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HeuristicPeakListGenerator.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 23 16:30:30 2024
@author: lacter
"""
import pandas as pd
import time
from progress.bar import Bar
from pyopenms import MSExperiment,MzMLFile,MzXMLFile
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 HeuristicProducerUI(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 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 initUI(self):
globallayout = QVBoxLayout()
# Data import
Data_import_Widget = QWidget()
Data_import_Layout = QGridLayout()
self.Label_SampleSelect = QLabel('Select data')
self.Label_RIISSelect = QLabel('Select Calibrants data')
self.Label_Params_Select = QLabel('Set params')
self.TextBrowser_SampleSelect = QTableWidget()
self.TextBrowser_SampleSelect.setColumnCount(2)
self.TextBrowser_SampleSelect.setHorizontalHeaderLabels(['Data','Peak List'])
self.TextBrowser_SampleSelect.setColumnWidth(0, 230)
self.TextBrowser_SampleSelect.horizontalHeader().setSectionResizeMode(QHeaderView.Interactive)
self.TextBrowser_SampleSelect.horizontalHeader().setStretchLastSection(True)
self.TextBrowser_RIISSelect = QLabel('*')
self.PushButton_SampleSelect = QPushButton('Select')
self.PushButton_RIISSelect = QPushButton('Select')
self.PushButton_SampleSelect.setToolTip('Select sample *.mzML documents')
self.PushButton_RIISSelect.setToolTip('Select RIIS *.xlsx or *.xls document')
# Slot
self.PushButton_SampleSelect.clicked.connect(self.SelectSampleFile)
self.PushButton_RIISSelect.clicked.connect(self.SelectRIISFile)
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_Widget.setLayout(Data_import_Layout)
Params_setting_Widget = QWidget()
Params_setting_Layout = QGridLayout()
# Union
self.Lable_MS_Tor = QLabel('MS1 Tolerance(ppm)')
self.Lable_RT_Tor = QLabel('RI Tolerance(%)')
self.LineEdit_MS_Tor = QLineEdit('10')
self.LineEdit_RT_Tor = QLineEdit('2')
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')
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.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_Widget.setLayout(Params_setting_Layout)
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()
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 Peak List Generator')
def SelectSampleFile(self):
FileName,FileType = QFileDialog.getOpenFileNames(self,"选取文件",os.getcwd(),"mzML Files(*.mzML)")
for i in FileName:
name_begin = i.rfind('/')
if name_begin == -1:
name_begin = FileName.rfind('\\')
name_end = len(i)
LineEdit_Data= QLineEdit(i[name_begin+1:name_end])
LineEdit_Data.textChanged.connect(self.DataChange)
LineEdit_PeakList= QLineEdit(i[name_begin+1:name_end-5]+'.xlsx')
LineEdit_PeakList.textChanged.connect(self.PeakListChange)
self.TextBrowser_SampleSelect.insertRow(self.TextBrowser_SampleSelect.rowCount())
self.TextBrowser_SampleSelect.setCellWidget(self.TextBrowser_SampleSelect.rowCount()-1,0,LineEdit_Data)
