更新

T7_client_sanwei.py

@@ -0,0 +1,116 @@
+import json
+import os
+import socket
+import struct
+
+import PySimpleGUI as sg
+
+
+def recvdata(conn, filepath):
+    header_size = struct.unpack('i', conn.recv(4))[0]
+    header_bytes = conn.recv(header_size)
+    header_json = header_bytes.decode('utf-8')
+    header_dic = json.loads(header_json)
+    content_len = header_dic['contentlen']
+    content_name = header_dic['contentname']
+    recv_len = 0
+    pdf = os.path.join(filepath, content_name)
+    with open(pdf, 'wb') as file:
+        while recv_len < content_len:
+            correntrecv = conn.recv(1024 * 1000)
+            file.write(correntrecv)
+            recv_len += len(correntrecv)
+
+
+def senddata(conn, path, librarynum, is_use_balance, is_use_max, datalimit, datalower):
+    name = os.path.basename(os.path.realpath(path))
+    try:
+        with open(path, 'rb') as file:
+            content = file.read()
+            headerdic = dict(
+                contentlen=len(content),
+                contentname=name,
+                librarynum=librarynum,
+                is_use_balance=is_use_balance,
+                is_use_max=is_use_max,
+                datalimit=datalimit,
+                datalower=datalower
+            )
+            headerjson = json.dumps(headerdic)
+            headerbytes = headerjson.encode('utf-8')
+            headersize = len(headerbytes)
+            conn.send(struct.pack('i', headersize))
+            conn.send(headerbytes)
+            conn.sendall(content)
+    except ConnectionResetError:
+        print('不存在这个文件！')
+
+
+def connect():
+    client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    dest_ip = '192.168.38.90'
+    dest_port = int(8291)
+    client.connect((dest_ip, dest_port))
+    return client
+
+
+def transclient(sendfile, resfile, librarynum, is_use_balance, is_use_max, datalimit, datalower):
+    conn = connect()
+    senddata(conn, sendfile, librarynum, is_use_balance, is_use_max, datalimit, datalower)
+    recvdata(conn, resfile)
+
+
+def make_gui():
+    sg.theme('DarkGreen1')
+
+    layout = [
+        [sg.Text('排样管数'), sg.Spin([i for i in range(150)], initial_value=130, size=(3, 1), key='_LIBRARYNUM_'),
+         sg.Text('单芯片量上限'), sg.Spin([i for i in range(2000)], initial_value=1750, size=(4, 1), key='_DATALIMIT_'),
+         sg.Text('单芯片量下限'), sg.Spin([i for i in range(2000)], initial_value=1700, size=(4, 1), key='_DATALOWER_')
+         ],
+        [sg.Text()],
+
+        [sg.Text('使用平衡文库'), sg.Radio("是", "is_use_balance", key='is_use_balance_key', default=True),
+         sg.Radio("否", "is_use_balance", key='is_not_use_balance_key')],
+
+        [sg.Text('使用扩容平衡性'), sg.Radio("是", "is_use_max", key='is_use_max_key'),
+         sg.Radio("否", "is_use_max", key='is_not_use_max_key', default=True)],
+        [sg.Text()],
+
+        [
+            sg.Text('导入排样excel')],
+        [
+            sg.Input(key='_FILE1_'), sg.FileBrowse('选择文件')],
+        [sg.Text()],
+        [
+            sg.Text('生成排样位置')],
+        [
+            sg.Input(key='_FILE2_'), sg.FolderBrowse('选择文件夹')],
+        [sg.Text()],
+        [
+            sg.OK('生成'), sg.Cancel('取消')]]
+    # iconpath = os.path.join(os.path.abspath(sys.path[0]), 'other', 'icon.ico')
+    window = sg.Window('解码排样T7程序_sanwei', layout, font='Helvetica 11', icon=r'D:\project\autulayout\other\icon.ico')
+    while True:
+        event, values = window.read()
+        if event == sg.WINDOW_CLOSED:
+            # 用户关闭了窗口，终止循环
+            break
+        is_use_balance = 1 if values['is_use_balance_key'] else 0
+        is_use_max = 1 if values['is_use_max_key'] else 0
+        print(is_use_balance, is_use_max)
+        if event == '生成':
+            if not values['_FILE1_'] or not values['_FILE2_']:
+                sg.popup_non_blocking('请正确提供参数')
+            else:
+                transclient(values['_FILE1_'], os.path.join(values['_FILE2_']), values['_LIBRARYNUM_'],
+                            is_use_balance, is_use_max, values['_DATALIMIT_'], values['_DATALOWER_'])
+                sg.Popup('排样成功！')
+                window.Close()
+        else:
+            window.Close()
+            break
+
+
+if __name__ == '__main__':
+    make_gui()

