1

api/api_v1/endpoints/data_auth.py

@@ -178,7 +178,7 @@ async def load_prop_quotas(request: Request,
             'id': item['name'],
             'data_type': data_type,
             'title': title,
-            'category': settings.CK_OPERATOR.get(data_type) or []
+            # 'category': settings.CK_OPERATOR.get(data_type) or []
         }
         event_props.append(event_prop)
     if data_in.model == 'scatter':
@@ -188,21 +188,18 @@ async def load_prop_quotas(request: Request,
                 "data_type": None,
                 "analysis": "times",
                 "title": "次数",
-                "category": []
             },
             {
                 "id": "*",
                 "data_type": None,
                 "analysis": "number_of_days",
                 "title": "天数",
-                "category": []
             },
             {
                 "id": "*",
                 "data_type": None,
                 "analysis": "number_of_hours",
                 "title": "小时数",
-                "category": []
             },
         ]
     else:
@@ -212,21 +209,18 @@ async def load_prop_quotas(request: Request,
                 "data_type": None,
                 "analysis": "total_count",
                 "title": "总次数",
-                "category": []
             },
             {
                 "id": "*",
                 "analysis": "touch_user_count",
                 "data_type": None,
                 "title": "触发用户数",
-                "category": []
             },
             {
                 "id": "*",
                 "analysis": "touch_user_avg",
                 "data_type": None,
                 "title": "人均次数",
-                "category": []
             },
         ]
 
@@ -263,7 +257,6 @@ async def load_filter_props(request: Request,
             'id': item['name'],
             'data_type': data_type,
             'title': title,
-            'category': settings.CK_FILTER.get(data_type) or []
         }
         event_props.append(event_prop)
 
@@ -277,7 +270,6 @@ async def load_filter_props(request: Request,
             'id': item['name'],
             'data_type': data_type,
             'title': title,
-            'category': settings.CK_FILTER.get(data_type) or []
         }
         user_props.append(user_prop)
 

api/api_v1/endpoints/query.py

@@ -136,8 +136,8 @@ async def event_model(
             #     df_group.reset_index(drop=True, inplace=True)
             q['groups'].append(groupby)
             q['values'].append(df['values'].to_list())
-            q['sum'].append(round(float(df['values'].sum()),2))
+            q['sum'].append(round(float(df['values'].sum()), 2))
-            q['avg'].append(round(float(df['values'].mean()),2))
+            q['avg'].append(round(float(df['values'].mean()), 2))
             for last_value in df['values'].values[::-1]:
                 if last_value > 0:
                     q['last_value'] = float(last_value)
@@ -165,8 +165,8 @@ async def event_model(
                 df_group = pd.concat([df_group, pd.DataFrame(concat_data, columns=df_group.columns)])
                 df_group.sort_values('date', inplace=True)
                 q['values'].append(df_group['values'].to_list())
-                q['sum'].append(round(float(df_group['values'].sum()),2))
+                q['sum'].append(round(float(df_group['values'].sum()), 2))
-                q['avg'].append(round(float(df_group['values'].mean()),2))
+                q['avg'].append(round(float(df_group['values'].mean()), 2))
                 for last_value in df['values'].values[::-1]:
                     if last_value > 0:
                         q['last_value'] = float(last_value)
@@ -188,8 +188,8 @@ async def event_model(
                 if last_value > 0:
                     q['last_value'] = float(last_value)
                     break
-            q['sum'].append(round(float(df['values'].sum()),2))
+            q['sum'].append(round(float(df['values'].sum()), 2))
-            q['avg'].append(round(float(df['values'].mean()),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',):
@@ -198,6 +198,15 @@ async def event_model(
             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:
+        sort_key = np.argsort(np.array(item['sum']))[::-1]
+        if item.get('groups'):
+            item['groups'] = np.array(item['groups'])[sort_key].tolist()
+        item['values'] = np.array(item['values'])[sort_key].tolist()
+        item['sum'] = np.array(item['sum'])[sort_key].tolist()
+        item['avg'] = np.array(item['avg'])[sort_key].tolist()
+
     return schemas.Msg(code=0, msg='ok', data=res)
 
 
@@ -267,13 +276,34 @@ async def retention_model(request: Request,
         'days': days,
         'date_range': [d.strftime('%Y-%m-%d') for d in date_range][:unit_num + 1],
         'title': title,
-        'filter_item_type':filter_item_type,
+        'filter_item_type': filter_item_type,
-        'filter_item':filter_item,
+        'filter_item': filter_item,
 
     }
     return schemas.Msg(code=0, msg='ok', data=resp)
 
 
+@router.post("/retention_model_export")
+async def retention_model_export(request: Request,
+                                 game: str,
+                                 ckdb: CKDrive = Depends(get_ck_db),
+                                 analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
+                                 current_user: schemas.UserDB = Depends(deps.get_current_user)
+                                 ):
+    """ 留存分析模型 数据导出"""
+    await analysis.init()
+    data = analysis.retention_model_sql2()
+    file_name = quote(f'留存分析.xlsx')
+    mime = mimetypes.guess_type(file_name)[0]
+
+    sql = data['sql']
+    df = await ckdb.query_dataframe(sql)
+    df_to_stream = DfToStream((df, '留存分析'))
+    with df_to_stream as d:
+        export = d.to_stream()
+    return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
+
+
 @router.post("/retention_model_del", deprecated=True)
 async def retention_model_del(
         request: Request,

api/api_v1/endpoints/xquery.py

@@ -1,9 +1,12 @@
+import mimetypes
 from collections import defaultdict
+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 starlette.responses import StreamingResponse
 
 import crud, schemas
 from common import *
@@ -16,6 +19,7 @@ from db.redisdb import get_redis_pool, RedisDrive
 from models.behavior_analysis import BehaviorAnalysis
 from models.user_analysis import UserAnalysis
 from models.x_analysis import XAnalysis
+from utils import DfToStream
 
 router = APIRouter()
 
@@ -60,3 +64,24 @@ async def ltv_model_sql(
     }
 
     return schemas.Msg(code=0, msg='ok', data=data)
+
+
+@router.post("/ltv_model_export")
+async def ltv_model_export(request: Request,
+                           game: str,
+                           ckdb: CKDrive = Depends(get_ck_db),
+                           analysis: XAnalysis = Depends(XAnalysis),
+                           current_user: schemas.UserDB = Depends(deps.get_current_user)
+                           ):
+    """ ltv分析 数据导出"""
+    await analysis.init()
+    data = analysis.ltv_model_sql()
+    file_name = quote(f'lvt.xlsx')
+    mime = mimetypes.guess_type(file_name)[0]
+
+    sql = data['sql']
+    df = await ckdb.query_dataframe(sql)
+    df_to_stream = DfToStream((df, 'ltv'))
+    with df_to_stream as d:
+        export = d.to_stream()
+    return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})

models/behavior_analysis.py

@@ -354,7 +354,7 @@ class BehaviorAnalysis:
                 qry = qry.order_by(sa.Column('date'))
             else:
                 qry = qry.order_by(sa.Column('values').desc())
-            qry = qry.limit(1000)
+            qry = qry.limit(10000)
 
             sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
             print(sql)
@@ -592,8 +592,8 @@ ORDER BY values desc"""
         }
 
     def retention_model_sql2(self):
-        filter_item_type = self.event_view.get('filter-item-type')
+        filter_item_type = self.event_view.get('filter_item_type')
-        filter_item = self.event_view.get('filter-item')
+        filter_item = self.event_view.get('filter_item')
         event_name_a = self.events[0]['eventName']
         event_name_b = self.events[1]['eventName']