From 94314df941a7c1880efd259877a18c147f651402 Mon Sep 17 00:00:00 2001
From: Guofu Li <li.guofu.l@gmail.com>
Date: Wed, 24 Aug 2022 17:57:19 +0800
Subject: [PATCH] bug fixes

---
 src/loader/DDBHFTLoader.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/loader/DDBHFTLoader.py b/src/loader/DDBHFTLoader.py
index 5bcb6d4..fe420b8 100644
--- a/src/loader/DDBHFTLoader.py
+++ b/src/loader/DDBHFTLoader.py
@@ -27,7 +27,7 @@ from .DDBLoader import DDBLoader
 class DDBHFTLoader(DDBLoader):
     """
     0. 从sql-server中读取calendar数据，并创建成员变量df_calendar，df_calendar可以保存在本地pickle作为缓存
-        |- `def make_calendar_df(self) -> df_calendar`
+        |- `def make_calendar_df(Nelf) -> df_calendar`
 
     1. 创建ddb中的数据库，分区性质从calendar数据中获取
         |- `def create_ddb_database(self, df_calendar) -> void`
@@ -416,7 +416,7 @@ class DDBHFTLoader(DDBLoader):
             with tqdm(total=num_rows, leave=False) as sub_pbar:
                 for _ in pool.imap_unordered(
                     functools.partial(
-                        dump_stock_daily_to_ddb, 
+                        DDBHFTLoader.dump_stock_daily_to_ddb, 
                         type_name = type_name,
                         stock_id = stock_id
                     ),
@@ -434,7 +434,7 @@ class DDBHFTLoader(DDBLoader):
         用于做多进程录入ddb的函数
         """
         blob = gzip.decompress(blob)
-        dataArray = eval(f"ProtoBuffEntitys.{type_name}Message_pb2.{type_name}Array()")
+        dataArray = eval(f"{type_name}Message_pb2.{type_name}Array()")
         dataArray.ParseFromString(blob)
 
         data_dict_list = [
@@ -452,10 +452,10 @@ class DDBHFTLoader(DDBLoader):
         df = pd.DataFrame(data_dict_list)
         #df['code'] = make_symbol(df['code'])
         df['code'] = stock_id
-        df['m_nDate'] = self.make_date(df['m_nDate'])
-        df['m_nTime'] = df['m_nDate'] + self.make_time(df['m_nTime'])
+        df['m_nDate'] = DDBLoader.make_date(df['m_nDate'])
+        df['m_nTime'] = df['m_nDate'] + DDBLoader.make_time(df['m_nTime'])
         for field_name in array_type_list:
-            df[field_name] = self.make_nparray(df[field_name])
+            df[field_name] = DDBLoader.make_nparray(df[field_name])
 
         #print(f"Did create ddb table for dataframe of shape {df.shape}")
         # self.make_table_skeleton(type_name, df.shape[0])
@@ -468,7 +468,7 @@ class DDBHFTLoader(DDBLoader):
         用于做多进程录入ddb的函数
         """
         df_table_name = type_name
-        df = make_stock_daily_df(row[2], type_name, stock_id)
+        df = DDBHFTLoader.make_stock_daily_df(row[2], type_name, stock_id)
 
         ddb_sess = ddb.session(DDBLoader.ddb_config['host'], 8848)
         ddb_sess.login(DDBLoader.ddb_config['username'], DDBLoader.ddb_config['password'])
@@ -477,8 +477,8 @@ class DDBHFTLoader(DDBLoader):
         # 因为在做Tick数据的时候，偶然发生'CHUNK[xxx] does not exist.'，所以在这里使用`append!`函数代换一下试试
         ddb_sess.run("append!(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
         #ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
-            dbPath = DDBLoader.ddb_path,
-            partitioned_table_name = type_name + DDBLoader.ddb_partition_table_suffix,
+            dbPath = DDBHFTLoader.ddb_path,
+            partitioned_table_name = type_name + DDBHFTLoader.ddb_partition_table_suffix,
             df_table_name = df_table_name
         ))