#!/usr/bin/env python3.2
# -*- coding: utf-8 -*-
# encoding: utf-8
"""
The spectrum class offers a python object for mass spectrometry data.
The spectrum object holds the basic information on the spectrum and offers
methods to interrogate properties of the spectrum.
Data, i.e. mass over charge (m/z) and intensity decoding is performed on demand
and can be accessed via their properties, e.g. :py:attr:`spec.Spectrum.peaks`.
The Spectrum class is used in the :py:class:`run.Run` class.
There each spectrum is accessible as a spectrum object.
Theoretical spectra can also be created using the setter functions.
For example, m/z values, intensities, and peaks can be set by the
corresponding properties: :py:attr:`spec.Spectrum.mz`,
:py:attr:`spec.Spectrum.i`, :py:attr:`spec.Spectrum.peaks`.
"""
#
# pymzml
#
# Copyright (C) 2010-2011 T. Bald, J. Barth, M. Specht, H. Roest, C. Fufezan
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see .
from __future__ import print_function
import sys
import math
import copy
# import random
import re
from base64 import b64decode as b64dec
from struct import unpack as unpack
from collections import defaultdict as ddict
from operator import itemgetter as itemgetter
import zlib
PROTON = 1.00727646677
ISOTOPE_AVERAGE_DIFFERENCE = 1.002
class Spectrum(dict):
def __init__(self, measuredPrecision = None , param=None):
"""
.. function:: __init__( measuredPrecision = value* )
Initializes a pymzml.spec.Spectrum class.
:param measuredPrecision: in m/z, mandatory
:type measuredPrecision: float
"""
assert isinstance( measuredPrecision , float ), \
"Require measured precision as input parameter..."
self.measuredPrecision = measuredPrecision
# this will also set and update internalPrecision
self.clear()
self._mz = []
self._i = []
#self._time = self._mz
self.param = param
self.ms = {}
self.dataType = "?"
return
def __add__(self, otherSpec):
"""
Adds two pymzml spectra together.
:param otherSpec: Spectrum object
:type otherSpec: object
Example:
>>> import pymzml
>>> s = pymzml.spec.Spectrum( measuredPrescision = 20e-6 )
>>> file_to_read = "../mzML_example_files/xy.mzML.gz"
>>> run = pymzml.run.Reader(file_to_read , MS1_Precision = 5e-6 , MSn_Precision = 20e-6)
>>> for spec in run:
... s += spec
"""
assert isinstance(otherSpec, Spectrum), \
"can only add two pymzML spectra together ..."
tmp = self.deRef()
if tmp._reprofiledPeaks is None:
tmp._reprofiledPeaks = tmp._reprofile_Peaks()
for mz, i in otherSpec.reprofiledPeaks:
tmp._reprofiledPeaks[mz] += i
# deleting original data since we have now a combination of specs
tmp_reprofiledPeaks = tmp._reprofiledPeaks
tmp.clear()
tmp._reprofiledPeaks = tmp_reprofiledPeaks
tmp['reprofiled'] = True
return tmp
def __sub__(self,otherSpec):
"""
Subtracts two pymzml spectra.
:param otherSpec: Spectrum object
:type otherSpec: object
"""
assert isinstance(otherSpec,Spectrum) , "can only subtract two pymzML spectra ..."
tmp = self.deRef()
if tmp._reprofiledPeaks is None:
tmp._reprofiledPeaks = tmp._reprofile_Peaks()
for mz,i in otherSpec.reprofiledPeaks:
tmp._reprofiledPeaks[mz] -= i
# deleting original data since we have now a combination of specs
tmp_reprofiledPeaks = tmp._reprofiledPeaks
tmp.clear()
tmp._reprofiledPeaks = tmp_reprofiledPeaks
tmp['reprofiled'] = True
return tmp
def __mul__(self, value):
"""
Multiplies each intensity with a float, i.e. scales the spectrum.
:param value: Value to multiply the spectrum
:type value: float
"""
assert isinstance(value, (int, float)), "require float or int of intensity values ..."
tmp = self.deRef()
if tmp._peaks is not None:
tmp.peaks = [(mz, i * float(value)) for mz, i in tmp.peaks]
if tmp._centroidedPeaks is not None:
tmp.centroidedPeaks = [(mz, i * float(value)) for mz, i in tmp.centroidedPeaks]
if tmp._reprofiledPeaks is not None:
for mz in tmp._reprofiledPeaks.keys():
tmp._reprofiledPeaks[mz] *= float(value)
return tmp
def __truediv__(self,value):
"""
Divides each intensity by a float, i.e. scales the spectrum.
:param value: Value to divide the spectrum
:type value: float, int
"""
assert isinstance( value , ( int , float ) ), "require float or int of intensity values ..."
tmp = self.deRef()
if tmp._peaks is not None:
tmp.peaks = [ (mz,i/float(value)) for mz,i in tmp.peaks ]
if tmp._centroidedPeaks is not None:
tmp.centroidedPeaks = [ (mz,i/float(value)) for mz,i in tmp.centroidedPeaks ]
if tmp._reprofiledPeaks is not None:
for mz in tmp._reprofiledPeaks.keys():
tmp._reprofiledPeaks[mz] /= float(value)
return tmp
def __div__(self,value):
return self.__truediv__(value)
def __del__(self):
self.clear()
del self
return
def clear(self, scope = 'all'):
"""
Clears the current spectrum object which means that all variables are
set to default or ``None``
"""
if scope == 'all':
for k in list(self.keys()):
del self[k]
self._mz = None
self._i = None
self._peaks = None
self._centroidedPeaks = None
self._reprofiledPeaks = None
self._deconvolutedPeaks = None
self._transformedMzWithError = None
self._transformedPeaks = None
self._transformed_deconvolutedPeaks = None
self._transformedMassWithError = None
self._extremeValues = None
self._tmzSet = None
self._tmassSet = None
self._centroidedPeaksSortedByI = None
self._xmlTree = None
self._iter = None
self['BinaryArrayOrder'] = []
self.ms = {}
return
def strip(self, scope = 'all'):
"""
Reduces the size of the spectrum. Interesting if specs need to be added
or stored.
