1.修改底层生成SQL代码

2.新增游戏埋点，并给配置新的表

api/api_v1/endpoints/query.py

@@ -394,8 +394,9 @@ async def event_model(
             groups = []
             for gitem in item['groups']:
                 gb = []
-                if '(' in gitem:
+                if '(' in gitem or '[' in gitem:
-                    gitem = gitem.strip('(').strip(')').replace(' ', '').replace("'", '')
+                    gitem = gitem.replace('(', '').replace(')', '').replace(' ', '').replace("'", '') \
+                        .replace('[', '').replace(']', '')
                 if isinstance(gitem, list):
                     true_list = gitem
                 else:
@@ -441,6 +442,7 @@ async def retention_model(request: Request,
                           current_user: schemas.UserDB = Depends(deps.get_current_user)
                           ) -> schemas.Msg:
     await analysis.init(data_where=current_user.data_where)
+    """留存分析模型"""
     try:
         res = await analysis.retention_model_sql2()  # 初始化开始时间结束时间，sql语句  字典
     except Exception as e:
@@ -456,6 +458,7 @@ async def retention_model(request: Request,
     filter_item_type = res['filter_item_type']  # all
     filter_item = res['filter_item']  # 列表  0,1,3,7,14,21,30
     df.set_index('reg_date', inplace=True)
+    #  补齐没有数据的日期
     for d in set(res['date_range']) - set(df.index):
         df.loc[d] = 0
 
