304 lines
12 KiB
Python
304 lines
12 KiB
Python
import pandas as pd
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from collections import defaultdict
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from datetime import datetime
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import time
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import os
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from .common import basedir, log
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class AutoLayout:
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"""
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自动化派样
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"""
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def __init__(self, path, output=basedir, data_limit=1520):
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self.path = path
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self.output = output
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self.data_limit = data_limit
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self.index_assignments = defaultdict(list)
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# 芯片数量量大小
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self.chip_size = dict()
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# 芯片是否极致
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self.chip_type = dict()
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# 芯片barcode
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self.chip_barcode_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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self.loc_chip_num = 1
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# 芯片客户
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self.chip_customer = defaultdict(set)
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self.rule = self.read_rule()
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# 甲基化文库不大于200,WGBS文库不大于200G
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self.chip_speciallib_size = dict()
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self.logger = log(os.path.basename(f'{path}.txt'))
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self.return_log = list()
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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 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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:param chipname:
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:param library_data:
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:param newer:
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:return:
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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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if newer:
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self.chip_size[chipname] = library_data['size']
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if library_data['classification'] in ['扩增子', '不平衡文库', '单细胞文库以及甲基化']:
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self.chip_speciallib_size[chipname] = library_data['size']
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else:
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self.chip_speciallib_size[chipname] = 0
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else:
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self.chip_size[chipname] += library_data['size']
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if library_data['classification'] in ['扩增子', '不平衡文库', '单细胞文库', '甲基化']:
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self.chip_speciallib_size[chipname] += library_data['size']
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self.chip_customer[chipname].add(library_data['customer'])
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def add_new_chip(self, library_data):
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"""
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要新增到芯片上的数据
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:param library_data:
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:return:
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"""
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chip_num_tmp = self.loc_chip_num
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while True:
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chip_num_tmp += 1
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chipname_tmp = f'chip{chip_num_tmp}'
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library = library_data['library']
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if chipname_tmp not in self.index_assignments:
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self.logger.error(f'{library} {chipname_tmp} 常规添加')
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self.add_new_data(chipname_tmp, library_data)
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break
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else:
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is_same_barcode = self.chip_barcode_recode[chipname_tmp].intersection(
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{item['barcode'] for item in library_data['data']})
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# 没有从重复的index,并且也不互斥的
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if ((self.chip_size[chipname_tmp] + library_data['size']) > self.data_limit):
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self.logger.error(f'{library} {chipname_tmp} 文库相加大于设定限制')
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if ((self.chip_speciallib_size[chipname_tmp] + library_data['size']) >= 200):
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self.logger.error(f'{library} {chipname_tmp} 不平衡文库相加大于设定限制')
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if is_same_barcode:
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self.logger.error(f'{library} {chipname_tmp} 文库有barcode重复')
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if self.use_rule(chipname_tmp, library_data['customer']):
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self.logger.error(f'{library} {chipname_tmp} 有互斥单位')
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if ((self.chip_size[chipname_tmp] + library_data['size']) <= self.data_limit) \
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and ((self.chip_speciallib_size[chipname_tmp] + library_data['size']) < 200) \
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and (not is_same_barcode) \
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and (not self.use_rule(chipname_tmp, library_data['customer'])):
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self.add_new_data(chipname_tmp, library_data, newer=False)
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break
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def dec_barcode_radio(self, chipname):
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data = self.index_assignments[chipname]
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df = pd.DataFrame(data)
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barcode_df = pd.DataFrame(df['barcode'].str.split('', expand=True).iloc[:, 1:-1].values,
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columns=['T' + str(x) for x in range(16)]).join(df['data_needed'])
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total = barcode_df['data_needed'].sum()
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is_not_balance_list = []
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for i in range(16):
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column = 'T' + str(i)
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col_df = barcode_df.groupby(column).agg({'data_needed': 'sum'})
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# 去掉N计数
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if 'N' in col_df.index:
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base_N_size = col_df.loc['N', 'data_needed']
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col_df = col_df.drop('N')
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else:
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base_N_size = 0
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col_df['ratio'] = (col_df['data_needed']) / (total - base_N_size)
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is_need_base = col_df.index[col_df['ratio'] < 0.088]
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need_base_list = list(is_need_base)
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ratio = col_df['ratio'].to_dict()
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for decbase in ['A', 'T', 'C', 'G']:
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if decbase not in ratio:
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ratio[decbase] = 0
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need_base_list.append(decbase)
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# 小于标准的base 是不是空的，空的说明都满足
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if need_base_list:
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is_not_balance_list.append(
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'%s 第%s位置, %s 有碱基不平衡，算出结果为 %s' % (chipname, i, need_base_list, ratio)
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)
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if len(is_not_balance_list) > 2:
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self.return_log.append('有碱基不平衡性！')
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self.return_log.extend(is_not_balance_list)
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print('有碱基不平衡性！\n', '\n'.join(is_not_balance_list))
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@staticmethod
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def level(row):
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if row['customer'] == '贞固':
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return 1
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if row['split_method'] == '极致周期':
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return 2
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# 医沐、清港泉、WES（自己建库）也是极致周期，