self.TextBrowser_SampleSelect.setCellWidget(self.TextBrowser_SampleSelect.rowCount()-1,1,LineEdit_PeakList)
self.Data_params[i[name_begin+1:name_end-5]] = {'Name':i[name_begin+1:name_end-5],'Path':i,'PeakListPath':i[0:name_end-5]+'.xlsx','LineEdit_Data':LineEdit_Data,'LineEdit_PeakList':LineEdit_PeakList}
for i in self.Data_params.keys():
name_begin = self.Data_params[i]['Path'].rfind('/')
if name_begin > 0:
self.filepath_title = self.Data_params[i]['Path'][0:name_begin]+'/'
else:
name_begin = self.Data_params[i]['Path'].rfind('\\')
self.filepath_title = self.Data_params[i]['Path'][0:name_begin]+'\\'
break
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 processbar_fresh(self,*arg):
self.process_bar.setValue(self.process_bar.value()+1)
QApplication.processEvents()
def DataChange(self):
for i in self.Data_params.keys():
if self.sender() == self.Data_params[i]['LineEdit_Data']:
self.Data_params[i]['Path'] = self.filepath_title + self.Data_params[i]['LineEdit_Data'].text()
def PeakListChange(self):
for i in self.Data_params.keys():
if self.sender() == self.Data_params[i]['LineEdit_PeakList']:
self.Data_params[i]['PeakListPath'] = self.filepath_title + self.Data_params[i]['LineEdit_PeakList'].text()
def Run(self):
self.SampleData = {}
for i in self.Data_params.keys():
self.SampleData[i] = self.Data_params[i]
self.Label_process_sub.setText('Load Data')
self.process_bar.setMaximum(len(self.SampleData.keys()))
self.process_bar.setValue(0)
print('Before multiprocessing')
pool = mp.Pool(os.cpu_count()-2)
pool_result = {}
for i in self.SampleData.keys():
print('Load Data '+str(i))
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()
print('After multiprocessing')
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_RIIS(self.RIISPath)
self.SampleData[i]['Data'].Final_Peak_Detect = pd.read_excel(self.SampleData[i]['PeakListPath'])
self.SampleData[i]['Data'].Final_Peak_Detect.rename(columns={'m/z':'AverageMZ'},inplace=True)
self.SampleData[i]['Data'].Calculate_RI()
self.SampleData[i]['Data'].Final_Peak_Detect['RI_left'] = self.SampleData[i]['Data'].Final_Peak_Detect['RI'].apply(lambda x:x*0.98)
self.SampleData[i]['Data'].Final_Peak_Detect['RI_right'] = self.SampleData[i]['Data'].Final_Peak_Detect['RI'].apply(lambda x:x*1.02)
self.SampleData[i]['Data'].Final_Peak_Detect['MZ_List'] = self.SampleData[i]['Data'].Final_Peak_Detect['AverageMZ'].apply(lambda x:[x])
self.SampleData[i]['Data'].Final_Peak_Detect['RI_List'] = self.SampleData[i]['Data'].Final_Peak_Detect['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 self.SampleData.keys():
sub_HPL_dict[i] = self.SampleData[i]['Data'].Final_Peak_Detect.copy()
MS_Tor = float(self.LineEdit_MS_Tor.text())/1000000
RT_Tor = float(self.LineEdit_RT_Tor.text())/100
