524 lines
21 KiB
Python
524 lines
21 KiB
Python
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 datetime import datetime
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import pandas as pd
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from tools.common import basedir, log
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# 定义一个格式化函数
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def format_date(date):
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return date.strftime('%Y-%m-%d')
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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, librarynum, is_use_balance=1, is_use_max=0, output=basedir, data_limit=1750):
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self.path = path
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self.output = output
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self.librarynum = int(librarynum)
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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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# 文库
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self.chip_classification = defaultdict(set)
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self.rule = self.read_rule()
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# 不平衡文库
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self.chip_speciallib_size = dict()
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# 甲基化文库
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self.chip_methylib_size = dict()
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# Nextera 文库大小
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self.chip_speciallib_nextera_size = dict()
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# 华大 文库
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self.chip_speciallib_huada_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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self.no_assign_data = list()
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self.ori_lib_data = list()
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self.need_cols = self.read_cols()
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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):
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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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df['barcode'] = df['barcode'].str.slice(0, 16)
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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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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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ratio = col_df['ratio'].to_dict()
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ratio_sites[i] = ratio
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A, B, C, D, E, F, G = list(), list(), list(), list(), list(), list(), list()
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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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if ratio[decbase] >= 0.6:
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A.append(decbase)
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if 0.2 <= ratio[decbase] < 0.6:
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B.append(decbase)
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if 0.15 <= ratio[decbase] < 0.2:
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C.append(decbase)
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if 0.1 <= ratio[decbase] < 0.15:
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D.append(decbase)
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if 0.08 <= ratio[decbase] < 0.1:
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E.append(decbase)
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if ratio[decbase] < 0.08:
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F.append(decbase)
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# 新增一个碱基可行规则
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if 0.125 <= ratio[decbase] <= 0.625:
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G.append(decbase)
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A_num, B_num, C_num, D_num, E_num, F_num, G_num = len(A), len(B), len(C), len(D), len(E), len(F), len(G)
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if not ((B_num + C_num + D_num == 4) or (F_num == 1 and (A_num + B_num) == 3) or (
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E_num == 1 and D_num == 1 and (A_num + B_num + C_num) == 2) or (
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E_num == 1 and (A_num + B_num + C_num) == 3) or (
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F_num == 1 and G_num == 3 and self.is_use_max)):
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is_not_balance_list.append(
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'第%s位置，算出结果为 %s' % (i, ratio)
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)
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return ratio_sites, is_not_balance_list
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def dec_barcode_radio(self, chipname):
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data = self.index_assignments[chipname]
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ratio_sites, is_not_balance_list = self.count_barcode_radio(data)
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if is_not_balance_list:
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desc = '\n'.join(is_not_balance_list)
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self.return_log.append(f'芯片{chipname}有碱基不平衡:\n{desc}')
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print(f'芯片{chipname}有碱基不平衡:\n{desc}')
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@staticmethod
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def level(row):
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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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if '华大' in row['classification']:
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return 11
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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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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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else:
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return 1000
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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_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 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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self.chip_customer[chipname].add(library_data['customer'])
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self.chip_classification[chipname].add(library_data['classification'])
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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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if library_data['is_balance_lib'] == '否':
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self.chip_speciallib_size[chipname] = library_data['size']
