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1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 | """Controlled experiment execution and report publication for the dashboard."""
from __future__ import annotations
import hashlib
import json
from datetime import datetime, timezone
from dataclasses import asdict, dataclass, replace
from pathlib import Path
from time import perf_counter
from types import CodeType, ModuleType
from typing import Any, Callable, Protocol, Sequence, cast
from src.research.data_v2 import prepare_research_data_v2
from src.research.experiment_recorder import ExperimentRecorder
from src.research.experiment_summary import (
write_detailed_experiment_summary,
write_statistics_artifact,
)
from src.research.protocol_registry import (
OPERATIONAL_RUNNERS,
PreregistrationError,
protocol_by_id,
protocol_catalog,
validate_operational_protocol,
)
from src.research.registry import ResearchRegistry
from src.research_assistant.airr import AIRRPipeline
from src.research_assistant.assistant import AnalysisBackend
from .models import JSONValue
class WorkflowValidationError(ValueError):
"""Raised when a workflow submission is not scientifically traceable."""
EPISTEMIC_LAYERS: dict[str, JSONValue] = {
"ui_state": "operator/dashboard progress only; not a scientific result",
"data": "DATA artifacts and deterministic statistics are authoritative run outputs",
"evid": "not_created; requires semantic validation, clean freeze and human review",
"interpretation": "post-hoc AI or human interpretation; never execution input",
}
class _ScientificRunLike(Protocol):
"""Typed read boundary used only for post-run execution validation."""
seed: int
condition: str
metrics: dict[str, Any]
def write_experiment_summary(
research_root: Path,
experiment_id: str,
ai_report: dict[str, object],
) -> str:
"""Write the canonical detailed, data-first experiment summary."""
return write_detailed_experiment_summary(research_root, experiment_id, ai_report)
@dataclass(frozen=True, slots=True)
class ExperimentWorkflow:
"""Validated, reproducible input for one controlled experiment run."""
experiment_id: str
question_id: str
hypothesis_id: str
title: str
conditions: str
ticks: int
notes: str
protocol: str
seeds: tuple[int, ...]
class ExperimentWorkflowService:
"""Run a fixed controller action and publish its research artifacts.
The service deliberately accepts a controller callback, never commands or
model-generated code. This keeps language-model assistance outside the
causal path of the experiment.
"""
def __init__(
self,
research_root: Path,
ai_backend: AnalysisBackend | None = None,
) -> None:
self._research_root = research_root
self._ai_backend = ai_backend
def catalog(self) -> dict[str, JSONValue]:
"""Return registry entries suitable for workflow selection."""
registry = ResearchRegistry(self._research_root / "registry").load_all()
return {
"questions": cast(
JSONValue,
[
{"id": question.id, "label": question.question}
for question in registry.questions.values()
],
),
"hypotheses": cast(
JSONValue,
[
{
"id": hypothesis.id,
"question_id": hypothesis.research_question,
"label": hypothesis.hypothesis,
}
for hypothesis in registry.hypotheses.values()
],
),
"protocols": cast(
JSONValue,
[
{
"id": "science_suite_v1",
"label": "Science Suite v1 (RQ-gesteuerter Runner + DATA + Manifest)",
},
{
"id": "science_all_v1",
"label": "Science ALL v1 (alle Science-Suite-Protokolle)",
},
{
"id": "science_time_v1",
"label": "Science TIME v1 (DATA + Manifest)",
},
{"id": "science_5d_v1", "label": "Science 5D v1 (DATA + Manifest)"},
{
"id": "learning_operator_v1",
"label": "Operator Learning v1 (DATA + Manifest + Report)",
},
{
"id": "runtime_ticks_v1",
"label": "Runtime-Ticks (Laufprotokoll)",
},
{
"id": "stdp_pair_timing_v1",
"label": "STDP Pair-Timing v1 (registriert)",
},
*protocol_catalog(self._research_root),
],
),
"next_experiment_id": self._next_experiment_id(),
}
def run_batch(
self,
body: dict[str, object],
*,
run_ticks: Callable[[int], object] | None = None,
before: Callable[[], dict[str, int]] | None = None,
after: Callable[[], dict[str, int]] | None = None,
) -> dict[str, object]:
"""Run selected registered protocols and publish one aggregate report."""