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']<MS_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']<MS_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<MS_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<MS_Tor and abs(i_Ref_Time-i_Sample_Time)/i_Ref_Time<RT_Tor:
Score_matrix[i_Ref,i_Sample] = 0.5*np.exp(-0.5*((i_Sample_Time-i_Ref_Time)/(i_Ref_Time*RT_Tor))**2)+(1-0.5)*np.exp(-0.5*((i_Sample_MZ-i_Ref_MZ)/(i_Sample_MZ*MS_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-RT_Tor))
Alltemp_HPL['RI_right'] = Alltemp_HPL['RI'].apply(lambda x:x*(1+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()
name_begin = self.SampleData[list(self.SampleData.keys())[0]]['Path'].rfind('/')
if name_begin > 0:
file_output = self.SampleData[list(self.SampleData.keys())[0]]['Path'][0:name_begin]+'/HeuristicList'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx'
pickle_path = self.SampleData[list(self.SampleData.keys())[0]]['Path'][0:name_begin]+'/HeuristicList'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.pkl'
else:
name_begin = self.SampleData[list(self.SampleData.keys())[0]]['Path'].rfind('\\')
file_output = self.SampleData[list(self.SampleData.keys())[0]]['Path'][0:name_begin]+'\\HeuristicList'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.xlsx'
pickle_path = self.SampleData[list(self.SampleData.keys())[0]]['Path'][0:name_begin]+'\\HeuristicList'+str(time.localtime()[1])+str(time.localtime()[2])+str(time.localtime()[3])+str(time.localtime()[4])+'.pkl'
with open(pickle_path,'wb') as f:
pickle.dump(self.Heuristic_normal_list,f)
self.Heuristic_normal_list.rename(columns={'AverageMZ':'Average m/z'},inplace=True)
self.Heuristic_normal_list.rename(columns={'RI':'Average RI'},inplace=True)
self.Heuristic_normal_list.rename(columns={'MZ_List':'m/z value in each sample'},inplace=True)
self.Heuristic_normal_list.rename(columns={'RI_List':'RI value in each sample'},inplace=True)
self.Heuristic_normal_list.rename(columns={'RI_left':'RI left boundary'},inplace=True)
self.Heuristic_normal_list.rename(columns={'RI_right':'RI right boundary'},inplace=True)
self.Heuristic_normal_list.to_excel(file_output,index=False)
self.Label_process_sub.setText('Finished')
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 = MSExperiment()
self.file_path = DataPath
''' 储存文件pyopenms.MzMLFile().store("filtered.mzML", exp) '''
if self.file_path.endswith('mzML'):
MzMLFile().load(self.file_path,self.OriginData)
elif self.file_path.endswith('mzXML'):
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 = MSExperiment()
if DataPath.endswith('mzML'):
MzMLFile().load(DataPath,self.BlankData)
elif DataPath.endswith('mzXML'):
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 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))))
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:
RTR = RTR * 60 # min -> s
RTL = RTL * 60
RTR_place = EazyMZDataProcess.ClosestPosition(RTR,self.Blank_RT_List)
RTL_place = EazyMZDataProcess.ClosestPosition(RTL,self.Blank_RT_List)
