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Replace the event-level n_sec confusion matrix with two step-resolved secondary-multiplicity comparisons: - sec_count_per_step: overlay histogram of how many secondaries a single step emits, rollout series vs reference. - sec_count_per_step_by_species: heatmap of per-step multiplicity of one species (zero row included) against species, drawn as one panel per rollout plus a reference panel, raw counts on a log color scale. Both are backed by a new sources.secondaries_by_step view, which tags each secondary with its emitting step — (event_id, parent_id, birth position) on the rollout side, the row index on the reference side — so neither plot needs a join against the step frame. Steps that emitted nothing are recovered by subtraction from the chunk's step count, keeping both specs sum-mergeable across condor chunks. The rollout multiplicity is derived from the actual secondary birth rows rather than the n_sec_pred column, which records the predicted count before the per-event max-tracks cap. _render_heatmap gained reference-panel and log-color support; marginal_distance_summary sets neither key and is unchanged. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
493 lines
16 KiB
Python
493 lines
16 KiB
Python
"""Render smoke test — skipped where plotstyle / LaTeX is unavailable."""
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from __future__ import annotations
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from pathlib import Path
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import numpy as np
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import pytest
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pytest.importorskip("plotstyle")
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from giant.analysis import render as render_mod # noqa: E402
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from giant.analysis.reduced import Reduced # noqa: E402
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def _try_render(reduced: list[Reduced], out: Path):
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from giant.analysis.render import render_all
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for r in reduced:
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r.save(out / "reduced" / f"{r.id}.json")
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return render_all(out / "reduced", out / "plots")
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def test_render_router_diagnostics_and_edge_cases(tmp_path: Path):
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reduced = [
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Reduced(
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"rg",
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"model",
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"router_gating",
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"Router gating",
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"pre-step energy [MeV]",
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{
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"log_x": True,
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"series": {
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"flow": {
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"n_experts": 2,
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"router_type": "energy",
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"rollout": {
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"centers": [1.0, 10.0, 100.0],
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"means": [[0.6, 0.4], [0.5, 0.5], [0.4, 0.6]],
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},
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"reference": {
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"centers": [1.0, 10.0, 100.0],
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"means": [[0.55, 0.45], [0.5, 0.5], [0.45, 0.55]],
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},
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},
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"wgan": {
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"n_experts": 2,
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"router_type": "energy",
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"rollout": {"centers": [1.0], "means": [[0.5, 0.5]]},
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"reference": {"centers": [1.0], "means": [[0.5, 0.5]]},
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},
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},
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},
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),
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Reduced(
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"rs",
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"model",
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"router_share",
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"Router share",
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"species",
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{
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"series": {
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"flow": {
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"categories": ["e-", "gamma"],
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"n_experts": 2,
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"router_type": "energy",
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"rollout": {"e-": [0.7, 0.3], "gamma": [0.2, 0.8]},
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"reference": {"e-": [0.6, 0.4], "gamma": [0.3, 0.7]},
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},
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},
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},
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),
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Reduced(
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"rp",
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"model",
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"router_share",
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"Router share by process (reference-only)",
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"process",
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{
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"series": {
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"flow": {
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"categories": ["compt", "phot"],
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"n_experts": 2,
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"router_type": "energy",
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"reference": {"compt": [0.4, 0.6], "phot": [0.9, 0.1]},
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},
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},
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},
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),
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Reduced(
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"rz",
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"model",
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"router_specialization",
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"Router specialization",
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"pre-step energy [MeV]",
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{
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"log_x": True,
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"series": {
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"flow": {
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"n_experts": 2,
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"chance_level": 0.5,
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"rollout": {"centers": [1.0, 10.0], "score": [0.6, 0.7]},
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"reference": {"centers": [1.0, 10.0], "score": [0.55, 0.65]},
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},
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"wgan": {
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"n_experts": 4,
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"chance_level": 0.25,
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"rollout": {"centers": [1.0, 10.0], "score": [0.3, 0.4]},
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"reference": {"centers": [], "score": []},
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},
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},
