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modules/reporting-engine/scripts/check_compatibility.py
37 KB · Oct 2, 2026 · 00:29 UTC
"""Check manifest-to-dataset compatibility using only mechanical evidence."""
from __future__ import annotations
# Direct CLI calls must select dependencies before importing workflow modules.
if __name__ == "__main__":
import runpy as _runpy
from pathlib import Path as _Path
for _parent in _Path(__file__).resolve().parents:
_launcher = _parent / "scripts" / "self_relaunch.py"
if not _launcher.is_file():
_launcher = _parent / "clara" / "scripts" / "self_relaunch.py"
if _launcher.is_file():
_runpy.run_path(str(_launcher))["ensure_running_in_managed_venv"](__file__)
break
else:
# Standalone components retain their host's dependency setup.
if any(
(_p / "components.json").is_file()
for _p in _Path(__file__).resolve().parents
):
raise SystemExit(
"Managed Python launcher is missing; rebuild the plugin package."
)
import argparse
import json
from pathlib import Path
from typing import Any
__all__ = [
"audit_manifest_against_dataset_profile",
"check_capability_compatibility",
"check_profile_compatibility",
"main",
]
REPORTING_ENGINE_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_MANIFEST = REPORTING_ENGINE_ROOT / "catalog" / "selection_manifest.json"
SEMANTIC_OR_PACKAGE_ISSUES = {
"requires_semantic_or_package_metric_source",
"requires_semantic_or_package_role",
}
def _load_json(path: Path) -> dict[str, Any]:
return json.loads(path.read_text(encoding="utf-8"))
def _columns_by_role(profile: dict[str, Any], role: str) -> list[str]:
return list((profile.get("roles") or {}).get(role) or [])
def _role_candidate_columns(profile: dict[str, Any], role: str) -> list[str]:
flattened = (profile.get("role_candidate_columns") or {}).get(role) or []
if flattened:
return [str(column) for column in flattened]
candidates = (profile.get("role_candidates") or {}).get(role) or []
columns: list[str] = []
for candidate in candidates:
if isinstance(candidate, dict) and candidate.get("column"):
columns.append(str(candidate["column"]))
elif isinstance(candidate, str):
columns.append(candidate)
return columns
def _metric_candidates(
profile: dict[str, Any], source_metric_role: dict[str, Any]
) -> list[str]:
metric_classes = profile.get("metric_classes") or {}
candidates: list[str] = []
for metric_class in source_metric_role.get("accepted_metric_classes") or []:
for column in metric_classes.get(metric_class) or []:
if column not in candidates:
candidates.append(column)
return candidates
def _column_profile(profile: dict[str, Any], column: str) -> dict[str, Any]:
columns = profile.get("columns") or {}
value = columns.get(column)
return value if isinstance(value, dict) else {}
def _candidate_available_rows(
profile: dict[str, Any], column: str, dataset_row_count: int
) -> int:
column_profile = _column_profile(profile, column)
if column_profile:
return dataset_row_count - int(column_profile.get("null_count") or 0)
derived = (profile.get("derived_metrics") or {}).get(column) or {}
produced_from = [str(value) for value in derived.get("produced_from") or []]
if not produced_from:
return 0
source_rows = [
dataset_row_count - int(_column_profile(profile, source).get("null_count") or 0)
for source in produced_from
if _column_profile(profile, source)
]
return min(source_rows) if len(source_rows) == len(produced_from) else 0
def _rejection_record(
profile: dict[str, Any], column: str, reason: str
) -> dict[str, Any]:
column_profile = _column_profile(profile, column)
return {
"column": column,
"reason": reason,
"source_role": column_profile.get("role"),
"metric_class": column_profile.get("metric_class"),
"cardinality_class": column_profile.get("cardinality_class"),
"null_ratio": column_profile.get("null_ratio"),
"period_parseability": column_profile.get("period_parseability"),
}
def _metric_rejections(
profile: dict[str, Any],
*,
accepted_metric_classes: list[str],
matched_columns: list[str],
limit: int = 20,
) -> dict[str, Any]:
accepted = set(accepted_metric_classes)
matched = set(matched_columns)
rejected: list[dict[str, Any]] = []
for column in _columns_by_role(profile, "metric"):
if column in matched:
continue
metric_class = _column_profile(profile, column).get("metric_class")
reason = (
"metric_class_not_accepted"
if metric_class not in accepted
else "not_selected_after_candidate_limit"
)
rejected.append(_rejection_record(profile, column, reason))