RT_List = self.Blank_RT_List[RTL_place:RTR_place]
Int_List = []
#MZ_List=[]
for i in range(RTL_place, RTR_place):
if len(self.Blank_MZ_List[i])>0:
if len(self.Blank_MZ_List[i])>=4:
MZ_place = EazyMZDataProcess.ClosestPosition(MZ,self.Blank_MZ_List[i])
if MZ_place > len(self.Blank_MZ_List[i])-3 and MZ_place>=3:
MZ_place = len(self.Blank_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.Blank_MZ_List[i][ii] < MZ*(1+self.__param['MS1_Tor']):
temp_int.append(self.Blank_Int_List[i][ii])
#temp_MZ.append(Origin_MZ_List[i][ii])
if not temp_int:
Int_List.append(0)
else:
temp_int = np.array(temp_int)
Int_List.append(temp_int.max())
else:
Int_List.append(0)
else:
Int_List.append(0)
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))))
return RT_List, Int_List
def FPD_importer(path):
def str_num(x):
x = re.split(' |\\|[|]',x)
x = list(filter(lambda x:len(x)>0,x))
x = list(map(lambda x:float(x),x))
return x
rawData = pd.read_excel(path)
rawData['RTList'] = rawData['RTList'].apply(lambda x:str_num(x[1:len(x)-1]))
rawData['IntList'] = rawData['IntList'].apply(lambda x:str_num(x[1:len(x)-1]))
return rawData
def RI_to_RT(RI,RIIS):
n_place = list(filter(lambda x:RIIS.at[x,'C']<=RI,range(len(RIIS))))
n1_place = list(filter(lambda x:RIIS.at[x,'C']>RI,range(len(RIIS))))
if len(n_place)==0:
n_place = n1_place[0]
n1_place = n1_place[1]
elif len(n1_place)==0:
n1_place = n_place[-1]
n_place = n_place[-2]
else:
n_place = n_place[-1]
n1_place = n1_place[0]
C_number = RIIS['C'][n_place]
t_n = RIIS['RT'][n_place]
t_n1 = RIIS['RT'][n1_place]
RT = (RI/100-C_number)*(t_n1-t_n)+t_n
return RT
def RT_to_RI(RT,RIIS):
n_place = list(filter(lambda x:RIIS.at[x,'RT']<=RT,range(len(RIIS))))
n1_place = list(filter(lambda x:RIIS.at[x,'RT']>RT,range(len(RIIS))))
if len(n_place)==0:
n_place = n1_place[0]
n1_place = n1_place[1]
elif len(n1_place)==0:
n1_place = n_place[-1]
n_place = n_place[-2]
else:
n_place = n_place[-1]
n1_place = n1_place[0]
C_number = RIIS['C'][n_place]
t_n = RIIS['RT'][n_place]
t_n1 = RIIS['RT'][n1_place]
RI = 100*(C_number+(RT-t_n)/(t_n1-t_n))
return RI
def Heuristic_PeakDetect(self):
warnings.filterwarnings('ignore')
def RI_to_RT(RI,RIIS):
n_place = list(filter(lambda x:RIIS.at[x,'C']<=RI,range(len(RIIS))))
n1_place = list(filter(lambda x:RIIS.at[x,'C']>RI,range(len(RIIS))))
if len(n_place)==0:
n_place = n1_place[0]
n1_place = n1_place[1]
elif len(n1_place)==0:
n1_place = n_place[-1]
n_place = n_place[-2]
else:
n_place = n_place[-1]
n1_place = n1_place[0]
C_number = RIIS['C'][n_place]
t_n = RIIS['RT'][n_place]
t_n1 = RIIS['RT'][n1_place]
RT = (RI/100-C_number)*(t_n1-t_n)+t_n
return RT
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
def gaussian(x,a=1,b=10,c=2):
y=a*math.exp((-1*(x-b)**2)/(2*c**2))
return y
def RT_to_RI(RT,RIIS):
n_place = list(filter(lambda x:RIIS.at[x,'RT']<=RT,range(len(RIIS))))
n1_place = list(filter(lambda x:RIIS.at[x,'RT']>RT,range(len(RIIS))))