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elif library_data['is_balance_lib'] == '甲基化':
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self.chip_methylib_size[chipname] = library_data['size']
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else:
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self.chip_speciallib_size[chipname] = 0
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self.chip_methylib_size[chipname] = 0
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if 'nextera' in library_data['classification'].lower():
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self.chip_speciallib_nextera_size[chipname] = library_data['size']
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else:
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self.chip_speciallib_nextera_size[chipname] = 0
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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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else:
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self.chip_speciallib_huada_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['is_balance_lib'] == '否':
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self.chip_speciallib_size[chipname] += library_data['size']
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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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if '华大' in library_data['classification']:
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self.chip_speciallib_huada_size[chipname] += library_data['size']
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def use_rule(self, chipname, classfication):
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may_classfic = set(self.rule[self.rule['c1'] == classfication]['c2'])
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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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def judge_data(self, chipname, library_data):
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"""
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约束条件
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"""
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size = library_data['size']
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classification = library_data['classification']
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is_balance_lib = library_data['is_balance_lib']
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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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# 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']}) or \
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self.chip_barcode_recode[chipname].intersection(
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{'N' * 8 + item['i7'] for item in library_data['data']}):
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notrepeatbarcode = False
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# 互斥的文库
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exclusive_classific = True
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if self.use_rule(chipname, classification):
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exclusive_classific = False
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# 不平衡文库大于250G 不能添加
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splibrary = True
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if is_balance_lib == '否' and self.chip_speciallib_size[chipname] + size > 250:
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splibrary = False
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# 甲基化文库不能大于250G
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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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spmethylibrary = False
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# 不使用平衡文库
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if not self.is_use_balance:
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splibrary = True
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spmethylibrary = True
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# 碱基不平衡不过不添加，保证前面的数据, 在数据达到1200G的时候开始
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base_balance = True
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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)
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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 splibrary and base_balance and spmethylibrary:
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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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"""
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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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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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if 0 < nextera_size < 50:
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# 有nextera文库，但是不满足50G 去除
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nextary_barcode = set()
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no_nextary_data = list()
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for libdata in self.index_assignments[chipname]:
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if libdata['classification'].lower() != 'nextera':
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no_nextary_data.append(libdata)
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else:
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self.no_assign_data.append(libdata)
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nextary_barcode.update(libdata['barcode'])
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self.index_assignments[chipname] = no_nextary_data
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self.chip_barcode_recode[chipname] -= nextary_barcode
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self.chip_speciallib_nextera_size[chipname] = 0
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self.chip_size[chipname] -= nextera_size
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flag = False
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if 0 < huada_size < 50:
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# 有华大文库，但是不满足50G 去除
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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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no_huada_data.append(libdata)
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else:
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self.no_assign_data.append(libdata)
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huada_barcode.update(libdata['barcode'])
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self.index_assignments[chipname] = no_huada_data
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self.chip_barcode_recode[chipname] -= huada_barcode
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self.chip_speciallib_huada_size[chipname] = 0
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self.chip_size[chipname] -= huada_size
