更新

tools/t7.py

@@ -5,16 +5,163 @@ from collections import defaultdict
 from datetime import datetime
 
 import pandas as pd
+from deap import base, creator, tools, algorithms
 
 from tools.common import basedir, log
 
 
 # 定义一个格式化函数
 def format_date(date):
-    if isinstance(date, pd.Timestamp):
     return date.strftime('%Y-%m-%d')
+
+
+def count_barcode_radio(data):
+    df = pd.DataFrame(data)
+    ratio_sites = dict()
+    is_not_balance_list = []
+    if df.empty:
+        return ratio_sites, is_not_balance_list
+
+    df['barcode'] = df['barcode'].str.slice(0, 16)
+    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'])
+    total = barcode_df['data_needed'].sum()
+
+    for i in range(16):
+        column = 'T' + str(i)
+        col_df = barcode_df.groupby(column).agg({'data_needed': 'sum'})
+        # 去掉N计数
+        if 'N' in col_df.index:
+            base_n_size = col_df.loc['N', 'data_needed']
+            col_df = col_df.drop('N')
         else:
-        return str(date)
+            base_n_size = 0
+        col_df['ratio'] = (col_df['data_needed']) / (total - base_n_size)
+        ratio = col_df['ratio'].to_dict()
+        ratio_sites[i] = ratio
+        A, B, C, D, E, F = list(), list(), list(), list(), list(), list()
+        for decbase in ['A', 'T', 'C', 'G']:
+            if decbase not in ratio:
+                ratio[decbase] = 0
+            if ratio[decbase] >= 0.6:
+                A.append(decbase)
+            if 0.2 <= ratio[decbase] < 0.6:
+                B.append(decbase)
+            if 0.15 <= ratio[decbase] < 0.2:
+                C.append(decbase)
+            if 0.1 <= ratio[decbase] < 0.15:
+                D.append(decbase)
+            if 0.08 <= ratio[decbase] < 0.1:
+                E.append(decbase)
+            if ratio[decbase] < 0.08:
+                F.append(decbase)
+
+        A_num, B_num, C_num, D_num, E_num, F_num = len(A), len(B), len(C), len(D), len(E), len(F)
+        if not ((B_num + C_num + D_num == 4) or (F_num == 1 and (A_num + B_num) == 3) or (
+                E_num == 1 and D_num == 1 and (A_num + B_num + C_num) == 2) or (
+                        E_num == 1 and (A_num + B_num + C_num) == 3)):
+            is_not_balance_list.append(
+                '第%s位置，算出结果为 %s' % (i, ratio)
+            )
+    return ratio_sites, is_not_balance_list
+
+
+# 定义遗传算法
+class Ga:
+    """
+    # 定义遗传算法
+    """
+
+    def __init__(self, sheets):
+        self.sheets = sheets
+
+    # 定义个体的生成方式
+    def generate_individual(self):
+        individual = copy.deepcopy(self.sheets)  # 初始解作为个体
+        return [individual]
+
+    # 定义评估函数
+    @staticmethod
+    def evaluate(individual):
+        total_data_needed_sum = 0
+        xchip = 0
+        try:
+            for sheetname, data in individual[0][0].items():
+                library_data = pd.DataFrame(data)
+
+                size = library_data['data_needed'].sum()
+
+                # 芯片大小不能超过设定限制
+                if size > 1700:
+                    return (0, 100000, 100000)
+
+                # barcode有重复
+                if len(library_data['barcode'].values) < len(set(library_data['barcode'].values)):
+                    return (0, 100000, 100000)
+
+                # 不平衡文库大于250G 不能添加
+                if library_data[library_data['is_balance_lib'] == '否']['data_needed'].sum() > 250:
+                    return (0, 100000, 100000)
+
+                # 碱基不平衡不过不添加，保证前面的数据, 在数据达到1200G的时候开始
+                ratio_sites, is_not_balance_list = count_barcode_radio(library_data)
+                if is_not_balance_list:
+                    return (0, 100000, 100000)
+
+                if library_data[library_data['classification'].str.lower() == 'nextera']['data_needed'].sum() <= 50:
+                    return (0, 100000, 100000)
+
+                # 计算每个sheet的data_needed之和
+                total_data_needed_sum += library_data['data_needed'].sum()
+
+                # 记录包含字母"A"的sheet数量
+                if any('极致' in value for value in library_data['split']):
+                    xchip += 1
+        except Exception:
+            return (0, 100000, 100000)
+
+        # 返回一个适应度值，目标是最大化总的data_needed之和，最小化sheet的数量, 最少的极致芯片
+        total_data_needed_sum, num_sheets, num_xchip = total_data_needed_sum, len(individual[0]), xchip
+        return total_data_needed_sum, num_sheets, num_xchip
+
+    def run(self):
+        # 定义遗传算法的参数
+        pop_size = 50
+        cxpb = 0.7  # 交叉概率
+        mutpb = 0.2  # 变异概率
+        ngen = 100  # 迭代次数
+
+        # 初始化遗传算法工具箱
+        creator.create("FitnessMax", base.Fitness, weights=(1.0, -1.0, -1.0,))  # 三个目标，一个最大化两个最小化
+        creator.create("Individual", list, fitness=creator.FitnessMax)
+
+        toolbox = base.Toolbox()
+
+        # 结构初始化器
+        toolbox.register("individual", tools.initRepeat, creator.Individual, self.generate_individual, n=3)
+        toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+        toolbox.register("evaluate", self.evaluate)
+
+        # 注册遗传算法所需的操作
+        toolbox.register("mate", tools.cxTwoPoint)
+        toolbox.register("mutate", tools.mutUniformInt, low=1, up=100, indpb=0.2)
+        toolbox.register("select", tools.selTournament, tournsize=3)
+        # 初始化种群
+        population = toolbox.population(n=pop_size)
+
+        # 运行遗传算法
+        algorithms.eaMuPlusLambda(population, toolbox, mu=pop_size, lambda_=pop_size * 2, cxpb=cxpb, mutpb=mutpb,
+                                  ngen=ngen, stats=None, halloffame=None)
+
+        # 输出结果
+        best_individual = tools.selBest(population, k=1)
+        print(best_individual)
+        optimized_sheets = best_individual[0]  # 获取最优解
+
+        # 将优化后的结果输出
+        # for i, sheet in enumerate(optimized_sheets):
+        #     sheet.to_excel(f'optimized_sheet_{i + 1}.xlsx', index=False)
+        return optimized_sheets
 