for column, derived_profile in (profile.get("derived_metrics") or {}).items():
if column in matched:
continue
metric_class = derived_profile.get("metric_class")
reason = (
"metric_class_not_accepted"
if metric_class not in accepted
else "not_selected_after_candidate_limit"
)
rejected.append(
{
"column": column,
"reason": reason,
"source_role": "derived_metric",
"metric_class": metric_class,
"cardinality_class": "derived",
"null_ratio": None,
"period_parseability": None,
}
)
return {
"rejected_count": len(rejected),
"samples": rejected[:limit],
}
def _period_rejections(
profile: dict[str, Any], *, matched_columns: list[str], limit: int = 20
) -> dict[str, Any]:
matched = set(matched_columns)
rejected = []
for column, column_profile in (profile.get("columns") or {}).items():
if column in matched:
continue
if column_profile.get("role") == "period":
reason = "not_selected_after_candidate_limit"
elif (column_profile.get("period_parseability") or {}).get("is_parseable"):
reason = "parseable_but_not_classified_as_period"
else:
reason = "not_parseable_as_period"
rejected.append(_rejection_record(profile, str(column), reason))
return {
"rejected_count": len(rejected),
"samples": rejected[:limit],
}
def _dimension_rejections(
profile: dict[str, Any],
*,
role: str,
role_resolution: dict[str, Any],
matched_columns: list[str],
limit: int = 20,
) -> dict[str, Any]:
matched = set(matched_columns)
resolution_type = str(role_resolution.get("resolution_type") or "")
if resolution_type == "semantic_or_package_role":
return {
"rejected_count": 0,
"samples": [],
"not_applicable_reason": "requires_semantic_or_package_role",
}
required_profile_roles = set(role_resolution.get("required_profile_roles") or [])
rejected: list[dict[str, Any]] = []
candidate_columns = _columns_by_role(profile, "dimension") + _columns_by_role(
profile, "identifier"
)
for column in dict.fromkeys(candidate_columns):
if column in matched:
continue
if resolution_type == "schema_role" and required_profile_roles:
reason = "does_not_match_required_profile_role"
elif resolution_type == "direct_rank_or_lane":
reason = "does_not_match_rank_or_lane_profile_role"
elif role in {"point_dimension", "product"}:
reason = "not_selected_as_entity_or_point_dimension"
else:
reason = "not_selected_after_role_token_ranking"
rejected.append(_rejection_record(profile, column, reason))
return {
"rejected_count": len(rejected),
"samples": rejected[:limit],
}
def _normalize_token(value: str) -> str:
return "".join(character for character in value.casefold() if character.isalnum())
def _special_dimension_candidates(profile: dict[str, Any], role: str) -> list[str]:
target = _normalize_token(role)
candidates = []
for column in _columns_by_role(profile, "dimension"):
normalized = _normalize_token(column)
if target in normalized or normalized in target:
candidates.append(column)
return candidates
def _direct_dimension_candidates(profile: dict[str, Any], role: str) -> list[str]:
if role in {"point_dimension", "product"}:
columns = _role_candidate_columns(profile, "entity_key") + _columns_by_role(
profile, "dimension"
)
else:
columns = _columns_by_role(profile, "dimension") + _columns_by_role(
profile, "identifier"
)
columns = list(dict.fromkeys(columns))
target = role.casefold()
role_tokens = [
_normalize_token(token)
for token in target.split("_")
if token not in {"category", "dimension", "driver", "item", "member", "point"}
]
matches: list[str] = []
for column in columns:
column_tokens = {
_normalize_token(token) for token in column.casefold().split("_")
}
column_tokens.add(_normalize_token(column))
if any(token and token in column_tokens for token in role_tokens):
matches.append(column)
fallback = [column for column in columns if column not in matches]
return (matches + fallback)[:30]
def _profile_role_candidates(profile: dict[str, Any], profile_role: str) -> list[str]:
if profile_role == "period":
return _columns_by_role(profile, "period")
if profile_role == "direct_dimension":
return _role_candidate_columns(profile, "direct_dimension") or _columns_by_role(
profile, "dimension"
)
if profile_role in {
"entity_key",
"ordered_stage",
"rank_or_lane",
"set_dimension",
"set_item",
"statement_line_item",
"statement_scenario",
}:
return _role_candidate_columns(profile, profile_role)
return []
def _resolve_dimension_role(
profile: dict[str, Any], role: str, requirement: dict[str, Any]
) -> dict[str, Any]:
"""Validate explicit role prerequisites without making semantic choices."""