:param scope: accepts currently ["all"]
:type scope: string
"all" will remove the raw and profiled data and some internal lookup
tables as well.
"""
if scope == 'all':
if self._peaks is None:
# decode, just in case ...
self.peaks
self._tmzSet = None
self._tmassSet = None
self._transformedMzWithError = None
self._transformedPeaks = None
self._transformed_deconvolutedPeaks = None
self._transformedMassWithError = None
if 'encodedData' in self.keys():
del self['encodedData']
del self['PY:0000000']
# this is the ID tag corresponding to 'encodedData'
else:
print("Dont understand strip request ", file = sys.stderr)
@property
def mz(self):
"""
Returns the list of m/z values. If the m/z values are encoded, the
function :py:func:`_decode()` is used to decode the encoded data.\n
The mz property can also be setted, e.g. for theoretical data.
However, it is recommended to use the peaks property to set mz and
intesity tuples at same time.
:rtype: list
:return: Returns a list of mz from the actual analysed spectrum
"""
if self._mz is None:
self._decode()
return self._mz
@mz.setter
def mz(self, mzList):
assert isinstance( mzList, list ), "require list of mz values ..."
self._mz = mzList
return
@property
def time(self):
"""
Returns the list of time values (retention time for chromatograms). If
the time values are encoded, the function :py:func:`_decode()` is used
to decode the encoded data.\n
The time property can also be setted, e.g. for theoretical data.
However, it is recommended to use the peaks property to set time and
intesity tuples at same time.
:rtype: list
:return: Returns a list of mz from the actual analysed spectrum
"""
if self._mz is None:
self._decode()
return self._mz
@time.setter
def time(self, timeList):
assert isinstance(timeList, list), "require list of time (RT) values ..."
self._mz = timeList
return
def extremeValues(self, key):
"""
Find extreme values, minimal and maximum mz and intensity
:param key: m/z : "mz" or intensity : "i"
:type key: string
:rtype: tuple
:return: tuple of minimal and maximum m/z or intensity
"""
if key not in ['mz', 'i']:
print("Dont understand extreme request ", file = sys.stderr)
if self._extremeValues is None:
self._extremeValues = {}
try:
if key == 'mz':
self._extremeValues['mz'] = (
min([mz for mz, i in self.peaks]),
max([mz for mz, i in self.peaks])
)
else:
self._extremeValues['i'] = (
min([i for mz, i in self.peaks]),
max([i for mz, i in self.peaks])
)
except ValueError:
# emtpy spectrum
self._extremeValues[key] = ()
return self._extremeValues[key]
@property
def i(self):
"""
Returns the list of the intensity values.
If the intensity values are encoded, the function :py:func:`_decode()`
is used to decode the encoded data.\n
The i property can also be setted, e.g. for theoretical data.However, it
is recommended to use the peaks property to set mz and intesity tuples
at same time.
:rtype: list
:return: Returns a list of intensity values from the actual analysed
spectrum.
"""
if self._i is None:
self._decode()
return self._i
@i.setter
def i(self, intensityList):
assert isinstance(intensityList, list), "require list of intensity values ..."
self._i = intensityList
return
@property
def peaks(self):
"""
Returns the list of peaks of the spectrum as tuples (m/z, intensity).
:rtype: list of tuples
:return: Returns list of tuples (m/z, intensity)
Example:
>>> import pymzml
>>> run = pymzml.run.Reader(spectra.mzMl.gz, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... for mz, i in spectrum.peaks:
... print(mz, i)
.. note::
The peaks property can also be setted, e.g. for theoretical data.
It requires a list of mz/intensity tuples.
"""
if 'reprofiled' in self.keys():
self.peaks = self._centroid_peaks()
elif self._peaks is None:
if self._mz is None and 'encodedData' not in self.keys():
self._peaks = []
else:
self._peaks = list(zip(self.mz , self.i))
return self._peaks
@property
def profile(self):
"""
Returns the list of peaks of the chromatogram as tuples (time, intensity).
:rtype: list of tuples
:return: Returns list of tuples (time, intensity)
Example:
>>> import pymzml
>>> run = pymzml.run.Reader(spectra.mzMl.gz, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... for time, i in spectrum.profile:
... print(time, i)
"""
if 'reprofiled' in self.keys():
self.peaks = self._centroid_peaks()
elif self._peaks is None:
if self._mz is None and 'encodedData' not in self.keys():
self._peaks = []
else:
self._peaks = list(zip(self.mz , self.i))
return self._peaks
@peaks.setter
def peaks(self, mz_i_tuple_list):
assert isinstance(mz_i_tuple_list, list), "require list of tuples (mz,intensity) ..."
if len(mz_i_tuple_list) == 0:
return
self._mz, self._i = map(list, zip(*mz_i_tuple_list))
self._peaks = mz_i_tuple_list
return self
@property
def centroidedPeaks(self):
"""
Returns the centroided version of a profile spectrum. Performs a Gauss
fit to determine centroided mz and intensities, if the spectrum is in
measured profile mode.
Returns a list of tuples of fitted m/z-intesity values. If the spectrum
peaks are already centroided, these peaks are returned.
:rtype: list of tuples
:return: Returns list of tuples (m/z, intensity)
Example:
>>> import pymzml
>>> run = pymzml.run.Reader(spectra.mzMl.gz, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... for mz, i in spectrum.centroidedPeaks:
... print(mz, i)
"""
if 'reprofiled' in self.keys():
self.peaks = self._centroid_peaks()
self._centroidedPeaks = self._peaks
if self._centroidedPeaks is None: #or self._reprofiledPeaks is not None:
self._centroidedPeaks = self._centroid_peaks()
return self._centroidedPeaks
@centroidedPeaks.setter
def centroidedPeaks(self, mz_i_tuple_list):
assert isinstance(mz_i_tuple_list, list), "require list of tuples (mz,intensity) ..."
