digital-psychology/apps/api/internal/scale/jungian.go

package scale

import (
	"encoding/json"
	"strconv"
)

// JungianQuestionMeta is parsed from scale_questions.body for OEJTS-style items.
type JungianQuestionMeta struct {
	Dimension string `json:"dimension"`
	Format    string `json:"format"`
	Left      string `json:"left"`
	Right     string `json:"right"`
	Prompt    string `json:"prompt"`
}

// ScoreJungian tallies 1–5 Likert answers per EI/SN/TF/JP (OEJTS rules).
// Left poles: E, S, F, J · Right poles: I, N, T, P · threshold = perDim*3.
func ScoreJungian(answers map[string]string, qMeta map[string]JungianQuestionMeta, perDim int) (typeCode string, raw map[string]int, pct map[string]int) {
	raw = map[string]int{"EI": 0, "SN": 0, "TF": 0, "JP": 0}
	if perDim <= 0 {
		perDim = 8
	}
	for qid, ans := range answers {
		meta, ok := qMeta[qid]
		if !ok || meta.Dimension == "" {
			continue
		}
		v, err := strconv.Atoi(ans)
		if err != nil || v < 1 || v > 5 {
			v = 3
		}
		raw[meta.Dimension] += v
	}
	thr := perDim * 3
	pick := func(score int, left, right string) string {
		if score > thr {
			return right
		}
		return left
	}
	typeCode = pick(raw["EI"], "E", "I") +
		pick(raw["SN"], "S", "N") +
		pick(raw["TF"], "F", "T") +
		pick(raw["JP"], "J", "P")

	pct = map[string]int{}
	minS, maxS := perDim, perDim*5
	span := float64(maxS - minS)
	rightPct := func(score int) int {
		p := int(float64(score-minS)/span*100 + 0.5)
		if p < 0 {
			return 0
		}
		if p > 100 {
			return 100
		}
		return p
	}
	eiR := rightPct(raw["EI"])
	snR := rightPct(raw["SN"])
	tfR := rightPct(raw["TF"])
	jpR := rightPct(raw["JP"])
	pct["E"], pct["I"] = 100-eiR, eiR
	pct["S"], pct["N"] = 100-snR, snR
	pct["F"], pct["T"] = 100-tfR, tfR
	pct["J"], pct["P"] = 100-jpR, jpR
	return typeCode, raw, pct
}

// ParseJungianMeta extracts dimension metadata from question body JSON.
func ParseJungianMeta(body json.RawMessage) JungianQuestionMeta {
	var m JungianQuestionMeta
	_ = json.Unmarshal(body, &m)
	return m
}

// BuildJungianResult builds rich result with 4-letter code + preference bars.
func BuildJungianResult(typeCode string, pct map[string]int) map[string]interface{} {
	label := MBTILabel(typeCode)
	base := mbtiPack(typeCode, label)
	base.Label = typeCode + " · " + label
	base.ShareLine = "我的类型探索：" + typeCode + " · " + label
	base.Dimensions = []map[string]interface{}{
		{"title": "E/I 能量", "score": maxPct(pct, "E", "I"), "note": prefNote("E", "I", pct, "外向互动", "内向思考")},
		{"title": "S/N 信息", "score": maxPct(pct, "S", "N"), "note": prefNote("S", "N", pct, "具体事实", "模式可能")},
		{"title": "T/F 决策", "score": maxPct(pct, "T", "F"), "note": prefNote("T", "F", pct, "逻辑一致", "价值与人际")},
		{"title": "J/P 节奏", "score": maxPct(pct, "J", "P"), "note": prefNote("J", "P", pct, "计划结构", "灵活开放")},
	}
	out := map[string]interface{}{
		"title":       "MBTI 探索结果",
		"style_key":   typeCode,
		"type_code":   typeCode,
		"label":       base.Label,
		"summary":     base.Summary,
		"overview":    base.Overview,
		"share_line":  base.ShareLine,
		"dimensions":  base.Dimensions,
		"preferences": pct,
		"strengths":   base.Strengths,
		"watchouts":   base.Watchouts,
		"tips":        base.Tips,
		"scripts":     base.Scripts,
		"growth_plan": base.GrowthPlan,
		"faq":         base.FAQ,
	}
	return out
}

func maxPct(pct map[string]int, a, b string) int {
	if pct[a] >= pct[b] {
		return pct[a]
	}
	return pct[b]
}

func prefNote(a, b string, pct map[string]int, aDesc, bDesc string) string {
	if pct[a] >= pct[b] {
		return a + " " + strconv.Itoa(pct[a]) + "% · " + aDesc
	}
	return b + " " + strconv.Itoa(pct[b]) + "% · " + bDesc
}