T7_server_sanwei.py

@@ -0,0 +1,96 @@
+import json
+import os
+import socket
+import struct
+import sys
+from datetime import datetime
+
+from tools.common import basedir
+from tools.t7 import AutoLayout as T7
+
+
+def recvdata(conn, path):
+    """
+    接受文件
+    :param conn:
+    :param path:
+    :return:
+    """
+    header_size = struct.unpack('i', conn.recv(4))[0]
+    header_bytes = conn.recv(header_size)
+    header_json = header_bytes.decode('utf-8')
+    header_dic = json.loads(header_json)
+    content_len = header_dic['contentlen']
+    content_name = header_dic['contentname']
+    librarynum = header_dic['librarynum']
+    is_use_balance = header_dic['is_use_balance']
+    is_use_max = header_dic['is_use_max']
+    datalimit = header_dic['datalimit']
+    datalower = header_dic['datalower']
+    recv_len = 0
+    fielpath = os.path.join(path, '%s_%s' % (datetime.now().strftime("%m%d%H%M"), content_name))
+    file = open(fielpath, 'wb')
+    while recv_len < content_len:
+        correntrecv = conn.recv(1024 * 1000)
+        file.write(correntrecv)
+        recv_len += len(correntrecv)
+    file.close()
+    return fielpath, librarynum, is_use_balance, is_use_max, datalimit, datalower
+
+
+def senddata(conn, path, message=None):
+    name = os.path.basename(os.path.realpath(path))
+    if not message:
+        with open(path, 'rb') as file:
+            content = file.read()
+        headerdic = dict(
+            contentlen=len(content),
+            contentname=name
+        )
+        headerjson = json.dumps(headerdic)
+        headerbytes = headerjson.encode('utf-8')
+        headersize = len(headerbytes)
+        conn.send(struct.pack('i', headersize))
+        conn.send(headerbytes)
+        conn.sendall(content)
+    else:
+        headerdic = dict(
+            contentlen=len(path),
+            contentname='message'
+        )
+        headerjson = json.dumps(headerdic)
+        headerbytes = headerjson.encode('utf-8')
+        headersize = len(headerbytes)
+        conn.send(struct.pack('i', headersize))
+        conn.send(headerbytes)
+        conn.sendall(path.encode('utf-8'))
+
+
+def server():
+    myserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    adrss = ("", 8291)
+    myserver.bind(adrss)
+    myserver.listen(5)
+
+    while True:
+        try:
+            myclient, adddr = myserver.accept()
+            recv_content, librarynum, is_use_balance, is_use_max, datalimit, datalower = recvdata(myclient,
+                                                                                                  os.path.join(basedir,
+                                                                                                               'example'))
+            print(recv_content, librarynum, is_use_balance, is_use_max, datalimit, datalower)
+            layout = T7(recv_content, librarynum, is_use_balance, is_use_max, data_limit=datalimit,
+                        data_lower=datalower)
+            outputpath = layout.run()
+            senddata(myclient, outputpath)
+        except Exception as e:
+            print(e)
+            continue
+
+
+if __name__ == '__main__':
+    if len(sys.argv) > 1:
+        layout = T7(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
+        outputpath = layout.run()
+    else:
+        server()

tools/t7.py

@@ -26,6 +26,8 @@ class AutoLayout:
         self.librarynum = int(librarynum)
         self.data_limit = data_limit
         self.data_lower = data_lower
+        self.get_col = list()
+        self.items = list()
 
         # 芯片原始数据读取
         self.ori_data = self.read_excel()
@@ -54,7 +56,7 @@ class AutoLayout:
         self.rule = self.read_rule()
         self.rule_exclusive_customer = self.read_rule_exclusive_customer()
 
-        # 子文库名称
+        # subsamplename
         self.chip_sublib = defaultdict(set)
 
         # 不平衡文库
@@ -93,11 +95,12 @@ class AutoLayout:
         原始数据处理
         :return:
         """
-        merge = pd.read_excel(self.path, None)
+        # 获取表头备注
-        ori_data = dict()
+        nrow = pd.read_excel(self.path, nrows=1)
-        for name, sheet in merge.items():
+        self.items = nrow.to_dict('records')
-            sheet.fillna('', inplace=True)
+        merge = pd.read_excel(self.path, skiprows=[1])
-            ori_data[name] = sheet.to_dict('records')
+        merge.fillna('', inplace=True)
+        ori_data = merge.to_dict('records')
         return ori_data
 
