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HeuristicProcessor.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Nov 8 20:24:43 2023
@author: lacter
"""
import pandas as pd
import time
from progress.bar import Bar
import pyopenms
import math
import numpy as np
import bisect
import re
import os
from scipy.optimize import linear_sum_assignment
import warnings
'''---------'''
import sys
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
import os
from PyQt5 import QtWidgets,QtCore
import pathos
import multiprocessing as mp
import pickle
class HeuristicProcessorUI(QWidget):
def __init__(self):
super().__init__()
self.initUI()
# 设置标题
self.resize(600,700)
self.centerWindow()
# 设置参数字典
self.Data_params={}
self.Calculate_params={'MS1_Tor':0.000010,'smooth':5,'min_Int':9000,'Points':17}
def CloseEvent(self,event):
os._exit(0)
def initUI(self):
globallayout = QVBoxLayout()
# Data import
Data_import_Widget = QWidget()
Data_import_Layout = QGridLayout()
# Union
self.Label_SampleSelect = QLabel('Select data')
self.Label_RIISSelect = QLabel('Select Calibrants data')
self.Label_HeuristicListSelect = QLabel('Select heuristic list')
self.Label_Params_Select = QLabel('Set params')
self.TextBrowser_SampleSelect = QTableWidget()
self.TextBrowser_SampleSelect.setColumnCount(4)
self.TextBrowser_SampleSelect.setHorizontalHeaderLabels(['Name','Type','Group','Index'])
self.TextBrowser_SampleSelect.horizontalHeader().setSectionResizeMode(QHeaderView.Interactive)
self.TextBrowser_SampleSelect.horizontalHeader().setStretchLastSection(True)
self.TextBrowser_RIISSelect = QLabel('*')
self.TextBrowser_HeuristicListSelect = QLabel('*')
self.PushButton_SampleSelect = QPushButton('Select')
self.PushButton_RIISSelect = QPushButton('Select')
self.PushButton_HeuristicListSelect = QPushButton('Select')
self.PushButton_SampleSelect.setToolTip('Select sample *.mzML documents')
self.PushButton_RIISSelect.setToolTip('Select RIIS *.xlsx or *.xls document')
self.PushButton_HeuristicListSelect.setToolTip('Select Heuristic List *.xlsx or *.xls document')
# Slot
self.PushButton_SampleSelect.clicked.connect(self.SelectSampleFile)
self.PushButton_RIISSelect.clicked.connect(self.SelectRIISFile)
self.PushButton_HeuristicListSelect.clicked.connect(self.SelectHeuristicList)
Data_import_Layout.addWidget(self.Label_SampleSelect,1,0)
Data_import_Layout.addWidget(self.TextBrowser_SampleSelect, 2, 0) # row 1， column 0
Data_import_Layout.addWidget(self.PushButton_SampleSelect, 2, 1) # row 1， column 1
Data_import_Layout.addWidget(self.Label_RIISSelect,5,0)
Data_import_Layout.addWidget(self.TextBrowser_RIISSelect, 6, 0)
Data_import_Layout.addWidget(self.PushButton_RIISSelect, 6, 1) # 行，列，行高，列宽
Data_import_Layout.addWidget(self.Label_HeuristicListSelect,7,0)
Data_import_Layout.addWidget(self.TextBrowser_HeuristicListSelect, 8, 0)
Data_import_Layout.addWidget(self.PushButton_HeuristicListSelect, 8, 1) # 行，列，行高，列宽
Data_import_Widget.setLayout(Data_import_Layout)
# Params setting
Params_setting_Widget = QWidget()
Params_setting_Layout = QGridLayout()
# Union
self.Lable_MS_Tor = QLabel('MS1 Tolerance(ppm)')
self.Lable_RT_Tor = QLabel('RT Tolerance(%)')
self.Lable_Int_min = QLabel('Min intensity')
self.Lable_Point = QLabel('Min scan points')
self.Lable_SN = QLabel('Signal/Noise')
self.Lable_SB = QLabel('Signal/Blank')
self.LineEdit_MS_Tor = QLineEdit('10')
self.LineEdit_RT_Tor = QLineEdit('2')
self.LineEdit_Int_min = QLineEdit('9000')
self.LineEdit_Point = QLineEdit('17')
self.LineEdit_SN = QLineEdit('10')
self.LineEdit_SB = QLineEdit('5')
self.LineEdit_MS_Tor.setToolTip('Under this threshold will be recognized as same m/z')
self.LineEdit_RT_Tor.setToolTip('Under this threshold will be recognized as same RT')
self.LineEdit_Int_min.setToolTip('Under this threshold will be deleted')
self.LineEdit_Point.setToolTip('Scan points under this threshold will be deleted')
self.LineEdit_SN.setToolTip('Under this threshold will be deleted')
self.LineEdit_SB.setToolTip('Under this threshold will be deleted')
Params_setting_Layout.addWidget(self.Label_Params_Select,0,0)
#Params_setting_Layout.addWidget(self.Lable_MS_Tor,1,0)
Params_setting_Layout.addWidget(self.Lable_MS_Tor,1,0)
Params_setting_Layout.addWidget(self.LineEdit_MS_Tor,1,1)
Params_setting_Layout.addWidget(self.Lable_RT_Tor,1,2)
Params_setting_Layout.addWidget(self.LineEdit_RT_Tor,1,3)
Params_setting_Layout.addWidget(self.Lable_Int_min,1,4)
Params_setting_Layout.addWidget(self.LineEdit_Int_min,1,5)
Params_setting_Layout.addWidget(self.Lable_Point,2,0)
Params_setting_Layout.addWidget(self.LineEdit_Point,2,1)
Params_setting_Layout.addWidget(self.Lable_SN,2,2)
Params_setting_Layout.addWidget(self.LineEdit_SN,2,3)
Params_setting_Layout.addWidget(self.Lable_SB,2,4)
Params_setting_Layout.addWidget(self.LineEdit_SB,2,5)
Params_setting_Widget.setLayout(Params_setting_Layout)
# Run button
Run_button_Widget = QWidget()
Run_button_Layout = QGridLayout()
# Union
self.PushButton_Run = QPushButton('Run')
self.Label_Run1 = QLabel('')
self.Label_Run2 = QLabel('')
self.Label_Run3 = QLabel('')
Run_button_Layout.addWidget(self.Label_Run1,0,0)
Run_button_Layout.addWidget(self.Label_Run2,0,1)
Run_button_Layout.addWidget(self.Label_Run3,0,2)
Run_button_Layout.addWidget(self.PushButton_Run,0,3)
Run_button_Widget.setLayout(Run_button_Layout)
self.PushButton_Run.clicked.connect(self.Run)
# statusbar
StatusBar_Widget = QWidget()
StatusBar_Layout = QGridLayout()
# Union
self.Label_process = QLabel('Processing bar')
self.Label_process_sub = QLabel('Step')
self.process_bar = QProgressBar()
self.process_bar.setStyleSheet("QProgressBar { border: 2px solid grey; border-radius: 5px; color: rgb(20,20,20); background-color: #FFFFFF; text-align: center;}QProgressBar::chunk {background-color: rgb(100,200,200); border-radius: 10px; margin: 0.1px; width: 1px;}")
font = QFont()
font.setBold(True)
font.setWeight(30)
self.process_bar.setFont(font)
self.process_bar.setMaximum(100)
self.process_bar.setMinimum(0)
self.process_bar.setValue(0)
StatusBar_Layout.addWidget(self.Label_process,0,0)