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},
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),
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Reduced(
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"ru",
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"model",
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"unavailable",
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"Router unavailable",
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"x",
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{"note": "router diagnostics unavailable: no router in this run"},
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),
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Reduced(
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"g4",
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"marginals",
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"grouped_hist",
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"Grouped (4)",
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"x",
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{
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"edges": [0, 1, 2],
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"groups": {
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lbl: {"series": {"flow": [1, 2], "wgan": [2, 1]}, "reference": [2, 1]}
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for lbl in ("a", "b", "c", "d")
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},
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"log_y": True,
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},
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),
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Reduced(
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"sl",
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"species",
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"single_hist",
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"Single (log-x)",
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"x",
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{"edges": [1, 10, 100], "series": {"flow": [5, 1], "wgan": [3, 2]}, "log_x": True, "log_y": True},
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),
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Reduced(
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"hm",
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"quality",
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"heatmap",
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"Distance summary (2 rollouts)",
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"grouping axis",
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{
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"series": {"flow": [[0.1, 0.2], [0.3, 0.4]], "wgan": [[0.5, 0.6], [0.7, 0.8]]},
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"row_labels": ["step_length", "edep"],
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"col_labels": ["overall", "energy"],
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"cbar_label": "KS statistic",
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"vmin": 0.0,
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"vmax": 1.0,
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},
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),
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]
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try:
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pdfs = _try_render(reduced, tmp_path)
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except RuntimeError as e: # LaTeX missing at render time
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pytest.skip(f"LaTeX rendering unavailable: {e}")
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assert len(pdfs) == len(reduced)
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assert all(p.exists() for p in pdfs)
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def test_render_all_run_gallery_invokes_subprocess(tmp_path: Path, monkeypatch):
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# render_mod.subprocess *is* the stdlib subprocess module, so a blanket
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# patch of .run would also swallow the real subprocess.run calls
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# matplotlib's texmanager makes to compile LaTeX during savefig — only
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# intercept the "gallery generate" call itself and pass everything else
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# (LaTeX included) through to the real subprocess.run.
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calls = []
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real_run = render_mod.subprocess.run
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def fake_run(*a, **k):
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if a and a[0] and a[0][0] == "gallery":
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calls.append((a, k))
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return None
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return real_run(*a, **k)
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monkeypatch.setattr(render_mod.subprocess, "run", fake_run)
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reduced = [
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Reduced(
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"s",
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"species",
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"single_hist",
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"Single",
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"x",
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{"edges": [0, 1, 2], "series": {"rollout": [5, 1]}},
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)
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]
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for r in reduced:
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r.save(tmp_path / "reduced" / f"{r.id}.json")
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try:
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render_mod.render_all(tmp_path / "reduced", tmp_path / "plots", run_gallery=True)
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except RuntimeError as e:
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pytest.skip(f"LaTeX rendering unavailable: {e}")
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assert len(calls) == 1
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args, kwargs = calls[0]
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assert args[0] == ["gallery", "generate", "--source", str(tmp_path / "plots")]
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assert kwargs == {"check": True}
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def test_render_run_glues_condor_run_meta_into_render_all(tmp_path: Path, monkeypatch):
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from giant.analysis import condor as condor_mod
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run_dir = tmp_path / "run"
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(run_dir / "reduced").mkdir(parents=True)
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merge_calls = []
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monkeypatch.setattr(condor_mod, "merge_all", lambda rd: merge_calls.append(Path(rd)))
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meta = condor_mod.RunMeta(
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rollouts=[{"name": "rollout", "path": "rollout.parquet", "plot_meta": {"checkpoint": "ckpt/best.pt"}}],
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reference="reference.parquet",
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run_dir=str(run_dir),
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title="my-run",
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)
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monkeypatch.setattr(condor_mod.RunMeta, "load", classmethod(lambda cls, p: meta))
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Reduced("s", "species", "single_hist", "Single", "x", {"edges": [0, 1], "series": {"rollout": [1]}}).save(
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run_dir / "reduced" / "s.json"
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)
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try:
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pdfs = render_mod.render_run(run_dir)
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except RuntimeError as e:
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pytest.skip(f"LaTeX rendering unavailable: {e}")