     @staticmethod
@@ -125,26 +128,26 @@ class AutoLayout:
         if df.empty:
             return ratio_sites, is_not_balance_list
         s, e = 0, 16
-        if maxt == 'i7':
+        if maxt == 'indexi7':
             s, e = 8, 16
-        if maxt == 'i5':
+        if maxt == 'indexi5':
             s, e = 0, 8
         num = e - s
-        df['barcode'] = df['barcode'].str.slice(s, e)
+        df['indexi5i7'] = df['indexi5i7'].str.slice(s, e)
-        barcode_df = pd.DataFrame(df['barcode'].str.split('', expand=True).iloc[:, 1:-1].values,
+        barcode_df = pd.DataFrame(df['indexi5i7'].str.split('', expand=True).iloc[:, 1:-1].values,
-                                  columns=['T' + str(x) for x in range(num)]).join(df['data_needed'])
+                                  columns=['T' + str(x) for x in range(num)]).join(df['orderdatavolume'])
-        total = barcode_df['data_needed'].sum()
+        total = barcode_df['orderdatavolume'].sum()
 
         for i in range(num):
             column = 'T' + str(i)
-            col_df = barcode_df.groupby(column).agg({'data_needed': 'sum'})
+            col_df = barcode_df.groupby(column).agg({'orderdatavolume': 'sum'})
             # 去掉N计数
             if 'N' in col_df.index:
-                base_n_size = col_df.loc['N', 'data_needed']
+                base_n_size = col_df.loc['N', 'orderdatavolume']
                 col_df = col_df.drop('N')
             else:
                 base_n_size = 0
-            col_df['ratio'] = (col_df['data_needed']) / (total - base_n_size)
+            col_df['ratio'] = (col_df['orderdatavolume']) / (total - base_n_size)
             ratio = col_df['ratio'].to_dict()
             ratio_sites[i] = ratio
             A, B, C, D, E, F, G = list(), list(), list(), list(), list(), list(), list()
@@ -191,25 +194,19 @@ class AutoLayout:
 
         today_date = datetime.now()
 
-        if 'nextera' in row['classification'].lower():
+        if 'nextera' in row['librarystructure'].lower():
             return 1000
 
-        if '华大' in row['classification']:
+        if '华大' in row['librarystructure']:
             return 1100
 
-        if '超加急' in row['priority']:
+        if row['cycletype'] == '极致周期' or '极致' in row['cycletype']:
-            return 1500
-
-        if row['拆分方式'] == '极致周期' or '极致' in row['拆分方式']:
             return 2000
 
-        if '加急' in row['priority']:
+        if row['retestflag'] == '是':
             return 3000
 
-        if '补测' in row['priority']:
+        mytime = row['receivedtime']
-            return 4000
-
-        mytime = row['time']
         # 判断日期是之前的还是之后的
         if mytime < today_date:
             return 5000
@@ -221,35 +218,20 @@ class AutoLayout:
         """
         barcode 有重复的极致样本 进行排列组合，汇集成新的可能性
         """
-        # 筛选有重复的行
-
-        # same_barcode_data = [data for data in self.ori_lib_data if data['level'] == 1900]
-        # same_barcode_sorted = sorted(same_barcode_data, key=lambda x: (-x['size']))
-        #
-        # same_barcode_dict = dict()
-        # for index, data in enumerate(same_barcode_sorted):
-        #     same_barcode_dict[data['library']] = data['level'] + index + 1
-        # correct_data = list()
-        # for data in self.ori_lib_data:
-        #     if data in same_barcode_sorted:
-        #         data['level'] = same_barcode_dict[data['library']]
-        #     correct_data.append(data)
-        # self.ori_lib_data = correct_data
-
         same_barcode_df = pd.DataFrame(
             [spdata for data in self.ori_lib_data if data['level'] == 1900 for spdata in data['data']])
 
-        # 按照 'barcode' 列进行分组
+        # 按照 'indexi5i7' 列进行分组
         if same_barcode_df.empty:
             return
-        grouped = same_barcode_df.groupby('barcode')
+        grouped = same_barcode_df.groupby('indexi5i7')
 
-        # 获取具有重复的 'barcode' 分组
+        # 获取具有重复的 'indexi5i7' 分组
         duplicate_groups = grouped.filter(lambda x: len(x) > 1)
 
         # 提取这些分组，计算文库重复次数
-        grouped_names = duplicate_groups.groupby('barcode')['#library'].apply(list).reset_index()
+        grouped_names = duplicate_groups.groupby('indexi5i7')['samplename'].apply(list).reset_index()
-        random_list = list(set(tuple(sublst) for sublst in list(grouped_names['#library'])))
+        random_list = list(set(tuple(sublst) for sublst in list(grouped_names['samplename'])))
         new_lst = [spdata for data in random_list for spdata in data]
         counts = Counter(new_lst)
 
@@ -270,17 +252,17 @@ class AutoLayout:
         """
         self.index_assignments[chipname].extend(library_data['data'])
 