@@ -498,6 +501,140 @@ async def retention_model(request: Request,
                 retention_avg_dict[rn]['cntn'] += rd[f'cnt{rn}']
                 retention_avg_dict[rn]['o_cnt0'] += rd['cnt0']
                 retention_avg_dict[rn]['o_cntn'] += rd[f'on{rn}']
+    # 算均值
+    tmp['p'] = []
+    tmp['n'] = []
+    tmp['p_outflow'] = []
+    tmp['n_outflow'] = []
+    tmp['d0'] = 0
+    for rt, rd in retention_avg_dict.items():
+        tmp['d0'] = int(df['cnt0'].sum())
+        n = round(rd['cntn'] * 100 / rd['cnt0'], 2)
+        n = 0 if np.isnan(n) else n
+        tmp['p'].append(n)
+        tmp['n'].append(rd['cntn'])
+        n = round(rd['o_cntn'] * 100 / rd['cnt0'], 2)
+        n = 0 if np.isnan(n) else n
+        tmp['p_outflow'].append(n)
+        tmp['n_outflow'].append(rd['o_cntn'])
+    # 次留数
+    title = ['日期', '用户数', '次留', *[f'{i + 1}留' for i in retention_n[1:]]]
+
+    # 未到达的日期需要补齐-
+    retention_length = len(retention_n)
+    for _, items in summary_values.items():
+        for key in ['p', 'n', 'p_outflow', 'n_outflow']:
+            items[key].extend(['-'] * (retention_length - len(items[key])))
+
+    resp = {
+        'summary_values': summary_values,
+        # 'values': values,
+        'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
+        'title': title,
+        'filter_item_type': filter_item_type,
+        'filter_item': filter_item,
+        'start_date': res['start_date'],
+        'end_date': res['end_date'],
+        'time_particle': res['time_particle']
+
+    }
+    return schemas.Msg(code=0, msg='ok', data=resp)
+
+
+@router.post("/retention_model_details")
+async def retention_model(request: Request,
+                          game: str,
+                          ckdb: CKDrive = Depends(get_ck_db),
+                          db: AsyncIOMotorDatabase = Depends(get_database),
+                          analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
+                          current_user: schemas.UserDB = Depends(deps.get_current_user)
+                          ) -> schemas.Msg:
+    await analysis.init(data_where=current_user.data_where)
+    """留存分析分组详情"""
+    try:
+        res = await analysis.retention_model_sql3()  # 初始化开始时间结束时间，sql语句  字典
+    except Exception as e:
+        return schemas.Msg(code=-9, msg='报表配置参数异常')
+    sql = res['sql']  # 获取到sql语句
+    df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
+    date_range = res['date_range']  # 时间  列表
+    # unit_num = res['unit_num']  # int
+    retention_n = res['retention_n']  # 列表 int
+    filter_item_type = res['filter_item_type']  # all
+    filter_item = res['filter_item']  # 列表  0,1,3,7,14,21,30
+    # 映射对应中文返回给前端展示
+    groupby_list=analysis.event_view.get('groupBy')
+    groupby = [i['columnName'] for i in groupby_list if i['tableType'] != 'user_label']
+    if len(groupby_list) == 1:
+        max_v = int(df[groupby_list[0]['columnName']].max())
+        min_v = int(df[groupby_list[0]['columnName']].min())
+    for i in groupby:
+        if i == 'svrindex':
+            if game == 'mfmh5':
+                game = 'mzmfmh5'
+            chinese = {}
+            resp = await crud.select_map.get_one(db, game, i)
+            for ii in resp:
+                chinese[ii['id']] = ii['title']
+            for k, v in chinese.items():
+                # 开始映射
+                df.loc[df['svrindex'] == k, 'svrindex'] = v
+    times=df['reg_date'][0]
+    df.set_index(groupby, inplace=True)
+    # for d in set(res['date_range']) - set(df.index):
+    #     df.loc[d] = 0
+
+    df.sort_index(inplace=True)
+    summary_values = {'均值': {}}
+    max_retention_n = 1
+    # 留存人数
+    avg = {}
+    # 流失人数
+    avgo = {}
+    for date, v in df.T.items():
+        # 字典中data存在时不替换，否则将data替换成空字典
+        tmp = summary_values.setdefault(date, dict())
+        tmp['d0'] = int(v.cnt0)
+        tmp['p'] = []
+        tmp['n'] = []
+        tmp['p_outflow'] = []
+        tmp['n_outflow'] = []
+        for i in retention_n:
+            n = (pd.Timestamp.now().date() - v[0]).days
+            if i > n:
+                continue
+            # max_retention_n = i if i > max_retention_n else max_retention_n
+            # 留存的人数
+            avg[i] = avg.setdefault(i, 0) + v[f'cnt{i}']
+            # 流失的人数
+            avgo[i] = avgo.setdefault(i, 0) + v[f'on{i}']
+            tmp['p'].append(v[f'p{i}'])
+            tmp['n'].append(v[f'cnt{i}'])
+            tmp['p_outflow'].append(v[f'op{i}'])
+            tmp['n_outflow'].append(v[f'on{i}'])
+    tmp = summary_values['均值']
+    retention_avg_dict = {}
+    # 多个分组项时，合成列表返回
+    if len(groupby)>1:
+        summary_valuess = {}
+        for k, v in summary_values.items():
+            if 'str' in str(type(k)):
+                summary_valuess[str([k])] = v
+            else:
+                summary_valuess[str(list(k))] = v
+    else:
+        summary_valuess=summary_values
+
+    for rn in retention_n:
+        for rt, rd in df.T.items():
+            if times + datetime.timedelta(days=rn) <= pd.datetime.now().date():
+                retention_avg_dict.setdefault(rn, {'cnt0': 0, 'cntn': 0, 'o_cnt0': 0, 'o_cntn': 0})
+                retention_avg_dict[rn]['cnt0'] += rd['cnt0']
+                retention_avg_dict[rn]['cntn'] += rd[f'cnt{rn}']
+                retention_avg_dict[rn]['o_cnt0'] += rd['cnt0']
+                retention_avg_dict[rn]['o_cntn'] += rd[f'on{rn}']
 
     tmp['p'] = []
     tmp['n'] = []
@@ -514,18 +651,66 @@ async def retention_model(request: Request,
         n = 0 if np.isnan(n) else n
         tmp['p_outflow'].append(n)
         tmp['n_outflow'].append(rd['o_cntn'])
+    #  如果分组项是int类型按选择的分组
 
