添加 i5 i7
parent
5ed962f9c3
commit
29a651f874
135
tools/t7.py
135
tools/t7.py
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@ -33,6 +33,7 @@ class AutoLayout:
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# 芯片barcode
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self.chip_barcode_recode = defaultdict(set)
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self.chip_barcodei7_recode = defaultdict(set)
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self.chip_barcodei5_recode = defaultdict(set)
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# 芯片原始数据读取
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self.ori_data = self.read_excel()
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# 当前锚芯片
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@ -64,15 +65,53 @@ class AutoLayout:
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self.is_use_balance = is_use_balance
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self.is_use_max = is_use_max
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def count_barcode_radio(self, data, maxt=False):
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@staticmethod
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def read_cols():
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df = pd.read_excel(os.path.join(basedir, 'rule', 'columns.xlsx'))
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cols = list(df['cols'].values)
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return cols
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def read_excel(self):
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"""
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原始数据处理
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:return:
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"""
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merge = pd.read_excel(self.path, None)
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ori_data = dict()
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for name, sheet in merge.items():
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sheet.fillna('', inplace=True)
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ori_data[name] = sheet.to_dict('records')
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return ori_data
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@staticmethod
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def read_rule():
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df = pd.read_excel(os.path.join(basedir, 'rule', 'exclusive_classfication.xlsx'))
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newdf = pd.DataFrame()
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newdf['c1'] = df['c2']
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newdf['c2'] = df['c1']
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res = pd.concat([df, newdf])
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return res.reset_index()
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@staticmethod
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def read_rule_exclusive_customer():
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df = pd.read_excel(os.path.join(basedir, 'rule', 'exclusive_customer.xlsx'))
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newdf = pd.DataFrame()
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newdf['customer1'] = df['customer2']
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newdf['customer2'] = df['customer1']
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res = pd.concat([df, newdf])
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return res.reset_index()
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def count_barcode_radio(self, data, maxt=''):
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df = pd.DataFrame(data)
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ratio_sites = dict()
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is_not_balance_list = []
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if df.empty:
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return ratio_sites, is_not_balance_list
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s, e = 0, 16
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if maxt:
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if maxt == 'i7':
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s, e = 8, 16
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if maxt == 'i5':
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s, e = 0, 8
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num = e - s
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df['barcode'] = df['barcode'].str.slice(s, e)
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barcode_df = pd.DataFrame(df['barcode'].str.split('', expand=True).iloc[:, 1:-1].values,
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@ -161,42 +200,6 @@ class AutoLayout:
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else:
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return 100000
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@staticmethod
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def read_rule():
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df = pd.read_excel(os.path.join(basedir, 'rule', 'exclusive_classfication.xlsx'))
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newdf = pd.DataFrame()
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newdf['c1'] = df['c2']
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newdf['c2'] = df['c1']
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res = pd.concat([df, newdf])
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return res.reset_index()
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@staticmethod
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def read_rule_exclusive_customer():
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df = pd.read_excel(os.path.join(basedir, 'rule', 'exclusive_customer.xlsx'))
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newdf = pd.DataFrame()
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newdf['customer1'] = df['customer2']
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newdf['customer2'] = df['customer1']
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res = pd.concat([df, newdf])
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return res.reset_index()
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@staticmethod
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def read_cols():
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df = pd.read_excel(os.path.join(basedir, 'rule', 'columns.xlsx'))
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cols = list(df['cols'].values)
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return cols
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def read_excel(self):
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"""
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原始数据处理
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:return:
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"""
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merge = pd.read_excel(self.path, None)
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ori_data = dict()
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for name, sheet in merge.items():
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sheet.fillna('', inplace=True)
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ori_data[name] = sheet.to_dict('records')
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return ori_data
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def combinations_same_barcode(self):
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"""
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barcode 有重复的极致样本 进行排列组合，汇集成新的可能性
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@ -250,6 +253,8 @@ class AutoLayout:
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"""
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self.index_assignments[chipname].extend(library_data['data'])
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self.chip_barcode_recode[chipname].update({item['barcode'] for item in library_data['data']})
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self.chip_barcodei7_recode[chipname].update({item['i7'] for item in library_data['data']})
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self.chip_barcodei5_recode[chipname].update({item['i5'] for item in library_data['data']})
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self.chip_customer[chipname].add(library_data['customer'])
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self.chip_classification[chipname].add(library_data['classification'])
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@ -280,7 +285,7 @@ class AutoLayout:
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if library_data['is_balance_lib'] == '甲基化':
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self.chip_methylib_size[chipname] += library_data['size']
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if 'nextera' in library_data['classification'].lower():
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self.chip_speciallib_huada_size[chipname] += library_data['size']
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self.chip_speciallib_nextera_size[chipname] += library_data['size']
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if '华大' in library_data['classification']:
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self.chip_speciallib_huada_size[chipname] += library_data['size']
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@ -297,7 +302,7 @@ class AutoLayout:
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return True
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return False
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def judge_data(self, chipname, library_data, max_barcode=False):
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def judge_data(self, chipname, library_data, max_barcode='all'):
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"""
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约束条件
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"""
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@ -362,17 +367,23 @@ class AutoLayout:
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use_huada = False
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# 开启i5或者i7
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if max_barcode:
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if max_barcode != 'all':
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base_balance = True
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# 开启i7:
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notrepeatbarcode = True
