Jesse_Chen
854 lines
34 KiB
Python
854 lines
34 KiB
Python
from __future__ import annotations
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import sys
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from pathlib import Path
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import pytest
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SCRIPTS = Path(__file__).resolve().parents[1] / "scripts"
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if str(SCRIPTS) not in sys.path:
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sys.path.insert(0, str(SCRIPTS))
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import jyotish_api_server as api_server # noqa: E402
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from jyotish_api_server import BadRequest, JyotishAPIHandler # noqa: E402
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def _handler() -> JyotishAPIHandler:
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return JyotishAPIHandler.__new__(JyotishAPIHandler)
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def test_active_rectification_questions_api_builds_choice_workflow() -> None:
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result = _handler()._compute_active_rectification_questions(
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{
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"birth_time": "1993-04-17 14:49",
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"uncertainty_minutes": 30,
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"step_minutes": 1,
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}
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)
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assert result["success"] is True
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assert result["endpoint"] == "active_rectification_questions"
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assert result["scope"] == "active_birth_time_rectification_questionnaire"
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assert result["candidate_scan"]["start"] == "1993-04-17 14:19"
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assert result["candidate_scan"]["end"] == "1993-04-17 15:19"
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assert result["candidate_scan"]["candidate_count"] == 61
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assert result["questions"]
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assert {option["key"] for option in result["questions"][0]["options"]} == {"A", "B", "C", "D"}
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assert "dynamic_candidate_cluster_scoring" in result["workflow"]
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def test_active_rectification_questions_api_accepts_location_for_true_recast() -> None:
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result = _handler()._compute_active_rectification_questions(
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{
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"birth_time": "1993-04-17 14:49",
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"uncertainty_minutes": 30,
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8,
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}
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)
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summary = result["candidate_scan"]["sensitivity_summary"]
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assert "true_varga_recast" in summary["computed_layers"]
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assert "true_arudha_recast" in summary["computed_layers"]
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assert "true_kp_cusp_recast" in summary["computed_layers"]
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assert "true_varga_recast" not in summary["blocked_layers"]
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assert "true_kp_cusp_recast" not in summary["blocked_layers"]
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def test_active_rectification_score_api_returns_rankings_and_next_questions() -> None:
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questionnaire = _handler()._compute_active_rectification_questions(
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{"birth_time": "1993-04-17 14:49", "uncertainty_minutes": 30}
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)
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": questionnaire,
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"answers": {
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"education_environment_shift": "A",
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"residence_relocation_shift": "B",
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"relationship_or_partner_entry": "D",
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"career_responsibility_pressure": "A",
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"research_tool_expression_shift": "C",
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},
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}
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)