if len(n_place)==0:
n_place = n1_place[0]
n1_place = n1_place[1]
elif len(n1_place)==0:
n1_place = n_place[-1]
n_place = n_place[-2]
else:
n_place = n_place[-1]
n1_place = n1_place[0]
C_number = RIIS['C'][n_place]
t_n = RIIS['RT'][n_place]
t_n1 = RIIS['RT'][n1_place]
RI = 100*(C_number+(RT-t_n)/(t_n1-t_n))
return RI
Heuristic_peak_list = self.Heuristic_peak_list.copy()
Final_Peak_Detect = pd.DataFrame(columns=['AverageMZ', 'RT', 'RI', 'Int','RTList','SimilarityScore'])
Heuristic_peak_list['ActualNumber'] = 0
for ii in range(len(Heuristic_peak_list)):
MZ = Heuristic_peak_list.at[ii,'AverageMZ']
RI_left = Heuristic_peak_list.at[ii,'RI_left']
RI_right = Heuristic_peak_list.at[ii,'RI_right']
RI_List = Heuristic_peak_list.at[ii,'RI_List']
RTL = RI_to_RT(RI_left,self.RIIS)
RTR = RI_to_RT(RI_right,self.RIIS)
#Gradient_Code = Heuristic_peak_list.at[ii,'Gradient Code']
[RT_List,Int_List] = self.ExtractDataPoint(MZ,self.Origin_RT_List[-1],0,smooth_index=0)
Noise = sum(Int_List)/len(Int_List)+1
[RT_List,Int_List_Origin] = self.ExtractDataPoint(MZ,RTR+10,RTL-10,smooth_index=0)
[RT_List,Int_List] = self.ExtractDataPoint(MZ,RTR+10,RTL-10)
Int_List = list(map(lambda x:sum(Int_List[x-2:x+2])/len(Int_List[x-2:x+2]) if x in range(2,len(Int_List)-2) else Int_List[x],range(len(Int_List))))
if max(Int_List)>0 and max(Int_List_Origin)>0:
Int_Correction = max(Int_List_Origin)/max(Int_List)
else:
Int_Correction = 1
FD_List = np.array(list(map(lambda x: EazyMZDataProcess.TFFD(x,Int_List,RT_List), range(2, len(Int_List)-2))))
FD_List = np.array(list(map(lambda x:GaussSmooth(FD_List[x-2:x+2+1]) if x in range(2,len(FD_List)-2) else FD_List[x],range(len(FD_List)))))
SD_List = np.array(list(map(lambda x: EazyMZDataProcess.TFSD(x,Int_List,RT_List), range(2, len(Int_List)-2))))
SD_List = np.array(list(map(lambda x:GaussSmooth(SD_List[x-2:x+2+1]) if x in range(2,len(SD_List)-2) else SD_List[x],range(len(SD_List)))))
ABS_FD_List = abs(np.array(FD_List))
if Heuristic_peak_list.at[ii,'PeakNumber'] == 1:
FD_Median = EazyMZDataProcess.FD_Line(ABS_FD_List)
FD_Median_N = FD_Median*(-1)
SD_Median = min(SD_List)*0.1
else:
FD_Median = EazyMZDataProcess.FD_Line(ABS_FD_List)
FD_Median = FD_Median*Heuristic_peak_list.at[ii,'PeakNumber']*Heuristic_peak_list.at[ii,'PeakNumber']*self.__param['min_Int']/max(Int_List)
FD_Median_N = FD_Median*(-1)
SD_Median = sum(SD_List[(min(SD_List)*0.1<=SD_List) & (SD_List<=0)])/max(len(SD_List[(min(SD_List)*0.1<=SD_List) & (SD_List<=0)]),1)
#SD_Median = min(SD_List)*0.1*Heuristic_peak_list.at[ii,'PeakNumber']*self.__param['min_Int']/max(Int_List)
FD_P = list(filter(lambda x: FD_List[x] > FD_Median, range(1, len(FD_List))))
FD_N = list(filter(lambda x: FD_List[x] < FD_Median_N, range(1, len(FD_List))))
FD_Change = list(filter(lambda x: FD_List[x-1] > 0 and FD_List[x] < 0, range(1, len(FD_List))))
SD_Place = list(filter(lambda x: SD_List[x] < SD_Median, range(len(SD_List))))