resolution_type = str(requirement.get("resolution_type") or "direct_dimension")
required_profile_roles = list(requirement.get("requires_profile_roles") or [])
prerequisite_matches = {
profile_role: _profile_role_candidates(profile, profile_role)
for profile_role in required_profile_roles
}
missing_profile_roles = [
profile_role
for profile_role, candidates in prerequisite_matches.items()
if not candidates
]
minimum_candidate_count = int(requirement.get("minimum_candidate_count") or 0)
candidate_count_role = str(
requirement.get("candidate_count_profile_role") or "direct_dimension"
)
available_candidate_count = len(
prerequisite_matches.get(candidate_count_role) or []
)
candidate_columns = set(prerequisite_matches.get("direct_dimension") or [])
multidimensional_relationships = [
relationship
for relationship in profile.get("dimension_relationships") or []
if relationship.get("left_column") in candidate_columns
and relationship.get("right_column") in candidate_columns
and bool(relationship.get("supports_multidimensional_path"))
]
requires_non_bijective_pair = bool(
requirement.get("requires_non_bijective_dimension_pair", False)
)
if resolution_type == "direct_dimension":
candidates = _direct_dimension_candidates(profile, role)
elif resolution_type == "direct_rank_or_lane":
candidates = _profile_role_candidates(profile, "rank_or_lane")
elif resolution_type == "schema_role":
profile_role = required_profile_roles[0] if required_profile_roles else role
candidates = _profile_role_candidates(profile, profile_role)
elif resolution_type == "semantic_or_package_role":
candidates = []
missing_profile_roles = required_profile_roles or ["semantic_or_package_role"]
else:
candidates = []
issues: list[str] = []
if resolution_type == "semantic_or_package_role":
issues.append("requires_semantic_or_package_role")
elif missing_profile_roles:
if resolution_type == "schema_role":
issues.append("missing_schema_role")
else:
issues.append("missing_role_prerequisites")
elif minimum_candidate_count > available_candidate_count:
issues.append("insufficient_role_prerequisites")
elif requires_non_bijective_pair and not multidimensional_relationships:
issues.append("no_non_redundant_dimension_pair")
elif (
resolution_type
in {
"direct_dimension",
"direct_rank_or_lane",
"schema_role",
}
and not candidates
):
issues.append("missing_role_candidates")
return {
"role": role,
"required": bool(requirement.get("required", True)),
"resolution_type": resolution_type,
"required_profile_roles": required_profile_roles,
"missing_profile_roles": missing_profile_roles,
"minimum_candidate_count": minimum_candidate_count,
"candidate_count_profile_role": candidate_count_role,
"available_candidate_count": available_candidate_count,
"requires_non_bijective_dimension_pair": requires_non_bijective_pair,
"multidimensional_relationship_evidence": multidimensional_relationships[:20],
"candidate_columns": candidates[:20],
"prerequisite_matches": {
key: values[:20] for key, values in prerequisite_matches.items()
},
"issues": issues,
"notes": requirement.get("notes"),
}
def _derived_metric_package_issues(
metric_requirements: dict[str, Any],
) -> list[str]:
for role in metric_requirements.get("derived_metric_roles") or []:
produced_from = role.get("produced_from") or []
if "attribute_evidence_package" in produced_from:
return ["requires_semantic_or_package_metric_source"]
return []
def _period_scope_preflight(
period_requirements: dict[str, Any],
period_columns: list[str],
) -> dict[str, Any]:
"""Expose period-scope requirements without making semantic choices."""
scope_contract = period_requirements.get("scope_contract") or {}
role = str(scope_contract.get("role") or period_requirements.get("role") or "none")
period_column_required = bool(
period_requirements.get(
"required", scope_contract.get("period_column_required", role != "none")
)
)
scope_required = bool(
scope_contract.get("scope_required_for_render", role == "filter")
)
explicit_all_data_allowed = bool(
scope_contract.get("explicit_all_data_allowed", role == "filter")
)
if role == "none":
status = "not_applicable"
warning = ""
elif not period_columns:
if period_column_required:
status = "blocked_missing_period_column"
warning = "No required period column candidate is available."
else:
status = "optional_filter_unavailable_all_data_only"
warning = (
"No optional period column candidate is available; this chart "
"can still render all records without period filtering."