self._centroidedPeaks = mz_i_tuple_list
return
def _centroid_peaks(self):
"""
Perform a Gauss fit to centroid the peaks for the property
:py:attr:`centroidedPeaks`
"""
isProfile = False
for k in self.keys():
if isinstance(k,str):
if 'profile' in k:
isProfile = True
break
if isProfile:
tmp = []
if 'reprofiled' in self.keys():
intensity_array = [ i for mz, i in self.reprofiledPeaks ]
mz_array = [ mz for mz, i in self.reprofiledPeaks ]
del self['reprofiled']
else:
intensity_array = self.i
mz_array = self.mz
for pos , i in enumerate(intensity_array[:-1]):
if pos <= 1:
continue
if 0 < intensity_array[pos - 1] < i > intensity_array[pos + 1] > 0:
# local maximum ...
#if 827 <= mz_array[pos] <= 828:
# print("::",i,"@",mz_array[pos])
# print("Found maximum",i,"@",mz_array[pos],intensity_array[pos-1] ,'<' ,i ,"> ",intensity_array[pos+1] )
x1 = mz_array[pos - 1]
y1 = intensity_array[pos - 1]
x2 = mz_array[pos]
y2 = intensity_array[pos]
x3 = mz_array[pos + 1]
y3 = intensity_array[pos + 1]
if x2 - x1 > (x3 - x2) * 10 or (x2 - x1) * 10 < x3 - x2:
# no gauss fit if distance between mz values is too large
continue
if y3 == y1:
# i.e. a reprofiledSpec
# we start a bit closer to the mid point.
before = 3
after = 4
while (not 0 < y1 < y2 > y3 > 0) and y1 == y3 and after < 10: #we dont want to go too far
if pos - before < 0:
lower_pos = 0
else:
lower_pos = pos - before
if pos+after >= len(mz_array):
upper_pos = len(mz_array) - 1
else:
upper_pos = pos + after
x1 = mz_array[ lower_pos ]
y1 = intensity_array[ lower_pos ]
x3 = mz_array[ upper_pos ]
y3 = intensity_array[ upper_pos ]
if before % 2 == 0:
after += 1
else:
before += 1
if not (0 < y1 < y2 > y3 > 0) or y1 == y3:
#If we dont check this, there is a chance to apply gauss fit to a section
#where there is no peak.
continue
try:
doubleLog = math.log(y2 / y1) / math.log(y3 / y1)
mue = (doubleLog * ( x1 * x1 - x3 * x3 ) - x1 * x1 + x2 * x2 ) / (2 * (x2 - x1) - 2 * doubleLog * (x3 - x1))
cSquarred = ( x2*x2 - x1*x1 - 2*x2*mue + 2*x1*mue )/ ( 2* math.log(y1/y2 ))
A = y1 * math.exp( (x1 - mue) * (x1 - mue) / ( 2 * cSquarred ) )
#if A > 1e20:
#print(mue, A, doubleLog, cSquarred)
#print(x1, "\t", y1)
#print(x2, "\t", y2)
#print(x3, "\t", y3)
#print()
except:
continue
tmp.append((mue, A))
#for mue, A in tmp:
#print(mue, "\t", A)
return tmp
else:
return self.peaks
@property
def xmlTree(self):
"""
xmlTree property returns an iterator over the original
xmlTree structure the spectrum was initilized with.
Example:
>>> for element in spectrum.xmlTree:
... print( element, element.tag, element.items() )
please refer to the xml documentation of Python and cElementTree
for more details.
"""
return self._xmlTree.getiterator()
@property
def xmlTreeIterFree(self):
return self._xmlTree
def determine_swath_IDs(self):
ID_tagline=self._xmlTree.get('id')
for header in re.findall( r'([a-zA-Z]*)=', ID_tagline ):
self[ header ] = re.findall( r'{0}=([0-9]*)'.format( header ), ID_tagline )[0]
@property
def tmzSet(self):
'''Create set out of transformed m/z values
(including all values in the defined imprecision).
:rtype: set
'''
if self._tmzSet is None:
self._tmzSet = set()
for mz, i in self.centroidedPeaks:
self._tmzSet |= set(
range(
int(round(
(mz - (mz * self.measuredPrecision)) * self.internalPrecision
)),
int(round(
(mz + (mz * self.measuredPrecision)) * self.internalPrecision)) + 1)
)
return self._tmzSet
@property
def tmassSet(self):
'''create a set out of transformed mass values
(including all values in the defined imprecision).
:rtype: set
'''
if self._tmassSet is None:
self._tmassSet = set(self._transformed_mass_with_error.keys())
return self._tmassSet
def deRef( self ):
"""
Strip some heavy data and return deepcopy of spectrum.
Example:
>>> run = pymzml.run.Reader(file_to_read, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spec in run:
... tmp = spec.deRef()
"""
self.strip()
return copy.deepcopy(self)
def reduce(self, mzRange = (None,None) ):
"""
Works on peaks and reduces spectrum to a m/z range.
Example:
>>> run = pymzml.run.Reader(file_to_read, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spec in run:
... spec.reduce( mzRange = (100,200) )
"""
# NOTE Total ion current should be adjusted as well, I guess ;)
assert isinstance(mzRange, tuple), \
"require tuple of (min,max) mz range to reduce spectrum"
if mzRange != (None, None):
tmp_peaks = [ (mz,i) for mz, i in self.peaks if mzRange[0] <= mz <= mzRange[1] ]
self.clear(scope = 'not_all')
self.peaks = tmp_peaks
return self
def removeNoise(self, mode = 'median', noiseLevel = None):
"""
Function to remove noise from peaks, centroided peaks and reprofiled
peaks.
:param mode: define mode for removing noise. Default = "median"
(other modes: "mean", "mad")
:type mode: string
:rtype: list of tuples
:return: Returns a list with tuples of m/z-intensity pairs above the
noise threshold
mad < median < mean
Threshold is calculated over the mad/median/mean of all intensity values.
