1

api/api_v1/endpoints/query.py

@@ -17,7 +17,7 @@ from db import get_database
 from db.ckdb import get_ck_db, CKDrive
 from db.redisdb import get_redis_pool, RedisDrive
 
-from models.behavior_analysis import BehaviorAnalysis
+from models.behavior_analysis import BehaviorAnalysis, CombinationEvent
 from models.user_analysis import UserAnalysis
 from utils import DfToStream
 
@@ -64,6 +64,8 @@ async def event_model_export(request: Request,
     mime = mimetypes.guess_type(file_name)[0]
     excels = []
     for item in sqls:
+        if not item.get('combination_event'):
+            continue
         sql = item['sql']
         event_name = item['event_name']
         df = await ckdb.query_dataframe(sql)
@@ -125,6 +127,17 @@ async def event_model(
             'end_date': item['end_date'],
             'time_particle': item['time_particle']
         }
+        # 处理组合问题
+        if item.get('combination_event'):
+            combination_event = CombinationEvent(res, item.get('combination_event'), item['format'])
+            values, sum_, avg = combination_event.parse()
+            q['values'].append(values)
+            q['sum'].append(sum_)
+            q['avg'].append(avg)
+            q['date_range'] = item['date_range']
+            res.append(q)
+            continue
+
         sql = item['sql']
         groupby = item['groupby']
         date_range = item['date_range']
@@ -193,16 +206,18 @@ async def event_model(
                     break
             q['sum'].append(round(float(df['values'].sum()), 2))
             q['avg'].append(round(float(df['values'].mean()), 2))
-        if item['time_particle'] in ('P1D', 'P1W'):
+
-            q['date_range'] = [d.strftime('%Y-%m-%d') for d in q['date_range']]
-        elif item['time_particle'] in ('P1M',):
-            q['date_range'] = [d.strftime('%Y-%m') for d in q['date_range']]
-        else:
-            q['date_range'] = [d.strftime('%Y-%m-%d %H:%M:%S') for d in q['date_range']]
         # q['eventNameDisplay']=item['event_name_display']
         res.append(q)
     # 按总和排序
     for item in res:
+        if item['time_particle'] in ('P1D', 'P1W'):
+            item['date_range'] = [d.strftime('%Y-%m-%d') for d in item['date_range']]
+        elif item['time_particle'] in ('P1M',):
+            item['date_range'] = [d.strftime('%Y-%m') for d in item['date_range']]
+        else:
+            item['date_range'] = [d.strftime('%Y-%m-%d %H:%M:%S') for d in item['date_range']]
+
         sort_key = np.argsort(np.array(item['sum']))[::-1]
         if item.get('groups'):
             item['groups'] = np.array(item['groups'])[sort_key].tolist()

models/behavior_analysis.py

@@ -19,6 +19,30 @@ from db import get_database
 from db.redisdb import get_redis_pool, RedisDrive
 
 
+class CombinationEvent:
+    def __init__(self, data, string, format):
+        self.data = data
+        self.string = string
+        self.pattern = re.compile('[+\-*/]')
+        self.format = format
+        self.events_name = []
+
+    def parse(self):
+        opts = self.pattern.findall(self.string)
+        factors = self.pattern.split(self.string)
+        result = pd.Series(self.data[int(factors[0])]['values'][0])
+        for i, opt in enumerate(opts):
+            b = pd.Series(self.data[int(factors[i + 1])]['values'][0])
+            result = settings.ARITHMETIC[opt](result, b)
+        if self.format == 'percent':
+            result = round(result * 100, 2)
+        elif self.format == 'float':
+            result = round(result, 2)
+        elif self.format == 'integer':
+            result = result.astype(int)
+        return result.to_list(), result.sum(), round(result.mean(), 2)
+
+
 class CustomEvent:
     def __init__(self, tbl, string, format):
         self.tbl = tbl
@@ -100,6 +124,7 @@ class BehaviorAnalysis:
         self.date_range = None
         self.unit_num = None
         self.report_name = None
+        self.combination_event = []
 
     async def init(self, *args, **kwargs):
 
@@ -321,6 +346,11 @@ class BehaviorAnalysis:
             event_name_col = getattr(self.event_tbl.c, '#event_name')
             format = event.get('format') or 'float'
 
+            # 兼容以前的结构
+            if event.get('customEvent'):
+                event['customType'] = event.get('customType') or 'formula'
+
+            if event.get('customType') == 'formula':
                 if event.get('customEvent'):
                     formula = event.get('customEvent')
                     custom = CustomEvent(self.event_tbl, formula, format).parse()
@@ -332,6 +362,19 @@ class BehaviorAnalysis:
                         *select_exprs,
                         custom['select']
                     ).where(*base_where, *where, *event_filter)
+
+            # 指标组合计算
+            elif event.get('customType') == 'combination':
+                sqls.append({'combination_event': event.get('customEvent'),
+                             'time_particle': self.time_particle,
+                             'start_date': self.start_date[:10],
+                             'end_date': self.end_date[:10],
+                             'event_name': event.get('eventNameDisplay'),
+                             'format': event.get('format') or 'float',
+                             'date_range': self.date_range
+                             }
+                            )
+                continue
             else:
                 event_name = event['event_name']
 
@@ -386,6 +429,7 @@ class BehaviorAnalysis:
                          'time_particle': self.time_particle,
                          'start_date': self.start_date[:10],
                          'end_date': self.end_date[:10],
+
                          })
 
         return sqls