修改卡特尔16PF测验提交

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/C16PFTScale.java

@@ -14,6 +14,8 @@ import java.text.DecimalFormat;
 import java.util.*;
 import java.util.stream.Collectors;
 
+import static com.rf.psychological.scale.resultBusiness.scaleResult.NEWCOMMONScale.commonComputeDimensionScore;
+
 /**
  * @Author: GaoYiXuan
  * @Description:卡特尔16PF测试
@@ -27,7 +29,7 @@ public class C16PFTScale extends BaseScale {
         super(jsonArray, resultJson);
     }
 
-    public  JSONObject scaleCalculate() throws Exception {
+    /*public  JSONObject scaleCalculate() throws Exception {
         //总分
         double score = 0;
         DecimalFormat df = new DecimalFormat("######0.00");
@@ -216,16 +218,139 @@ public class C16PFTScale extends BaseScale {
         returnJson.put(ScaleConstant.ResultEnum.RESULT_NEW_FIELD.getKeyword(), newResult);
 
         return returnJson;
-    }
+    }*/
+
+    public  JSONObject scaleCalculate() throws Exception {
+        //总分
+        double score = 0;
+        DecimalFormat df = new DecimalFormat("######0.00");
+
+        Map<String, String> resultMap0 = new LinkedHashMap<>();
+        List<Map<String, String>> resultMapList = new ArrayList<>();
+        //新版本数据格式
+        Map<String, Object> newResult = new LinkedHashMap<>();
 