(mad = mean absolute deviation)
Example:
>>> import pymzml
>>> run = pymzml.run.Reader(spectra.mzML.gz, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... for mz, i in spectrum.removeNoise( mode = 'mean'):
... print(mz, i)
"""
need_to_be_called_before_nose_can_be_removed = self.peaks
need_to_be_called_before_nose_can_be_removed =self.centroidedPeaks
# Thanks to JD Hogan for pointing it out!
if noiseLevel is None:
noiseLevel = self.estimatedNoiseLevel(mode = mode)
if self._centroidedPeaks is not None:
self.centroidedPeaks = [ (mz, i) for mz, i in self.centroidedPeaks if i >= noiseLevel]
if self._peaks is not None:
self.peaks = [ (mz, i) for mz, i in self.peaks if i >= noiseLevel]
self._reprofiledPeaks = None
return self
def highestPeaks(self, n):
"""
Function to retrieve the n-highest centroided peaks of the spectrum.
:param n: Number of n-highest peaks
:type n: int
:rtype: list
:return: list of centroided peaks (mz, intensity tuples)
Example:
>>> run = pymzml.run.Reader("../mzML_example_files/deconvolution.mzML.gz", MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... if spectrum["ms level"] == 2:
... if spectrum["id"] == 1770:
... for mz,i in spectrum.highestPeaks(5):
... print(mz,i)
"""
if self._centroidedPeaksSortedByI is None:
self._centroidedPeaksSortedByI = sorted(
self.centroidedPeaks, key = itemgetter(1)
)
return self._centroidedPeaksSortedByI[-n:]
def estimatedNoiseLevel(self, mode = 'median'):
"""
Calculates noise threshold for function :py:func:`removeNoise`
"""
if self.centroidedPeaks == []:
return 0
if 'noiseLevelEstimate' not in self.keys():
self['noiseLevelEstimate'] = {}
if mode not in self['noiseLevelEstimate'].keys():
if mode == 'median':
self['noiseLevelEstimate']['median'] = self._median([ i for mz, i in self.centroidedPeaks])
elif mode == 'mad':
median = self.estimatedNoiseLevel(mode='median')
self['noiseLevelEstimate']['mad'] = self._median(sorted([ abs(i - median) for mz,i in self.centroidedPeaks]))
elif mode == 'mean':
mean = sum([i for mz, i in self.centroidedPeaks]) / float(len(self.centroidedPeaks))
self['noiseLevelEstimate']['mean'] = mean
self['noiseLevelEstimate']['variance'] = sum([(i - mean) * (i - mean) for mz, i in self.centroidedPeaks]) / float(len(self.centroidedPeaks))
else:
print("dont understand noise level estimation method call", mode, file = sys.stderr)
return self['noiseLevelEstimate'][mode]
def _median(self, data):
if len(data) == 0:
return None
data.sort()
l = len(data)
if not l % 2:
median = (data[int(math.floor(float(l)/float(2)))] + data[int(math.ceil(float(l)/float(2)))] ) / float(2.0)
else:
median = data[int(l/2)]
return median
@property
def reprofiledPeaks(self):
"""
Returns the reprofiled version of a centroided spectrum.
:rtype: list of reprofiled mz,i tuples
:return: Reprofiled peaks as tuple list
Example:
>>> import pymzml
>>> run = pymzml.run.Reader(spectra.mzMl.gz, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... for mz, i in spectrum.reprofiledPeaks:
... print(mz, i)
"""
#NOTE self._reprofiledPeaks is a defaultdict(int) with k:mz, v:i
if self._reprofiledPeaks is None:
if self.mz != []:
self._reprofiledPeaks = self._reprofile_Peaks()
else:
self._reprofiledPeaks = ddict(int)
return sorted(self._reprofiledPeaks.items())
def _reprofile_Peaks(self):
"""
Performs reprofiling for property :py:func:`reprofiledPeaks`
"""
tmp = ddict(int)
for mz, i in self.centroidedPeaks:
# Let the measured precision be 2 sigma of the signal width
s = mz * self.measuredPrecision * 2
s2 = s * s
floor = mz - 5.0 * s # Gauss curve +- 3 sigma
ceil = mz + 5.0 * s
ip = self.internalPrecision / 4
# more spacing, i.e. less points describing the gauss curve -> faster adding
for _ in range( int(round(floor * ip)) , int(round(ceil * ip)) + 1 ):
if _ % int(5) == 0 :
a = float(_) / float(ip)
y = i * math.exp( -1 * ((mz - a) * (mz - a)) / (2 * s2) )
tmp[ a ] += y
self['reprofiled'] = True
return tmp
@property
def measuredPrecision(self):
"""
Sets the measured and internal precision
:param value: measured precision (e.g. 5e-6)
:type value: float
"""
return self._measuredPrecision
@measuredPrecision.setter
def measuredPrecision(self, value):
self._measuredPrecision = value
self.internalPrecision = int(round(50000.0 / (value * 1e6)))
return
def _link(self, idTag=None, value = None, name = None):
try:
v = float(value)
except:
v = value
if idTag not in self:
self[idTag] = v
else:
oldValue = self[idTag]
self[idTag] = [oldValue]
self[idTag].append(v)
self[name] = self[idTag]
return
def _decodeNumpress(self, inData, compression):
"""
Decodes numpress encoded base 64 data.
:param inData: Input string, base64 encoded and numpress compressed
:type mz2find: string
:param compression: De-Compression algorithm to be used (valid are 'ms-np-linear', 'ms-np-pic', 'ms-np-slof')
:type mz2find: string
:rtype: array
:return: Returns the unpacked data as an array of floats.
"""
try:
import pyopenms
except ImportError:
print("Could not import pyOpenMS to decode numpress-encoded data -- please install the module to enable this functionality.")
exit(1)
result = []
coder = pyopenms.MSNumpressCoder()
np_config = pyopenms.NumpressConfig()
np_config.estimate_fixed_point = True
if compression == 'ms-np-linear':
np_config.np_compression = pyopenms.MSNumpressCoder.NumpressCompression.LINEAR
elif compression == 'ms-np-pic':
np_config.np_compression = pyopenms.MSNumpressCoder.NumpressCompression.PIC
elif compression == 'ms-np-slof':
np_config.np_compression = pyopenms.MSNumpressCoder.NumpressCompression.SLOF
coder.decodeNP(inData, result, False, np_config)
return result
def _decode(self):
"""
Decodes the base 64 encoded and packed strings from the data.