protocol_ids_value = body.get("protocols")
if not isinstance(protocol_ids_value, list) or not protocol_ids_value:
raise WorkflowValidationError("protocols must contain at least one protocol id")
protocol_ids = [item for item in protocol_ids_value if isinstance(item, str) and item]
if not protocol_ids:
raise WorkflowValidationError("protocols must contain valid protocol selections")
catalog = {
str(item["id"]): item
for item in protocol_catalog(self._research_root)
if isinstance(item.get("id"), str)
}
exploratory: dict[str, tuple[str, str]] = {}
known_protocols: list[str] = []
for protocol_id in protocol_ids:
if protocol_id.startswith("exploratory:"):
parts = protocol_id.split(":", 2)
if len(parts) != 3 or not parts[1] or not parts[2]:
raise WorkflowValidationError(f"Invalid exploratory selection: {protocol_id}")
exploratory[protocol_id] = (parts[1], parts[2])
else:
known_protocols.append(protocol_id)
unknown = [protocol_id for protocol_id in known_protocols if protocol_id not in catalog]
if unknown:
raise WorkflowValidationError(f"Unknown operational protocols: {', '.join(unknown)}")
batch_id_value = body.get("batch_id")
batch_id = str(batch_id_value).strip() if isinstance(batch_id_value, str) else ""
if not batch_id:
stamp = datetime.now(timezone.utc).strftime("%Y%m%d%H%M%S")
batch_id = f"EXP-BATCH-{stamp}"
if not batch_id.startswith("EXP-"):
raise WorkflowValidationError("batch_id must use the EXP-* convention")
ticks = body.get("ticks", 1000)
if not isinstance(ticks, int) or isinstance(ticks, bool) or ticks < 1:
raise WorkflowValidationError("ticks must be a positive integer")
seeds = body.get("seeds", "42-44")
options_value = body.get("protocol_options", {})
protocol_options = options_value if isinstance(options_value, dict) else {}
title_prefix = str(body.get("title_prefix") or "Experiment workflow").strip()
conditions = str(body.get("conditions") or "Registered protocol conditions").strip()
notes = str(body.get("notes") or "").strip()
results: list[dict[str, object]] = []
for index, protocol_id in enumerate(protocol_ids, start=1):
option_value = protocol_options.get(protocol_id)
option = option_value if isinstance(option_value, dict) else {}
child_ticks = option.get("ticks", ticks)
child_seeds = option.get("seeds", seeds)
if not isinstance(child_ticks, int) or isinstance(child_ticks, bool) or child_ticks < 1:
raise WorkflowValidationError(f"Invalid ticks for {protocol_id}")
if protocol_id in exploratory:
question_id, hypothesis_id = exploratory[protocol_id]
protocol = "runtime_ticks_v1"
title = f"{title_prefix}: exploratory {question_id}"
else:
contract = catalog[protocol_id]
question_id = str(contract["research_question"])
hypothesis_id = str(contract["hypothesis"])
protocol = protocol_id
title = f"{title_prefix}: {protocol_id}"
experiment_id = f"{batch_id}-{index:02d}"
child = {
"experiment_id": experiment_id,
"question_id": question_id,
"hypothesis_id": hypothesis_id,
"title": title,
"conditions": conditions,
"ticks": child_ticks,
"seeds": child_seeds,
"notes": notes,
"protocol": protocol,
"exploratory": protocol_id in exploratory,
}
try:
if protocol_id in exploratory:
if run_ticks is None or before is None or after is None:
raise WorkflowValidationError(
"Exploratory batch runs require an attached runtime controller."
)
result = self.run(child, run_ticks, before(), after)
summary = write_experiment_summary(
self._research_root,
experiment_id,
{"status": "unavailable", "reason": "Exploratory runtime run; AI post-hoc analysis not required.", "scientific_evidence": False},
)
result["summary"] = summary
result["research_run_mode"] = "EXPLORATORY"
result["test_run"] = True
else:
result = self.run_science(child)
results.append({"protocol": protocol_id, "ticks": child_ticks, "seeds": child_seeds, "status": "completed", **result})
except Exception as exc:
results.append({
"protocol": protocol_id,
"ticks": child_ticks,
"seeds": child_seeds,
"experiment_id": experiment_id,
"status": "failed",
"error": f"{type(exc).__name__}: {exc}",
})
workflow_root = self._research_root / "workflows"
workflow_root.mkdir(parents=True, exist_ok=True)
report_path = workflow_root / f"{batch_id}.json"
completed = sum(item["status"] == "completed" for item in results)
failed = len(results) - completed
report = {
"workflow_id": batch_id,
"created_at": datetime.now(timezone.utc).isoformat(),
"protocols": protocol_ids,
"requested_ticks": ticks,
"seeds": seeds,
"completed": completed,
"failed": failed,
"results": results,
}
report_path.write_text(json.dumps(report, indent=2, ensure_ascii=True, default=str) + "\n", encoding="utf-8")
markdown_path = report_path.with_suffix(".md")
markdown_path.write_text(
"\n".join([
f"# {batch_id}: Experiment workflow",
"",
f"- Requested ticks: `{ticks}`",
f"- Seeds: `{seeds}`",
f"- Completed: `{completed}`",
f"- Failed: `{failed}`",
"",
"## Protocol results",
*[f"- `{item['protocol']}`: **{item['status']}**" + (f" — {item['error']}" if item.get("error") else "") for item in results],
"",
"The workflow report is an aggregate index; each completed protocol keeps its own manifest, raw data, statistics and report.",
]) + "\n",
encoding="utf-8",
)
return {
"workflow_id": batch_id,
"report": f"workflows/{batch_id}.json",
"report_markdown": f"workflows/{batch_id}.md",
"completed": completed,
"failed": failed,
"results": results,
}
def run_science(
self, body: dict[str, object], *, seeds: tuple[int, ...] | None = None
) -> dict[str, object]:
"""Execute the RQ-compatible science runner and persist complete artifacts."""
science_body = dict(body)
science_body.setdefault("protocol", "science_suite_v1")
workflow = self._validate(science_body)
runner_name = self._science_runner(science_body, workflow)
effective_seeds = seeds if seeds is not None else workflow.seeds
workflow = replace(workflow, seeds=effective_seeds)
output_dir = self._research_root / "experiments" / workflow.experiment_id
if (output_dir / "manifest.json").exists():
raise WorkflowValidationError(
f"Experiment '{workflow.experiment_id}' already exists."