)
elif scope_required:
status = "caller_scope_required_before_render"
warning = (
"This chart has a period filter. A caller must pass a bounded "
"analysis period or explicitly request all available data."
)
elif role in {"axis", "axis_or_table"}:
status = "scope_optional_available_period_axis"
warning = (
"This chart can use the available period axis; bounded questions "
"should still pass an explicit period scope."
)
else:
status = "scope_optional"
warning = ""
return {
"role": role,
"status": status,
"period_column_required": period_column_required,
"scope_required_for_render": scope_required,
"explicit_all_data_allowed": explicit_all_data_allowed,
"accepted_scope_controls": list(
scope_contract.get("accepted_scope_controls") or []
),
"period_candidates": period_columns[:20],
"unscoped_default": scope_contract.get("unscoped_default"),
"pre_render_warning": warning,
}
def _capability_profile_match(
capability: dict[str, Any], profile: dict[str, Any]
) -> dict[str, Any]:
contract = capability["selection_contract"]
requirements = contract["dataset_requirements"]
metric_requirements = requirements["metrics"]
period_requirements = requirements["period"]
dimension_requirements = requirements["dimensions"]
observation_requirements = requirements.get("observations") or {
"minimum_non_null_rows": 1,
"scope": "rendered_analysis_scope",
}
source_metric_matches = []
missing_source_metric_roles = []
mechanical_role_matches: list[dict[str, Any]] = []
rejected_column_evidence: list[dict[str, Any]] = []
for source_metric_role in metric_requirements["source_metric_roles"]:
role_required = bool(source_metric_role.get("required", True))
candidates = _metric_candidates(profile, source_metric_role)
accepted_metric_classes = (
source_metric_role.get("accepted_metric_classes") or []
)
source_metric_matches.append(
{
"role": source_metric_role["role"],
"required": role_required,
"accepted_metric_classes": accepted_metric_classes,
"candidate_columns": candidates,
}
)
if role_required and not candidates:
missing_source_metric_roles.append(source_metric_role["role"])
mechanical_role_matches.append(
_mechanical_role_match(
kind="metric",
role=str(source_metric_role["role"]),
candidate_columns=candidates,
required=role_required,
issue=(
"missing_source_metric_role"
if role_required and not candidates
else None
),
accepted_metric_classes=accepted_metric_classes,
)
)
rejected_column_evidence.append(
{
"kind": "metric",
"role": str(source_metric_role["role"]),
**_metric_rejections(
profile,
accepted_metric_classes=list(accepted_metric_classes),
matched_columns=candidates,
),
}
)
period_columns = _columns_by_role(profile, "period")
period_role = str(period_requirements.get("role") or "none")
period_required = bool(period_requirements.get("required", period_role != "none"))
minimum_distinct_period_values = int(
period_requirements.get("minimum_distinct_values") or 0
)
period_value_evidence = [
{
"column": column,
"distinct_count": int(
((profile.get("columns") or {}).get(column) or {}).get("distinct_count")
or 0
),
"ordered_values": list(
((profile.get("columns") or {}).get(column) or {}).get("ordered_values")
or []
)[:20],
}
for column in period_columns
]
available_distinct_period_values = max(
(item["distinct_count"] for item in period_value_evidence), default=0
)
period_scope = _period_scope_preflight(period_requirements, period_columns)
period_scope.update(
{
"comparison_pair_required": bool(
period_requirements.get("comparison_pair_required", False)
),
"minimum_distinct_period_values": minimum_distinct_period_values,
"available_distinct_period_values": available_distinct_period_values,
"period_value_evidence": period_value_evidence,
}
)
if period_role in {"axis", "axis_or_table", "filter"}:
mechanical_role_matches.append(
_mechanical_role_match(
kind="period",
role="period_axis" if period_role != "filter" else "period_filter",
candidate_columns=period_columns,
required=period_required,
issue=(
"missing_period_role"
if period_required and not period_columns
else None
),
period_role=period_role,
)
)
rejected_column_evidence.append(
{
"kind": "period",
"role": "period_axis" if period_role != "filter" else "period_filter",
**_period_rejections(profile, matched_columns=period_columns),
}
)
required_dimension_count = dimension_requirements.get("minimum_count", 0)
