1.修改设备LTV计算公式

2.修改多次付费占比公式
3.优化超限范围的异常问题
4.修改下载多次付费占比数据没有百分比的问题
5.优化看板设置调整后提示文本异常问题

api/api_v1/authz/authz.py

@@ -46,9 +46,10 @@ async def get_permissions_for_user_in_domain(
     """
     获取域内用户或角色的权限
     """
-
+    #data为列表
     data = casbin_enforcer.get_permissions_for_user_in_domain(data_in.role_id, data_in.game)
     paths = {i[2] for i in data}
+    #列表形式的coll_name
     all_api = await crud.api_list.all_api(db)
     for item in all_api:
         if item['path'] in paths:

api/api_v1/endpoints/query.py

@@ -151,10 +151,11 @@ async def event_model(
     res = []
     is_hide = []
 
-    for idx, item in enumerate(sqls):
+    for idx, item in enumerate(sqls):  #列出索引下标
         if item.get('is_show') == False:
             is_hide.append(idx)
-
+        #event_name:事件名，日充总额
+        #format：float浮点型
         q = {
             'groups': [],
             'values': [],
@@ -167,7 +168,7 @@ async def event_model(
             'end_date': item['end_date'],
             'time_particle': item['time_particle']
         }
-        # 处理组合问题
+        # 处理组合问题，如combination_event不存在则跳过
         if item.get('combination_event'):
             combination_event = CombinationEvent(res, item.get('combination_event'), item['format'])
             values, sum_, avg = combination_event.parse()
@@ -181,13 +182,14 @@ async def event_model(
                     break
             res.append(q)
             continue
-
+        #sql语句
         sql = item['sql']
         groupby = item['groupby']
-        date_range = item['date_range']
+        date_range = item['date_range']  #获取的要查询的每一天的时间
-        q['date_range'] = date_range
+        q['date_range'] = date_range  #把要查询的时间加入q字典中
-        df = await ckdb.query_dataframe(sql)
+        df = await ckdb.query_dataframe(sql)  #以sql语句查出数据，df是二维列表
-        df.fillna(0, inplace=True)
+        df.fillna(0, inplace=True)#以0填补空数据
+        #获取第一矩阵的长度
         if df.shape[0] == 0:
             df = pd.DataFrame({'date': date_range, 'values': 0 * len(date_range)})
             # continue
@@ -214,6 +216,7 @@ async def event_model(
         if groupby and (set(groupby) & set(df)) == set(groupby):
             # 有分组
             for group, df_group in df.groupby(groupby):
+                #在原数据上将索引重新转换为列，新索引的列删除
                 df_group.reset_index(drop=True, inplace=True)
                 q['groups'].append(str(group))
 
@@ -238,7 +241,9 @@ async def event_model(
             concat_data = []
             for i in set(date_range) - set(df['date']):
                 concat_data.append((i, 0))
+                #纵向拼接两个表
             df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
+            #在原数据上按data排序
             df.sort_values('date', inplace=True)
             if len(df) >= 2:
                 q['chain_ratio'] = division((df.iloc[-1, 1] - df.iloc[-2, 1]) * 100, df.iloc[-2, 1], 2)
@@ -249,7 +254,9 @@ async def event_model(
                 if last_value > 0:
                     q['last_value'] = float(last_value)
                     break
+            #求所有值的和
             q['sum'].append(round(float(df['values'].sum()), 2))
+            #求平均值
             q['avg'].append(round(float(df['values'].mean()), 2))
 
         # q['eventNameDisplay']=item['event_name_display']
@@ -257,7 +264,7 @@ async def event_model(
     # 按总和排序
     for item in res:
         try:
-            if item['time_particle'] in ('P1D', 'P1W'):
+            if item['time_particle'] in ('P1D', 'P1W'): #按格式修改年月日
                 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']]
@@ -266,7 +273,7 @@ async def event_model(
         except:
             pass
 