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if self.chip_barcodei7_recode[chipname].intersection({item['i7'] for item in library_data['data']}):
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if self.chip_barcodei7_recode[chipname].intersection({item['i7'] for item in library_data['data']}) and max_barcode == 'i7':
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notrepeatbarcode = False
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if self.chip_barcodei5_recode[chipname].intersection({item['i5'] for item in library_data['data']}) and max_barcode == 'i5':
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notrepeatbarcode = False
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#是个N的取消
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if ('N' * 8 in {item['i5'] for item in library_data['data']}) and max_barcode == 'i5':
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notrepeatbarcode = False
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if ('N' * 8 in {item['i7'] for item in library_data['data']}) and max_barcode == 'i7':
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notrepeatbarcode = False
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if self.chip_size[chipname] > 900:
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current_data = copy.deepcopy(self.index_assignments[chipname])
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new_data = library_data['data']
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current_data.extend(new_data)
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ratio_sites, is_not_balance_list = self.count_barcode_radio(current_data, maxt=True)
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ratio_sites, is_not_balance_list = self.count_barcode_radio(current_data, maxt=max_barcode)
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if is_not_balance_list:
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base_balance = False
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@ -380,12 +391,12 @@ class AutoLayout:
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return True
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return False
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def add_loc_num(self):
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def add_loc_num(self, chipname):
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"""
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锚定芯片号增加
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"""
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# 有nextera, 华大文库 必须满足大于50G 到了芯片结算
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chipname = f'chip{self.loc_chip_num}'
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# chipname = f'chip{self.loc_chip_num}'
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nextera_size = self.chip_speciallib_nextera_size[chipname]
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huada_size = self.chip_speciallib_huada_size[chipname]
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flag = True
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@ -409,7 +420,7 @@ class AutoLayout:
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huada_barcode = set()
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no_huada_data = list()
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for libdata in self.index_assignments[chipname]:
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if libdata['classification'] != '华大':
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if '华大' not in libdata['classification']:
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no_huada_data.append(libdata)
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else:
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self.no_assign_data.append(libdata)
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@ -538,14 +549,14 @@ class AutoLayout:
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break
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j += 1
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else:
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self.add_loc_num()
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self.add_loc_num(chipname)
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if self.chip_size[chipname] > self.data_limit:
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self.add_loc_num()
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self.add_loc_num(chipname)
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def assign_again(self):
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def assign_again_size(self, max_barcode='all'):
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"""
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剩余的数据，开放i5或者i7再排下
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剩余的数据
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"""
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left_data = list()
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no_need_chipname = list()
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@ -555,7 +566,6 @@ class AutoLayout:
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df = pd.DataFrame(chip_assignments)
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if df['data_needed'].sum() < 1700:
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left_data.extend(chip_assignments)
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# del self.index_assignments[chip_idx]
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no_need_chipname.append(chip_idx)
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for chip_idx in no_need_chipname:
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del self.index_assignments[chip_idx]
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@ -576,11 +586,11 @@ class AutoLayout:
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data=library_df[self.need_cols].to_dict('records')
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))
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ori_lib_data = sorted(ori_lib_data, key=lambda x: (x['level'], -x['size']))
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ori_lib_data = sorted(ori_lib_data, key=lambda x: (x['level'], x['time'], -x['size']))
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self.loc_chip_num = 100
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while ori_lib_data:
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library_data = ori_lib_data[0]
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chipname = f'chipB{self.loc_chip_num}'
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chipname = f'chipB{self.loc_chip_num}_{max_barcode}' if max_barcode != 'all' else f'chipB{self.loc_chip_num}'
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# 空白芯片直接添加
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if chipname not in self.index_assignments:
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@ -589,22 +599,22 @@ class AutoLayout:
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continue
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# 判断条件
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if self.judge_data(chipname, library_data, max_barcode=True):
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if self.judge_data(chipname, library_data, max_barcode=max_barcode):
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self.add_new_data(chipname, library_data, newer=False)
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ori_lib_data.remove(library_data)
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else:
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for j in range(len(ori_lib_data)):
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newlibrary_data = ori_lib_data[j]
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if self.judge_data(chipname, newlibrary_data, max_barcode=True):
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if self.judge_data(chipname, newlibrary_data, max_barcode=max_barcode):
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ori_lib_data.remove(newlibrary_data)
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self.add_new_data(chipname, newlibrary_data, newer=False)
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break
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j += 1
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else:
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self.loc_chip_num += 1
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self.add_loc_num(chipname)
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if self.chip_size[chipname] > self.data_limit:
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self.loc_chip_num += 1
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self.add_loc_num(chipname)
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def run(self):
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# print('# 测试代码')
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@ -612,7 +622,10 @@ class AutoLayout:
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# self.assign_again()
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try:
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self.assign_samples()
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self.assign_again()
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self.assign_again_size()
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# self.assign_again_size(max_barcode='i7')
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# self.assign_again_size(max_barcode='i5')
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# self.assign_again_size()
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except Exception as e:
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self.return_log.append(f'T7排样出错， 请联系！{e}')
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self.index_assignments = {}
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@ -633,8 +646,8 @@ class AutoLayout:
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else:
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addname = ''
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other_name = ''
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if 'chipB' in chip_idx and df['barcode'].duplicated().any():
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other_name = '_i7'
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# if 'chipB' in chip_idx and df['barcode'].duplicated().any():
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# other_name = '_i7'
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if df['data_needed'].sum() < 1600 and not addname:
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df['note'] = '排样数据量不足1600G'
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