+    # 默认区间
+    if analysis.event_view.get('groupBy')[0]['intervalType'] == 'def':
+        summary_valuess.pop('均值')
+        interval = (max_v - min_v) // 10 or 1
+        lens = len(summary_valuess[max_v]['n'])
+        ress = {}
+        for i in range(min_v, max_v, interval):
+            d0 = 0
+            n1 = []
+            n_outflow1 = []
+            for k, v in summary_valuess.items():
+                if k >= i and k < i + interval:
+                    d0 += v['d0']
+                    n1.append(v['n'])
+                    n_outflow1.append(v['n_outflow'])
+            if len(n1) > 0:
+                re_dict = {}
+                n = np.sum([ii for ii in n1], axis=0).tolist()
+                n_outflow = np.sum([iii for iii in n_outflow1], axis=0).tolist()
+                p = [round(nu*100 / d0, 2) for nu in n]
+                p_outflow = [round(num*100 / d0, 2) for num in n_outflow]
+                re_dict['d0'] = d0
+                re_dict['n'] = n
+                re_dict['n_outflow'] = n_outflow
+                re_dict['p'] = p
+                re_dict['p_outflow'] = p_outflow
+                ress[f"[{i},{i + interval})"] = re_dict
+            else:
+                re_dict = {'d0': 0}
+                n = []
+                n_outflow = []
+                p = []
+                p_outflow = []
+                for cishu in range(0, lens):
+                    n.append(0)
+                    n_outflow.append(0)
+                    p.append(0)
+                    p_outflow.append(0)
+                re_dict['n'] = n
+                re_dict['n_outflow'] = n_outflow
+                re_dict['p'] = p
+                re_dict['p_outflow'] = p_outflow
+                ress[f"[{i},{i + interval})"] = re_dict
+        summary_valuess=ress
+    # 自定义区间
+    elif analysis.event_view.get('groupBy')[0]['intervalType'] == 'user_defined':
+        pass
     #  次留数
-    title = ['日期', '用户数', '次留', *[f'{i + 1}留' for i in retention_n[1:]]]
+    title = ['分组项', '用户数', '次留', *[f'{i + 1}留' for i in retention_n[1:]]]
 
     # 未到达的日期需要补齐-
     retention_length = len(retention_n)
-    for _, items in summary_values.items():
+    for _, items in summary_valuess.items():
         for key in ['p', 'n', 'p_outflow', 'n_outflow']:
             items[key].extend(['-'] * (retention_length - len(items[key])))
 
     resp = {
-        'summary_values': summary_values,
+        'summary_values': summary_valuess,
         # 'values': values,
         'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
         'title': title,
@@ -832,7 +1017,7 @@ async def funnel_model(
 
         _ = date_data.setdefault(key.strftime('%Y-%m-%d'), {})
         _['总体'] = tmp
-
+    # 分组
     if groupby:
         # 补齐数据
         concat_data = []
@@ -843,7 +1028,18 @@ async def funnel_model(
 
         df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
         # df.sort_values(list((*groupby, 'level')), inplace=True, ascending=False)
-
+        # 映射对应中文返回给前端展示
+        for i in groupby:
+            if i == 'svrindex':
+                if game == 'mfmh5':
+                    game = 'mzmfmh5'
+                chinese = {}
+                resp = await crud.select_map.get_one(db, game, i)
+                for ii in resp:
+                    chinese[ii['id']] = ii['title']
+                for k, v in chinese.items():
+                    # 开始映射
+                    df.loc[df[i] == k, i] = v
         for key, tmp_df in df.groupby(groupby):
             tmp = {'title': key}
             tmp_df = tmp_df.groupby('level').sum()
@@ -1247,18 +1443,19 @@ async def scatter_model(
             #按天分组
             sql = sql.replace(f'toDate(addHours({game}.event."#event_time", 8)) AS date', f'`{columnName}` as va',
                               1)
-            sql = sql.replace(f'toDate(addHours({game}.event."#event_time", 8))', columnName, 1)
+            sql = sql.replace(f'toDate(addHours({game}.event."#event_time", 8))', f'`{columnName}`', 1)
             #按周分组
             sql = sql.replace(f'toStartOfWeek(addHours({game}.event."#event_time", 8)) AS date', f'`{columnName}` as va',
                               1)
-            sql = sql.replace(f'toStartOfWeek(addHours({game}.event."#event_time", 8))', columnName, 1)
+            sql = sql.replace(f'toStartOfWeek(addHours({game}.event."#event_time", 8))', f'`{columnName}`', 1)
             #按月分组
             sql = sql.replace(f'toStartOfMonth(addHours({game}.event."#event_time", 8)) AS date', f'`{columnName}` as va',
                               1)
-            sql = sql.replace(f'toStartOfMonth(addHours({game}.event."#event_time", 8))', columnName, 1)
+            sql = sql.replace(f'toStartOfMonth(addHours({game}.event."#event_time", 8))', f'`{columnName}`', 1)
             #合计
             if analysis.event_view.get('timeParticleSize') == "total":
-                sql = sql.replace(f'SELECT', f'SELECT {columnName} as va,', 1)
+                sql = sql.replace(f'SELECT', f'SELECT `{columnName}` as va,', 1)
+        print(sql)
         df = await ckdb.query_dataframe(sql)
         if df.empty:
             return schemas.Msg(code=-9, msg='无数据', data=None)

core/config.py

@@ -399,21 +399,21 @@ class Settings(BaseSettings):
         case_sensitive = True
 