dimension_columns = _columns_by_role(profile, "dimension")
required_dimension_roles = dimension_requirements["required_roles"]
optional_dimension_roles = dimension_requirements.get("optional_roles") or []
all_dimension_roles = [*required_dimension_roles, *optional_dimension_roles]
role_requirements = dimension_requirements.get("role_requirements") or {
role: {
"role": role,
"required": role in required_dimension_roles,
"resolution_type": "direct_dimension",
"requires_profile_roles": ["direct_dimension"],
}
for role in all_dimension_roles
}
role_resolutions = [
_resolve_dimension_role(profile, role, role_requirements.get(role, {}))
for role in all_dimension_roles
]
dimension_contract = dimension_requirements.get("dimension_contract") or {}
requires_non_bijective_dimension_pair = bool(
dimension_contract.get("requires_non_bijective_dimension_pair", False)
)
non_bijective_dimension_pairs = [
relationship
for relationship in profile.get("dimension_relationships") or []
if relationship.get("supports_multidimensional_path")
]
mechanical_role_matches.extend(
_dimension_role_match(role_resolution) for role_resolution in role_resolutions
)
for role_resolution in role_resolutions:
rejected_column_evidence.append(
{
"kind": "dimension",
"role": str(role_resolution["role"]),
**_dimension_rejections(
profile,
role=str(role_resolution["role"]),
role_resolution=role_resolution,
matched_columns=list(role_resolution.get("candidate_columns") or [])
+ [
str(value)
for values in (
role_resolution.get("prerequisite_matches") or {}
).values()
for value in values
],
),
}
)
issues = []
if missing_source_metric_roles:
issues.append("missing_source_metric_roles")
if period_required and not period_columns:
issues.append("missing_required_period_column")
if (
minimum_distinct_period_values > 0
and available_distinct_period_values < minimum_distinct_period_values
):
issues.append("insufficient_distinct_period_values")
if len(dimension_columns) < required_dimension_count:
issues.append("insufficient_dimension_columns")
for role_resolution in role_resolutions:
if not role_resolution.get("required", True):
continue
issues.extend(role_resolution["issues"])
if requires_non_bijective_dimension_pair and not non_bijective_dimension_pairs:
issues.append("no_non_redundant_dimension_pair")
raw_dataset_row_count = profile.get("row_count")
observation_count_known = isinstance(raw_dataset_row_count, int)
dataset_row_count = int(raw_dataset_row_count) if observation_count_known else None
metric_role_available_rows = (
{
str(match["role"]): max(
(
_candidate_available_rows(profile, column, int(dataset_row_count))
for column in match["candidate_columns"]
),
default=0,
)
for match in source_metric_matches
if match.get("required", True)
}
if observation_count_known
else {}
)
available_observation_rows = (
min(metric_role_available_rows.values())
if metric_role_available_rows
else dataset_row_count
)
minimum_observation_rows = int(
observation_requirements.get("minimum_non_null_rows") or 1
)
if (
available_observation_rows is not None
and available_observation_rows < minimum_observation_rows
):
issues.append("insufficient_observation_rows")
issues.extend(_derived_metric_package_issues(metric_requirements))
issues = sorted(set(issues))
status = "mechanically_compatible" if not issues else "mechanically_incomplete"
return {
"capability_id": capability["capability_id"],
"status": status,
"issues": issues,
"selection_emphasis": capability["selection_emphasis"],
"source_metric_matches": source_metric_matches,
"derived_metric_roles": [
role["role"] for role in metric_requirements["derived_metric_roles"]
],
"period_candidates": period_columns,
"period_scope": period_scope,
"required_dimension_count": required_dimension_count,
"observation_evidence": {
"required_minimum_non_null_rows": minimum_observation_rows,
"available_dataset_rows": dataset_row_count,
"available_rows_by_required_metric_role": metric_role_available_rows,
"available_observation_rows": available_observation_rows,
"observation_count_known": observation_count_known,
"requirement_scope": observation_requirements.get("scope"),
"scope_warning": (
"A selected period or filter must retain at least the required "
"number of non-null rows. Dataset-wide compatibility does not "
"prove every narrower scope is renderable."