)
from src.research import experiment_suite
config_path = (
self._research_root.parent / "configs" / "learning_experiment.yaml"
)
if not config_path.exists():
config_path = Path("configs/learning_experiment.yaml")
config_path = config_path.resolve()
config = _load_yaml(config_path)
config_digest = _sha256_file(config_path)
started = perf_counter()
if hasattr(experiment_suite, runner_name):
runner_module: ModuleType = experiment_suite
else:
from src.research import followup_experiments
runner_module = followup_experiments
runner = getattr(runner_module, runner_name)
runner_source_value = getattr(runner_module, "__file__", None)
if not isinstance(runner_source_value, str):
raise WorkflowValidationError(
f"Cannot resolve source path for runner module {runner_module.__name__}."
)
runner_source = runner_source_value
runtime_runner_digest, source_runner_digest = (
_assert_loaded_callable_matches_source(
runner, Path(runner_source).resolve(), runner_name
)
)
summary_runtime_digest, summary_source_digest = (
_assert_loaded_callable_matches_source(
write_detailed_experiment_summary,
Path(write_detailed_experiment_summary.__code__.co_filename).resolve(),
"write_detailed_experiment_summary",
)
)
tick_aware_runners = {
"run_all",
"run_ping",
"run_ping_v2",
"run_5d",
"run_time",
"run_temporal",
"run_recurrence_map",
"run_replication",
"run_5d_matched",
"run_regulation_recovery",
"run_temporal_order",
"run_performance_profile",
"run_recurrence_scale",
"run_sustained_stability",
}
if runner_name in tick_aware_runners:
runs = runner(config, seeds=effective_seeds, ticks=workflow.ticks)
else:
runs = runner(config, seeds=effective_seeds)
runs = [replace(run, experiment_id=workflow.experiment_id) for run in runs]
duration = perf_counter() - started
tick_validation = self._validate_tick_execution(
runner_name,
workflow.ticks,
effective_seeds,
cast(Sequence[_ScientificRunLike], runs),
)
recorder = ExperimentRecorder(workflow.experiment_id, output_dir=output_dir)
data_path = output_dir / "DATA" / "runs.json"
data_path.parent.mkdir(parents=True, exist_ok=True)
serialized_runs = [asdict(run) for run in runs]
# Compute statistics from the complete in-memory observations first. Large
# per-tick traces are then moved to compressed sidecars so runs.json remains
# reviewable without discarding raw observations.
statistics_path = write_statistics_artifact(output_dir, serialized_runs)
statistics_payload = json.loads(statistics_path.read_text(encoding="utf-8"))
data_v2 = prepare_research_data_v2(
output_dir, serialized_runs, statistics_payload
)
trace_paths = list(data_v2.raw_paths)
data_path.write_text(
json.dumps(serialized_runs, indent=2, sort_keys=True, default=list) + "\n",
encoding="utf-8",
)
recorder.record_research_links([workflow.question_id], [workflow.hypothesis_id])
operational_protocol = protocol_by_id(self._research_root, workflow.protocol)
if operational_protocol is not None:
preregistration = validate_operational_protocol(
self._research_root,
question_id=workflow.question_id,
hypothesis_id=workflow.hypothesis_id,
protocol_id=workflow.protocol,
seed_count=len(effective_seeds),
)
recorder.record_research_run_mode(
str(preregistration.get("mode", "EXPLORATORY")).upper()
)
recorder.record_config(str(config_path), config_digest)
recorder.record_simulation_params(
seed=effective_seeds[0],
ticks=workflow.ticks,
seeds=list(effective_seeds),
protocol=workflow.protocol,
)
recorder.record_artifact("data", "DATA/runs.json")
recorder.record_artifact("data_index", "DATA/runs_index.json")
recorder.record_artifact("ai_packet", "analysis/ai_packet.json")
recorder.record_artifact("ai_packet_digest", "analysis/ai_packet_digest.json")
recorder.record_artifact("statistics", "analysis/statistics.json")
recorder.record_artifact("workflow", "workflow.json")
recorder.record_artifact("report", "report.md")
recorder.record_results(
run_count=len(runs),
protocol=workflow.protocol,
runner=runner_name,
ticks_requested=workflow.ticks,
tick_contract=tick_validation,
)
report_path = output_dir / "report.md"
report_path.write_text(
self._render_science_report(
workflow, len(runs), duration, runner_name, tick_validation
),
encoding="utf-8",
)
workflow_path = output_dir / "workflow.json"
workflow_path.write_text(
json.dumps(
{
"experiment_id": workflow.experiment_id,
"research_question": workflow.question_id,
"hypothesis": workflow.hypothesis_id,
"title": workflow.title,
"conditions": workflow.conditions,
"ticks": workflow.ticks,
"ticks_contract": "minimum requested observation window",
"seeds": list(effective_seeds),
"protocol": workflow.protocol,
"resolved_runner": runner_name,
"tick_validation": tick_validation,
"notes": workflow.notes,
"execution": "registered experiment_suite runner",
"assistant_policy": "AI is post-hoc interpretation only.",
"epistemic_layers": EPISTEMIC_LAYERS,