StatusBar_Layout.addWidget(self.Label_process_sub,1,0)
StatusBar_Layout.addWidget(self.process_bar,1,1)
StatusBar_Widget.setLayout(StatusBar_Layout)
# Global Layout setting
globallayout.addWidget(Data_import_Widget)
globallayout.addWidget(Params_setting_Widget)
globallayout.addWidget(Run_button_Widget)
globallayout.addWidget(StatusBar_Widget)
self.setLayout(globallayout)
self.setWindowTitle('Heuristic Processor')
def centerWindow(self):
screen = QDesktopWidget().screenGeometry()
size = self.geometry()
LeftValue = int((screen.width()-size.width())/2)
TopValue = int((screen.height()-size.height())/2)
self.move(LeftValue,TopValue)
def SelectSampleFile(self):
FileName,FileType = QFileDialog.getOpenFileNames(self,"选取文件",os.getcwd(),"mzML Files(*.mzML)")
for i in FileName:
Combox_Type = QComboBox()
Combox_Type.addItems(['Sample','QC','Blank','MS2'])
Combox_Type.setCurrentText('Sample')
Combox_Type.currentIndexChanged.connect(self.SampleTypeChange)
LineEdit_Index= QLineEdit(str(self.TextBrowser_SampleSelect.rowCount()+1))
LineEdit_Index.textChanged.connect(self.SampleIndexChange)
LineEdit_Group= QLineEdit('1')
LineEdit_Group.textChanged.connect(self.SampleGroupChange)
name_begin = i.rfind('/')
if name_begin == -1:
name_begin = FileName.rfind('\\')
name_end = len(i)
self.Data_params[i[name_begin+1:name_end-5]] = {'Name':i[name_begin+1:name_end-5],'Path':i,'Type':'Sample','Group':1,'Index':str(self.TextBrowser_SampleSelect.rowCount()),'Combox':Combox_Type,'LineEdit_Index':LineEdit_Index,'LineEdit_Group':LineEdit_Group,'rowCount':self.TextBrowser_SampleSelect.rowCount()}
self.TextBrowser_SampleSelect.insertRow(self.TextBrowser_SampleSelect.rowCount())
self.TextBrowser_SampleSelect.setItem(self.TextBrowser_SampleSelect.rowCount()-1,0,QTableWidgetItem(i[name_begin+1:name_end-5]))
self.TextBrowser_SampleSelect.setCellWidget(self.TextBrowser_SampleSelect.rowCount()-1,1,self.Data_params[i[name_begin+1:name_end-5]]['Combox'])
self.TextBrowser_SampleSelect.setCellWidget(self.TextBrowser_SampleSelect.rowCount()-1,2,self.Data_params[i[name_begin+1:name_end-5]]['LineEdit_Group'])
self.TextBrowser_SampleSelect.setCellWidget(self.TextBrowser_SampleSelect.rowCount()-1,3,self.Data_params[i[name_begin+1:name_end-5]]['LineEdit_Index'])
QApplication.processEvents()
def SelectRIISFile(self):
FileName,FileType = QFileDialog.getOpenFileName(self,"选取文件",os.getcwd(),"Excel Files(*.xlsx)")
name_begin = FileName.rfind('/')
if name_begin == -1:
name_begin = FileName.rfind('\\')
name_end = len(FileName)
self.TextBrowser_RIISSelect.setText(FileName[name_begin+1:name_end])
self.RIISPath = FileName
def SelectHeuristicList(self):
FileName,FileType = QFileDialog.getOpenFileName(self,"选取文件",os.getcwd(),"Pickle Files(*.pkl)")
name_begin = FileName.rfind('/')
if name_begin == -1:
name_begin = FileName.rfind('\\')
name_end = len(FileName)
self.TextBrowser_HeuristicListSelect.setText(FileName[name_begin+1:name_end])
self.HeuristicListPath = FileName
def SampleTypeChange(self):
for i in self.Data_params.keys():
if self.sender() == self.Data_params[i]['Combox']:
self.Data_params[i]['Type'] = self.Data_params[i]['Combox'].currentText()
if self.Data_params[i]['Combox'].currentText() == 'QC':
self.Data_params[i]['Group'] = 'QC'
self.Data_params[i]['LineEdit_Group'] = QLineEdit('QC')
self.Data_params[i]['LineEdit_Group'].setReadOnly(True)
self.TextBrowser_SampleSelect.setCellWidget(self.Data_params[i]['rowCount'],2,self.Data_params[i]['LineEdit_Group'])
elif self.Data_params[i]['Combox'].currentText() == 'MS2':
self.Data_params[i]['Group'] = 'MS2'
self.Data_params[i]['LineEdit_Group'] = QLineEdit('MS2')
self.Data_params[i]['LineEdit_Group'].setReadOnly(True)
self.TextBrowser_SampleSelect.setCellWidget(self.Data_params[i]['rowCount'],2,self.Data_params[i]['LineEdit_Group'])
elif self.Data_params[i]['Combox'].currentText() == 'Blank':
self.Data_params[i]['Group'] = 'Blank'
self.Data_params[i]['LineEdit_Group'] = QLineEdit('Blank')
self.Data_params[i]['LineEdit_Group'].setReadOnly(True)
self.TextBrowser_SampleSelect.setCellWidget(self.Data_params[i]['rowCount'],2,self.Data_params[i]['LineEdit_Group'])
else:
self.Data_params[i]['LineEdit_Group'] = QLineEdit('1')
self.TextBrowser_SampleSelect.setCellWidget(self.Data_params[i]['rowCount'],2,self.Data_params[i]['LineEdit_Group'])
QApplication.processEvents()
def SampleIndexChange(self):
for i in self.Data_params.keys():
if self.sender() == self.Data_params[i]['LineEdit_Index']:
self.Data_params[i]['Index'] = self.Data_params[i]['LineEdit_Index'].text()
def SampleGroupChange(self):
for i in self.Data_params.keys():
if self.sender() == self.Data_params[i]['LineEdit_Group']:
self.Data_params[i]['Group'] = self.Data_params[i]['LineEdit_Group'].text()
def processbar_fresh(self,*arg):
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
def Run(self):
self.SampleData = {}
self.BlankData = {}
self.SampleClass = {}
self.MS2_Data = {}
self.QCData = {}
for i in self.Data_params.keys():
if self.Data_params[i]['Type'] == 'Sample':
self.SampleData[i] = self.Data_params[i]
self.SampleClass[i] = self.Data_params[i]['Group']
elif self.Data_params[i]['Type'] == 'MS2':
self.MS2_Data[i] = self.Data_params[i]
elif self.Data_params[i]['Type'] == 'Blank':
self.BlankData[i] = self.Data_params[i]
elif self.Data_params[i]['Type'] == 'QC':
self.QCData[i] = self.Data_params[i]
for i in self.SampleData.keys():
name_begin = self.SampleData[i]['Path'].rfind('/')
if name_begin > 0:
self.filepath_title = self.SampleData[i]['Path'][0:name_begin]+'/'
else:
name_begin = self.SampleData[i]['Path'].rfind('\\')
self.filepath_title = self.SampleData[i]['Path'][0:name_begin]+'\\'
break
ClassList = []
for i in self.SampleClass.keys():
ClassList.append(self.SampleClass[i])
ClassList = list(set(ClassList))
self.Align_path = self.filepath_title+'Alignment-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx'
''' --- Load Sample Data ---'''
self.Label_process_sub.setText('Load Data')
self.process_bar.setMaximum(len(self.SampleData.keys()))
self.process_bar.setValue(0)