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flag = False
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if flag:
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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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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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ori_library_df = pd.DataFrame(self.ori_data['未测'])
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# 检查提供excel 是否有必须表头
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get_col = set(ori_library_df.columns)
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unhave_col = set(self.need_cols) - get_col
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if unhave_col:
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unhave_from = '; '.join(unhave_col)
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raise UserWarning(f'未测表里没有{unhave_from} 表头,请核查！')
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# 数据标准格式
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numeric_mask = pd.to_numeric(ori_library_df['data_needed'], errors='coerce').notna()
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time_mask = pd.to_datetime(ori_library_df['time'], errors='coerce').notna()
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# 添加处理status列的逻辑
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status_mask = ori_library_df['status'] == '暂不排样'
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# 非正常barcode
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barcode_mask = ori_library_df['barcode'].str.len() != 16
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ori_library_df['note'] = ''
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ori_library_df.loc[~numeric_mask, 'note'] = 'data_needed 列非数字'
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ori_library_df.loc[~time_mask, 'note'] = 'time 列非日期'
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ori_library_df.loc[status_mask, 'note'] = '暂不排样'
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ori_library_df.loc[barcode_mask, 'note'] = '非16位barcode'
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no_ori_data = ori_library_df[~(numeric_mask & time_mask) | status_mask | barcode_mask]
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self.no_assign_data.extend(no_ori_data.to_dict('records'))
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# 使用布尔索引筛选出不是数字和非日期的行，并且不是暂不排样的行
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ori_library_df = ori_library_df[(numeric_mask & time_mask) & ~status_mask & ~barcode_mask]
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# 某个客户的检测的数据超过1个T就单独处理
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# summary = ori_library_df.groupby('customer').agg({'data_needed': 'sum'})
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# print(summary)
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# 时间格式化
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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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for library, library_df in ori_library_df.groupby('#library'):
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size = library_df['data_needed'].sum()
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# 文库内部有重复
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if len(library_df['barcode'].values) > len(set(library_df['barcode'].values)):
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library_df['note'] = '文库内部有重复'
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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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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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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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# 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.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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# 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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self.ori_lib_data.remove(library_data)
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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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self.ori_lib_data.remove(library_data)
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else:
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for j in range(len(self.ori_lib_data)):
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newlibrary_data = self.ori_lib_data[j]
|
||
if self.judge_data(chipname, newlibrary_data):
|
||
self.ori_lib_data.remove(newlibrary_data)
|
||
self.add_new_data(chipname, newlibrary_data, newer=False)
|
||
break
|
||
j += 1
|
||
else:
|
||
self.add_loc_num()
|
||
|
||
if self.chip_size[chipname] > self.data_limit:
|
||
self.add_loc_num()
|
||
|
||
def run(self):
|
||
# self.assign_samples()
|
||
try:
|
||
self.assign_samples()
|
||
except Exception as e:
|
||
self.return_log.append(f'T7排样出错， 请联系！{e}')
|
||
self.index_assignments = {}
|
||
outputname = 'assignments_%s_%s' % (datetime.now().strftime("%m%d%H%M"), os.path.basename(self.path))
|
||
outputpath = os.path.join(self.output, 'result', outputname)
|
||
writer = pd.ExcelWriter(outputpath)
|
||
|
||
chip_loc = 1
|
||
librarynum = 0
|
||
for chip_idx, chip_assignments in self.index_assignments.items():
|
||
if not chip_assignments:
|
||
continue
|
||
df = pd.DataFrame(chip_assignments)
|
||
df['time'] = df['time'].dt.strftime('%Y-%m-%d')
|
||
|
||
if [method for method in df['拆分方式'].values if '极致' in method]:
|
||
addname = 'X'
|
||
else:
|
||
addname = ''
|
||
|
||
if df['data_needed'].sum() < 1600 and not addname:
|
||
df['note'] = '排样数据量不足1600G'
|
||
self.no_assign_data.extend(df.to_dict('records'))
|
||
continue
|
||
if librarynum > self.librarynum:
|
||
df['note'] = '排样管数超标'
|
||
self.no_assign_data.extend(df.to_dict('records'))
|
||
continue
|
||
librarynum += len(set(df['#library'].values))
|
||
|
||
self.dec_barcode_radio(chip_idx)
|
||
chipname = addname + chip_idx
|
||
|
||
sum_list = list()
|
||
for library, library_df in df.groupby('#library'):
|
||
sum_list.append(dict(
|
||
二次拆分=library,
|
||
客户=library_df['customer'].values[0],
|
||
类型=library_df['classification'].values[0],
|
||
打折前=library_df['data_needed'].sum()
|
||
))
|
||
df_sum = pd.DataFrame(sum_list)
|
||
res_df = pd.concat([df, df_sum], axis=1)
|
||
res_df.to_excel(writer, sheet_name=chipname, index=False)
|
||
chip_loc += 1
|
||
# self.no_assign_data.extend(self.diffic_assign_data)
|
||
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)
|
||
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)
|
||
if self.return_log:
|
||
pd.DataFrame(self.return_log).to_excel(writer, sheet_name='log', index=False)
|
||
writer.close()
|
||
return outputpath
|
||
|
||
|
||
if __name__ == '__main__':
|
||
start_time = time.time()
|
||
filepath = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'example', 'input排样表.xlsx')
|
||
output_file = ''
|
||
layout = AutoLayout(filepath, output_file)
|
||
layout.run()
|
||
end_time = time.time()
|
||
execution_time = end_time - start_time
|
||
print(f"代码执行时间为：{execution_time} 秒")
|
||
|
||
# server()
|