-        self.chip_barcode_recode[chipname].update({item['barcode'] for item in library_data['data']})
+        self.chip_barcode_recode[chipname].update({item['indexi5i7'] for item in library_data['data']})
-        self.chip_barcodei7_recode[chipname].update({item['i7'] for item in library_data['data']})
+        self.chip_barcodei7_recode[chipname].update({item['indexi7'] for item in library_data['data']})
-        self.chip_barcodei5_recode[chipname].update({item['i5'] for item in library_data['data']})
+        self.chip_barcodei5_recode[chipname].update({item['indexi5'] for item in library_data['data']})
 
         # 华大的 文库 i7 不能重复，添加N+i7
         if '华大' in library_data['classification']:
-            self.chip_barcode_recode[chipname].update({'N' * 8 + item['i7'] for item in library_data['data']})
+            self.chip_barcode_recode[chipname].update({'N' * 8 + item['indexi7'] for item in library_data['data']})
-            # self.chip_barcode_recode[chipname].update({item['i5'] + 'N' * 8 for item in library_data['data']})
+            # self.chip_barcode_recode[chipname].update({item['indexi5'] + 'N' * 8 for item in library_data['data']})
 
         # 子文库
-        self.chip_sublib[chipname].update({item['sublibrary'] for item in library_data['data']})
+        self.chip_sublib[chipname].update({item['subsamplename'] for item in library_data['data']})
 
         self.chip_customer[chipname].add(library_data['customer'])
         self.chip_classification[chipname].add(library_data['classification'])
@@ -288,13 +270,11 @@ class AutoLayout:
         if newer:
             self.chip_size[chipname] = library_data['size']
             self.chip_size_N[chipname] = 0
-            if 'N' in library_data['data'][0]['barcode']:
+            if 'N' in library_data['data'][0]['indexi5i7']:
-                # print(library_data['data'][0]['barcode'])
                 self.chip_size_N[chipname] = library_data['size']
-            # if library_data['classification'] in ['扩增子', '不平衡文库', '单细胞文库以及甲基化']:
             if library_data['is_balance_lib'] == '否':
                 self.chip_speciallib_size[chipname] = library_data['size']
-            elif library_data['is_balance_lib'] == '甲基化':
+            elif '甲基化' in library_data['classification']:
                 self.chip_methylib_size[chipname] = library_data['size']
             else:
                 self.chip_speciallib_size[chipname] = 0
@@ -312,24 +292,26 @@ class AutoLayout:
             self.chip_size[chipname] += library_data['size']
             if library_data['is_balance_lib'] == '否':
                 self.chip_speciallib_size[chipname] += library_data['size']
-            if library_data['is_balance_lib'] == '甲基化':
+            if '甲基化' in library_data['classification']:
                 self.chip_methylib_size[chipname] += library_data['size']
             if 'nextera' in library_data['classification'].lower():
                 self.chip_speciallib_nextera_size[chipname] += library_data['size']
             if '华大' in library_data['classification']:
                 self.chip_speciallib_huada_size[chipname] += library_data['size']
-
+            if 'N' in library_data['data'][0]['indexi5i7']:
-            if 'N' in library_data['data'][0]['barcode']:
-                # print(library_data['data'][0]['barcode'])
                 self.chip_size_N[chipname] += library_data['size']
 
-    def use_rule(self, chipname, classfication):
+    def use_rule_exclusive_classfication(self, chipname, classfication):
+        """
+        文库不能排在一起
+        """
         may_classfic = set(self.rule[self.rule['c1'] == classfication]['c2'])
         if self.chip_customer[chipname].intersection(may_classfic):
             return True
         return False
 
     def use_rule_exclusive_customer(self, chipname, customer):
+        """文库不能排在一起"""
         may_classfic = set(
             self.rule_exclusive_customer[self.rule_exclusive_customer['customer1'] == customer]['customer2'])
         if self.chip_customer[chipname].intersection(may_classfic):
@@ -342,12 +324,12 @@ class AutoLayout:
         """
         size = library_data['size']
         size_N = 0
-        if 'N' in library_data['data'][0]['barcode']:
+        if 'N' in library_data['data'][0]['indexi5i7']:
             size_N = library_data['size']
         classification = library_data['classification']
         customer = library_data['customer']
         is_balance_lib = library_data['is_balance_lib']
-        library = library_data['library']
+        # library = library_data['library']
 