 
-class Debug(Settings):
-    MDB_HOST: str = '10.0.0.9'
-    MDB_PORT: int = 27017
-    MDB_USER: str = 'root'
-    MDB_PASSWORD: str = 'iamciniao'
-    MDB_DB: str = 'xdata'
-
-    DATABASE_URI = f'mongodb://{MDB_USER}:{MDB_PASSWORD}@{MDB_HOST}:{MDB_PORT}/admin'
-#本地MongoDB的库测试
 # class Debug(Settings):
-#     MDB_HOST: str = '127.0.0.1'
+#     MDB_HOST: str = '10.0.0.9'
 #     MDB_PORT: int = 27017
+#     MDB_USER: str = 'root'
+#     MDB_PASSWORD: str = 'iamciniao'
 #     MDB_DB: str = 'xdata'
 #
-#     DATABASE_URI = f'mongodb://{MDB_HOST}:{MDB_PORT}/admin'
+#     DATABASE_URI = f'mongodb://{MDB_USER}:{MDB_PASSWORD}@{MDB_HOST}:{MDB_PORT}/admin'
+#本地MongoDB的库测试
+class Debug(Settings):
+    MDB_HOST: str = '127.0.0.1'
+    MDB_PORT: int = 27017
+    MDB_DB: str = 'xdata'
+
+    DATABASE_URI = f'mongodb://{MDB_HOST}:{MDB_PORT}/admin'
 
 class Produce(Settings):
     MDB_HOST: str = '127.0.0.1'

crud/__init__.py

@@ -21,3 +21,4 @@ from .crud_url_list import url_list
 from .crud_user_url import user_url
 from .crud_api_module import api_module
 from .crud_event_list import event_list
+from .crud_event_point import event_point

crud/crud_event_point.py

@@ -0,0 +1,29 @@
+from motor.motor_asyncio import AsyncIOMotorDatabase
+import schemas
+from crud.base import CRUDBase
+
+__all__ = 'event_point',
+
+from utils import get_uid
+
+
+class CRUDProjectNumber(CRUDBase):
+    # 获取对应游戏的数据，默认把基础属性的数据也获取出来
+    async def all_event(self, db: AsyncIOMotorDatabase, game):
+        return await self.find_many(db, {'game': {'$in':[game,'basics_attr']}})
+
+    # # 修改数据
+    # async def update(self, db: AsyncIOMotorDatabase, data_in: schemas.AddProjectnumber):
+    #     game = data_in.game
+    #     add_ditch = []
+    #     for member in data_in.ditch:
+    #         add_ditch.append(member.dict())
+    #     await self.update_one(db, {'game': game}, {'$set': {'ditch': add_ditch}})
+    #
+    # # 插入数据
+    # async def create(self, db: AsyncIOMotorDatabase, data_in: schemas.ProjectnumberInsert):
+    #     # await self.update_one(db, {'xiangmu': data_in.xiangmu}, {'$set': data_in.dict()}, upsert=True)
+    #     await self.update_one(db, {data_in.game, data_in.ditch}, upsert=True)
+
+
+event_point = CRUDProjectNumber('event_point')

main.py

@@ -162,5 +162,5 @@ async def add_process_time_header(request: Request, call_next):
 
 
 if __name__ == '__main__':
-    #uvicorn.run(app='main:app', host="10.0.0.240", port=7899, reload=True, debug=True)
+    uvicorn.run(app='main:app', host="10.0.0.240", port=7899, reload=True, debug=True)
-    uvicorn.run(app='main:app', host="0.0.0.0", port=7899, reload=True, debug=True)
+    #uvicorn.run(app='main:app', host="0.0.0.0", port=7899, reload=True, debug=True)

models/behavior_analysis.py

@@ -19,7 +19,8 @@ from db import get_database
 
 from db.redisdb import get_redis_pool, RedisDrive
 from models.user_label import UserClusterDef
-from utils import get_week, strptime, start_end_month, strptime1
+from utils import get_week, strptime, start_end_month, strptime1, get_event
+
 