},
indent=2,
ensure_ascii=True,
)
+ "\n",
encoding="utf-8",
)
recorder.record_provenance_digests(
code_digest=_sha256_file(Path(experiment_suite.__file__)),
config_digest=config_digest,
prompt_digest=hashlib.sha256(b"NO_PROMPT").hexdigest(),
data_digest=_sha256_files(
[
data_path,
data_v2.raw_index_path,
data_v2.ai_packet_path,
data_v2.ai_packet_digest_path,
*trace_paths,
],
output_dir,
),
)
recorder.record_runtime(duration).mark_completed().save()
ai_report = self._append_ai_report(workflow.experiment_id)
manifest_path = output_dir / "manifest.json"
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
artifacts = manifest.setdefault("artifacts", {})
artifacts["statistics"] = str(statistics_path.relative_to(output_dir)).replace(
"\\", "/"
)
manifest["execution_contract"] = {
"resolved_runner": runner_name,
"ticks_requested": workflow.ticks,
"tick_validation": tick_validation,
"question_id": workflow.question_id,
"hypothesis_id": workflow.hypothesis_id,
"protocol": workflow.protocol,
"runtime_runner_bytecode_sha256": runtime_runner_digest,
"source_runner_bytecode_sha256": source_runner_digest,
"runtime_summary_bytecode_sha256": summary_runtime_digest,
"source_summary_bytecode_sha256": summary_source_digest,
"source_runtime_consistency": "MATCH",
}
manifest["epistemic_layers"] = EPISTEMIC_LAYERS
artifacts["raw_run_index"] = "DATA/runs_index.json"
artifacts["current_run"] = "DATA/current_run.json"
artifacts["ai_packet"] = "analysis/ai_packet.json"
artifacts["ai_packet_digest"] = "analysis/ai_packet_digest.json"
if operational_protocol is not None:
artifacts["preregistration"] = str(operational_protocol["preregistration"])
if ai_report.get("status") == "generated":
artifacts["ai_report_json"] = str(ai_report["json"])
artifacts["ai_report_markdown"] = str(ai_report["markdown"])
manifest_path.write_text(
json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
summary_path = write_experiment_summary(
self._research_root, workflow.experiment_id, ai_report
)
return {
"experiment_id": workflow.experiment_id,
"manifest": f"experiments/{workflow.experiment_id}/manifest.json",
"report": f"experiments/{workflow.experiment_id}/report.md",
"workflow": f"experiments/{workflow.experiment_id}/workflow.json",
"statistics": f"experiments/{workflow.experiment_id}/analysis/statistics.json",
"ai_packet": f"experiments/{workflow.experiment_id}/analysis/ai_packet.json",
"raw_run_index": f"experiments/{workflow.experiment_id}/DATA/runs_index.json",
"data_id": f"DATA-{workflow.experiment_id}",
"ai_report": ai_report,
"summary": summary_path,
"result": {
"run_count": len(runs),
"duration_seconds": duration,
"runner": runner_name,
"ticks_requested": workflow.ticks,
"seeds_executed": list(effective_seeds),
"tick_validation": tick_validation,
},
"epistemic_layers": EPISTEMIC_LAYERS,
}
@staticmethod
def _science_runner(body: dict[str, object], workflow: ExperimentWorkflow) -> str:
"""Resolve execution from protocol and registered research question.
Generated experiment IDs are labels only. They must never silently select
a scientific runner, because that previously allowed TEMP questions to be
executed as PING recurrence experiments.
"""
protocol = str(body.get("protocol") or workflow.protocol)
question_id = workflow.question_id
operational_runner = OPERATIONAL_RUNNERS.get(protocol)
if operational_runner is not None:
return operational_runner
if protocol == "science_all_v1":
return "run_all"
if protocol == "science_time_v1":
if not question_id.startswith("RQ-TIME-"):
raise WorkflowValidationError(
"science_time_v1 requires a RQ-TIME-* research question."
)
return "run_time"
if protocol == "science_5d_v1":
if not question_id.startswith("RQ-5D-"):
raise WorkflowValidationError(
"science_5d_v1 requires a RQ-5D-* research question."
)
return "run_5d"
if protocol != "science_suite_v1":
raise WorkflowValidationError(
f"Protocol '{protocol}' is not a Science Suite runner protocol."
)
rq_runners = (
("RQ-PING-", "run_ping"),
("RQ-TEMP-", "run_temporal"),
("RQ-TIME-", "run_time"),
("RQ-5D-", "run_5d"),
("RQ-STDP-", "run_stdp"),
("RQ-REG-", "run_regulation"),
)
for prefix, runner_name in rq_runners:
if question_id.startswith(prefix):
return runner_name
if question_id == "RQ-SNN-001":
# Keep the UI workflow executable, but never relabel an impulse-response
# PING run as primary evidence for long-term stability. The complete
# suite is diagnostic only; _semantic_status deliberately keeps this RQ
# at MISMATCH until a dedicated sustained-activity protocol exists.
return "run_all"
if question_id == "RQ-SNN-002":
return "run_ping"
if question_id == "RQ-SNN-005":
return "run_learning_repeat"
raise WorkflowValidationError(
f"No science runner is registered for research question '{question_id}'. "
"Select a compatible registered protocol instead of falling back to PING."
