main
parent
6ba6b137dd
commit
d1a3778f1c
132
tools/t7.py
132
tools/t7.py
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@ -1,7 +1,7 @@
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import copy
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import os
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import time
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from collections import defaultdict
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from collections import defaultdict, Counter
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from datetime import datetime
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import pandas as pd
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@ -132,27 +132,27 @@ class AutoLayout:
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today_date = datetime.now()
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if 'nextera' in row['classification'].lower():
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return 10
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return 1000
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if '华大' in row['classification']:
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return 11
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return 1100
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if row['拆分方式'] == '极致周期' or '极致' in row['拆分方式']:
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return 20
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return 2000
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if '加急' in row['priority']:
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return 3000
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if '补测' in row['priority']:
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return 4000
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mytime = row['time']
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# 判断日期是之前的还是之后的
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if mytime < today_date:
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return 30
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if '加急' in row['priority']:
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return 40
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if '补测' in row['priority']:
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return 50
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return 5000
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else:
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return 1000
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return 100000
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@staticmethod
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def read_rule():
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@ -190,6 +190,49 @@ class AutoLayout:
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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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"""
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# 筛选有重复的行
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# same_barcode_data = [data for data in self.ori_lib_data if data['level'] == 1900]
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# same_barcode_sorted = sorted(same_barcode_data, key=lambda x: (-x['size']))
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#
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# same_barcode_dict = dict()
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# for index, data in enumerate(same_barcode_sorted):
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# same_barcode_dict[data['library']] = data['level'] + index + 1
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# correct_data = list()
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# for data in self.ori_lib_data:
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# if data in same_barcode_sorted:
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# data['level'] = same_barcode_dict[data['library']]
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# correct_data.append(data)
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# self.ori_lib_data = correct_data
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same_barcode_df = pd.DataFrame(
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[spdata for data in self.ori_lib_data if data['level'] == 1900 for spdata in data['data']])
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# 按照 'barcode' 列进行分组
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if same_barcode_df.empty:
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return
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grouped = same_barcode_df.groupby('barcode')
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# 获取具有重复的 'barcode' 分组
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duplicate_groups = grouped.filter(lambda x: len(x) > 1)
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# 提取这些分组，计算文库重复次数
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grouped_names = duplicate_groups.groupby('barcode')['#library'].apply(list).reset_index()
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random_list = list(set(tuple(sublst) for sublst in list(grouped_names['#library'])))
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new_lst = [spdata for data in random_list for spdata in data]
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counts = Counter(new_lst)
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correct_data = list()
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for data in self.ori_lib_data:
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if data['library'] in counts:
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data['level'] -= counts[data['library']]
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correct_data.append(data)
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self.ori_lib_data = correct_data
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def add_new_data(self, chipname, library_data, newer=True):
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"""
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增加新数据到已知芯片上
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@ -241,7 +284,8 @@ class AutoLayout:
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return False
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def use_rule_exclusive_customer(self, chipname, customer):
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may_classfic = set(self.rule_exclusive_customer[self.rule_exclusive_customer['customer1'] == customer]['customer2'])
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may_classfic = set(
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self.rule_exclusive_customer[self.rule_exclusive_customer['customer1'] == customer]['customer2'])
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if self.chip_customer[chipname].intersection(may_classfic):
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return True
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return False
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@ -285,8 +329,9 @@ class AutoLayout:
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splibrary = False
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# 甲基化文库不能大于250G
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# 甲基化更改成100G
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spmethylibrary = True
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if is_balance_lib == '甲基化' and self.chip_methylib_size[chipname] + size > 250:
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if is_balance_lib == '甲基化' and self.chip_methylib_size[chipname] + size > 100:
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spmethylibrary = False
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# 不使用平衡文库
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@ -304,7 +349,12 @@ class AutoLayout:
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if is_not_balance_list:
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base_balance = False
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if sizelimit and notrepeatbarcode and exclusive_classific and exclusive_customer and splibrary and base_balance and spmethylibrary:
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# 华大的文库不能超过限制的一半
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use_huada = True
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if self.chip_speciallib_huada_size[chipname] > self.data_limit / 2:
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use_huada = False
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if sizelimit and notrepeatbarcode and exclusive_classific and exclusive_customer and splibrary and base_balance and spmethylibrary and use_huada:
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return True
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return False
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@ -312,7 +362,7 @@ class AutoLayout:
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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
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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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nextera_size = self.chip_speciallib_nextera_size[chipname]
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huada_size = self.chip_speciallib_huada_size[chipname]
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@ -351,7 +401,7 @@ class AutoLayout:
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self.loc_chip_num += 1
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def assign_samples(self):
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ori_library_data = list()
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# ori_library_data = list()
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if '未测' not in self.ori_data.keys():
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raise UserWarning('提供excel没有 未测 sheet ,请核查！')
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@ -396,9 +446,10 @@ class AutoLayout:
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ori_library_df['time'] = pd.to_datetime(ori_library_df['time'], errors='coerce')
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ori_library_df['level'] = ori_library_df.apply(self.level, axis=1)
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# 极致客户有重复的，把等级调到0，防止放到了最后，到了未测里
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ori_library_df.loc[
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(ori_library_df.duplicated(subset='barcode')) & (ori_library_df['level'] == 20), 'level'] = 19
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# 极致客户有重复的，把等级调到19，防止放到了最后，到了未测里
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must_lib_df = ori_library_df[ori_library_df['level'] == 2000]
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must_lib = set(must_lib_df[must_lib_df.duplicated(subset='barcode', keep=False)]['#library'].to_list())
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ori_library_df.loc[ori_library_df['#library'].isin(must_lib), 'level'] = 1900
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for library, library_df in ori_library_df.groupby('#library'):
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@ -410,13 +461,23 @@ class AutoLayout:
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self.no_assign_data.extend(library_df.to_dict('records'))
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continue
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# 拆分处理
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flag = False
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# 拆分处理 分为了2个大文库
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if size > (self.data_limit) / 2:
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library_df['data_needed'] = library_df['data_needed'] / 2
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flag = True
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self.return_log.append(f'文库{library} 已做拆分处理, 请注意！！！ ')
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self.ori_lib_data.append(dict(
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library=library,
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is_balance_lib=library_df['is_balance_lib'].values[0],
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size=library_df['data_needed'].sum(),
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split_method=library_df['拆分方式'].values[0],
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time=library_df['time'].values[0],
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level=library_df['level'].values[0],
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customer=library_df['customer'].values[0],
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classification=library_df['classification'].values[0],
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data=library_df[self.need_cols].to_dict('records')
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))
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ori_library_data.append(dict(
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self.ori_lib_data.append(dict(
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library=library,
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is_balance_lib=library_df['is_balance_lib'].values[0],
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size=library_df['data_needed'].sum(),
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@ -428,23 +489,9 @@ class AutoLayout:
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data=library_df[self.need_cols].to_dict('records')
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))
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# 拆分对半
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if flag:
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self.return_log.append(f'文库{library} 已做拆分处理, 请注意！！！ ')
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ori_library_data.append(dict(
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library=library,
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is_balance_lib=library_df['is_balance_lib'].values[0],
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size=library_df['data_needed'].sum(),
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split_method=library_df['拆分方式'].values[0],
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time=library_df['time'].values[0],
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level=library_df['level'].values[0],
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customer=library_df['customer'].values[0],
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classification=library_df['classification'].values[0],
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data=library_df[self.need_cols].to_dict('records')
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))
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self.ori_lib_data = sorted(ori_library_data, key=lambda x: (x['level'], x['time']))
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self.combinations_same_barcode()
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self.ori_lib_data = sorted(self.ori_lib_data, key=lambda x: (x['level'], x['time']))
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# self.ori_lib_data = ori_sort_data
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while self.ori_lib_data:
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library_data = self.ori_lib_data[0]
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chipname = f'chip{self.loc_chip_num}'
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@ -474,6 +521,7 @@ class AutoLayout:
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self.add_loc_num()
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def run(self):
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# print('# 测试代码')
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# self.assign_samples()
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try:
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self.assign_samples()
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@ -522,7 +570,7 @@ class AutoLayout:
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res_df = pd.concat([df, df_sum], axis=1)
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res_df.to_excel(writer, sheet_name=chipname, index=False)
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chip_loc += 1
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# self.no_assign_data.extend(self.diffic_assign_data)
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no_assign_df = pd.DataFrame(self.no_assign_data)
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no_assign_df = no_assign_df.applymap(lambda x: format_date(x) if isinstance(x, pd.Timestamp) else x)
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if not no_assign_df.empty:
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@ -543,5 +591,3 @@ if __name__ == '__main__':
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end_time = time.time()
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execution_time = end_time - start_time
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print(f"代码执行时间为：{execution_time} 秒")
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# server()
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