         # 芯片大小不能超过设定限制
         sizelimit = True
@@ -357,17 +339,17 @@ class AutoLayout:
 
         # barcode有重复
         notrepeatbarcode = True
-        if self.chip_barcode_recode[chipname].intersection({item['barcode'] for item in library_data['data']}) or \
+        if self.chip_barcode_recode[chipname].intersection({item['indexi5i7'] for item in library_data['data']}) or \
                 self.chip_barcode_recode[chipname].intersection(
-                    {'N' * 8 + item['i7'] for item in library_data['data']}) or \
+                    {'N' * 8 + item['indexi7'] for item in library_data['data']}) or \
                 self.chip_barcode_recode[chipname].intersection(
-                    {item['i5'] + 'N' * 8 for item in library_data['data']}):
+                    {item['indexi5'] + 'N' * 8 for item in library_data['data']}):
             notrepeatbarcode = False
             # print(chipname, library, 'barcode有重复')
 
         # 互斥的文库
         exclusive_classific = True
-        if self.use_rule(chipname, classification):
+        if self.use_rule_exclusive_classfication(chipname, classification):
             exclusive_classific = False
             # print(chipname, library, '互斥的文库')
 
@@ -422,15 +404,15 @@ class AutoLayout:
             base_balance = True
             notrepeatbarcode = True
             if self.chip_barcodei7_recode[chipname].intersection(
-                    {item['i7'] for item in library_data['data']}) and max_barcode == 'i7':
+                    {item['indexi7'] for item in library_data['data']}) and max_barcode == 'indexi7':
                 notrepeatbarcode = False
             if self.chip_barcodei5_recode[chipname].intersection(
-                    {item['i5'] for item in library_data['data']}) and max_barcode == 'i5':
+                    {item['indexi5'] for item in library_data['data']}) and max_barcode == 'indexi5':
                 notrepeatbarcode = False
             # 是个N的取消
-            if ('N' * 8 in {item['i5'] for item in library_data['data']}) and max_barcode == 'i5':
+            if ('N' * 8 in {item['indexi5'] for item in library_data['data']}) and max_barcode == 'indexi5':
                 notrepeatbarcode = False
-            if ('N' * 8 in {item['i7'] for item in library_data['data']}) and max_barcode == 'i7':
+            if ('N' * 8 in {item['indexi7'] for item in library_data['data']}) and max_barcode == 'indexi7':
                 notrepeatbarcode = False
             if self.chip_size[chipname] > 900:
                 current_data = copy.deepcopy(self.index_assignments[chipname])
@@ -442,7 +424,7 @@ class AutoLayout:
 
         # 子文库名不能重复
         notrepeatsublib = True
-        if self.chip_sublib[chipname].intersection({item['sublibrary'] for item in library_data['data']}):
+        if self.chip_sublib[chipname].intersection({item['subsamplename'] for item in library_data['data']}):
             notrepeatsublib = False
 
         if sizelimit and notrepeatbarcode and \
@@ -475,7 +457,7 @@ class AutoLayout:
                     no_nextary_data.append(libdata)
                 else:
                     self.no_assign_data.append(libdata)
-                    nextary_barcode.update(libdata['barcode'])
+                    nextary_barcode.update(libdata['indexi5i7'])
             self.index_assignments[chipname] = no_nextary_data
             self.chip_barcode_recode[chipname] -= nextary_barcode
             self.chip_speciallib_nextera_size[chipname] = 0
@@ -490,7 +472,7 @@ class AutoLayout:
                     no_huada_data.append(libdata)
                 else:
                     self.no_assign_data.append(libdata)
-                    huada_barcode.update(libdata['barcode'])
+                    huada_barcode.update(libdata['indexi5i7'])
             self.index_assignments[chipname] = no_huada_data
             self.chip_barcode_recode[chipname] -= huada_barcode
             self.chip_speciallib_huada_size[chipname] = 0
@@ -500,63 +482,45 @@ class AutoLayout:
             self.loc_chip_num += 1
 
     def assign_samples(self):
-        # ori_library_data = list()
 
-        if '未测' not in self.ori_data.keys():
+        # if '未测' not in self.ori_data.keys():
-            raise UserWarning('提供excel没有 未测 sheet ,请核查！')
+        #     raise UserWarning('提供excel没有 未测 sheet ,请核查！')
-        ori_library_df = pd.DataFrame(self.ori_data['未测'])
+        ori_library_df = pd.DataFrame(self.ori_data)
-
-        # 检查提供excel 是否有必须表头
-        get_col = set(ori_library_df.columns)
-        unhave_col = set(self.need_cols) - get_col
-
-        if unhave_col:
-            unhave_from = '; '.join(unhave_col)
-            raise UserWarning(f'未测表里没有{unhave_from}  表头,请核查！')
 
