; the tiny per-event result is digitized in numpy.
+    """
+    per_event = (
+        lf.group_by("event_id")
+        .agg(pl.col("pre_E").max().alias("incident_E"))
+        .collect(engine="streaming")
+        .sort("event_id")
+    )
+    event_ids = per_event["event_id"].to_numpy()
+    incident = per_event["incident_E"].to_numpy()
+    bin_idx = np.clip(np.digitize(incident, edges[1:-1]), 0, len(edges) - 2)
+    return event_ids, bin_idx.astype(np.int64)
diff --git a/giant/analysis/reduce.py b/giant/analysis/reduce.py
new file mode 100644
index 0000000..15089f6
--- /dev/null
+++ b/giant/analysis/reduce.py
@@ -0,0 +1,267 @@
+"""Streaming compute primitives — the reduce half of the analysis.
+
+Everything here turns a (possibly larger-than-RAM) LazyFrame into a *compact*
+numpy/DataFrame artifact in bounded memory, and never imports plotstyle so it can
+run on an HTCondor worker. Efficiency rules (see the plan's "Histogram
+efficiency" section):
+
+* ``hist1d`` is a single streaming ``group_by([group, bin]).len()`` pass against
+  **fixed** edges (no min/max range pass) with a strict column projection — only
+  the columns the value/group expressions reference are read from the parquet.
+* the multi-quantity reductions (``event_scalars``, profiles, ``species_share``,
+  ``leakage_fraction``) each emit *all* their outputs from one ``group_by``.
+* per-event -> per-row lookups (shower entry/axis) use ``replace_strict`` (a hash
+  map applied as an expression, bounded memory), never a streaming join.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import polars as pl
+
+from giant.constants import TERM_ESCAPED
+
+# ---------------------------------------------------------------------------
+# 1-D histogram primitive
+# ---------------------------------------------------------------------------
+
+
+def _bin_expr(value: pl.Expr, lo: float, hi: float, nbins: int) -> pl.Expr:
+    """Uniform bin index of ``value`` over ``[lo, hi]`` into ``nbins`` bins."""
+    return ((value - lo) / (hi - lo) * nbins).floor().cast(pl.Int32).clip(0, nbins - 1)
+
+
+def hist1d(
+    lf: pl.LazyFrame,
+    value: pl.Expr,
+    edges: np.ndarray,
+    group: pl.Expr | None = None,
+) -> dict[object, np.ndarray]:
+    """Streaming histogram of ``value`` over fixed uniform ``edges``, by ``group``.
+
+    Returns ``{group_key: counts}`` (counts is an ``int64`` array of length
+    ``len(edges)-1``). One hash pass; runtime is independent of group cardinality,
+    so every pdg/material/energy stratum falls out together. Only the tiny
+    ``(n_groups x nbins)`` result is materialized.
+    """
+    lo, hi, nbins = float(edges[0]), float(edges[-1]), len(edges) - 1
+    group = pl.lit(0, dtype=pl.Int64) if group is None else group
+    res = (
+        lf.select(group.alias("_g"), _bin_expr(value, lo, hi, nbins).alias("_b"))
+        .group_by("_g", "_b")
+        .agg(pl.len().alias("_n"))
+        .collect(engine="streaming")
+    )
+    out: dict[object, np.ndarray] = {}
+    for g, b, n in res.iter_rows():
+        out.setdefault(g, np.zeros(nbins, dtype=np.int64))[b] = n
+    return out
+
+
+def sum_merge(dicts: list[dict[str, Any]]) -> dict[str, Any]:
+    """Elementwise-sum a list of sum-mergeable count/total dicts (JSON-safe keys).
+
+    Used to merge chunked ``hist1d``/``species_share``-style partials, whose
+    values bin/group against edges or keys fixed by ``Context`` — a chunk's raw
+    count dict is exactly a partial sum, so merging is a plain elementwise sum
+    over the union of keys (a key absent from some chunk is all-zero there).
+    Values may be per-bin count lists or plain scalar totals; both round-trip
+    through ``np.asarray``/``.tolist()`` unchanged in shape.
+    """
+    out: dict[str, np.ndarray] = {}
+    for d in dicts:
+        for k, v in d.items():
+            arr = np.asarray(v)
+            out[k] = arr.copy() if k not in out else out[k] + arr
+    return {k: v.tolist() for k, v in out.items()}
+
+
+# ---------------------------------------------------------------------------
+# Per-event scalar observables (one bounded group_by pass)
+# ---------------------------------------------------------------------------
+
+
+def event_scalars(lf: pl.LazyFrame) -> pl.DataFrame:
+    """One row per event: total/mean deposited energy, path length, step count.
+
+    Columns: ``event_id, total_edep, total_length, n_steps, mean_length,
+    incident_E`` (incident = ``max(pre_E)``, the primary). The caller chooses
+    whether ``lf`` includes the rollout's synthetic termination rows — pass the
+    full scan for energy totals (they carry the deposited remainder), physical
+    steps only for step-count / mean-length.
+    """
+    return (
+        lf.group_by("event_id")
+        .agg(
+            pl.col("edep").sum().alias("total_edep"),
+            pl.col("step_length").sum().alias("total_length"),
+            pl.len().alias("n_steps"),
+            pl.col("pre_E").max().alias("incident_E"),
+        )
+        .with_columns((pl.col("total_length") / pl.col("n_steps")).alias("mean_length"))
+        .collect(engine="streaming")
+    )
+
+
+# ---------------------------------------------------------------------------
+# Shower shape: entry/axis + edep-weighted longitudinal / transverse profiles
+# ---------------------------------------------------------------------------
+
+
+def entry_axis(lf: pl.LazyFrame) -> pl.DataFrame:
+    """Per-event shower entry point and axis (from the highest-``pre_E`` step).
+
+    One bounded ``group_by``: the primary's ``pre_pos`` becomes the entry point
+    and its ``pre_dir`` the shower axis.
+    """
+    return (
+        lf.group_by("event_id")
+        .agg(
+            pl.col("pre_x").get(pl.col("pre_E").arg_max()).alias("entry_x"),
+            pl.col("pre_y").get(pl.col("pre_E").arg_max()).alias("entry_y"),
+            pl.col("pre_z").get(pl.col("pre_E").arg_max()).alias("entry_z"),
+            pl.col("pre_dx").get(pl.col("pre_E").arg_max()).alias("axis_x"),
+            pl.col("pre_dy").get(pl.col("pre_E").arg_max()).alias("axis_y"),
+            pl.col("pre_dz").get(pl.col("pre_E").arg_max()).alias("axis_z"),
+        )
+        .collect(engine="streaming")
+        .sort("event_id")
+    )
+
+
+_ENTRY_AXIS_COLS = ("entry_x", "entry_y", "entry_z", "axis_x", "axis_y", "axis_z")
+
+
+def attach_entry_axis(lf: pl.LazyFrame, entry: pl.DataFrame) -> pl.LazyFrame:
+    """Broadcast each event's entry/axis onto its rows via ``replace_strict``.
+
+    A hash map applied as an expression — streams in bounded memory, unlike a
+    join which would buffer the whole file-sized left side.
+    """
+    ids = entry["event_id"].to_numpy()
+    return lf.with_columns(
+        pl.col("event_id")
+        .replace_strict(ids, entry[col].to_numpy(), return_dtype=pl.Float64)
+        .alias(col)
+        for col in _ENTRY_AXIS_COLS
+    )
+
+
+def depth_expr() -> pl.Expr:
+    """Signed distance of ``post_pos`` from the entry point along the shower axis."""
+    dx = pl.col("post_x") - pl.col("entry_x")
+    dy = pl.col("post_y") - pl.col("entry_y")
+    dz = pl.col("post_z") - pl.col("entry_z")
+    return dx * pl.col("axis_x") + dy * pl.col("axis_y") + dz * pl.col("axis_z")
+
+
+def transverse_expr() -> pl.Expr:
+    """Perpendicular distance of ``post_pos`` from the shower axis."""
+    dx = pl.col("post_x") - pl.col("entry_x")
+    dy = pl.col("post_y") - pl.col("entry_y")
+    dz = pl.col("post_z") - pl.col("entry_z")
+    depth = dx * pl.col("axis_x") + dy * pl.col("axis_y") + dz * pl.col("axis_z")
+    tx = dx - depth * pl.col("axis_x")
+    ty = dy - depth * pl.col("axis_y")
+    tz = dz - depth * pl.col("axis_z")
+    return (tx**2 + ty**2 + tz**2).sqrt()
+
+
+def profile_partial(
+    lf: pl.LazyFrame,
+    coord: pl.Expr,
+    edges: np.ndarray,
+    weight: pl.Expr,
+) -> tuple[np.ndarray, np.ndarray]:
+    """One chunk's per-event x bin ``weight``-sum matrix: ``(event_ids, matrix)``.
+
+    One streaming ``group_by(event_id, bin)`` sums ``weight`` per (event, bin).
+    A chunk's matrix rows are only the events present in that chunk, so chunks'
+    matrices stack cleanly with no cross-chunk lookup — this requires chunking
+    to be event-disjoint (every row of an event lands in one chunk).
+    ``coord``/``weight`` require the entry/axis columns attached.
+    """
+    lo, hi, nbins = float(edges[0]), float(edges[-1]), len(edges) - 1
+    grid = (
+        lf.select(
+            "event_id",
+            _bin_expr(coord, lo, hi, nbins).alias("_b"),
+            weight.alias("_w"),
+        )
+        .group_by("event_id", "_b")
+        .agg(pl.col("_w").sum().alias("_ws"))
+        .collect(engine="streaming")
+    )
+    ev = grid["event_id"].to_numpy()
+    uniq, inv = np.unique(ev, return_inverse=True)
+    mat = np.zeros((len(uniq), nbins), dtype=np.float64)
+    np.add.at(mat, (inv, grid["_b"].to_numpy()), grid["_ws"].to_numpy())
+    return uniq, mat
+
+
+def profile_finalize(mats: list[np.ndarray]) -> tuple[np.ndarray, np.ndarray]:
+    """Collapse per-chunk per-event x bin matrices into the final mean/std profile.
+
+    Chunks are event-disjoint, so row-wise concatenation of their matrices
+    reconstructs the full per-event matrix; the mean/event-RMS collapse must
+    happen once over that full matrix — an average of per-chunk means/stds
+    would be wrong (chunks generally hold different numbers of events).
+    Returns ``(mean, std)``, each length ``nbins``.
+    """
+    full = np.concatenate(mats, axis=0)
+    return full.mean(axis=0), full.std(axis=0)
+
+
+def weighted_profile(
+    lf: pl.LazyFrame,
+    coord: pl.Expr,
+    edges: np.ndarray,
+    weight: pl.Expr,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Single-pass profile of ``coord`` (mean +/- event-RMS band over events).
+
+    Convenience wrapper for the unchunked (whole-dataset) case; ``mean_std +
+    profile_partial`` is what a chunked compute/finalize split uses instead.
+    """
+    _, mat = profile_partial(lf, coord, edges, weight)
+    return profile_finalize([mat])
+
+
+# ---------------------------------------------------------------------------
+# Species contribution and leakage
+# ---------------------------------------------------------------------------
+
+
+def species_share(lf: pl.LazyFrame) -> pl.DataFrame:
+    """Total deposited energy per PDG species (``pdg, total_edep``), one pass."""
+    return (
+        lf.group_by("pdg")
+        .agg(pl.col("edep").sum().alias("total_edep"))
+        .collect(engine="streaming")
+        .sort("total_edep", descending=True)
+    )
+
+
+def leakage_fraction(lf: pl.LazyFrame) -> np.ndarray:
+    """Per-event escaped-energy fraction (rollout only), one bounded pass.
+
+    Escaped rows carry the leaked energy in ``pre_E`` (``edep`` is 0 there); the
+    fraction is ``escaped / (deposited + escaped)`` per event.
+    """
+    per_event = (
+        lf.group_by("event_id")
+        .agg(
+            pl.col("edep").sum().alias("deposited"),
+            pl.col("pre_E")
+            .filter(pl.col("termination_reason") == TERM_ESCAPED)
+            .sum()
+            .alias("escaped"),
+        )
+        .collect(engine="streaming")
+    )
+    deposited = per_event["deposited"].to_numpy()
+    escaped = per_event["escaped"].fill_null(0.0).to_numpy()
+    total = deposited + escaped
+    return np.where(total > 0, escaped / total, 0.0)
diff --git a/giant/analysis/reduced.py b/giant/analysis/reduced.py
new file mode 100644
index 0000000..2188800
--- /dev/null
+++ b/giant/analysis/reduced.py
@@ -0,0 +1,65 @@
+"""The compact, self-describing artifact a compute job produces per plot.
+
+Serialized as small JSON (no pickle, no per-event arrays) so it is trivially
+transferable off the batch worker and human-inspectable. ``render.py`` dispatches
+on ``kind`` and needs nothing but this file.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+
+# Reduced.kind values:
+#   "overlay_hist"   rollout vs reference density histogram over shared edges
+#   "grouped_hist"   one panel per group (energy/pdg/material), each an overlay
+#   "profile"        edep-weighted mean +/- event-RMS vs depth/radius, two series
+#   "bar"            per-category rollout vs reference bars (share / counts)
+#   "single_hist"    one series only (e.g. rollout leakage; reference has none)
+#   "router_gating"  stacked mean MoE gate weight vs energy, rollout + reference
+#   "router_share"   stacked bar of MoE top-1 dispatch share by category
+#   "unavailable"    plot not applicable to this run (e.g. non-MoE checkpoint)
+
+
+@dataclass
+class Reduced:
+    id: str
+    family: str
+    kind: str
+    title: str
+    xlabel: str
+    payload: dict
+    meta: dict = field(default_factory=dict)
+
+    def save(self, path: str | Path) -> None:
+        Path(path).parent.mkdir(parents=True, exist_ok=True)
+        Path(path).write_text(json.dumps(asdict(self)))
+
+    @classmethod
+    def load(cls, path: str | Path) -> "Reduced":
+        return cls(**json.loads(Path(path).read_text()))
+
+
+@dataclass
+class Partial:
+    """The raw, not-yet-finalized output of one ``(plot, chunk)`` compute job.
+
+    ``data`` holds whatever shape that plot's ``PlotSpec.compute_partial``
+    returns — a raw sum-mergeable count dict, or a raw per-event/per-secondary
+    array to be concatenated across chunks — never a finished histogram/profile.
+    ``PlotSpec.finalize`` is the only thing that knows how to interpret it.
+    """
+
+    id: str
+    family: str
+    chunk: int
+    data: dict
+
+    def save(self, path: str | Path) -> None:
+        Path(path).parent.mkdir(parents=True, exist_ok=True)
+        Path(path).write_text(json.dumps(asdict(self)))
+
+    @classmethod
+    def load(cls, path: str | Path) -> "Partial":
+        return cls(**json.loads(Path(path).read_text()))
diff --git a/giant/analysis/render.py b/giant/analysis/render.py
new file mode 100644
index 0000000..3941981
--- /dev/null
+++ b/giant/analysis/render.py
@@ -0,0 +1,361 @@
+"""Render reduced artifacts to styled PDFs + gallery metadata (the local step).
+
+This is the *only* module that imports ``plotstyle`` (ETPlot's KIT matplotlib
+theme), which renders through a real LaTeX toolchain — so it runs on the
+submit/login node, never on a compute worker. It reads nothing but the small
+``Reduced`` JSON files a run produced, so it is fully decoupled from the heavy
+streaming compute.
+
+For each reduced artifact it writes ``<out>/<family>/<id>.pdf`` plus a sibling
+``<id>.yaml`` (per-plot gallery metadata) and a per-family ``metadata.yaml``.
+Optionally runs ``gallery generate`` to build the static HTML site.
+"""
+
+from __future__ import annotations
+
+import subprocess
+from pathlib import Path
+
+import numpy as np
+import plotstyle as ps
+import yaml
+
+from giant.analysis.reduced import Reduced
+
+_SERIES_LABELS = {"rollout": "rollout", "reference": "reference (Geant4)"}
+
+
+def _density(counts: list[int] | np.ndarray, edges: np.ndarray) -> np.ndarray:
+    counts = np.asarray(counts, dtype=np.float64)
+    total = counts.sum()
+    if total == 0:
+        return counts
+    return counts / (total * (edges[1] - edges[0]))
+
+
+def _overlay(ax, edges: np.ndarray, series: dict[str, list], log_y: bool) -> None:
+    for key in ("reference", "rollout"):
+        if key in series:
+            ax.stairs(_density(series[key], edges), edges, label=_SERIES_LABELS[key])
+    if log_y:
+        ax.set_yscale("log")
+
+
+def _router_summary(model_config: dict) -> str:
+    r = model_config.get("router") or {}
+    if not r.get("enabled"):
+        return "off"
+    return f"{r.get('type', '?')}×{r.get('n_experts', '?')}"
+
+
+def _figure_params(run_meta: dict) -> dict:
+    """Curated run identity for the figure subtitle (``new_figure(params=...)``).
+
+    ``run_meta``/each plot's own ``<id>.yaml`` (see ``_plot_metadata``) already
+    carry every threaded model/training/rollout/dataset parameter for
+    after-the-fact lookup — this picks only the handful that matter for
+    telling figures apart at a glance while flipping through a gallery, since
+    the subtitle is one unwrapped line of text. The last slot is
+    architecture-conditional: flow/ddpm runs show the ODE ``steps`` used for
+    this rollout, wgan runs show ``noise_dim`` instead since wgan sampling is
+    single-pass and has no ODE step count.
+    """
+    mc = run_meta.get("model_config") or {}
+    mode = mc.get("mode")
+    params: dict = {}
+    if mc.get("hidden_dim") is not None:
+        params["hidden_dim"] = mc["hidden_dim"]
+    if mc.get("n_blocks") is not None:
+        params["n_blocks"] = mc["n_blocks"]
+    if mode is not None:
+        params["mode"] = mode
+    if mc.get("conditioning") is not None:
+        params["conditioning"] = mc["conditioning"]
+    params["router"] = _router_summary(mc)
+    if run_meta.get("training_epoch") is not None:
+        params["epoch"] = run_meta["training_epoch"]
+    if run_meta.get("best_val_loss") is not None:
+        params["best_val_loss"] = round(run_meta["best_val_loss"], 4)
+    if mode == "wgan":
+        if mc.get("noise_dim") is not None:
+            params["noise_dim"] = mc["noise_dim"]
+    elif run_meta.get("steps") is not None:
+        params["steps"] = run_meta["steps"]
+    return params
+
+
+def _render_overlay(r: Reduced, params: dict):
+    edges = np.asarray(r.payload["edges"])
+    fig, ax = ps.new_figure("thesis-single", title=r.title, params=params)
+    _overlay(ax, edges, r.payload, r.payload.get("log_y", False))
+    ax.set_xlabel(r.xlabel)
+    ax.set_ylabel("density")
+    ps.style_legend(ax, title="source")
+    return fig
+
+
+def _render_single(r: Reduced, params: dict):
+    edges = np.asarray(r.payload["edges"])
+    fig, ax = ps.new_figure("thesis-single", title=r.title, params=params)
+    ax.stairs(
+        _density(r.payload["rollout"], edges), edges, label=_SERIES_LABELS["rollout"]
+    )
+    if r.payload.get("log_y"):
+        ax.set_yscale("log")
+    ax.set_xlabel(r.xlabel)
+    ax.set_ylabel("density")
+    ps.style_legend(ax, title="source")
+    return fig
+
+
+def _render_grouped(r: Reduced, params: dict):
+    edges = np.asarray(r.payload["edges"])
+    groups = r.payload["groups"]
+    labels = list(groups)
+    n = len(labels)
+    ncols = min(3, n) or 1
+    nrows = (n + ncols - 1) // ncols
+    fig, axes = ps.new_figure(
+        "slide-16x9",
+        title=r.title,
+        params=params,
+        nrows=nrows,
+        ncols=ncols,
+        squeeze=False,
+    )
+    flat = axes.ravel()
+    for i, lbl in enumerate(labels):
+        ax = flat[i]
+        _overlay(ax, edges, groups[lbl], r.payload.get("log_y", False))
+        ax.set_title(lbl, fontsize=8)
+        ax.set_xlabel(r.xlabel)
+    for j in range(n, len(flat)):
+        flat[j].set_visible(False)
+    ps.style_legend(flat[0], title="source")
+    return fig
+
+
+def _render_profile(r: Reduced, params: dict):
+    edges = np.asarray(r.payload["edges"])
+    centers = 0.5 * (edges[:-1] + edges[1:])
+    fig, ax = ps.new_figure("thesis-single", title=r.title, params=params)
+    for key in ("reference", "rollout"):
+        mean = np.asarray(r.payload[f"{key}_mean"])
+        std = np.asarray(r.payload[f"{key}_std"])
+        (line,) = ax.plot(centers, mean, label=_SERIES_LABELS[key])
+        ax.fill_between(
+            centers, mean - std, mean + std, alpha=0.2, color=line.get_color()
+        )
+    ax.set_xlabel(r.xlabel)
+    ax.set_ylabel(r.payload.get("ylabel", "mean deposited energy [MeV]"))
+    ps.style_legend(ax, title="source")
+    return fig
+
+
+def _render_bar(r: Reduced, params: dict):
+    labels = r.payload["labels"]
+    x = np.arange(len(labels))
+    width = 0.4
+    fig, ax = ps.new_figure("thesis-single", title=r.title, params=params)
+    ax.bar(
+        x - width / 2, r.payload["reference"], width, label=_SERIES_LABELS["reference"]
+    )
+    ax.bar(x + width / 2, r.payload["rollout"], width, label=_SERIES_LABELS["rollout"])
+    ax.set_xticks(x)
+    ax.set_xticklabels(labels, rotation=45, ha="right")
+    ax.set_ylabel(r.payload.get("ylabel", "value"))
+    ps.style_legend(ax, title="source")
+    return fig
+
+
+def _render_router_gating(r: Reduced, params: dict):
+    n_experts = r.payload["n_experts"]
+    log_x = r.payload.get("log_x", False)
+    fig, axes = ps.new_figure(
+        "slide-16x9", title=r.title, params=params, nrows=1, ncols=2, squeeze=False
+    )
+    flat = axes.ravel()
+    for ax, key in zip(flat, ("rollout", "reference")):
+        side = r.payload.get(key, {})
+        centers = np.asarray(side.get("centers", []))
+        means = np.asarray(side.get("means", []))
+        if len(centers) and means.size:
+            cum = np.zeros(len(centers))
+            for i in range(n_experts):
+                ax.fill_between(
+                    centers, cum, cum + means[:, i], alpha=0.7, label=f"expert {i}"
+                )
+                cum = cum + means[:, i]
+        if log_x:
+            ax.set_xscale("log")
+        ax.set_ylim(0, 1)
+        ax.set_title(_SERIES_LABELS[key], fontsize=8)
+        ax.set_xlabel(r.xlabel)
+    flat[0].set_ylabel("mean gate weight")
+    ps.style_legend(flat[0], title=f"{r.payload.get('router_type', '')} router")
+    return fig
+
+
+def _render_router_share(r: Reduced, params: dict):
+    categories = r.payload["categories"]
+    n_experts = r.payload["n_experts"]
+    x = np.arange(len(categories))
+    present = [k for k in ("rollout", "reference") if k in r.payload]
+    fig, axes = ps.new_figure(
+        "slide-16x9",
+        title=r.title,
+        params=params,
+        nrows=1,
+        ncols=len(present),
+        squeeze=False,
+    )
+    flat = axes.ravel()
+    for ax, key in zip(flat, present):
+        side = r.payload[key]
+        shares = np.array([side[c] for c in categories])  # (n_cat, n_experts)
+        bottom = np.zeros(len(categories))
+        for i in range(n_experts):
+            ax.bar(x, shares[:, i], bottom=bottom, label=f"expert {i}")
+            bottom += shares[:, i]
+        ax.set_xticks(x)
+        ax.set_xticklabels(categories, rotation=45, ha="right")
+        ax.set_ylim(0, 1)
+        ax.set_title(_SERIES_LABELS[key], fontsize=8)
+    flat[0].set_ylabel("share of rows dispatched to expert")
+    ps.style_legend(flat[0], title=f"{r.payload.get('router_type', '')} router")
+    return fig
+
+
+def _render_unavailable(r: Reduced, params: dict):
+    fig, ax = ps.new_figure("thesis-single", title=r.title, params=params)
+    ax.axis("off")
+    ax.text(
+        0.5,
+        0.5,
+        r.payload.get("note", "not available"),
+        ha="center",
+        va="center",
+        wrap=True,
+        fontsize=10,
+        transform=ax.transAxes,
+    )
+    return fig
+
+
+_RENDERERS = {
+    "overlay_hist": _render_overlay,
+    "single_hist": _render_single,
+    "grouped_hist": _render_grouped,
+    "profile": _render_profile,
+    "bar": _render_bar,
+    "router_gating": _render_router_gating,
+    "router_share": _render_router_share,
+    "unavailable": _render_unavailable,
+}
+
+
+def render(r: Reduced, run_meta: dict | None = None):
+    """Build the matplotlib figure for one reduced artifact (dispatch on kind)."""
+    return _RENDERERS[r.kind](r, _figure_params(run_meta or {}))
+
+
+def _plot_metadata(r: Reduced, run_meta: dict) -> dict:
+    meta = {
+        "title": r.title,
+        "description": f"Rollout vs reference: {r.title}.",
+        "plot_type": r.kind,
+        "family": r.family,
+    }
+    meta.update(r.meta)
+    if "note" in r.payload:
+        meta["note"] = r.payload["note"]
+    if run_meta:
+        # Every threaded model/training/rollout/dataset parameter, so a
+        # single plot's metadata is self-contained for later comparison
+        # without cross-referencing the run's root metadata.yaml.
+        meta["parameters"] = {k: v for k, v in run_meta.items() if k != "title"}
+    return meta
+
+
+def render_all(
+    reduced_dir: str | Path,
+    out_dir: str | Path,
+    run_meta: dict | None = None,
+    *,
+    run_gallery: bool = False,
+) -> list[Path]:
+    """Render every reduced artifact under ``reduced_dir`` to a PDF tree.
+
+    Writes ``<out>/<family>/<id>.pdf`` + ``<id>.yaml`` and a per-family
+    ``metadata.yaml`` (carrying the run's checkpoint/paths as gallery params).
+    Returns the list of PDF paths written.
+    """
+    ps.use()
+    run_meta = run_meta or {}
+    reduced_dir, out_dir = Path(reduced_dir), Path(out_dir)
+    pdfs: list[Path] = []
+    families: set[str] = set()
+
+    for jf in sorted(reduced_dir.glob("*.json")):
+        r = Reduced.load(jf)
+        family_dir = out_dir / r.family
+        family_dir.mkdir(parents=True, exist_ok=True)
+        families.add(r.family)
+        fig = render(r, run_meta)
+        ps.savefig(fig, str(family_dir / r.id), formats=("pdf",))
+        (family_dir / f"{r.id}.yaml").write_text(
+            yaml.safe_dump(_plot_metadata(r, run_meta), sort_keys=False)
+        )
+        pdfs.append(family_dir / f"{r.id}.pdf")
+        import matplotlib.pyplot as plt
+
+        plt.close(fig)
+
+    # Root + per-family gallery metadata.
+    out_dir.mkdir(parents=True, exist_ok=True)
+    (out_dir / "metadata.yaml").write_text(
+        yaml.safe_dump(
+            {
+                "title": run_meta.get("title", "GIANT rollout analysis"),
+                "description": "Autoregressive rollout compared against held-out Geant4 reference steps.",
+                "experiment": "GIANT",
+                "parameters": {k: v for k, v in run_meta.items() if k != "title"},
+            },
+            sort_keys=False,
+        )
+    )
+    for fam in families:
+        (out_dir / fam / "metadata.yaml").write_text(
+            yaml.safe_dump(
+                {"title": fam, "description": f"{fam} plots."}, sort_keys=False
+            )
+        )
+
+    if run_gallery:
+        subprocess.run(["gallery", "generate", "--source", str(out_dir)], check=True)
+    return pdfs
+
+
+def render_run(run_dir: str | Path, *, run_gallery: bool = False) -> list[Path]:
+    """Render a prepped run directory: ``<run_dir>/reduced`` → ``<run_dir>/plots``.
+
+    First joins every plot's chunk partials (``reduced_partial/<id>__*.json``)
+    into ``reduced/<id>.json`` via ``merge_all`` — a no-op merge when the run
+    wasn't chunked (``n_chunks=1``) — then pulls the rollout provenance
+    (checkpoint, paths, cutoffs) from ``run_meta.json`` into every plot's
+    gallery metadata and renders.
+    """
+    from giant.analysis.condor import RunMeta, merge_all
+
+    run_dir = Path(run_dir)
+    merge_all(run_dir)
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    run_meta = {
