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Specter_Standalone.py
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Specter_Standalone.py
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import sys
import time
import pandas as pd
import pymzml
import numpy as np
from numpy import linalg
from scipy import stats
from scipy import integrate
from scipy import sparse
from scipy.optimize import fsolve
from scipy.stats import mvn
import os
import random
import itertools
import cPickle as pickle
from functools import partial
import sparse_nnls
import multiprocessing
import sqlite3
import struct
import zlib
import csv
import re
import tqdm
from contextlib import closing
def RegressSpectraOntoLibrary(DIASpectrum,Library,tol,maxWindowOffset):
precMZ = float(DIASpectrum[1])
precRT = float(DIASpectrum[2]) #MS2 scan retention time, in minutes
index = DIASpectrum[3]
windowWidth = DIASpectrum[4]
DIASpectrum = np.array(DIASpectrum[0])
RefSpectraLibrary = Library
LibraryCoeffs = []
if len(DIASpectrum.shape) == 2:
if windowWidth > 0:
CandidateRefSpectraLibrary = [spectrum['Spectrum'] for key,spectrum in RefSpectraLibrary.iteritems() if
abs(float(spectrum['PrecursorMZ']) - precMZ) < windowWidth/2]
MassWindowCandidates = [key for key,spectrum in RefSpectraLibrary.iteritems() if
abs(float(spectrum['PrecursorMZ']) - precMZ) < windowWidth/2]
else:
CandidateRefSpectraLibrary = [spectrum['Spectrum'] for key,spectrum in RefSpectraLibrary.iteritems() if
float(spectrum['PrecursorMZ']) > precMZ - maxWindowOffset/2]
MassWindowCandidates = [key for key,spectrum in RefSpectraLibrary.iteritems() if
float(spectrum['PrecursorMZ']) > precMZ - maxWindowOffset/2]
#MERGING OF POINTS IN ACQUIRED SPECTRUM WITH NEARBY M/Z COORDINATES
MergedDIASpecCoordIndices = np.searchsorted(DIASpectrum[:,0]+tol*DIASpectrum[:,0],DIASpectrum[:,0])
MergedDIASpecCoords = DIASpectrum[np.unique(MergedDIASpecCoordIndices),0]
MergedDIASpecIntensities = [np.mean(DIASpectrum[np.where(MergedDIASpecCoordIndices == i)[0],1]) for i in np.unique(MergedDIASpecCoordIndices)]
DIASpectrum = np.array((MergedDIASpecCoords,MergedDIASpecIntensities)).transpose()
#FILTER LIBRARY SPECTRA BY THE CONDITION THAT SOME NUMBER OF THEIR 10 MOST INTENSE PEAKS BELONG TO THE DIA SPECTRUM
CentroidBreaks = np.concatenate((DIASpectrum[:,0]-tol*DIASpectrum[:,0],DIASpectrum[:,0]+tol*DIASpectrum[:,0]))
CentroidBreaks = np.sort(CentroidBreaks)
LocateReferenceCoordsInDIA = [np.searchsorted(CentroidBreaks,M[:,0]) for M in CandidateRefSpectraLibrary]
TopTenPeaksCoordsInDIA = [np.searchsorted(CentroidBreaks,M[np.argsort(-M[:,1])[0:min(10,M.shape[0])],0]) for M in CandidateRefSpectraLibrary]
ReferencePeaksInDIA = [i for i in range(len(MassWindowCandidates)) if
len([a for a in TopTenPeaksCoordsInDIA[i] if a % 2 == 1]) > 5] #min(3,CandidateRefSpectraLibrary[i].shape[0])]
ProportionOfReferencePeaksInDIA = [len([a for a in TopTenPeaksCoordsInDIA[i] if a % 2 == 1])/CandidateRefSpectraLibrary[i].shape[0]
for i in range(len(MassWindowCandidates))]
RefPeptideCandidatesLocations = [LocateReferenceCoordsInDIA[i] for i in ReferencePeaksInDIA]