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assert merge_calls == [run_dir]
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assert len(pdfs) == 1
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plot_meta = (run_dir / "plots" / "species" / "s.yaml").read_text()
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assert "checkpoint" in plot_meta
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root_meta = (run_dir / "plots" / "metadata.yaml").read_text()
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assert "my-run" in root_meta
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def test_render_one_of_each_kind(tmp_path: Path):
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reduced = [
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Reduced(
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"m",
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"marginals",
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"overlay_hist",
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"Overlay",
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"x",
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{
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"edges": [0, 1, 2, 3],
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"series": {"flow": [1, 2, 3], "wgan": [2, 2, 2]},
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"reference": [3, 2, 1],
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"log_y": False,
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},
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),
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Reduced(
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"g",
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"marginals",
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"grouped_hist",
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"Grouped",
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"x",
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{
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"edges": [0, 1, 2],
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"groups": {"a": {"series": {"flow": [1, 2]}, "reference": [2, 1]}},
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"log_y": False,
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},
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),
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Reduced(
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"p",
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"shower",
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"profile",
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"Profile",
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"depth",
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{
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"edges": [0, 1, 2],
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"series": {"flow": {"mean": [1, 2], "std": [0.1, 0.2]}},
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"reference": {"mean": [1.1, 1.9], "std": [0.1, 0.1]},
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"ylabel": "e",
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},
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),
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Reduced(
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"b",
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"species",
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"bar",
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"Bar",
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"species",
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{
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"labels": ["e-", "gamma"],
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"series": {"flow": [0.6, 0.4], "wgan": [0.55, 0.45]},
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"reference": [0.5, 0.5],
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"ylabel": "frac",
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},
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),
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Reduced(
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"s",
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"species",
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"single_hist",
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"Single",
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"x",
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{"edges": [0, 1, 2], "series": {"flow": [5, 1]}, "log_y": True},
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),
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Reduced(
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"hm1",
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"secondaries",
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"heatmap",
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"Heatmap (single rollout)",
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"predicted",
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{
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"series": {"flow": [[1, 0], [0, 1]]},
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"reference": [[2, 0], [0, 1]],
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"row_labels": ["0", "1+"],
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"col_labels": ["0", "1+"],
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"cbar_label": "count",
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"log_color": True,
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},
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),
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]
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try:
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pdfs = _try_render(reduced, tmp_path)
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except RuntimeError as e: # LaTeX missing at render time
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pytest.skip(f"LaTeX rendering unavailable: {e}")
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assert len(pdfs) == len(reduced)
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assert all(p.exists() for p in pdfs)
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assert (tmp_path / "plots" / "metadata.yaml").exists()
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# ── pure-function helpers: no matplotlib figure needed ──────────────────
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def test_density_zero_total_returns_counts_unchanged():
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counts = np.array([0.0, 0.0, 0.0])
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out = render_mod._density(counts, np.array([0.0, 1.0, 2.0, 3.0]))
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np.testing.assert_array_equal(out, counts)
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def test_density_normalizes_by_total_and_bin_width():
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counts = [1, 3]
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edges = np.array([0.0, 2.0, 4.0]) # bin width 2
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out = render_mod._density(counts, edges)
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np.testing.assert_allclose(out, np.array([1, 3]) / (4 * 2))
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def test_router_summary_disabled_is_off():
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assert render_mod._router_summary({"enabled": False, "type": "energy"}) == "off"
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assert render_mod._router_summary({}) == "off"
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def test_router_summary_enabled_formats_type_and_n_experts():
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cfg = {"enabled": True, "type": "energy", "n_experts": 8}
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assert render_mod._router_summary(cfg) == "energy×8"
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def test_figure_params_v2_basics_and_router_and_epoch():
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mc = {
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"stage1_model": {
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"hidden_dim": 256,
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"n_res_blocks": 4,
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"generator": "flow",
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"router": {"enabled": True, "type": "energy", "n_experts": 4},