)
@staticmethod
def _validate_tick_execution(
runner_name: str,
requested_ticks: int,
seeds: tuple[int, ...],
runs: Sequence[_ScientificRunLike],
) -> dict[str, object]:
"""Verify that tick-aware runners really respected the requested window."""
exact_window_runners = {
"run_ping",
"run_ping_v2",
"run_5d",
"run_temporal",
"run_recurrence_map",
"run_replication",
"run_5d_matched",
"run_regulation_recovery",
"run_temporal_order",
"run_performance_profile",
"run_recurrence_scale",
}
if runner_name in exact_window_runners:
observed_ints: list[int] = []
for run in runs:
value = run.metrics.get("ticks_executed")
if (
isinstance(value, bool)
or not isinstance(value, int)
or value < requested_ticks
):
raise WorkflowValidationError(
f"Tick contract violated: runner {runner_name} did not execute at least {requested_ticks} ticks in every run."
)
observed_ints.append(value)
if not observed_ints:
raise WorkflowValidationError(
f"Tick contract violated: runner {runner_name} produced no runs."
)
return {
"status": "SATISFIED",
"mode": "minimum_per_run",
"requested_ticks": requested_ticks,
"observed_min": min(observed_ints),
"observed_max": max(observed_ints),
}
if runner_name == "run_sustained_stability":
observed_ints = [
run.metrics.get("ticks_executed")
for run in runs
if isinstance(run.metrics.get("ticks_executed"), int)
]
if len(observed_ints) != len(runs) or not observed_ints:
raise WorkflowValidationError(
"Sustained stability runner produced incomplete tick observations."
)
if min(observed_ints) < requested_ticks:
raise WorkflowValidationError(
"Sustained stability runner did not execute the requested tick budget in every run."
)
if len(runs) != len(seeds) * 2:
raise WorkflowValidationError(
"Sustained stability runner must produce control and treatment runs for every seed."
)
return {
"status": "SATISFIED",
"mode": "minimum_per_run_with_control_and_treatment",
"requested_ticks": requested_ticks,
"observed_min": min(observed_ints),
"observed_max": max(observed_ints),
"run_count": len(runs),
}
if runner_name == "run_time":
missing_seeds: list[int] = []
for seed in seeds:
if not any(
run.seed == seed and run.metrics.get("ticks") == requested_ticks
for run in runs
):
missing_seeds.append(seed)
if missing_seeds:
raise WorkflowValidationError(
"TIME tick ladder does not contain the requested terminal tick count "
f"for seeds: {missing_seeds}."
)
return {
"status": "SATISFIED",
"mode": "terminal_tick_per_seed",
"requested_ticks": requested_ticks,
}
if runner_name == "run_all":
tick_groups = ("ping:", "temporal:", "5d:")
relevant = [
run
for run in runs
if any(str(run.condition).startswith(prefix) for prefix in tick_groups)
]
if not relevant or any(
not isinstance(run.metrics.get("ticks_executed"), int)
or run.metrics["ticks_executed"] < requested_ticks
for run in relevant
):
raise WorkflowValidationError(
"Science ALL tick contract violated in a tick-aware subgroup."
)
for seed in seeds:
if not any(
run.seed == seed
and str(run.condition).startswith("time:")
and run.metrics.get("ticks") == requested_ticks
for run in runs
):
raise WorkflowValidationError(
f"Science ALL TIME subgroup lacks requested tick terminal for seed {seed}."
)
return {
"status": "SATISFIED",
"mode": "mixed_protocol_tick_contract",
"requested_ticks": requested_ticks,
}
return {
"status": "NOT_APPLICABLE",
"mode": "protocol_defined_internal_trials",
"requested_ticks": requested_ticks,
}
def _append_ai_report(self, experiment_id: str) -> dict[str, object]:
"""Generate AIRR only after completion, or expose unavailable explicitly."""
if self._ai_backend is None:
return {"status": "unavailable", "reason": "AI backend not configured"}
try:
report = AIRRPipeline(self._research_root).analyze(
experiment_id, self._ai_backend
)
except Exception as exc:
return {
"status": "failed",
"error": type(exc).__name__,
"message": str(exc),
}
return {
"status": "generated",
"report_id": report.report_id,
"json": f"experiments/{experiment_id}/reports/{report.report_id}.json",
"markdown": f"experiments/{experiment_id}/reports/{report.report_id}.md",
"human_review": "PENDING",
"scientific_evidence": False,
}
def run(
self,
body: dict[str, object],
run_ticks: Callable[[int], object],
before: dict[str, int],
after: Callable[[], dict[str, int]],
) -> dict[str, object]:
"""Execute a bounded tick run and write manifest, plan, and report."""
workflow = self._validate(body)
output_dir = self._research_root / "experiments" / workflow.experiment_id
manifest_path = output_dir / "manifest.json"
if manifest_path.exists():
raise WorkflowValidationError(
f"Experiment '{workflow.experiment_id}' already exists."