 class CombinationEvent:
     def __init__(self, data, string, format):
@@ -255,7 +256,7 @@ class BehaviorAnalysis:
         :param relation:
         :return:
         """
-
+        #  事件,留存，分布，漏斗分析生成sql经过，
         user_filters = []
         event_filters = []
         for filter in filters:
@@ -281,9 +282,56 @@ class BehaviorAnalysis:
                     continue
                 else:
                     continue
-
+                #  表名
                 tbl = getattr(self, f'{item["tableType"]}_tbl')
+                #  字段名
                 col = getattr(tbl.c, item['columnName'])
+                #  判断是否是同一个事件
+                yuan_event=self.events[0].get('eventName') or self.events[0].get('event_name')  # 指标中的事件名
+                biao_event=self.events[0].get('customEvent','').split('.')[0]
+                event= await get_event(item['columnName'],self.game)  # 获取对应事件名或基础属性
+                if event != yuan_event and event != biao_event and event != '基础属性' and self.game != 'debug':
+                    event_time_col = getattr(self.event_tbl.c, '#event_time')
+                    event_name_col = getattr(self.event_tbl.c, '#event_name')
+                    base_where = [
+                        func.addHours(event_time_col, self.zone_time) >= self.start_date,
+                        func.addHours(event_time_col, self.zone_time) <= self.end_date,]
+                    event_name_where = []
+                    event_name_where.append(settings.CK_CALC_SYMBO['=='](event_name_col, event))
+                    ftv = item['ftv']
+                    if comparator == '==':
+                        if len(ftv) > 1:
+                            event_name_where.append(or_(*[col == v for v in ftv]))
+                        else:
+                            event_name_where.append(col == ftv[0])
+                    elif comparator == '>=':
+                        event_name_where.append(col >= ftv[0])
+                    elif comparator == '<=':
+                        event_name_where.append(col <= ftv[0])
+                    elif comparator == '>':
+                        event_name_where.append(col > ftv[0])
+                    elif comparator == '<':
+                        event_name_where.append(col < ftv[0])
+                    elif comparator == 'range':
+                        event_name_where.append(col > ftv[0])
+                        event_name_where.append(col <= ftv[1])
+                    elif comparator == 'is not null':
+                        event_name_where.append(col.isnot(None))
+                    elif comparator == 'is null':
+                        event_name_where.append(col.is_(None))
+                    elif comparator == 'like':
+                        event_name_where.append(col.like(f'%{ftv[0]}%'))
+                    elif comparator == 'not like':
+                        event_name_where.append(col.notlike(f'%{ftv[0]}%'))
+                    elif comparator == 'in':
+                        event_name_where.append(col.in_(ftv))
+                    elif comparator == '!=':
+                        event_name_where.append(col != ftv[0])
+                    sub_qry=sa.select(sa.Column('#account_id')).select_from(
+                        sa.select(sa.Column('#account_id')).where(and_(*base_where,*event_name_where))
+                    )
+                    event_filter.append(sa.Column('#account_id').in_(sub_qry))
+                    continue
                 # 日期类型处理时区
                 if item.get('data_type') == 'datetime':
                     col = func.addHours(col, self.zone_time)
@@ -404,6 +452,7 @@ class BehaviorAnalysis:
                 }
 
     async def event_model_sql(self):
+        """事件分析生成sql会经过"""
         sqls = []
         event_time_col = getattr(self.event_tbl.c, '#event_time')
         for event in self.events:
@@ -776,19 +825,29 @@ ORDER BY level
             }
 
     async def trace_model_sql(self):
+        #  路径分析生成SQL
         session_interval = self.event_view.get('session_interval')
         session_type = self.event_view.get('session_type')
         session_type_map = {
             'minute': 60,
             'second': 1,
             'hour': 3600
-
         }
         interval_ts = session_interval * session_type_map.get(session_type, 60)
         event_names = self.events.get('event_names')
         source_event = self.events.get('source_event', {}).get('eventName')
         source_type = self.events.get('source_event', {}).get('source_type')
-
+        wheres = self.events['user_filter']['filts']
+        sql_one=''
+        if wheres != []:  # 有筛选条件的时候使用
+            columnName=wheres[0]['columnName'] # 获取字段名
+            event=await get_event(columnName,self.game) # 获取字段属于那个事件，或者是否是基础属性
+            if event == '基础属性':
+                sql_one=f""" and `{columnName}` {wheres[0]['comparator']} '{wheres[0]['ftv'][0]}' and `#event_name` in evnet_all) """
+            else:# 如果包含有事件则进入下面的逻辑
+                sql_one=f"""and `#account_id` in ( SELECT `#account_id` FROM {self.game}.event WHERE `#event_name`= '{event}' and `{columnName}` = 
+                '{wheres[0]['ftv'][0]}' and  addHours(`#event_time`, {self.zone_time}) >= start_data
+              and addHours(`#event_time`, {self.zone_time}) <= end_data ) and `#event_name` in evnet_all)"""
         sql_a = f"""with
     '{source_event}' as start_event,
     {tuple(event_names)} as evnet_all,
@@ -879,6 +938,8 @@ GROUP BY event_chain,`#account_id`
 ORDER BY values desc"""
 
         sql = sql_a if source_type == 'initial_event' else sql_b
+        if sql_one != '':
+            sql= sql.replace('and `#event_name` in evnet_all)',sql_one,1)
         print(sql)
         return {
             'sql': sql,
@@ -888,10 +949,11 @@ ORDER BY values desc"""
         }
 
     async def retention_model_sql2(self):
+        # 留存分析生成SQL
         filter_item_type = self.event_view.get('filter_item_type')
         filter_item = self.event_view.get('filter_item')
-        event_name_a = self.events[0]['eventName']
+        event_name_a = self.events[0]['eventName']  # 初始的事件名
-        event_name_b = self.events[1]['eventName']
+        event_name_b = self.events[1]['eventName']  # 回访的事件名
 
         visit_name = self.events[0].get('event_attr_id')
 
@@ -973,3 +1035,107 @@ group by a.reg_date) log on reg.date=log.reg_date
             'start_date': self.start_date[:10],
             'end_date': self.end_date[:10],
         }
+
+    async def retention_model_sql3(self):
+        # 留存分析分组详情生成SQL
+        filter_item_type = self.event_view.get('filter_item_type')
+        filter_item = self.event_view.get('filter_item')
+        event_name_a = self.events[0]['eventName']  # 初始的事件名
+        event_name_b = self.events[1]['eventName']  # 回访的事件名
+        groupby_list=self.event_view.get('groupBy')
+        #  判断是基础的还是标签
+        groupby = [i['columnName'] for i in groupby_list if i['tableType'] != 'user_label']
+
+        visit_name = self.events[0].get('event_attr_id')
+
+        where, _ = await self.handler_filts((self.events[0]['filts'], self.events[0].get('relation', 'and')),
+                                            (self.global_filters, self.global_relation)
+                                            , self.ext_filters)
+        where_a = '1'
+        if where:
+            qry = sa.select().where(*where)
+            sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
+            where_a = 'WHERE '.join(sql.split('WHERE ')[1:])
+        # 任意事件
+        event_name_b = 1 if event_name_b == '*' else f"`#event_name` = '{event_name_b}'"
+
+        days = (arrow.get(self.end_date).date() - arrow.get(self.start_date).date()).days
+        keep = []
+        cnt = []
+        retention_n = [*[k for k in range(1, 60)], 70 - 1, 75 - 1, 80 - 1, 85 - 1, 90 - 1, 95 - 1, 100 - 1, 110 - 1,
+                       120 - 1, 150 - 1, 180 - 1, 210 - 1, 240 - 1, 270 - 1, 300 - 1,
+                       360 - 1]
+
+        """
+               cnt0-cnt1 as on1,
+       round(on1 * 100 / cnt0, 2)   as `0p1`,
+        """
+
+        for i in retention_n:
+            keep.append(
+                f"""cnt{i},
+                round(cnt{i} * 100 / cnt0, 2) as `p{i}`,
+                cnt0-cnt{i} as on{i},
+                round(on{i} * 100 / cnt0, 2)   as `op{i}`
+                """)
+            cnt.append(f"""sum(if(dateDiff('day',a.reg_date,b.visit_date)={i},1,0)) as cnt{i}""")
+        keep_str = ','.join(keep)
+        cnt_str = ','.join(cnt)
+        if len(groupby) > 0:
+            groupbys = ','.join([f"`{i}`" for i in groupby])
+            groupby_on = ' and '.join([f"reg.{ii} = log.{ii}" for ii in [f"`{i}`" for i in groupby]])
+            sql=f"""
+    with '{event_name_a}' as start_event,
+        {event_name_b} as retuen_visit,
+        `{visit_name}` as visit,
+        '{self.start_date}' as start_data,
+        '{self.end_date}' as end_data,
+        toDate(addHours(`#event_time`, {self.zone_time})) as date
+    select reg_date,
+           {groupbys},
+           cnt0 ,
+           {keep_str}
+           from(select date,{groupbys},uniqExact(visit) as cnt0 from {self.game}.event
+    where `#event_name` = start_event and addHours(`#event_time`, {self.zone_time}) >= start_data  and addHours(`#event_time`, {self.zone_time}) <= end_data and {where_a}
+    group by date,{groupbys}) reg left join
+    (select a.reg_date,{groupbys},
+           {cnt_str}
+    from (select {groupbys},date as reg_date, visit from {self.game}.event where `#event_name` = start_event and addHours(`#event_time`, {self.zone_time}) >= start_data  and addHours(`#event_time`, {self.zone_time}) <= end_data and {where_a} group by reg_date, visit,{groupbys}) a
+             left join (select date as visit_date, visit from {self.game}.event where retuen_visit and addHours(`#event_time`, {self.zone_time}) >= start_data group by visit_date, visit) b on
+    a.visit = b.visit
+    group by a.reg_date,{groupbys}) log on reg.date=log.reg_date and {groupby_on}
+    """
+        else:
+            sql = f"""
+    with '{event_name_a}' as start_event,
+        {event_name_b} as retuen_visit,
+        `{visit_name}` as visit,
+        '{self.start_date}' as start_data,
+        '{self.end_date}' as end_data,
+        toDate(addHours(`#event_time`, {self.zone_time})) as date
+    select reg_date,
+           cnt0 ,
+           {keep_str}
+           from(select date, uniqExact(visit) as cnt0 from {self.game}.event
+    where `#event_name` = start_event and addHours(`#event_time`, {self.zone_time}) >= start_data  and addHours(`#event_time`, {self.zone_time}) <= end_data and {where_a}
+    group by date) reg left join
+    (select a.reg_date,
+           {cnt_str}
+    from (select date as reg_date, visit from {self.game}.event where `#event_name` = start_event and addHours(`#event_time`, {self.zone_time}) >= start_data  and addHours(`#event_time`, {self.zone_time}) <= end_data and {where_a} group by reg_date, visit) a
+             left join (select date as visit_date, visit from {self.game}.event where retuen_visit and addHours(`#event_time`, {self.zone_time}) >= start_data group by visit_date, visit) b on
+    a.visit = b.visit
+    group by a.reg_date) log on reg.date=log.reg_date
+    """
+        print(sql)
+        return {
+            'sql': sql,
+            'group_label': self.group_label,
+            'date_range': self.date_range,
+            'unit_num': self.unit_num,
+            'retention_n': retention_n,
+            'filter_item_type': filter_item_type,
+            'filter_item': filter_item,
+            'time_particle': self.time_particle,
+            'start_date': self.start_date[:10],
+            'end_date': self.end_date[:10],
+        }

models/user_label.py

@@ -43,7 +43,7 @@ class UserClusterDef:
         res_json = await self.rdb.get(f'{self.game}_user')
         columns = json.loads(res_json).keys()
         metadata = sa.MetaData(schema=self.game)
-        #self.user_tbl = sa.Table('user_view', metadata, *[sa.Column(column) for column in columns])修改了这里，这是原本的
+        # self.user_tbl = sa.Table('user_view', metadata, *[sa.Column(column) for column in columns]) #  修改了这里，这是原本的
         self.user_tbl = sa.Table('event', metadata, *[sa.Column(column) for column in columns])
         self.u_account_id_col = getattr(self.user_tbl.c, '#account_id')
         self.e_account_id_col = getattr(self.event_tbl.c, '#account_id')

models/x_analysis.py

@@ -15,6 +15,8 @@ import schemas
 from core.config import settings
 from db import get_database
 from db.redisdb import get_redis_pool, RedisDrive
+from models.user_label import UserClusterDef
+from utils import get_event
 
 
 class XAnalysis:
@@ -30,7 +32,11 @@ class XAnalysis:
         self.date_range = []
 
         self.ext_filters = (self.data_in.ext_filter.get('filts', []), self.data_in.ext_filter.get('relation', 'and'))
-
+        self.start_date = None
+        self.end_date = None
+        self.data_where=[]
+        self.event_tbl=None
+        self.zone_time:int = 0
     def _get_global_filters(self):
         return self.event_view.get('filts') or []   #获取event_view字典里面filts的值，或返回空列表
 
@@ -124,8 +130,8 @@ class XAnalysis:
 
         return event_filters
 
-
     def ltv_model_sql(self):
+        #  ltv的生成sql
         days = (arrow.get(self.event_view['endTime']).date() - arrow.get(self.event_view['startTime']).date()).days
         quota = self.event_view['quota']
         select_ltv = []

schemas/__init__.py

@@ -25,3 +25,4 @@ from .url_list import *
 from .user_url import *
 from .api_module import *
 from .event_list import *
+from .event_point import *

schemas/event_point.py

@@ -0,0 +1,8 @@
+from pydantic import BaseModel
+from typing import List
+
+
+class Eventpoint(BaseModel):
+    game: str  # 游戏名
+    event_name: str  # 事件名
+    event_attr: List[str]  # 事件属性

utils/func.py

@@ -1,4 +1,3 @@
-
 import random
 import time
 import datetime
@@ -7,6 +6,12 @@ import pandas as pd
 from datetime import timedelta
 from datetime import datetime as p1
 import calendar
+
+import crud
+import schemas
+from db import get_database
+
+
 def get_uid():
     return hex(int(time.time() * 10 ** 7) + random.randint(0, 10000))[2:]
 
@@ -27,6 +32,8 @@ def estimate_data(data_type):
         return "Nullable(DateTime('UTC'))"
     else:
         return "Nullable(String)"
+
+
 # 将字典变成字符串
 def dict_to_str(dic):
     c = str()
@@ -41,6 +48,7 @@ def dict_to_str(dic):
             c += "\"%s\":\"%s\"}" % (k, v)
     return c
 
+
 def getEveryDay(begin_date, end_date):
     # 前闭后闭
     date_list = []
@@ -51,6 +59,8 @@ def getEveryDay(begin_date,end_date):
         date_list.append(date_str)
         begin_date += datetime.timedelta(days=1)
     return date_list
+
+
 # print(getEveryDay('2016-01-01','2017-05-11'))
 def Download_xlsx(df, name):
     """
@@ -69,6 +79,7 @@ def Download_xlsx(df,name):
     Download = StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
     return Download
 
+
 def jiange_insert(list_date):
     """
     间隔1条插入一条数据插入数据
@@ -81,6 +92,7 @@ def jiange_insert(list_date):
         i += 2
     return list_date
 
+
 def create_df(resp):
     """
     分布分析外部下载功能的df数据
@@ -118,6 +130,8 @@ def create_df(resp):
     columns.insert(0, '事件发生时间')
     df = pd.DataFrame(data=date, columns=columns)
     return df
+
+
 def create_neidf(resp, columnName):
     """
     分布分析内部下载功能的df数据
@@ -156,9 +170,11 @@ def create_neidf(resp,columnName):
     df = pd.DataFrame(data=date, columns=columns)
     return df
 
+
 def get_week(date_str=None):
     if date_str and isinstance(date_str, str):
-        now_time = (p1.strptime(date_str + " 00:00:00", "%Y-%m-%d %H:%M:%S")+ datetime.timedelta(days=1)).strftime("%Y-%m-%d %H:%M:%S")
+        now_time = (p1.strptime(date_str + " 00:00:00", "%Y-%m-%d %H:%M:%S") + datetime.timedelta(days=1)).strftime(
+            "%Y-%m-%d %H:%M:%S")
     else:
         now_time = p1.now().replace(hour=0, minute=0, second=0, microsecond=0)
     now_time = strptime(now_time)
@@ -169,6 +185,7 @@ def get_week(date_str=None):
     week_end_time = week_start_time + timedelta(days=6, hours=23, minutes=59, seconds=59)
     return week_start_time, week_end_time
 
+
 def strptime(date_string):
     """
     将字符串转换成datetime.datetime类型
@@ -177,6 +194,7 @@ def strptime(date_string):
     """
     return p1.strptime(date_string, '%Y-%m-%d %H:%M:%S')
 
+
 def strptime1(date_str):
     """
     将字符串转换成datetime.datetime类型
@@ -185,6 +203,7 @@ def strptime1(date_str):
     """
     return p1.strptime(date_str + " 00:00:00", "%Y-%m-%d %H:%M:%S")
 
+
 def start_end_month(time):
     """
     获取某个月的起始时间和结束时间
@@ -196,3 +215,18 @@ def start_end_month(time):
     this_month_end = datetime.datetime(now.year, now.month, calendar.monthrange(now.year, now.month)[1])
     this_month_end1 = this_month_end + timedelta(hours=23, minutes=59, seconds=59)
     return this_month_start, this_month_end1
+
+
+async def get_event(attr, game):
+    """
+    :param attr: str 事件属性
+    :param game: str 游戏名
+    :return: str 事件名
+    """
+    res = await crud.event_point.all_event(get_database(),game)
+    event_name=''
+    for i in res:
+        if attr in i['event_attr']:
+            event_name = i['event_name']
+            break
+    return event_name