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if row['customer'] == '医沐' or row['customer'] == '清港泉':
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return 3
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# 赛福、桐树基因的文库尽量跟极致周期测人的样本排一起上机
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if row['customer'] == '赛福' or row['customer'] == '桐树基因':
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return 7
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if row['classification'] == 'Nextera':
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return 5
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if '华大' in row['classification']:
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return 6
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else:
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return 100
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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.xlsx'))
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newdf = pd.DataFrame()
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newdf['customer1'] = df['customer2']
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newdf['customer1'] = df['customer1']
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return pd.concat([df, newdf])
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def use_rule(self, chipname, customer):
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may_customer = set(self.rule[self.rule['customer1'] == customer]['customer2'])
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if self.chip_customer[chipname].intersection(may_customer):
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return True
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return False
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def judge_data(self, chipname, library_data):
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size = library_data['size']
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customer = library_data['customer']
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library = library_data['library']
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# 芯片大小不能超过设定限制
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sizelimit = True
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if self.chip_size[chipname] + size > self.data_limit:
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sizelimit = False
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self.logger.error(f'{library} {chipname} 文库相加大于设定限制')
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# barcode有重复
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notrepeatbarcode = True
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if self.chip_barcode_recode[chipname].intersection({item['barcode'] for item in library_data['data']}):
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notrepeatbarcode = False
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self.logger.error(f'{library} {chipname} 文库有barcode重复')
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# 互斥的客户
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exclusivecostom = True
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if self.use_rule(chipname, customer):
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exclusivecostom = False
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self.logger.error(f'{library} {chipname} 有互斥单位')
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# 不平衡文库大于200G 不能添加
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splibrary = True
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if library_data['classification'] in ['扩增子', '不平衡文库', '单细胞文库', '甲基化'] \
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and self.chip_speciallib_size[chipname] + size > 200:
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splibrary = False
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self.logger.error(f'{library} {chipname} 不平衡文库相加大于设定限制')
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if sizelimit and notrepeatbarcode and exclusivecostom and splibrary:
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return True
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return False
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def assign_samples(self):
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ori_library_data = list()
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ori_library_df = pd.DataFrame(self.ori_data['未测'])
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ori_library_df['level'] = ori_library_df.apply(self.level, axis=1)
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for library, library_df in ori_library_df.groupby('#library'):
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ori_library_data.append(dict(
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library=library,
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size=library_df['data_needed'].sum(),
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split_method=library_df['split_method'].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.to_dict('records')
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))
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ori_sort_data = sorted(ori_library_data, key=lambda x: (x['level'], -x['size'], x['time']))
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i = 0
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while ori_sort_data:
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library_data = ori_sort_data[0]
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chipname = f'chip{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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self.add_new_data(chipname, library_data)
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ori_sort_data.remove(library_data)
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i += 1
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continue
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# 判断条件
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if self.judge_data(chipname, library_data):
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self.add_new_data(chipname, library_data, newer=False)
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ori_sort_data.remove(library_data)
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i += 1
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else:
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for j in range(len(ori_sort_data)):
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newlibrary_data = ori_sort_data[j]
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if self.judge_data(chipname, newlibrary_data):
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ori_sort_data.remove(newlibrary_data)
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i += 1
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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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if self.chip_size[chipname] > 1500:
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self.loc_chip_num += 1
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def assign_again(self):
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pass
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def run(self):
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try:
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self.assign_samples()
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except Exception as e:
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self.return_log.append(f'排样出错， 请联系！{e}')
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self.index_assignments = {}
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outputname = 'assignments_%s_%s' % (datetime.now().strftime("%m%d%H%M"), os.path.basename(self.path))
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outputpath = os.path.join(self.output, 'result', outputname)
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writer = pd.ExcelWriter(outputpath)
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no_assign_data = list()
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for chip_idx, chip_assignments in self.index_assignments.items():
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self.dec_barcode_radio(chip_idx)
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df = pd.DataFrame(chip_assignments)
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if df['data_needed'].sum() < 1400:
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no_assign_data.extend(chip_assignments)
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continue
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if '极致周期' in df['split_method'].values:
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addname = 'X'
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else:
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addname = ''
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df.to_excel(writer, sheet_name=addname + chip_idx, index=False)
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pd.DataFrame(no_assign_data).to_excel(writer, sheet_name='未测', index=False)
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if self.return_log:
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pd.DataFrame(self.return_log).to_excel(writer, sheet_name='log', index=False)
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writer.close()
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return outputpath
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if __name__ == '__main__':
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start_time = time.time()
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excel_file = 'example/07031754_20230703.xlsx'
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output_file = ''
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layout = AutoLayout(excel_file, output_file)
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layout.run()
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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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