From 6225342e331688bb7c85517c22d5cb5a88da6a9f Mon Sep 17 00:00:00 2001 From: chaopower Date: Tue, 12 Mar 2024 14:58:32 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8D=E5=B9=B3=E8=A1=A1=E6=96=87=E5=BA=93?= =?UTF-8?q?=E5=A4=A7=E4=BA=8E250G=20=E6=8B=86=E5=88=86=E5=A4=84=E7=90=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tools/t7.py | 70 ++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 58 insertions(+), 12 deletions(-) diff --git a/tools/t7.py b/tools/t7.py index 8ac0243..12427e2 100644 --- a/tools/t7.py +++ b/tools/t7.py @@ -25,21 +25,26 @@ class AutoLayout: self.librarynum = int(librarynum) self.data_limit = data_limit + # 芯片原始数据读取 + self.ori_data = self.read_excel() + + # 记录所有的排好的芯片数据 self.index_assignments = defaultdict(list) - # 芯片数量量大小 + + # 记录每个芯片数量大小 self.chip_size = dict() - # 芯片是否极致 - self.chip_type = dict() - # 芯片barcode + + # 记录芯片barcode, i7, i5 barcode信息 self.chip_barcode_recode = defaultdict(set) self.chip_barcodei7_recode = defaultdict(set) self.chip_barcodei5_recode = defaultdict(set) - # 芯片原始数据读取 - self.ori_data = self.read_excel() + # 当前锚芯片 self.loc_chip_num = 1 + # 芯片客户 self.chip_customer = defaultdict(set) + # 文库 self.chip_classification = defaultdict(set) self.rule = self.read_rule() @@ -55,16 +60,21 @@ class AutoLayout: self.chip_speciallib_nextera_size = dict() # 华大 文库 self.chip_speciallib_huada_size = dict() + # 排序好的文库数据 + self.ori_lib_data = list() self.logger = log(os.path.basename(f'{path}.txt')) self.return_log = list() self.no_assign_data = list() - self.ori_lib_data = list() + self.need_cols = self.read_cols() self.is_use_balance = is_use_balance self.is_use_max = is_use_max + # 记录拆分的不平衡文库 + self.split_lib = set() + @staticmethod def read_cols(): df = pd.read_excel(os.path.join(basedir, 'rule', 'columns.xlsx')) @@ -252,6 +262,7 @@ class AutoLayout: :return: """ self.index_assignments[chipname].extend(library_data['data']) + self.chip_barcode_recode[chipname].update({item['barcode'] for item in library_data['data']}) self.chip_barcodei7_recode[chipname].update({item['i7'] for item in library_data['data']}) self.chip_barcodei5_recode[chipname].update({item['i5'] for item in library_data['data']}) @@ -318,6 +329,7 @@ class AutoLayout: # barcode有重复 notrepeatbarcode = True + if self.chip_barcode_recode[chipname].intersection({item['barcode'] for item in library_data['data']}) or \ self.chip_barcode_recode[chipname].intersection( {'N' * 8 + item['i7'] for item in library_data['data']}) or \ @@ -370,14 +382,16 @@ class AutoLayout: if max_barcode != 'all': base_balance = True notrepeatbarcode = True - if self.chip_barcodei7_recode[chipname].intersection({item['i7'] for item in library_data['data']}) and max_barcode == 'i7': + if self.chip_barcodei7_recode[chipname].intersection( + {item['i7'] for item in library_data['data']}) and max_barcode == 'i7': notrepeatbarcode = False - if self.chip_barcodei5_recode[chipname].intersection({item['i5'] for item in library_data['data']}) and max_barcode == 'i5': + if self.chip_barcodei5_recode[chipname].intersection( + {item['i5'] for item in library_data['data']}) and max_barcode == 'i5': notrepeatbarcode = False - #是个N的取消 + # 是个N的取消 if ('N' * 8 in {item['i5'] for item in library_data['data']}) and max_barcode == 'i5': notrepeatbarcode = False - if ('N' * 8 in {item['i7'] for item in library_data['data']}) and max_barcode == 'i7': + if ('N' * 8 in {item['i7'] for item in library_data['data']}) and max_barcode == 'i7': notrepeatbarcode = False if self.chip_size[chipname] > 900: current_data = copy.deepcopy(self.index_assignments[chipname]) @@ -487,6 +501,7 @@ class AutoLayout: for library, library_df in ori_library_df.groupby('#library'): size = library_df['data_needed'].sum() + is_balance_lib = library_df['is_balance_lib'].values[0] # 文库内部有重复 if len(library_df['barcode'].values) > len(set(library_df['barcode'].values)): @@ -494,6 +509,30 @@ class AutoLayout: self.no_assign_data.extend(library_df.to_dict('records')) continue + # 不平衡文库 大于250G 的数据 先进行拆分 + if is_balance_lib == '否' and size > 250: + self.return_log.append(f'文库{library} 是不平衡文库, 数据为{size}, 大于250G, 已做拆分处理, 请注意！！！ ') + data_needed = library_df['data_needed'].copy() + for num in range(int(size), 0, -200): + addnum = 200 + if num <= 200: + addnum = num + library_df['data_needed'] = (addnum / size) * data_needed + + 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=1950, + customer=library_df['customer'].values[0], + classification=library_df['classification'].values[0], + data=library_df[self.need_cols].to_dict('records') + )) + self.split_lib.add(library) + continue + # 拆分处理 分为了2个大文库 if size > (self.data_limit) / 2: library_df['data_needed'] = library_df['data_needed'] / 2 @@ -574,13 +613,17 @@ class AutoLayout: 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'): + level = library_df['level'].values[0] + if library in self.split_lib: + level = 1950 + 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], + level=level, customer=library_df['customer'].values[0], classification=library_df['classification'].values[0], data=library_df[self.need_cols].to_dict('records') @@ -677,6 +720,9 @@ class AutoLayout: 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_not_balance = ','.join(set([lib for lib in no_assign_df['#library'] if lib in self.split_lib])) + if no_assign_df_not_balance: + self.return_log.append(f'文库{no_assign_df_not_balance}有做不平衡文库拆分处理，并且没有排完，请核查！') if not no_assign_df.empty: no_assign_df = no_assign_df[self.need_cols] no_assign_df.to_excel(writer, sheet_name='未测', index=False)