pool = mp.Pool(os.cpu_count()-2)
pool_result = {}
for i in self.SampleData.keys():
pool_result[i] = pool.apply_async(EazyMZDataProcess,args=(self.SampleData[i]['Path'],))
pool.close()
pool.join()
for i in self.SampleData.keys():
self.SampleData[i]['Data'] = pool_result[i].get()
for i in self.SampleData.keys():
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
self.SampleData[i]['Data'].set_param('MS1_Tor',float(self.LineEdit_MS_Tor.text())/1000000)
self.SampleData[i]['Data'].set_param('RT_Tor',float(self.LineEdit_RT_Tor.text())/100)
self.SampleData[i]['Data'].set_param('min_Int',int(self.LineEdit_Int_min.text()))
self.SampleData[i]['Data'].set_param('Points',int(self.LineEdit_Point.text()))
self.SampleData[i]['Data'].set_RIIS(self.RIISPath)
self.Label_process_sub.setText('Load QC Data')
''' --- Load QC Data ---'''
pool = mp.Pool(os.cpu_count()-2)
pool_result = {}
for i in self.QCData.keys():
pool_result[i] = pool.apply_async(EazyMZDataProcess,args=(self.QCData[i]['Path'],))
pool.close()
pool.join()
for i in self.QCData.keys():
self.QCData[i]['Data'] = pool_result[i].get()
for i in self.QCData.keys():
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
self.QCData[i]['Data'].set_param('MS1_Tor',float(self.LineEdit_MS_Tor.text())/1000000)
self.QCData[i]['Data'].set_param('RT_Tor',float(self.LineEdit_RT_Tor.text())/100)
self.QCData[i]['Data'].set_param('min_Int',int(self.LineEdit_Int_min.text()))
self.QCData[i]['Data'].set_param('Points',int(self.LineEdit_Point.text()))
self.QCData[i]['Data'].set_RIIS(self.RIISPath)
temp_key = list(self.SampleData.keys())[0]
self.Polarity = self.SampleData[temp_key]['Data'].get_param('Polarity')
''' --- Load Blank Data ---'''
self.Label_process_sub.setText('Load Blank Data')
self.process_bar.setMaximum(len(self.BlankData.keys()))
self.process_bar.setValue(0)
for i in self.BlankData.keys():
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
self.BlankData[i]['Data'] = EazyMZDataProcess(self.BlankData[i]['Path'])
self.BlankData[i]['Data'].set_RIIS(self.RIISPath)
self.BlankData[i]['Data'].set_param('MS1_Tor',float(self.LineEdit_MS_Tor.text())/1000000)
self.BlankData[i]['Data'].set_param('RT_Tor',float(self.LineEdit_Point.text())/100)
''' --- Load MS2 Data ---'''
self.Label_process_sub.setText('Load MS2 Data')
self.process_bar.setMaximum(len(self.MS2_Data.keys()))
self.process_bar.setValue(0)
for i in self.MS2_Data.keys():
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
self.MS2_Data[i]['Data'] = EazyMZDataProcess(self.MS2_Data[i]['Path'])
self.MS2_Data[i]['Data'].set_RIIS(self.RIISPath)
self.MS2_Data[i]['Data'].set_param('MS1_Tor',float(self.LineEdit_MS_Tor.text())/1000000)
self.MS2_Data[i]['Data'].set_param('RT_Tor',float(self.LineEdit_Point.text())/100)
''' --- Heuristic List ---'''
self.Label_process_sub.setText('Set Heuristic List')
MS_Tor = float(self.LineEdit_MS_Tor.text())/1000000
with open(self.HeuristicListPath,'rb') as f:
Alltemp_HPL = pickle.load(f)
temp_Heuristic_peak_list = []
for ii in range(len(Alltemp_HPL)):
if len(temp_Heuristic_peak_list) == 0:
temp_Heuristic_peak_list = Alltemp_HPL.iloc[[0],:].copy()
temp_Heuristic_peak_list['PeakNumber'] = 1
else:
MZ = Alltemp_HPL.at[ii,'AverageMZ']
RI_left = Alltemp_HPL.at[ii,'RI_left']
RI_right = Alltemp_HPL.at[ii,'RI_right']
RI = Alltemp_HPL.at[ii,'RI']
same_list = list(filter(lambda x:EazyMZDataProcess.ppm_compare(MZ,temp_Heuristic_peak_list.at[x,'AverageMZ'])<MS_Tor and
((RI_left<=temp_Heuristic_peak_list.at[x,'RI_right'] and RI_right>=temp_Heuristic_peak_list.at[x,'RI_right']) or
(RI_left<=temp_Heuristic_peak_list.at[x,'RI_left'] and RI_right>=temp_Heuristic_peak_list.at[x,'RI_left'])),range(len(temp_Heuristic_peak_list))))
contain_list = list(filter(lambda x:EazyMZDataProcess.ppm_compare(MZ,temp_Heuristic_peak_list.at[x,'AverageMZ'])<MS_Tor and (RI_left>=temp_Heuristic_peak_list.at[x,'RI_left'] and RI_right<=temp_Heuristic_peak_list.at[x,'RI_right']),range(len(temp_Heuristic_peak_list))))
if len(same_list) == 0 :
if len(contain_list) == 0:
iii = Alltemp_HPL.iloc[[ii],:].copy()
iii['PeakNumber'] = 1
temp_Heuristic_peak_list = pd.concat([temp_Heuristic_peak_list,iii])
temp_Heuristic_peak_list.reset_index(drop=True,inplace=True)
temp_Heuristic_peak_list.loc[len(temp_Heuristic_peak_list)-1,'RI_List'] = [RI]
elif len(contain_list) == 1:
temp_Heuristic_peak_list.loc[contain_list[0],'PeakNumber'] = temp_Heuristic_peak_list.loc[contain_list[0],'PeakNumber']+1
temp_Heuristic_peak_list.loc[contain_list[0],'MZ_List'].append(MZ)
temp_Heuristic_peak_list.loc[contain_list[0],'RI_List'].append(RI)
temp_Heuristic_peak_list.loc[contain_list[0],'AverageMZ'] = np.mean(temp_Heuristic_peak_list.loc[contain_list[0],'MZ_List'])
elif len(same_list) == 1 :
if RI_left<temp_Heuristic_peak_list.at[same_list[0],'RI_left']:
temp_Heuristic_peak_list.loc[same_list[0],'RI_left'] = RI_left
if RI_right>temp_Heuristic_peak_list.at[same_list[0],'RI_right']:
temp_Heuristic_peak_list.loc[same_list[0],'RI_right'] = RI_right
temp_Heuristic_peak_list.loc[same_list[0],'MZ_List'].append(MZ)
temp_Heuristic_peak_list.loc[same_list[0],'RI_List'].append(RI)
temp_Heuristic_peak_list.loc[same_list[0],'AverageMZ'] = np.mean(temp_Heuristic_peak_list.loc[same_list[0],'MZ_List'])
temp_Heuristic_peak_list.loc[same_list[0],'PeakNumber'] = temp_Heuristic_peak_list.loc[same_list[0],'PeakNumber']+1
elif len(same_list) == 2 :
iii = Alltemp_HPL.iloc[[ii],:].copy()
iii['PeakNumber'] = temp_Heuristic_peak_list.loc[same_list[0],'PeakNumber'] + temp_Heuristic_peak_list.loc[same_list[1],'PeakNumber'] + 1
iii.loc[ii,'RI_right'] = max([RI_right,temp_Heuristic_peak_list.loc[same_list[0],'RI_right'],temp_Heuristic_peak_list.loc[same_list[1],'RI_right']])
iii.loc[ii,'RI_left'] = min([RI_left,temp_Heuristic_peak_list.loc[same_list[0],'RI_left'],temp_Heuristic_peak_list.loc[same_list[1],'RI_left']])
iii['MZ_List'] = iii['MZ_List'].astype('object')
iii.at[ii,'MZ_List'] = list(temp_Heuristic_peak_list.loc[same_list[0],'MZ_List'] + temp_Heuristic_peak_list.loc[same_list[1],'MZ_List'] + iii.loc[ii,'MZ_List'])