+        "title": meta.title,
+        "rollout": meta.rollout,
+        "reference": meta.reference,
+        **meta.plot_meta,
+    }
+    return render_all(
+        run_dir / "reduced", run_dir / "plots", run_meta, run_gallery=run_gallery
+    )
diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py
new file mode 100644
index 0000000..6dbc3f8
--- /dev/null
+++ b/giant/analysis/router_gating.py
@@ -0,0 +1,339 @@
+"""Router gating diagnostic: where a MoE checkpoint's decision boundaries sit.
+
+Unlike everything else in this package, this reduction needs a live PyTorch
+model — soft expert gate weights aren't columns in a rollout/predict parquet,
+they only exist by calling `Router.gate(cond_cont, cond_cat)` (see
+`giant.model.network.Router`) against the checkpoint that produced the
+rollout. That's a deliberate, narrow exception to the rest of the catalog's
+"polars/numpy only" contract; it still runs fine as a `compute-one` HTCondor
+job since torch is already installed there (the same env trains checkpoints).
+
+The routing axis is fixed to pre-step energy: every router type at least
+indirectly depends on it (`EnergyRouter` reads it directly; `PdgRouter` and
+`ProcessRouter` correlate with it through the physics), and it's the one axis
+a reader can interpret without knowing the checkpoint's specific router
+config. `x` is binned into equal-population (quantile) bins rather than
+equal-width ones, since energy is heavy-tailed and equal-width bins would
+leave the upper end almost empty. Mean gate weight per bin is stacked as
+filled areas per expert — since `gate` rows are a partition of unity, the
+stack always fills exactly to 1, and the crossover bands are the router's
+soft decision boundaries (where two experts' means cross ~0.5).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import polars as pl
+
+from giant.analysis.grouping import pdg_label
+from giant.analysis.reduced import Reduced
+
+if TYPE_CHECKING:
+    import torch
+
+    from giant.data.transforms import Normalizer
+
+_SAMPLE_ROWS = 200_000
+_N_BINS = 40
+_TOP_K_PROCESS = 8
+
+_COLS = (
+    "pre_x",
+    "pre_y",
+    "pre_z",
+    "pre_E",
+    "pre_dx",
+    "pre_dy",
+    "pre_dz",
+    "layer_id",
+    "pdg",
+    "material",
+)
+
+
+@dataclass
+class _RouterHandle:
+    router: "torch.nn.Module"
+    pdg_map: dict[int, int]
+    mat_map: dict[str, int]
+    cond_normalizer: "Normalizer"
+    conditioning: str
+    router_type: str
+
+
+def load_router(checkpoint: str | Path) -> _RouterHandle | None:
+    """Load a checkpoint's Stage-1 router, or None if it isn't a MoE checkpoint."""
+    import torch
+
+    from giant.data.transforms import Normalizer
+    from giant.model.network import build_models
+
+    ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False)
+    model_cfg = ckpt.get("model_config") or {}
+    router_cfg = model_cfg.get("router")
+    if not router_cfg or not router_cfg.get("enabled"):
+        return None
+
+    stage1, _ = build_models(model_cfg)
+    stage1.load_state_dict(ckpt["model"])
+    stage1.eval()
+
+    return _RouterHandle(
+        router=stage1.router,
+        pdg_map={int(k): v for k, v in ckpt["pdg_map"].items()},
+        mat_map={str(k): v for k, v in ckpt["mat_map"].items()},
+        cond_normalizer=Normalizer.from_dict(ckpt["normalizer"]["cond"]),
+        conditioning=model_cfg.get("conditioning", "embedding"),
+        router_type=router_cfg["type"],
+    )
+
+
+def _subsample(
+    lf: pl.LazyFrame, n: int, seed: int, extra_cols: tuple = ()
+) -> pl.DataFrame:
+    total = lf.select(pl.len()).collect(engine="streaming").item()
+    if total > n:
+        threshold = int(n / total * 2**32)
+        lf = lf.filter((pl.col("pre_E").hash(seed=seed) % 2**32) < threshold)
+    return lf.select(*_COLS, *extra_cols).collect(engine="streaming")
+
+
+def _gate_for_df(
+    handle: _RouterHandle, df: pl.DataFrame
+) -> tuple[pl.DataFrame, np.ndarray]:
+    """(filtered df, gate_weights) for rows in ``df`` with a known pdg/material.
+
+    Rows whose species or material never appeared in the checkpoint's
+    training vocab can't be embedded — dropped here the same way
+    `giant.rollout`'s own known-pdg gate drops them at inference. The
+    returned df keeps every original column (filtered to the same rows), so
+    callers can key gate weights by any of them (energy, pdg, process, ...).
+    """
+    import torch
+
+    from giant.data.transforms import build_cond_features
+
+    known = np.array(
+        [
+            int(p) in handle.pdg_map and str(m) in handle.mat_map
+            for p, m in zip(df["pdg"].to_list(), df["material"].to_list())
+        ]
+    )
+    if not known.any():
+        return df.clear(), np.zeros((0, handle.router.n_experts))
+    df = df.filter(pl.Series(known, dtype=pl.Boolean))
+
+    data = {
+        "pre_pos": np.column_stack(
+            [df["pre_x"].to_numpy(), df["pre_y"].to_numpy(), df["pre_z"].to_numpy()]
+        ),
+        "pre_E": df["pre_E"].to_numpy(),
+        "pre_dir": np.column_stack(
+            [df["pre_dx"].to_numpy(), df["pre_dy"].to_numpy(), df["pre_dz"].to_numpy()]
+        ),
+        "layer_id": df["layer_id"].to_numpy(),
+        "pdg": df["pdg"].to_numpy(),
+        "material": df["material"].to_numpy(),
+    }
+    cond_cont, cond_cat = build_cond_features(
+        data,
+        handle.pdg_map,
+        handle.mat_map,
+        cond_normalizer=handle.cond_normalizer,
+        conditioning=handle.conditioning,
+    )
+    with torch.no_grad():
+        gate = handle.router.gate(
+            torch.from_numpy(cond_cont).float(), torch.from_numpy(cond_cat).long()
+        ).numpy()
+    return df, gate
+
+
+def _quantile_bins(x: np.ndarray, gate: np.ndarray, n_bins: int) -> dict:
+    order = np.argsort(x)
+    x_sorted, g_sorted = x[order], gate[order]
+    edges = np.quantile(x_sorted, np.linspace(0, 1, n_bins + 1))
+    edges[-1] = np.nextafter(edges[-1], np.inf)  # include the max value
+    bin_idx = np.clip(np.digitize(x_sorted, edges[1:-1]), 0, n_bins - 1)
+
+    n_experts = gate.shape[1]
+    centers = np.full(n_bins, np.nan)
+    means = np.full((n_bins, n_experts), np.nan)
+    for b in range(n_bins):
+        mask = bin_idx == b
+        if mask.any():
+            centers[b] = x_sorted[mask].mean()
+            means[b] = g_sorted[mask].mean(axis=0)
+    valid = ~np.isnan(centers)
+    return {"centers": centers[valid].tolist(), "means": means[valid].tolist()}
+
+
+def _top1_shares(
+    categories: np.ndarray, idx: np.ndarray, order: list, n_experts: int
+) -> dict[str, list[float]]:
+    """Fraction of each category's rows hard-dispatched to each expert.
+
+    Uses `Router.top1` (argmax), not the soft `gate` mean — grouped top-1
+    dispatch is what `_route_forward` actually runs in eval mode (rollout,
+    predict), so this answers "which expert does a photon/Compton step
+    actually go through", not just its average soft weight.
+    """
+    shares: dict[str, list[float]] = {}
+    for key in order:
+        mask = categories == key
+        total = int(mask.sum())
+        if total == 0:
+            shares[str(key)] = [0.0] * n_experts
+            continue
+        counts = np.bincount(idx[mask], minlength=n_experts)
+        shares[str(key)] = (counts / total).tolist()
+    return shares
+
+
+_NOTE_NOT_MOE = (
+    "checkpoint has no enabled MoE router (model.router.enabled is "
+    "false/absent) — nothing to show"
+)
+
+_TITLES = {
+    "router_gating": "Router gating (mixture-of-experts decision boundaries)",
+    "router_share_by_pdg": "Router expert share by particle species",
+    "router_share_by_process": "Router expert share by physics process",
+}
+
+
+def _unavailable(spec_id: str) -> Reduced:
+    return Reduced(
+        id=spec_id,
+        family="model",
+        kind="unavailable",
+        title=_TITLES[spec_id],
+        xlabel="n/a",
+        payload={"note": _NOTE_NOT_MOE},
+    )
+
+
+def compute_router_gating(
+    checkpoint: str | Path | None,
+    r_phys: pl.LazyFrame,
+    t_phys: pl.LazyFrame,
+    seed: int = 0,
+) -> Reduced:
+    """`Reduced` for the router-gating figure, or an explanatory note if n/a."""
+    handle = load_router(checkpoint) if checkpoint else None
+    if handle is None:
+        return _unavailable("router_gating")
+
+    sides: dict[str, dict] = {}
+    for name, lf in (("rollout", r_phys), ("reference", t_phys)):
+        df = _subsample(lf, _SAMPLE_ROWS, seed)
+        df, gate = _gate_for_df(handle, df)
+        x = df["pre_E"].to_numpy()
+        sides[name] = (
+            _quantile_bins(x, gate, _N_BINS) if len(x) else {"centers": [], "means": []}
+        )
+
+    return Reduced(
+        id="router_gating",
+        family="model",
+        kind="router_gating",
+        title=_TITLES["router_gating"],
+        xlabel="pre-step energy [MeV]",
+        payload={
+            "router_type": handle.router_type,
+            "n_experts": handle.router.n_experts,
+            "log_x": True,
+            **sides,
+        },
+    )
+
+
+def compute_router_share_by_pdg(
+    checkpoint: str | Path | None,
+    r_phys: pl.LazyFrame,
+    t_phys: pl.LazyFrame,
+    top_pdgs: list[int],
+    seed: int = 0,
+) -> Reduced:
+    """Stacked-bar share of each particle species dispatched to each expert."""
+    handle = load_router(checkpoint) if checkpoint else None
+    if handle is None:
+        return _unavailable("router_share_by_pdg")
+
+    labels = [pdg_label(p) for p in top_pdgs]
+    sides: dict[str, dict] = {}
+    for name, lf in (("rollout", r_phys), ("reference", t_phys)):
+        df = _subsample(lf, _SAMPLE_ROWS, seed)
+        df, gate = _gate_for_df(handle, df)
+        if len(df):
+            idx = gate.argmax(axis=1)
+            shares = _top1_shares(
+                df["pdg"].to_numpy(), idx, top_pdgs, handle.router.n_experts
+            )
+        else:
+            shares = {str(p): [0.0] * handle.router.n_experts for p in top_pdgs}
+        sides[name] = {labels[i]: shares[str(p)] for i, p in enumerate(top_pdgs)}
+
+    return Reduced(
+        id="router_share_by_pdg",
+        family="model",
+        kind="router_share",
+        title=_TITLES["router_share_by_pdg"],
+        xlabel="particle species",
+        payload={
+            "router_type": handle.router_type,
+            "n_experts": handle.router.n_experts,
+            "categories": labels,
+            **sides,
+        },
+    )
+
+
+def compute_router_share_by_process(
+    checkpoint: str | Path | None,
+    t_phys: pl.LazyFrame,
+    seed: int = 0,
+    top_k: int = _TOP_K_PROCESS,
+) -> Reduced:
+    """Stacked-bar share of each physics process dispatched to each expert.
+
+    Reference-only: ``process`` is the true post-step physics process — a
+    label the rollout side has no equivalent of (see
+    `giant.model.network.ProcessRouter`, which predicts it from pre-step
+    conditioning alone, never observes it at eval time). This plot instead
+    checks *after the fact*, on real data, how well the router's conditioning
+    -based dispatch lines up with the true process.
+    """
+    handle = load_router(checkpoint) if checkpoint else None
+    if handle is None:
+        return _unavailable("router_share_by_process")
+
+    df = _subsample(t_phys, _SAMPLE_ROWS, seed, extra_cols=("process",))
+    df, gate = _gate_for_df(handle, df)
+    if len(df):
+        counts = df["process"].value_counts().sort("count", descending=True)
+        order = counts["process"].to_list()[:top_k]
+        idx = gate.argmax(axis=1)
+        shares = _top1_shares(
+            df["process"].to_numpy(), idx, order, handle.router.n_experts
+        )
+    else:
+        order, shares = [], {}
+
+    return Reduced(
+        id="router_share_by_process",
+        family="model",
+        kind="router_share",
+        title=_TITLES["router_share_by_process"],
+        xlabel="physics process",
+        payload={
+            "router_type": handle.router_type,
+            "n_experts": handle.router.n_experts,
+            "categories": order,
+            "reference": {p: shares[p] for p in order},
+        },
+    )
diff --git a/giant/analysis/runtime_estimate.py b/giant/analysis/runtime_estimate.py
new file mode 100644
index 0000000..f5279f7
--- /dev/null
+++ b/giant/analysis/runtime_estimate.py
@@ -0,0 +1,117 @@
+"""Per-(plot, chunk) HTCondor walltime estimates for `giant analyze submit`.
+
+Each catalog spec's compute cost is close to linear in the number of input
+rows a `compute-one` job streams over — every spec is one (or a couple of)
+streaming `group_by` pass(es) over the chunk (see `catalog.py`/`reduce.py`).
+`_COST_MODEL` below is ``spec_id -> (intercept_s, seconds_per_row)``.
+``n_rows`` is the combined rollout+reference row count of the job's input:
+the chunk's row count for `chunkable=True` specs, the whole dataset's for the
+three `chunkable=False` router specs (they always run as a single job
+regardless of chunk count).
+
+Calibrated 2026-07-27 from real HTCondor timings (`condor_history`
+``RemoteWallClockTime``) of a production run: prediction ``563f5ee3``
+(PbWO4, 50 GeV) analyzed with ``--chunks 4`` against
+``giant/analysis/runtime_estimate.py``'s prior (local-synthetic-only) model —
+see the ``analysis-rollout-plots`` branch history for the raw data. That run's
+4 chunks came out at nearly identical row counts (~63-64M rows each, ~254M
+total), so this real data has no genuine row-count spread to fit a slope
+against — instead each spec's ``per_row`` here is a single line through the
+origin (``intercept=0``) hitting that spec's *median* wall-clock time across
+its 4 chunks at that run's row count. A handful of (spec, chunk) pairs showed
+3-8x spikes in one chunk only (e.g. ``marginal_edep_by_material``: 88, 88, 90,
+722s) — almost certainly shared ``/ceph`` contention from ~130 jobs landing on
+the filesystem at once right after submission, not a real per-row cost, so
+the median (not the max) was fit to avoid baking that noise into a rate that
+would then wrongly scale up with a bigger dataset. `RUNTIME_SAFETY_MARGIN` is
+deliberately generous (4x total) specifically to absorb that kind of
+contention spike instead. Rerun this calibration (pull fresh
+`condor_history`/`run_meta.json`, refit) if the catalog changes or timings
+drift — a synthetic local rebaseline via `scripts/profile_analysis_costs.py`
+is a reasonable fallback when no real cluster data is available yet, but
+undershoots real wall time badly (it can't see docker pull / `/ceph` I/O
+latency), which is exactly why this file moved off it.
+"""
+
+from __future__ import annotations
+
+import math
+
+# Multiplicative pad applied to every job's estimated walltime. The one knob
+# this feature was asked to expose. Set generously (4x total, i.e. 3.0 here)
+# to absorb the shared-/ceph-contention spikes described above rather than
+# encoding them into individual specs' per-row rates.
+RUNTIME_SAFETY_MARGIN = 3.00
+
+# Fixed per-job overhead (docker start, `.venv/bin/giant` startup, initial
+# `/ceph` read latency) — calibrated as the fastest observed real spec
+# (`leakage_fraction`, median 51s) rounded up, since even the cheapest spec
+# streams the whole chunk once.
+_FIXED_OVERHEAD_S = 60.0
+
+# Router diagnostics run a live torch checkpoint (bounded inference over
+# <=200k subsampled rows, independent of chunk size) instead of a row-based
+# scan. Calibrated from the 3 real router jobs' observed wall times (119, 66,
+# 124s) — max minus _FIXED_OVERHEAD_S, on top of it.
+_ROUTER_FIXED_S = 64.0
+_ROUTER_IDS = frozenset(
+    {"router_gating", "router_share_by_pdg", "router_share_by_process"}
+)
+
+# Conservative fallback for any catalog id not in _COST_MODEL (e.g. a plot
+# added after the last calibration run) — the most expensive fitted per-row
+# rate observed, plus a small constant pad.
+_DEFAULT_COST = (5.0, 3.0e-6)
+
+# spec_id -> (intercept_s, seconds_per_row), fit 2026-07-27 from real
+# HTCondor `RemoteWallClockTime` (see module docstring for methodology).
+_COST_MODEL: dict[str, tuple[float, float]] = {
+    "marginal_step_length": (0.0, 5.199e-07),
+    "marginal_step_length_by_energy": (0.0, 1.678e-06),
+    "marginal_step_length_by_pdg": (0.0, 4.569e-07),
+    "marginal_step_length_by_material": (0.0, 2.269e-06),
+    "marginal_edep": (0.0, 2.804e-06),
+    "marginal_edep_by_energy": (0.0, 1.386e-06),
+    "marginal_edep_by_pdg": (0.0, 4.490e-07),
+    "marginal_edep_by_material": (0.0, 4.333e-07),
+    "marginal_delta_e": (0.0, 5.042e-07),
+    "marginal_delta_e_by_energy": (0.0, 1.678e-06),
+    "marginal_delta_e_by_pdg": (0.0, 4.727e-07),
+    "marginal_delta_e_by_material": (0.0, 4.490e-07),
+    "marginal_post_E": (0.0, 4.805e-07),
+    "marginal_post_E_by_energy": (0.0, 1.284e-06),
+    "marginal_post_E_by_pdg": (0.0, 4.569e-07),
+    "marginal_post_E_by_material": (0.0, 4.490e-07),
+    "marginal_cos_scatter": (0.0, 5.436e-07),
+    "marginal_cos_scatter_by_energy": (0.0, 1.363e-06),
+    "marginal_cos_scatter_by_pdg": (0.0, 4.727e-07),
+    "marginal_cos_scatter_by_material": (0.0, 4.727e-07),
+    "event_total_edep": (0.0, 4.490e-07),
+    "event_total_edep_by_energy": (0.0, 4.411e-07),
+    "event_mean_length": (0.0, 4.333e-07),
+    "event_n_steps": (0.0, 4.569e-07),
+    "shower_longitudinal": (0.0, 2.348e-06),
+    "shower_transverse": (0.0, 2.899e-06),
+    "species_edep_share": (0.0, 4.333e-07),
+    "leakage_fraction": (0.0, 0.0),
+    "sec_count_per_event": (0.0, 4.727e-07),
+    "sec_count_per_species": (0.0, 4.963e-07),
+    "sec_energy": (0.0, 4.727e-07),
+    "sec_cos_angle": (0.0, 2.749e-06),
+}
+
+
+def estimate_runtime_s(spec_id: str, n_rows: int) -> int:
+    """Estimated `+RequestWalltime` (seconds) for one (plot, chunk) job.
+
+    ``n_rows`` is the rollout+reference row count of that job's input slice.
+    Includes `_FIXED_OVERHEAD_S`/`_ROUTER_FIXED_S` and `RUNTIME_SAFETY_MARGIN`
+    — callers should pass this straight through to the submit description.
+    """
+    if spec_id in _ROUTER_IDS:
+        compute_s = _ROUTER_FIXED_S
+    else:
+        intercept, per_row = _COST_MODEL.get(spec_id, _DEFAULT_COST)
+        compute_s = intercept + per_row * n_rows
+    total = _FIXED_OVERHEAD_S + compute_s
+    return math.ceil(total * (1 + RUNTIME_SAFETY_MARGIN))
diff --git a/giant/analysis/sources.py b/giant/analysis/sources.py
new file mode 100644
index 0000000..2248c90
--- /dev/null
+++ b/giant/analysis/sources.py
@@ -0,0 +1,179 @@
+"""Canonical world-frame LazyFrame builders for the two sides of a comparison.
+
+The analysis compares one autoregressive ``giant rollout`` (the *generated* side)
+against a raw miniCaloSim steps file (the *reference* / real side). Both carry a
+**shared world-frame physical column subset** under identical names, so no
+renaming or coordinate decode is needed — everything is already in world-frame
+mm / MeV:
+
+    event_id, track_id, step_no, pdg,
+    pre_x, pre_y, pre_z, pre_E, pre_dx, pre_dy, pre_dz,
+    post_x, post_y, post_z, post_E, post_dx, post_dy, post_dz,
+    edep, step_length, material, layer_id
+
+(rollout: ``rollout.py:_RECORD_KEYS``; reference: minicalosim ``RunAction.cc``
+Steps ntuple passed through by ``dwarf convert``.)
+
+The two files differ in their *extra* columns — the rollout adds ``parent_id``,
+``generation``, ``n_sec_pred``, ``termination_reason``; the reference adds
+``process``, field columns, ``child_track_ids`` and the ``sec_*_list`` secondary
+birth-state lists. Those are only touched by the side-specific helpers here
+(synthetic-row filtering, the secondary view).
+
+Nothing in this module (or ``reduce.py``) imports plotstyle — compute runs on
+HTCondor workers that have no LaTeX toolchain.
+"""
+
+from __future__ import annotations
+
+from enum import Enum
+from pathlib import Path
+
+import polars as pl
+import pyarrow.parquet as pq
+
+from giant.constants import (
+    PREDICT_COORD_METADATA_KEY,
+    ROLLOUT_COORD_VALUE,
+    TERM_ENERGY_CUTOFF,
+    TERM_ESCAPED,
+    TERM_MAX_STEPS,
+    TERM_UNKNOWN_PDG,
+)
+
+# The world-frame physical columns both sides share under identical names.
+PHYS_COLS: tuple[str, ...] = (
+    "event_id",
+    "pdg",
+    "pre_x",
+    "pre_y",
+    "pre_z",
+    "pre_E",
+    "pre_dx",
+    "pre_dy",
+    "pre_dz",
+    "post_x",
+    "post_y",
+    "post_z",
+    "post_E",
+    "post_dx",
+    "post_dy",
+    "post_dz",
+    "edep",
+    "step_length",
+    "material",
+    "layer_id",
+)
+
+# Rollout rows written purely for bookkeeping (a track's forced stop): they carry
+# step_length=0, post_pos=pre_pos, and — for every reason but escaped — the
+# track's whole remaining pre_E dumped into edep so the shower still conserves
+# energy. They are not physical steps (the reference has no equivalent), so a
+# per-step marginal comparison must drop them; a per-event energy total must keep
+# them. See rollout.py's terminal-row handling.
+SYNTHETIC_TERMINATION_REASONS: frozenset[str] = frozenset(
+    {TERM_ESCAPED, TERM_UNKNOWN_PDG, TERM_ENERGY_CUTOFF, TERM_MAX_STEPS}
+)
+
+
+class Side(str, Enum):
+    """Which of the two comparison inputs a file is."""
+
+    rollout = "rollout"
+    reference = "reference"
+
+
+def _check_rollout_metadata(path: Path) -> None:
+    """Raise if ``path`` carries coord metadata that isn't the rollout tag.
+
+    A missing tag (older rollout output, predating tagging) is allowed through,
+    matching ``giant rollout``'s own leniency; a tag that is present but wrong is
+    a real mismatch and worth failing on before the column layout is trusted.
+    """
+    metadata = pq.read_schema(path).metadata or {}
+    coord = metadata.get(PREDICT_COORD_METADATA_KEY.encode())
+    if coord is not None and coord.decode() != ROLLOUT_COORD_VALUE:
+        raise ValueError(
+            f"{path} is not a rollout file (coord={coord.decode()!r}); "
+            "expected `giant rollout` output"
+        )
+
+
+def open_side(source: str | Path | pl.LazyFrame, side: Side) -> pl.LazyFrame:
+    """Lazily scan one side's file, verifying the rollout tag when applicable.
+
+    Returns the *full* lazy scan (no column projection) so downstream reductions
+    can push their own narrow projection into the parquet read — the single
+    biggest lever on a larger-than-RAM file. ``pl.LazyFrame`` inputs pass straight
+    through (used by tests).
+
+    ``pdg`` is cast to a canonical ``Int64`` here: the rollout writer and the
+    reference file's upstream ROOT→parquet conversion don't agree on integer
+    width, and an uncast mismatch only surfaces later as a ``pl.concat``
+    ``SchemaError`` (e.g. in ``build_context``'s pdg-count merge).
+    """
+    if isinstance(source, pl.LazyFrame):
+        return source.with_columns(pl.col("pdg").cast(pl.Int64))
+    path = Path(source)
+    if side is Side.rollout:
+        _check_rollout_metadata(path)
+        lf = pl.scan_parquet(path)
+    else:
+        # The reference (a rollout's seed `dataset`) may be a directory of
+        # parquet shards rather than a single file — scan them all.
+        lf = (
+            pl.scan_parquet(str(path / "**/*.parquet"))
+            if path.is_dir()
+            else pl.scan_parquet(path)
+        )
+    return lf.with_columns(pl.col("pdg").cast(pl.Int64))
+
+
+def physical_steps(lf: pl.LazyFrame, side: Side) -> pl.LazyFrame:
+    """Real, physical steps only — drops the rollout's synthetic termination rows.
+
+    The predicate is pushed down so the dropped rows are never decoded. The
+    reference has no such rows, so it is returned unchanged.
+    """
+    if side is Side.reference:
+        return lf
+    return lf.filter(
+        ~pl.col("termination_reason").is_in(list(SYNTHETIC_TERMINATION_REASONS))
+    )
+
+
+def secondaries(lf: pl.LazyFrame, side: Side) -> pl.LazyFrame:
+    """Per-secondary birth state, one row per produced secondary.
+
+    Canonical columns: ``event_id, energy, pdg, sdx, sdy, sdz`` (birth energy in
+    MeV, PDG code, birth unit direction in the world frame). The two sides encode
+    secondaries differently:
+
+    - rollout: each secondary is its own track, so its birth state is the row with
+      ``generation > 0`` and ``step_no == 0`` (``pre_E`` / ``pre_dir`` there).
+    - reference: secondaries live in per-parent-step ``sec_*_list`` columns; the
+      lists are exploded together and empty (no-secondary) steps drop out.
+    """
+    if side is Side.rollout:
+        return lf.filter((pl.col("generation") > 0) & (pl.col("step_no") == 0)).select(
+            "event_id",
+            pl.col("pre_E").alias("energy"),
+            "pdg",
+            pl.col("pre_dx").alias("sdx"),
+            pl.col("pre_dy").alias("sdy"),
+            pl.col("pre_dz").alias("sdz"),
+        )
+    lists = ["sec_E_list", "sec_pdg_list", "sec_dx_list", "sec_dy_list", "sec_dz_list"]
+    return (
+        lf.select("event_id", *lists)
+        .explode(lists)
+        .drop_nulls("sec_E_list")
+        .select(
+            "event_id",
+            pl.col("sec_E_list").alias("energy"),
+            pl.col("sec_pdg_list").alias("pdg"),
+            pl.col("sec_dx_list").alias("sdx"),
+            pl.col("sec_dy_list").alias("sdy"),
+            pl.col("sec_dz_list").alias("sdz"),
+        )
+    )
diff --git a/giant/analysis/variables.py b/giant/analysis/variables.py
new file mode 100644
index 0000000..2688e1e
--- /dev/null
+++ b/giant/analysis/variables.py
@@ -0,0 +1,28 @@
+"""Per-step value expressions shared by ``context`` (range sizing) and ``catalog``.
+
+Kept separate from both so the range-sizing prep and the plot registry agree on
+exactly what each variable *is*, with no import cycle. plotstyle-free.
+"""
+
+from __future__ import annotations
+
+import polars as pl
+
+# Ranged marginal variables: name -> (axis label, value expression). Their
+# histogram ranges are sized from data in `context.build_context`.
+RANGED_VARS: dict[str, tuple[str, pl.Expr]] = {
+    "step_length": ("step length [mm]", pl.col("step_length")),
+    "edep": ("deposited energy [MeV]", pl.col("edep")),
+    "delta_e": ("energy loss [MeV]", pl.col("pre_E") - pl.col("post_E")),
+    "post_E": ("post-step energy [MeV]", pl.col("post_E")),
+}
+
+
+def cos_scatter_expr() -> pl.Expr:
+    """cos of the scattering angle: ``pre_dir . post_dir`` (both unit), in [-1, 1]."""
+    dot = (
+        pl.col("pre_dx") * pl.col("post_dx")
+        + pl.col("pre_dy") * pl.col("post_dy")
+        + pl.col("pre_dz") * pl.col("post_dz")
+    )
+    return dot.clip(-1.0, 1.0)
diff --git a/giant/cli.py b/giant/cli.py
index d985efc..9ba234f 100644
--- a/giant/cli.py
+++ b/giant/cli.py
@@ -891,7 +891,7 @@ def _seed_from_data(files: list[Path], n_events: int | None) -> dict[str, np.nda
 
