1.新增分布分析分组显示数据
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@ -30,11 +30,11 @@ async def event_list(
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#获取事件名
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try:
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event_list = await ckdb.distinct(game, 'event', '#event_name')
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except Exception as e:
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return schemas.Msg(code=-9, msg='查无数据', data='')
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# 获取事件量
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event_count = await ckdb.yesterday_event_count(game)
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event_meta = await crud.event_mana.find_many(db, {'game': game}) or {}
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except Exception as e:
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return schemas.Msg(code=-9, msg='查无数据', data='')
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if event_meta:
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event_meta = pd.DataFrame(event_meta).set_index('event_name').fillna('').T.to_dict()
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@ -990,14 +990,14 @@ async def scatter_model(
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#转换数据类型为int
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df['values'] = df['values'].astype(int)
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interval_type = res['interval_type']
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analysis = res['analysis']
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analysi = res['analysis']
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groupby = res['groupby']
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quota_interval_arr = res['quota_interval_arr']
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# 兼容合计的
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if res['time_particle'] == 'total':
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df['date'] = '合计'
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if analysis != 'number_of_days' and interval_type != 'discrete':
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if analysi != 'number_of_days' and interval_type != 'discrete':
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max_v = int(df['values'].max())
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min_v = int(df['values'].min())
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interval = (max_v - min_v) // 10 or 1
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@ -1035,19 +1035,19 @@ async def scatter_model(
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'p': round(bins_s * 100 / total, 2).to_list(),
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'title': '总体'}
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# 分组的
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if groupby:
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for key, tmp_df in df.groupby(['date', *groupby]):
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bins_s = pd.cut(tmp_df['values'], bins=bins,
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right=False).value_counts()
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bins_s.sort_index(inplace=True)
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total = int(bins_s.sum())
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title = '.'.join(key[1:])
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date = key[0]
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resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
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'p': round((bins_s * 100 / total).fillna(0),
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2).to_list(),
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'title': title
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}
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# if groupby:
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# for key, tmp_df in df.groupby(['date', *groupby]):
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# bins_s = pd.cut(tmp_df['values'], bins=bins,
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# right=False).value_counts()
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# bins_s.sort_index(inplace=True)
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# total = int(bins_s.sum())
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# title = '.'.join(key[1:])
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# date = key[0]
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# resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
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# 'p': round((bins_s * 100 / total).fillna(0),
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# 2).to_list(),
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# 'title': title
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# }
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return schemas.Msg(code=0, msg='ok', data=resp)
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# elif analysis == 'number_of_days':
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@ -1122,7 +1122,91 @@ async def scatter_model(
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# resp['list'][key.strftime('%Y-%m-%d')]['总体'] = {'n': bins_s.to_list(), 'total': total,
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# 'p': round(bins_s * 100 / total, 2).to_list(),
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# 'title': '总体'}
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@router.post("/scatter_model_details")
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async def scatter_model(
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request: Request,
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game: str,
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ckdb: CKDrive = Depends(get_ck_db),
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db: AsyncIOMotorDatabase = Depends(get_database),
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analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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await analysis.init(data_where=current_user.data_where)
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try:
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res = await analysis.scatter_model_sql()
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except Exception as e:
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return schemas.Msg(code=-9, msg='报表配置参数异常')
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sql=res['sql']
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if analysis.event_view['groupBy'] != []:
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columnName=analysis.event_view['groupBy'][-1]['columnName']
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sql=sql.replace(f'toDate(addHours({game}.event."#event_time", 8)) AS date', f'max(`{columnName}`) as va', 1)
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sql=sql.replace(f'toDate(addHours({game}.event."#event_time", 8)),','',1)
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='无数据', data=None)
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df.fillna(0, inplace=True)
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# 转换数据类型为int
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df['values'] = df['values'].astype(int)
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interval_type = res['interval_type']
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analysi = res['analysis']
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groupby = res['groupby']
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quota_interval_arr = res['quota_interval_arr']
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# 兼容合计的
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if res['time_particle'] == 'total':
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df['date'] = '合计'
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if analysi != 'number_of_days' and interval_type != 'discrete':
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max_v = int(df['values'].max())
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min_v = int(df['values'].min())
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interval = (max_v - min_v) // 10 or 1
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resp = {'list': dict(),
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'start_date': res['start_date'],
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'end_date': res['end_date'],
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'time_particle': res['time_particle'],
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'biaotou':columnName
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}
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if not quota_interval_arr:
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resp['label'] = [f'[{i},{i + interval})' for i in range(min_v, max_v, interval)]
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bins = [i for i in range(min_v, max_v + interval, interval)]
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else:
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quota_interval_arr = [-float('inf')] + quota_interval_arr + [float('inf')]
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resp['label'] = []
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bins = [quota_interval_arr[0]]
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for i, v in enumerate(quota_interval_arr[1:]):
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resp['label'].append(f'[{quota_interval_arr[i]},{v})')
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bins.append(v)
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# 这是分组的
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for key, tmp_df in df.groupby('va'):
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bins_s = pd.cut(tmp_df['values'], bins=bins,
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right=False).value_counts()
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bins_s.sort_index(inplace=True)
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total = int(bins_s.sum())
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if res['time_particle'] == 'total':
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resp['list']['合计'] = dict()
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resp['list']['合计']['总体'] = {'n': bins_s.to_list(), 'total': total,
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'p': round(bins_s * 100 / total, 2).to_list(),
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'title': '总体'}
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else:
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p=round(bins_s * 100 / total, 2).to_list()
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for i in range(len(p)):
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if str(p[i]) == 'nan':
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p[i] = 0
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if 'time' not in columnName:
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resp['list'][key] = dict()
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resp['list'][key] = {'n': bins_s.to_list(), 'total': total,
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'p': p,
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'title': '总体'}
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else:
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resp['list'][key.strftime('%Y-%m-%d %H:%M:%S')] = dict()
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resp['list'][key.strftime('%Y-%m-%d %H:%M:%S')] = {'n': bins_s.to_list(), 'total': total,
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'p': p,
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'title': '总体'}
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return schemas.Msg(code=0, msg='ok', data=resp)
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else:
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return schemas.Msg(code=-9, msg='没有添加分组项', data='')
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@router.post("/trace_model_sql")
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async def trace_model_sql(
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@ -651,10 +651,14 @@ ORDER BY level
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.where(and_(*where)) \
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.group_by(*self.groupby, e_account_id_col)
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else:
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# qry = sa.select(event_date_col, e_account_id_col,
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# settings.CK_FUNC[analysis](event_attr_col).label('values')) \
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# .where(and_(*where)) \
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# .group_by(event_date_col, *self.groupby, e_account_id_col)
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qry = sa.select(event_date_col, e_account_id_col,
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settings.CK_FUNC[analysis](event_attr_col).label('values')) \
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.where(and_(*where)) \
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.group_by(event_date_col, *self.groupby, e_account_id_col)
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.group_by(event_date_col,e_account_id_col)
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sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
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print(sql)
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return {
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