更新

tools/t7.py

@@ -1,7 +1,7 @@
 import copy
 import os
 import time
-from collections import defaultdict
+from collections import defaultdict, Counter
 from datetime import datetime
 
 import pandas as pd
@@ -132,27 +132,27 @@ class AutoLayout:
         today_date = datetime.now()
 
         if 'nextera' in row['classification'].lower():
-            return 10
+            return 1000
 
         if '华大' in row['classification']:
-            return 11
+            return 1100
 
         if row['拆分方式'] == '极致周期' or '极致' in row['拆分方式']:
-            return 20
+            return 2000
+
+        if '加急' in row['priority']:
+            return 3000
+
+        if '补测' in row['priority']:
+            return 4000
 
         mytime = row['time']
         # 判断日期是之前的还是之后的
         if mytime < today_date:
-            return 30
+            return 5000
-
-        if '加急' in row['priority']:
-            return 40
-
-        if '补测' in row['priority']:
-            return 50
 
         else:
-            return 1000
+            return 100000
 
     @staticmethod
     def read_rule():
@@ -190,6 +190,49 @@ class AutoLayout:
             ori_data[name] = sheet.to_dict('records')
         return ori_data
 
+    def combinations_same_barcode(self):
+        """
+        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
+        if same_barcode_df.empty:
+            return
+        grouped = same_barcode_df.groupby('barcode')
+
+        # 获取具有重复的 'barcode' 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()
+        random_list = list(set(tuple(sublst) for sublst in list(grouped_names['#library'])))
+        new_lst = [spdata for data in random_list for spdata in data]
+        counts = Counter(new_lst)
+
+        correct_data = list()
+        for data in self.ori_lib_data:
+            if data['library'] in counts:
+                data['level'] -= counts[data['library']]
+            correct_data.append(data)
+        self.ori_lib_data = correct_data
+
     def add_new_data(self, chipname, library_data, newer=True):
         """
         增加新数据到已知芯片上
@@ -241,7 +284,8 @@ class AutoLayout:
         return False
 
     def use_rule_exclusive_customer(self, chipname, customer):
-        may_classfic = set(self.rule_exclusive_customer[self.rule_exclusive_customer['customer1'] == customer]['customer2'])
+        may_classfic = set(
+            self.rule_exclusive_customer[self.rule_exclusive_customer['customer1'] == customer]['customer2'])
         if self.chip_customer[chipname].intersection(may_classfic):
             return True
         return False
@@ -285,8 +329,9 @@ class AutoLayout:
             splibrary = False
 
         # 甲基化文库不能大于250G
+        # 甲基化更改成100G
         spmethylibrary = True
-        if is_balance_lib == '甲基化' and self.chip_methylib_size[chipname] + size > 250:
+        if is_balance_lib == '甲基化' and self.chip_methylib_size[chipname] + size > 100:
             spmethylibrary = False
 
         # 不使用平衡文库
@@ -304,7 +349,12 @@ class AutoLayout:
             if is_not_balance_list:
                 base_balance = False
 
-        if sizelimit and notrepeatbarcode and exclusive_classific and exclusive_customer and splibrary and base_balance and spmethylibrary:
+        # 华大的文库不能超过限制的一半
+        use_huada = True
+        if self.chip_speciallib_huada_size[chipname] > self.data_limit / 2:
+            use_huada = False
+
+        if sizelimit and notrepeatbarcode and exclusive_classific and exclusive_customer and splibrary and base_balance and spmethylibrary and use_huada:
             return True
         return False
 
@@ -312,7 +362,7 @@ class AutoLayout:
         """
         锚定芯片号增加
         """
-        # 有nextera, 华大文库 必须满足大于50G
+        # 有nextera, 华大文库 必须满足大于50G 到了芯片结算
         chipname = f'chip{self.loc_chip_num}'
         nextera_size = self.chip_speciallib_nextera_size[chipname]
         huada_size = self.chip_speciallib_huada_size[chipname]
@@ -351,7 +401,7 @@ class AutoLayout:
             self.loc_chip_num += 1
 
     def assign_samples(self):
-        ori_library_data = list()
+        # ori_library_data = list()
 