     Streams conditioning columns and keeps the highest-pre_E step per event_id —
     the codebase's convention for the primary (a secondary always carries less
-    energy than its parent). See giant/analysis.py:_entry_axis_and_bin_edges.
+    energy than its parent). See giant/analysis/reduce.py:entry_axis.
     """
     best_E: dict[int, float] = {}
     best: dict[int, tuple] = {}
@@ -1024,6 +1024,9 @@ def rollout(
         )
         raise typer.Exit(1)
 
+    gconfig.warn_if_checkpoint_config_mismatch(checkpoint)
+    training_cfg = gconfig.load_checkpoint_config(checkpoint)
+
     model_cfg = ckpt["model_config"]
     conditioning = model_cfg.get("conditioning", "embedding")
     pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()}
@@ -1104,7 +1107,26 @@ def rollout(
             "max_steps": max_steps,
             "steps": steps,
             "max_tracks_per_event": max_tracks_per_event,
+            "escape_threshold": escape_threshold,
+            "n_events": n_events,
             "n_seed_events": int(len(seeds["event_id"])),
+            "weights": weights.value,
+            "batch_size": batch_size,
+            "device": str(_device),
+            "rollout_seed": seed,
+            "n_rows": summary["n_rows"],
+            "termination_reason_counts": summary["termination_reason_counts"],
+            # Full architecture spec baked into the checkpoint — includes the
+            # entire router sub-dict, not just a hand-picked subset, so any
+            # model knob (router type/n_experts, noise_dim, vocab sizes, ...)
+            # is available downstream without touching this command again.
+            "model_config": dict(model_cfg),
+            "training_epoch": ckpt.get("epoch"),
+            "best_val_loss": ckpt.get("best_val_loss"),
+            # [train]/[meta] from the sibling config.toml (giant.config.save_config)
+            # — empty dicts if the checkpoint has no config.toml next to it.
+            "training_config": dict(training_cfg.get("train", {})),
+            "training_meta": dict(training_cfg.get("meta", {})),
         }
     )
     ref_path.write_text(yaml.dump(ref, default_flow_style=False, sort_keys=False))
@@ -1114,5 +1136,186 @@ def rollout(
     typer.echo(f"reference: {ref_path}")
 
 
+analyze_app = typer.Typer(
+    no_args_is_help=True,
+    help="Rollout-vs-reference analysis: parallel compute on HTCondor + local render.",
+)
+app.add_typer(analyze_app, name="analyze")
+
+
+@analyze_app.command("prep")
+def analyze_prep(
+    rollout_yaml: Annotated[
+        Path,
+        typer.Argument(
+            help="giant rollout YAML sidecar (names the rollout + reference files)"
+        ),
+    ],
+    run_dir: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--run-dir",
+            "-o",
+            help="Override the run directory (default: <cwd>/analysis_runs/analysis_<id>)",
+        ),
+    ] = None,
+    n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4,
+    n_marginal_bins: Annotated[int, typer.Option("--bins")] = 50,
+    top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6,
+    chunks: Annotated[
+        int,
+        typer.Option(
+            "--chunks", help="Split each plot's data into this many event_id chunks"
+        ),
+    ] = 1,
+) -> None:
+    """Read the rollout YAML → shared.json + run_meta.json in the run directory."""
+    from giant.analysis import prep
+
+    path = prep(
+        rollout_yaml,
+        run_dir,
+        n_chunks=chunks,
+        default_base=Path.cwd() / "analysis_runs",
+        n_energy_bins=n_energy_bins,
+        n_marginal_bins=n_marginal_bins,
+        top_k_pdg=top_k_pdg,
+    )
+    typer.echo(f"run directory: {path}")
+
+
+@analyze_app.command("compute-one")
+def analyze_compute_one(
+    id: Annotated[
+        str, typer.Option("--id", help="Catalog plot id (see `analyze list`)")
+    ],
+    run_dir: Annotated[
+        Path, typer.Option("--run-dir", help="Run directory from `analyze prep`")
+    ],
+    chunk: Annotated[
+        int, typer.Option("--chunk", help="Chunk index (see `analyze prep --chunks`)")
+    ] = 0,
+) -> None:
+    """Run one (plot, chunk)'s streaming reduction (this is what each condor job runs)."""
+    from giant.analysis import compute_one
+
+    path = compute_one(id, run_dir, chunk_index=chunk)
+    typer.echo(f"wrote {path}")
+
+
+@analyze_app.command("merge-one")
+def analyze_merge_one(
+    id: Annotated[
+        str, typer.Option("--id", help="Catalog plot id (see `analyze list`)")
+    ],
+    run_dir: Annotated[
+        Path, typer.Option("--run-dir", help="Run directory from `analyze prep`")
+    ],
+) -> None:
+    """Merge one plot's chunk partials into its final reduced JSON.
+
+    Runs automatically as part of `analyze render`; useful standalone to
+    debug a specific plot without re-rendering everything.
+    """
+    from giant.analysis import merge_one
+
+    path = merge_one(id, run_dir)
+    typer.echo(f"wrote {path}")
+
+
+@analyze_app.command("list")
+def analyze_list() -> None:
+    """Print every catalog plot id."""
+    from giant.analysis import catalog_ids
+
+    for pid in catalog_ids():
+        typer.echo(pid)
+
+
+@analyze_app.command("render")
+def analyze_render(
+    run_dir: Annotated[
+        Path, typer.Argument(help="Run directory from `analyze prep` (holds reduced/)")
+    ],
+    gallery: Annotated[
+        bool,
+        typer.Option(
+            "--gallery/--no-gallery", help="Run `gallery generate` after rendering"
+        ),
+    ] = False,
+) -> None:
+    """Render reduced artifacts to styled PDFs + gallery metadata (local; needs LaTeX)."""
+    from giant.analysis.render import render_run
+
+    pdfs = render_run(run_dir, run_gallery=gallery)
+    typer.echo(f"rendered {len(pdfs)} plots → {Path(run_dir) / 'plots'}")
+
+
+@analyze_app.command("submit")
+def analyze_submit(
+    rollout_yaml: Annotated[Path, typer.Argument(help="giant rollout YAML sidecar")],
+    accounting_group: Annotated[str, typer.Option("--accounting-group")],
+    run_dir: Annotated[
+        Optional[Path],
+        typer.Option(
+            "--run-dir",
+            "-o",
+            help="Override the run directory (default: <cwd>/analysis_runs/analysis_<id>)",
+        ),
+    ] = None,
+    docker_image: Annotated[
+        str, typer.Option("--docker-image")
+    ] = "cverstege/alma9-gridjob",
+    request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 8192,
+    remote: Annotated[
+        bool,
+        typer.Option("--remote/--local", help="+RemoteJob vs ProvidesETPResources"),
+    ] = False,
+    chunks: Annotated[
+        int,
+        typer.Option(
+            "--chunks",
+            help="Split each plot's data into this many event_id chunks/jobs",
+        ),
+    ] = 1,
+    n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4,
+    n_marginal_bins: Annotated[int, typer.Option("--bins")] = 50,
+    top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6,
+    dry_run: Annotated[
+        bool, typer.Option("--dry-run", help="Write files but don't condor_submit")
+    ] = False,
+) -> None:
+    """prep + write the HTCondor submit description (one job per plot x chunk), then submit."""
+    import subprocess
+
+    from giant.analysis import SubmitConfig, prep, write_submit
+
+    path = prep(
+        rollout_yaml,
+        run_dir,
+        n_chunks=chunks,
+        default_base=Path.cwd() / "analysis_runs",
+        n_energy_bins=n_energy_bins,
+        n_marginal_bins=n_marginal_bins,
+        top_k_pdg=top_k_pdg,
+    )
+    cfg = SubmitConfig(
+        run_dir=path,
+        accounting_group=accounting_group,
+        repo_dir=Path.cwd(),
+        docker_image=docker_image,
+        request_memory_mb=request_memory,
+        remote=remote,
+        n_chunks=chunks,
+    )
+    sub = write_submit(cfg)
+    typer.echo(f"run directory: {path}")
+    typer.echo(f"wrote submit description: {sub}")
+    if dry_run:
+        typer.echo("dry-run: not submitting")
+        return
+    subprocess.run(["condor_submit", str(sub)], check=True)
+
+
 if __name__ == "__main__":
     app()
diff --git a/giant/config.py b/giant/config.py
index cfd7a1b..2de0df5 100644
--- a/giant/config.py
+++ b/giant/config.py
@@ -188,6 +188,21 @@ def warn_if_git_hash_mismatch(file_cfg: dict, config_path: Path) -> None:
         )
 