),
},
"required_dimension_roles": required_dimension_roles,
"optional_dimension_roles": optional_dimension_roles,
"dimension_pair_evidence": {
"required": requires_non_bijective_dimension_pair,
"status": (
"available"
if non_bijective_dimension_pairs
else (
"missing"
if requires_non_bijective_dimension_pair
else "not_required"
)
),
"candidate_pairs": non_bijective_dimension_pairs[:20],
},
"mechanical_role_matches": mechanical_role_matches,
"rejected_column_evidence": rejected_column_evidence,
"unmatched_required_roles": [
{
"kind": match["kind"],
"role": match["role"],
"issue": match.get("issue"),
}
for match in mechanical_role_matches
if match["required"] and match["fit_status"] != "satisfied"
],
"ambiguous_required_roles": [
{
"kind": match["kind"],
"role": match["role"],
"candidate_count": match["candidate_count"],
"candidate_columns": match["candidate_columns"][:8],
}
for match in mechanical_role_matches
if match["required"] and match["ambiguity_status"] == "ambiguous"
],
"unavailable_optional_roles": [
{
"kind": match["kind"],
"role": match["role"],
"issue": match.get("issue"),
}
for match in mechanical_role_matches
if not match["required"] and match["fit_status"] == "optional_not_matched"
],
"ambiguous_available_roles": [
{
"kind": match["kind"],
"role": match["role"],
"required": match["required"],
"candidate_count": match["candidate_count"],
"candidate_columns": match["candidate_columns"][:8],
}
for match in mechanical_role_matches
if match["ambiguity_status"] == "ambiguous"
],
"special_dimension_matches": {
role: _special_dimension_candidates(profile, role)
for role in all_dimension_roles
},
"role_resolutions": role_resolutions,
"dimension_candidates_sample": dimension_columns[:20],
"analysis_validity_status": "not_checked",
}
def _mechanical_role_match(
*,
kind: str,
role: str,
candidate_columns: list[str],
required: bool,
issue: str | None = None,
**metadata: Any,
) -> dict[str, Any]:
candidate_columns = list(dict.fromkeys(candidate_columns))
candidate_count = len(candidate_columns)
if issue:
fit_status = "missing"
elif candidate_count:
fit_status = "satisfied"
elif required:
fit_status = "missing"
issue = "missing_required_role"
else:
fit_status = "optional_not_matched"
ambiguity_status = "ambiguous" if candidate_count > 1 else "unambiguous"
return {
"kind": kind,
"role": role,
"required": required,
"fit_status": fit_status,
"ambiguity_status": ambiguity_status,
"candidate_count": candidate_count,
"candidate_columns": candidate_columns[:30],
"example_column": candidate_columns[0] if candidate_columns else None,
"issue": issue,
**metadata,
}
def _dimension_role_match(role_resolution: dict[str, Any]) -> dict[str, Any]:
issues = list(role_resolution.get("issues") or [])
prerequisite_matches = role_resolution.get("prerequisite_matches") or {}
matched_columns: list[str] = []
for values in prerequisite_matches.values():
matched_columns.extend(str(value) for value in values)
matched_columns.extend(
str(value) for value in role_resolution.get("candidate_columns") or []
)
if "requires_semantic_or_package_role" in issues:
fit_status = "semantic_or_package_gap"
issue = "requires_semantic_or_package_role"
elif issues:
fit_status = "missing"
issue = ",".join(issues)
else:
fit_status = "satisfied"
issue = None
match = _mechanical_role_match(
kind="dimension",
role=str(role_resolution["role"]),
candidate_columns=list(dict.fromkeys(matched_columns)),
required=bool(role_resolution.get("required", True)),
issue=issue if fit_status != "satisfied" else None,
resolution_type=role_resolution.get("resolution_type"),
required_profile_roles=role_resolution.get("required_profile_roles") or [],
missing_profile_roles=role_resolution.get("missing_profile_roles") or [],
)
match["fit_status"] = fit_status
if fit_status == "semantic_or_package_gap":
match["ambiguity_status"] = "not_applicable"
return match
def check_profile_compatibility(
manifest: dict[str, Any],
profile: dict[str, Any],
*,
manifest_source: str = "in_memory",
profile_source: str = "in_memory",
) -> dict[str, Any]:
"""Return all-capability mechanical compatibility evidence."""
results = [
_capability_profile_match(capability, profile)
for capability in manifest["capabilities"].values()
]
counts: dict[str, int] = {}
for result in results:
counts[result["status"]] = counts.get(result["status"], 0) + 1
return {
"schema_version": "0.2",
"manifest": manifest_source,
"dataset_profile": profile_source,
"dataset_id": profile["dataset_id"],
"dataset_profile_schema_version": profile.get("schema_version"),
"counts": dict(sorted(counts.items())),
"selector_boundary": (
"This audit only checks mechanical compatibility between the dataset "
"profile and chart manifest. It does not decide whether the analysis "
"makes business sense."
