Jesse_Chen
311 lines
9.0 KiB
TypeScript
311 lines
9.0 KiB
TypeScript
import assert from "node:assert/strict";
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import test from "node:test";
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import {
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parseBirthTimeProfile,
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parseCandidateResult,
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parseRectificationQuestionnaire,
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parseRectificationScoring,
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} from "../src/lib/birth-time-journey-adapters.ts";
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const coordinates = {
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latitude: 31.2304,
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longitude: 121.4737,
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timezone_offset: 8,
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} as const;
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test("birth time profile adapter parses an exact hospital declaration", () => {
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const assessment = parseBirthTimeProfile({
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birth_date: "1993-04-17",
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reported_birth_time: "08:16:00",
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birth_time_source: "hospital_record",
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uncertainty_before_minutes: 2,
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uncertainty_after_minutes: 2,
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...coordinates,
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});
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assert.equal(assessment.source, "hospital_record");
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if (assessment.source === "hospital_record") {
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assert.equal(assessment.reportedTime, "08:16");
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assert.equal(assessment.location.lon, 121.4737);
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}
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});
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test("birth time profile adapter normalizes PostgreSQL date values", () => {
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const assessment = parseBirthTimeProfile({
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birth_date: new Date("1993-04-17T00:00:00.000Z"),
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reported_birth_time: "08:16:00",
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birth_time_source: "hospital_record",
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uncertainty_before_minutes: 2,
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uncertainty_after_minutes: 2,
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...coordinates,
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});
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assert.equal(assessment.date, "1993-04-17");
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});
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test("birth time profile adapter parses a period without inventing a time", () => {
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const assessment = parseBirthTimeProfile({
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birth_date: "1993-04-17",
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reported_birth_time: null,
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birth_time_source: "period_only",
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birth_time_period: "evening",
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...coordinates,
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});
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assert.equal(assessment.source, "period_only");
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assert.equal("reportedTime" in assessment, false);
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});
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test("birth time profile adapter rejects missing location coordinates", () => {
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assert.throws(() => parseBirthTimeProfile({
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birth_date: "1993-04-17",
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reported_birth_time: "08:16:00",
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birth_time_source: "hospital_record",
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uncertainty_before_minutes: 2,
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uncertainty_after_minutes: 2,
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}));
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});
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test("rectification adapter normalizes Python questionnaire samples and options", () => {
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const questionnaire = parseRectificationQuestionnaire({
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questions: [{
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id: "education_environment_shift",
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prompt: "是否有明显学业变化？",
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options: [
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{ key: "A", label: "明确有" },
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{ key: "D", label: "不记得" },
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],
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}],
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candidate_scan: {
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samples: [{
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ascendant: { sign: "Cancer" },
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varga_lagna: {
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D9: { sign: "Leo" },
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D10: { sign: "Virgo" },
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},
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}],
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},
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});
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assert.deepEqual(questionnaire.questions[0]?.options, [
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{ key: "A", label: "明确有" },
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{ key: "D", label: "不记得" },
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]);
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assert.deepEqual(questionnaire.samples[0], {
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ascendantSign: "Cancer",
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d4Sign: null,
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d9Sign: "Leo",
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d10Sign: "Virgo",
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d24Sign: null,
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d30Sign: null,
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a7Sign: null,
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ulSign: null,
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a10Sign: null,
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});
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});
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test("rectification adapter preserves real engine sample times and named Varga keys", () => {
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const questionnaire = parseRectificationQuestionnaire({
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questions: [],
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candidate_scan: {
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samples: [{
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time: "1997-08-08 06:00",
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ascendant: { sign: "Cancer" },
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varga_lagna: {
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D2_Hora: { sign: "Cancer" },
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D4_Turyamsa: { sign: "Aries" },
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D9_Navamsa: { sign: "Aquarius" },
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D10_Dasamsa: { sign: "Scorpio" },
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D11_Rudramsa: { sign: "Libra" },
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D24_Siddhamsa: { sign: "Pisces" },
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D30_Trimsamsa: { sign: "Pisces" },
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},
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arudha: {
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A7: { sign: "Scorpio" },
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UL: { sign: "Virgo" },
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A10: { sign: "Capricorn" },
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},
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}],
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},
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});
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const rawScan = questionnaire.raw.candidate_scan as {
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samples: Array<{ time?: unknown }>;
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};
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assert.equal(rawScan.samples[0]?.time, "1997-08-08 06:00");
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assert.deepEqual(questionnaire.samples[0], {
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ascendantSign: "Cancer",
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d2Sign: "Cancer",
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d4Sign: "Aries",
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d9Sign: "Aquarius",
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d10Sign: "Scorpio",
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d11Sign: "Libra",
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d24Sign: "Pisces",
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d30Sign: "Pisces",
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a7Sign: "Scorpio",
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ulSign: "Virgo",
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a10Sign: "Capricorn",
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});
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});
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test("rectification adapter rejects a malformed Python questionnaire", () => {
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assert.throws(() => parseRectificationQuestionnaire({
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questions: [{ id: "missing_prompt" }],
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candidate_scan: { samples: [] },
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}));
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});
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test("rectification adapter normalizes all evidence-domain Varga signs", () => {