         # 数据标准格式
-        numeric_mask = pd.to_numeric(ori_library_df['data_needed'], errors='coerce').notna()
+        numeric_mask = pd.to_numeric(ori_library_df['orderdatavolume'], errors='coerce').notna()
-        time_mask = pd.to_datetime(ori_library_df['time'], errors='coerce').notna()
+        time_mask = pd.to_datetime(ori_library_df['receivedtime'], errors='coerce').notna()
-
-        # 添加处理status列的逻辑
-        status_mask = ori_library_df['status'] == '暂不排样'
 
         # 非正常barcode
-        barcode_mask = ori_library_df['barcode'].str.len() != 16
+        barcode_mask = ori_library_df['indexi5i7'].str.len() != 16
 
         ori_library_df['note'] = ''
         ori_library_df.loc[~numeric_mask, 'note'] = 'data_needed 列非数字'
         ori_library_df.loc[~time_mask, 'note'] = 'time 列非日期'
-        ori_library_df.loc[status_mask, 'note'] = '暂不排样'
         ori_library_df.loc[barcode_mask, 'note'] = '非16位barcode'
-
+        no_ori_data = ori_library_df[~(numeric_mask & time_mask) | barcode_mask]
-        no_ori_data = ori_library_df[~(numeric_mask & time_mask) | status_mask | barcode_mask]
 
         self.no_assign_data.extend(no_ori_data.to_dict('records'))
 
-        # 使用布尔索引筛选出不是数字和非日期的行，并且不是暂不排样的行
+        # 使用布尔索引筛选出不是数字和非日期的行，并且不是暂不排样的行, 以及非16位置barcode
-        ori_library_df = ori_library_df[(numeric_mask & time_mask) & ~status_mask & ~barcode_mask]
+        ori_library_df = ori_library_df[(numeric_mask & time_mask) & ~barcode_mask]
-
-        # 某个客户的检测的数据超过1个T就单独处理
-        # summary = ori_library_df.groupby('customer').agg({'data_needed': 'sum'})
-        # print(summary)
 
         # 时间格式化
-        ori_library_df['time'] = pd.to_datetime(ori_library_df['time'], errors='coerce')
+        ori_library_df['receivedtime'] = pd.to_datetime(ori_library_df['receivedtime'], errors='coerce')
         ori_library_df['level'] = ori_library_df.apply(self.level, axis=1)
 
-        # 极致客户有重复的，把等级调到19，防止放到了最后，到了未测里
+        # 极致客户有重复的，把等级调到1900，防止放到了最后，到了未测里
         must_lib_df = ori_library_df[ori_library_df['level'] == 2000]
-        must_lib = set(must_lib_df[must_lib_df.duplicated(subset='barcode', keep=False)]['#library'].to_list())
+        must_lib = set(must_lib_df[must_lib_df.duplicated(subset='indexi5i7', keep=False)]['samplename'].to_list())
-        ori_library_df.loc[ori_library_df['#library'].isin(must_lib), 'level'] = 1900
+        ori_library_df.loc[ori_library_df['samplename'].isin(must_lib), 'level'] = 1900
 
-        for library, library_df in ori_library_df.groupby('#library'):
+        for library, library_df in ori_library_df.groupby('samplename'):
 
-            size = library_df['data_needed'].sum()
+            size = library_df['orderdatavolume'].sum()
-            is_balance_lib = library_df['is_balance_lib'].values[0]
+            is_balance_lib = library_df['librarybalancedflag'].values[0]
 
             # 文库内部有重复
-            if len(library_df['barcode'].values) > len(set(library_df['barcode'].values)):
+            if len(library_df['indexi5i7'].values) > len(set(library_df['indexi5i7'].values)):
                 library_df['note'] = '文库内部有重复'
                 self.no_assign_data.extend(library_df.to_dict('records'))
                 continue
@@ -564,53 +528,53 @@ class AutoLayout:
             # 不平衡文库 大于250G 的数据 先进行拆分
             if is_balance_lib == '否' and size > 250:
                 self.return_log.append(f'文库{library} 是不平衡文库, 数据为{size}, 大于250G, 已做拆分处理, 请注意！！！ ')
-                data_needed = library_df['data_needed'].copy()
+                data_needed = library_df['orderdatavolume'].copy()
                 for num in range(int(size), 0, -200):
                     addnum = 200
                     if num <= 200:
                         addnum = num
-                    library_df['data_needed'] = (addnum / size) * data_needed
+                    library_df['orderdatavolume'] = (addnum / size) * data_needed
 
                     self.ori_lib_data.append(dict(
                         library=library,
-                        is_balance_lib=library_df['is_balance_lib'].values[0],
+                        is_balance_lib=library_df['librarybalancedflag'].values[0],
-                        size=library_df['data_needed'].sum(),
+                        size=library_df['orderdatavolume'].sum(),
-                        split_method=library_df['拆分方式'].values[0],
+                        split_method=library_df['cycletype'].values[0],
-                        time=library_df['time'].values[0],
+                        time=library_df['receivedtime'].values[0],
                         level=1950,
-                        customer=library_df['customer'].values[0],
+                        customer=library_df['companynamea'].values[0],
-                        classification=library_df['classification'].values[0],
+                        classification=library_df['librarystructure'].values[0],
-                        data=library_df[self.need_cols].to_dict('records')
+                        data=library_df.to_dict('records')
                     ))
                 self.split_lib.add(library)
                 continue
 