RefPeptideCandidateList = [CandidateRefSpectraLibrary[i] for i in ReferencePeaksInDIA]
RefPeptideCandidates = [MassWindowCandidates[i] for i in ReferencePeaksInDIA]
NormalizedRefPeptideCandidateList = [M[:,1]/sum(M[:,1]) for M in RefPeptideCandidateList]
RefSpectraLibrarySparseRowIndices = (np.array([i for v in RefPeptideCandidatesLocations for i in v if i % 2 == 1]) + 1)/2
RefSpectraLibrarySparseRowIndices = RefSpectraLibrarySparseRowIndices - 1 #Respect the 0-indexing
RefSpectraLibrarySparseColumnIndices = np.array([i for j in range(len(RefPeptideCandidates)) for i in [j]*len([k for k in RefPeptideCandidatesLocations[j] if k % 2 == 1])])
RefSpectraLibrarySparseMatrixEntries = np.array([NormalizedRefPeptideCandidateList[k][i] for k in range(len(NormalizedRefPeptideCandidateList)) for i in range(len(NormalizedRefPeptideCandidateList[k]))
if RefPeptideCandidatesLocations[k][i] % 2 == 1])
if (len(RefSpectraLibrarySparseRowIndices) > 0 and len(RefSpectraLibrarySparseColumnIndices) > 0 and len(RefSpectraLibrarySparseMatrixEntries) > 0):
UniqueRowIndices = [i for i in set(RefSpectraLibrarySparseRowIndices)]
UniqueRowIndices.sort()
DIASpectrumIntensities=DIASpectrum[UniqueRowIndices,1] #Project the spectrum to those m/z bins at which at least one column of the coefficient matrix has a nonzero entry
DIASpectrumIntensities=np.append(DIASpectrumIntensities,[0]) #Add a zero to the end of the DIA data vector to penalize
#peaks of library spectra not present in the DIA spectrum
#AUGMENT THE LIBRARY MATRIX WITH TOTAL ION INTENSITIES OF PEAKS OF LIBRARY SPECTRA THAT DON'T CORRESPOND TO PEAKS IN DIA SPECTRUM
ReferencePeaksNotInDIA = np.array([k for v in RefPeptideCandidatesLocations for k in range(len(v)) if v[k] % 2 == 0])
SparseColumnIndicesForPeaksNotInDIA = np.arange(len(RefPeptideCandidates))
NumRowsOfLibraryMatrix = max(UniqueRowIndices)
SparseRowIndicesForPeaksNotInDIA = [NumRowsOfLibraryMatrix+1]*len(SparseColumnIndicesForPeaksNotInDIA)
#Duplicate (i,j) entries are summed together, yielding total ion intensities
SparseMatrixEntriesForPeaksNotInDIA = np.array([np.sum([NormalizedRefPeptideCandidateList[j][k]
for k in range(len(NormalizedRefPeptideCandidateList[j]))
if RefPeptideCandidatesLocations[j][k] % 2 == 0])
for j in range(len(NormalizedRefPeptideCandidateList))])
SparseRowIndices=np.append(RefSpectraLibrarySparseRowIndices,SparseRowIndicesForPeaksNotInDIA)
SparseColumnIndices=np.append(RefSpectraLibrarySparseColumnIndices,SparseColumnIndicesForPeaksNotInDIA)
SparseMatrixEntries=np.append(RefSpectraLibrarySparseMatrixEntries,SparseMatrixEntriesForPeaksNotInDIA)
SparseRowIndices = stats.rankdata(SparseRowIndices,method='dense').astype(int) - 1 #Renumber the row indices according to the projected spectrum,
#respecting the 0-indexing
LibrarySparseMatrix = sparse.coo_matrix((SparseMatrixEntries,(SparseRowIndices,SparseColumnIndices)))
LibraryCoeffs = sparse_nnls.lsqnonneg(LibrarySparseMatrix,DIASpectrumIntensities,{'show_progress': False})
LibraryCoeffs = LibraryCoeffs['x']
NonzeroCoeffs = [c for c in LibraryCoeffs if c != 0]
NonzeroCoeffsAboveThreshold = NonzeroCoeffs
Output = [[0,index,0,0,0,0]]
if len(NonzeroCoeffs) > 0:
RefSpectraIDs = [RefPeptideCandidates[j] for j in range(len(RefPeptideCandidates)) if LibraryCoeffs[j] != 0]
Output = [[NonzeroCoeffsAboveThreshold[i],index,RefSpectraIDs[i][0],RefSpectraIDs[i][1],precMZ,precRT] for i in range(len(NonzeroCoeffsAboveThreshold))]
return Output
def RegressSpectraOntoLibraryWithDecoys(DIASpectrum,Library,tol,maxWindowOffset):
precMZ = float(DIASpectrum[1])
precRT = float(DIASpectrum[2]) #MS2 scan retention time, in minutes
index = DIASpectrum[3]
windowWidth = DIASpectrum[4]
DIASpectrum = np.array(DIASpectrum[0])
RefSpectraLibrary = Library
LibraryCoeffs = []
if len(DIASpectrum.shape) == 2:
if windowWidth > 0:
CandidateRefSpectraLibrary = [spectrum['Spectrum'] for key,spectrum in RefSpectraLibrary.iteritems() if
abs(float(spectrum['PrecursorMZ']) - precMZ) < windowWidth/2]
MassWindowCandidates = [key for key,spectrum in RefSpectraLibrary.iteritems() if
abs(float(spectrum['PrecursorMZ']) - precMZ) < windowWidth/2]
CandidateDecoyLibrary = [spectrum['Spectrum'] for key,spectrum in RefSpectraLibrary.iteritems() if
windowWidth/2 <= abs(float(spectrum['PrecursorMZ']) - precMZ) <= windowWidth]
MassWindowDecoyCandidates = [("DECOY_"+key[0],key[1]) for key,spectrum in RefSpectraLibrary.iteritems() if
windowWidth/2 <= abs(float(spectrum['PrecursorMZ']) - precMZ) <= windowWidth]
else:
CandidateRefSpectraLibrary = [spectrum['Spectrum'] for key,spectrum in RefSpectraLibrary.iteritems() if
float(spectrum['PrecursorMZ']) > precMZ - maxWindowOffset/2]
MassWindowCandidates = [key for key,spectrum in RefSpectraLibrary.iteritems() if
float(spectrum['PrecursorMZ']) > precMZ - maxWindowOffset/2]
CandidateDecoyLibrary = [spectrum['Spectrum'] for key,spectrum in RefSpectraLibrary.iteritems() if
precMZ - maxWindowOffset <= float(spectrum['PrecursorMZ']) <= precMZ - maxWindowOffset/2]
MassWindowDecoyCandidates = [("DECOY_"+key[0],key[1]) for key,spectrum in RefSpectraLibrary.iteritems() if
precMZ - maxWindowOffset <= float(spectrum['PrecursorMZ']) <= precMZ - maxWindowOffset/2]
#FILTER LIBRARY SPECTRA BY THE CONDITION THAT SOME NUMBER OF THEIR 10 MOST INTENSE PEAKS BELONG TO THE DIA SPECTRUM
CentroidBreaks = np.concatenate((DIASpectrum[:,0]-tol*DIASpectrum[:,0],DIASpectrum[:,0]+tol*DIASpectrum[:,0]))
CentroidBreaks.sort()
LocateReferenceCoordsInDIA = [np.searchsorted(CentroidBreaks,M[:,0]) for M in CandidateRefSpectraLibrary]
#Hard cutoff - at least 5 of the 10 most intense peaks (or all peaks if there are fewer than 3) of reference spectrum must appear in acquired spectrum
TopTenPeaksCoordsInDIA = [np.searchsorted(CentroidBreaks,M[np.argsort(-M[:,1])[0:min(10,M.shape[0])],0]) for M in CandidateRefSpectraLibrary]
ReferencePeaksInDIA = [i for i in range(len(MassWindowCandidates)) if
len([a for a in TopTenPeaksCoordsInDIA[i] if a % 2 == 1]) > 5] #min(3,CandidateRefSpectraLibrary[i].shape[0])]
#SHIFT ALL FRAGMENT ION PEAKS OF ALL DECOY SPECTRA BY 20 M/Z TO ENSURE DISSIMILARITY FROM REAL SPECTRA
LocateDecoyCoordsInDIA = [np.searchsorted(CentroidBreaks,M[:,0]+20) for M in CandidateDecoyLibrary]
TopTenPeaksCoordsInDIA = [np.searchsorted(CentroidBreaks,M[np.argsort(-M[:,1])[0:min(10,M.shape[0])],0]+20) for M in CandidateDecoyLibrary]
DecoyPeaksInDIA = [i for i in range(len(MassWindowDecoyCandidates)) if
len([a for a in TopTenPeaksCoordsInDIA[i] if a % 2 == 1]) > 5] #min(3,CandidateRefSpectraLibrary[i].shape[0])]
RefPeptideCandidatesLocations = [LocateReferenceCoordsInDIA[i] for i in ReferencePeaksInDIA]
RefPeptideCandidateList = [CandidateRefSpectraLibrary[i] for i in ReferencePeaksInDIA]
RefPeptideCandidates = [MassWindowCandidates[i] for i in ReferencePeaksInDIA]
NormalizedRefPeptideCandidateList = [M[:,1]/sum(M[:,1]) for M in RefPeptideCandidateList]
DecoyCandidatesLocations = [LocateDecoyCoordsInDIA[i] for i in DecoyPeaksInDIA]
DecoyCandidateList = [CandidateDecoyLibrary[i] for i in DecoyPeaksInDIA]
DecoyCandidates = [MassWindowDecoyCandidates[i] for i in DecoyPeaksInDIA]
NormalizedDecoyCandidateList = [M[:,1]/sum(M[:,1]) for M in DecoyCandidateList]
RefSpectraLibrarySparseRowIndices = (np.array([i for v in RefPeptideCandidatesLocations for i in v if i % 2 == 1]) + 1)/2
RefSpectraLibrarySparseRowIndices = RefSpectraLibrarySparseRowIndices - 1 #Respect the 0-indexing
RefSpectraLibrarySparseColumnIndices = np.array([i for j in range(len(RefPeptideCandidates)) for i in [j]*len([k for k in RefPeptideCandidatesLocations[j] if k % 2 == 1])])
RefSpectraLibrarySparseMatrixEntries = np.array([NormalizedRefPeptideCandidateList[k][i] for k in range(len(NormalizedRefPeptideCandidateList)) for i in range(len(NormalizedRefPeptideCandidateList[k]))
if RefPeptideCandidatesLocations[k][i] % 2 == 1])
if (len(RefSpectraLibrarySparseRowIndices) > 0 and len(RefSpectraLibrarySparseColumnIndices) > 0 and len(RefSpectraLibrarySparseMatrixEntries) > 0):
DecoyLibrarySparseRowIndices = (np.array([i for v in DecoyCandidatesLocations for i in v if i % 2 == 1]) + 1)/2
DecoyLibrarySparseRowIndices = DecoyLibrarySparseRowIndices - 1 #Respect the 0-indexing
DecoyLibrarySparseColumnIndices = max(RefSpectraLibrarySparseColumnIndices) + 1 + np.array([i for j in range(len(DecoyCandidates)) for i in [j]*len([k for k in DecoyCandidatesLocations[j] if k % 2 == 1])])
DecoyLibrarySparseMatrixEntries = np.array([NormalizedDecoyCandidateList[k][i] for k in range(len(NormalizedDecoyCandidateList)) for i in range(len(DecoyCandidatesLocations[k]))
if DecoyCandidatesLocations[k][i] % 2 == 1])
UniqueRowIndices = [i for i in set(np.concatenate((RefSpectraLibrarySparseRowIndices,DecoyLibrarySparseRowIndices)))]
UniqueRowIndices.sort()
DIASpectrumIntensities=DIASpectrum[UniqueRowIndices,1] #Project the spectrum to those m/z bins at which at least one column of the coefficient matrix has a nonzero entry
DIASpectrumIntensities=np.append(DIASpectrumIntensities,[0]) #Add a zero to the end of the DIA data vector to penalize
#peaks of library spectra not present in the DIA spectrum
#AUGMENT THE LIBRARY MATRIX WITH TOTAL ION INTENSITIES OF PEAKS OF LIBRARY SPECTRA THAT DON'T CORRESPOND TO PEAKS IN DIA SPECTRUM
ReferencePeaksNotInDIA = np.array([k for v in RefPeptideCandidatesLocations for k in range(len(v)) if v[k] % 2 == 0])
SparseColumnIndicesForPeaksNotInDIA = np.arange(len(RefPeptideCandidates))
NumRowsOfLibraryMatrix = max(UniqueRowIndices)
SparseRowIndicesForPeaksNotInDIA = [NumRowsOfLibraryMatrix+1]*len(SparseColumnIndicesForPeaksNotInDIA)
#Duplicate (i,j) entries are summed together, yielding total ion intensities
SparseMatrixEntriesForPeaksNotInDIA = np.array([np.sum([NormalizedRefPeptideCandidateList[j][k]
for k in range(len(NormalizedRefPeptideCandidateList[j]))
if RefPeptideCandidatesLocations[j][k] % 2 == 0])
for j in range(len(NormalizedRefPeptideCandidateList))])
RefSpectraLibrarySparseRowIndices=np.append(RefSpectraLibrarySparseRowIndices,SparseRowIndicesForPeaksNotInDIA)
RefSpectraLibrarySparseColumnIndices=np.append(RefSpectraLibrarySparseColumnIndices,SparseColumnIndicesForPeaksNotInDIA)
RefSpectraLibrarySparseMatrixEntries=np.append(RefSpectraLibrarySparseMatrixEntries,SparseMatrixEntriesForPeaksNotInDIA)
DecoyPeaksNotInDIA = np.array([k for v in DecoyCandidatesLocations for k in range(len(v)) if v[k] % 2 == 0])
SparseColumnIndicesForDecoyPeaksNotInDIA = np.arange(len(DecoyCandidates))
NumRowsOfLibraryMatrix = max(UniqueRowIndices)
SparseRowIndicesForDecoyPeaksNotInDIA = [NumRowsOfLibraryMatrix+1]*len(SparseColumnIndicesForDecoyPeaksNotInDIA)
#Duplicate (i,j) entries are summed together, yielding total ion intensities
SparseMatrixEntriesForDecoyPeaksNotInDIA = np.array([np.sum([NormalizedDecoyCandidateList[j][k]
for k in range(len(NormalizedDecoyCandidateList[j]))
if DecoyCandidatesLocations[j][k] % 2 == 0])
for j in range(len(NormalizedDecoyCandidateList))])
DecoyLibrarySparseRowIndices = np.append(DecoyLibrarySparseRowIndices,SparseRowIndicesForDecoyPeaksNotInDIA)
DecoyLibrarySparseColumnIndices=np.append(DecoyLibrarySparseColumnIndices,max(RefSpectraLibrarySparseColumnIndices)+SparseColumnIndicesForDecoyPeaksNotInDIA + 1)
DecoyLibrarySparseMatrixEntries=np.append(DecoyLibrarySparseMatrixEntries,SparseMatrixEntriesForDecoyPeaksNotInDIA)
SparseRowIndices = np.concatenate((RefSpectraLibrarySparseRowIndices,DecoyLibrarySparseRowIndices))
SparseColumnIndices = np.concatenate((RefSpectraLibrarySparseColumnIndices,DecoyLibrarySparseColumnIndices))
SparseMatrixEntries = np.concatenate((RefSpectraLibrarySparseMatrixEntries,DecoyLibrarySparseMatrixEntries))
SparseRowIndices = stats.rankdata(SparseRowIndices,method='dense').astype(int) - 1 #Renumber the row indices according to the projected spectrum,
#respecting the 0-indexing
LibrarySparseMatrix = sparse.coo_matrix((SparseMatrixEntries,(SparseRowIndices,SparseColumnIndices)))
LibraryCoeffs = sparse_nnls.lsqnonneg(LibrarySparseMatrix,DIASpectrumIntensities,{'show_progress': False})
LibraryCoeffs = LibraryCoeffs['x']
NonzeroCoeffs = [c for c in LibraryCoeffs if c != 0]
NonzeroCoeffsAboveThreshold = NonzeroCoeffs
Output = [[0,index,0,0,0,0]]
if len(NonzeroCoeffs) > 0:
RefSpectraIDs = [RefPeptideCandidates[j] for j in range(len(RefPeptideCandidates)) if LibraryCoeffs[j] != 0]
DecoyIDs = [DecoyCandidates[j] for j in range(len(DecoyCandidates)) if LibraryCoeffs[max(RefSpectraLibrarySparseColumnIndices)+1+j] != 0]
RefSpectraIDs = RefSpectraIDs+DecoyIDs
Output = [[NonzeroCoeffsAboveThreshold[i],index,RefSpectraIDs[i][0],RefSpectraIDs[i][1],precMZ,precRT] for i in range(len(NonzeroCoeffsAboveThreshold))]
return Output
if __name__ == "__main__":
args = sys.argv
mzMLname = args[1] #dirName = args[1]
libName = args[2]
Index = 0
if len(args) >= 4:
Index = int(args[3])
StartOrEnd = "start"
if len(args) >= 5:
StartOrEnd = args[4]
numProcessors = multiprocessing.cpu_count()
if len(args) >= 6:
numProcessors = int(args[5])
instrument = 'orbitrap'
if len(args) == 7:
instrument=args[6]
delta = 10
if len(args) == 8:
delta=float(args[7])
#Cast the spectral library as a dictionary
start = time.time()
libPath = os.path.expanduser(libName+'.blib')
if os.path.exists(libPath):
Lib = sqlite3.connect(libPath)
LibPrecursorInfo = pd.read_sql("SELECT * FROM RefSpectra",Lib)
SpectraLibrary = {}
for i in range(len(LibPrecursorInfo)):
precursorID = str(LibPrecursorInfo['id'][i])
precursorKey = (LibPrecursorInfo['peptideModSeq'][i],LibPrecursorInfo['precursorCharge'][i])
NumPeaks = pd.read_sql("SELECT numPeaks FROM RefSpectra WHERE id = "+precursorID,Lib)['numPeaks'][0]
SpectrumMZ = pd.read_sql("SELECT peakMZ FROM RefSpectraPeaks WHERE RefSpectraID = " + precursorID,Lib)['peakMZ'][0]
SpectrumIntensities = pd.read_sql("SELECT peakIntensity FROM RefSpectraPeaks WHERE RefSpectraID = "+precursorID,Lib)['peakIntensity'][0]
if len(SpectrumMZ) == 8*NumPeaks and len(SpectrumIntensities) == 4*NumPeaks:
SpectraLibrary.setdefault(precursorKey,{})
SpectrumMZ = struct.unpack('d'*NumPeaks,SpectrumMZ)
SpectrumIntensities = struct.unpack('f'*NumPeaks,SpectrumIntensities)
SpectraLibrary[precursorKey]['Spectrum'] = np.array((SpectrumMZ,SpectrumIntensities)).T
SpectraLibrary[precursorKey]['PrecursorMZ'] = LibPrecursorInfo['precursorMZ'][i]
SpectraLibrary[precursorKey]['PrecursorRT'] = LibPrecursorInfo['retentionTime'][i] #The library retention time is given in minutes
elif len(SpectrumIntensities) == 4*NumPeaks:
SpectraLibrary.setdefault(precursorKey,{})
SpectrumMZ = struct.unpack('d'*NumPeaks,zlib.decompress(SpectrumMZ))
SpectrumIntensities = struct.unpack('f'*NumPeaks,SpectrumIntensities)
SpectraLibrary[precursorKey]['Spectrum'] = np.array((SpectrumMZ,SpectrumIntensities)).T
SpectraLibrary[precursorKey]['PrecursorMZ'] = LibPrecursorInfo['precursorMZ'][i]
SpectraLibrary[precursorKey]['PrecursorRT'] = LibPrecursorInfo['retentionTime'][i]
elif len(SpectrumMZ) == 8*NumPeaks:
SpectraLibrary.setdefault(precursorKey,{})
SpectrumMZ = struct.unpack('d'*NumPeaks,SpectrumMZ)
SpectrumIntensities = struct.unpack('f'*NumPeaks,zlib.decompress(SpectrumIntensities))
SpectraLibrary[precursorKey]['Spectrum'] = np.array((SpectrumMZ,SpectrumIntensities)).T
SpectraLibrary[precursorKey]['PrecursorMZ'] = LibPrecursorInfo['precursorMZ'][i]
SpectraLibrary[precursorKey]['PrecursorRT'] = LibPrecursorInfo['retentionTime'][i]
else:
SpectraLibrary = pickle.load(open(libName,"rb"))
print "Library loaded in {} minutes".format(round((time.time()-start)/60,1))
path = os.path.expanduser(mzMLname+'.mzML')
DIArun = pymzml.run.Reader(path)
E = enumerate(DIArun)