)
output_dir.mkdir(parents=True, exist_ok=False)
relative_root = Path("experiments") / workflow.experiment_id
plan_path = output_dir / "workflow.json"
plan_path.write_text(
json.dumps(
{
"title": workflow.title,
"research_question": workflow.question_id,
"hypothesis": workflow.hypothesis_id,
"conditions": workflow.conditions,
"ticks": workflow.ticks,
"protocol": workflow.protocol,
"seeds": list(workflow.seeds),
"notes": workflow.notes,
"execution": "controller.step",
"assistant_policy": "No AI-generated input is executed or used as evidence.",
"epistemic_layers": EPISTEMIC_LAYERS,
},
indent=2,
ensure_ascii=True,
)
+ "\n",
encoding="utf-8",
)
recorder = ExperimentRecorder(workflow.experiment_id, output_dir=output_dir)
recorder.record_research_links(
[workflow.question_id], [workflow.hypothesis_id]
).record_simulation_params(
ticks=workflow.ticks,
seed=workflow.seeds[0],
seeds=list(workflow.seeds),
protocol=workflow.protocol,
).record_artifact(
"workflow", str(relative_root / plan_path.name).replace("\\", "/")
)
data_path = output_dir / "DATA" / "runs.json"
data_path.parent.mkdir(parents=True, exist_ok=True)
started = perf_counter()
try:
run_ticks(workflow.ticks)
except Exception as exc:
recorder.record_runtime_error(
tick=before["tick"],
phase="controller.step",
exception_type=type(exc).__name__,
message=str(exc),
fatal=True,
).mark_failed()
recorder.record_runtime(perf_counter() - started).save()
raise
runtime = after()
duration = perf_counter() - started
observed_ticks = runtime["tick"] - before["tick"]
if observed_ticks < workflow.ticks:
recorder.record_runtime_error(
tick=runtime["tick"],
phase="tick_contract",
exception_type="TickContractViolation",
message=f"Requested {workflow.ticks}, observed {observed_ticks} ticks.",
fatal=True,
).mark_failed()
recorder.record_runtime(duration).save()
raise WorkflowValidationError(
f"Runtime tick contract violated: requested {workflow.ticks}, observed {observed_ticks}."
)
recorder.record_results(
passed=True,
start=before,
end=runtime,
observed_ticks=observed_ticks,
ticks_requested=workflow.ticks,
tick_contract="SATISFIED",
).record_artifact("data", "DATA/runs.json").record_runtime(duration).mark_completed().save()
data_path.write_text(
json.dumps(
[{
"experiment_id": workflow.experiment_id,
"condition": "exploratory_runtime_ticks",
"seed": workflow.seeds[0],
"metrics": {
"ticks_requested": workflow.ticks,
"ticks_executed": observed_ticks,
"start_tick": before["tick"],
"end_tick": runtime["tick"],
"neurons": runtime["neurons"],
"synapses": runtime["synapses"],
},
"runtime_error": None,
}],
indent=2,
ensure_ascii=True,
)
+ "\n",
encoding="utf-8",
)
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
manifest["epistemic_layers"] = EPISTEMIC_LAYERS
manifest_path.write_text(
json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
report_path = output_dir / "report.md"
report_path.write_text(
self._render_report(workflow, before, runtime, duration), encoding="utf-8"
)
return {
"experiment_id": workflow.experiment_id,
"manifest": str(relative_root / manifest_path.name).replace("\\", "/"),
"report": str(relative_root / report_path.name).replace("\\", "/"),
"result": {"start": before, "end": runtime, "duration_seconds": duration},
"epistemic_layers": EPISTEMIC_LAYERS,
}
def _validate(self, body: dict[str, object]) -> ExperimentWorkflow:
def required(name: str) -> str:
value = body.get(name)
if not isinstance(value, str) or not value.strip():
raise WorkflowValidationError(f"Missing '{name}'.")
return value.strip()
experiment_id_value = body.get("experiment_id", "")
if not isinstance(experiment_id_value, str):
raise WorkflowValidationError("Experiment ID must be text.")
experiment_id = experiment_id_value.strip() or self._next_experiment_id()
if not experiment_id.startswith("EXP-") or len(experiment_id) > 64:
raise WorkflowValidationError(
"Experiment ID must use the EXP-* convention."
)
ticks = body.get("ticks")
protocol = str(body.get("protocol") or "runtime_ticks_v1")
tick_limit = 1_000_000 if protocol == "science_time_v1" else 100_000
if (
not isinstance(ticks, int)
or isinstance(ticks, bool)
or not 1 <= ticks <= tick_limit
):
raise WorkflowValidationError(
f"Ticks must be an integer between 1 and {tick_limit}."
)
seeds = self._parse_seeds(body.get("seeds"))
question_id = required("question_id")
hypothesis_id = required("hypothesis_id")
registry = ResearchRegistry(self._research_root / "registry").load_all()
question = registry.questions.get(question_id)
hypothesis = registry.hypotheses.get(hypothesis_id)
if question is None:
raise WorkflowValidationError(f"Unknown research question '{question_id}'.")
exploratory_runtime = protocol == "runtime_ticks_v1" and body.get("exploratory") is True
if exploratory_runtime and hypothesis_id == "EXPLORATORY-UNSPECIFIED":
hypothesis = None
elif hypothesis is None or hypothesis.research_question != question.id:
raise WorkflowValidationError(
"The selected hypothesis does not belong to the research question."