 
 class AutoLayout:
@@ -53,6 +200,7 @@ class AutoLayout:
         self.logger = log(os.path.basename(f'{path}.txt'))
         self.return_log = list()
         self.no_assign_data = list()
+        self.need_cols = self.read_cols()
 
     def read_excel(self):
         """
@@ -99,7 +247,8 @@ class AutoLayout:
             if 'nextera' in library_data['classification'].lower():
                 self.chip_speciallib_nextera_size[chipname] += library_data['size']
 
-    def count_barcode_radio(self, data):
+    @staticmethod
+    def count_barcode_radio(data):
         df = pd.DataFrame(data)
         ratio_sites = dict()
         is_not_balance_list = []
@@ -116,11 +265,11 @@ class AutoLayout:
             col_df = barcode_df.groupby(column).agg({'data_needed': 'sum'})
             # 去掉N计数
             if 'N' in col_df.index:
-                base_N_size = col_df.loc['N', 'data_needed']
+                base_n_size = col_df.loc['N', 'data_needed']
                 col_df = col_df.drop('N')
             else:
-                base_N_size = 0
+                base_n_size = 0
-            col_df['ratio'] = (col_df['data_needed']) / (total - base_N_size)
+            col_df['ratio'] = (col_df['data_needed']) / (total - base_n_size)
             ratio = col_df['ratio'].to_dict()
             ratio_sites[i] = ratio
             A, B, C, D, E, F = list(), list(), list(), list(), list(), list()
@@ -195,6 +344,12 @@ class AutoLayout:
         res = pd.concat([df, newdf])
         return res.reset_index()
 