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assert scored["success"] is True
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assert scored["endpoint"] == "active_rectification_score"
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assert scored["scope"] == "active_birth_time_rectification_scoring"
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assert scored["answered_count"] == 5
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assert scored["candidate_cluster_rankings"]
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assert scored["next_round_questions"]
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assert scored["candidate_cluster_rankings"][0]["score"] >= scored["candidate_cluster_rankings"][-1]["score"]
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def test_active_rectification_questions_api_validates_request() -> None:
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with pytest.raises(BadRequest, match="birth_time must be a string"):
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_handler()._compute_active_rectification_questions({})
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with pytest.raises(BadRequest, match="uncertainty_minutes must be between 1 and 180"):
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_handler()._compute_active_rectification_questions(
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{"birth_time": "1993-04-17 14:49", "uncertainty_minutes": 0}
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)
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with pytest.raises(BadRequest, match="step_minutes must be between 1 and 30"):
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_handler()._compute_active_rectification_questions(
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{"birth_time": "1993-04-17 14:49", "step_minutes": 31}
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)
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def test_active_rectification_score_api_validates_payload() -> None:
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with pytest.raises(BadRequest, match="questionnaire must be an object"):
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_handler()._compute_active_rectification_score({"answers": {}})
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with pytest.raises(BadRequest, match="answers must be an object"):
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_handler()._compute_active_rectification_score({"questionnaire": {}})
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def _answered_rectification_score() -> dict:
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questionnaire = _handler()._compute_active_rectification_questions(
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{
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"birth_time": "1993-04-17 14:49",
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"uncertainty_minutes": 30,
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"step_minutes": 1,
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8,
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}
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)
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return _handler()._compute_active_rectification_score(
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{
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"questionnaire": questionnaire,
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"answers": {
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"education_environment_shift": "A",
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"residence_relocation_shift": "B",
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"relationship_or_partner_entry": "D",
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"career_responsibility_pressure": "A",
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"research_tool_expression_shift": "C",
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},
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}
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)
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def test_rectification_score_exposes_narayana_cross_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"narayana_cross_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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def test_rectification_technique_audit_mentions_narayana_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Narayana Dasha Rectification"
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and row.get("status") in {"used", "partial"}
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for row in audit_rows
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)
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def test_narayana_conflict_downgrades_without_replacing_vimshottari_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"narayana_cross_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert top["conflict_policy"] == "downgrade_without_replacement"
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def test_rectification_claim_remains_candidate_not_birth_time_truth_red() -> None:
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scored = _answered_rectification_score()
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assert scored["claim_status"] == "candidate"
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assert scored["truth_status"] != "birth_time_truth"
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def test_rectification_score_exposes_jaimini_karaka_cross_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"jaimini_karaka_cross_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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def test_rectification_technique_audit_mentions_jaimini_karaka_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Jaimini Karaka Rectification"
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and row.get("status") == "partial"
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for row in audit_rows
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)
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def test_jaimini_karaka_conflict_downgrades_without_replacing_primary_rank_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"jaimini_karaka_cross_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "jaimini_karaka" in top["downgrade_reasons"]
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def test_rectification_score_exposes_vimsopaka_avastha_cross_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"vimsopaka_avastha_cross_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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def test_rectification_technique_audit_mentions_vimsopaka_avastha_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Vimsopaka Avastha Rectification"
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and row.get("status") == "partial"
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for row in audit_rows
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)
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def test_vimsopaka_avastha_conflict_downgrades_without_replacing_primary_rank_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"vimsopaka_avastha_cross_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "vimsopaka_avastha" in top["downgrade_reasons"]
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def test_rectification_score_exposes_shadbala_av_observation_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"shadbala_av_observation_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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assert scored["formula_unit_parity_status"] == "partial"
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def test_rectification_technique_audit_mentions_shadbala_av_low_weight_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Shadbala Ashtakavarga Rectification"
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and row.get("status") == "partial_observation"
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and row.get("weight_policy") == "low_weight_only"
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for row in audit_rows
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)
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def test_shadbala_av_conflict_downgrades_without_replacing_primary_rank_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"shadbala_av_observation_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "shadbala_av" in top["downgrade_reasons"]
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def test_rectification_score_exposes_gochara_observation_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"gochara_transit_observation_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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assert scored["timing_claim_status"] == "exploratory_unvalidated"
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def test_rectification_technique_audit_mentions_gochara_holdout_gate_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Gochara Transit Rectification"
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and row.get("status") == "blocked_from_verified_timing"
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and row.get("holdout_gate") == "negative_holdout_required"
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for row in audit_rows
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)
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def test_gochara_conflict_downgrades_without_verified_timing_claim_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"gochara_transit_observation_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "gochara_transit" in top["downgrade_reasons"]
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assert scored["timing_claim_status"] == "exploratory_unvalidated"
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def test_high_rigor_event_rectification_requires_real_vedastro_candidate_discrimination(monkeypatch) -> None:
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original_loader = api_server._load_local_module
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class LocalScorer:
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@staticmethod