 
+def load_checkpoint_config(ckpt_path: str | Path) -> dict:
+    """Load the full ``[train]``/``[model]``/``[meta]`` config.toml written
+    alongside a checkpoint by ``save_config``.
+
+    Returns ``{}`` if no config.toml sits next to the checkpoint (older runs,
+    or a checkpoint moved without its sidecar) — this is best-effort
+    provenance for threading into a rollout's YAML sidecar, not a hard
+    requirement for using the checkpoint itself.
+    """
+    config_path = Path(ckpt_path).parent / "config.toml"
+    if not config_path.exists():
+        return {}
+    return load_toml(config_path)
+
+
 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.
 
diff --git a/giant/data/transforms.py b/giant/data/transforms.py
index 9b74ea7..a4a5222 100644
--- a/giant/data/transforms.py
+++ b/giant/data/transforms.py
@@ -228,6 +228,54 @@ class _WelfordAccumulator:
         return norm
 
 
+class _ReservoirSampler:
+    """Uniform random sample of a fixed capacity drawn from a data stream.
+
+    Algorithm R (Vitter 1985), vectorized per chunk so it stays cheap over
+    hundreds of millions of rows: use to get a representative subsample of
+    a column for a distribution estimate (e.g. quantiles) without
+    materializing the full column.
+
+        sampler = _ReservoirSampler(capacity=100_000)
+        for chunk in data:
+            sampler.update(chunk)
+        sample = sampler.sample
+    """
+
+    def __init__(self, capacity: int, seed: int = 0) -> None:
+        self.capacity = capacity
+        self.n_seen = 0
+        self._rng = np.random.default_rng(seed)
+        self._reservoir = np.empty(0, dtype=np.float64)
+
+    def update(self, values: np.ndarray) -> None:
+        values = np.asarray(values, dtype=np.float64).reshape(-1)
+        if values.size == 0:
+            return
+        n_before = self.n_seen
+        if n_before < self.capacity:
+            take = min(values.size, self.capacity - n_before)
+            self._reservoir = np.concatenate([self._reservoir, values[:take]])
+            values = values[take:]
+            n_before += take
+        self.n_seen = n_before + values.size
+        if values.size == 0 or self.capacity == 0:
+            return
+        # remaining elements are past the fill phase: element at 1-based
+        # stream position j replaces a uniformly random reservoir slot with
+        # probability capacity/j, which yields a uniform sample overall.
+        positions = n_before + np.arange(1, values.size + 1)
+        accept = self._rng.random(values.size) < (self.capacity / positions)
+        accept_idx = np.nonzero(accept)[0]
+        if accept_idx.size > 0:
+            slots = self._rng.integers(0, self.capacity, size=accept_idx.size)
+            self._reservoir[slots] = values[accept_idx]
+
+    @property
+    def sample(self) -> np.ndarray:
+        return self._reservoir.astype(np.float32)
+
+
 def travel_direction(pre_pos: np.ndarray, post_pos: np.ndarray) -> np.ndarray:
     """World-frame unit vector pointing from pre_pos to post_pos.
 
@@ -530,11 +578,44 @@ def build_cond_features(
     cond_cat = np.column_stack([pdg_idx, mat_idx])
 
     if cond_normalizer is not None:
-        cond_cont = cond_normalizer.transform(cond_cont)
+        cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, conditioning)
 
     return cond_cont, cond_cat
 
 
+def _cond_normalizer_transform(
+    cond_cont: np.ndarray, cond_normalizer: "Normalizer", conditioning: str
+) -> np.ndarray:
+    """Apply ``cond_normalizer``, padding a legacy narrower normalizer if needed.
+
+    Checkpoints trained before physical-property conditioning (``COND_DIM``
+    8->15, ``giant/constants.py``) saved a ``COND_DIM_BASE``-wide (8) cond
+    normalizer, fit before ``build_cond_features`` grew the extra physical
+    columns. In "embedding" mode those columns are never read by
+    ``ConditionEncoder`` (``giant/model/network.py``), so padding the missing
+    entries with mean=0/std=1 is a safe no-op that keeps such checkpoints
+    usable under the current, always-``COND_DIM``-wide contract. In
+    "physical" mode the physical columns are load-bearing, so a mismatch
+    there is a real incompatibility, not something to paper over.
+    """
+    mean, std = cond_normalizer.mean, cond_normalizer.std
+    assert mean is not None and std is not None, "Normalizer not fitted"
+    width = cond_cont.shape[-1]
+    if mean.shape[-1] < width:
+        if conditioning != "embedding":
+            raise ValueError(
+                f"cond normalizer has {mean.shape[-1]} columns, expected "
+                f"{width}, and conditioning={conditioning!r} reads the "
+                "physical columns directly — this checkpoint predates "
+                "physical-property conditioning and can't be safely padded; "
+                "retrain it under the current code."
+            )
+        pad = width - mean.shape[-1]
+        mean = np.concatenate([mean, np.zeros(pad, dtype=mean.dtype)])
+        std = np.concatenate([std, np.ones(pad, dtype=std.dtype)])
+    return ((cond_cont - mean) / std).astype(np.float32)
+
+
 def build_features(
     data: dict[str, np.ndarray],
     pdg_map: dict[int, int],
diff --git a/giant/model/network.py b/giant/model/network.py
index 5596a17..5345747 100644
--- a/giant/model/network.py
+++ b/giant/model/network.py
@@ -1,6 +1,7 @@
 import inspect
 import math
 import re
+from collections.abc import Sequence
 
 import torch
 import torch.nn as nn
@@ -600,10 +601,14 @@ class EnergyRouter(Router):
     """Soft turn-on gate over normalized pre-step log-energy.
 
     Reads `cond_cont[:, energy_idx]` (ignores cond_cat). Learnable (or
-    fixed) 1-D centers, initialized spread across [-2, 2] — roughly the
-    z-normalized energy range. `gate(e) = softmax_i(-(e - c_i)^2 / tau)`,
-    differentiable in e; as tau -> 0 this hardens to nearest-center
-    (Voronoi) selection, which is exactly what `top1` uses at eval.
+    fixed) 1-D centers. By default initialized spread evenly across
+    [-2, 2] — an assumed-uniform z-normalized energy range that may not
+    match the true (often skewed) distribution and can leave experts
+    overlapping instead of partitioning the range; pass `centers_init` to
+    seed them from data (e.g. energy quantiles) instead.
+    `gate(e) = softmax_i(-(e - c_i)^2 / tau)`, differentiable in e; as
+    tau -> 0 this hardens to nearest-center (Voronoi) selection, which is
+    exactly what `top1` uses at eval.
     """
 
     def __init__(
@@ -612,11 +617,20 @@ class EnergyRouter(Router):
         temperature: float = 0.5,
         learn_centers: bool = True,
         energy_idx: int = 3,
+        centers_init: Sequence[float] | None = None,
     ) -> None:
         super().__init__(n_experts)
         self.temperature = temperature
         self.energy_idx = energy_idx
-        centers = torch.linspace(-2.0, 2.0, n_experts)
+        if centers_init is None:
+            centers = torch.linspace(-2.0, 2.0, n_experts)
+        else:
+            if len(centers_init) != n_experts:
+                raise ValueError(
+                    f"centers_init has {len(centers_init)} values, "
+                    f"expected n_experts={n_experts}"
+                )
+            centers = torch.tensor(list(centers_init), dtype=torch.float32)
         if learn_centers:
             self.centers = nn.Parameter(centers)
         else:
diff --git a/giant/pipeline.py b/giant/pipeline.py
index c06a0d0..6518059 100644
--- a/giant/pipeline.py
+++ b/giant/pipeline.py
@@ -20,7 +20,7 @@ from giant.data.loader import (
     build_index_maps_from_files,
     build_process_map_from_files,
 )
-from giant.data.transforms import build_features, _WelfordAccumulator
+from giant.data.transforms import build_features, _WelfordAccumulator, _ReservoirSampler
 from giant.data.dataset import make_event_split, StreamingStepsDataset
 from giant.model.network import build_models, build_critics
 from giant.train import train as run_training
@@ -83,6 +83,18 @@ def run_train_job(
     cond_acc = _WelfordAccumulator(COND_DIM)
     tgt_acc = _WelfordAccumulator(X_DIM)
     sec_phys_acc = _WelfordAccumulator(PARTICLE_PHYS_DIM)
+    # EnergyRouter's default center spread (linspace over [-2, 2]) assumes
+    # the z-normalized energy column is roughly uniform, which real energy
+    # spectra rarely are — collect a reservoir sample here (reusing this
+    # same pass, not a second scan) so centers can instead be seeded from
+    # actual data quantiles below.
+    energy_router_active = (
+        router_cfg.get("enabled") and router_cfg.get("type") == "energy"
+    )
+    energy_idx = router_cfg.get("energy_idx", 3)
+    energy_sampler = (
+        _ReservoirSampler(capacity=100_000) if energy_router_active else None
+    )
     for path in files:
         for chunk in iter_file_chunks(path):
             mask = np.isin(chunk["event_id"], events_arr)
@@ -99,6 +111,8 @@ def run_train_job(
             )
             cond_acc.update(cond_cont)
             tgt_acc.update(target_s1)
+            if energy_sampler is not None:
+                energy_sampler.update(cond_cont[:, energy_idx])
             sec_valid = np.arange(K_MAX)[None, :] < n_sec[:, None]
             sec_phys = sec_cont[:, :, 4:6][sec_valid]
             if len(sec_phys) > 0:
@@ -107,6 +121,18 @@ def run_train_job(
     tgt_norm = tgt_acc.to_normalizer()
     sec_phys_norm = sec_phys_acc.to_normalizer()
 
+    if energy_sampler is not None and energy_sampler.n_seen > 0:
+        assert cond_norm.mean is not None and cond_norm.std is not None
+        normalized_sample = (
+            energy_sampler.sample - cond_norm.mean[energy_idx]
+        ) / cond_norm.std[energy_idx]
+        quantiles = np.linspace(0.0, 1.0, router_cfg["n_experts"])
+        centers_init = np.quantile(normalized_sample, quantiles).astype(np.float32)
+        router_cfg["centers_init"] = centers_init.tolist()
+        echo(
+            f"  seeded EnergyRouter centers from data quantiles: {router_cfg['centers_init']}"
+        )
+
     train_ds = StreamingStepsDataset(
         files=files,
         split_events=train_events,
diff --git a/pyproject.toml b/pyproject.toml
index bca28c4..303071c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,9 @@ analysis = [
     "matplotlib>=3.8,<4",
     "polars>=1.0,<2",
     "ipykernel>=7.3.0",
+    # KIT matplotlib theme, published from git.larsbogner.de. Only the local
+    # `giant analyze render` step imports it; compute workers never do.
+    "plotstyle>=1.0.0",
 ]
 
 [project.scripts]
@@ -65,6 +68,7 @@ torch = [
     { index = "pytorch-cpu", extra = "cpu" },
     { index = "pytorch-cu118", extra = "cuda" },
 ]
+plotstyle = { index = "larsbogner" }
 