-    private static void putDimResult(Map<String, String> resultMap0, JSONObject resultJson, DimensionEntity dimensionEntity, NewResultDto newResultDto1, ScaleMarksEntity scaleMarksEntity) {
-        resultMap0.put(dimensionEntity.getName() + "常模参考值", scaleMarksEntity.getReference());
-        resultMap0.put(dimensionEntity.getName() + "症状", scaleMarksEntity.getSymptom());
-        resultJson.put(dimensionEntity.getName() + "结论", scaleMarksEntity.getSymptom());
-        resultMap0.put(dimensionEntity.getName() + "因子解释", scaleMarksEntity.getNameExplain());
-        resultMap0.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultJson.put(dimensionEntity.getName() + "指导语", scaleMarksEntity.getImprovementSuggestions());
-        resultMap0.put(dimensionEntity.getName() + "建议", scaleMarksEntity.getSuggestion());
-        BeanUtils.copyProperties(scaleMarksEntity, newResultDto1);
+        //获取答案列表
+        List<AnswerEntity> answerEntities = (List<AnswerEntity>) resultJson.get("answerEntities");
+        //获取评分规则列表
+        List<ScaleMarksEntity> scaleMarksEntities = (List<ScaleMarksEntity>) resultJson.get("scaleMarksEntities");
+        //获取维度信息列表
+        List<DimensionEntity> dimensionEntities = (List<DimensionEntity>) resultJson.get("dimensionEntities");
+        //计算每个维度得分dimensionEntities
+        double[] dimensionScore = new double[dimensionEntities.size()];
+        Map<String,Double> dimensionScoreMap = new HashMap<>();
+        //jsonArray:用户自己选择的题目选项
+        for (int i = 0; i < jsonArray.size(); i++) {
+            JSONObject jsonObject1 = jsonArray.getJSONObject(i);
+            //计算总分
+            for (AnswerEntity answerEntity : answerEntities) {
+                if (answerEntity.getQuestionNo().equals(jsonObject1.getString("questionNo"))) {
+                    if (answerEntity.getName().replaceAll("\\s", "").equals(jsonObject1.getString("checkItems").replaceAll("\\s", ""))) {
+                        score += Double.parseDouble(answerEntity.getScore());
+                        break;
+                    }
+                }
+            }
+            //计算维度分数
+            for (int y = 0; y < dimensionEntities.size(); y++) {
+                //获取维度信息
+                DimensionEntity dimensionEntity = dimensionEntities.get(y);
+                //获取该维度下有哪些选项
+                String questionNos = dimensionEntity.getQuestionNo();
+                //删除空白字符后得到进行划分得到选择题号
+                String[] questionNo = questionNos.replaceAll("\\s", "").split("；");
+                //计算每道题的得分
+                for (String question : questionNo) {
+                    if (question.equals(jsonObject1.getString("questionNo"))) {
+                        //获取所有答案中本道题目的答案list数组
+                        List<AnswerEntity> nowQuestionAnswerList = answerEntities.stream().filter(answerEntity -> answerEntity.getQuestionNo().equals(question)).collect(Collectors.toList());
+                        //本题目的正确答案与自己的选项进行对比，正确的话相应分数进行相加
+                        for (AnswerEntity answerEntity : nowQuestionAnswerList) {
+                            if (answerEntity.getName().replaceAll("\\s", "").equals(jsonObject1.getString("checkItems").replaceAll("\\s", ""))) {
+                                dimensionScoreMap.put(dimensionEntity.getId(),dimensionScoreMap.get(dimensionEntity.getId())==null?Double.parseDouble(answerEntity.getScore()):(dimensionScoreMap.get(dimensionEntity.getId()) +Double.parseDouble(answerEntity.getScore())));
+                                dimensionScore[y] += Double.parseDouble(answerEntity.getScore());
+                                break;
+                            }
+                        }
+                    }
+                }
+            }
+            resultJson.put("dimensionScoreMap",dimensionScoreMap);
+        }
+        //计算标准分
+        for (int y = 0; y < dimensionEntities.size(); y++) {
+            for (int i = 0; i < scaleMarksEntities.size(); i++) {
+                if (dimensionEntities.get(y).getName().equals(scaleMarksEntities.get(i).getName())) {
+                    String[] splits = scaleMarksEntities.get(i).getReference().split("±");
+                    double a = Double.valueOf(splits[0]);
+                    double b = Double.valueOf(splits[1]);
+                    dimensionScore[y] = Math.floor(((dimensionScore[y] - a)/b)*1.5 + 5.5);
+                    break;
+                }
+            }
+        }
+
+        //返回值以及存数据库格式
+        List<NewResultDto> newResultDtos = new ArrayList<>();
+
+        //判断最高分
+        double[] dimensionScores = new double[dimensionScore.length];
+        for (int y = 0; y < dimensionScore.length; y++) {
+            dimensionScores[y] = dimensionScore[y];
+        }
+        Arrays.sort(dimensionScores);
+        JSONObject jsonObject = new JSONObject(true);
+        for (int y = 0; y < dimensionScore.length; y++) {
+            if (dimensionScore[y] == dimensionScores[dimensionScores.length-1]){
+                jsonObject.put(dimensionEntities.get(y).getName(),dimensionScores[dimensionScores.length-1]);
+            }
+        }
+        Set<String> keys = jsonObject.keySet();
+        List<DimensionEntity> dimensionEntitieList = new ArrayList<>();
+        int b = 0;
+        for (String key : keys) {
+            for (DimensionEntity dimensionEntity : dimensionEntities) {
+                if (dimensionEntity.getName().equals(key)){
+                    dimensionEntitieList.add(dimensionEntity);
+                    dimensionScore[b] = jsonObject.getInteger(key);
+                    b++;
+                }
+            }
+        }
+
+
+        //将量表总维度根据维度名称进行分组
+        Map<String, List<ScaleMarksEntity>> scaleMarksMap = scaleMarksEntities.stream().collect(Collectors.groupingBy(ScaleMarksEntity::getName));
+        JSONObject iconInfo = new JSONObject();
+        //雷达图需要的维度以及最大值
+        List<Map<String, Object>> indicator = new LinkedList<>();
+        //雷达图所需要的常模参考值
+        LinkedList<String> reference = new LinkedList<>();
+        //雷达图需要的分数
+        LinkedList<String> scoreList = new LinkedList<>();
+        //计算维度所在评分区间
+        commonComputeDimensionScore(df, resultMap0, resultJson, dimensionEntitieList, dimensionScore, scaleMarksMap, newResultDtos, indicator, reference, scoreList);
+        //需要图表展示的
+        iconInfo.put("indicator", indicator);
+        //判断是否有常模参考值，即reference的内容如果全为0，则不进行返回
+        if (!reference.stream().allMatch("0"::equals)) {
+            iconInfo.put("reference", reference);
+        }
+        iconInfo.put("scoreList", scoreList);
+        if (indicator.size() == 0 && reference.size() == 0) {
+            newResult.put("iconInfo", "");
+        } else {
+            newResult.put("iconInfo", iconInfo);
+        }
+
+        newResult.put("result", newResultDtos);
+        resultMapList.add(resultMap0);
+        JSONObject returnJson = new JSONObject(true);
+        returnJson.put("resultMapList", resultMapList);
+        returnJson.put("resultJson", resultJson);
+        returnJson.put(ScaleConstant.ResultEnum.RESULT_NEW_FIELD.getKeyword(), newResult);
+
+
+        return returnJson;
     }
 }

src/main/java/com/rf/psychological/scale/resultBusiness/scaleResult/TTTScale.java

@@ -164,9 +164,9 @@ public class TTTScale extends BaseScale{
         }
         DecimalFormat df = new DecimalFormat("######0.00");
 
-        int totalScore = score1 + score2 + score3 + score4;
-        resultMap0.put("总分", String.valueOf(totalScore));
-        resultJson.put("总分", String.valueOf(totalScore));
+        //int totalScore = score1 + score2 + score3 + score4;
+        //resultMap0.put("总分", String.valueOf(totalScore));
+        //resultJson.put("总分", String.valueOf(totalScore));
 
 
         //将量表总维度根据维度名称进行分组