732642856
228 lines
10 KiB
Python
228 lines
10 KiB
Python
from scripts.reference_transparency_contract import (
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build_reference_transparency_contract,
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select_similar_public_cases,
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)
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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def _chart(ascendant: str, moon: str, domain_lord_sign: str) -> dict:
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return {
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"ascendant": {"sign": ascendant},
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"planets": {
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"Moon": {"sign": moon},
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"Saturn": {"sign": domain_lord_sign},
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},
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"houses": {"house_7": {"lord": "Venus"}, "house_10": {"lord": "Saturn"}},
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}
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def _varga_chart() -> dict:
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return {
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"ascendant": {"sign": "Leo", "lon": 149.0634},
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"planets": {
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"Moon": {"sign": "Pisces", "lon": 344.5165},
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"Saturn": {"sign": "Libra", "lon": 207.9320},
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"Sun": {"sign": "Aquarius", "lon": 312.5175},
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"Venus": {"sign": "Sagittarius", "lon": 267.9412},
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"Rahu": {"sign": "Sagittarius", "lon": 249.2743},
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"Ketu": {"sign": "Gemini", "lon": 69.2743},
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},
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"houses": {"house_10": {"lord": "Saturn"}},
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}
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def test_select_similar_cases_shares_only_high_similarity_same_domain() -> None:
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user_chart = _chart("Leo", "Pisces", "Libra")
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cases = [
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{
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"case_id": "matching_career_case",
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"subject": {"name": "Public Example"},
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"chart": _chart("Leo", "Pisces", "Libra"),
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"source": {"url": "https://example.com/birth", "source_grade": "primary"},
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"event_outcomes": [{
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"domain": "career", "event_type": "career_breakthrough", "event_date": "2007-01-09",
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"outcome": "Public career event", "source": {"url": "https://example.com/event", "source_grade": "primary"},
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}],
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"replay": {"outcome_replay_status": "replayed", "do_not_use_for_prediction": False},
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},
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{
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"case_id": "wrong_domain_case",
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"subject": {"name": "Different Example"},
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"chart": _chart("Aries", "Aries", "Aries"),
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"source": {"url": "https://example.com/birth-2", "source_grade": "primary"},
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"event_outcomes": [{
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"domain": "marriage", "event_type": "legal_marriage", "event_date": "2011-04-29",
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"outcome": "Public marriage event", "source": {"url": "https://example.com/event-2", "source_grade": "primary"},
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}],
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"replay": {"outcome_replay_status": "replayed", "do_not_use_for_prediction": False},
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},
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]
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selected = select_similar_public_cases(user_chart, ["career"], cases=cases)
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assert selected["status"] == "high_similarity_public_references_available"
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assert [case["case_id"] for case in selected["cases"]] == ["matching_career_case"]
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assert selected["cases"][0]["similarity"]["score"] == 1.0
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assert "node_axis" not in selected["cases"][0]["similarity"]["matching_factors"]
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assert selected["cases"][0]["event_source"]["url"] == "https://example.com/event"
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assert selected["does_not_predict_user_outcome"] is True
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assert selected["coverage"] == {
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"available_event_domains": ["career", "marriage"],
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"requested_uncovered_domains": [],
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}
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def test_reference_contract_preserves_dates_and_discloses_parallel_methods() -> None:
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contract = build_reference_transparency_contract(
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_chart("Leo", "Pisces", "Libra"),
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["career"],
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timing={"candidate_windows": [{"start": "2026-08-12", "end": "2026-08-16"}]},
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cases=[],
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)
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assert contract["timing_display"]["exact_triggers"] == "display_as_technical_trigger_not_guarantee"
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assert contract["external_engine_observations"]["VedAstro hosted"]["deployment_identity"] == "not_publicly_proven"
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assert contract["method_variants"]["display"] == "show_parallel_methods_with_sources"
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assert contract["similar_public_cases"]["status"] == "no_high_similarity_public_reference"
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assert contract["similar_public_cases"]["coverage"]["requested_uncovered_domains"] == ["career"]
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def test_default_public_manifest_can_surface_a_matching_replayed_case() -> None:
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from scripts.domain_calculation_service import compute_chart
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jobs_chart = compute_chart({
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"year": 1955, "month": 2, "day": 24, "hour": 19, "minute": 15,
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"lat": 37.7833, "lon": -122.4167, "tz": -8.0,
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"ayanamsa": "lahiri", "node_mode": "mean",
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})
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selected = select_similar_public_cases(jobs_chart, ["career"])
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assert selected["status"] == "high_similarity_public_references_available"
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assert selected["cases"][0]["case_id"] == "jobs_iphone_2007"
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assert selected["cases"][0]["reference_only"] is True
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def test_same_event_date_compares_vimshottari_mahadasha() -> None:
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from scripts.domain_calculation_service import compute_chart
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jobs_chart = compute_chart({
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"year": 1955, "month": 2, "day": 24, "hour": 19, "minute": 15,
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"lat": 37.7833, "lon": -122.4167, "tz": -8.0,
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"ayanamsa": "lahiri", "node_mode": "mean",
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})