 [[tool.uv.index]]
 name = "pytorch-cpu"
@@ -75,3 +79,8 @@ explicit = true
 name = "pytorch-cu118"
 url = "https://download.pytorch.org/whl/cu118"
 explicit = true
+
+[[tool.uv.index]]
+name = "larsbogner"
+url = "https://git.larsbogner.de/api/packages/lars/pypi/simple/"
+explicit = true
diff --git a/scripts/profile_analysis_costs.py b/scripts/profile_analysis_costs.py
new file mode 100644
index 0000000..de97a2b
--- /dev/null
+++ b/scripts/profile_analysis_costs.py
@@ -0,0 +1,242 @@
+"""Benchmark `giant analyze compute-one`'s per-job cost against synthetic data.
+
+Generates mock rollout+reference parquet files at a few row counts, times
+`compute_reduced` for every chunkable catalog spec at each size (a single
+chunk covering the whole mock file), fits a straight line (intercept, seconds
+per row) through the timings, and prints the result as a Python dict literal
+ready to paste into `giant/analysis/runtime_estimate.py::_COST_MODEL`.
+
+The three `chunkable=False` router specs (`router_gating`,
+`router_share_by_pdg`, `router_share_by_process`) need a live MoE checkpoint
+to do any real work; without one (this machine has no `/ceph` access, so no
+real checkpoint) they short-circuit almost instantly and are excluded here —
+see `runtime_estimate.py`'s `_ROUTER_FIXED_S` for how those are handled
+instead.
+
+Usage: ``uv run python scripts/profile_analysis_costs.py``
+"""
+
+from __future__ import annotations
+
+import time
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import numpy as np
+import polars as pl
+
+from giant.analysis.catalog import catalog_ids, get_spec
+from giant.analysis.condor import compute_reduced
+from giant.analysis.context import build_context
+
+# Row counts (per side) to benchmark at. Kept in local memory/CPU range so the
+# whole sweep finishes in about a minute; the fit is linear so it extrapolates
+# fine to real multi-GB rollouts.
+SIDE_ROW_COUNTS = [20_000, 100_000, 500_000, 2_000_000]
+
+_MATERIALS = ["G4_PbWO4", "G4_Pb", "G4_lAr", "G4_Si"]
+_PDGS = [11, -11, 22, 2112, 2212, 211, -211, 13]
+_ROUTER_IDS = {"router_gating", "router_share_by_pdg", "router_share_by_process"}
+
+
+def _unit_vectors(n: int, rng: np.random.Generator) -> np.ndarray:
+    v = rng.normal(size=(n, 3))
+    return v / np.linalg.norm(v, axis=1, keepdims=True)
+
+
+def _ragged_lists(k: np.ndarray, rng: np.random.Generator, lo: float, hi: float):
+    total = int(k.sum())
+    flat = rng.uniform(lo, hi, size=total)
+    idx = np.cumsum(k)[:-1]
+    return [arr.tolist() for arr in np.split(flat, idx)]
+
+
+def _make_rollout(n: int, n_events: int, seed: int) -> pl.DataFrame:
+    rng = np.random.default_rng(seed)
+    event_id = rng.integers(0, n_events, size=n)
+    is_secondary = rng.random(n) < 0.15  # generation>0, step_no==0 birth rows
+    is_synthetic = rng.random(n) < 0.05  # bookkeeping termination rows
+
+    pre_E = rng.lognormal(mean=3.0, sigma=1.5, size=n)
+    edep = rng.uniform(0, 1, size=n) * pre_E * 0.3
+    post_E = np.clip(pre_E - edep, 0.0, None)
+    pre_dir = _unit_vectors(n, rng)
+    post_dir = _unit_vectors(n, rng)
+    pos = rng.uniform(-50, 300, size=(n, 3))
+    step_length = rng.uniform(0.1, 10.0, size=n)
+    post_pos = pos + pre_dir * step_length[:, None]
+
+    reasons = np.where(
+        is_synthetic,
+        rng.choice(["escaped", "energy_cutoff", "max_steps", "unknown_pdg"], size=n),
+        "natural_end",
+    )
+
+    return pl.DataFrame(
+        {
+            "event_id": event_id,
+            "track_id": rng.integers(0, 5, size=n),
+            "parent_id": np.where(is_secondary, 0, -1),
+            "generation": is_secondary.astype(np.int64),
+            "step_no": np.where(is_secondary, 0, rng.integers(0, 20, size=n)),
+            "pdg": rng.choice(_PDGS, size=n),
+            "pre_x": pos[:, 0],
+            "pre_y": pos[:, 1],
+            "pre_z": pos[:, 2],
+            "pre_E": pre_E,
+            "pre_dx": pre_dir[:, 0],
+            "pre_dy": pre_dir[:, 1],
+            "pre_dz": pre_dir[:, 2],
+            "post_x": post_pos[:, 0],
+            "post_y": post_pos[:, 1],
+            "post_z": post_pos[:, 2],
+            "post_E": post_E,
+            "post_dx": post_dir[:, 0],
+            "post_dy": post_dir[:, 1],
+            "post_dz": post_dir[:, 2],
+            "edep": np.where(
+                is_synthetic, np.where(reasons == "escaped", 0.0, pre_E), edep
+            ),
+            "step_length": np.where(is_synthetic, 0.0, step_length),
+            "material": rng.choice(_MATERIALS, size=n),
+            "layer_id": rng.integers(0, 30, size=n),
+            "n_sec_pred": rng.integers(0, 4, size=n),
+            "termination_reason": reasons,
+        }
+    )
+
+
+def _make_reference(n: int, n_events: int, seed: int) -> pl.DataFrame:
+    rng = np.random.default_rng(seed + 1)
+    event_id = rng.integers(0, n_events, size=n)
+    pre_E = rng.lognormal(mean=3.0, sigma=1.5, size=n)
+    edep = rng.uniform(0, 1, size=n) * pre_E * 0.3
+    post_E = np.clip(pre_E - edep, 0.0, None)
+    pre_dir = _unit_vectors(n, rng)
+    post_dir = _unit_vectors(n, rng)
+    pos = rng.uniform(-50, 300, size=(n, 3))
+    step_length = rng.uniform(0.1, 10.0, size=n)
+    post_pos = pos + pre_dir * step_length[:, None]
+
+    k = rng.poisson(0.3, size=n).clip(max=5).astype(np.int64)
+    sec_pdg = _ragged_lists(k, rng, 0, 1)  # placeholder, overwritten below
+    sec_E = _ragged_lists(k, rng, 0.1, 50.0)
+    sec_dx = _ragged_lists(k, rng, -1.0, 1.0)
+    sec_dy = _ragged_lists(k, rng, -1.0, 1.0)
+    sec_dz = _ragged_lists(k, rng, -1.0, 1.0)
+    total = int(k.sum())
+    flat_pdg = rng.choice(_PDGS, size=total).tolist()
+    idx = np.cumsum(k)[:-1]
+    sec_pdg = [list(x) for x in np.split(np.array(flat_pdg), idx)]
+
+    return pl.DataFrame(
+        {
+            "event_id": event_id,
+            "track_id": rng.integers(0, 5, size=n),
+            "step_no": rng.integers(0, 20, size=n),
+            "pdg": rng.choice(_PDGS, size=n),
+            "pre_x": pos[:, 0],
+            "pre_y": pos[:, 1],
+            "pre_z": pos[:, 2],
+            "pre_E": pre_E,
+            "pre_dx": pre_dir[:, 0],
+            "pre_dy": pre_dir[:, 1],
+            "pre_dz": pre_dir[:, 2],
+            "post_x": post_pos[:, 0],
+            "post_y": post_pos[:, 1],
+            "post_z": post_pos[:, 2],
+            "post_E": post_E,
+            "post_dx": post_dir[:, 0],
+            "post_dy": post_dir[:, 1],
+            "post_dz": post_dir[:, 2],
+            "edep": edep,
+            "step_length": step_length,
+            "material": rng.choice(_MATERIALS, size=n),
+            "layer_id": rng.integers(0, 30, size=n),
+            "process": rng.choice(["compt", "phot", "eBrem", "eIoni", "conv"], size=n),
+            "sec_E_list": sec_E,
+            "sec_pdg_list": sec_pdg,
+            "sec_dx_list": sec_dx,
+            "sec_dy_list": sec_dy,
+            "sec_dz_list": sec_dz,
+        }
+    )
+
+
+def _time(
+    spec_id: str, rollout: Path, reference: Path, shared: Path, out: Path
+) -> float:
+    t0 = time.perf_counter()
+    compute_reduced(
+        spec_id,
+        rollout,
+        reference,
+        shared,
+        out,
+        checkpoint=None,
+        chunk_index=0,
+        n_chunks=1,
+    )
+    return time.perf_counter() - t0
+
+
+def main() -> None:
+    ids = [i for i in catalog_ids() if get_spec(i).chunkable]
+    timings: dict[str, list[tuple[int, float]]] = {i: [] for i in ids}
+
+    with TemporaryDirectory(prefix="giant-profile-") as tmp:
+        tmp_path = Path(tmp)
+        for n_side in SIDE_ROW_COUNTS:
+            n_events = max(n_side // 20, 10)
+            rollout = tmp_path / f"rollout_{n_side}.parquet"
+            reference = tmp_path / f"reference_{n_side}.parquet"
+            _make_rollout(n_side, n_events, seed=0).write_parquet(rollout)
+            _make_reference(n_side, n_events, seed=0).write_parquet(reference)
+
+            shared = tmp_path / f"shared_{n_side}.json"
+            ctx = build_context(
+                rollout,
+                reference,
+                n_energy_bins=4,
+                n_marginal_bins=50,
+                top_k_pdg=6,
+                sample_rows=min(n_side, 200_000),
+            )
+            ctx.save(shared)
+
+            # warm the OS page cache so the timed pass measures compute, not
+            # the one-time cold read of a freshly-written file.
+            pl.scan_parquet(rollout).select(pl.len()).collect()
+            pl.scan_parquet(reference).select(pl.len()).collect()
+
+            n_rows = 2 * n_side  # rollout + reference rows in this "chunk"
+            for spec_id in ids:
+                out = tmp_path / f"{spec_id}_{n_side}.json"
+                dt = _time(spec_id, rollout, reference, shared, out)
+                timings[spec_id].append((n_rows, dt))
+                print(f"{spec_id:35s} n_rows={n_rows:>9d} time={dt:7.3f}s")
+
+            rollout.unlink()
+            reference.unlink()
+            shared.unlink()
+
+    print("\n# spec_id -> (intercept_s, seconds_per_row), fit by least squares")
+    print("_COST_MODEL: dict[str, tuple[float, float]] = {")
+    for spec_id in ids:
+        xs = np.array([n for n, _ in timings[spec_id]], dtype=float)
+        ys = np.array([t for _, t in timings[spec_id]], dtype=float)
+        slope, intercept = np.polyfit(xs, ys, 1)
+        intercept = max(intercept, 0.0)
+        slope = max(slope, 0.0)
+        print(f'    "{spec_id}": ({intercept:.6f}, {slope:.9f}),')
+    print("}")
+
+    if _ROUTER_IDS:
+        print(
+            "\n# router_* specs excluded: need a live MoE checkpoint to do real\n"
+            "# work, none available on this machine — see _ROUTER_FIXED_S instead."
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test-cuda.py b/test-cuda.py
deleted file mode 100644
index 2b95e64..0000000
--- a/test-cuda.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch
-import torch.version
-
-print(f"PyTorch version: {torch.__version__}")
-print(f"CUDA available:  {torch.cuda.is_available()}")
-
-if torch.cuda.is_available():
-    print(f"CUDA version:    {torch.version.cuda}")
-    print(f"Device count:    {torch.cuda.device_count()}")
-    print(f"Device name:     {torch.cuda.get_device_name(0)}")
-
-    # Run a small tensor op on the GPU
-    a = torch.randn(1000, 1000, device="cuda")
-    b = torch.randn(1000, 1000, device="cuda")
-    c = a @ b
-    torch.cuda.synchronize()
-    print(f"Matrix multiply:  OK  (result shape {c.shape}, device {c.device})")
-else:
-    print("No CUDA device found — check driver/CUDA installation.")
diff --git a/tests/test_analysis.py b/tests/test_analysis.py
deleted file mode 100644
index 5ad88b2..0000000
--- a/tests/test_analysis.py
+++ /dev/null
@@ -1,1175 +0,0 @@
-import matplotlib
-
-matplotlib.use("Agg")  # no display needed for plot smoke tests
-
-import numpy as np
-import pandas as pd
-import polars as pl
-import pyarrow as pa
-import pyarrow.parquet as pq
-import pytest
-
-from giant.analysis import (
-    RAW_TARGET_NAMES,
-    RolloutVsTruth,
-    compute_event_observables_pl,
-    compute_rollout_vs_truth_observables_pl,
-    constraint_report_pl,
-    correlation_matrices_pl,
-    marginal_table_pl,
-    pdg_contribution_table_pl,
-    plot_constraint_violations,
-    plot_correlation_matrices,
-    plot_direction_alignment,
-    plot_kl_bars_pl,
-    plot_longitudinal_profile,
-    plot_marginals,
-    plot_mean_energy_per_step,
-    plot_mean_length_per_step,
-    plot_pairwise,
-    plot_pdg_energy_share,
-    plot_pdg_length_share,
-    plot_shower_max_depth,
-    plot_total_energy,
-    plot_total_length,
-    plot_transverse_profile,
-)
-from giant.constants import (
-    LOCAL_TARGET_NAMES,
-    PREDICT_COORD_METADATA_KEY,
-    PREDICT_SCHEMA_VERSION,
-    PREDICT_SCHEMA_VERSION_KEY,
-    ROLLOUT_COORD_VALUE,
-    TERM_ENERGY_CUTOFF,
-    TERM_NATURAL_END,
-)
-from giant.data.transforms import (
-    energy_simplex_decode,
-    inv_log_transform,
-    local_frame_rotation,
-    log_transform,
-    reconstruct_post_pos,
-    travel_direction,
-)
-
-# ---------------------------------------------------------------------------
-# Fixtures: predict `--coord local` parquet writers
-# ---------------------------------------------------------------------------
-
-
-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 _write_predicted_local_parquet(path, n=50, metadata=None, rng=None):
-    """Mimic `giant predict --coord local`'s output schema for the loader tests.
-
-    Column 0 is a log-scaled step_length; columns 1–2 are the deposit/secondary
-    ALR energy logits (unconstrained reals, decoded against pre_E); columns 3–8
-    are direction components.
-    """
-    rng = rng or np.random.default_rng(0)
-    true_log_local = rng.standard_normal((n, 9)).astype(np.float32)
-    true_log_local[:, 0] = log_transform(rng.uniform(0.1, 5.0, n).astype(np.float32))
-    pred_log_local = true_log_local + rng.normal(0, 0.01, (n, 9)).astype(np.float32)
-    pre_E = rng.uniform(1.0, 100.0, n).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": pre_E,
-            "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, pre_E
-
-
-def _predicted_local_path(tmp_path, n=200, seed=0):
-    path = tmp_path / "predicted_local.parquet"
-    _write_predicted_local_parquet(
-        path,
-        n=n,
-        rng=np.random.default_rng(seed),
-        metadata={
-            PREDICT_COORD_METADATA_KEY: "local",
-            PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
-        },
-    )
-    return path
-
-
-# ---------------------------------------------------------------------------
-# Inline numpy oracle: the ground truth the streaming functions are checked
-# against. This is the raw-space decoding the old `SampleCollection` path did,
-# recomputed directly from the small fixture rather than in the module.
-# ---------------------------------------------------------------------------
-
-
-def _raw_from_parquet(path):
-    """(real_raw, gen_raw, pdg, material, pre_E) in physical units, via numpy."""
-    df = pl.read_parquet(path)
-    pre_E = df["pre_E"].to_numpy().astype(np.float32)
-
-    def decode(prefix):
-        log_local = np.column_stack(
-            [df[f"{prefix}_{name}"].to_numpy() for name in LOCAL_TARGET_NAMES]
-        ).astype(np.float32)
-        raw = log_local.copy()
-        raw[:, 0] = inv_log_transform(log_local[:, 0])
-        edep, _e_sec, _post_E, delta_e = energy_simplex_decode(log_local[:, 1:3], pre_E)
-        raw[:, 1] = delta_e
-        raw[:, 2] = edep
-        return raw
-
-    return (
-        decode("true"),
-        decode("pred"),
-        df["pdg"].to_numpy(),
-        df["material"].to_numpy(),
-        pre_E,
-    )
-
-
-def _histogram_kl(p_samples, q_samples, bins=50, eps=1e-8):
-    """KL(P || Q) between two 1D samples via a shared histogram (numpy oracle)."""
-    lo = min(p_samples.min(), q_samples.min())
-    hi = max(p_samples.max(), q_samples.max())
-    if hi <= lo:
-        return 0.0
-    edges = np.linspace(lo, hi, bins + 1)
-    p_hist, _ = np.histogram(p_samples, bins=edges)
-    q_hist, _ = np.histogram(q_samples, bins=edges)
-    p = p_hist.astype(np.float64) + eps
-    q = q_hist.astype(np.float64) + eps
-    p /= p.sum()
-    q /= q.sum()
-    return float(np.sum(p * np.log(p / q)))
-
-
-def _group_masks(pdg, material, pre_E, group_by, n_energy_bins=4):
-    """Replicate the module's `_group` labelling so oracle labels line up."""
-    n = len(pdg)
-    if group_by is None:
-        return [("all", np.ones(n, dtype=bool))]
-    if group_by == "pdg":
-        return [(f"pdg={int(v)}", pdg == v) for v in np.unique(pdg)]
-    if group_by == "material":
-        return [(f"material={v}", material == v) for v in np.unique(material)]
-    if group_by == "energy":
-        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(group_by)
-
-
-def _numpy_marginal_table(real, gen, pdg, material, pre_E, group_by, bins=50):
-    rows = []
-    for label, mask in _group_masks(pdg, material, pre_E, group_by):
-        if mask.sum() < 2:
-            continue
-        r, g = real[mask], gen[mask]
-        for j, name in enumerate(RAW_TARGET_NAMES):
-            rows.append(
-                {
-                    "group": label,
-                    "dim": name,
-                    "n": int(mask.sum()),
-                    "real_mean": r[:, j].mean(),
-                    "gen_mean": g[:, j].mean(),
-                    "real_std": r[:, j].std(),
-                    "gen_std": g[:, j].std(),
-                    "kl_real_gen": _histogram_kl(r[:, j], g[:, j], bins=bins),
-                }
-            )
-    return pd.DataFrame(rows).sort_values(["group", "dim"]).reset_index(drop=True)
-
-
-def _numpy_constraint_report(gen, norm_tol=0.05):
-    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[:3]):
-        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)
-
-
-# ---------------------------------------------------------------------------
-# Tier 1: stratified marginals
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.parametrize("group_by", [None, "pdg", "material", "energy"])
-def test_marginal_table_pl_matches_numpy_oracle(tmp_path, group_by):
-    path = _predicted_local_path(tmp_path)
-    real, gen, pdg, material, pre_E = _raw_from_parquet(path)
-
-    expected = _numpy_marginal_table(real, gen, pdg, material, pre_E, group_by)
-    actual = (
-        marginal_table_pl(path, group_by=group_by).sort(["group", "dim"]).to_pandas()
-    )
-
-    assert list(expected["group"]) == list(actual["group"])
-    assert list(expected["n"]) == list(actual["n"])
-    for col in ["real_mean", "gen_mean", "real_std", "gen_std"]:
-        np.testing.assert_allclose(
-            expected[col].to_numpy(), actual[col].to_numpy(), atol=1e-4, rtol=1e-4
-        )
-    # KL uses np.histogram (oracle) vs polars binning (lazy path); the two bin the
-    # boundary (min/max) sample differently, so allow a small absolute discrepancy
-    # rather than requiring bit-identical estimates.
-    np.testing.assert_allclose(
-        expected["kl_real_gen"].to_numpy(), actual["kl_real_gen"].to_numpy(), atol=2e-2
-    )
-
-
-def test_marginal_table_pl_aggregate_has_all_dims(tmp_path):
-    table = marginal_table_pl(_predicted_local_path(tmp_path))
-    assert set(table["dim"].to_list()) == set(RAW_TARGET_NAMES)
-    assert (table["group"] == "all").all()
-
-
-def test_marginal_table_pl_accepts_lazyframe(tmp_path):
-    path = _predicted_local_path(tmp_path)
-    from_path = marginal_table_pl(path).sort(["group", "dim"])
-    from_lf = marginal_table_pl(pl.scan_parquet(path)).sort(["group", "dim"])
-    np.testing.assert_allclose(
-        from_lf["kl_real_gen"].to_numpy(), from_path["kl_real_gen"].to_numpy()
-    )
-
-
-def test_marginal_table_pl_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"):
-        marginal_table_pl(path)
-
-
-def test_marginal_table_pl_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"):
-        marginal_table_pl(path)
-
-
-def test_marginal_table_pl_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"):
-        marginal_table_pl(path)
-
-
-@pytest.mark.parametrize("group_by", [None, "pdg", "material", "energy"])
-def test_plot_kl_bars_pl_runs_without_error(tmp_path, group_by):
-    fig = plot_kl_bars_pl(_predicted_local_path(tmp_path), group_by=group_by)
-    assert fig is not None
-
-
-def test_plot_marginals_runs_without_error(tmp_path):
-    fig = plot_marginals(_predicted_local_path(tmp_path))
-    assert len(fig.axes) == len(RAW_TARGET_NAMES)  # single "all" row
-
-
-def test_plot_marginals_grouped_runs_without_error(tmp_path):
-    fig = plot_marginals(_predicted_local_path(tmp_path), group_by="material")
-    assert fig is not None
-
-
-def test_plot_marginals_caps_groups(tmp_path):
-    path = _predicted_local_path(tmp_path, n=400)
-    df = pl.read_parquet(path).with_columns(
-        pl.Series("pdg", np.arange(400) % 8, dtype=pl.Int64)
-    )
-    fig = plot_marginals(df.lazy(), group_by="pdg", max_groups=3)
-    n_rows = len(fig.axes) // len(RAW_TARGET_NAMES)
-    assert n_rows <= 3
-
-
-# ---------------------------------------------------------------------------
-# Tier 2: joint structure
-# ---------------------------------------------------------------------------
-
-
-def test_correlation_matrices_pl_matches_numpy(tmp_path):
-    path = _predicted_local_path(tmp_path)
-    real, gen, *_ = _raw_from_parquet(path)
-
-    real_corr, gen_corr = correlation_matrices_pl(path)
-
-    np.testing.assert_allclose(
-        real_corr, np.corrcoef(real, rowvar=False), atol=1e-4, rtol=1e-4
-    )
-    np.testing.assert_allclose(
-        gen_corr, np.corrcoef(gen, rowvar=False), atol=1e-4, rtol=1e-4
-    )
-    for corr in (real_corr, gen_corr):
-        np.testing.assert_allclose(np.diag(corr), 1.0, atol=1e-6)
-        np.testing.assert_allclose(corr, corr.T, atol=1e-6)
-
-
-def test_plot_correlation_matrices_runs_without_error(tmp_path):
-    fig = plot_correlation_matrices(_predicted_local_path(tmp_path))
-    assert len(fig.axes) >= 3  # real, generated, difference (+ colorbars)
-
-
-def test_plot_pairwise_runs_without_error(tmp_path):
-    fig = plot_pairwise(_predicted_local_path(tmp_path), n_sample=100)
-    assert len(fig.axes) == 6  # 2 rows (real/gen) × 3 default pairs
-
-
-def test_plot_direction_alignment_runs_without_error(tmp_path):
-    fig = plot_direction_alignment(_predicted_local_path(tmp_path))
-    assert fig is not None
-
-
-# ---------------------------------------------------------------------------
-# Tier 3: physical constraints
-# ---------------------------------------------------------------------------
-
-
-def test_constraint_report_pl_matches_numpy_oracle(tmp_path):
-    path = _predicted_local_path(tmp_path)
-    _real, gen, *_ = _raw_from_parquet(path)
-
-    expected = _numpy_constraint_report(gen)
-    actual = constraint_report_pl(path).to_pandas()
-
-    assert list(expected["check"]) == list(actual["check"])
-    np.testing.assert_allclose(
-        expected["violation_rate"].to_numpy(),
-        actual["violation_rate"].to_numpy(),
-        atol=1e-6,
-    )
-    np.testing.assert_allclose(
-        expected["mean_abs_error"].to_numpy(),
-        actual["mean_abs_error"].to_numpy(),
-        atol=1e-4,
-    )
-
-
-def test_constraint_report_pl_flags_bad_direction_norms(tmp_path):
-    path = _predicted_local_path(tmp_path)
-    # Force the generated post_dir off the unit sphere for every row: the fixed
-    # (1, 1, 1) vector has norm √3 ≈ 1.73, well outside the tolerance.
-    df = pl.read_parquet(path).with_columns(
-        pl.lit(1.0).alias("pred_post_dx"),
-        pl.lit(1.0).alias("pred_post_dy"),
-        pl.lit(1.0).alias("pred_post_dz"),
-    )
-    report = constraint_report_pl(df.lazy()).to_pandas()
-    rate = dict(zip(report["check"], report["violation_rate"]))
-    assert rate["post_dir unit norm"] == 1.0
-
-
-def test_plot_constraint_violations_runs_without_error(tmp_path):
-    fig = plot_constraint_violations(_predicted_local_path(tmp_path))
-    assert len(fig.axes) == 2 + 3  # 2 direction norms + 3 scalar dims
-
-
-# ---------------------------------------------------------------------------
-# Tier 4: event-level (shower) observables
-# ---------------------------------------------------------------------------
-
-
-def _make_event_level_arrays(rng):
-    """3 events (3/2/4 steps), each with an unambiguous highest-pre_E row.
-
-    The forced max-pre_E rows (indices 1, 3, 7) fix a known shower axis/entry
-    point per event, so the expected event_table can be re-derived independently
-    in the test without depending on compute_event_observables_pl.
-    """
-    event_id = np.array([0, 0, 0, 1, 1, 2, 2, 2, 2], dtype=np.int64)
-    n = len(event_id)
-    pre_pos = rng.uniform(-5.0, 5.0, (n, 3)).astype(np.float32)
-    pre_dir = _unit_vectors(rng, n)
-    pre_E = rng.uniform(1.0, 50.0, n).astype(np.float32)
-    pre_E[1] = 100.0  # event 0's entry step
-    pre_E[3] = 100.0  # event 1's entry step
-    pre_E[7] = 100.0  # event 2's entry step
-
-    def _local_block():
-        block = rng.standard_normal((n, 9)).astype(np.float32)
-        block[:, 0] = log_transform(rng.uniform(0.1, 5.0, n).astype(np.float32))
-        # cols 1–2 stay as random ALR energy logits (decoded against pre_E)
-        block[:, 3:6] = _unit_vectors(rng, n)
-        block[:, 6:9] = _unit_vectors(rng, n)
-        return block
-
-    true_log_local = _local_block()
-    pred_log_local = _local_block()
-    return event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local
-
-
-def _write_event_level_parquet(path, rng=None):
-    rng = rng or np.random.default_rng(7)
-    event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local = (
-        _make_event_level_arrays(rng)
-    )
-    n = len(event_id)
-    pdg = rng.choice([11, -11, 22], n)
-    table = pa.table(
-        {
-            "event_id": event_id,
-            "pdg": pdg,
-            "pre_x": pre_pos[:, 0],
-            "pre_y": pre_pos[:, 1],
-            "pre_z": pre_pos[:, 2],
-            "pre_E": pre_E,
-            "pre_dx": pre_dir[:, 0],
-            "pre_dy": pre_dir[:, 1],
-            "pre_dz": pre_dir[:, 2],
-            "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)
-            },
-        }
-    )
-    table = table.replace_schema_metadata(
-        {
-            PREDICT_COORD_METADATA_KEY: "local",
-            PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
-        }
-    )
-    pq.write_table(table, path)
