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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>
75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
"""The compact, self-describing artifact a compute job produces per plot.
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Serialized as small JSON (no pickle, no per-event arrays) so it is trivially
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transferable off the batch worker and human-inspectable. ``render.py`` dispatches
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on ``kind`` and needs nothing but this file.
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"""
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from __future__ import annotations
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import json
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from dataclasses import asdict, dataclass, field
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from pathlib import Path
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# Reduced.kind values (payload keys a rollout series by name under
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# payload["series"], with the reference — where one exists — kept as one
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# distinguished payload["reference"] entry; see catalog.py's module
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# docstring for the full per-kind payload shape):
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# "overlay_hist" N-rollout-series vs reference density histogram over shared edges
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# "grouped_hist" one panel per group (energy/pdg/material), each an overlay
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# "profile" edep-weighted mean +/- event-RMS vs depth/radius, N series + reference
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# "bar" per-category N-rollout-series vs reference bars (share / counts)
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# "single_hist" rollout-only series (e.g. leakage; reference has none)
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# "router_gating" stacked mean MoE gate weight vs energy, one rollout+reference
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# panel-pair per rollout with an enabled MoE router
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# "router_share" stacked bar of MoE top-1 dispatch share by category, one
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# panel per rollout with an enabled MoE router
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# "router_specialization" max gate weight vs energy (one scalar trend line
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# summarizing "router_gating"), per rollout with an enabled router
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# "heatmap" row x col matrix + colorbar, one panel per rollout (a
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# distance scorecard)
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# "unavailable" plot not applicable to this run (e.g. no MoE checkpoint)
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@dataclass
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class Reduced:
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id: str
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family: str
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kind: str
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title: str
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xlabel: str
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payload: dict
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meta: dict = field(default_factory=dict)
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def save(self, path: str | Path) -> None:
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Path(path).parent.mkdir(parents=True, exist_ok=True)
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Path(path).write_text(json.dumps(asdict(self)))
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@classmethod
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def load(cls, path: str | Path) -> "Reduced":
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return cls(**json.loads(Path(path).read_text()))
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@dataclass
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class Partial:
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"""The raw, not-yet-finalized output of one ``(plot, chunk)`` compute job.
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``data`` holds whatever shape that plot's ``PlotSpec.compute_partial``
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returns — a raw sum-mergeable count dict, or a raw per-event/per-secondary
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array to be concatenated across chunks — never a finished histogram/profile.
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``PlotSpec.finalize`` is the only thing that knows how to interpret it.
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"""
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id: str
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family: str
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chunk: int
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data: dict
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def save(self, path: str | Path) -> None:
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Path(path).parent.mkdir(parents=True, exist_ok=True)
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Path(path).write_text(json.dumps(asdict(self)))
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@classmethod
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def load(cls, path: str | Path) -> "Partial":
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return cls(**json.loads(Path(path).read_text()))
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