iii['RI_List'] = iii['RI_List'].astype('object')
iii.at[ii,'RI_List'] = list(temp_Heuristic_peak_list.loc[same_list[0],'RI_List'] + temp_Heuristic_peak_list.loc[same_list[1],'RI_List'] + [iii.loc[ii,'RI']])
iii.loc[ii,'AverageMZ'] = float(np.mean(iii.loc[ii,'MZ_List']))
temp_Heuristic_peak_list.drop(same_list,inplace=True)
temp_Heuristic_peak_list = pd.concat([temp_Heuristic_peak_list,iii])
temp_Heuristic_peak_list.reset_index(drop=True,inplace=True)
self.HeuristicList = temp_Heuristic_peak_list.copy()
''' --- Peak Picking Sample ---'''
for i in self.SampleData.keys():
self.SampleData[i]['Data'].Heuristic_peak_list = self.HeuristicList.copy()
self.Label_process_sub.setText('Heuristic detect')
pool = mp.Pool(os.cpu_count()-2)
pool_result = {}
self.process_bar.setMaximum(len(self.SampleData.keys()))
self.process_bar.setValue(0)
QApplication.processEvents()
for i in self.SampleData.keys():
print('Load Data '+str(i))
pool_result[i]=pool.apply_async(self.SampleData[i]['Data'].Heuristic_PeakDetect,callback=self.processbar_fresh)
pool.close()
pool.join()
for i in self.SampleData.keys():
self.SampleData[i]['Data'].Final_Peak_Detect = pool_result[i].get()[0].copy()
self.SampleData[i]['Data'].Heuristic_peak_list = pool_result[i].get()[1].copy()
self.Label_process.setText('Calculate RI & SN')
for i in self.SampleData.keys():
self.SampleData[i]['Data'].Calculate_RI()
self.SampleData[i]['Data'].Calculate_SN(drop=True,Threshold=int(self.LineEdit_SN.text()))
for i in self.SampleData.keys():
self.SampleData[i]['Data'].OutputSinglePeakListPath = self.SampleData[i]['Path'][0:len(self.SampleData[i]['Path'])-5]+'-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx'
pool = mp.Pool(os.cpu_count()-2)
for i in self.SampleData.keys():
#pool.apply_async(self.SampleData[i]['Data'].Final_Peak_Detect.to_excel,(self.SampleData[i]['Path'][0:len(self.SampleData[i]['Path'])-5]+'-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx',))
pool.apply_async(self.SampleData[i]['Data'].OutputSinglePeakList,)
pool.close()
pool.join()
''' --- Peak Picking QC ---'''
self.Label_process_sub.setText('QC Data Heuristic detect')
for i in self.QCData.keys():
self.QCData[i]['Data'].Heuristic_peak_list = self.HeuristicList.copy()
pool = mp.Pool(os.cpu_count()-2)
pool_result = {}
self.process_bar.setMaximum(len(self.QCData.keys()))
self.process_bar.setValue(0)
QApplication.processEvents()
for i in self.QCData.keys():
pool_result[i]=pool.apply_async(self.QCData[i]['Data'].Heuristic_PeakDetect,callback=self.processbar_fresh)
pool.close()
pool.join()
for i in self.QCData.keys():
self.QCData[i]['Data'].Final_Peak_Detect = pool_result[i].get()[0].copy()
self.QCData[i]['Data'].Heuristic_peak_list = pool_result[i].get()[1].copy()
''' --- Alignment ---'''
self.Label_process.setText('Data Alignment')
self.Align = DataAlignment(self.Label_process,self.process_bar)
for i in self.SampleData.keys():
self.Align.add_Data(self.SampleData[i]['Data'],Tag='Sample')
for i in self.QCData.keys():
self.Align.add_Data(self.QCData[i]['Data'],Tag='QC')
for i in self.BlankData.keys():
self.Align.add_Data(self.BlankData[i]['Data'],Tag='Blank')
self.Align.set_param('RI_Alignment',True)
self.Align.set_param('MZ_Tor',float(self.LineEdit_MS_Tor.text())/1000000)
self.Align.set_param('RT_Tor',float(self.LineEdit_Point.text())/100)
self.Align.RenewRefList()
''' MS2 assine'''
for i in self.MS2_Data.keys():
V_List_Num = list(self.MS2_Data[i]['Data'].MS2_Data['Pre_MZ'][int(len(self.MS2_Data[i]['Data'].MS2_Data['Pre_MZ'])/2):int(len(self.MS2_Data[i]['Data'].MS2_Data['Pre_MZ'])/2)+50])
V_List_Count = []
Count = 1
for ii in range(1,len(V_List_Num)):
if abs(V_List_Num[ii-1]-V_List_Num[ii])/V_List_Num[ii] <= float(self.LineEdit_MS_Tor.text())/1000000:
Count = Count + 1
else:
V_List_Count.append(Count)
Count = 1
V_List_Count = pd.Series(V_List_Count[2:len(V_List_Count)-2])
V_List = []
for ii in range(V_List_Count.mode()[0]):
V_List.append('CE'+str(ii+1))
break
for i in self.MS2_Data.keys():
self.MS2_Data[i]['Data'].MS2_Data['CE'] = V_List[0]
for ii in range(1,len(self.MS2_Data[i]['Data'].MS2_Data)):
if self.MS2_Data[i]['Data'].MS2_Data['Pre_MZ'][ii] == self.MS2_Data[i]['Data'].MS2_Data['Pre_MZ'][ii-1]:
V_Place = list(filter(lambda x:V_List[x]==self.MS2_Data[i]['Data'].MS2_Data['CE'][ii-1],range(len(V_List))))[0]
if V_Place == len(V_List)-1:
self.MS2_Data[i]['Data'].MS2_Data['CE'][ii] = V_List[0]
else:
self.MS2_Data[i]['Data'].MS2_Data['CE'][ii] = V_List[V_Place+1]
else:
self.MS2_Data[i]['Data'].MS2_Data['CE'][ii] = V_List[0]
self.Align.RefList['MS2_MZ'] = self.Align.RefList['MS2_MZ'].astype('object')
self.Align.RefList['MS2_Int'] = self.Align.RefList['MS2_Int'].astype('object')
self.Label_process.setText('MS2 Data Processing')
self.Label_process_sub.setText('MS2 assign')
self.process_bar.setMaximum(len(self.Align.RefList))
self.process_bar.setValue(0)
QApplication.processEvents()
self.MergeMS2 = []
for i in self.MS2_Data.keys():
self.MS2_Data[i]['Data'].Calculate_MS2_RI()
if len(self.MergeMS2) == 0:
self.MergeMS2 = self.MS2_Data[i]['Data'].MS2_Data.copy()
else:
self.MergeMS2 = pd.concat([self.MergeMS2,self.MS2_Data[i]['Data'].MS2_Data])
if len(self.MergeMS2)>0:
self.MergeMS2.reset_index(drop=True,inplace=True)
temp_MZ_Tor = self.Align.AlignmentParam['MZ_Tor']
for i in range(len(self.Align.RefList)):
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
Valified_List = list(filter(lambda x:abs(self.Align.RefList.at[i,'m/z']-self.MergeMS2.at[x,'Pre_MZ'])/self.Align.RefList.at[i,'m/z']<temp_MZ_Tor and self.Align.RefList.at[i,'RI']*(1-float(self.LineEdit_RT_Tor.text())/100)<self.MergeMS2.at[x,'Scan_RI']<self.Align.RefList.at[i,'RI']*((1+float(self.LineEdit_RT_Tor.text())/100)),range(len(self.MergeMS2))))
if len(Valified_List) >0:
Valified_Index = Valified_List[np.where(abs(np.array(self.MergeMS2.loc[Valified_List,'Scan_RI'])-self.Align.RefList.at[i,'RI'])==min(abs(np.array(self.MergeMS2.loc[Valified_List,'Scan_RI'])-self.Align.RefList.at[i,'RI'])))[0][0]]