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def score_life_events(request):
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return {
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"result_id": "local-result",
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"confidence": "high",
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"can_apply": True,
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"winning_segment": {
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"start_time": "14:30",
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"end_time": "14:30",
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"representative_time": "14:30",
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"width_minutes": 1,
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},
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"event_count": len(request["events"]),
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"domain_count": len({event["domain"] for event in request["events"]}),
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"top_score": 30,
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"second_score": 20,
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"margin_percent": 33.33,
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"reasons": [],
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"evidence": [],
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"algorithm_version": "fixture",
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"canonical_input_hash": "canonical-fixture",
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"calculation_contract": {"events": request["events"]},
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"stability_diagnostics": {
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"neighbor_stability": {"all_required_passed": True},
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"leave_one_event_out": {"status": "pass"},
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},
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"missing_layers": [],
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"candidate_ranking_summary": [
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{"rank": 1, "time": "14:30", "score": 30, "tied_minute_count": 1},
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{"rank": 2, "time": "14:31", "score": 20, "tied_minute_count": 1},
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],
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}
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class VedAstroAdapter:
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@staticmethod
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def run_rectification_minute_snapshot_for_case(case, case_id="user_chart"):
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minute = case["minute"]
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return {
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"available": True,
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"status": "ok",
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"source": "vedastro_official",
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"layers": {
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"ascendant_house_boundaries": {
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"status": "ok",
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"fingerprint": f"asc-{minute}",
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"ascendant": {"sign": "Leo", "degree_in_sign": minute / 10},
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"houses": {"House1": {}},
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},
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"D9": {
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"status": "ok",
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"fingerprint": f"d9-{minute}",
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"houses": {"House1": {}},
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"planets": {},
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},
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"D10": {
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"status": "ok",
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"fingerprint": "d10-same",
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"houses": {"House1": {}},
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"planets": {},
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},
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"dasha_boundaries": {
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"status": "ok",
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"fingerprint": f"dasha-{minute}",
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"boundary_count": 3,
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},
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"kp_cusp_sub_lord": {
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"status": "unsupported_by_verified_official_interface",
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"reason": "not supported by verified official interface",
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},