-    return event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local, pdg
-
-
-def _expected_event_table(
-    event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local
-):
-    """Independent re-derivation of total/centroid/RMS per event, for comparison."""
-    expected = {}
-    for e in sorted(np.unique(event_id).tolist()):
-        mask = event_id == e
-        entry_idx = np.where(mask)[0][np.argmax(pre_E[mask])]
-        entry_pos = pre_pos[entry_idx]
-        axis_dir = pre_dir[entry_idx]
-
-        def agg(log_local, mask=mask, entry_pos=entry_pos, axis_dir=axis_dir):
-            step_length = inv_log_transform(log_local[mask, 0])
-            edep, _e_sec, _post_E, _delta_e = energy_simplex_decode(
-                log_local[mask, 1:3], pre_E[mask]
-            )
-            travel_dir_local = log_local[mask, 6:9]
-            post_pos = reconstruct_post_pos(
-                pre_pos[mask], pre_dir[mask], step_length, travel_dir_local
-            )
-            disp = post_pos - entry_pos
-            depth = disp @ axis_dir
-            transverse = np.linalg.norm(disp - depth[:, None] * axis_dir, axis=1)
-            total_edep = float(edep.sum())
-            total_length = float(step_length.sum())
-            centroid = float((edep * depth).sum() / total_edep)
-            rms = float(np.sqrt((edep * transverse**2).sum() / total_edep))
-            return total_edep, total_length, centroid, rms
-
-        real_total_edep, real_total_length, real_centroid, real_rms = agg(
-            true_log_local
-        )
-        gen_total_edep, gen_total_length, gen_centroid, gen_rms = agg(pred_log_local)
-        expected[e] = (
-            real_total_edep,
-            gen_total_edep,
-            real_total_length,
-            gen_total_length,
-            real_centroid,
-            gen_centroid,
-            real_rms,
-            gen_rms,
-        )
-    return expected
-
-
-def test_compute_event_observables_pl_matches_manual_reconstruction(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local, _pdg = (
-        _write_event_level_parquet(path)
-    )
-    expected = _expected_event_table(
-        event_id, pre_pos, pre_dir, pre_E, true_log_local, pred_log_local
-    )
-
-    obs = compute_event_observables_pl(path, depth_bins=5, transverse_bins=5)
-    real_table = obs.real_table.sort("event_id")
-    gen_table = obs.gen_table.sort("event_id")
-
-    for i, eid in enumerate(real_table["event_id"].to_list()):
-        (
-            real_total_edep,
-            gen_total_edep,
-            real_total_length,
-            gen_total_length,
-            real_centroid,
-            gen_centroid,
-            real_rms,
-            gen_rms,
-        ) = expected[eid]
-        np.testing.assert_allclose(
-            real_table["total_edep"][i], real_total_edep, rtol=1e-4
-        )
-        np.testing.assert_allclose(
-            gen_table["total_edep"][i], gen_total_edep, rtol=1e-4
-        )
-        np.testing.assert_allclose(
-            real_table["total_length"][i], real_total_length, rtol=1e-4
-        )
-        np.testing.assert_allclose(
-            gen_table["total_length"][i], gen_total_length, rtol=1e-4
-        )
-        np.testing.assert_allclose(
-            real_table["centroid_depth"][i], real_centroid, rtol=1e-3, atol=1e-4
-        )
-        np.testing.assert_allclose(
-            gen_table["centroid_depth"][i], gen_centroid, rtol=1e-3, atol=1e-4
-        )
-        np.testing.assert_allclose(
-            real_table["transverse_rms"][i], real_rms, rtol=1e-3, atol=1e-4
-        )
-        np.testing.assert_allclose(
-            gen_table["transverse_rms"][i], gen_rms, rtol=1e-3, atol=1e-4
-        )
-
-
-def test_compute_event_observables_pl_profile_shapes(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    _write_event_level_parquet(path)
-    obs = compute_event_observables_pl(path, depth_bins=7, transverse_bins=4)
-
-    assert obs.depth_edges.shape == (8,)
-    assert obs.transverse_edges.shape == (5,)
-    assert obs.real_depth_profile.shape == (7,)
-    assert obs.gen_depth_profile.shape == (7,)
-    assert obs.real_transverse_profile.shape == (4,)
-    assert obs.gen_transverse_profile.shape == (4,)
-    assert len(obs.real_table) == 3
-    assert len(obs.gen_table) == 3
-
-
-def test_compute_event_observables_pl_accepts_lazyframe(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    _write_event_level_parquet(path)
-
-    obs_from_path = compute_event_observables_pl(path, depth_bins=5, transverse_bins=5)
-    obs_from_lf = compute_event_observables_pl(
-        pl.scan_parquet(path), depth_bins=5, transverse_bins=5
-    )
-
-    np.testing.assert_allclose(
-        obs_from_lf.real_table.sort("event_id")["total_edep"].to_numpy(),
-        obs_from_path.real_table.sort("event_id")["total_edep"].to_numpy(),
-    )
-
-
-def test_compute_event_observables_pl_approx_median(tmp_path):
-    """The streaming log-bin median approximates the true per-event median."""
-    rng = np.random.default_rng(11)
-    n = 4000  # one event, many steps → a well-defined median
-    true_log = rng.standard_normal((n, 9)).astype(np.float32)
-    true_log[:, 0] = log_transform(rng.uniform(0.1, 5.0, n).astype(np.float32))
-    for s in (3, 6):
-        v = rng.standard_normal((n, 3)).astype(np.float32)
-        true_log[:, s : s + 3] = v / np.linalg.norm(v, axis=1, keepdims=True)
-    pre_E = rng.uniform(1.0, 100.0, n).astype(np.float32)
-    pre = rng.standard_normal((n, 3)).astype(np.float32)
-    pdir = _unit_vectors(rng, n)
-    pre_E[0] = 1000.0  # entry step
-
-    path = tmp_path / "one_event.parquet"
-    table = pa.table(
-        {
-            "event_id": np.zeros(n, dtype=np.int64),
-            "pdg": rng.choice([11, 22], n),
-            "pre_x": pre[:, 0],
-            "pre_y": pre[:, 1],
-            "pre_z": pre[:, 2],
-            "pre_E": pre_E,
-            "pre_dx": pdir[:, 0],
-            "pre_dy": pdir[:, 1],
-            "pre_dz": pdir[:, 2],
-            "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_{nm}": true_log[:, j] for j, nm in enumerate(LOCAL_TARGET_NAMES)},
-            **{f"true_{nm}": true_log[:, j] for j, nm in enumerate(LOCAL_TARGET_NAMES)},
-        }
-    ).replace_schema_metadata(
-        {
-            PREDICT_COORD_METADATA_KEY: "local",
-            PREDICT_SCHEMA_VERSION_KEY: PREDICT_SCHEMA_VERSION,
-        }
-    )
-    pq.write_table(table, path)
-
-    true_edep = energy_simplex_decode(true_log[:, 1:3], pre_E)[0]
-    true_length = inv_log_transform(true_log[:, 0])
-    exact_edep_median = np.median(true_edep)
-    exact_length_median = np.median(true_length)
-
-    obs = compute_event_observables_pl(path)
-    approx_edep = obs.real_table["median_edep"][0]
-    approx_length = obs.real_table["median_length"][0]
-
-    # log-bin interpolation: expect within a few percent of the true median.
-    np.testing.assert_allclose(approx_edep, exact_edep_median, rtol=0.05)
-    np.testing.assert_allclose(approx_length, exact_length_median, rtol=0.05)
-
-
-def test_event_level_plots_run_without_error(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    _write_event_level_parquet(path)
-    obs = compute_event_observables_pl(path, depth_bins=5, transverse_bins=5)
-
-    assert plot_total_energy(obs) is not None
-    assert plot_total_length(obs) is not None
-    assert plot_longitudinal_profile(obs) is not None
-    assert plot_transverse_profile(obs) is not None
-    assert plot_shower_max_depth(obs) is not None
-
-
-# ---------------------------------------------------------------------------
-# Particle-species (pdg) contribution shares
-# ---------------------------------------------------------------------------
-
-
-def test_pdg_contribution_table_pl_matches_manual_sums(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    _, _, _, pre_E, true_log_local, pred_log_local, pdg = _write_event_level_parquet(
-        path
-    )
-
-    real_edep = energy_simplex_decode(true_log_local[:, 1:3], pre_E)[0]
-    gen_edep = energy_simplex_decode(pred_log_local[:, 1:3], pre_E)[0]
-    real_length = inv_log_transform(true_log_local[:, 0])
-    gen_length = inv_log_transform(pred_log_local[:, 0])
-
-    expected = {}
-    for p in np.unique(pdg):
-        mask = pdg == p
-        expected[int(p)] = (
-            float(real_edep[mask].sum()),
-            float(gen_edep[mask].sum()),
-            float(real_length[mask].sum()),
-            float(gen_length[mask].sum()),
-        )
-
-    table = pdg_contribution_table_pl(path).sort("pdg")
-    for i, p in enumerate(table["pdg"].to_list()):
-        real_e, gen_e, real_l, gen_l = expected[int(p)]
-        np.testing.assert_allclose(table["real_total_edep"][i], real_e, rtol=1e-4)
-        np.testing.assert_allclose(table["gen_total_edep"][i], gen_e, rtol=1e-4)
-        np.testing.assert_allclose(table["real_total_length"][i], real_l, rtol=1e-4)
-        np.testing.assert_allclose(table["gen_total_length"][i], gen_l, rtol=1e-4)
-
-
-def test_pdg_contribution_table_pl_accepts_lazyframe(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    _write_event_level_parquet(path)
-
-    from_path = pdg_contribution_table_pl(path).sort("pdg")
-    from_lf = pdg_contribution_table_pl(pl.scan_parquet(path)).sort("pdg")
-
-    np.testing.assert_allclose(
-        from_lf["real_total_edep"].to_numpy(), from_path["real_total_edep"].to_numpy()
-    )
-
-
-def test_pdg_pie_plots_run_without_error(tmp_path):
-    path = tmp_path / "event_level.parquet"
-    _write_event_level_parquet(path)
-    table = pdg_contribution_table_pl(path)
-
-    assert plot_pdg_energy_share(table) is not None
-    assert plot_pdg_length_share(table) is not None
-
-
-def test_plot_pdg_energy_share_caps_slices():
-    table = pl.DataFrame(
-        {
-            "pdg": list(range(10)),
-            "real_total_edep": [float(10 - i) for i in range(10)],
-            "gen_total_edep": [float(10 - i) for i in range(10)],
-            "real_total_length": [float(10 - i) for i in range(10)],
-            "gen_total_length": [float(10 - i) for i in range(10)],
-        }
-    )
-    fig = plot_pdg_energy_share(table, max_slices=4)
-    for ax in fig.axes:
-        assert len(ax.patches) == 4
-
-
-# ---------------------------------------------------------------------------
-# Rollout vs. held-out truth: unpaired `RolloutVsTruth` source (Tier 1-3) and
-# `compute_rollout_vs_truth_observables_pl` (Tier 4)
-# ---------------------------------------------------------------------------
-
-_WORLD_FRAME_COLS = [
-    "event_id",
-    "pdg",
-    "material",
-    "layer_id",
-    "pre_x",
-    "pre_y",
-    "pre_z",
-    "pre_E",
-    "pre_dx",
-    "pre_dy",
-    "pre_dz",
-    "post_x",
-    "post_y",
-    "post_z",
-    "post_E",
-    "post_dx",
-    "post_dy",
-    "post_dz",
-    "edep",
-    "step_length",
-]
-
-
-def _write_world_frame_parquet(
-    path, n=200, rng=None, rollout=True, termination_reasons=None
-):
-    """A `giant rollout` output or truth-schema steps file, in raw world-frame units.
-
-    `rollout=True` adds `track_id`/`termination_reason`/`n_sec_pred` (rollout-
-    only columns `RolloutVsTruth` reads) and tags `ROLLOUT_COORD_VALUE`
-    metadata; `rollout=False` is a plain truth-schema file (no metadata tag,
-    matching a real held-out/val parquet). `termination_reasons`, if given,
-    overrides the (rollout-only) `termination_reason` column — pass a fixed
-    array to test synthetic-row dropping deterministically.
-    """
-    rng = rng or np.random.default_rng(0)
-    event_id = rng.integers(0, 5, n)
-    pre_pos = rng.standard_normal((n, 3)).astype(np.float32) * 5
-    pre_dir = _unit_vectors(rng, n)
-    pre_E = rng.uniform(1.0, 100.0, n).astype(np.float32)
-    post_dir = _unit_vectors(rng, n)
-    step_length = rng.uniform(0.1, 5.0, n).astype(np.float32)
-    travel_dir_world = _unit_vectors(rng, n)
-    post_pos = pre_pos + step_length[:, None] * travel_dir_world
-    post_E = (pre_E * rng.uniform(0.3, 0.99, n)).astype(np.float32)
-    edep = rng.uniform(0, 1, n).astype(np.float32)
-
-    columns = {
-        "event_id": event_id,
-        "pdg": rng.choice([11, -11, 22], n),
-        "material": rng.choice(["W", "Pb"], n),
-        "layer_id": rng.integers(0, 10, n).astype(np.int32),
-        "pre_x": pre_pos[:, 0],
-        "pre_y": pre_pos[:, 1],
-        "pre_z": pre_pos[:, 2],
-        "pre_E": pre_E,
-        "pre_dx": pre_dir[:, 0],
-        "pre_dy": pre_dir[:, 1],
-        "pre_dz": pre_dir[:, 2],
-        "post_x": post_pos[:, 0],
-        "post_y": post_pos[:, 1],
-        "post_z": post_pos[:, 2],
-        "post_E": post_E,
-        "post_dx": post_dir[:, 0],
-        "post_dy": post_dir[:, 1],
-        "post_dz": post_dir[:, 2],
-        "edep": edep,
-        "step_length": step_length,
-    }
-    metadata = None
-    if rollout:
-        columns["track_id"] = rng.integers(0, 3, n).astype(np.int64)
-        columns["termination_reason"] = (
-            termination_reasons
-            if termination_reasons is not None
-            else rng.choice(["", TERM_NATURAL_END], n)
-        )
-        columns["n_sec_pred"] = rng.integers(0, 3, n).astype(np.int32)
-        metadata = {PREDICT_COORD_METADATA_KEY: ROLLOUT_COORD_VALUE}
-
-    table = pa.table(columns)
-    if metadata is not None:
-        table = table.replace_schema_metadata(metadata)
-    pq.write_table(table, path)
-    return columns
-
-
-def _raw_from_world_frame(columns):
-    """(N, 9) `RAW_TARGET_NAMES` oracle for a world-frame steps dict, via numpy.
-
-    Independent re-derivation of `_world_frame_local_exprs`'s forward Rodrigues
-    rotation, using `giant.data.transforms.local_frame_rotation`/
-    `travel_direction` directly rather than the module's polars expressions.
-    """
-    pre_pos = np.column_stack(
-        [columns["pre_x"], columns["pre_y"], columns["pre_z"]]
-    ).astype(np.float32)
-    pre_dir = np.column_stack(
-        [columns["pre_dx"], columns["pre_dy"], columns["pre_dz"]]
-    ).astype(np.float32)
-    post_pos = np.column_stack(
-        [columns["post_x"], columns["post_y"], columns["post_z"]]
-    ).astype(np.float32)
-    post_dir = np.column_stack(
-        [columns["post_dx"], columns["post_dy"], columns["post_dz"]]
-    ).astype(np.float32)
-    pre_E = np.asarray(columns["pre_E"], dtype=np.float32)
-    post_E = np.asarray(columns["post_E"], dtype=np.float32)
-
-    post_dir_local = local_frame_rotation(pre_dir, post_dir)
-    travel_dir_local = local_frame_rotation(
-        pre_dir, travel_direction(pre_pos, post_pos)
-    )
-    return np.column_stack(
-        [
-            np.asarray(columns["step_length"], dtype=np.float32),
-            pre_E - post_E,
-            np.asarray(columns["edep"], dtype=np.float32),
-            post_dir_local,
-            travel_dir_local,
-        ]
-    ).astype(np.float32)
-
-
-def test_rollout_vs_truth_decodes_raw_targets_correctly(tmp_path):
-    rng = np.random.default_rng(1)
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    rollout_cols = _write_world_frame_parquet(
-        rollout_path, n=150, rng=rng, rollout=True
-    )
-    truth_cols = _write_world_frame_parquet(truth_path, n=200, rng=rng, rollout=False)
-
-    expected_gen = _raw_from_world_frame(rollout_cols)
-    expected_real = _raw_from_world_frame(truth_cols)
-
-    source = RolloutVsTruth(rollout=rollout_path, truth=truth_path)
-    table = marginal_table_pl(source).to_pandas().set_index("dim")
-
-    assert (table["n"] == len(truth_cols["pdg"])).all()
-    assert (table["n_gen"] == len(rollout_cols["pdg"])).all()
-    for j, name in enumerate(RAW_TARGET_NAMES):
-        np.testing.assert_allclose(
-            table.loc[name, "real_mean"], expected_real[:, j].mean(), atol=1e-3
-        )
-        np.testing.assert_allclose(
-            table.loc[name, "gen_mean"], expected_gen[:, j].mean(), atol=1e-3
-        )
-
-
-def test_rollout_vs_truth_allows_unpaired_lengths(tmp_path):
-    rng = np.random.default_rng(2)
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    _write_world_frame_parquet(rollout_path, n=30, rng=rng, rollout=True)
-    _write_world_frame_parquet(truth_path, n=500, rng=rng, rollout=False)
-
-    source = RolloutVsTruth(rollout=rollout_path, truth=truth_path)
-    table = marginal_table_pl(source)
-    assert (table["n"] == 500).all()
-    assert (table["n_gen"] == 30).all()
-
-
-def test_rollout_vs_truth_drops_synthetic_termination_rows(tmp_path):
-    rng = np.random.default_rng(3)
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    n = 100
-    # Half the rollout rows are synthetic bookkeeping (dropped), half are real.
-    reasons = np.where(np.arange(n) % 2 == 0, TERM_ENERGY_CUTOFF, "")
-    rollout_cols = _write_world_frame_parquet(
-        rollout_path, n=n, rng=rng, rollout=True, termination_reasons=reasons
-    )
-    _write_world_frame_parquet(truth_path, n=50, rng=rng, rollout=False)
-
-    kept = reasons != TERM_ENERGY_CUTOFF
-    expected_gen = _raw_from_world_frame(
-        {k: np.asarray(v)[kept] for k, v in rollout_cols.items()}
-    )
-
-    source = RolloutVsTruth(rollout=rollout_path, truth=truth_path)
-    table = marginal_table_pl(source)
-    assert (table["n_gen"] == kept.sum()).all()
-    step_length_row = table.filter(pl.col("dim") == "step_length")
-    np.testing.assert_allclose(
-        step_length_row["gen_mean"][0], expected_gen[:, 0].mean(), atol=1e-3
-    )
-
-
-def test_rollout_vs_truth_rejects_wrong_coord_metadata(tmp_path):
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    _write_world_frame_parquet(rollout_path, rollout=True)
-    _write_world_frame_parquet(truth_path, rollout=False)
-    # Overwrite with a mismatched coord tag (predict-schema "local").
-    table = pq.read_table(rollout_path).replace_schema_metadata(
-        {PREDICT_COORD_METADATA_KEY: "local"}
-    )
-    pq.write_table(table, rollout_path)
-
-    source = RolloutVsTruth(rollout=rollout_path, truth=truth_path)
-    with pytest.raises(ValueError, match="not a rollout file"):
-        marginal_table_pl(source)
-
-
-@pytest.mark.parametrize("group_by", [None, "pdg", "material", "energy"])
-def test_rollout_vs_truth_downstream_plots_run_without_error(tmp_path, group_by):
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    _write_world_frame_parquet(rollout_path, n=150, rollout=True)
-    _write_world_frame_parquet(truth_path, n=150, rollout=False)
-    source = RolloutVsTruth(rollout=rollout_path, truth=truth_path)
-
-    assert plot_marginals(source, group_by=group_by) is not None
-    assert plot_kl_bars_pl(source, group_by=group_by) is not None
-
-
-def test_rollout_vs_truth_joint_and_constraint_checks_run(tmp_path):
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    _write_world_frame_parquet(rollout_path, n=150, rollout=True)
-    _write_world_frame_parquet(truth_path, n=150, rollout=False)
-    source = RolloutVsTruth(rollout=rollout_path, truth=truth_path)
-
-    assert plot_correlation_matrices(source) is not None
-    assert plot_pairwise(source, n_sample=50) is not None
-    assert plot_direction_alignment(source) is not None
-    assert plot_constraint_violations(source) is not None
-    report = constraint_report_pl(source).to_pandas()
-    assert set(report["check"]) == {
-        "post_dir unit norm",
-        "travel_dir unit norm",
-        "step_length >= 0",
-        "delta_e >= 0",
-        "edep >= 0",
-    }
-
-
-def _write_truth_shower(path, event_id, n_steps_per_event, rng):
-    """One-track-per-event world-frame shower: highest-`pre_E` row fixes the axis."""
-    rows_per_event = n_steps_per_event
-    rows = []
-    for i, eid in enumerate(event_id):
-        n = rows_per_event
-        pre_pos = rng.normal(size=(n, 3)).astype(np.float32) * 5
-        pre_E = rng.uniform(1.0, 50.0, n).astype(np.float32)
-        pre_E[0] = 1000.0  # entry step
-        axis = _unit_vectors(rng, 1)[0]
-        pre_dir = np.tile(axis, (n, 1)).astype(np.float32)
-        step_length = rng.uniform(0.1, 5.0, n).astype(np.float32)
-        post_pos = pre_pos + step_length[:, None] * axis
-        edep = rng.uniform(0, 1, n).astype(np.float32)
-        for j in range(n):
-            rows.append(
-                {
-                    "event_id": eid,
-                    "pdg": 11,
-                    "material": "Pb",
-                    "layer_id": 0,
-                    "pre_x": pre_pos[j, 0],
-                    "pre_y": pre_pos[j, 1],
-                    "pre_z": pre_pos[j, 2],
-                    "pre_E": pre_E[j],
-                    "pre_dx": pre_dir[j, 0],
-                    "pre_dy": pre_dir[j, 1],
-                    "pre_dz": pre_dir[j, 2],
-                    "post_x": post_pos[j, 0],
-                    "post_y": post_pos[j, 1],
-                    "post_z": post_pos[j, 2],
-                    "edep": edep[j],
-                    "step_length": step_length[j],
-                    "track_id": i,
-                    "termination_reason": TERM_NATURAL_END if j == n - 1 else "",
-                    "n_sec_pred": 0,
-                }
-            )
-    return rows
-
-
-def test_compute_rollout_vs_truth_observables_pl_matches_manual_reconstruction(
-    tmp_path,
-):
-    rng = np.random.default_rng(4)
-    rollout_rows = _write_truth_shower(
-        tmp_path / "_r", event_id=[0, 1, 2], n_steps_per_event=15, rng=rng
-    )
-    truth_rows = _write_truth_shower(
-        tmp_path / "_t", event_id=[0, 1, 2], n_steps_per_event=20, rng=rng
-    )
-
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    pq.write_table(
-        pa.table(
-            {k: [r[k] for r in rollout_rows] for k in rollout_rows[0]}
-        ).replace_schema_metadata({PREDICT_COORD_METADATA_KEY: ROLLOUT_COORD_VALUE}),
-        rollout_path,
-    )
-    truth_cols = [
-        "event_id",
-        "pdg",
-        "material",
-        "layer_id",
-        "pre_x",
-        "pre_y",
-        "pre_z",
-        "pre_E",
-        "pre_dx",
-        "pre_dy",
-        "pre_dz",
-        "post_x",
-        "post_y",
-        "post_z",
-        "edep",
-        "step_length",
-    ]
-    pq.write_table(
-        pa.table({k: [r[k] for r in truth_rows] for k in truth_cols}), truth_path
-    )
-
-    obs = compute_rollout_vs_truth_observables_pl(
-        rollout_path, truth_path, depth_bins=5, transverse_bins=5
-    )
-
-    rollout_df = pd.DataFrame(rollout_rows)
-    truth_df = pd.DataFrame(truth_rows)
-    expected_gen_total = rollout_df.groupby("event_id")["edep"].sum().sort_index()
-    expected_real_total = truth_df.groupby("event_id")["edep"].sum().sort_index()
-
-    gen_table = obs.gen_table.sort("event_id")
-    real_table = obs.real_table.sort("event_id")
-    np.testing.assert_allclose(
-        gen_table["total_edep"].to_numpy(), expected_gen_total.to_numpy(), rtol=1e-4
-    )
-    np.testing.assert_allclose(
-        real_table["total_edep"].to_numpy(), expected_real_total.to_numpy(), rtol=1e-4
-    )
-    assert (gen_table["n_tracks"].to_numpy() == 1).all()
-    assert (gen_table["leaked_E"].to_numpy() == 0).all()
-
-
-def test_compute_rollout_vs_truth_observables_pl_profile_shapes(tmp_path):
-    rng = np.random.default_rng(5)
-    rollout_rows = _write_truth_shower(
-        tmp_path / "_r", event_id=[0, 1], n_steps_per_event=10, rng=rng
-    )
-    truth_rows = _write_truth_shower(
-        tmp_path / "_t", event_id=[0, 1, 2], n_steps_per_event=10, rng=rng
-    )
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    pq.write_table(
-        pa.table(
-            {k: [r[k] for r in rollout_rows] for k in rollout_rows[0]}
-        ).replace_schema_metadata({PREDICT_COORD_METADATA_KEY: ROLLOUT_COORD_VALUE}),
-        rollout_path,
-    )
-    truth_cols = [
-        "event_id",
-        "pdg",
-        "material",
-        "layer_id",
-        "pre_x",
-        "pre_y",
-        "pre_z",
-        "pre_E",
-        "pre_dx",
-        "pre_dy",
-        "pre_dz",
-        "post_x",
-        "post_y",
-        "post_z",
-        "edep",
-        "step_length",
-    ]
-    pq.write_table(
-        pa.table({k: [r[k] for r in truth_rows] for k in truth_cols}), truth_path
-    )
-
-    obs = compute_rollout_vs_truth_observables_pl(
-        rollout_path, truth_path, depth_bins=6, transverse_bins=4
-    )
-    assert obs.depth_edges.shape == (7,)
-    assert obs.transverse_edges.shape == (5,)
-    assert len(obs.gen_table) == 2
-    assert len(obs.real_table) == 3
-
-    assert plot_total_energy(obs) is not None
-    assert plot_total_length(obs) is not None
-    assert plot_mean_energy_per_step(obs) is not None
-    assert plot_mean_length_per_step(obs) is not None
-    assert plot_longitudinal_profile(obs) is not None
-    assert plot_transverse_profile(obs) is not None
-    assert plot_shower_max_depth(obs) is not None
-
-
-def test_compute_rollout_vs_truth_observables_pl_rejects_wrong_coord_metadata(
-    tmp_path,
-):
-    rollout_path = tmp_path / "rollout.parquet"
-    truth_path = tmp_path / "truth.parquet"
-    _write_world_frame_parquet(rollout_path, rollout=True)
-    _write_world_frame_parquet(truth_path, rollout=False)
-    table = pq.read_table(rollout_path).replace_schema_metadata(
-        {PREDICT_COORD_METADATA_KEY: "local"}
-    )
-    pq.write_table(table, rollout_path)
-
-    with pytest.raises(ValueError, match="not a rollout file"):
-        compute_rollout_vs_truth_observables_pl(rollout_path, truth_path)
diff --git a/tests/test_analysis_reduce.py b/tests/test_analysis_reduce.py
new file mode 100644
index 0000000..877f274