:rtype: tuple
:return: Returns the unpacked data as a tuple. Returns an empty list if
there is no raw data or raises an exception if data could not be
decoded.
"""
if 'encodedData' in self.keys():
compressionStated = True
n_BinaryArrayOrder = len(self['BinaryArrayOrder'])
if n_BinaryArrayOrder == 4:
compressionStated = False
#
for pos in range(0, n_BinaryArrayOrder, int(n_BinaryArrayOrder / 2)):
if compressionStated:
arrayType, compression, encodingType = [value for key, value in sorted([self['BinaryArrayOrder'][pos] , self['BinaryArrayOrder'][pos + 1], self['BinaryArrayOrder'][pos + 2]])]
else:
arrayType, encodingType = [value for key, value in sorted([self['BinaryArrayOrder'][pos] , self['BinaryArrayOrder'][pos + 1]])]
compression = 'no'
if encodingType == '32-bit float':
floattype = 'f'
elif encodingType == '64-bit float':
floattype = 'd'
else:
floattype = None
print("New data encoding detected, please adjust parser",
file = sys.stderr
)
unpackedData = []
if self['encodedData'][int(pos*0.5)] is None:
pass
elif len(self['encodedData'][int(pos*0.5)]) == 0:
pass
elif len(self['encodedData'][int(pos*0.5)]) != 0:
base64Data = self['encodedData'][int(pos * 0.5)].encode("utf-8")
decodedData = b64dec(base64Data)
if compression == 'zlib':
decodedData = zlib.decompress(decodedData)
elif compression in ['ms-np-linear', 'ms-np-pic', 'ms-np-slof']:
unpackedData = self._decodeNumpress(base64Data, compression)
elif compression == 'no':
pass
else:
print("New data compression ({0}) detected, please adjust parser".format(compression), file = sys.stderr)
exit(1)
fmt = "{endian}{arraylength}{floattype}".format( endian = "<" , arraylength = self['defaultArrayLength'] , floattype = floattype )
try:
if compression in ["no", "zlib"]:
unpackedData = unpack(fmt, decodedData)
except: # NOTE raises struct.error, but cannot be checked for here
print("Couldn't extract data {0} fmt: {1}".format(arrayType, fmt), file = sys.stderr)
print(len(self['encodedData'][int(pos * 0.5)]), file = sys.stderr)
exit(1)
if arrayType == 'mz' or arrayType == 'time':
self._mz = unpackedData
elif arrayType == 'i':
self._i = unpackedData
else:
print("Arraytype {0} not supported ...".format(arrayType), file = sys.stderr)
exit(1)
return
def hasPeak(self, mz2find):
"""
Checks if a Spectrum has a certain peak.
Needs a certain mz value as input and returns a list of peaks if a peak
is found in the spectrum, otherwise ``[]`` is returned.
Every peak is a tuple of m/z and intensity.
:param mz2find: mz value which should be found
:type mz2find: float
:rtype: list
:return: m/z and intensity as tuple in list
Example:
>>> import pymzml, get_example_file
>>> example_file = get_example_file.open_example('deconvolution.mzML.gz')
>>> run = pymzml.run.Reader(example_file, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... if spectrum["ms level"] == 2:
... peak_to_find = spectrum.hasPeak(1016.5404)
... print(peak_to_find)
[(1016.5404, 19141.735187697403)]
"""
value = self.transformMZ(mz2find)
return self._transformed_mz_with_error[value]
# NOTE this can return a result if a peak is found within 20.08 ppm (for a 20 ppm spectrum) ...
def hasDeconvolutedPeak(self, mass2find):
"""
Checks if a deconvoluted spectrum contains a certain peak.
Needs a mass value as input and returns a list of peaks if a peak
is found in the spectrum. If the mass is not found ``[]`` is
returned.
Every peak is a tuple of m/z and intensity.
:param mass2find: mass value which should be found
:type mass2find: float
:rtype: list
:return: mass and intensity as tuple in list if mass is found,
otherwise ``[]``
Example:
>>> import pymzml, get_example_file
>>> example_file = get_example_file.open_example('deconvolution.mzML.gz')
>>> run = pymzml.run.Reader(example_file, MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>> for spectrum in run:
... if spectrum["ms level"] == 2:
... peak_to_find = spectrum.hasDeconvolutedPeak(1044.5804)
... print(peak_to_find)
[(1044.5596, 3809.4356300564586)]
"""
value = self.transformMZ(mass2find)
return self._transformed_mass_with_error[value]
@property
def _transformed_mz_with_error(self):
"""
Returns transformed m/z value with error
:rtype: dictionary
:return: Transformed m/z values in dictionary {m/z_with_error :
[(m/z,intensity), ...], ...}
"""
if self._transformedMzWithError is None:
self._transformedMzWithError = ddict(list)
for mz, i in self.centroidedPeaks:
for t_mz_with_error in range(int(round((mz - (mz * self.measuredPrecision)) * self.internalPrecision)),
int(round((mz + (mz * self.measuredPrecision)) * self.internalPrecision)) + 1):
self._transformedMzWithError[t_mz_with_error].append((mz, i))
return self._transformedMzWithError
@property
def _transformed_mass_with_error(self):
"""
Returns transformed mass value with error
:rtype: dictionary
:return: Transformed mass values in dictionary {mass_with_error:
(mass,intensity), ...}
"""
if self._transformedMassWithError is None:
self._transformedMassWithError = ddict(list)
for mass, i in self.deconvolutedPeaks:
for t_mass_with_error in range(int(round((mass - (mass * self.measuredPrecision)) * self.internalPrecision)),
int(round((mass + (mass * self.measuredPrecision)) * self.internalPrecision)) + 1):
self._transformedMassWithError[t_mass_with_error].append((mass, i))
return self._transformedMassWithError
@property
def transformedPeaks(self):
"""
m/z value is multiplied by the internal precision
:rtype: list of tuples
:return: Returns a list of peaks (tuples of mz and intensity). Float m/z
values are adjusted by the internal precision to integers.