             # 拆分处理 分为了2个大文库
-            if size > (self.data_limit) / 2:
+            if size > self.data_limit / 2:
-                library_df['data_needed'] = library_df['data_needed'] / 2
+                library_df['orderdatavolume'] = library_df['orderdatavolume'] / 2
                 self.return_log.append(f'文库{library} 已做拆分处理, 请注意！！！ ')
                 self.ori_lib_data.append(dict(
                     library=library,
-                    is_balance_lib=library_df['is_balance_lib'].values[0],
+                    is_balance_lib=library_df['librarybalancedflag'].values[0],
-                    size=library_df['data_needed'].sum(),
+                    size=library_df['orderdatavolume'].sum(),
-                    split_method=library_df['拆分方式'].values[0],
+                    split_method=library_df['cycletype'].values[0],
-                    time=library_df['time'].values[0],
+                    time=library_df['receivedtime'].values[0],
                     level=library_df['level'].values[0],
-                    customer=library_df['customer'].values[0],
+                    customer=library_df['companynamea'].values[0],
-                    classification=library_df['classification'].values[0],
+                    classification=library_df['librarystructure'].values[0],
-                    data=library_df[self.need_cols].to_dict('records')
+                    data=library_df.to_dict('records')
                 ))
 
             self.ori_lib_data.append(dict(
                 library=library,
-                is_balance_lib=library_df['is_balance_lib'].values[0],
+                is_balance_lib=library_df['librarybalancedflag'].values[0],
-                size=library_df['data_needed'].sum(),
+                size=library_df['orderdatavolume'].sum(),
-                split_method=library_df['拆分方式'].values[0],
+                split_method=library_df['cycletype'].values[0],
-                time=library_df['time'].values[0],
+                time=library_df['receivedtime'].values[0],
                 level=library_df['level'].values[0],
-                customer=library_df['customer'].values[0],
+                customer=library_df['companynamea'].values[0],
-                classification=library_df['classification'].values[0],
+                classification=library_df['librarystructure'].values[0],
-                data=library_df[self.need_cols].to_dict('records')
+                data=library_df.to_dict('records')
             ))
 
         self.combinations_same_barcode()
@@ -655,37 +619,38 @@ class AutoLayout:
             if not chip_assignments:
                 continue
             df = pd.DataFrame(chip_assignments)
-            if df['data_needed'].sum() < self.data_lower:
+            if df['orderdatavolume'].sum() < self.data_lower:
                 left_data.extend(chip_assignments)
                 no_need_chipname.append(chip_idx)
         for chip_idx in no_need_chipname:
             del self.index_assignments[chip_idx]
-
+        if not left_data:
+            return
         ori_library_df = pd.DataFrame(left_data)
         ori_library_df['level'] = ori_library_df.apply(self.level, axis=1)
         ori_lib_data = list()
-        for library, library_df in ori_library_df.groupby('#library'):
+        for library, library_df in ori_library_df.groupby('samplename'):
             level = library_df['level'].values[0]
             if library in self.split_lib:
                 level = 1950
 
             ori_lib_data.append(dict(
                 library=library,
-                is_balance_lib=library_df['is_balance_lib'].values[0],
+                is_balance_lib=library_df['librarybalancedflag'].values[0],
-                size=library_df['data_needed'].sum(),
+                size=library_df['orderdatavolume'].sum(),
-                split_method=library_df['拆分方式'].values[0],
+                split_method=library_df['cycletype'].values[0],
-                time=library_df['time'].values[0],
+                time=library_df['receivedtime'].values[0],
                 level=level,
-                customer=library_df['customer'].values[0],
+                customer=library_df['companynamea'].values[0],
-                classification=library_df['classification'].values[0],
+                classification=library_df['librarystructure'].values[0],
-                data=library_df[self.need_cols].to_dict('records')
+                data=library_df.to_dict('records')
             ))
 
         ori_lib_data = sorted(ori_lib_data, key=lambda x: (x['level'], x['time'], -x['size']))
         self.loc_chip_num = 100
         while ori_lib_data:
             library_data = ori_lib_data[0]
-            chipname = f'chipB{self.loc_chip_num}_{max_barcode}' if max_barcode != 'all' else f'chipB{self.loc_chip_num}'
+            chipname = f'chip{self.loc_chip_num}_{max_barcode}' if max_barcode != 'all' else f'chip{self.loc_chip_num}'
 