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},
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"conditioning": {"particle": {"type": "physical"}},
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}
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meta = {"training_epoch": 12, "best_val_loss": 0.123456, "steps": 10, "model_config": mc}
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params = render_mod._figure_params({"rollouts": {"rollout": meta}})
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assert params == {
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"hidden_dim": 256,
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"n_res_blocks": 4,
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"mode": "flow",
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"conditioning": "physical",
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"router": "energy×4",
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"epoch": 12,
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"best_val_loss": 0.1235,
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"steps": 10,
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}
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def test_figure_params_v2_wgan_reports_noise_dim_not_steps():
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mc = {
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"stage1_model": {
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"generator": "wgan",
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"wgan": {"noise_dim": 32},
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},
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}
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meta = {"model_config": mc, "steps": 10}
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params = render_mod._figure_params({"rollouts": {"rollout": meta}})
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assert params["mode"] == "wgan"
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assert params["noise_dim"] == 32
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assert "steps" not in params
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def test_figure_params_v2_reports_mode_s2_only_when_it_differs():
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same = {
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"stage1_model": {"generator": "flow"},
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"stage2_model": {"generator": "flow"},
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}
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assert "mode_s2" not in render_mod._figure_params({"rollouts": {"rollout": {"model_config": same}}})
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mixed = {
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"stage1_model": {"generator": "flow"},
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"stage2_model": {"generator": "wgan"},
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}
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params = render_mod._figure_params({"rollouts": {"rollout": {"model_config": mixed}}})
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assert params["mode_s2"] == "wgan"
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def test_figure_params_old_shape_basics():
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meta = {
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"model_config": {
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"hidden_dim": 128,
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"n_blocks": 3,
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"mode": "ddpm",
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"conditioning": "embedding",
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"router": {"enabled": False},
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},
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"training_epoch": 5,
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"best_val_loss": 0.5,
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"steps": 20,
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}
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params = render_mod._figure_params({"rollouts": {"rollout": meta}})
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assert params == {
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"hidden_dim": 128,
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"n_blocks": 3,
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"mode": "ddpm",
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"conditioning": "embedding",
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"router": "off",
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"epoch": 5,
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"best_val_loss": 0.5,
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"steps": 20,
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}
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def test_figure_params_old_shape_wgan_reports_noise_dim_not_steps():
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meta = {
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"model_config": {"mode": "wgan", "noise_dim": 16},
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"steps": 20,
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}
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params = render_mod._figure_params({"rollouts": {"rollout": meta}})
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assert params["noise_dim"] == 16
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assert "steps" not in params
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def test_figure_params_multi_rollout_names_the_series():
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run_meta = {"rollouts": {"flow": {"model_config": {"mode": "flow"}}, "wgan": {"model_config": {"mode": "wgan"}}}}
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assert render_mod._figure_params(run_meta) == {"rollouts": "flow, wgan"}
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def test_figure_params_empty_rollouts_is_empty():
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assert render_mod._figure_params({}) == {}
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assert render_mod._figure_params({"rollouts": {}}) == {}
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def test_plot_metadata_includes_note_and_run_meta_parameters():
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r = Reduced("u", "router", "unavailable", "Unavailable", "x", {"note": "no router data"})
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meta = render_mod._plot_metadata(r, {"title": "run-1", "reference": "ref.parquet", "rollouts": {"rollout": {}}})
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assert meta["note"] == "no router data"
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assert meta["parameters"] == {"reference": "ref.parquet", "rollouts": {"rollout": {}}}
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assert "title" not in meta["parameters"]
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def test_plot_metadata_omits_parameters_when_run_meta_empty():
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r = Reduced("s", "species", "single_hist", "Single", "x", {"edges": [0, 1]})
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meta = render_mod._plot_metadata(r, {})
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assert "parameters" not in meta
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assert "note" not in meta
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def test_tex_escape_handles_percent_and_other_special_chars():
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assert render_mod._tex_escape("90% of deposited energy") == r"90\% of deposited energy"
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assert render_mod._tex_escape(r"a_b & c#d $e {f} \bar") == r"a\_b \& c\#d \$e \{f\} \textbackslash{}bar"
|
||
|
||
|
||
def test_render_survives_title_and_xlabel_with_literal_percent(tmp_path: Path):
|
||
# Regression test for gitea #81: a literal "%" in a catalog title (e.g.
|
||
# "Shower containment depth (90% of deposited energy)") crashed the whole
|
||
# LaTeX render, since usetex treats an unescaped "%" as a comment marker.
|
||
reduced = [
|
||
Reduced(
|
||
"shower_containment_depth_90",
|
||
"shower",
|
||
"single_hist",
|
||
"Shower containment depth (90% of deposited energy)",
|
||
"depth containing 90% of deposited energy [mm]",
|
||
{"edges": [0, 1, 2], "series": {"flow": [5, 1]}},
|
||
),
|
||
]
|
||
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) == 1
|
||
assert pdfs[0].exists()
|