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},
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"raw_response": {"must_not": "leak"},
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}
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@staticmethod
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def run_range_scan_for_case(case, _domain, _start, _end, case_id="user_chart"):
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return {
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"available": True,
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"status": "ok",
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"event_count": 1,
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"top_event": {"event_id": f"event-{case_id}"},
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"evidence_ledger": [{"signal_lift": 1}],
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"raw_response": {"must_not": "leak"},
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}
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monkeypatch.setattr(
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api_server,
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"_load_local_module",
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lambda name: LocalScorer if name == "active_rectification_events" else VedAstroAdapter if name == "vedastro_service_adapter" else original_loader(name),
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)
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monkeypatch.setattr(
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"scripts.rectification_three_engine_packet.build_packet",
|
|
lambda _case: {
|
|
"engine_status": {"local": "ok", "pyjhora": "ok", "jyotishganit": "ok"},
|
|
"match_count": 3,
|
|
"mismatch_count": 0,
|
|
},
|
|
)
|
|
monkeypatch.setattr(
|
|
JyotishAPIHandler,
|
|
"_compute_vedastro_gateway_run",
|
|
lambda *_args, **_kwargs: {
|
|
"status": "ok",
|
|
"official_closure_state": "official_verified",
|
|
"official_closure_reason": "official_raw_response_present",
|
|
"official_raw_response": {"must_not": "leak"},
|
|
},
|
|
)
|
|
result = _handler()._compute_active_rectification_events(
|
|
{
|
|
"birth_date": "1993-04-17",
|
|
"start_time": "14:29",
|
|
"end_time": "14:31",
|
|
"lat": 36.683333,
|
|
"lon": 114.35,
|
|
"tz": 8,
|
|
"high_rigor": True,
|
|
"events": [
|
|
{
|
|
"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
|
|
"domain": "education",
|
|
"date": "2011-09",
|
|
"precision": "month",
|
|
"summary": "2011 年 9 月离开家乡开始大学生活",
|
|
},
|
|
{"id": "0790866c-ad5e-4a45-b2b4-a5c73f6be6ea", "domain": "career", "date": "2019-07-01", "precision": "day"},
|
|
{"id": "0ef52e51-ab5f-453b-81e5-adb44a929224", "domain": "relationship", "date": "2021", "precision": "year"},
|
|
],
|
|
}
|
|
)
|
|
receipt = result["three_engine_packet"]["vedastro"]
|
|
assert receipt["status"] == "official_verified"
|
|
contract = result["technique_contract"]
|
|
validation = contract["external_engines"]["validation"]
|
|
assert result["can_apply"] is True
|
|
assert contract["confirmation_allowed"] is True
|
|
assert contract["decision"] == "confirm_minute"
|
|
assert contract["canonical_input_hash"]
|
|
assert contract["gates"]["vedastro_minute_sensitive_validation"]["status"] == "pass"
|
|
assert validation["minute_sensitive_validation"]["discriminated"] is True
|
|
assert validation["minute_sensitive_validation"]["discriminated_layers"]
|
|
assert validation["event_background_validation"]["used_for_decision"] is False
|
|
assert validation["event_background_validation"]["candidates"][0]["metric"] == validation["event_background_validation"]["candidates"][1]["metric"]
|
|
assert "must_not" not in str(validation)
|
|
assert result["calculation_contract"]["events"][0]["summary"] == "2011 年 9 月离开家乡开始大学生活"
|
|
|
|
|
|
def test_long_real_conversation_reaches_vedastro_after_local_range_is_narrow(monkeypatch) -> None:
|
|
original_loader = api_server._load_local_module
|
|
vedastro_calls: list[tuple[str, str, str, str]] = []
|
|
|
|
class VedAstroAdapter:
|
|
@staticmethod
|
|
def run_rectification_minute_snapshot_for_case(case, case_id="user_chart"):
|
|
candidate_time = f'{case["hour"]:02d}:{case["minute"]:02d}'
|
|
return {
|
|
"available": True,
|
|
"status": "ok",
|
|
"source": "vedastro_official",
|
|
"layers": {
|
|
"ascendant_house_boundaries": {
|
|
"status": "ok",
|
|
"fingerprint": f"asc-{candidate_time}",
|
|
"ascendant": {"sign": "Leo", "degree_in_sign": case["minute"] / 10},
|
|
"houses": {"House1": {}},
|
|
},
|
|
"D9": {
|
|
"status": "ok",
|
|
"fingerprint": f"d9-{candidate_time}",
|
|
"houses": {"House1": {}},
|
|
"planets": {},
|
|
},
|
|
"D10": {
|
|
"status": "ok",
|
|
"fingerprint": f"d10-{candidate_time}",
|
|
"houses": {"House1": {}},
|
|
"planets": {},
|
|
},
|
|
"dasha_boundaries": {
|
|
"status": "ok",
|
|
"fingerprint": f"dasha-{candidate_time}",
|
|
"boundary_count": 3,
|
|
},
|
|
"kp_cusp_sub_lord": {
|
|
"status": "unsupported_by_verified_official_interface",
|
|
"reason": "not supported by verified official interface",
|
|
},
|
|
},
|
|
}
|
|
|
|
@staticmethod
|
|