-        sort_key = np.argsort(np.array(item['sum']))[::-1]
+        sort_key = np.argsort(np.array(item['sum']))[::-1]#将sum中的元素从小到大排列后的结果，提取其对应的索引，然后倒着输出到变量之中
         if item.get('groups'):
             item['groups'] = np.array(item['groups'])[sort_key].tolist()
         item['values'] = np.array(item['values'])[sort_key].tolist()
@@ -299,17 +306,17 @@ 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)
-    res = await analysis.retention_model_sql2()
+    res = await analysis.retention_model_sql2()  #初始化开始时间结束时间，sql语句  字典
-    sql = res['sql']
+    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']
+    date_range = res['date_range']   #时间  列表
-    unit_num = res['unit_num']
+    unit_num = res['unit_num']  #int
-    retention_n = res['retention_n']
+    retention_n = res['retention_n']   #列表 int
-    filter_item_type = res['filter_item_type']
+    filter_item_type = res['filter_item_type']   #all
-    filter_item = res['filter_item']
+    filter_item = res['filter_item']   #列表  0,1,3,7,14,21,30
     df.set_index('reg_date', inplace=True)
     for d in set(res['date_range']) - set(df.index):
         df.loc[d] = 0
@@ -320,6 +327,7 @@ async def retention_model(request: Request,
     avg = {}
     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'] = []
@@ -516,6 +524,7 @@ async def funnel_model(
     await analysis.init(data_where=current_user.data_where)
     res = await analysis.funnel_model_sql()
     sql = res['sql']
+    #查询的时间
     date_range = res['date_range']
     cond_level = res['cond_level']
     groupby = res['groupby']
@@ -530,6 +539,7 @@ async def funnel_model(
         for item in not_exists_level:
             key = key if isinstance(key, tuple) else (key,)
             concat_data.append((*key, item, 0))
+            #合并数据
     df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
 
     # df.set_index('date',inplace=True)
@@ -539,7 +549,9 @@ async def funnel_model(
         return schemas.Msg(code=0, msg='ok', data={'list': data_list, 'level': cond_level})
 
     tmp = {'title': '总体'}
+    #以level分组后的和
     tmp_df = df[['level', 'values']].groupby('level').sum()
+    #在原数据上对索引进行排序
     tmp_df.sort_index(inplace=True)
     for i in tmp_df.index:
         tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
@@ -785,10 +797,18 @@ async def scatter_model(
     await analysis.init(data_where=current_user.data_where)
     res = await analysis.scatter_model_sql()
     sql = res['sql']
+
+    #查询买量渠道owner为kuaiyou3的日注册玩家等级分布
+    # sql_list=sql.split("GROUP BY")
+    # sql01 = """and xiangsu.event.owner_name='kuaiyou3'GROUP BY"""""
+    # new_sql=sql_list[0]+sql01+sql_list[1]
+
+
     df = await ckdb.query_dataframe(sql)
     if df.empty:
         return schemas.Msg(code=-9, msg='无数据', data=None)
     df.fillna(0, inplace=True)
+    #转换数据类型为int
     df['values'] = df['values'].astype(int)
     interval_type = res['interval_type']
     analysis = res['analysis']
@@ -866,14 +886,37 @@ async def scatter_model(
                 dt = '合计'
             else:
                 dt = key.strftime('%Y-%m-%d')
+
             labels_dict = {}
             for key2, tmp_df2 in tmp_df.groupby('values'):
                 label = str(key2)
                 n = len(tmp_df2)
                 labels_dict[label] = n
 
-            resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
+            #如需要2之后所有之和，则执行下面代码，返回值为字典的labels_dict01
-                                       'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
+            labels_dict01={}
+            v=-1
+            for i in  labels:
+                v +=1
+                if int(i) == 1:
+                    labels_dict01["1"]=labels_dict["1"]
+                else:
+                    # for number in labels_dict.keys():
+                    #     if number >=i:
+                            values=list(labels_dict.values())
+                            n=sum(values[v:])
+                            labels_dict01[i]=n
+            #传入百分比数据
+            list_p=[]
+            for i in labels:
+                number_int=round(labels_dict01.get(i, 0) * 100 / total, 2)
+                number_str=str(number_int)+'%'
+                list_p.append(number_str)
+
+            resp['list'][dt] = {'总体': {'n': [labels_dict01.get(i, 0) for i in labels], 'total': total,
+                                       'p': list_p}}
+            # resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
+            #                            'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
         return schemas.Msg(code=0, msg='ok', data=resp)
 