--- /dev/null
+++ b/tests/test_analysis_reduce.py
@@ -0,0 +1,171 @@
+"""Tests for the streaming compute primitives (giant.analysis.reduce/sources/grouping)."""
+
+from __future__ import annotations
+
+import numpy as np
+import polars as pl
+
+from giant.analysis import grouping as G
+from giant.analysis import reduce as R
+from giant.analysis.sources import (
+    SYNTHETIC_TERMINATION_REASONS,
+    Side,
+    physical_steps,
+    secondaries,
+)
+
+
+def _rollout_frame() -> pl.LazyFrame:
+    # event 1: primary (2 steps) + 1 secondary track + 1 escaped bookkeeping row
+    # event 2: primary (1 step)
+    return pl.DataFrame(
+        {
+            "event_id": [1, 1, 1, 1, 2],
+            "track_id": [0, 0, 1, 0, 0],
+            "parent_id": [-1, -1, 0, -1, -1],
+            "generation": [0, 0, 1, 0, 0],
+            "step_no": [0, 1, 0, 99, 0],
+            "pdg": [11, 11, 22, 11, 11],
+            "pre_x": [0.0, 0.0, 0.0, 0.0, 0.0],
+            "pre_y": [0.0, 0.0, 0.0, 0.0, 0.0],
+            "pre_z": [0.0, 1.0, 1.0, 2.0, 0.0],
+            "pre_E": [100.0, 60.0, 20.0, 30.0, 50.0],
+            "pre_dx": [0.0, 0.0, 1.0, 0.0, 0.0],
+            "pre_dy": [0.0, 0.0, 0.0, 0.0, 0.0],
+            "pre_dz": [1.0, 1.0, 0.0, 1.0, 1.0],
+            "post_x": [0.0, 0.0, 1.0, 0.0, 0.0],
+            "post_y": [0.0, 0.0, 0.0, 0.0, 0.0],
+            "post_z": [1.0, 2.0, 1.0, 2.0, 1.0],
+            "post_E": [60.0, 30.0, 0.0, 0.0, 20.0],
+            "post_dx": [0.0, 0.0, 1.0, 0.0, 0.0],
+            "post_dy": [0.0, 0.0, 0.0, 0.0, 0.0],
+            "post_dz": [1.0, 1.0, 0.0, 1.0, 1.0],
+            "edep": [40.0, 30.0, 20.0, 0.0, 30.0],
+            "step_length": [1.0, 1.0, 1.0, 0.0, 1.0],
+            "material": ["G4_PbWO4"] * 5,
+            "layer_id": [0, 1, 1, -1, 0],
+            "n_sec_pred": [1, 0, 0, 0, 0],
+            "termination_reason": [
+                "",
+                "natural_end",
+                "natural_end",
+                "escaped",
+                "natural_end",
+            ],
+        }
+    ).lazy()
+
+
+def _reference_frame() -> pl.LazyFrame:
+    return pl.DataFrame(
+        {
+            "event_id": [1, 1, 2],
+            "track_id": [0, 0, 0],
+            "step_no": [0, 1, 0],
+            "pdg": [11, 11, 11],
+            "pre_x": [0.0, 0.0, 0.0],
+            "pre_y": [0.0, 0.0, 0.0],
+            "pre_z": [0.0, 1.0, 0.0],
+            "pre_E": [100.0, 60.0, 50.0],
+            "pre_dx": [0.0, 0.0, 0.0],
+            "pre_dy": [0.0, 0.0, 0.0],
+            "pre_dz": [1.0, 1.0, 1.0],
+            "post_x": [0.0, 0.0, 0.0],
+            "post_y": [0.0, 0.0, 0.0],
+            "post_z": [1.0, 2.0, 1.0],
+            "post_E": [60.0, 30.0, 20.0],
+            "post_dx": [0.0, 0.0, 0.0],
+            "post_dy": [0.0, 0.0, 0.0],
+            "post_dz": [1.0, 1.0, 1.0],
+            "edep": [40.0, 30.0, 30.0],
+            "step_length": [1.0, 1.0, 1.0],
+            "material": ["G4_PbWO4", "G4_PbWO4", "G4_Pb"],
+            "layer_id": [0, 1, 0],
+            "sec_E_list": [[20.0], [], [10.0]],
+            "sec_pdg_list": [[22], [], [22]],
+            "sec_dx_list": [[1.0], [], [0.0]],
+            "sec_dy_list": [[0.0], [], [0.0]],
+            "sec_dz_list": [[0.0], [], [1.0]],
+        }
+    ).lazy()
+
+
+def test_hist1d_overall_and_grouped():
+    lf = _rollout_frame()
+    edges = np.linspace(0.0, 50.0, 6)  # width 10
+    h = R.hist1d(lf, pl.col("edep"), edges)
+    # edep values: 40,30,20,0,30 -> bins [0),[10),[20),[30),[40)
+    assert h[0].tolist() == [1, 0, 1, 2, 1]
+    # grouped by pdg: pdg 22 has a single edep=20
+    hg = R.hist1d(lf, pl.col("edep"), edges, group=pl.col("pdg"))
+    assert hg[22].tolist() == [0, 0, 1, 0, 0]
+    assert hg[11].sum() == 4
+
+
+def test_physical_steps_drops_synthetic_rollout_rows_only():
+    lf = _rollout_frame()
+    phys = physical_steps(lf, Side.rollout).collect()
+    assert phys.height == 4  # dropped the escaped bookkeeping row
+    assert "escaped" not in phys["termination_reason"].to_list()
+    assert SYNTHETIC_TERMINATION_REASONS  # non-empty guard
+    # reference passes through unchanged
+    ref = _reference_frame()
+    assert physical_steps(ref, Side.reference).collect().height == ref.collect().height
+
+
+def test_event_scalars_totals_include_all_rows():
+    lf = _rollout_frame()
+    es = R.event_scalars(lf).sort("event_id")
+    row1 = es.filter(pl.col("event_id") == 1).to_dicts()[0]
+    assert row1["total_edep"] == 90.0  # 40+30+20+0
+    assert row1["incident_E"] == 100.0
+    assert row1["n_steps"] == 4
+
+
+def test_secondaries_rollout_vs_reference_align():
+    r = secondaries(_rollout_frame(), Side.rollout).collect().sort("event_id")
+    assert r["energy"].to_list() == [20.0]  # only the generation>0, step_no==0 row
+    assert r["pdg"].to_list() == [22]
+    t = secondaries(_reference_frame(), Side.reference).collect().sort("event_id")
+    # two secondaries (event 1 and event 2); empty list dropped
+    assert sorted(t["energy"].to_list()) == [10.0, 20.0]
+    assert t["pdg"].to_list() == [22, 22]
+
+
+def test_leakage_fraction():
+    frac = R.leakage_fraction(_rollout_frame())
+    # event 1: escaped pre_E=30, deposited=90 -> 30/120 = 0.25; event 2: 0
+    assert sorted(round(f, 6) for f in frac) == [0.0, 0.25]
+
+
+def test_weighted_profile_matches_manual_bincount():
+    lf = _rollout_frame()
+    ea = R.entry_axis(lf)
+    lf2 = R.attach_entry_axis(lf, ea)
+    edges = np.linspace(0.0, 3.0, 4)  # depth bins along +z
+    mean, std = R.weighted_profile(lf2, R.depth_expr(), edges, pl.col("edep"))
+    assert mean.shape == (3,)
+    # totals conserved: sum over bins == mean total edep per event
+    assert np.isclose(mean.sum() * 1, (90.0 + 30.0) / 2)  # 2 events
+
+
+def test_energy_bins_edges_and_event_map():
+    incident = np.array([100.0, 100.0, 1000.0, 1000.0])
+    edges = G.energy_bin_edges(incident, n_bins=2)
+    assert len(edges) == 3 and edges[0] <= 100.0 < edges[-1]
+    ids, bins = G.event_energy_bins(_rollout_frame(), edges)
+    assert set(bins.tolist()) <= {0, 1}
+    assert len(ids) == 2
+
+
+def test_digitize_expr_matches_numpy():
+    edges = np.array([0.0, 10.0, 100.0, 1000.0])
+    df = pl.DataFrame({"v": [5.0, 50.0, 500.0, 2000.0]})
+    got = df.select(G.digitize_expr(pl.col("v"), edges).alias("b"))["b"].to_list()
+    assert got == np.digitize([5.0, 50.0, 500.0, 2000.0], edges[1:-1]).tolist()
+
+
+def test_pdg_and_material_labels():
+    assert G.pdg_label(22) == "gamma"
+    assert G.pdg_label(999999) == "999999"
+    assert G.material_label("G4_PbWO4") == "PbWO4"
diff --git a/tests/test_catalog.py b/tests/test_catalog.py
new file mode 100644
index 0000000..c8a47f0
--- /dev/null
+++ b/tests/test_catalog.py
@@ -0,0 +1,153 @@
+"""Tests for the plot catalog: id uniqueness + every spec computes a valid Reduced."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from giant.analysis import build_catalog, catalog_ids, get_spec
+from giant.analysis.catalog import Bundle, PlotSpec
+from giant.analysis.context import Context, build_context
+from tests.test_analysis_reduce import _reference_frame, _rollout_frame
+
+
+def _build_ctx() -> Context:
+    r, t = _rollout_frame(), _reference_frame()
+    return build_context(
+        r, t, n_energy_bins=2, n_marginal_bins=10, top_k_pdg=3, sample_rows=1000
+    )
+
+
+@pytest.fixture(scope="module")
+def ctx() -> Context:
+    return _build_ctx()
+
+
+@pytest.fixture(scope="module")
+def bundle(ctx: Context) -> Bundle:
+    return Bundle.open(_rollout_frame(), _reference_frame(), ctx)
+
+
+def test_catalog_ids_unique_and_nonempty():
+    ids = catalog_ids()
+    assert ids and len(ids) == len(set(ids))
+    # the required families are all present
+    fams = {s.family for s in build_catalog()}
+    assert {"marginals", "event", "shower", "species", "secondaries"} <= fams
+
+
+def test_get_spec_roundtrip_and_unknown():
+    spec = get_spec("marginal_edep")
+    assert spec.id == "marginal_edep" and spec.family == "marginals"
+    with pytest.raises(KeyError):
+        get_spec("does_not_exist")
+
+
+def test_every_spec_computes_valid_reduced(bundle: Bundle):
+    for spec in build_catalog():
+        r = spec.finalize([spec.compute_partial(bundle)], bundle.ctx)
+        assert r.id == spec.id
+        assert r.kind in {
+            "overlay_hist",
+            "grouped_hist",
+            "profile",
+            "bar",
+            "single_hist",
+            "router_gating",
+            "router_share",
+            "unavailable",
+        }
+        assert r.title and r.xlabel
+        _validate_payload(r)
+
+
+def _validate_payload(r) -> None:
+    p = r.payload
+    if r.kind == "overlay_hist":
+        n = len(p["edges"]) - 1
+        assert len(p["rollout"]) == n and len(p["reference"]) == n
+    elif r.kind == "single_hist":
+        assert len(p["rollout"]) == len(p["edges"]) - 1
+    elif r.kind == "grouped_hist":
+        n = len(p["edges"]) - 1
+        assert p["groups"], "grouped hist must have at least one group"
+        for g in p["groups"].values():
+            assert len(g["rollout"]) == n and len(g["reference"]) == n
+    elif r.kind == "profile":
+        n = len(p["edges"]) - 1
+        for k in ("rollout_mean", "rollout_std", "reference_mean", "reference_std"):
+            assert len(p[k]) == n
+    elif r.kind == "bar":
+        assert len(p["labels"]) == len(p["rollout"]) == len(p["reference"])
+    elif r.kind == "unavailable":
+        assert p["note"]
+    elif r.kind == "router_gating":
+        for side in ("rollout", "reference"):
+            if side in p:
+                assert len(p[side]["centers"]) == len(p[side]["means"])
+    elif r.kind == "router_share":
+        for cat in p["categories"]:
+            for side in ("rollout", "reference"):
+                if side in p:
+                    assert cat in p[side]
+
+
+# ---------------------------------------------------------------------------
+# chunked (compute_partial x N -> finalize) must match the unchunked (N=1) result
+# ---------------------------------------------------------------------------
+
+# One representative id per merge shape: sum-mergeable (marginal_edep,
+# sec_count_per_species via pdg-keyed sums), concat-then-finalize with
+# data-dependent edges (event_total_edep), concat-then-mean/std (shower_
+# longitudinal), concat-then-max-edge (leakage_fraction), pdg-keyed sum with a
+# ratio (species_edep_share), and a chunkable=False passthrough (router_gating).
+_CHUNK_EQUIVALENCE_IDS = [
+    "marginal_edep",
+    "species_edep_share",
+    "event_total_edep",
+    "shower_longitudinal",
+    "leakage_fraction",
+    "sec_count_per_species",
+    "router_gating",
+]
+
+
+def _assert_payload_close(a, b, path: str = "payload") -> None:
+    """Recursively compare two JSON-shaped payloads (float-tolerant)."""
+    assert type(a) is type(b), f"{path}: {type(a)} != {type(b)}"
+    if isinstance(a, dict):
+        assert set(a) == set(b), f"{path}: key mismatch {set(a)} != {set(b)}"
+        for k in a:
+            _assert_payload_close(a[k], b[k], f"{path}.{k}")
+    elif isinstance(a, list):
+        assert len(a) == len(b), f"{path}: length mismatch"
+        for i, (x, y) in enumerate(zip(a, b)):
+            _assert_payload_close(x, y, f"{path}[{i}]")
+    elif isinstance(a, float):
+        assert np.isclose(a, b, atol=1e-9), f"{path}: {a} != {b}"
+    else:
+        assert a == b, f"{path}: {a} != {b}"
+
+
+@pytest.mark.parametrize("spec_id", _CHUNK_EQUIVALENCE_IDS)
+def test_chunked_matches_unchunked(ctx: Context, spec_id: str):
+    """A plot computed over N event-disjoint chunks then merged must equal the
+    same plot computed in one unchunked pass — the core chunking correctness
+    guarantee (see the analysis-rollout-plots chunking plan)."""
+    spec: PlotSpec = get_spec(spec_id)
+    r, t = _rollout_frame(), _reference_frame()
+
+    unchunked_bundle = Bundle.open(r, t, ctx)
+    unchunked = spec.finalize([spec.compute_partial(unchunked_bundle)], ctx)
+
+    # 4 chunks over only 2 distinct event_ids also exercises empty chunks.
+    n_chunks = 4 if spec.chunkable else 1
+    parts = [
+        spec.compute_partial(Bundle.open(r, t, ctx, chunk=(k, n_chunks)))
+        for k in range(n_chunks)
+    ]
+    chunked = spec.finalize(parts, ctx)
+
+    assert chunked.id == unchunked.id
+    assert chunked.kind == unchunked.kind
+    _assert_payload_close(unchunked.payload, chunked.payload)
diff --git a/tests/test_condor.py b/tests/test_condor.py
new file mode 100644
index 0000000..c4259ac
--- /dev/null
+++ b/tests/test_condor.py
@@ -0,0 +1,270 @@
+"""Tests for the rollout-YAML → run-directory flow, compute, and submit."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pyarrow.parquet as pq
+import pytest
+import yaml
+
+from giant.analysis import (
+    RunMeta,
+    SubmitConfig,
+    catalog_ids,
+    compute_one,
+    compute_reduced,
+    derive_run_dir,
+    load_rollout_yaml,
+    merge_one,
+    prep,
+    write_submit,
+)
+from giant.analysis.catalog import get_spec
+from giant.analysis.condor import Context
+from giant.analysis.reduced import Partial, Reduced
+from giant.constants import PREDICT_COORD_METADATA_KEY, ROLLOUT_COORD_VALUE
+from tests.test_analysis_reduce import _reference_frame, _rollout_frame
+
+
+def _write_inputs(tmp_path: Path) -> Path:
+    """Materialize rollout+reference parquet and a rollout YAML; return the YAML path."""
+    rollout = tmp_path / "rollout.parquet"
+    reference = tmp_path / "reference.parquet"
+    tbl = _rollout_frame().collect().to_arrow()
+    tbl = tbl.replace_schema_metadata({PREDICT_COORD_METADATA_KEY: ROLLOUT_COORD_VALUE})
+    pq.write_table(tbl, rollout)
+    _reference_frame().collect().write_parquet(reference)
+
+    yaml_path = tmp_path / "run.yaml"
+    yaml_path.write_text(
+        yaml.safe_dump(
+            {
+                "prediction_id": "abcd1234ef",
+                "output": str(rollout),
+                "dataset": str(reference),
+                "checkpoint": "/ckpt/best.pt",
+                "kind": "rollout",
+                "energy_cutoff": 0.1,
+                "steps": 10,
+            }
+        )
+    )
+    return yaml_path
+
+
+def _fake_venv(repo_dir: Path) -> None:
+    """Stand in for a `uv sync`'d venv: write_submit checks `.venv/bin/giant` exists."""
+    giant = repo_dir / ".venv" / "bin" / "giant"
+    giant.parent.mkdir(parents=True, exist_ok=True)
+    giant.write_text("#!/bin/bash\n")
+    giant.chmod(0o755)
+
+
+def _prep(
+    rollout_yaml: Path, run_dir: str | Path | None = None, chunks: int = 1
+) -> Path:
+    """``prep`` with small test-sized context bins/sampling."""
+    return prep(
+        rollout_yaml,
+        run_dir,
+        n_chunks=chunks,
+        n_energy_bins=2,
+        n_marginal_bins=8,
+        top_k_pdg=3,
+        sample_rows=1000,
+    )
+
+
+def test_load_rollout_yaml_requires_paths(tmp_path: Path):
+    bad = tmp_path / "bad.yaml"
+    bad.write_text(yaml.safe_dump({"output": "x.parquet"}))  # no dataset
+    with pytest.raises(ValueError):
+        load_rollout_yaml(bad)
+
+
+def test_derive_run_dir_next_to_rollout():
+    y = {"output": "/data/roll.parquet", "prediction_id": "abcd1234ef", "dataset": "d"}
+    assert derive_run_dir(y) == Path("/data/analysis_abcd1234")
+    assert derive_run_dir(y, "/somewhere") == Path("/somewhere")
+
+
+def test_derive_run_dir_default_base():
+    y = {"output": "/data/roll.parquet", "prediction_id": "abcd1234ef", "dataset": "d"}
+    assert derive_run_dir(y, default_base="/work/lbogner/giant2/analysis_runs") == Path(
+        "/work/lbogner/giant2/analysis_runs/analysis_abcd1234"
+    )
+    # an explicit run_dir still wins over default_base
+    assert derive_run_dir(y, "/somewhere", default_base="/other") == Path("/somewhere")
+
+
+def test_prep_lays_out_run_dir(tmp_path: Path):
+    yaml_path = _write_inputs(tmp_path)
+    run_dir = _prep(yaml_path)
+    assert run_dir == tmp_path / "analysis_abcd1234"
+    assert (run_dir / "shared.json").exists()
+    ctx = Context.load(run_dir / "shared.json")
+    assert set(ctx.var_ranges) == {"step_length", "edep", "delta_e", "post_E"}
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    assert meta.reference.endswith("reference.parquet")
+    assert meta.plot_meta["checkpoint"] == "/ckpt/best.pt"
+    assert "best.pt" in meta.title
+    assert meta.n_chunks == 1
+    assert meta.rows_per_chunk == [meta.total_rows]  # single chunk holds everything
+    assert meta.total_rows == 8  # 5 rollout rows + 3 reference rows
+
+
+def test_prep_splits_rows_per_chunk(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path), chunks=2)
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    assert len(meta.rows_per_chunk) == 2
+    assert sum(meta.rows_per_chunk) == meta.total_rows == 8
+
+
+def test_compute_one_from_run_dir(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))
+    out = compute_one("marginal_edep", run_dir)
+    assert out == run_dir / "reduced_partial" / "marginal_edep__0.json"
+    partial = Partial.load(out)
+    assert partial.id == "marginal_edep" and partial.chunk == 0
+    assert "r" in partial.data and "t" in partial.data
+
+
+def test_compute_reduced_explicit_paths(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    out = compute_reduced(
+        "marginal_step_length",
+        meta.rollout,
+        meta.reference,
+        run_dir / "shared.json",
+        tmp_path / "r.json",
+    )
+    assert Partial.load(out).id == "marginal_step_length"
+
+
+def test_merge_one_produces_reduced(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))
+    compute_one("marginal_edep", run_dir)
+    out = merge_one("marginal_edep", run_dir)
+    assert out == run_dir / "reduced" / "marginal_edep.json"
+    reduced = Reduced.load(out)
+    assert reduced.id == "marginal_edep"
+    assert len(reduced.payload["rollout"]) == len(reduced.payload["edges"]) - 1
+
+
+def test_merge_one_fails_loudly_on_missing_chunk(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path), chunks=2)
+    compute_one("marginal_edep", run_dir, chunk_index=0)  # chunk 1 never computed
+    with pytest.raises(FileNotFoundError, match="missing chunk"):
+        merge_one("marginal_edep", run_dir)
+
+
+def test_chunked_compute_and_merge_matches_unchunked(tmp_path: Path):
+    (tmp_path / "a").mkdir()
+    (tmp_path / "b").mkdir()
+    unchunked_dir = _prep(_write_inputs(tmp_path / "a"))
+    compute_one("marginal_step_length", unchunked_dir)
+    unchunked = Reduced.load(merge_one("marginal_step_length", unchunked_dir))
+
+    chunked_dir = _prep(_write_inputs(tmp_path / "b"), chunks=2)
+    for k in range(2):
+        compute_one("marginal_step_length", chunked_dir, chunk_index=k)
+    chunked = Reduced.load(merge_one("marginal_step_length", chunked_dir))
+
+    assert chunked.payload == unchunked.payload
+
+
+def test_compute_reduced_rejects_out_of_range_chunk(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))  # n_chunks=1 (default)
+    with pytest.raises(ValueError, match="out of range"):
+        compute_one("marginal_edep", run_dir, chunk_index=1)
+
+
+def test_write_submit_description(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))
+    _fake_venv(tmp_path)
+    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path)
+    txt = write_submit(cfg).read_text()
+    assert "universe = docker" in txt
+    assert "docker_image = cverstege/alma9-gridjob" in txt
+    assert "requirements = TARGET.ProvidesETPResources" in txt
+    assert "accounting_group = cms" in txt
+    assert "+RequestWalltime = $(walltime)" in txt
+    assert "queue plotid,chunk,walltime from" in txt
+    jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()]
+    assert [i for i, _, _ in jobs] == catalog_ids()
+    assert all(k == "0" for _, k, _ in jobs)  # n_chunks=1 default
+    assert all(int(w) > 0 for _, _, w in jobs)
+    wrapper = run_dir / "run_compute.sh"
+    assert wrapper.exists() and (wrapper.stat().st_mode & 0o111)
+    body = wrapper.read_text()
+    assert "giant analyze compute-one --id" in body
+    assert "--chunk" in body and "--run-dir" in body
+
+
+def test_write_submit_requires_synced_venv(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))
+    cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path)
+    with pytest.raises(FileNotFoundError, match="uv sync"):
+        write_submit(cfg)
+
+
+def test_write_submit_remote_flag(tmp_path: Path):
+    run_dir = _prep(_write_inputs(tmp_path))
+    _fake_venv(tmp_path)
+    cfg = SubmitConfig(
+        run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, remote=True
+    )
+    txt = write_submit(cfg).read_text()
+    assert "+RemoteJob = True" in txt
+    assert "ProvidesETPResources" not in txt
+
+
+def test_write_submit_chunks_respect_chunkable(tmp_path: Path):
+    assert get_spec("router_gating").chunkable is False
+    run_dir = _prep(_write_inputs(tmp_path), chunks=4)
+    _fake_venv(tmp_path)
+    cfg = SubmitConfig(
+        run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4
+    )
+    write_submit(cfg)
+    jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()]
+    counts: dict[str, int] = {}
+    for spec_id, _, _ in jobs:
+        counts[spec_id] = counts.get(spec_id, 0) + 1
+    assert counts["marginal_edep"] == 4
+    assert counts["router_gating"] == 1  # chunkable=False, ignores n_chunks
+
+
+def test_estimate_runtime_s_scales_with_rows_and_margin():
+    from giant.analysis import RUNTIME_SAFETY_MARGIN, estimate_runtime_s
+    from giant.analysis.runtime_estimate import _FIXED_OVERHEAD_S
+
+    assert RUNTIME_SAFETY_MARGIN > 0
+    small = estimate_runtime_s("marginal_edep", 1_000)
+    large = estimate_runtime_s("marginal_edep", 100_000_000)
+    assert small >= (1 + RUNTIME_SAFETY_MARGIN) * _FIXED_OVERHEAD_S
+    assert large > small  # bigger chunk -> longer estimate
+
+
+def test_write_submit_walltime_grows_with_chunk_rows(tmp_path: Path):
+    """A chunked run's later job walltimes track that chunk's row count."""
+    from giant.analysis.runtime_estimate import estimate_runtime_s
+
+    run_dir = _prep(_write_inputs(tmp_path), chunks=2)
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    _fake_venv(tmp_path)
+    cfg = SubmitConfig(
+        run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=2
+    )
+    write_submit(cfg)
+    jobs = {
+        (i, int(k)): int(w)
+        for i, k, w in (
+            line.split(",") for line in (run_dir / "jobs.txt").read_text().split()
+        )
+    }
+    for chunk in range(2):
+        expected = estimate_runtime_s("marginal_edep", meta.rows_per_chunk[chunk])
+        assert jobs[("marginal_edep", chunk)] == expected
diff --git a/tests/test_render.py b/tests/test_render.py
new file mode 100644
index 0000000..49bb84c
--- /dev/null
+++ b/tests/test_render.py
@@ -0,0 +1,92 @@
+"""Render smoke test — skipped where plotstyle / LaTeX is unavailable."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+pytest.importorskip("plotstyle")
+
+from giant.analysis.reduced import Reduced  # noqa: E402
+
+
+def _try_render(reduced: list[Reduced], out: Path):
+    from giant.analysis.render import render_all
+
+    for r in reduced:
+        r.save(out / "reduced" / f"{r.id}.json")
+    return render_all(out / "reduced", out / "plots")
+
+
+def test_render_one_of_each_kind(tmp_path: Path):
+    reduced = [
+        Reduced(
+            "m",
+            "marginals",
+            "overlay_hist",
+            "Overlay",
+            "x",
+            {
+                "edges": [0, 1, 2, 3],
+                "rollout": [1, 2, 3],
+                "reference": [3, 2, 1],
+                "log_y": False,
+            },
+        ),
+        Reduced(
+            "g",
+            "marginals",
+            "grouped_hist",
+            "Grouped",
+            "x",
+            {
+                "edges": [0, 1, 2],
+                "groups": {"a": {"rollout": [1, 2], "reference": [2, 1]}},
+                "log_y": False,
+            },
+        ),
+        Reduced(
+            "p",
+            "shower",
+            "profile",
+            "Profile",
+            "depth",
+            {
+                "edges": [0, 1, 2],
+                "rollout_mean": [1, 2],
+                "rollout_std": [0.1, 0.2],
+                "reference_mean": [1.1, 1.9],
+                "reference_std": [0.1, 0.1],
+                "ylabel": "e",
+            },
+        ),
+        Reduced(
+            "b",
+            "species",
+            "bar",
+            "Bar",
+            "species",
+            {
+                "labels": ["e-", "gamma"],
+                "rollout": [0.6, 0.4],
+                "reference": [0.5, 0.5],
+                "ylabel": "frac",
+            },
+        ),
+        Reduced(
+            "s",
+            "species",
+            "single_hist",
+            "Single",
+            "x",
+            {"edges": [0, 1, 2], "rollout": [5, 1], "log_y": True},
+        ),
+    ]
+    try:
+        pdfs = _try_render(reduced, tmp_path)
+    except RuntimeError as e:  # LaTeX missing at render time
+        pytest.skip(f"LaTeX rendering unavailable: {e}")
+    assert len(pdfs) == len(reduced)
+    assert all(p.exists() for p in pdfs)
+    assert (tmp_path / "plots" / "metadata.yaml").exists()
diff --git a/tests/test_router.py b/tests/test_router.py
index 8f3fa2a..62a8c86 100644
--- a/tests/test_router.py
+++ b/tests/test_router.py
@@ -97,6 +97,42 @@ def test_build_router_ignores_unrecognized_kwargs():
     assert router.temperature == 0.3
 