"""
if self._transformedPeaks is None:
self._transformedPeaks = [(self.transformMZ(mz), i) for mz, i in self.centroidedPeaks]
return self._transformedPeaks
@property
def transformed_deconvolutedPeaks(self):
"""
Deconvoluted mz value is multiplied by the internal precision
:rtype: list of tuples
:return: Returns a list of peaks (tuples of mz and intensity). Float m/z
values are adjusted by the internal precision to integers.
"""
if self._transformed_deconvolutedPeaks is None:
self._transformed_deconvolutedPeaks = [(self.transformMZ(mass), i) for mass, i in self.deconvolutedPeaks]
return self._transformed_deconvolutedPeaks
def _mz2mass(self, mz, charge):
"""
Calculate the uncharged mass for a given mz value
:param mz: m/z value
:type mz: float
:param charge: charge
:type charge: int
:rtype: float
:return: Returns mass of a given m/z value
"""
return ((mz - PROTON) * charge)
def _group(self, peaks):
"""
Group mz (or mass) values according to the given ppm value. The mean
value of grouped peaks is stored. When an intensity tuple is given, the
corresponding intensity are summed up and stored.
:rtype: list
:return: list of peaks
"""
mz_tuple, intensity_tuple = zip(*peaks)
count_ungrouped = 0
mz_list_grouped = []
i = 0
# iterate over all entries for grouping
while i < len(mz_tuple):
target = self.ppm2abs(mz_tuple[i], self.measuredPrecision, 1, 1)
j = i + 1
while j < len(mz_tuple) and mz_tuple[j] <= target:
j += 1
j = j- 1
if i == j:
# no peaks have to be grouped, just add the current peak to the result and go in with the next peak
mz_list_grouped.append(tuple([mz_tuple[i], intensity_tuple[i]]))
i += 1
else:
# potential overlapping peaks are found.
# check whether the mz value of the j index does not overlap with the next j+1 index
k = j + 1
group = True
if k < len(mz_tuple):
target_new = self.ppm2abs(mz_tuple[j], self.measuredPrecision, 1, 1)
if target_new >= mz_tuple[k]:
group = False
if group:
# group the peaks, calculate mean
mean = sum(mz_tuple[i:j+1])/len(mz_tuple[i:j+1])
intensity_sum = sum(intensity_tuple[i:j+1])
mz_list_grouped.append(tuple([mean, intensity_sum]))
i = j + 1
else:
# peaks are ambiguous, no grouping is applied --> every peak is stored
# this incident is counted.
count_ungrouped += j - i
# adding each element between i and j
for k in range(i, j + 1):
mz_list_grouped.append(tuple([mz_tuple[k], intensity_tuple[k]]))
i = j + 1
if count_ungrouped:
# if ungrouped entries occurred, this is reported
print('{0} elements could not be grouped due to an overlap.'.format(count_ungrouped), file = sys.stderr)
return mz_list_grouped
def _get_deisotopedMZ_for_chargeDeconvolution(self, ppmFactor = 4, minCharge = 1, maxCharge = 8, maxNextPeaks = 100):
"""
Calculates the deisotoped m/z value as an input for the charge deconvolution
:param ppmFactor: ppm factor
:type ppmFactor: int
:param minCharge: minimum charge considered
:type minCharge: int
:param maxCharge: maximum charge considered
:type maxCharge: int
:param maxNextPeaks: maximum length for isotope envelope
:type maxNextPaks: int
:rtype: list of tuples
:return: Monoisotopic peak [(m/z, intensity_sum, charge, found),...]
.. note::
The argument *maxNextPeaks* is just to make sure that the isotope
envelope doesnt get too long. This limit is not reached usually.
"""
try:
mz_list, intensities_list = zip(*self.centroidedPeaks)
except ValueError:
# empty spectrum
sys.exit()
mz = []
intensities = []
monoisotopicPeaks = []
length = len(mz_list)
override = False
for i in range(length):
for charge in range(maxCharge, minCharge - 1, -1):
# check absence of isotope envelope peaks before the current peak
#print("Analyzing mz_list, charge:", mz_list[i], charge)
found = False
if i == 0:
# the current peak is the first peak, no preceding peak is available, so this is a monoisotopic candidate
pass
else:
j = i - 1
target = mz_list[i] - ISOTOPE_AVERAGE_DIFFERENCE / charge
target_min = self.ppm2abs(target, self.measuredPrecision, -1, ppmFactor) # min and max should be calculated in one step (so that self.ppm() is not called twice)
target_max = self.ppm2abs(target, self.measuredPrecision, 1, ppmFactor)
while j >= 0 and mz_list[j] >= target_min:
if mz_list[j] <= target_max:
found = True
# Found preceeding peak, break goes to the next peak
break
j = j - 1
# if a potential preceding peak for the current peak is found, jump to the next peak
if found:
break
''' check presence of isotope envelope after the current peak'''
found = 1
intensity_sum = intensities_list[i]
last_intensity = intensities_list[i]
#last_mz = mz_list[i]
local_max = False
for i_envelope in range(1, maxNextPeaks + 1):
if (i + i_envelope) >= len(mz_list):
break
target = mz_list[i] + (ISOTOPE_AVERAGE_DIFFERENCE * i_envelope)/ charge
#target = last_mz + ISOTOPE_AVERAGE_DIFFERENCE / charge
hasPeak_result = self.hasPeak(target)
if len(hasPeak_result) > 1:
print("Found more than one peak. This is not expected")
sys.exit(1)
elif len(hasPeak_result) == 0:
break
# an isotope envelope is not supposed to have missing peaks
else:
mz, intensity = hasPeak_result[0]
if intensity < last_intensity:
# the peak before was the local maximum
local_max = True
elif local_max == True and intensity > last_intensity:
# this would be a second local max, so stop searching the isotope envelope
break
found += 1
intensity_sum += intensity
#last_mz = mz
if found > 1:
monoisotopicPeaks.append(tuple([mz_list[i], intensity_sum, charge, found]))
break
# as the first peak of the isotope envelope is added here, this is a monoisotopic peak.
# the charge derived from the isotope envelope is the highest charge which is possible.
return monoisotopicPeaks
@property
def deconvolutedPeaks(self):
"""
Calling :py:func:`spec.Spectrum.deconvolute_peaks` with standard
parameters, which calculates uncharged masses and returns deconvoluted
peaks.