),
"results": results,
}
def check_capability_compatibility(
capability_id: str,
profile: dict[str, Any],
*,
manifest: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""Return mechanical compatibility for one known capability."""
resolved_manifest = manifest or _load_json(DEFAULT_MANIFEST)
capability = (resolved_manifest.get("capabilities") or {}).get(capability_id)
if not isinstance(capability, dict):
raise KeyError(f"Unknown capability: {capability_id}")
return _capability_profile_match(capability, profile)
def audit_manifest_against_dataset_profile(
manifest_path: Path, profile_path: Path
) -> dict[str, Any]:
"""Load persisted inputs and return all-capability compatibility evidence."""
return check_profile_compatibility(
_load_json(manifest_path),
_load_json(profile_path),
manifest_source=str(manifest_path),
profile_source=str(profile_path),
)
def _write_markdown(path: Path, audit: dict[str, Any]) -> None:
lines = [
f"# Dataset Profile Chart Compatibility: {audit['dataset_id']}",
"",
"## Counts",
"",
]
for status, count in audit["counts"].items():
lines.append(f"- `{status}`: `{count}`")
lines.extend(
["", "## Boundary", "", audit["selector_boundary"], "", "## Results", ""]
)
for result in audit["results"]:
lines.append(
f"- `{result['capability_id']}`: `{result['status']}`"
f" / `{result['selection_emphasis']}`"
)
if result["issues"]:
lines.append(f" - Issues: `{', '.join(result['issues'])}`")
for match in result["source_metric_matches"]:
candidates = ", ".join(
f"`{column}`" for column in match["candidate_columns"][:8]
)
lines.append(
f" - Metric `{match['role']}` candidates: {candidates or '`none`'}"
)
if result["period_candidates"]:
periods = ", ".join(
f"`{column}`" for column in result["period_candidates"][:4]
)
lines.append(f" - Period candidates: {periods}")
period_scope = result.get("period_scope") or {}
if period_scope.get("status") not in {None, "not_applicable"}:
lines.append(
" - Period scope: "
f"`{period_scope.get('status')}`"
f" / role `{period_scope.get('role')}`"
f" / required `{period_scope.get('scope_required_for_render')}`"
)
if period_scope.get("pre_render_warning"):
lines.append(f" - {period_scope['pre_render_warning']}")
for match in result.get("mechanical_role_matches") or []:
candidates = ", ".join(
f"`{column}`" for column in match.get("candidate_columns", [])[:5]
)
lines.append(
" - Role "
f"`{match['kind']}.{match['role']}`: "
f"`{match['fit_status']}`"
f" / `{match['ambiguity_status']}`"
f" / candidates: {candidates or '`none`'}"
)
for rejected in result.get("rejected_column_evidence") or []:
samples = ", ".join(
f"`{sample['column']}` ({sample['reason']})"
for sample in rejected.get("samples", [])[:5]
)
not_applicable = rejected.get("not_applicable_reason")
suffix = (
f" / `{not_applicable}`"
if not_applicable
else f" / samples: {samples or '`none`'}"
)
lines.append(
" - Rejected "
f"`{rejected['kind']}.{rejected['role']}`: "
f"`{rejected['rejected_count']}`"
f"{suffix}"
)
path.write_text("\n".join(lines) + "\n", encoding="utf-8")
def main(argv: list[str] | None = None) -> int:
"""Write JSON and Markdown compatibility evidence."""
parser = argparse.ArgumentParser(
description="Audit chart manifest mechanical compatibility against a dataset profile."
)
parser.add_argument("dataset_profile", type=Path)
parser.add_argument("--manifest", type=Path, default=DEFAULT_MANIFEST)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args(argv)
audit = audit_manifest_against_dataset_profile(args.manifest, args.dataset_profile)
output = args.output
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(
json.dumps(audit, indent=2, ensure_ascii=False) + "\n", encoding="utf-8"
)
markdown_output = output.with_suffix(".md")
_write_markdown(markdown_output, audit)
return 0
if __name__ == "__main__":
raise SystemExit(main())
SHA-256: edae39a15858bedfb260ca3b0c6f4cbadb0e9aa1f3bc660cb9a3c066dd0490cf