             # 空白芯片直接添加
             if chipname not in self.index_assignments:
@@ -714,13 +679,12 @@ class AutoLayout:
     def run(self):
         # print('# 测试代码')
         # self.assign_samples()
-        # self.assign_again()
+        # self.assign_again_size()
         try:
             self.assign_samples()
             self.assign_again_size()
-            # self.assign_again_size(max_barcode='i7')
+            # self.assign_again_size(max_barcode='indexi7')
-            # self.assign_again_size(max_barcode='i5')
+            # self.assign_again_size(max_barcode='indexi5')
-            # self.assign_again_size()
         except Exception as e:
             self.return_log.append(f'T7排样出错， 请联系！{e}')
             self.index_assignments = {}
@@ -728,23 +692,22 @@ class AutoLayout:
         outputpath = os.path.join(self.output, 'result', outputname)
         writer = pd.ExcelWriter(outputpath)
 
+        res = list()
         chip_loc = 1
         librarynum = 0
         for chip_idx, chip_assignments in self.index_assignments.items():
             if not chip_assignments:
                 continue
             df = pd.DataFrame(chip_assignments)
-            df['time'] = df['time'].dt.strftime('%Y-%m-%d')
+            df['receivedtime'] = df['receivedtime'].dt.strftime('%Y-%m-%d')
 
-            if [method for method in df['拆分方式'].values if '极致' in method]:
+            if [method for method in df['cycletype'].values if '极致' in method]:
                 addname = 'X'
             else:
                 addname = ''
             other_name = ''
-            # if 'chipB' in chip_idx and df['barcode'].duplicated().any():
-            #     other_name = '_i7'
 
-            if df['data_needed'].sum() < (self.data_lower - 50) and not addname:
+            if df['orderdatavolume'].sum() < (self.data_lower - 50) and not addname:
                 df['note'] = f'排样数据量不足{self.data_lower - 50}G'
                 self.no_assign_data.extend(df.to_dict('records'))
                 continue
@@ -752,31 +715,29 @@ class AutoLayout:
                 df['note'] = '排样管数超标'
                 self.no_assign_data.extend(df.to_dict('records'))
                 continue
-            librarynum += len(set(df['#library'].values))
+            librarynum += len(set(df['samplename'].values))
 
             self.dec_barcode_radio(chip_idx)
             chipname = addname + chip_idx + other_name
-
+            df['lanepackcode'] = chipname
-            sum_list = list()
+            # df = pd.concat([pd.DataFrame(self.items), df]).reset_index(drop=True)
-            for library, library_df in df.groupby('#library'):
+            #
-                sum_list.append(dict(
+            # df.to_excel(writer, sheet_name=chipname, index=False)
-                    二次拆分=library,
+            res.extend(df.to_dict('records'))
-                    客户=library_df['customer'].values[0],
-                    类型=library_df['classification'].values[0],
-                    打折前=library_df['data_needed'].sum()
-                ))
-            df_sum = pd.DataFrame(sum_list)
-            res_df = pd.concat([df, df_sum], axis=1)
-            res_df.to_excel(writer, sheet_name=chipname, index=False)
             chip_loc += 1
+        res_df = pd.DataFrame(res)
+        res_df = pd.concat([pd.DataFrame(self.items), res_df]).reset_index(drop=True)
+        res_df.to_excel(writer, sheet_name='assignment', index=False)
 
         no_assign_df = pd.DataFrame(self.no_assign_data)
+        if not no_assign_df.empty:
             no_assign_df = no_assign_df.applymap(lambda x: format_date(x) if isinstance(x, pd.Timestamp) else x)
-        no_assign_df_not_balance = ','.join(set([lib for lib in no_assign_df['#library'] if lib in self.split_lib]))
+            no_assign_df_not_balance = ','.join(set([lib for lib in no_assign_df['samplename'] if lib in self.split_lib]))
             if no_assign_df_not_balance:
                 self.return_log.append(f'文库{no_assign_df_not_balance}有做不平衡文库拆分处理，并且没有排完，请核查！')
-        if not no_assign_df.empty:
+            # if not no_assign_df.empty:
-            no_assign_df = no_assign_df[self.need_cols]
+            #     no_assign_df = no_assign_df[self.need_cols]
+            no_assign_df = pd.concat([pd.DataFrame(self.items), no_assign_df]).reset_index(drop=True)
             no_assign_df.to_excel(writer, sheet_name='未测', index=False)
         if self.return_log:
             pd.DataFrame(self.return_log).to_excel(writer, sheet_name='log', index=False)