CE_Index = list(filter(lambda x:V_List[x]==self.MergeMS2.at[Valified_Index+3,'CE'],range(len(V_List))))[0]
if Valified_Index-CE_Index > 0 and Valified_Index+len(V_List)-CE_Index < len(self.MergeMS2):
Valified_Index_List = list(range(Valified_Index-CE_Index,Valified_Index+len(V_List)-CE_Index))
elif Valified_Index-CE_Index < 0:
Valified_Index_List = list(range(0,Valified_Index+len(V_List)-CE_Index))
elif Valified_Index+len(V_List)-CE_Index > len(self.MergeMS2):
Valified_Index_List = list(range(Valified_Index-CE_Index,len(self.MergeMS2)))
MZ_List = []
Int_List = []
for ii in Valified_Index_List:
if abs(self.MergeMS2.at[ii,'Pre_MZ'] - self.Align.RefList.at[i,'m/z']) / self.Align.RefList.at[i,'m/z']<temp_MZ_Tor:
if len(MZ_List) == 0:
MZ_List = self.MergeMS2.at[i,'MZ_List']
Int_List = self.MergeMS2.at[i,'Int_List']
else:
for iii in range(len(self.MergeMS2.at[i,'MZ_List'])):
MS2_Merge_Index = list(filter(lambda x:abs(MZ_List[x]-self.MergeMS2.at[i,'MZ_List'][iii])/MZ_List[x]<temp_MZ_Tor,range(len(MZ_List))))
if len(MS2_Merge_Index) == 0:
MZ_List.append(self.MergeMS2.at[i,'MZ_List'][iii])
Int_List.append(self.MergeMS2.at[i,'Int_List'][iii])
elif Int_List[MS2_Merge_Index[0]] < self.MergeMS2.at[i,'Int_List'][iii]:
MZ_List[MS2_Merge_Index[0]] = self.MergeMS2.at[i,'MZ_List'][iii]
Int_List[MS2_Merge_Index[0]] = self.MergeMS2.at[i,'Int_List'][iii]
temp_range = pd.DataFrame({'MZ_List':MZ_List,'Int_List':Int_List})
temp_range.sort_values(by='MZ_List',inplace=True)
temp_range.reset_index(drop=True,inplace=True)
MZ_List = list(temp_range['MZ_List'])
Int_List = list(temp_range['Int_List'])
self.Align.RefList.at[i,'MS2_MZ'] = MZ_List.copy()
self.Align.RefList.at[i,'MS2_Int'] = Int_List.copy()
''' Filter '''
for i in ClassList:
self.Align.RefList['Fill Group '+str(i)] = 0
for ii in range(len(self.Align.RefList)):
Number_Class = 0
for iii in self.SampleClass.keys():
if self.SampleClass[iii] == i:
Number_Class += 1
if self.Align.RefList[iii][ii] >0:
self.Align.RefList['Fill Group '+str(i)][ii] += 1
self.Align.RefList['Fill Group '+str(i)][ii] = self.Align.RefList['Fill Group '+str(i)][ii]/Number_Class
#self.Align.RefList.to_excel(self.filepath_title+'Alignment-Origin-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx',index=False)
self.Align.BlankFilter()
''' --- Output ---'''
self.Align.RefList.insert(0,'ID',[0]*len(self.Align.RefList))
self.Align.RefList.sort_values(by='m/z',ascending=True,inplace=True)
self.Align.RefList.reset_index(drop=True,inplace=True)
for ID_i in range(len(self.Align.RefList)):
self.Align.RefList['ID'][ID_i] = ID_i+1
self.Align.RefList['m/z'][ID_i] = np.around(self.Align.RefList['m/z'][ID_i],5)
self.Align.RefList['RI'][ID_i] = np.around(self.Align.RefList['RI'][ID_i],1)
self.Align.RefList['RT'][ID_i] = np.around(self.Align.RefList['RT'][ID_i],2)
self.Align.RefList.rename(columns={'m/z':'Average m/z'},inplace=True)
mgf_output = ''
csv_mgf = pd.DataFrame(columns=['row ID','row m/z','row retention time','Peak height'])
for i in range(len(self.Align.RefList)):
if type(self.Align.RefList['MS2_MZ'][i]) == list:
MZ = np.around(self.Align.RefList['Average m/z'][i],5)
RT = np.around(self.Align.RefList['RT'][i]/60,2)
Int = self.Align.RefList['Int'][i]
temp_csv_mgf = pd.DataFrame([(i+1,MZ,RT,Int)],columns=['row ID','row m/z','row retention time','Peak height'])
csv_mgf = pd.concat([csv_mgf,temp_csv_mgf])
if self.Polarity == 'Positive':
mgf_output = mgf_output+'BEGIN IONS\nFEATURE_ID='+str(i+1)+'\nPEPMASS='+str(MZ)+'\nRTINSECONDS='+str(np.around(RT*60,3))+'\nCHARGE=1+\nMSLEVEL=2\n'
elif self.Polarity == 'Negative':
mgf_output = mgf_output+'BEGIN IONS\nFEATURE_ID='+str(i+1)+'\nPEPMASS='+str(MZ)+'\nRTINSECONDS='+str(np.around(RT*60,3))+'\nCHARGE=1-\nMSLEVEL=2\n'
for ii in range(len(self.Align.RefList['MS2_MZ'][i])):
mgf_output = mgf_output+str(np.around(self.Align.RefList['MS2_MZ'][i][ii],5))+' '+str(np.around(self.Align.RefList['MS2_Int'][i][ii],3))+'\n'
mgf_output = mgf_output+'END IONS\n\n'
if len(mgf_output)>0:
with open(self.filepath_title+'tandemMS-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.mgf','w')as mgfFile:
mgfFile.write(mgf_output)
csv_mgf.to_csv(self.filepath_title+'tandemMS-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.csv',index=False)
self.Align.RefList.rename(columns={'Int':'Average Intensity'},inplace=True)
self.Align.RefList.rename(columns={'RT':'Average RT(s)'},inplace=True)
self.Align.RefList.rename(columns={'RI':'Average RI'},inplace=True)
self.Align.RefList.rename(columns={'MS_List':'m/z value in each sample'},inplace=True)
self.Align.RefList.rename(columns={'RT_List':'RI value in each sample'},inplace=True)
self.Align.RefList.rename(columns={'MS2_Int':'Intensity values of product ions'},inplace=True)
self.Align.RefList.rename(columns={'MS2_MZ':'m/z values of product ions'},inplace=True)
self.Align.RefList.rename(columns={'SimilarityScore':'Average Similarity Score'},inplace=True)
self.Align.RefList.rename(columns={'SC_List':'Similarity Score in each sample'},inplace=True)
self.Align.RefList.rename(columns={'max_SampleInt':'Max sample intensity'},inplace=True)
self.Align.RefList.rename(columns={'mean_BlankInt':'Average blank intensity'},inplace=True)
self.Align.RefList.to_excel(self.filepath_title+'Alignment-'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx',index=False)
self.Label_process_sub.setText('Finished')
self.process_bar.setMaximum(100)
self.process_bar.setValue(100)
class ProgressBar(QDialog):
def __init__(self,parent=None):
super(ProgressBar,self).__init__(parent)
self.resize(500,32)
self.progressBar = QProgressBar(self)
self.progressBar.setMaximum(500)
self.progressBar.setMinimum(0)
self.progressBar.setValue(0)
self.centerWindow()
self.show()
def setValue(self,task_Number,total_task_Number,value):
if total_task_Number == 1:
self.setWindowTitle('Processing')
else:
self.setWindowTitle('Processing '+str(task_Number)+'/'+str(total_task_Number))
self.progressBar.setValue(value)
def centerWindow(self):
screen = QDesktopWidget().screenGeometry()
size = self.geometry()
LeftValue = int((screen.width()-size.width())/2)