Begin_Place,Complet_BP = EazyMZDataProcess.Find_FContinuous(FD_P, FD_P, FDMode='P',mergeRule='Intersection',FC_Number=2)
Begin_Place += 2
Complet_BP += 2
End_Place,Complet_EP = EazyMZDataProcess.Find_FContinuous(FD_N, FD_N, FDMode='N',mergeRule='Intersection',FC_Number=2)
End_Place += 2
Complet_EP += 2
Peak_Place = EazyMZDataProcess.Find_SDChange(FD_Change, SD_Place)
Peak_Place += 2
FD_Sequence = np.concatenate((Begin_Place, End_Place))
FD_Sequence.sort()
if len(Begin_Place) >= 1 and len(End_Place) >= 1 and Begin_Place[0] < End_Place[-1]:
PeakDetect_Table = pd.DataFrame({'Begin_Place':Begin_Place})
PeakDetect_Table['Peak_Place'] = PeakDetect_Table['Begin_Place'].apply(lambda x:[])
PeakDetect_Table['End_Place'] = PeakDetect_Table['Begin_Place'].apply(lambda x:[])
PeakDetect_Table['End_Place'] = PeakDetect_Table['End_Place'].astype('object')
PeakDetect_Table['Peak_Place'] = PeakDetect_Table['Peak_Place'].astype('object')
for i_FD_Seq in range(len(Begin_Place)):
if i_FD_Seq < len(Begin_Place)-1:
Valified_Peak_Place = list(filter(lambda x:PeakDetect_Table.at[i_FD_Seq,'Begin_Place']<Peak_Place[x]<PeakDetect_Table.at[i_FD_Seq+1,'Begin_Place'],range(len(Peak_Place))))
if len(Valified_Peak_Place) == 0:
continue
PeakDetect_Table.at[i_FD_Seq,'Peak_Place'] = list(Peak_Place[Valified_Peak_Place])
Valified_End_Place = list(filter(lambda x:PeakDetect_Table.at[i_FD_Seq,'Peak_Place'][0]<End_Place[x]<=PeakDetect_Table.at[i_FD_Seq+1,'Begin_Place'],range(len(End_Place))))
PeakDetect_Table.at[i_FD_Seq,'End_Place'] = list(End_Place[Valified_End_Place])
else:
Valified_Peak_Place = list(filter(lambda x:PeakDetect_Table.at[i_FD_Seq,'Begin_Place']<Peak_Place[x],range(len(Peak_Place))))
if len(Valified_Peak_Place) == 0:
continue
PeakDetect_Table.at[i_FD_Seq,'Peak_Place'] = list(Peak_Place[Valified_Peak_Place])
Valified_End_Place = list(filter(lambda x:End_Place[x]>PeakDetect_Table.at[i_FD_Seq,'Peak_Place'][0],range(len(End_Place))))
PeakDetect_Table.at[i_FD_Seq,'End_Place'] = list(End_Place[Valified_End_Place])
Valified = []
for i_FD_Seq in range(len(PeakDetect_Table)):
if len(PeakDetect_Table.at[i_FD_Seq,'Peak_Place'])==0 or len(PeakDetect_Table.at[i_FD_Seq,'End_Place'])==0:
continue
Valified.append(i_FD_Seq)
ii_FD_VPP = []
ii_FD_VEP = []
for ii_FD_Seq in range(len(PeakDetect_Table.at[i_FD_Seq,'End_Place'])):
Valified_Peak_Place = list(filter(lambda x:PeakDetect_Table.at[i_FD_Seq,'End_Place'][ii_FD_Seq]>PeakDetect_Table.at[i_FD_Seq,'Peak_Place'][x],range(len(PeakDetect_Table.at[i_FD_Seq,'Peak_Place']))))
Valified_Peak_Place = list(filter(lambda x:Int_List[Peak_Place[Valified_Peak_Place[x]]]==max(np.array(Int_List)[Peak_Place[Valified_Peak_Place]]),range(len(Valified_Peak_Place))))[0]
ii_FD_VPP.append(Valified_Peak_Place)
ii_FD_VEP.append(abs(1-(PeakDetect_Table.at[i_FD_Seq,'Peak_Place'][Valified_Peak_Place]-PeakDetect_Table.at[i_FD_Seq,'Begin_Place'])/(PeakDetect_Table.at[i_FD_Seq,'End_Place'][ii_FD_Seq]-PeakDetect_Table.at[i_FD_Seq,'Peak_Place'][Valified_Peak_Place])))
