def __init__(self, path): """ Initialize the Reader instance and check if the file is a valid peaks.mzML file and put it in a fileHandling.FileHandle instance. @type path: string @param path: The path of the feature XML file """ # filepath self.path = path # if the file at path does not start with <?xml, raise an exception that the xml file is invalid file = fileHandling.FileHandle(self.path) file.isXML() file.isMzML() # the current element self.element = None # current userParam # a list of all the keys that can be used for __getItem__ self.__spectraKeySet = [] # element dictionary to contain all the elements # uses collections.defaultidct to enable unknown keys to be added to the dictionary self.spectraInfo = collections.defaultdict(dict) self.simpleFlag = True
def __init__(self, path): """ Initialize the Reader instance and check if the file is a valid featureXML file and put it in a fileHandling.FileHandle instance. @type path: string @param path: The path of the feature XML file """ # filepath self.path = path # if the file at path does not start with <?xml, raise an exception that the xml file is invalid file = fileHandling.FileHandle(self.path) file.isXML() # if the second line of the file does not start with <featureMap, raise an exception that the file is not a featureXML file file.isFeatureXML() # a flag to see if simpleFeatureInfo or allFeatureInfo is used. This makes a difference in the __getItem__ function self.simpleFlag = True # the current element self.element = None # a list of all the keys that can be used for __getItem__ self.__elementKeySet = set([]) # element dictionary to contain all the elements # uses collections.defaultidct to enable unknown keys to be added to the dictionary self.elementInfo = collections.defaultdict(dict) # add the keys to _elementKeySet that __getitem__ takes self.__elementKeySet.add('intensity') self.__elementKeySet.add('overallquality') self.__elementKeySet.add('userParam') self.__elementKeySet.add('convexhull') self.__elementKeySet.add('mz') self.__elementKeySet.add('retention time') self.__elementKeySet.add('quality') self.__elementKeySet.add('charge') self.__elementKeySet.add('content') self.__elementKeySet.add('id') return
def test_isMascot(self): validMascot = testFolder + 'test_mascot.xml' fileHandler = fileHandling.FileHandle(validMascot) fileHandler.isMascot()
def test_isMascotException(self): invalidMascot = testFolder + 'featurexmlTestFile_1.featureXML' fileHandler = fileHandling.FileHandle(invalidMascot) self.assertRaises(IOError, fileHandler.isMascot)
def test_isFeatureXML_invalidException(self): invalidFeatureXML = testFolder + 'invalidFeatureXML_noheader.featureXML' fileHandler = fileHandling.FileHandle(invalidFeatureXML) # test if isFeatureXML gives the right error (IOError) when called with an invalid XML file self.assertRaises(IOError, fileHandler.isFeatureXML)
def test_isMzML(self): validMzML = testFolder + 'mzml_test_file_1.mzML' fileHandler = fileHandling.FileHandle(validMzML) fileHandler.isMzML() self.assertEqual(fileHandler.isMzML(), None)
def test_isFeatureXML(self): validFeatureXML = testFolder + 'featurexmlTestFile_1.featureXML' # if no error is given test passes fileHandler = fileHandling.FileHandle(validFeatureXML) self.assertEqual(fileHandler.isFeatureXML(), None)
def test_isXML_invalidException(self): invalidXmlFile = testFolder + '/invalidXML.XML' fileHandler = fileHandling.FileHandle(invalidXmlFile) # test if isXML gives the right error (IOError) when called with an invalid XML file self.assertRaises(IOError, fileHandler.isXML)
def test_isXML(self): validXmlFile = testFolder + 'validXML.XML' fileHandler = fileHandling.FileHandle(validXmlFile) # if no error is given test passes self.assertEqual(fileHandler.isXML(), None)
def test_getFile(self): fileHandle = fileHandling.FileHandle(testFolder + 'featurexmlTestFile_1.featureXML') self.assertEqual(fileHandle.getFile(), testFolder + 'featurexmlTestFile_1.featureXML')
def compareCoordinate(mzmlFile, featureFile, writeCSV=False, writeTo='precursorPerFeature.csv'): r""" Compare the precursors scan time and m/z values of a spectrum with all the retention time and m/z values in the convexhull of a feature. The spectrum information can come from a mzml File or a peaks.mzml file. It returns a dictionary with 3 keys: totalPrecursorsInFeatures, averagePrecursorsInFeatures and featPerPrecursorDict. totalPrecursorsInFeatures is a numeric value: the total amount of precursors that are in all features, averagePrecursorsInFeatures is a numeric value: the average amount of precursors in a feature and totalPrecursorsInFeatures is a dictionary with as key every feature and as value the amount of precursors per feature. A third input is writeCSV. If this is set to true, totalPrecursorsInFeatures is written out to a CSV file with a column featureID and a column # of precursors. @type mzmlFile: string @param mzmlFile: The path of the .mzML file @type featureFile: string @param featureFile: The path of the .featureXML file @type writeCSV: bool @param writeCSV: Flag if a CSV file has to be written out of the precursor per feature data (default: false) @type writeTo: string @param writeTo: The file and path where writeCSV has to be written to, default is precursorPerFeature.csv in the same folder as the script @rtype: Dictionary @returns: A dictionary with 3 keys: totalPrecursorsInFeatures, averagePrecursorsInFeatures and featPerPrecursorDict. totalPrecursorsInFeatures is a numeric value: the total amount of precursors that are in all features, averagePrecursorsInFeatures is a numeric value: the average amount of precursors in a feature and totalPrecursorsInFeatures is a dictionary with as key every feature and as value the amount of precursors per feature B{Examples}: Print the return value: >>> print compareCoordinate('example_mzML_file.mzML', 'example_feature_file.featureXML') {'totalPrecursorsInFeatures': 2, 'featPerPrecursorDict': {'f_43922326584371237334': 1, 'f_8613715360396561740': 0, 'f_13020522388175237334': 1}, 'averagePrecursorsInFeatures': 0.66666666666666663} Write the results to a csv file: >>> compareCoordinate(testFolder+'mzmlTestFile.mzML', testFolder+'featurexmlTestFile.featureXML', True, testFolder+'testPrecursorPerFeature.csv') # note the True """ fileHandle = fileHandling.FileHandle(os.path.abspath(mzmlFile)) # getting the absolute path of the given mzml file mzmlFile = os.path.abspath(mzmlFile) # parsing of mzml file msrun = pymzml.run.Reader(mzmlFile) # get the retention times and m/z of all precursors in msrun retentionTime = mzmlFunctions.getPrecursorRtMz(msrun) featureFile = os.path.abspath(featureFile) # make an instance of the parseFeatureXML.Reader object, with file as input featureXML = parseFeatureXML.Reader(featureFile) # featPrecursor will hold the amount of precursors per feature, with id as key and amount of precursors as feature featPrecursor = {} totalPrecursor = 0 countZero = 0 x = 0 # get all features out of featureXML for feature in featureXML.getSimpleFeatureInfo(): # set the amount of precursor per feature to 0 at every feature precursorPerFeature = 0 # get the coordinates of all features featureCoordinates = featureFunctions.getFeatureConvexhullCoordinates( feature) # loop for every feature coordinate through every MS/MS precursor coordinate for mzAndRT in retentionTime: # if the retention time (*60 to go from minutes to seconds) is larger than xMin and smaller than xMax and the m/z is # larger than xMin and smaller than xMax, count the precursors if float(mzAndRT['rt']) * 60 > float(featureCoordinates[feature]['rtMin']) and float(mzAndRT['rt'] * 60) < float(featureCoordinates[feature]['rtMax']) \ and float(mzAndRT['mz']) > float(featureCoordinates[feature]['mzMin']) and float(mzAndRT['mz']) < float(featureCoordinates[feature]['mzMax']): precursorPerFeature += 1 totalPrecursor += 1 if precursorPerFeature == 0: countZero += 1 featPrecursor[featureXML['id']] = precursorPerFeature x += 1 # if writeCSV flag is set to True, write out csv file to the absolute path of writeTo (default: precursorPerFeature.csv in the same folder) if writeCSV: compareDataWriter = output.CompareDataWriter(os.path.abspath(writeTo)) compareDataWriter.precursorPerFeatureCsvWriter(featPrecursor) # calculate the average precursor per feature averagePrecursFeature = float(totalPrecursor) / float(len(featPrecursor)) return { 'totalPrecursorsInFeatures': totalPrecursor, 'averagePrecursorsInFeatures': averagePrecursFeature, 'featPerPrecursorDict': featPrecursor }