+    @staticmethod
+    def read_cols():
+        df = pd.read_excel(os.path.join(basedir, 'rule', 'columns.xlsx'))
+        cols = list(df['cols'].values)
+        return cols
+
     def use_rule(self, chipname, classfication):
         may_classfic = set(self.rule[self.rule['c1'] == classfication]['c2'])
         if self.chip_customer[chipname].intersection(may_classfic):
@@ -204,7 +359,7 @@ class AutoLayout:
     def judge_data(self, chipname, library_data):
         size = library_data['size']
         # customer = library_data['customer']
-        library = library_data['library']
+        # library = library_data['library']
         classification = library_data['classification']
         is_balance_lib = library_data['is_balance_lib']
 
@@ -269,12 +424,13 @@ 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 = ['status', '#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', '备注',
+        #             'classification', 'priority', 'time', '拆分方式', 'barcode', 'is_balance_lib', '备注',
-                    'TIPS1', 'TIPS2', 'TIPS3'
+        #             'TIPS1', 'TIPS2', 'TIPS3'
-                    ]
+        #             ]
+        self.need_cols = self.read_cols()
         get_col = set(ori_library_df.columns)
-        unhave_col = set(need_col) - get_col
+        unhave_col = set(self.need_cols) - get_col
 
         if unhave_col:
             unhave_fom = '; '.join(unhave_col)
@@ -291,10 +447,10 @@ class AutoLayout:
         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')
+        # need_col.append('note')
 
         no_ori_data = ori_library_df[~(numeric_mask & time_mask) | status_mask]
-        no_ori_data.loc[:, 'time'] = no_ori_data['time'].apply(format_date)
+
         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
@@ -332,7 +488,7 @@ class AutoLayout:
                 level=library_df['level'].values[0],
                 customer=library_df['customer'].values[0],
                 classification=library_df['classification'].values[0],
-                data=library_df[need_col].to_dict('records')
+                data=library_df[self.need_cols].to_dict('records')
             ))
 
             if flag:
@@ -346,9 +502,9 @@ class AutoLayout:
                     level=library_df['level'].values[0],
                     customer=library_df['customer'].values[0],
                     classification=library_df['classification'].values[0],
-                    data=library_df[need_col].to_dict('records')
+                    data=library_df[self.need_cols].to_dict('records')
                 ))
-        ori_sort_data = sorted(ori_library_data, key=lambda x: (x['level'], x['time']))
+        ori_sort_data = sorted(ori_library_data, key=lambda x: (x['level'], x['time'], -x['size']))
 
         i = 0
         while ori_sort_data:
@@ -397,6 +553,9 @@ class AutoLayout:
         outputpath = os.path.join(self.output, 'result', outputname)
         writer = pd.ExcelWriter(outputpath)
 
+        # ga = Ga(sheets=self.index_assignments)
+        # self.index_assignments = ga.run()
+
         chip_loc = 1
         librarynum = 0
         for chip_idx, chip_assignments in self.index_assignments.items():
@@ -415,9 +574,23 @@ class AutoLayout:
             else:
                 addname = ''
             self.dec_barcode_radio(chip_idx)
-            df.to_excel(writer, sheet_name=addname + chip_idx, index=False)
+            chipname = addname + chip_idx
+
+            sum_list = list()
+            for library, library_df in df.groupby('#library'):
+                sum_list.append(dict(
+                    二次拆分=library,
+                    客户=library_df['customer'].values[0],
+                    类型=library_df['classification'].values[0],
+                    打折前=library_df['data_needed'].sum()
+                ))
+            df_sum = pd.DataFrame(sum_list)
+            res_df = pd.concat([df, df_sum], axis=1)
+            res_df.to_excel(writer, sheet_name=chipname, index=False)
             chip_loc += 1
         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)
+        no_assign_df = no_assign_df[self.need_cols]
         no_assign_df.to_excel(writer, sheet_name='未测', index=False)
         if self.return_log:
             pd.DataFrame(self.return_log).to_excel(writer, sheet_name='log', index=False)