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const questionnaire = parseRectificationQuestionnaire({
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questions: [],
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candidate_scan: {
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samples: [{
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ascendant: { sign: "Cancer" },
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varga_lagna: {
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D4: { sign: "Aries" },
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D9: { sign: "Leo" },
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D10: { sign: "Virgo" },
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D24: { sign: "Gemini" },
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D30: { sign: "Pisces" },
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},
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}],
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},
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});
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assert.deepEqual(questionnaire.samples[0], {
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ascendantSign: "Cancer",
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d4Sign: "Aries",
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d9Sign: "Leo",
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d10Sign: "Virgo",
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d24Sign: "Gemini",
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d30Sign: "Pisces",
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a7Sign: null,
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ulSign: null,
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a10Sign: null,
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});
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});
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test("rectification adapter preserves scoring maps needed after the third answer", () => {
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const questionnaire = parseRectificationQuestionnaire({
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questions: [{
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id: "education_environment_shift",
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prompt: "是否有明显学业变化？",
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round: 1,
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options: [{ key: "A", label: "明确有" }],
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scoring_map: {
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A: { cluster: "early_candidate_cluster", points: 3 },
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},
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}],
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candidate_scan: { samples: [] },
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});
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assert.deepEqual(questionnaire.raw.questions, [{
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id: "education_environment_shift",
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prompt: "是否有明显学业变化？",
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round: 1,
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options: [{ key: "A", label: "明确有" }],
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scoring_map: {
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A: { cluster: "early_candidate_cluster", points: 3 },
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},
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}]);
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});
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test("rectification adapter normalizes scoring without elevating confidence", () => {
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const scoring = parseRectificationScoring({
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answered_count: 3,
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candidate_cluster_rankings: [
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{ cluster: "middle_candidate_cluster", score: 5 },
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],
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next_round: 2,
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next_round_questions: [{
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id: "health_crisis_or_low_period",
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prompt: "是否有明显健康或低谷阶段？",
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options: [{ key: "A", label: "明确有" }],
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}],
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});
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assert.equal(scoring.answeredCount, 3);
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assert.deepEqual(scoring.candidateClusterRankings, [
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{ cluster: "middle_candidate_cluster", score: 5 },
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]);
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assert.equal(scoring.nextRound, 2);
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assert.deepEqual(scoring.nextRoundQuestions, [{
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id: "health_crisis_or_low_period",
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prompt: "是否有明显健康或低谷阶段？",
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options: [{ key: "A", label: "明确有" }],
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}]);
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});
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test("rectification adapter normalizes an event-scored candidate result", () => {
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const result = parseCandidateResult({
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result_id: "1d8ee348-61a3-433d-8907-ff6d281b9992",
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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: "14:22",
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end_time: "14:26",
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representative_time: "14:24",
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width_minutes: 5,
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},
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event_count: 4,
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domain_count: 3,
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top_score: 16,
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second_score: 10,
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margin_percent: 37.5,
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reasons: [],
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evidence: [{
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event_id: "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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domain: "career",
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candidate_time: "14:24",
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rule_ids: ["vim_md_domain_house"],
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points: 4,
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legacy_server_metadata: { source: "existing-engine" },
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}],
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algorithm_version: "birth-time-event-scoring-v1",
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technique_contract: {
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calculation_status: "evaluated",
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used_divisional_charts: ["D10"],
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used_arudha: [],
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dasha_tracks: ["vimshottari"],
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missing_layers: [],
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auxiliary_layers: [],
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hard_blockers: ["neighbor_stability"],
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confirmation_allowed: false,
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decision: "continue_rectification",
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gates: {
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neighbor_stability: {
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status: "diagnostic_fail",
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reason: "diagnostic_only_unique_lead_at_plus_minus_1_2_5_minutes",
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},
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},
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},
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});
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assert.equal(result.resultId, "1d8ee348-61a3-433d-8907-ff6d281b9992");
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assert.equal(result.winningSegment?.representativeTime, "14:24");
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assert.equal(result.canApply, false, "an old or incomplete engine receipt cannot open minute confirmation");
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assert.deepEqual(result.evidence[0]?.ruleIds, ["vim_md_domain_house"]);
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assert.equal(result.techniqueReceipt?.gates?.neighbor_stability?.status, "fail");
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assert.equal("legacy_server_metadata" in (result.evidence[0] ?? {}), false);
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});
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test("candidate compatibility result accepts event counts beyond the former safety cap", () => {
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const lowCandidate = {
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result_id: "1d8ee348-61a3-433d-8907-ff6d281b9992",
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confidence: "low",
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can_apply: false,
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winning_segment: null,
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event_count: 10,
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domain_count: 5,
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top_score: 0,
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second_score: 0,
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margin_percent: 0,
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reasons: ["safety_cap"],
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evidence: [],
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algorithm_version: "birth-time-choice-scoring-v2",
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} as const;
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assert.equal(parseCandidateResult(lowCandidate).eventCount, 10);
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assert.equal(parseCandidateResult({ ...lowCandidate, event_count: 11 }).eventCount, 11);
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});
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