:rtype: list
:return: list of deconvoluted peaks (mass (instead of m/z) / intensity tuples)
"""
if self._deconvolutedPeaks is None:
self._deconvolutedPeaks = self.deconvolute_peaks(ppmFactor = 4, minCharge = 1, maxCharge = 8, maxNextPeaks = 100)
return self._deconvolutedPeaks
def deconvolute_peaks(self, ppmFactor = 4, minCharge = 1, maxCharge = 8, maxNextPeaks = 100, returnCharge = False, debug = False):
"""
Calculating uncharged masses and returning deconvoluted peaks.
The deconvolution of spectra is done by first identifying isotope envelopes and
the charge state of this envelopes. The first peak of an isotope envelope is choosen
as the monoisotopic peak for which the mass is calculated from the m/z ratio.
Isotope envelopes are identified by searching the centroided spectrum for peaks
which show no preceding isotope peak within a specified mass accuracy. To be
sure, the measured mass accuracy is multiplied by a user adjustable factor
(``ppmFactor``). When the current peak meets the criteria with no preceding peaks, the
following peaks are analysed. The following peaks are considered to be part of
the isotope envelope, as long as they fit within the measured precision and
only one local maximum is present. The second local maximum is not considered
as the starting point of a new isotope envelope as one cannot be sure were this
isotope envelope starts. However, the last peak before the second local maximum
is considered to be part of the isotope envelope from the first local maximum,
as the intensity of this peak shouldn't have a big influence on the whole
isotope envelope intensity.
The charge range for detecting isotope envelopes can be specified (``minCharge``,
``maxCharge``). An isotope envelope always gets the highest possible charge.
With the charge the mass can be calculated from the m/z value of the first peak
of the isotope envelope. The intensity of the deconvoluted peak results from
the sum of all isotope envelope peaks.
In a last step, deconvoluted peaks are grouped together within the measured
precision. This is necessary because isotope envelopes from the same fragment
but with different charge states can leed to slightly different deconvoluted
peaks.
:param ppmFactor: ppm factor (imprecision factor)
:type ppmFactor: int
:param minCharge: minimum charge considered
:type minCharge: int
:param maxCharge: maximum charge considered
:type maxCharge: int
:param maxNextPeaks: maximum length for isotope envelope
:type maxNextPaks: int
:rtype: tuple (mass, intensity)
:return: Deconvoluted peaks, mass (instead of m/z) and intensity are
returned
"""
if self.measuredPrecision > 50e-6:
print("{0} ppm is too high for deconvolution. Please make sure to use spectra with < 50 ppm.".format(self.measuredPrecision * 1e6), file = sys.stderr)
exit(1)
if debug == True:
masses2mz = ddict(list)
# calculate monoisotopic m/z and charge
interestingPeaks = self._get_deisotopedMZ_for_chargeDeconvolution(ppmFactor, minCharge, maxCharge, maxNextPeaks)
# charge deconvolution
result = []
if returnCharge == True:
for mz, intensity, charge, n in interestingPeaks:
mass = self._mz2mass(mz, charge)
result.append(tuple([mass, intensity, charge]))
# sort the result corresponding to the mass (due to the mz to mass conversion, the values are no longer sorted)
result = sorted(result)
# check on empty result list
if len(result) == 0:
# no peaks could be identified for charge deconvolution.
return []
# group peaks
return result
else:
for mz, intensity, charge, n in interestingPeaks:
mass = self._mz2mass(mz, charge)
result.append(tuple([mass, intensity]))
if debug == True:
masses2mz[mass].append((mz, intensity, charge, n))
# sort the result corresponding to the mass (due to the mz to mass conversion, the values are no longer sorted)
result = sorted(result)
# check on empty result list
if len(result) == 0:
# no peaks could be identified for charge deconvolution.
return []
if debug is True:
return self._group(result), masses2mz
# group peaks
return self._group(result)
def ppm2abs(self, value, ppmValue, direction = 1, factor = 1):
'''
Returns the value plus (or minus, dependent on direction) the
imprecession for this value.
:param value: m/z value
:type value: float
:param ppmvalue: ppm value
:type ppmvalue: int
:param direction: plus or minus the considered m/z value. The argument
*direction* should be 1 or -1
:type direction: int
:param factor: multiplication factor for the imprecision.The argument
*factor* should be bigger than 0.
:type factor: int
:rtype: float
:return: imprecision for a given value
'''
result = value + (value * (ppmValue * factor)) * direction
return result
def hasOverlappingPeak(self, mz):
"""
Checks if a spetrum has more than one peak for a given m/z value and within the measured precision
:param mz: m/z value which should be checked
:type mz: float
:return: Returns ``True`` if a nearby peak is detected, otherwise ``False``
:rtype: bool
"""
for minus_or_plus in [-1, 1]:
target = self.ppm2abs(mz, self.measuredPrecision, minus_or_plus, 1)
temp = self.hasPeak(self.ppm2abs(mz, self.measuredPrecision) )
if temp and len(temp) > 1:
return True
return False
def similarityTo(self,spec2):
"""
Compares two spectra and returns cosine
:param spec2: another pymzml spectrum that is compated to the current spectrum.
:type spec2: pymzml.spec.Spectrum
:return: value between 0 and 1, i.e. the cosine between the two spectra.
:rtype: float
.. note::
Spectra data is transformed into an n-dimensional vector,
whereas mz values are binned in bins of 10 m/z and the intensities are added up.
Then the cosine is calculated between those two vectors.
The more similar the specs are, the closer the value is to 1.
