更新nextera

tools/t7.py

@@ -38,6 +38,10 @@ class AutoLayout:
         self.rule = self.read_rule()
         # 甲基化文库不大于200,WGBS文库不大于200G
         self.chip_speciallib_size = dict()
+
+        # Nextera 文库大小
+        self.chip_speciallib_nextera_size = dict()
+
         self.logger = log(os.path.basename(f'{path}.txt'))
         self.return_log = list()
         self.no_assign_data = list()
@@ -50,7 +54,7 @@ class AutoLayout:
         merge = pd.read_excel(self.path, None)
         ori_data = dict()
         for name, sheet in merge.items():
-            sheet.fillna('.', inplace=True)
+            sheet.fillna('', inplace=True)
             ori_data[name] = sheet.to_dict('records')
         return ori_data
 
@@ -65,6 +69,9 @@ 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_customer[chipname].add(library_data['customer'])
+        self.chip_classification[chipname].add(library_data['classification'])
+
         if newer:
             self.chip_size[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']:
@@ -72,13 +79,17 @@ class AutoLayout:
                 self.chip_speciallib_size[chipname] = library_data['size']
             else:
                 self.chip_speciallib_size[chipname] = 0
+            if 'Nextera' in library_data['classification'].lower():
+                self.chip_speciallib_nextera_size[chipname] = library_data['size']
+            else:
+                self.chip_speciallib_nextera_size[chipname] = 0
+
         else:
             self.chip_size[chipname] += library_data['size']
             if library_data['is_balance_lib'] == '否':
                 self.chip_speciallib_size[chipname] += library_data['size']
-
+            if 'Nextera' in library_data['classification'].lower():
-        self.chip_customer[chipname].add(library_data['customer'])
+                self.chip_speciallib_nextera_size[chipname] += library_data['size']
-        self.chip_classification[chipname].add(library_data['classification'])
 
     def count_barcode_radio(self, data):
         df = pd.DataFrame(data)
@@ -146,10 +157,14 @@ class AutoLayout:
         # 将时间字符串转换为 datetime 对象
         # mytime = datetime.strptime(row['time'], "%Y-%m-%d")
         # mytime = row['time'].strftime("%Y-%m-%d")
-        mytime = row['time']
+
+        if 'nextera' in row['classification'].lower():
+            return 1
+
         if row['拆分方式'] == '极致周期' or '极致' in row['拆分方式']:
             return 2
 
+        mytime = row['time']
         # 判断日期是之前的还是之后的
         if mytime < today_date:
             return 3
@@ -219,6 +234,25 @@ class AutoLayout:
             return True
         return False
 
+    def add_loc_num(self):
+        # 有nextera文库 必须满足大于50G
+        nextera_size = self.chip_speciallib_nextera_size[f'chip{self.loc_chip_num}']
+        if nextera_size > 50 or nextera_size == 0:
+            self.loc_chip_num += 1
+        else:
+            # 有nextera文库，但是不满足50G 去除
+            nextary_barcode = set()
+            no_nextary_data = list()
+            for libdata in self.index_assignments[self.loc_chip_num]:
+                if libdata['classification'].lower() != 'nextera':
+                    no_nextary_data.append(libdata)
+
+                else:
+                    self.no_assign_data.extend(libdata['data'])
+                    nextary_barcode.update(set([nextera_data['barcode'] for nextera_data in libdata['data']]))
+
+            self.chip_barcode_recode[self.loc_chip_num] -= nextary_barcode
+
     def assign_samples(self):
         ori_library_data = list()
 
@@ -226,8 +260,8 @@ class AutoLayout:
             raise UserWarning('提供excel没有 未测 sheet ,请核查！')
         ori_library_df = pd.DataFrame(self.ori_data['未测'])
 
-        need_col = ['#library', 'sublibrary', 'i5', 'i7', 'data_needed', 'real_data', 'customer',
+        need_col = ['status', '#library', 'sublibrary', 'i5', 'i7', 'data_needed', 'real_data', 'customer',
-                    'classification', 'priority', 'time', '拆分方式', 'barcode', 'is_balance_lib'
+                    'classification', 'priority', 'time', '拆分方式', 'barcode', 'is_balance_lib', '备注'
                     ]
         get_col = set(ori_library_df.columns)
         unhave_col = set(need_col) - get_col
@@ -239,17 +273,22 @@ class AutoLayout:
         numeric_mask = pd.to_numeric(ori_library_df['data_needed'], errors='coerce').notna()
         time_mask = pd.to_datetime(ori_library_df['time'], errors='coerce').notna()
 
+        # 添加处理status列的逻辑
+        status_mask = ori_library_df['status'] == '暂不排样'
+
         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'
 
         need_col.append('note')
 
-        no_ori_data = ori_library_df[~(numeric_mask & time_mask)]
+        no_ori_data = ori_library_df[~(numeric_mask & time_mask) | status_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
-        ori_library_df = ori_library_df[(numeric_mask & time_mask)]
+        ori_library_df = ori_library_df[(numeric_mask & time_mask) & ~status_mask]
+
         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0，防止放到了最后，到了未测里
@@ -320,9 +359,11 @@ class AutoLayout:
                         break
                     j += 1
                 else:
-                    self.loc_chip_num += 1
+                    # self.loc_chip_num += 1
+                    self.add_loc_num()
             if self.chip_size[chipname] > self.data_limit:
-                self.loc_chip_num += 1
+                # self.loc_chip_num += 1
+                self.add_loc_num()
 
     def assign_again(self):
         pass