)
if protocol_by_id(self._research_root, protocol) is not None:
try:
validate_operational_protocol(
self._research_root,
question_id=question_id,
hypothesis_id=hypothesis_id,
protocol_id=protocol,
seed_count=len(seeds),
)
except PreregistrationError as exc:
raise WorkflowValidationError(str(exc)) from exc
return ExperimentWorkflow(
experiment_id=experiment_id,
question_id=question_id,
hypothesis_id=hypothesis_id,
title=required("title"),
conditions=required("conditions"),
ticks=ticks,
notes=str(body.get("notes", "")).strip(),
protocol=protocol,
seeds=seeds,
)
@staticmethod
def _parse_seeds(value: object) -> tuple[int, ...]:
"""Parse comma-separated seeds and compact ranges from the runner UI."""
if value is None or value == "":
return (42, 43, 44)
if isinstance(value, str):
tokens: list[object] = [
token.strip() for token in value.split(",") if token.strip()
]
elif isinstance(value, (list, tuple)):
tokens = list(cast(list[object] | tuple[object, ...], value))
else:
raise WorkflowValidationError("Seeds must be comma-separated integers.")
parsed: list[int] = []
for token in tokens:
if isinstance(token, bool):
raise WorkflowValidationError("Seeds must be non-negative integers.")
text = str(token).strip()
if "-" in text and text.count("-") == 1:
start_text, end_text = (part.strip() for part in text.split("-"))
if start_text.isdigit() and end_text.isdigit():
start, end = int(start_text), int(end_text)
if end < start or end - start > 63:
raise WorkflowValidationError(
"Seed ranges must contain 64 values or fewer."
)
parsed.extend(range(start, end + 1))
continue
if not text.isdigit():
raise WorkflowValidationError("Seeds must be non-negative integers.")
parsed.append(int(text))
unique = tuple(dict.fromkeys(parsed))
if not unique or len(unique) > 64:
raise WorkflowValidationError("Provide between 1 and 64 unique seeds.")
return unique
def _next_experiment_id(self) -> str:
"""Return the first available generated experiment identifier."""
experiments_dir = self._research_root / "experiments"
for number in range(1, 10_000):
experiment_id = f"EXP-GEN-{number:04d}"
if not (experiments_dir / experiment_id).exists():
return experiment_id
raise WorkflowValidationError("No generated experiment IDs are available.")
@staticmethod
def _render_science_report(
workflow: ExperimentWorkflow,
run_count: int,
duration: float,
runner_name: str,
tick_validation: dict[str, object],
) -> str:
return "\n".join(
[
f"# {workflow.experiment_id}: {workflow.title}",
"",
"## Forschungszuordnung",
f"Forschungsfrage: `{workflow.question_id}`",
f"Hypothese: `{workflow.hypothesis_id}`",
f"Aufgeloester Runner: `{runner_name}`",
"",
"## Protokoll und Einstellungen",
f"Protokoll: `{workflow.protocol}`",
f"Angeforderte Mindest-Ticks: `{workflow.ticks}`",
f"Seeds: `{', '.join(str(seed) for seed in workflow.seeds)}`",
f"Tick-Vertrag: `{json.dumps(tick_validation, ensure_ascii=False, sort_keys=True)}`",
"",
"## Bedingungen",
workflow.conditions,
f"Runs: {run_count}; Dauer: {duration:.6f} s",
"",
"## Daten und Statistik",
"Kompakte Run-Projektion: `DATA/runs.json`",
"Unveränderlicher Rohdatenindex: `DATA/runs_index.json`",
"KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)",
"Deterministische deskriptive Statistik: `analysis/statistics.json`",
"Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.",
"",
"## Epistemische Ebenen",
"UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.",
"DATA: Rohdaten, Run-Index und deterministische Statistik.",
"EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.",
"Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.",
"",
"## Evidenzstatus",
"DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.",
"",
"## Hinweise",
workflow.notes or "Keine.",
"",
]
)
@staticmethod
def _render_report(
workflow: ExperimentWorkflow,
before: dict[str, int],
after: dict[str, int],
duration: float,
) -> str:
observed_ticks = after["tick"] - before["tick"]
return "\n".join(
[
f"# {workflow.experiment_id}: {workflow.title}",
"",
"## Forschungsfrage",
workflow.question_id,
"",
"## Hypothese",
workflow.hypothesis_id,
"",
"## Bedingungen",
workflow.conditions,
"",
"## Ausfuehrung",
f"Controller: `step({workflow.ticks})`",
"Ausfuehrungsmodus: kontrollierter Runtime-Lauf",
f"Dauer: {duration:.6f} s",
"",
"## Ergebnis",
f"Tick: {before['tick']} -> {after['tick']}",
f"Neuronen: {before['neurons']} -> {after['neurons']}",
f"Synapsen: {before['synapses']} -> {after['synapses']}",
f"Angeforderte Ticks: {workflow.ticks}",
f"Beobachtete Ticks: {observed_ticks}",