"""
assert isinstance(spec2, Spectrum), "Spectrum2 is not a pymzML spectrum"
vector1 = ddict(int)
vector2 = ddict(int)
mzs = set()
for mz, i in self.peaks:
vector1[round(mz, 1)] += i
mzs.add(round(mz, 1))
for mz, i in spec2.peaks:
vector2[round(mz, 1)] += i
mzs.add(round(mz, 1))
z = 0
n_v1 = 0
n_v2 = 0
for mz in mzs:
int1 = vector1[mz]
int2 = vector2[mz]
z += int1 * int2
n_v1 += int1 * int1
n_v2 += int2 * int2
try:
cosine = z / (math.sqrt(n_v1) * math.sqrt(n_v2))
except:
cosine = 0.0
return cosine
def transformMZ(self, value):
"""
pymzml uses an internal precision to different tasks. This precision depends on the
measured prescision and is calculated when :py:func:`spec.Spectrum.measuredPrecision` is invoked.
transformMZ can be used to transform mz values into the internal standard.
:param value: mz value
:type value: float
:return: transformed value
:rtype: float
this value can be used to probe internal dictionaries, lists or sets, e.g. spectrum.tmzSet.
Example:
>>> import pymzml
>>> mzValues_to_test = set()
>>> run = pymzml.run.Reader( "test.mzML.gz" , MS1_Precision = 5e-6, MSn_Precision = 20e-6)
>>>
>>> for spectrum in run:
... if spectrum["ms level"] == 2:
... peak_to_find = spectrum.hasDeconvolutedPeak(1044.5804)
... print(peak_to_find)
[(1044.5596, 3809.4356300564586)]
"""
return int(round(value * self.internalPrecision))
def initFromTreeObjectWithRef(self, treeObject, refObject):
"""
initializes first from the treeObject and then goes
through the refObject and assigns any other parameters from there...
"""
self.initFromTreeObject(treeObject)
reference = ""
for element in treeObject.getiterator():
if element.tag.endswith('}referenceableParamGroupRef'):
reference = element.get('ref')
break
for element in refObject:
if element.tag.endswith('}referenceableParamGroup'):
refid = element.get('id')
if refid == reference:
self.readAccession(element)
return
def readAccession(self, parElement):
for element in parElement.getiterator():
accession = element.get('accession')
self.ms[accession] = element
if element.tag.endswith('cvParam'):
if accession in self.param['accessions']:
for mzmlTag in self.param['accessions'][accession]['valuesToExtract']:
try:
self._link(idTag = accession,
value = element.get(mzmlTag),
name = self.param['accessions'][accession]['name']
)
except KeyError:
if mzmlTag == 'unitName':
continue
# this allows parsing of mzML files generated with ProteomeDiscoverer
else:
print("kind of 'unitName' issue again ... with {0}".format(mzmlTag))
sys.exit()
if self.param['accessions'][accession]['name'] == 'intensity array':
self['BinaryArrayOrder'].append(('arrayType', 'i'))
elif self.param['accessions'][accession]['name'] == 'm/z array':
self['BinaryArrayOrder'].append(('arrayType', 'mz'))
elif self.param['accessions'][accession]['name'] == 'time array':
self['BinaryArrayOrder'].append(('arrayType', 'time'))
elif self.param['accessions'][accession]['name'] == '32-bit float':
self['BinaryArrayOrder'].append(('encoding', '32-bit float'))
elif self.param['accessions'][accession]['name'] == '64-bit float':
self['BinaryArrayOrder'].append(('encoding', '64-bit float'))
elif self.param['accessions'][accession]['name'] == 'zlib compression':
self['BinaryArrayOrder'].append(('compression', 'zlib'))
elif self.param['accessions'][accession]['name'] == 'no compression':
self['BinaryArrayOrder'].append(('compression', 'no'))
elif self.param['accessions'][accession]['name'] == 'MS-Numpress linear prediction compression':
self['BinaryArrayOrder'].append(('compression', 'ms-np-linear'))
elif self.param['accessions'][accession]['name'] == 'MS-Numpress positive integer compression':
self['BinaryArrayOrder'].append(('compression', 'ms-np-pic'))
elif self.param['accessions'][accession]['name'] == 'MS-Numpress short logged float compression':
self['BinaryArrayOrder'].append(('compression', 'ms-np-slof'))
elif element.tag.endswith('precursorList'):
# TODO remove this completely?
self['precursors'] = []
elif element.tag.endswith('selectedIon'):
if 'precursors' not in self.keys():
self['precursors'] = []
self['precursors'].append({'mz': None, 'charge': None})
for subElement in element.getiterator():
if subElement.tag.endswith('cvParam'):
accession = subElement.get('accession')
if accession == 'MS:1000040':
try:
self['precursors'][-1]['mz'] = float(subElement.get('value'))
except ValueError:
self['precursors'][-1]['mz'] = subElement.get('value')
elif accession == 'MS:1000041':
try:
self['precursors'][-1]['charge'] = int(subElement.get('value'))
except ValueError:
self['precursors'][-1]['charge'] = subElement.get('value')
elif accession == 'MS:1000744':
try:
self['precursors'][-1]['mz'] = float(subElement.get('value'))
except ValueError:
self['precursors'][-1]['mz'] = subElement.get('value')
else:
pass
elif element.tag.endswith('binary'):
self._link(
idTag = 'PY:0000000',
value = element.text,
name = 'encodedData'
)
return
def initFromTreeObject(self, treeObject):
"""
treeObject.get('nativeID')
print(treeObject)
print(treeObject.items())
for _ in treeObject.getiterator():
print(_.tag,_.items())
"""
self.clear()
self._xmlTree = treeObject
#
if treeObject.tag.endswith('chromatogram'):
self['id'] = treeObject.get('id')
self['ms level'] = None
self.dataType = "chromatogram"
else:
try:
'''
1.1.0 >>
1.1.0 >>
1.0.0 >>
0.99.1 >>
so far regex hold for this ...
'''
self['id'] = int(re.search( r'[0-9]*$', treeObject.get('id') ).group())
except:
self['id'] = None
self.dataType = "spectrum"
self['defaultArrayLength'] = int(treeObject.get('defaultArrayLength'))
self.readAccession(treeObject)
try:
if self['ms level'] == 1:
self.measuredPrecision = self.param['MS1_Precision']
else:
self.measuredPrecision = self.param['MSn_Precision']
except KeyError:
pass
return
if __name__ == '__main__':
print(__doc__)