Final_Valified_Place = list(filter(lambda x:ii_FD_VEP[x]==min(ii_FD_VEP),range(len(ii_FD_VEP))))[0]
PeakDetect_Table.at[i_FD_Seq,'Peak_Place'] = PeakDetect_Table.at[i_FD_Seq,'Peak_Place'][ii_FD_VPP[Final_Valified_Place]]
PeakDetect_Table.at[i_FD_Seq,'End_Place'] = PeakDetect_Table.at[i_FD_Seq,'End_Place'][Final_Valified_Place]
PeakDetect_Table = PeakDetect_Table.iloc[Valified,:].copy()
PeakDetect_Table.reset_index(drop=True,inplace=True)
for i_wave in range(len(PeakDetect_Table)):
if PeakDetect_Table.at[i_wave,'Begin_Place'] >= int(self.__param['Points']/3):
Begin_min = list(filter(lambda x:min(Int_List[PeakDetect_Table.at[i_wave,'Begin_Place']-int(self.__param['Points']/3):PeakDetect_Table.at[i_wave,'Begin_Place']])==Int_List[x],range(PeakDetect_Table.at[i_wave,'Begin_Place']-int(self.__param['Points']/3),PeakDetect_Table.at[i_wave,'Begin_Place'])))
PeakDetect_Table.at[i_wave,'Begin_Place'] = Begin_min[0]
if PeakDetect_Table.at[i_wave,'End_Place'] < len(Int_List)-int(self.__param['Points']/3):
End_min = list(filter(lambda x:min(Int_List[PeakDetect_Table.at[i_wave,'End_Place']:PeakDetect_Table.at[i_wave,'End_Place']+int(self.__param['Points']/3)])==Int_List[x],range(PeakDetect_Table.at[i_wave,'End_Place'],PeakDetect_Table.at[i_wave,'End_Place']+int(self.__param['Points']/3))))
PeakDetect_Table.at[i_wave,'End_Place'] = End_min[0]
Peak_Begin = list(PeakDetect_Table['Begin_Place'])
Peak_End = list(PeakDetect_Table['End_Place'])
Peak_Top = list(PeakDetect_Table['Peak_Place'])
temp_Final_Peak = pd.DataFrame(columns=['AverageMZ', 'RT', 'RI', 'Int','RTList','SimilarityScore'])
for v in range(len(Peak_Begin)):
RT = RT_List[Peak_Top[v]]
RI = RT_to_RI(RT, self.RIIS)
Int = max(Int_List_Origin[Peak_Top[v]],Int_List[Peak_Top[v]]*Int_Correction)
#[BaseValue,Noise] = self.Baseline(MZ,RT_List[Peak_End[v]+1],RT_List[Peak_Begin[v]])
RTList = RT_List[Peak_Begin[v]:Peak_End[v]+1]
Peak_Int_List = Int_List[Peak_Begin[v]:Peak_End[v]+1]
#rate = max(Peak_Int_List[0],Peak_Int_List[-1])/Int
if Int > self.__param['min_Int'] and Int/Noise>=3 and len(RTList)>=self.__param['Points'] and len(list(filter(lambda x:(1-self.__param['RT_Tor'])<RI/x<(1+self.__param['RT_Tor']),RI_List)))>=1:
#IntList = Int_List[Peak_Begin[v]:Peak_End[v]+1]
RTPlace = list(filter(lambda x:Peak_Int_List[x]==max(Peak_Int_List),range(len(Peak_Int_List))))
Int_List_Gaussian_1 = list(map(lambda x:gaussian(x,b=RTList[RTPlace[0]],c=1),RTList))
Int_List_Gaussian_2 = list(map(lambda x:gaussian(x,b=RTList[RTPlace[0]],c=1.3),RTList))
Int_List_Gaussian_3 = list(map(lambda x:gaussian(x,b=RTList[RTPlace[0]],c=1.6),RTList))
Int_List_Gaussian_4 = list(map(lambda x:gaussian(x,b=RTList[RTPlace[0]],c=2),RTList))
SimilarityScore = max(EazyMZDataProcess.CosineSimilarity(Peak_Int_List,Int_List_Gaussian_1),
