新增

tools/t7.py

@@ -3,15 +3,22 @@ import os
 import time
 from collections import defaultdict, Counter
 from datetime import datetime
+from io import BytesIO
 
+import openpyxl
 import pandas as pd
 
-from tools.common import basedir, log
+from tools.common import basedir
 
 
-# 定义一个格式化函数
+# 复制样式函数
-def format_date(date):
+def copy_cell_style(src_cell, dest_cell):
-    return date.strftime('%Y-%m-%d')
+    dest_cell.font = copy.copy(src_cell.font)
+    dest_cell.border = copy.copy(src_cell.border)
+    dest_cell.fill = copy.copy(src_cell.fill)
+    dest_cell.number_format = copy.copy(src_cell.number_format)
+    dest_cell.protection = copy.copy(src_cell.protection)
+    dest_cell.alignment = copy.copy(src_cell.alignment)
 
 
 class AutoLayout:
@@ -19,8 +26,8 @@ class AutoLayout:
     自动化派样
     """
 
-    def __init__(self, path, librarynum, is_use_balance=1, is_use_max=0, output=basedir, data_limit=1750,
+    def __init__(self, path, librarynum, is_use_balance=1, is_use_max=0, output=basedir, data_limit=1650,
-                 data_lower=1700):
+                 data_lower=1600):
         self.path = path
         self.output = output
         self.librarynum = int(librarynum)
@@ -72,14 +79,14 @@ class AutoLayout:
         # 排序好的文库数据
         self.ori_lib_data = list()
 
-        self.logger = log(os.path.basename(f'{path}.txt'))
+        # self.logger = log(os.path.basename(f'{path}.txt'))
         self.return_log = list()
         self.no_assign_data = list()
 
         # 包lane处理
         self.order_assign_data = list()
 
-        self.need_cols = self.read_cols()
+        # self.need_cols = self.read_cols()
 
         self.is_use_balance = is_use_balance
         self.is_use_max = is_use_max
@@ -197,6 +204,9 @@ class AutoLayout:
 
         today_date = datetime.now()
 
+        if '贞固' in row['companynamea'].lower():
+            return 999
+
         if 'nextera' in row['librarystructure'].lower():
             return 1000
 
@@ -206,10 +216,7 @@ class AutoLayout:
         if row['cycletype'] == '极致周期' or '极致' in row['cycletype']:
             return 2000
 
-        if row['retestflag'] == '是':
+        mytime = row['createdtime']
-            return 3000
-
-        mytime = row['receivedtime']
         # 判断日期是之前的还是之后的
         if mytime < today_date:
             return 5000
@@ -233,8 +240,8 @@ class AutoLayout:
         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('indexi5i7')['samplename'].apply(list).reset_index()
+        grouped_names = duplicate_groups.groupby('indexi5i7')['presamplename'].apply(list).reset_index()
-        random_list = list(set(tuple(sublst) for sublst in list(grouped_names['samplename'])))
+        random_list = list(set(tuple(sublst) for sublst in list(grouped_names['presamplename'])))
         new_lst = [spdata for data in random_list for spdata in data]
         counts = Counter(new_lst)
 
@@ -430,6 +437,11 @@ class AutoLayout:
         if self.chip_sublib[chipname].intersection({item['subsamplename'] for item in library_data['data']}):
             notrepeatsublib = False
 
+        # 不平衡文库不能放散样1
+        is_not_balance_lib_chip1 = True
+        if is_balance_lib == '否' and self.loc_chip_num == 1 :
+            is_not_balance_lib_chip1 = False
+
         if sizelimit and notrepeatbarcode and \
                 exclusive_classific and \
                 exclusive_customer and \
@@ -438,7 +450,8 @@ class AutoLayout:
                 spmethylibrary and \
                 use_huada and \
                 notrepeatsublib and \
-                sizelimit_N:
+                sizelimit_N and \
+                is_not_balance_lib_chip1:
             return True
         return False
 
@@ -490,7 +503,7 @@ class AutoLayout:
 
         # 数据标准格式
         numeric_mask = pd.to_numeric(ori_library_df['orderdatavolume'], errors='coerce').notna()
-        time_mask = pd.to_datetime(ori_library_df['receivedtime'], errors='coerce').notna()
+        time_mask = pd.to_datetime(ori_library_df['createdtime'], errors='coerce').notna()
 
         # 非正常barcode
         barcode_mask = ori_library_df['indexi5i7'].str.len() != 16
@@ -510,19 +523,19 @@ class AutoLayout:
 
         self.order_assign_data = ori_library_df[orderlane_mask].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, 以及非16位置barcode
+        # 使用布尔索引筛选出不是数字和非日期的行，包lane的
         ori_library_df = ori_library_df[(numeric_mask & time_mask) & (~orderlane_mask)]
 
         # 时间格式化
-        ori_library_df['receivedtime'] = pd.to_datetime(ori_library_df['receivedtime'], errors='coerce')
+        ori_library_df['createdtime'] = pd.to_datetime(ori_library_df['createdtime'], 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='indexi5i7', keep=False)]['samplename'].to_list())
+        must_lib = set(must_lib_df[must_lib_df.duplicated(subset='indexi5i7', keep=False)]['presamplename'].to_list())
-        ori_library_df.loc[ori_library_df['samplename'].isin(must_lib), 'level'] = 1900
+        ori_library_df.loc[ori_library_df['presamplename'].isin(must_lib), 'level'] = 1900
 
-        for library, library_df in ori_library_df.groupby('samplename'):
+        for library, library_df in ori_library_df.groupby('presamplename'):
 
             size = library_df['orderdatavolume'].sum()
             is_balance_lib = library_df['librarybalancedflag'].values[0]
@@ -639,13 +652,14 @@ class AutoLayout:
         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('samplename'):
+        for library, library_df in ori_library_df.groupby('presamplename'):
             level = library_df['level'].values[0]
             if library in self.split_lib:
                 level = 1950
 
             ori_lib_data.append(dict(
                 library=library,
+                sample_code=library_df['sampleCode'].values[0],
                 is_balance_lib=library_df['librarybalancedflag'].values[0],
                 size=library_df['orderdatavolume'].sum(),
                 split_method=library_df['cycletype'].values[0],
@@ -693,12 +707,11 @@ class AutoLayout:
         try:
             self.assign_samples()
             self.assign_again_size()
-            # self.assign_again_size(max_barcode='indexi7')
-            # self.assign_again_size(max_barcode='indexi5')
         except Exception as e:
             self.return_log.append(f'T7排样出错， 请联系！{e}')
             self.index_assignments = {}
         outputname = 'assignments_%s_%s' % (datetime.now().strftime("%m%d%H%M"), os.path.basename(self.path))
+
         outputpath = os.path.join(self.output, 'result', outputname)
         writer = pd.ExcelWriter(outputpath)
 
@@ -708,7 +721,7 @@ class AutoLayout:
             if not chip_assignments:
                 continue
             df = pd.DataFrame(chip_assignments)
-            df['receivedtime'] = df['receivedtime'].dt.strftime('%Y-%m-%d')
+            # df['receivedtime'] = df['receivedtime'].dt.strftime('%Y-%m-%d')
 
             if [method for method in df['cycletype'].values if '极致' in method]:
                 addname = 'X'
@@ -724,14 +737,15 @@ class AutoLayout:
                 df['note'] = '排样管数超标'
                 self.no_assign_data.extend(df.to_dict('records'))
                 continue
-            librarynum += len(set(df['samplename'].values))
+            librarynum += len(set(df['presamplename'].values))
 
             self.dec_barcode_radio(chip_idx)
             chipname = addname + chip_idx + other_name
             sum_list = list()
-            for library, library_df in df.groupby('samplename'):
+            for library, library_df in df.groupby('presamplename'):
                 sum_list.append(dict(
                     预排文库编号=library_df['sampleCode'].values[0],
+                    预排样本名称=library_df['presamplename'].values[0],
                     二次拆分=library,
                     客户=library_df['companynamea'].values[0],
                     类型=library_df['librarystructure'].values[0],
@@ -739,6 +753,7 @@ class AutoLayout:
                 ))
             df_sum = pd.DataFrame(sum_list)
             res_df = pd.concat([df, df_sum], axis=1)
+            res_df = pd.concat([pd.DataFrame(self.items), res_df]).reset_index(drop=True)
             res_df.to_excel(writer, sheet_name=chipname, index=False)
             chip_loc += 1
 
@@ -753,22 +768,21 @@ class AutoLayout:
 
         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['samplename'] if lib in self.split_lib]))
+                set([lib for lib in no_assign_df['presamplename'] 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:
-            #     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)
         order_assign_df = pd.DataFrame(self.order_assign_data)
+
         if not order_assign_df.empty:
             order_assign_df = pd.concat([pd.DataFrame(self.items), order_assign_df]).reset_index(drop=True)
             order_assign_df.to_excel(writer, sheet_name='包lane', index=False)
         if self.return_log:
             pd.DataFrame(self.return_log).to_excel(writer, sheet_name='log', index=False)
         writer.close()
+
         return outputpath