 
+def test_energy_router_default_centers_are_linspace():
+    router = EnergyRouter(n_experts=4)
+    torch.testing.assert_close(router.centers, torch.linspace(-2.0, 2.0, 4))
+
+
+def test_energy_router_centers_init_overrides_default():
+    centers_init = [-1.0, 0.0, 0.5, 3.0]
+    router = EnergyRouter(n_experts=4, centers_init=centers_init)
+    torch.testing.assert_close(router.centers, torch.tensor(centers_init))
+
+
+def test_energy_router_centers_init_wrong_length_raises():
+    try:
+        EnergyRouter(n_experts=4, centers_init=[0.0, 1.0])
+    except ValueError:
+        return
+    raise AssertionError("expected ValueError for centers_init length mismatch")
+
+
+def test_energy_router_centers_init_respects_learn_centers_flag():
+    learned = EnergyRouter(
+        n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=True
+    )
+    fixed = EnergyRouter(
+        n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=False
+    )
+    assert isinstance(learned.centers, torch.nn.Parameter)
+    assert not isinstance(fixed.centers, torch.nn.Parameter)
+
+
+def test_build_router_threads_centers_init_through_energy_router():
+    centers_init = [-1.5, -0.5, 0.5, 1.5]
+    router = build_router("energy", 4, centers_init=centers_init)
+    torch.testing.assert_close(router.centers, torch.tensor(centers_init))
+
+
 def test_build_router_unknown_type_raises():
     try:
         build_router("nonexistent", 4)
diff --git a/tests/test_router_gating.py b/tests/test_router_gating.py
new file mode 100644
index 0000000..51123d3
--- /dev/null
+++ b/tests/test_router_gating.py
@@ -0,0 +1,126 @@
+"""Tests for the MoE router-gating diagnostic (giant.analysis.router_gating)."""
+
+from __future__ import annotations
+
+import numpy as np
+import polars as pl
+import torch
+
+from giant.analysis.router_gating import (
+    compute_router_gating,
+    compute_router_share_by_pdg,
+    compute_router_share_by_process,
+)
+from giant.data.transforms import Normalizer
+from giant.model.network import build_models
+
+_PDG_MAP = {11: 0, 22: 1}
+_MAT_MAP = {"G4_PbWO4": 0, "G4_Pb": 1}
+
+
+def _model_cfg() -> dict:
+    return {
+        "router": {
+            "enabled": True,
+            "type": "energy",
+            "n_experts": 2,
+            "temperature": 0.5,
+            "learn_centers": True,
+            "energy_idx": 3,
+        },
+        "pdg_vocab": len(_PDG_MAP),
+        "mat_vocab": len(_MAT_MAP),
+        "conditioning": "embedding",
+    }
+
+
+def _write_checkpoint(tmp_path) -> str:
+    cfg = _model_cfg()
+    stage1, _ = build_models(cfg)
+    norm = Normalizer()
+    norm.mean = np.zeros(15, dtype=np.float32)
+    norm.std = np.ones(15, dtype=np.float32)
+    ckpt = {
+        "model_config": cfg,
+        "model": stage1.state_dict(),
+        "pdg_map": _PDG_MAP,
+        "mat_map": _MAT_MAP,
+        "normalizer": {"cond": norm.to_dict()},
+    }
+    path = tmp_path / "ckpt.pt"
+    torch.save(ckpt, path)
+    return str(path)
+
+
+def _steps_frame(process: bool = False) -> pl.LazyFrame:
+    n = 40
+    rng = np.random.default_rng(0)
+    pre_e = np.concatenate([rng.uniform(1, 10, n // 2), rng.uniform(100, 1000, n // 2)])
+    pdg = np.where(np.arange(n) % 2 == 0, 11, 22)
+    material = np.where(np.arange(n) % 3 == 0, "G4_Pb", "G4_PbWO4")
+    data = {
+        "event_id": np.arange(n),
+        "pdg": pdg,
+        "pre_x": np.zeros(n),
+        "pre_y": np.zeros(n),
+        "pre_z": np.zeros(n),
+        "pre_E": pre_e,
+        "pre_dx": np.zeros(n),
+        "pre_dy": np.zeros(n),
+        "pre_dz": np.ones(n),
+        "post_x": np.zeros(n),
+        "post_y": np.zeros(n),
+        "post_z": np.ones(n),
+        "post_E": pre_e * 0.5,
+        "post_dx": np.zeros(n),
+        "post_dy": np.zeros(n),
+        "post_dz": np.ones(n),
+        "edep": pre_e * 0.5,
+        "step_length": np.ones(n),
+        "material": material,
+        "layer_id": np.zeros(n, dtype=np.int64),
+    }
+    if process:
+        data["process"] = np.where(pdg == 11, "eIoni", "compt")
+    return pl.DataFrame(data).lazy()
+
+
+def test_compute_router_gating_shapes(tmp_path):
+    checkpoint = _write_checkpoint(tmp_path)
+    lf = _steps_frame()
+    r = compute_router_gating(checkpoint, lf, lf)
+    assert r.kind == "router_gating"
+    assert r.payload["n_experts"] == 2
+    for side in ("rollout", "reference"):
+        means = r.payload[side]["means"]
+        assert means, f"{side} produced no bins"
+        assert all(abs(sum(row) - 1.0) < 1e-5 for row in means)
+
+
+def test_compute_router_gating_missing_checkpoint_is_unavailable():
+    lf = _steps_frame()
+    r = compute_router_gating(None, lf, lf)
+    assert r.kind == "unavailable"
+    assert "note" in r.payload
+    assert r.title
+
+
+def test_compute_router_share_by_pdg(tmp_path):
+    checkpoint = _write_checkpoint(tmp_path)
+    lf = _steps_frame()
+    r = compute_router_share_by_pdg(checkpoint, lf, lf, top_pdgs=[11, 22])
+    assert r.kind == "router_share"
+    for side in ("rollout", "reference"):
+        assert set(r.payload[side]) == {"e-", "gamma"}
+        for shares in r.payload[side].values():
+            assert abs(sum(shares) - 1.0) < 1e-5
+
+
+def test_compute_router_share_by_process(tmp_path):
+    checkpoint = _write_checkpoint(tmp_path)
+    lf = _steps_frame(process=True)
+    r = compute_router_share_by_process(checkpoint, lf)
+    assert r.kind == "router_share"
+    assert set(r.payload["categories"]) <= {"eIoni", "compt"}
+    for shares in r.payload["reference"].values():
+        assert abs(sum(shares) - 1.0) < 1e-5
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index a7af975..c9bc7ac 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -397,3 +397,46 @@ def test_build_cond_features_mass_charge_override(fake_material_props):
         cond_cont[:, COND_DIM_BASE], log_transform(np.array([123.0, 456.0]))
     )
     np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 1], [2.0, -2.0])
+
+
+def test_build_cond_features_pads_legacy_normalizer_in_embedding_mode():
+    """A pre-physical-conditioning checkpoint's cond normalizer is COND_DIM_BASE
+    (8) wide, fit before build_cond_features grew the extra physical columns.
+    In "embedding" mode those columns are never read downstream, so a legacy
+    normalizer should be usable as-is (padded, not rejected)."""
+    data = _minimal_step_data(3)
+    pdg_map, mat_map = {11: 0}, {"PbWO4": 0}
+    legacy_norm = Normalizer()
+    legacy_norm.mean = np.zeros(COND_DIM_BASE, dtype=np.float32)
+    legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32)
+
+    cond_cont, _ = build_cond_features(
+        data, pdg_map, mat_map, cond_normalizer=legacy_norm, conditioning="embedding"
+    )
+
+    assert cond_cont.shape[-1] == COND_DIM
+    # padded physical columns are zero-filled pre-normalization and
+    # mean=0/std=1 post-normalization, so they should come out as zero
+    np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE:], 0.0)
+
+
+def test_build_cond_features_rejects_legacy_normalizer_in_physical_mode(
+    fake_material_props,
+):
+    """Unlike "embedding" mode, "physical" mode actually reads the physical
+    columns, so a legacy 8-wide normalizer can't be silently padded — that
+    would silently feed the network un-normalized physical properties."""
+    data = _minimal_step_data(3)
+    pdg_map, mat_map = {11: 0}, {"PbWO4": 0}
+    legacy_norm = Normalizer()
+    legacy_norm.mean = np.zeros(COND_DIM_BASE, dtype=np.float32)
+    legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32)
+
+    with pytest.raises(ValueError, match="predates physical-property conditioning"):
+        build_cond_features(
+            data,
+            pdg_map,
+            mat_map,
+            cond_normalizer=legacy_norm,
+            conditioning="physical",
+        )
diff --git a/uv.lock b/uv.lock
index ef69653..3604491 100644
--- a/uv.lock
+++ b/uv.lock
@@ -459,6 +459,7 @@ dependencies = [
 analysis = [
     { name = "ipykernel" },
     { name = "matplotlib" },
+    { name = "plotstyle" },
     { name = "polars" },
 ]
 convert = [
@@ -477,6 +478,7 @@ dev = [
     { name = "awkward" },
     { name = "ipykernel" },
     { name = "matplotlib" },
+    { name = "plotstyle" },
     { name = "polars" },
     { name = "pytest" },
     { name = "ruff" },
@@ -497,6 +499,7 @@ requires-dist = [
     { name = "numpy", specifier = ">=1.26,<3" },
     { name = "pandas", specifier = ">=2.2,<4" },
     { name = "particle", specifier = ">=1.0,<2" },
+    { name = "plotstyle", marker = "extra == 'analysis'", specifier = ">=1.0.0", index = "https://git.larsbogner.de/api/packages/lars/pypi/simple/" },
     { name = "polars", marker = "extra == 'analysis'", specifier = ">=1.0,<2" },
     { name = "polars", marker = "extra == 'convert'", specifier = ">=1.0,<2" },
     { name = "pyarrow", specifier = ">=16,<25" },
@@ -1307,6 +1310,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/e6/cd9575ac904136b3cbf7aa7ee819ef86eedb7274e46f230e94ea4342e729/platformdirs-4.10.0-py3-none-any.whl", hash = "sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a", size = 22743, upload-time = "2026-05-28T03:32:52.175Z" },
 ]
 
+[[package]]
+name = "plotstyle"
+version = "1.0.0"
+source = { registry = "https://git.larsbogner.de/api/packages/lars/pypi/simple/" }
+dependencies = [
+    { name = "matplotlib" },
+]
+sdist = { url = "https://git.larsbogner.de/api/packages/lars/pypi/files/plotstyle/1.0.0/plotstyle-1.0.0.tar.gz", hash = "sha256:30f0c429077e5909ab1c89e0982c4351ffb71dc0688b177f78c35ed31b9bfc92" }
+wheels = [
+    { url = "https://git.larsbogner.de/api/packages/lars/pypi/files/plotstyle/1.0.0/plotstyle-1.0.0-py3-none-any.whl", hash = "sha256:f222a866fc81e7166cab78a8a30f2fc8e1436012c98812d39deb1103bd7e99ef" },
+]
+
 [[package]]
 name = "pluggy"
 version = "1.6.0"