f"Tick-Vertrag: {'SATISFIED' if observed_ticks >= workflow.ticks else 'VIOLATED'}",
"",
"## Epistemische Ebenen",
"UI-Zustand ist nur Dashboard-Steuerung und Fortschritt.",
"DATA sind Manifest, Workflow und gemessene Laufwerte.",
"EVID wurde nicht erzeugt.",
"Interpretation bleibt nachgelagert und hat keine Ausfuehrungsautoritaet.",
"",
"## Reproduzierbarkeit",
"Git-Commit, Laufzeitumgebung und Runtime-Parameter stehen im Manifest.",
"",
"## Evidenzstatus",
"Keine EVID erzeugt. Fuer wissenschaftliche Evidenz sind ein sauberer Source-Freeze, ein registriertes Protokoll, kontrollierte unabhaengige Variablen und definierte Messgroessen erforderlich.",
"",
"## Hinweise",
workflow.notes or "Keine.",
"",
"KI-Ausgaben sind weder Ausfuehrungseingabe noch Evidenz. Eine finale Antwort auf die Forschungsfrage bleibt einer menschlichen wissenschaftlichen Bewertung vorbehalten.",
"",
]
)
def _find_named_code(code: CodeType, name: str) -> CodeType | None:
if code.co_name == name:
return code
for value in code.co_consts:
if not isinstance(value, CodeType):
continue
found = _find_named_code(value, name)
if found is not None:
return found
return None
def _code_digest(code: CodeType) -> str:
"""Hash executable code semantics while ignoring path/debug metadata.
Raw marshal bytes include interpreter- and compilation-specific metadata that
can differ between an imported function and the same freshly compiled source.
The source-freeze gate needs semantic identity instead: bytecode, exception
tables, signature/flags, referenced names and recursively hashed constants.
"""
digest = hashlib.sha256()
def update_value(value: object) -> None:
if isinstance(value, CodeType):
digest.update(b"code\0")
update_code(value)
return
if isinstance(value, tuple):
digest.update(b"tuple\0")
tuple_items = cast(tuple[object, ...], value)
for item in tuple_items:
update_value(item)
digest.update(b"\0")
return
if isinstance(value, frozenset):
digest.update(b"frozenset\0")
set_items = cast(frozenset[object], value)
encoded_items = sorted(
f"{type(item).__qualname__}:{item!r}".encode(
"utf-8", errors="backslashreplace"
)
for item in set_items
)
for item in encoded_items:
digest.update(item)
digest.update(b"\0")
return
if isinstance(value, bytes):
digest.update(b"bytes\0")
digest.update(value)
return
digest.update(type(value).__qualname__.encode("utf-8"))
digest.update(b":")
digest.update(repr(value).encode("utf-8", errors="backslashreplace"))
def update_code(item: CodeType) -> None:
for number in (
item.co_argcount,
item.co_posonlyargcount,
item.co_kwonlyargcount,
item.co_nlocals,
item.co_stacksize,
item.co_flags,
):
digest.update(str(number).encode("ascii"))
digest.update(b"\0")
digest.update(item.co_code)
digest.update(b"\0")
digest.update(item.co_exceptiontable)
digest.update(b"\0")
for values in (
item.co_names,
item.co_varnames,
item.co_freevars,
item.co_cellvars,
):
update_value(tuple(values))
digest.update(b"\0")
update_value(tuple(item.co_consts))
update_code(code)
return digest.hexdigest()
def _assert_loaded_callable_matches_source(
function: Callable[..., object], source_path: Path, function_name: str
) -> tuple[str, str]:
"""Block scientific runs when a long-lived process still holds stale code."""
runtime_digest = _code_digest(function.__code__)
compiled = compile(
source_path.read_text(encoding="utf-8"), str(source_path), "exec"
)
source_code = _find_named_code(compiled, function_name)
if source_code is None:
raise WorkflowValidationError(
f"Cannot verify runtime/source consistency for {function_name}."
)
source_digest = _code_digest(source_code)
if runtime_digest != source_digest:
raise WorkflowValidationError(
f"Running process contains stale code for {function_name}. Restart the Brain-5D "
"dashboard/runtime before creating a scientific experiment."
)
return runtime_digest, source_digest
def _sha256_files(paths: Sequence[Path], root: Path) -> str:
"""Hash a set of artifacts using experiment-relative names for portability."""
digest = hashlib.sha256()
for path in sorted(paths, key=lambda item: item.relative_to(root).as_posix()):
digest.update(path.relative_to(root).as_posix().encode("utf-8"))
digest.update(b"\0")
digest.update(path.read_bytes())
digest.update(b"\0")
return digest.hexdigest()
def _load_yaml(path: Path) -> dict[str, Any]:
import yaml
loaded = yaml.safe_load(path.read_text(encoding="utf-8"))
if not isinstance(loaded, dict):
raise WorkflowValidationError("Science configuration must be a mapping.")
return cast(dict[str, Any], loaded)
def _sha256_file(path: Path) -> str:
return hashlib.sha256(path.read_bytes()).hexdigest()
|