开启i7

tools/t7.py

@@ -32,6 +32,7 @@ class AutoLayout:
         self.chip_type = dict()
         # 芯片barcode
         self.chip_barcode_recode = defaultdict(set)
+        self.chip_barcodei7_recode = defaultdict(set)
         # 芯片原始数据读取
         self.ori_data = self.read_excel()
         # 当前锚芯片
@@ -63,19 +64,22 @@ class AutoLayout:
         self.is_use_balance = is_use_balance
         self.is_use_max = is_use_max
 
-    def count_barcode_radio(self, data):
+    def count_barcode_radio(self, data, maxt=False):
         df = pd.DataFrame(data)
         ratio_sites = dict()
         is_not_balance_list = []
         if df.empty:
             return ratio_sites, is_not_balance_list
-
+        s, e = 0, 16
-        df['barcode'] = df['barcode'].str.slice(0, 16)
+        if maxt:
+            s, e = 8, 16
+        num = e - s
+        df['barcode'] = df['barcode'].str.slice(s, e)
         barcode_df = pd.DataFrame(df['barcode'].str.split('', expand=True).iloc[:, 1:-1].values,
-                                  columns=['T' + str(x) for x in range(16)]).join(df['data_needed'])
+                                  columns=['T' + str(x) for x in range(num)]).join(df['data_needed'])
         total = barcode_df['data_needed'].sum()
 
-        for i in range(16):
+        for i in range(num):
             column = 'T' + str(i)
             col_df = barcode_df.groupby(column).agg({'data_needed': 'sum'})
             # 去掉N计数
@@ -137,6 +141,9 @@ class AutoLayout:
         if '华大' in row['classification']:
             return 1100
 
+        if '超加急' in row['priority']:
+            return 1500
+
         if row['拆分方式'] == '极致周期' or '极致' in row['拆分方式']:
             return 2000
 
@@ -290,7 +297,7 @@ class AutoLayout:
             return True
         return False
 
-    def judge_data(self, chipname, library_data):
+    def judge_data(self, chipname, library_data, max_barcode=False):
         """
         约束条件
         """
@@ -354,6 +361,21 @@ class AutoLayout:
         if (self.chip_speciallib_huada_size[chipname] > self.data_limit / 2) and ('华大' in classification):
             use_huada = False
 
+        # 开启i5或者i7
+        if max_barcode:
+            base_balance = True
+            # 开启i7:
+            notrepeatbarcode = True
+            if self.chip_barcodei7_recode[chipname].intersection({item['i7'] for item in library_data['data']}):
+                notrepeatbarcode = False
+            if self.chip_size[chipname] > 900:
+                current_data = copy.deepcopy(self.index_assignments[chipname])
+                new_data = library_data['data']
+                current_data.extend(new_data)
+                ratio_sites, is_not_balance_list = self.count_barcode_radio(current_data, maxt=True)
+                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 and use_huada:
             return True
         return False
@@ -491,6 +513,7 @@ 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]
@@ -520,11 +543,76 @@ class AutoLayout:
             if self.chip_size[chipname] > self.data_limit:
                 self.add_loc_num()
 
+    def assign_again(self):
+        """
+        剩余的数据，开放i5或者i7再排下
+        """
+        left_data = list()
+        no_need_chipname = list()
+        for chip_idx, chip_assignments in self.index_assignments.items():
+            if not chip_assignments:
+                continue
+            df = pd.DataFrame(chip_assignments)
+            if df['data_needed'].sum() < 1700:
+                left_data.extend(chip_assignments)
+                # del self.index_assignments[chip_idx]
+                no_need_chipname.append(chip_idx)
+        for chip_idx in no_need_chipname:
+            del self.index_assignments[chip_idx]
+
+        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'):
+            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')
+            ))
+
+        ori_lib_data = sorted(ori_lib_data, key=lambda x: (x['level'], -x['size']))
+        self.loc_chip_num = 100
+        while ori_lib_data:
+            library_data = ori_lib_data[0]
+            chipname = f'chipB{self.loc_chip_num}'
+
+            # 空白芯片直接添加
+            if chipname not in self.index_assignments:
+                self.add_new_data(chipname, library_data)
+                ori_lib_data.remove(library_data)
+                continue
+
+            # 判断条件
+            if self.judge_data(chipname, library_data, max_barcode=True):
+                self.add_new_data(chipname, library_data, newer=False)
+                ori_lib_data.remove(library_data)
+            else:
+                for j in range(len(ori_lib_data)):
+                    newlibrary_data = ori_lib_data[j]
+                    if self.judge_data(chipname, newlibrary_data, max_barcode=True):
+                        ori_lib_data.remove(newlibrary_data)
+                        self.add_new_data(chipname, newlibrary_data, newer=False)
+                        break
+                    j += 1
+                else:
+                    self.loc_chip_num += 1
+
+            if self.chip_size[chipname] > self.data_limit:
+                self.loc_chip_num += 1
+
     def run(self):
         # print('# 测试代码')
         # self.assign_samples()
+        # self.assign_again()
         try:
             self.assign_samples()
+            self.assign_again()
         except Exception as e:
             self.return_log.append(f'T7排样出错， 请联系！{e}')
             self.index_assignments = {}
@@ -544,6 +632,9 @@ class AutoLayout:
                 addname = 'X'
             else:
                 addname = ''
+            other_name = ''
+            if 'chipB' in chip_idx and df['barcode'].duplicated().any():
+                other_name = 'i7'
 
             if df['data_needed'].sum() < 1600 and not addname:
                 df['note'] = '排样数据量不足1600G'
@@ -556,7 +647,7 @@ class AutoLayout:
             librarynum += len(set(df['#library'].values))
 
             self.dec_barcode_radio(chip_idx)
-            chipname = addname + chip_idx
+            chipname = addname + chip_idx + other_name
 
             sum_list = list()
             for library, library_df in df.groupby('#library'):