         if '未测' not in self.ori_data.keys():
             raise UserWarning('提供excel没有 未测 sheet ,请核查！')
@@ -396,9 +446,10 @@ class AutoLayout:
         ori_library_df['time'] = pd.to_datetime(ori_library_df['time'], 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0，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19，防止放到了最后，到了未测里
-        ori_library_df.loc[
+        must_lib_df = ori_library_df[ori_library_df['level'] == 2000]
-            (ori_library_df.duplicated(subset='barcode')) & (ori_library_df['level'] == 20), 'level'] = 19
+        must_lib = set(must_lib_df[must_lib_df.duplicated(subset='barcode', keep=False)]['#library'].to_list())
+        ori_library_df.loc[ori_library_df['#library'].isin(must_lib), 'level'] = 1900
 
         for library, library_df in ori_library_df.groupby('#library'):
 
@@ -410,28 +461,11 @@ class AutoLayout:
                 self.no_assign_data.extend(library_df.to_dict('records'))
                 continue
 
-            # 拆分处理
+            # 拆分处理 分为了2个大文库
-            flag = False
             if size > (self.data_limit) / 2:
                 library_df['data_needed'] = library_df['data_needed'] / 2
-                flag = True
-
-            ori_library_data.append(dict(
-                library=library,
-                is_balance_lib=library_df['is_balance_lib'].values[0],
-                size=library_df['data_needed'].sum(),
-                split_method=library_df['拆分方式'].values[0],
-                time=library_df['time'].values[0],
-                level=library_df['level'].values[0],
-                customer=library_df['customer'].values[0],
-                classification=library_df['classification'].values[0],
-                data=library_df[self.need_cols].to_dict('records')
-            ))
-
-            # 拆分对半
-            if flag:
                 self.return_log.append(f'文库{library} 已做拆分处理, 请注意！！！ ')
-                ori_library_data.append(dict(
+                self.ori_lib_data.append(dict(
                     library=library,
                     is_balance_lib=library_df['is_balance_lib'].values[0],
                     size=library_df['data_needed'].sum(),
@@ -442,9 +476,22 @@ class AutoLayout:
                     classification=library_df['classification'].values[0],
                     data=library_df[self.need_cols].to_dict('records')
                 ))
-        self.ori_lib_data = sorted(ori_library_data, key=lambda x: (x['level'], x['time']))
 
-        # self.ori_lib_data = ori_sort_data
+            self.ori_lib_data.append(dict(
+                library=library,
+                is_balance_lib=library_df['is_balance_lib'].values[0],
+                size=library_df['data_needed'].sum(),
+                split_method=library_df['拆分方式'].values[0],
+                time=library_df['time'].values[0],
+                level=library_df['level'].values[0],
+                customer=library_df['customer'].values[0],
+                classification=library_df['classification'].values[0],
+                data=library_df[self.need_cols].to_dict('records')
+            ))
+
+        self.combinations_same_barcode()
+        self.ori_lib_data = sorted(self.ori_lib_data, key=lambda x: (x['level'], x['time']))
+
         while self.ori_lib_data:
             library_data = self.ori_lib_data[0]
             chipname = f'chip{self.loc_chip_num}'
@@ -474,6 +521,7 @@ class AutoLayout:
                 self.add_loc_num()
 
     def run(self):
+        # print('# 测试代码')
         # self.assign_samples()
         try:
             self.assign_samples()
@@ -522,7 +570,7 @@ class AutoLayout:
             res_df = pd.concat([df, df_sum], axis=1)
             res_df.to_excel(writer, sheet_name=chipname, index=False)
             chip_loc += 1
-        # self.no_assign_data.extend(self.diffic_assign_data)
+
         no_assign_df = pd.DataFrame(self.no_assign_data)
         no_assign_df = no_assign_df.applymap(lambda x: format_date(x) if isinstance(x, pd.Timestamp) else x)
         if not no_assign_df.empty:
@@ -543,5 +591,3 @@ if __name__ == '__main__':
     end_time = time.time()
     execution_time = end_time - start_time
     print(f"代码执行时间为：{execution_time} 秒")
-
-    # server()