TopValue = int((screen.height()-size.height())/2)
self.move(LeftValue,TopValue)
class EazyMZDataProcess(object):
def __init__(self,DataPath):
if DataPath.rfind('/') != -1 :
self.DataName = DataPath[DataPath.rfind('/')+1:len(DataPath)-5]
else:
self.DataName = DataPath[DataPath.rfind('\\')+1:len(DataPath)-5]
self.OriginData = pyopenms.MSExperiment()
self.file_path = DataPath
''' 储存文件pyopenms.MzMLFile().store("filtered.mzML", exp) '''
if self.file_path.endswith('mzML'):
pyopenms.MzMLFile().load(self.file_path,self.OriginData)
elif self.file_path.endswith('mzXML'):
pyopenms.MzXMLFile().load(self.file_path,self.OriginData)
self.OriginData.sortSpectra(True)
self.__param = {'MS1_Tor':0.000010,'RT_Tor':2,'min_Int':10000,'min_RT':6,'max_Noise':2000,
'Deconvolution':False,'FeatureDetectPlot':3,'MergeRule':'Intersection',
'UpDown_gap':10,'saveAutoList':False,'smooth':5,'Points':25}
self.Origin_RT_List = np.array([])
self.Origin_MZ_List = []
self.Origin_Int_List = []
self.MS2_Pre = []
self.MS2_RT_List = []
self.MS2_MZ_List = []
self.MS2_Int_List = []
self.MS2_RelInt=[]
for i in self.OriginData:
if i.getMSLevel()==1:
MZ_temp, Int_temp = i.get_peaks()
self.Origin_MZ_List.append(np.around(MZ_temp,5))
self.Origin_Int_List.append(np.around(Int_temp,0))
self.Origin_RT_List = np.append(self.Origin_RT_List,i.getRT())
if i.getMSLevel()==2:
Pre_temp = i.getPrecursors()[0].getMZ()
MZ_temp,Int_temp=i.get_peaks()
temp_range = list(filter(lambda x:MZ_temp[x]<=Pre_temp,range(len(MZ_temp)))) # and Int_temp[x]>1000
temp_range = list(filter(lambda x:len(np.where(abs(MZ_temp[temp_range]-MZ_temp[x])/MZ_temp[x]<self.__param['MS1_Tor']))==1 or
Int_temp[x]==max(Int_temp[np.where(abs(MZ_temp[temp_range]-MZ_temp[x])/MZ_temp[x]<self.__param['MS1_Tor'])]),temp_range))
if len(temp_range)>0:
MZ_temp = MZ_temp[temp_range]
Int_temp = Int_temp[temp_range]
self.MS2_Pre.append(Pre_temp)
self.MS2_RT_List.append(i.getRT())
self.MS2_MZ_List.append(np.around(MZ_temp,5))
self.MS2_Int_List.append(np.around(Int_temp,0))
self.MS2_RelInt.append(max(Int_temp[temp_range]))
else:
MZ_temp = []
Int_temp = []
self.MS2_Pre.append(Pre_temp)
self.MS2_RT_List.append(i.getRT())
self.MS2_MZ_List.append(np.around(MZ_temp,5))
self.MS2_Int_List.append(np.around(Int_temp,0))
self.MS2_RelInt.append(0)
self.Origin_RT_List = np.around(self.Origin_RT_List,3)
self.MS2_RT_List = np.around(self.MS2_RT_List,3)
self.MS1_Data = {'Scan_Time':self.Origin_RT_List,'MZ_List':self.Origin_MZ_List,'Int_List':self.Origin_Int_List}
self.MS1_Data = pd.DataFrame(self.MS1_Data)
self.MS2_Data = {'Scan_Time':self.MS2_RT_List,'Pre_MZ':self.MS2_Pre,'MZ_List':self.MS2_MZ_List,'Int_List':self.MS2_Int_List,'Rel_Int':self.MS2_RelInt}
self.MS2_Data = pd.DataFrame(self.MS2_Data)
self.__param['Flow_RT'] = int(self.__param['min_RT']/(4*sum(np.diff(self.Origin_RT_List))/len(np.diff(self.Origin_RT_List))))+1
if self.OriginData[0].getInstrumentSettings().getPolarity() == 1:
self.__param['Polarity'] = 'Positive'
elif self.OriginData[0].getInstrumentSettings().getPolarity() == 2:
self.__param['Polarity'] = 'Negative'
else:
self.__param['Polarity'] = 'Not give'
print('Uncertain Polarity')
self.OriginData = []
def add_0(x):
x.append(0)
return x
def add_Blank(self,DataPath):
self.BlankData = pyopenms.MSExperiment()
if DataPath.endswith('mzML'):
pyopenms.MzMLFile().load(DataPath,self.BlankData)
elif DataPath.endswith('mzXML'):
pyopenms.MzXMLFile().load(DataPath,self.BlankData)
self.BlankData.sortSpectra(True)
self.Blank_RT_List = np.array([])
self.Blank_MZ_List = []
self.Blank_Int_List = []
for i in self.BlankData:
if i.getMSLevel()==1:
MZ_temp, Int_temp = i.get_peaks()
self.Blank_MZ_List.append(np.around(MZ_temp,5))
self.Blank_Int_List.append(np.around(Int_temp,0))
self.Blank_RT_List = np.append(self.Blank_RT_List,i.getRT())
self.Blank_RT_List = np.around(self.Blank_RT_List,3)
def add_RT(x, RT):
x.append(RT)
return x
def add_param(self,name,value):
self.__param[name]=value
print('set',name,' = ',value)
def add_Heuristic_peak_list(self,path):
Heuristic_peak_list = pd.read_excel(path)
Heuristic_peak_list['RI_left'] = Heuristic_peak_list['RI'].apply(lambda x:x*0.98)
Heuristic_peak_list['RI_right'] = Heuristic_peak_list['RI'].apply(lambda x:x*1.02)
Heuristic_peak_list['MZ_List'] = Heuristic_peak_list['AverageMZ'].apply(lambda x:[x])
Heuristic_peak_list['RI_List'] = Heuristic_peak_list['RI'].apply(lambda x:[x])
Alltemp_HPL=pd.DataFrame(columns=['AverageMZ','RT','MZ_List','RT_List'])
Alltemp_HPL['AverageMZ'] = Alltemp_HPL['AverageMZ'].map(lambda x:'%.4f'%x)
sub_HPL_dict = {}
for i in list(set(Heuristic_peak_list['Gradient Code'])):
sub_Heuristic_peak_list = Heuristic_peak_list[Heuristic_peak_list['Gradient Code']==i].copy()
sub_Heuristic_peak_list.reset_index(drop=True,inplace=True)
sub_HPL_dict[i] = sub_Heuristic_peak_list.copy()
for i in sub_HPL_dict.keys():
sub_HPL_dict[i].sort_values('AverageMZ',ascending=(False),inplace=True)
sub_HPL_dict[i].reset_index(drop=True,inplace=True)
Alltemp_HPL.sort_values('AverageMZ',ascending=(False),inplace=True)
Alltemp_HPL.reset_index(drop=True,inplace=True)
temp_Data = sub_HPL_dict[i].copy()
init_RefMZ = pd.DataFrame(columns=['AverageMZ','MZList','RowIndex'])
init_SampleMZ = pd.DataFrame(columns=['AverageMZ','MZList','RowIndex'])
Sample_Time = temp_Data.loc[:,'RI']
for ii in range(len(Alltemp_HPL)):
if len(init_RefMZ)==0:
init_RefMZ.loc[len(init_RefMZ)] = [Alltemp_HPL.at[ii,'AverageMZ'],[Alltemp_HPL.at[ii,'AverageMZ']],[ii]]
else:
if abs(init_RefMZ.at[len(init_RefMZ)-1,'AverageMZ']-Alltemp_HPL.at[ii,'AverageMZ'])/Alltemp_HPL.at[ii,'AverageMZ']<self.__param['MS1_Tor']:
init_RefMZ.at[len(init_RefMZ)-1,'MZList'].append(Alltemp_HPL.at[ii,'AverageMZ'])
init_RefMZ.at[len(init_RefMZ)-1,'RowIndex'].append(ii)
init_RefMZ.at[len(init_RefMZ)-1,'AverageMZ']=np.mean(init_RefMZ.at[len(init_RefMZ)-1,'MZList'])
else:
init_RefMZ.loc[len(init_RefMZ)] = [Alltemp_HPL.at[ii,'AverageMZ'],[Alltemp_HPL.at[ii,'AverageMZ']],[ii]]
for ii in range(len(temp_Data)):
if len(init_SampleMZ)==0:
init_SampleMZ.loc[len(init_SampleMZ)] = [temp_Data.at[ii,'AverageMZ'],[temp_Data.at[ii,'AverageMZ']],[ii]]
else:
if abs(init_SampleMZ.at[len(init_SampleMZ)-1,'AverageMZ']-temp_Data.at[ii,'AverageMZ'])/temp_Data.at[ii,'AverageMZ']<self.__param['MS1_Tor']:
init_SampleMZ.at[len(init_SampleMZ)-1,'MZList'].append(temp_Data.at[ii,'AverageMZ'])
init_SampleMZ.at[len(init_SampleMZ)-1,'RowIndex'].append(ii)
init_SampleMZ.at[len(init_SampleMZ)-1,'AverageMZ']=np.mean(init_SampleMZ.at[len(init_SampleMZ)-1,'MZList'])
else:
init_SampleMZ.loc[len(init_SampleMZ)] = [temp_Data.at[ii,'AverageMZ'],[temp_Data.at[ii,'AverageMZ']],[ii]]
add_MZ = []
add_RT = []
add_MS_List = []
add_RT_List = []
for ii in range(len(init_SampleMZ)):
Sample_MZ = init_SampleMZ.at[ii,'AverageMZ']
match_Index = list(filter(lambda x:abs(init_RefMZ.at[x,'AverageMZ']-Sample_MZ)/Sample_MZ<self.__param['MS1_Tor'],range(len(init_RefMZ))))
if len(match_Index)>1:
Ref_Index = init_RefMZ.at[match_Index[0],'RowIndex']
for i_mI in range(1,len(match_Index)):
Ref_Index = Ref_Index + init_RefMZ.at[match_Index[i_mI],'RowIndex']
elif len(match_Index)==1:
Ref_Index = init_RefMZ.at[match_Index[0],'RowIndex']
else:
for i_add in init_SampleMZ.at[ii,'RowIndex']:
add_MZ.append(temp_Data.at[i_add,'AverageMZ'])
add_RT.append(Sample_Time[i_add])
add_MS_List.append([temp_Data.at[i_add,'AverageMZ']])
add_RT_List.append([Sample_Time[i_add]])
continue
Score_matrix= np.zeros([len(Ref_Index),len(init_SampleMZ.at[ii,'RowIndex'])])
for i_Ref in range(len(Ref_Index)):
for i_Sample in range(len(init_SampleMZ.at[ii,'RowIndex'])):
i_Ref_MZ = Alltemp_HPL.at[Ref_Index[i_Ref],'AverageMZ']
i_Sample_MZ = temp_Data.at[init_SampleMZ.at[ii,'RowIndex'][i_Sample],'AverageMZ']
i_Ref_Time = Alltemp_HPL.at[Ref_Index[i_Ref],'RT']
i_Sample_Time = Sample_Time[init_SampleMZ.at[ii,'RowIndex'][i_Sample]]
if abs(i_Ref_MZ-i_Sample_MZ)/i_Ref_MZ<self.__param['MS1_Tor'] and abs(i_Ref_Time-i_Sample_Time)/i_Ref_Time<self.__param['RT_Tor']:
Score_matrix[i_Ref,i_Sample] = 0.5*np.exp(-0.5*((i_Sample_Time-i_Ref_Time)/(i_Ref_Time*self.__param['RT_Tor']))**2)+(1-0.5)*np.exp(-0.5*((i_Sample_MZ-i_Ref_MZ)/(i_Sample_MZ*self.__param['MS1_Tor']))**2)
Sm_row,Sm_col = linear_sum_assignment(Score_matrix,True)
for i_Sm_row,i_Sm_col in zip(Sm_row,Sm_col):
if Score_matrix[i_Sm_row,i_Sm_col] != 0:
Alltemp_HPL.at[Ref_Index[i_Sm_row],'MZ_List'].append(temp_Data.at[init_SampleMZ.at[ii,'RowIndex'][i_Sm_col],'AverageMZ'])
Alltemp_HPL.at[Ref_Index[i_Sm_row],'RT_List'].append(Sample_Time[init_SampleMZ.at[ii,'RowIndex'][i_Sm_col]])
Alltemp_HPL.at[Ref_Index[i_Sm_row],'RT'] = np.mean(Alltemp_HPL.at[Ref_Index[i_Sm_row],'RT_List'])
Alltemp_HPL.at[Ref_Index[i_Sm_row],'AverageMZ'] = np.mean(Alltemp_HPL.at[Ref_Index[i_Sm_row],'MZ_List'])
else:
add_MZ.append(temp_Data.at[init_SampleMZ.at[ii,'RowIndex'][i_Sm_col],'AverageMZ'])
add_RT.append(Sample_Time[init_SampleMZ.at[ii,'RowIndex'][i_Sm_col]])
add_MS_List.append([temp_Data.at[init_SampleMZ.at[ii,'RowIndex'][i_Sm_col],'AverageMZ']])
add_RT_List.append([Sample_Time[init_SampleMZ.at[ii,'RowIndex'][i_Sm_col]]])
miss_col = list(filter(lambda x:x not in Sm_col,range(len(init_SampleMZ.at[ii,'RowIndex']))))
for i_miss in miss_col:
add_MZ.append(temp_Data.at[init_SampleMZ.at[ii,'RowIndex'][i_miss],'AverageMZ'])
add_RT.append(Sample_Time[init_SampleMZ.at[ii,'RowIndex'][i_miss]])
add_MS_List.append([temp_Data.at[init_SampleMZ.at[ii,'RowIndex'][i_miss],'AverageMZ']])
add_RT_List.append([Sample_Time[init_SampleMZ.at[ii,'RowIndex'][i_miss]]])
add_RefList = pd.DataFrame({'AverageMZ': add_MZ,'RT': add_RT,'MZ_List':add_MS_List,'RT_List':add_RT_List})
Alltemp_HPL = pd.concat([Alltemp_HPL, add_RefList])
Alltemp_HPL.reset_index(drop=True, inplace=True)
Alltemp_HPL = Alltemp_HPL.fillna(0)
Alltemp_HPL['RT'] = Alltemp_HPL['RT_List'].apply(lambda x:sum(x)/len(x))
Alltemp_HPL.rename(columns={'RT':'RI'},inplace=True)
Alltemp_HPL.rename(columns={'RT_List':'RI_List'},inplace=True)
Alltemp_HPL['RI_left'] = Alltemp_HPL['RI'].apply(lambda x:x*(1-self.__param['RT_Tor']))
Alltemp_HPL['RI_right'] = Alltemp_HPL['RI'].apply(lambda x:x*(1+self.__param['RT_Tor']))
Alltemp_HPL.sort_values('AverageMZ',ascending=(True),inplace=True)
Alltemp_HPL.reset_index(drop=True,inplace=True)
self.Heuristic_normal_list = Alltemp_HPL.copy()
temp_Heuristic_peak_list = []
for ii in range(len(Alltemp_HPL)):
if len(temp_Heuristic_peak_list) == 0:
temp_Heuristic_peak_list = Alltemp_HPL.iloc[[0],:].copy()
temp_Heuristic_peak_list['PeakNumber'] = 1
else:
MZ = Alltemp_HPL.at[ii,'AverageMZ']
RI_left = Alltemp_HPL.at[ii,'RI_left']
RI_right = Alltemp_HPL.at[ii,'RI_right']
RI = Alltemp_HPL.at[ii,'RI']
same_list = list(filter(lambda x:EazyMZDataProcess.ppm_compare(MZ,temp_Heuristic_peak_list.at[x,'AverageMZ'])<self.__param['MS1_Tor'] and
((RI_left<=temp_Heuristic_peak_list.at[x,'RI_right'] and RI_right>=temp_Heuristic_peak_list.at[x,'RI_right']) or
(RI_left<=temp_Heuristic_peak_list.at[x,'RI_left'] and RI_right>=temp_Heuristic_peak_list.at[x,'RI_left'])),range(len(temp_Heuristic_peak_list))))
contain_list = list(filter(lambda x:EazyMZDataProcess.ppm_compare(MZ,temp_Heuristic_peak_list.at[x,'AverageMZ'])<self.__param['MS1_Tor'] and (RI_left>=temp_Heuristic_peak_list.at[x,'RI_left'] and RI_right<=temp_Heuristic_peak_list.at[x,'RI_right']),range(len(temp_Heuristic_peak_list))))
if len(same_list) == 0 :
if len(contain_list) == 0:
iii = Alltemp_HPL.iloc[[ii],:].copy()
iii['PeakNumber'] = 1
temp_Heuristic_peak_list = pd.concat([temp_Heuristic_peak_list,iii])
temp_Heuristic_peak_list.reset_index(drop=True,inplace=True)
temp_Heuristic_peak_list.loc[len(temp_Heuristic_peak_list)-1,'RI_List'] = [RI]
elif len(contain_list) == 1:
temp_Heuristic_peak_list.loc[contain_list[0],'PeakNumber'] = temp_Heuristic_peak_list.loc[contain_list[0],'PeakNumber']+1
temp_Heuristic_peak_list.loc[contain_list[0],'MZ_List'].append(MZ)
temp_Heuristic_peak_list.loc[contain_list[0],'RI_List'].append(RI)
temp_Heuristic_peak_list.loc[contain_list[0],'AverageMZ'] = np.mean(temp_Heuristic_peak_list.loc[contain_list[0],'MZ_List'])
elif len(same_list) == 1 :