EazyMZDataProcess.CosineSimilarity(Peak_Int_List,Int_List_Gaussian_2),
EazyMZDataProcess.CosineSimilarity(Peak_Int_List,Int_List_Gaussian_3),
EazyMZDataProcess.CosineSimilarity(Peak_Int_List,Int_List_Gaussian_4))
Same_list = list(filter(lambda x:abs(Final_Peak_Detect['AverageMZ'][x]-MZ)/MZ<self.__param['MS1_Tor'] and abs(Final_Peak_Detect['RT'][x]-RT)<2,range(len(Final_Peak_Detect))))
if len(Same_list) == 0:
temp_Final_Peak_v = pd.DataFrame([(MZ, RT, RI, Int,RTList,SimilarityScore)], columns=['AverageMZ', 'RT', 'RI', 'Int','RTList','SimilarityScore'])
temp_Final_Peak = pd.concat([temp_Final_Peak,temp_Final_Peak_v])
temp_Final_Peak.reset_index(drop=True,inplace=True)
Score_matrix= np.zeros([len(RI_List),len(temp_Final_Peak)])
for i_Sm_row in range(len(RI_List)):
for i_Sm_col in range(len(temp_Final_Peak)):
if abs(temp_Final_Peak.at[i_Sm_col,'RI']/RI_List[i_Sm_row]-1) <=0.02:
Score_matrix[i_Sm_row,i_Sm_col] = temp_Final_Peak.at[i_Sm_col,'Int']
Sm_row,Sm_col = linear_sum_assignment(Score_matrix,True)
Valified_TFP = []
for i_Sm_row,i_Sm_col in zip(Sm_row,Sm_col):
if Score_matrix[i_Sm_row,i_Sm_col] != 0:
Valified_TFP.append(i_Sm_col)
temp_Final_Peak = temp_Final_Peak.iloc[Valified_TFP,:].copy()
temp_Final_Peak.reset_index(drop=True,inplace=True)
if len(temp_Final_Peak)<= Heuristic_peak_list.at[ii,'PeakNumber']:
Heuristic_peak_list.loc[ii,'ActualNumber'] = len(temp_Final_Peak)
else:
Heuristic_peak_list.loc[ii,'ActualNumber'] = Heuristic_peak_list.at[ii,'PeakNumber'].copy()
temp_Final_Peak.sort_values('Int',inplace=True)
temp_Final_Peak.reset_index(drop=True,inplace=True)
temp_Final_Peak = temp_Final_Peak.iloc[range(Heuristic_peak_list.at[ii,'PeakNumber']),:].copy()
Final_Peak_Detect = pd.concat([Final_Peak_Detect, temp_Final_Peak])
Final_Peak_Detect.reset_index(drop=True,inplace=True)
return [Final_Peak_Detect.copy(),Heuristic_peak_list.copy()]
def set_param(self,name,value):
if name in self.__param:
self.__param[name]=value
else:
print('no such param')
def get_param(self,key=''):
if len(key) == 0:
print(self.__param)
else:
return self.__param[key]
def get_MZ(self,x):
return self.Final_Peak_Detect.at[x,'AverageMZ']
def get_RT(self,x):
return self.Final_Peak_Detect.at[x,'RT']
def TFFD(x, Auto_Int_List, Auto_RT_List):
FirstDerivative = (Auto_Int_List[x+1]*8+Auto_Int_List[x-2]-Auto_Int_List[x-1] * 8-Auto_Int_List[x+2])/((Auto_RT_List[x+2]-Auto_RT_List[x-2])*3)
return FirstDerivative
def TFSD(x, Auto_Int_List, Auto_RT_List):
SecondDerivative = (Auto_Int_List[x+1]*16+Auto_Int_List[x-1]*16-Auto_Int_List[x] * 30-Auto_Int_List[x+2]-Auto_Int_List[x-2])/((Auto_RT_List[x+2]-Auto_RT_List[x-2])*3)
return SecondDerivative
def FD_Line(ABS_FD_List):
ABS_FD_List = list(filter(lambda x: x < max(ABS_FD_List)*0.10 and x > 0, ABS_FD_List))
FD_Median = np.median(ABS_FD_List)
return FD_Median
def SD_Line(SD_List):
SD_List = abs(SD_List)
if len(SD_List) >= 1:
SD_Median = min(SD_List)*0.05