         # bins_s = pd.cut(tmp_df['values'], bins=bins,

api/api_v1/endpoints/report.py

@@ -40,7 +40,8 @@ async def edit(
     """编辑报表"""
     res = await crud.report.update_one(db, {'_id': data_in.report_id, 'user_id': request.user.id},
                                        {'$set': {'query': data_in.query, 'name': data_in.name, 'desc': data_in.desc}})
-    if not res.matched_count:
+    #if not res.matched_count:
+    if res.matched_count:
         return schemas.Msg(code=-1, msg='只能报表所有者编辑')
     return schemas.Msg(code=0, msg='ok', data='编辑成功')
 

api/api_v1/endpoints/xquery.py

@@ -1,11 +1,14 @@
+import datetime
 import mimetypes
 from collections import defaultdict
+import time
 from urllib.parse import quote
 
 import pandas as pd
 import numpy as np
 from fastapi import APIRouter, Depends, Request
 from motor.motor_asyncio import AsyncIOMotorDatabase
+from pandas import DataFrame
 from starlette.responses import StreamingResponse
 
 import crud, schemas
@@ -50,50 +53,105 @@ async def ltv_model_sql(
 
     res = analysis.ltv_model_sql()
     sql = res['sql']
-    quota = res['quota']
+    quota = res['quota']  #字段名
     ltv_n = res['ltv_n']
     df = await ckdb.query_dataframe(sql)
     if df.empty:
         return schemas.Msg(code=-9, msg='查无数据')
-    df.fillna(0, inplace=True)
+    df.fillna(0, inplace=True)   #修改原对象，以0填补空缺值
+
+    # for d in set(res['date_range']) - set(df['date']):  # 时间的差集运算 最后为空
+    #     df.loc[len(df)] = 0
+    #     df.loc[len(df) - 1, 'date'] = d
+    #     days = (pd.Timestamp.now().date() - d).days  # 时间差
+    #     # if days + 2 >= ltv_len:
+    #     #     continue
+    #     df.iloc[len(df) - 1, days + 3:] = '-'
+    # df.sort_values('date', inplace=True)  # 根据date进行倒叙排序
+
     for d in set(res['date_range']) - set(df['date']):
-        df.loc[len(df)] = 0
+        #在有效日期最后一行补充行数据（值都为0），补充的行数为两个集合的差集长度
+        df.loc[len(df)] = '-'
+        #在date此列补充多行数据（值为两个集合差集的子元素）
         df.loc[len(df) - 1, 'date'] = d
-        days = (pd.Timestamp.now().date() - d).days
+        # days = (d-pd.Timestamp.now().date()).days
-        # if days + 2 >= ltv_len:
+        # # if days + 2 >= ltv_len:
-        #     continue
+        # #     continue
-        df.iloc[len(df) - 1, days+3:] = '-'
+        # if days>0:
+        #     df.iloc[len(df) - 1, 1:] = '-'
+
     df.sort_values('date', inplace=True)
-
+    df.rename(columns={'date': '注册日期'}, inplace=True)   #True为将结果返回赋值给原变量，修改原对象，columns为列名
-    df.rename(columns={'date': '注册日期'}, inplace=True)
     cat = '角色数'
-    if quota == '#distinct_id':
+    if quota == '#distinct_id':  #如果字段名=字段名
         cat = '设备数'
-    df.rename(columns={'cnt1': cat}, inplace=True)
+    df.rename(columns={'cnt1': cat}, inplace=True)   #原数据基础上修改df里面列名为cnt1为设备数
-
+    df1 = df[['注册日期', cat, *[f'LTV{i}' for i in ltv_n]]]   #1, 2, 3, 4, 5, 6, 7, 8, 9, ~~到360
-    df1 = df[['注册日期', cat, *[f'LTV{i}' for i in ltv_n]]]
     df2 = df[['注册日期', cat, *[f'sumpay_{i}' for i in ltv_n]]]
-    df2.replace('-', 0, inplace=True)
+    df2.replace('-', 0, inplace=True)  #True改变原数据，前面是需要替换的值，后面是替换后的值。 在原数据把下划线替换成0
-    avg_ltv = (df2[[f'sumpay_{i}' for i in ltv_n]].sum() / df2[cat].sum()).round(2)
+    #修改下面代码
-    df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avg_ltv]
+    #求相差天数
+    str_time = str(res['date_range'][0])
+    split_time = str_time.split('-')
+    now_time = time.strftime("%Y-%m-%d", time.localtime())
+    split_now_time = now_time.split('-')
+    today = datetime.datetime(int(split_time[0]), int(split_time[1]), int(split_time[2]))
+    now_day = datetime.datetime(int(split_now_time[0]), int(split_now_time[1]), int(split_now_time[2]))
+    newday = (now_day - today).days + 1
+    #计算方法运算每个LTV的均值
+    _listData = {}
+    for i in ltv_n:
+        if i <=newday:
+            avgLtv=(df2[[f'sumpay_{i}']][0:newday+1-i].sum()/df2[cat][0:newday+1-i].sum()).round(2)
+
+            new_avgLtv=str(avgLtv).split('\n')[0].split(' ')
+            new_avgLtv01=new_avgLtv[len(new_avgLtv)-1]
+
+            _listData[f'sumpay_{i}'] = new_avgLtv01
+        else:
+            _listData[f'sumpay_{i}']=0
+    avgLtvlist = pd.Series(_listData)
+    _listname=[]
+
+    #计算总累计LTV最后一个值
+    for k, v in _listData.items():
+        if v !=0:
+            _listname.append(k)
+    max_nmu=max(_listname)
+    max_num=(df2[[max_nmu]].sum()/df2[cat].sum()).round(2)
+    max_number=str(max_num[0])
+    df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avgLtvlist]
+
+    # avg_ltv = (df2[[f'sumpay_{i}' for i in ltv_n]].sum() / df2[cat].sum()).round(2)
+    #df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avg_ltv]
     df1.insert(2, '累计LTV', 0)
-
-
-
     last_ltv = []
     for items in df1.values:
         for item in items[::-1]:
             if item != '-':
                 last_ltv.append(item)
                 break
-    df1['累计LTV'] = last_ltv
+    #修改累计LTV中最后一个值
-    days = (pd.Timestamp.now().date() - pd.to_datetime(res['start_date']).date()).days
+    last_ltv[-1]=max_number
 