start = time.time()
if StartOrEnd == "end":
if instrument == 'orbitrap':
res = [[spectrum.peaks,spectrum['precursors'][0]['mz'],spectrum['MS:1000016'],i] for i,spectrum in E if spectrum['ms level'] == 2.0 and i < Index]
if instrument == 'tof':
res = [[spectrum.peaks,spectrum['precursors'][0]['mz'],spectrum['MS:1000016'],i] for i,spectrum in E if 'precursors' in spectrum.keys() and i < Index]
else:
if instrument == 'orbitrap':
res = [[spectrum.peaks,spectrum['precursors'][0]['mz'],spectrum['MS:1000016'],i] for i,spectrum in E if spectrum['ms level'] == 2.0 and i >= Index]
if instrument == 'tof':
res = [[spectrum.peaks,spectrum['precursors'][0]['mz'],spectrum['MS:1000016'],i] for i,spectrum in E if 'precursors' in spectrum.keys() and i >= Index]
print "Loaded {} MS2 spectra from {} in {} minutes.".format(len(res),path,round((time.time()-start)/60,1))
res=[(res[i][0],float(res[i][1]),float(res[i][2]),float(res[i][3]),float(res[i+1][1]) - float(res[i][1])) for i in range(len(res)-1)]
header=[[x[1],x[2],x[3]] for x in res]
absolutePath = mzMLname.rsplit('/',1)[0]
noPathName = mzMLname.rsplit('/',1)[1]
if '/' in libName:
libName = libName.rsplit('/',1)[1]
outputDir = os.path.expanduser(absolutePath+'/SpecterResults')
if not os.path.exists(outputDir):
os.makedirs(outputDir)
headerPath = os.path.expanduser(absolutePath+'/SpecterResults/'+noPathName+'_'+libName+'_header.csv')
with open(headerPath, "ab") as f:
writer = csv.writer(f)
writer.writerows(header)
print "Header written to {}.".format(headerPath)
MaxWindowPrecMZ = max(np.array([x[1] for x in res])) + max(np.array([x[4] for x in res]))
MaxOffset = max(np.array([x[4] for x in res]))
SpectraLibrary = {k:SpectraLibrary[k] for k in SpectraLibrary if SpectraLibrary[k]['PrecursorMZ'] < MaxWindowPrecMZ}
pool = multiprocessing.Pool(numProcessors)
with closing(pool) as p:
results = list(tqdm.tqdm(p.imap(partial(RegressSpectraOntoLibrary,Library=SpectraLibrary,tol=delta*1e-6,maxWindowOffset=MaxOffset), res), total=len(res)))
p.terminate()
output = [p.get() for p in results]
output = [[output[i][j][0],output[i][j][1],output[i][j][2],output[i][j][3],
output[i][j][4],output[i][j][5]] for i in range(len(output)) for j in range(len(output[i]))]
outputPath = os.path.expanduser(absolutePath+'/SpecterResults/'+noPathName+'_'+libName+'_SpecterCoeffs.csv')
with open(outputPath, "ab") as f:
writer = csv.writer(f)
writer.writerows(output)
print "Output written to {}.".format(outputPath)
with closing(pool) as p:
results = list(tqdm.tqdm(p.imap(partial(RegressSpectraOntoLibraryWithDecoys,Library=SpectraLibrary,tol=delta*1e-6,maxWindowOffset=MaxOffset), res), total=len(res)))
p.terminate()
output = [p.get() for p in results]
output = [[output[i][j][0],output[i][j][1],output[i][j][2],output[i][j][3],
output[i][j][4],output[i][j][5]] for i in range(len(output)) for j in range(len(output[i]))]
outputPath = os.path.expanduser(absolutePath+'/SpecterResults/'+noPathName+'_'+libName+'_SpecterCoeffsDecoys.csv')
with open(outputPath, "ab") as f:
writer = csv.writer(f)
writer.writerows(output)
print "Output written to {}.".format(outputPath)