diff --git a/tools/t7.py b/tools/t7.py index b954eea..ac997f0 100644 --- a/tools/t7.py +++ b/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 - - df['barcode'] = df['barcode'].str.slice(0, 16) + s, e = 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'):