+
+    df1['累计LTV'] = last_ltv
+
+
+    #把列中累计LTV等于0的值改成'-'
+    df1.loc[df1['累计LTV']==0, '累计LTV'] = '-'
+    #剔除行，列的累计LTV=='-'的剔除出去
+    df3 = df1.drop(df1[(df1.累计LTV=='-')].index)
+
+    days = (pd.Timestamp.now().date() - pd.to_datetime(res['start_date']).date()).days
     df1.iloc[len(df1) - 1, days + 4:] = '-'
 
     data = {
-        'title': df1.columns.tolist(),
+        #'title': df1.columns.tolist(),
-        'rows': df1.values.tolist(),
+        #'rows': df1.values.tolist(),
+        'title': df3.columns.tolist(),
+        'rows': df3.values.tolist(),
+
         'start_date': res['start_date'],
         'end_date': res['end_date']
     }

main.py

@@ -115,4 +115,5 @@ async def add_process_time_header(request: Request, call_next):
 
 
 if __name__ == '__main__':
-    uvicorn.run(app='main:app', host="0.0.0.0", 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)

models/x_analysis.py

@@ -32,7 +32,7 @@ class XAnalysis:
         self.ext_filters = (self.data_in.ext_filter.get('filts', []), self.data_in.ext_filter.get('relation', 'and'))
 
     def _get_global_filters(self):
-        return self.event_view.get('filts') or []
+        return self.event_view.get('filts') or []   #获取event_view字典里面filts的值，或返回空列表
 
     async def init(self, *args, **kwargs):
         if self.data_in.report_id:
@@ -152,11 +152,12 @@ class XAnalysis:
         ]
         if quota == '#distinct_id':
             where.append(sa.Column('is_new_device') == 1)
+
         qry = sa.select().where(*where)
         sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
         where_str = sql.split('WHERE ')[1]
 
-        where_order = self.handler_filts((self.global_filters, self.global_relation))
+        where_order = self.handler_filts((self.global_filters, self.global_relation))   #global_relation就是  and
         where_order_str = 1
         if where_order:
             qry = sa.select().where(*where_order)
@@ -176,9 +177,9 @@ class XAnalysis:
                {select_ltv_str},
                {sum_money_str}
         FROM (SELECT toDate(addHours(`#event_time`, `#zone_offset`)) as date, uniqExact(`{quota}`) cnt1
-              FROM {self.game}.event
+              FROM {self.game}.event  
               where `#event_name` = 'create_account'
-                AND {where_str} AND {where_account_str}
+                AND {where_str} AND {where_account_str} 
               GROUP BY toDate(addHours(`#event_time`, `#zone_offset`))) as reg
                  left join
              (select a.date,