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selected = select_similar_public_cases(
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jobs_chart,
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["career"],
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reference_date="2007-01-09",
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)
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similarity = selected["cases"][0]["similarity"]
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assert "vimshottari_mahadasha" in similarity["matching_factors"]
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assert similarity["timing_state"]["status"] == "matched"
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assert "vimshottari_antardasha" in similarity["uncompared_layers"]
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def test_pending_health_case_is_context_only_not_calibration() -> None:
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user_chart = _chart("Leo", "Pisces", "Libra")
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cases = [{
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"case_id": "public_health_context",
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"subject": {"name": "Public Example"},
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"chart": _chart("Leo", "Pisces", "Libra"),
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"source": {"url": "https://example.com/birth", "source_grade": "primary"},
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"event_outcomes": [{
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"domain": "health", "event_type": "serious_injury", "event_date": "1925-09",
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"outcome": "Public health event", "source": {"url": "https://example.com/event", "source_grade": "verified_secondary"},
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}],
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"replay": {"outcome_replay_status": "pending", "do_not_use_for_prediction": True},
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}]
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selected = select_similar_public_cases(user_chart, ["health"], cases=cases)
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assert selected["status"] == "high_similarity_public_references_available"
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assert selected["cases"][0]["reference_status"] == "public_context_only"
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assert selected["cases"][0]["reference_only"] is True
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assert selected["coverage"]["available_event_domains"] == ["health"]
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def test_career_similarity_adds_d10_only_when_both_charts_have_longitudes() -> None:
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chart = _varga_chart()
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cases = [{
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"case_id": "d10_match", "subject": {"name": "Public Example"}, "chart": chart,
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"source": {"url": "https://example.com/birth", "source_grade": "primary"},
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"event_outcomes": [{
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"domain": "career", "event_type": "career_breakthrough", "event_date": "2007-01-09",
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"outcome": "Public career event", "source": {"url": "https://example.com/event", "source_grade": "primary"},
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}],
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"replay": {"outcome_replay_status": "replayed", "do_not_use_for_prediction": False},
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}]
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selected = select_similar_public_cases(chart, ["career"], cases=cases)
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similarity = selected["cases"][0]["similarity"]
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assert {"d10_ascendant", "d10_sun"}.issubset(similarity["matching_factors"])
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assert "D10" not in similarity["uncompared_layers"]
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def test_marriage_similarity_adds_d9_only_when_both_charts_have_longitudes() -> None:
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chart = _varga_chart()
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cases = [{
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"case_id": "d9_match", "subject": {"name": "Public Example"}, "chart": chart,
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"source": {"url": "https://example.com/birth", "source_grade": "primary"},
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"event_outcomes": [{
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"domain": "marriage", "event_type": "legal_marriage", "event_date": "2011-04-29",
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"outcome": "Public marriage event", "source": {"url": "https://example.com/event", "source_grade": "primary"},
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}],
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"replay": {"outcome_replay_status": "replayed", "do_not_use_for_prediction": False},
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}]
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selected = select_similar_public_cases(chart, ["marriage"], cases=cases)
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similarity = selected["cases"][0]["similarity"]
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assert {"d9_ascendant", "d9_venus"}.issubset(similarity["matching_factors"])
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assert "D9" not in similarity["uncompared_layers"]
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def test_api_house_shape_keeps_theme_lord_in_similarity() -> None:
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chart = _varga_chart()
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chart["houses"] = {10: {"sign": "Taurus", "sign_idx": 1}}
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cases = [{
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"case_id": "api_house_shape", "subject": {"name": "Public Example"}, "chart": chart,
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"source": {"url": "https://example.com/birth", "source_grade": "primary"},
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"event_outcomes": [{
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"domain": "career", "event_type": "career_breakthrough", "event_date": "2007-01-09",
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"outcome": "Public career event", "source": {"url": "https://example.com/event", "source_grade": "primary"},
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}],
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"replay": {"outcome_replay_status": "replayed", "do_not_use_for_prediction": False},
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}]
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selected = select_similar_public_cases(chart, ["career"], cases=cases)
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assert "domain_lord_sign" in selected["cases"][0]["similarity"]["matching_factors"]
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def test_default_manifest_exposes_kahlo_health_as_context_only() -> None:
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from scripts.domain_calculation_service import compute_chart
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kahlo_chart = compute_chart({
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"year": 1907, "month": 7, "day": 6, "hour": 8, "minute": 30,
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"lat": 19.3333, "lon": -99.1667, "tz": -6.6111,
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"ayanamsa": "lahiri", "node_mode": "mean",
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})
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selected = select_similar_public_cases(kahlo_chart, ["health"])
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assert [case["case_id"] for case in selected["cases"]] == ["kahlo_bus_injury_1925"]
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assert selected["cases"][0]["reference_status"] == "public_context_only"
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assert selected["coverage"]["available_event_domains"] == ["career", "health", "marriage"]
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def test_consultation_api_exposes_reference_transparency_contract() -> None:
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source = (ROOT / "scripts" / "jyotish_api_server.py").read_text(encoding="utf-8")
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assert "result['reference_transparency'] = build_reference_transparency_contract(" in source
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assert "reference_date=_consultation_reference_date(body).date().isoformat()" in source
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