模式回归到多个sheet，包lane列出

tools/t7.py

@@ -76,6 +76,9 @@ class AutoLayout:
         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.is_use_balance = is_use_balance
@@ -483,8 +486,6 @@ class AutoLayout:
 
     def assign_samples(self):
 
-        # if '未测' not in self.ori_data.keys():
-        #     raise UserWarning('提供excel没有 未测 sheet ,请核查！')
         ori_library_df = pd.DataFrame(self.ori_data)
 
         # 数据标准格式
@@ -494,18 +495,23 @@ class AutoLayout:
         # 非正常barcode
         barcode_mask = ori_library_df['indexi5i7'].str.len() != 16
         ori_library_df.loc[barcode_mask, 'indexi5i7'] = ori_library_df.loc[barcode_mask, 'indexi5'].str[-8:] + \
-                                                   ori_library_df.loc[barcode_mask, 'indexi7'].str[-8:]
+                                                        ori_library_df.loc[barcode_mask, 'indexi7'].str[-8:]
 
         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[barcode_mask, 'note'] = '非16位barcode'
+        ori_library_df.loc[barcode_mask, 'note'] = '非16位barcode，已修改'
         no_ori_data = ori_library_df[~(numeric_mask & time_mask)]
 
         self.no_assign_data.extend(no_ori_data.to_dict('records'))
 
+        # 包lane的剔除
+        orderlane_mask = ori_library_df['productname'].str.contains('包lane')
+
+        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
-        ori_library_df = ori_library_df[(numeric_mask & time_mask) & ~barcode_mask]
+        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')
@@ -539,6 +545,7 @@ class AutoLayout:
 
                     self.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],
@@ -557,6 +564,7 @@ class AutoLayout:
                 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,
+                    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],
@@ -569,6 +577,7 @@ class AutoLayout:
 
             self.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],
@@ -581,7 +590,6 @@ class AutoLayout:
 
         self.combinations_same_barcode()
         self.ori_lib_data = sorted(self.ori_lib_data, key=lambda x: (x['level'], x['time']))
-        # self.ori_lib_data = sorted(self.ori_lib_data, key=lambda x: (x['level'] != 100000, -x['size']))
 
         while self.ori_lib_data:
             library_data = self.ori_lib_data[0]
@@ -694,8 +702,6 @@ class AutoLayout:
         outputpath = os.path.join(self.output, 'result', outputname)
         writer = pd.ExcelWriter(outputpath)
 
-        res = list()
-        sum_res = list()
         chip_loc = 1
         librarynum = 0
         for chip_idx, chip_assignments in self.index_assignments.items():
@@ -722,43 +728,44 @@ class AutoLayout:
 
             self.dec_barcode_radio(chip_idx)
             chipname = addname + chip_idx + other_name
-            df['lanepackcode'] = chipname
-            # df = pd.concat([pd.DataFrame(self.items), df]).reset_index(drop=True)
-            #
-            # df.to_excel(writer, sheet_name=chipname, index=False)
-            res.extend(df.to_dict('records'))
-            chip_loc += 1
-
             sum_list = list()
             for library, library_df in df.groupby('samplename'):
                 sum_list.append(dict(
+                    预排文库编号=library_df['sampleCode'].values[0],
                     二次拆分=library,
                     客户=library_df['companynamea'].values[0],
-                    类型=library_df['classification'].values[0],
+                    类型=library_df['librarystructure'].values[0],
                     打折前=library_df['orderdatavolume'].sum()
                 ))
             df_sum = pd.DataFrame(sum_list)
-            sum_res.append({'sheetname': chipname, 'data': df_sum})
+            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.DataFrame(res)
-        res_df = pd.concat([pd.DataFrame(self.items), res_df]).reset_index(drop=True)
+        # res_df = pd.concat([pd.DataFrame(self.items), res_df]).reset_index(drop=True)
-        res_df.to_excel(writer, sheet_name='assignment', index=False)
+        # res_df.to_excel(writer, sheet_name='assignment', index=False)
 
-        for sum_sheet in sum_res:
+        # for sum_sheet in sum_res:
-            sheetname = sum_sheet.get('sheetname')
+        #     sheetname = sum_sheet.get('sheetname')
-            df_data = sum_sheet.get('data')
+        #     df_data = sum_sheet.get('data')
-            df_data.to_excel(writer, sheet_name=sheetname, index=False)
+        #     df_data.to_excel(writer, sheet_name=sheetname, 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['samplename'] 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:
             #     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()