def run_range_scan_for_case(case, domain, start, end, case_id="user_chart"):
|
|
candidate_time = f'{case["hour"]:02d}:{case["minute"]:02d}'
|
|
vedastro_calls.append((candidate_time, domain, start, end))
|
|
return {
|
|
"available": True,
|
|
"status": "ok",
|
|
"event_count": 1,
|
|
"top_event": {"event_id": f"event-{case_id}"},
|
|
"evidence_ledger": [{"signal_lift": 1}],
|
|
}
|
|
|
|
monkeypatch.setattr(
|
|
api_server,
|
|
"_load_local_module",
|
|
lambda name: VedAstroAdapter if name == "vedastro_service_adapter" else original_loader(name),
|
|
)
|
|
monkeypatch.setattr(
|
|
"scripts.rectification_three_engine_packet.build_packet",
|
|
lambda _case: {
|
|
"engine_status": {"local": "ok", "pyjhora": "ok", "jyotishganit": "ok"},
|
|
"match_count": 3,
|
|
"mismatch_count": 0,
|
|
},
|
|
)
|
|
monkeypatch.setattr(
|
|
JyotishAPIHandler,
|
|
"_compute_vedastro_gateway_run",
|
|
lambda *_args, **_kwargs: {
|
|
"status": "ok",
|
|
"official_closure_state": "official_verified",
|
|
"official_closure_reason": "official_raw_response_present",
|
|
"official_raw_response": {"status": "ok"},
|
|
},
|
|
)
|
|
|
|
result = _handler()._compute_active_rectification_events(
|
|
{
|
|
"birth_date": "1997-08-08",
|
|
"start_time": "04:00",
|
|
"end_time": "07:59",
|
|
"lat": 36.420487,
|
|
"lon": 114.209936,
|
|
"tz": 8,
|
|
"high_rigor": True,
|
|
"events": [
|
|
{"id": "00000000-0000-4000-8000-000000000001", "domain": "education", "date": "2016-09", "precision": "month", "summary": "离家去外地上大学"},
|
|
{"id": "00000000-0000-4000-8000-000000000002", "domain": "career", "date": "2020-04", "precision": "month", "summary": "去石油化工研究院实习做研究员"},
|
|
{"id": "00000000-0000-4000-8000-000000000003", "domain": "career", "date": "2020-10", "precision": "month", "summary": "从研究院辞职"},
|
|
{"id": "00000000-0000-4000-8000-000000000004", "domain": "education", "date": "2020-12", "precision": "month", "summary": "参加研究生考试结果不理想"},
|
|
{"id": "00000000-0000-4000-8000-000000000005", "domain": "relocation", "date": "2021-01", "precision": "month", "summary": "回家备考并长期在家"},
|
|
{"id": "00000000-0000-4000-8000-000000000006", "domain": "education", "date": "2022-12", "precision": "month", "summary": "考研结束后转向自学前端"},
|
|
{"id": "00000000-0000-4000-8000-000000000007", "domain": "career", "date": "2023-04", "precision": "month", "summary": "去北京入职医疗器械公司"},
|
|
{"id": "00000000-0000-4000-8000-000000000008", "domain": "relationship", "date": "2024-08-08", "precision": "day", "summary": "恋爱关系发生重大转折"},
|
|
{"id": "00000000-0000-4000-8000-000000000009", "domain": "relationship", "date": "2024-10", "precision": "month", "summary": "短暂复联后主动断联"},
|
|
{"id": "00000000-0000-4000-8000-000000000010", "domain": "finance", "date": "2026-01", "precision": "month", "summary": "公司无法正常发放工资"},
|
|
{"id": "00000000-0000-4000-8000-000000000011", "domain": "career", "date": "2026-07-10", "precision": "day", "summary": "与朋友正式决定创业"},
|
|
{"id": "00000000-0000-4000-8000-000000000012", "domain": "career", "date": "2026-07-21", "precision": "day", "summary": "提交公司注册材料"},
|
|
],
|
|
}
|
|
)
|
|
|
|
assert result["winning_segment"] == {
|
|
"start_time": "05:07",
|
|
"end_time": "05:08",
|
|
"representative_time": "05:07",
|
|
"width_minutes": 2,
|
|
}
|
|
assert result["stability_diagnostics"]["neighbor_stability"]["all_required_passed"] is False
|
|
assert result["stability_diagnostics"]["leave_one_event_out"]["status"] != "pass"
|
|
assert len(vedastro_calls) == 6
|
|
assert {call[0] for call in vedastro_calls} == {"05:07", "05:21"}
|
|
assert {call[1] for call in vedastro_calls} == {"career", "wealth", "marriage"}
|
|
assert all(start == end for _, _, start, end in vedastro_calls)
|
|
assert {
|
|
(domain, start)
|
|
for candidate, domain, start, _ in vedastro_calls
|
|
if candidate == "05:07"
|
|
} == {
|
|
("career", "2026-07-21"),
|
|
("wealth", "2026-01-16"),
|
|
("marriage", "2024-08-08"),
|
|
}
|
|
assert {
|
|
candidate
|
|
for candidate, _, _, _ in vedastro_calls
|
|
} == {
|
|
item["time"]
|
|
for item in result["candidate_ranking_summary"][:2]
|
|
}
|
|
validation = result["technique_contract"]["external_engines"]["validation"]
|
|
event_validation = validation["event_background_validation"]
|
|
assert event_validation["eligible_event_count"] == 12
|
|
assert event_validation["supported_event_count"] == 3
|
|
assert event_validation["used_for_decision"] is False
|
|
assert event_validation["candidates"][0]["metric"] == event_validation["candidates"][1]["metric"]
|
|
assert "one_strongest_event_per_native_adapter_domain" in event_validation["selection_policy"]
|
|
assert result["three_engine_packet"]["vedastro"]["status"] == "official_verified"
|
|
assert result["three_engine_packet"]["vedastro"]["search_events_role"] == "background_only"
|
|
assert result["technique_contract"]["gates"]["vedastro_minute_sensitive_validation"]["status"] == "pass"
|
|
assert validation["minute_sensitive_validation"]["discriminated"] is True
|
|
assert result["technique_contract"]["confirmation_allowed"] is True
|
|
assert result["technique_contract"]["decision"] == "confirm_minute"
|
|
assert result["can_apply"] is True
|
|
assert "neighbor_stability_not_passed" not in result["technique_contract"]["hard_blockers"]
|
|
assert "leave_one_event_out_not_passed" not in result["technique_contract"]["hard_blockers"]
|
|
|
|
|
|
def test_identical_vedastro_minute_sensitive_snapshots_do_not_discriminate_candidates() -> None:
|
|
layers = {
|
|
name: {"status": "ok", "fingerprint": f"same-{name}"}
|
|
for name in api_server._VEDASTRO_MINUTE_SENSITIVE_LAYERS
|
|
}
|
|
snapshots = [
|
|
{"candidate_time": "05:07", "available": True, "layers": layers},
|
|
{"candidate_time": "05:21", "available": True, "layers": layers},
|
|
]
|
|
|
|
comparison = api_server._compare_vedastro_minute_snapshots(snapshots)
|
|
|
|
assert comparison["comparison_ready"] is True
|
|
assert comparison["discriminated"] is False
|
|
assert comparison["discriminated_layers"] == []
|
|
assert all(item["status"] == "same" for item in comparison["differences"].values())
|
|
|
|
|
|
def test_search_events_difference_cannot_override_identical_minute_snapshots(monkeypatch) -> None:
|
|
original_loader = api_server._load_local_module
|
|
|
|
class LocalScorer:
|
|
@staticmethod
|
|
def score_life_events(request):
|
|
return {
|
|
"result_id": "local-result",
|
|
"confidence": "high",
|
|
"can_apply": True,
|
|
"winning_segment": {
|
|
"start_time": "14:30",
|
|
"end_time": "14:30",
|
|
"representative_time": "14:30",
|
|
"width_minutes": 1,
|
|
},
|
|
"event_count": len(request["events"]),
|
|
"domain_count": len({event["domain"] for event in request["events"]}),
|
|
"top_score": 30,
|
|
"second_score": 20,
|
|
"margin_percent": 33.33,
|
|
"reasons": [],
|
|
"evidence": [],
|
|
"algorithm_version": "fixture",
|
|
"canonical_input_hash": "canonical-fixture",
|
|
"calculation_contract": {"events": request["events"]},
|
|
"stability_diagnostics": {
|
|
"neighbor_stability": {"all_required_passed": True},
|
|
"leave_one_event_out": {"status": "pass"},
|
|
},
|
|
"missing_layers": [],
|
|
"candidate_ranking_summary": [
|
|
{"rank": 1, "time": "14:30", "score": 30, "tied_minute_count": 1},
|
|
{"rank": 2, "time": "14:31", "score": 20, "tied_minute_count": 1},
|
|
],
|
|
}
|
|
|
|
class VedAstroAdapter:
|
|
@staticmethod
|
|
def run_rectification_minute_snapshot_for_case(_case, case_id="user_chart"):
|
|
layers = {
|
|
name: {
|
|
"status": "ok",
|
|
"fingerprint": f"same-{name}",
|
|
"houses": {"House1": {}},
|
|
"planets": {},
|
|
"boundary_count": 3,
|
|
}
|
|
for name in api_server._VEDASTRO_MINUTE_SENSITIVE_LAYERS
|
|
}
|
|
layers["ascendant_house_boundaries"]["ascendant"] = {
|
|
"sign": "Leo",
|
|
"degree_in_sign": 12.5,
|
|
}
|
|
layers["kp_cusp_sub_lord"] = {
|
|
"status": "unsupported_by_verified_official_interface",
|
|
"reason": "not supported by verified official interface",
|
|
}
|
|
return {
|
|
"available": True,
|
|
"status": "ok",
|
|
"source": "vedastro_official",
|
|
"layers": layers,
|
|
}
|
|
|
|
@staticmethod
|
|
def run_range_scan_for_case(case, _domain, _start, _end, case_id="user_chart"):
|
|
event_count = 10 if case["minute"] == 30 else 1
|
|
return {
|
|
"available": True,
|
|
"status": "ok",
|
|
"event_count": event_count,
|
|
"top_event": {"event_id": f"event-{case_id}"},
|
|
"evidence_ledger": [{"signal_lift": event_count}],
|
|
}
|
|
|
|
monkeypatch.setattr(
|
|
api_server,
|
|
"_load_local_module",
|
|
lambda name: LocalScorer if name == "active_rectification_events" else VedAstroAdapter if name == "vedastro_service_adapter" else original_loader(name),
|
|
)
|
|
monkeypatch.setattr(
|
|
"scripts.rectification_three_engine_packet.build_packet",
|
|
lambda _case: {
|
|
"engine_status": {"local": "ok", "pyjhora": "ok", "jyotishganit": "ok"},
|
|
"match_count": 3,
|
|
"mismatch_count": 0,
|
|
},
|
|
)
|
|
monkeypatch.setattr(
|
|
JyotishAPIHandler,
|
|
"_compute_vedastro_gateway_run",
|
|
lambda *_args, **_kwargs: {
|
|
"status": "ok",
|
|
"official_closure_state": "official_verified",
|
|
"official_closure_reason": "official_raw_response_present",
|
|
},
|
|
)
|
|
|
|
result = _handler()._compute_active_rectification_events(
|
|
{
|
|
"birth_date": "1993-04-17",
|
|
"start_time": "14:29",
|
|
"end_time": "14:31",
|
|
"lat": 36.683333,
|
|
"lon": 114.35,
|
|
"tz": 8,
|
|
"high_rigor": True,
|
|
"events": [
|
|
{"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5", "domain": "education", "date": "2011-09", "precision": "month"},
|
|
{"id": "0790866c-ad5e-4a45-b2b4-a5c73f6be6ea", "domain": "career", "date": "2019-07-01", "precision": "day"},
|
|
{"id": "0ef52e51-ab5f-453b-81e5-adb44a929224", "domain": "relationship", "date": "2021", "precision": "year"},
|
|
],
|
|
}
|
|
)
|
|
|
|
validation = result["technique_contract"]["external_engines"]["validation"]
|
|
background_candidates = validation["event_background_validation"]["candidates"]
|
|
assert background_candidates[0]["metric"] != background_candidates[1]["metric"]
|
|
assert validation["event_background_validation"]["used_for_decision"] is False
|
|
assert validation["minute_sensitive_validation"]["discriminated"] is False
|
|
assert result["can_apply"] is False
|
|
assert result["technique_contract"]["confirmation_allowed"] is False
|
|
assert "vedastro_minute_sensitive_layers_not_discriminated" in result["reasons"]
|
|
assert "vedastro_candidate_not_discriminated" not in result["reasons"]
|