if RI_left<temp_Heuristic_peak_list.at[same_list[0],'RI_left']:
temp_Heuristic_peak_list.loc[same_list[0],'RI_left'] = RI_left
if RI_right>temp_Heuristic_peak_list.at[same_list[0],'RI_right']:
temp_Heuristic_peak_list.loc[same_list[0],'RI_right'] = RI_right
temp_Heuristic_peak_list.loc[same_list[0],'MZ_List'].append(MZ)
temp_Heuristic_peak_list.loc[same_list[0],'RI_List'].append(RI)
temp_Heuristic_peak_list.loc[same_list[0],'AverageMZ'] = np.mean(temp_Heuristic_peak_list.loc[same_list[0],'MZ_List'])
temp_Heuristic_peak_list.loc[same_list[0],'PeakNumber'] = temp_Heuristic_peak_list.loc[same_list[0],'PeakNumber']+1
elif len(same_list) == 2 :
iii = Alltemp_HPL.iloc[[ii],:].copy()
iii['PeakNumber'] = temp_Heuristic_peak_list.loc[same_list[0],'PeakNumber'] + temp_Heuristic_peak_list.loc[same_list[1],'PeakNumber'] + 1
iii.loc[ii,'RI_right'] = max([RI_right,temp_Heuristic_peak_list.loc[same_list[0],'RI_right'],temp_Heuristic_peak_list.loc[same_list[1],'RI_right']])
iii.loc[ii,'RI_left'] = min([RI_left,temp_Heuristic_peak_list.loc[same_list[0],'RI_left'],temp_Heuristic_peak_list.loc[same_list[1],'RI_left']])
iii['MZ_List'] = iii['MZ_List'].astype('object')
iii.at[ii,'MZ_List'] = list(temp_Heuristic_peak_list.loc[same_list[0],'MZ_List'] + temp_Heuristic_peak_list.loc[same_list[1],'MZ_List'] + iii.loc[ii,'MZ_List'])
iii['RI_List'] = iii['RI_List'].astype('object')
iii.at[ii,'RI_List'] = list(temp_Heuristic_peak_list.loc[same_list[0],'RI_List'] + temp_Heuristic_peak_list.loc[same_list[1],'RI_List'] + [iii.loc[ii,'RI']])
iii.loc[ii,'AverageMZ'] = float(np.mean(iii.loc[ii,'MZ_List']))
temp_Heuristic_peak_list.drop(same_list,inplace=True)
temp_Heuristic_peak_list = pd.concat([temp_Heuristic_peak_list,iii])
temp_Heuristic_peak_list.reset_index(drop=True,inplace=True)
self.Heuristic_peak_list = temp_Heuristic_peak_list.copy()
def ThreeZero(x):
if len(x) >= 3:
if x[-1] == 0 and x[-2] == 0 and x[-3] == 0:
return False
else:
return True
else:
return True
def ClosestPosition(TargetNumber, List):
if TargetNumber >= List[-1]:
#print("Close error +")
return len(List)-1
elif TargetNumber <= List[0]:
#print("Close error -")
return 0
position = bisect.bisect_left(List, TargetNumber)
before = List[position-1]
after = List[position]
if after - TargetNumber < TargetNumber - before:
return position
else:
return position-1
def ppm_compare(a, b):
ppm_result = abs(a-b)/b
return ppm_result
'''
def Draw_TIC(self):
TIC_Int_List = []
for i in range(len(self.Origin_Int_List)):
TIC_Int_List.append(sum(self.Origin_Int_List[i]))
plt.plot(self.Origin_RT_List,TIC_Int_List)
plt.show()
TIC_Table = {'ScanTime':self.Origin_RT_List,'Intensity':TIC_Int_List}
TIC_Table = pd.DataFrame(TIC_Table)
#TIC_Table.to_excel(OutputPath+'TIC.xlsx')
return TIC_Table
'''
def CosineSimilarity(ExestList, NewList, LOG=False):
if len(ExestList) == 0 :
return 0
else:
if len(ExestList) != len(NewList):
print("CosineSimilarity error", len(ExestList), len(NewList))
return 0
if LOG == True:
temp_ExestList = []
for i in range(len(ExestList)):
if ExestList[i] != 0:
temp_ExestList.append(math.log(ExestList[i]))
else:
temp_ExestList.append(0)
ExestList = temp_ExestList
temp_NewList = []
for ii in range(len(NewList)):
if NewList[ii] != 0:
temp_NewList.append(math.log(NewList[ii]))
else:
temp_NewList.append(0)
NewList = temp_NewList
ExestList = np.array(ExestList)
NewList = np.array(NewList)
CosineSimilarityValue = ExestList.dot(
NewList)/(np.linalg.norm(ExestList) * np.linalg.norm(NewList))
return CosineSimilarityValue
def Calculate_SB(self,drop=False,limit=5):
self.Final_Peak_Detect['BLANK'] = 0
for i in range(len(self.Final_Peak_Detect)):
RTL = self.Final_Peak_Detect.at[i,'RTList'][0]
RTR = self.Final_Peak_Detect.at[i,'RTList'][-1]
MZ = self.Final_Peak_Detect.at[i,'AverageMZ']
[RT_List,Int_List] = self.ExtractBlankPoint(MZ,RTR,RTL,smooth_index=5)
self.Final_Peak_Detect.at[i,'BLANK'] = max(1,max(Int_List))
self.Final_Peak_Detect['S/B'] = self.Final_Peak_Detect['Int']/self.Final_Peak_Detect['BLANK']
if drop == True:
self.Final_Peak_Detect = self.Final_Peak_Detect[self.Final_Peak_Detect['S/B']>limit].copy()
self.Final_Peak_Detect.reset_index(drop=True,inplace=True)
def ExtractDataPoint(self,MZ, RTR, RTL,s_min=False, plt_for_test=False,smooth_index=5):
smooth_index =(smooth_index-1)//2
if smooth_index<0:
smooth_index=0
if s_min == True:
RTR = RTR * 60 # min -> s
RTL = RTL * 60
RTR_place = EazyMZDataProcess.ClosestPosition(RTR,self.Origin_RT_List)
RTL_place = EazyMZDataProcess.ClosestPosition(RTL,self.Origin_RT_List)
RT_List = self.Origin_RT_List[RTL_place:RTR_place]
Int_List = []
#MZ_List=[]
for i in range(RTL_place, RTR_place):
if len(self.Origin_MZ_List[i])>0:
if len(self.Origin_MZ_List[i])>=4:
MZ_place = EazyMZDataProcess.ClosestPosition(MZ,self.Origin_MZ_List[i])
if MZ_place > len(self.Origin_MZ_List[i])-3 and MZ_place>=3:
MZ_place = len(self.Origin_MZ_List[i])-3
elif MZ_place < 2:
MZ_place = 2
temp_int = []
#temp_MZ = []
for ii in range(MZ_place-2, MZ_place+2):
if MZ*(1-self.__param['MS1_Tor']) < self.Origin_MZ_List[i][ii] < MZ*(1+self.__param['MS1_Tor']):
temp_int.append(self.Origin_Int_List[i][ii])
#temp_MZ.append(Origin_MZ_List[i][ii])
if not temp_int:
Int_List.append(0)
#MZ_List.append(0)
else:
temp_int = np.array(temp_int)
Int_List.append(temp_int.max())
#MZ_List.append()
else:
Int_List.append(0)
else:
Int_List.append(0)
# smooth
def GaussSmooth(x):
if len(x)==5:
op = x[0]*0.07+x[1]*0.23+x[2]*0.4+x[3]*0.23+x[4]*0.07
elif len(x)==3:
op = x[0]*0.17 +x[1]*0.66 +x[2]*0.17
else:
op = sum(x)/len(x)
return op
if smooth_index != 0:
Int_List = list(map(lambda x:GaussSmooth(Int_List[x-smooth_index:x+smooth_index+1]) if x in range(smooth_index,len(Int_List)-smooth_index) else Int_List[x],range(len(Int_List))))
'''
if plt_for_test == True:
plt.figure()
plt.title(str(MZ))
plt.plot(RT_List,Int_List)
plt.figure()
'''
return RT_List, Int_List
def ExtractBlankPoint(self,MZ, RTR, RTL,s_min=False, plt_for_test=False,smooth_index=5):
smooth_index =(smooth_index-1)//2
if smooth_index<0:
smooth_index=0
if s_min == True: