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unidec.py
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import os
import time
import shutil
from copy import deepcopy
import subprocess
import zipfile
import fnmatch
import string
import numpy as np
from scipy.interpolate import interp1d
from scipy import signal
from unidec_modules import unidecstructure, peakstructure, MassFitter
import unidec_modules.unidectools as ud
import unidec_modules.IM_functions as IM_func
import unidec_modules.MassSpecBuilder as MSBuild
from unidec_modules.unidec_enginebase import UniDecEngine
__author__ = 'Michael.Marty'
class UniDec(UniDecEngine):
def __init__(self):
"""
UniDec Engine
Consists of three main subclasses: Config, DataContiner, Peaks
:return: None
"""
UniDecEngine.__init__(self)
self.autopeaks = None
self.peakparams = None
self.massfit = None
self.massfitdat = None
self.errorgrid = None
pass
def open_file(self, file_name, file_directory=None, *args, **kwargs):
"""
Open text or mzML file. Will create _unidecfiles directory if it does not exist.
If it finds a _conf.dat file in _unidecfiles, it will import the old configuration.
Otherwise, it keeps the existing configuration but resets file names.
If silent=True is passed in **kwargs, prints are suppressed.
:param file_name: Name of file to open. May be in x y or x y z text format or in mzML format.
May be tab or space delimited
:param file_directory: Directory in which filename is located. Default is current directory.
:return: None
"""
if file_directory is None:
file_directory = os.path.dirname(file_name)
file_name = os.path.basename(file_name)
tstart = time.clock()
self.pks = peakstructure.Peaks()
self.data = unidecstructure.DataContainer()
# Handle Paths
self.config.filename = file_name
if "silent" not in kwargs or not kwargs["silent"]:
print "Opening File: ", self.config.filename
self.config.outfname = os.path.splitext(self.config.filename)[0]
self.config.extension = os.path.splitext(self.config.filename)[1]
self.config.default_file_names()
self.config.dirname = file_directory
file_directory = os.path.join(self.config.dirname, self.config.filename)
os.chdir(self.config.dirname)
# Import Data
self.data.rawdata = ud.load_mz_file(file_directory, self.config)
if self.data.rawdata.shape[1] == 3:
self.config.imflag = 1
self.config.discreteplot = 1
self.config.poolflag = 1
mzaxis = np.unique(self.data.rawdata[:, 0])
dtaxis = np.unique(self.data.rawdata[:, 1])
intgrid = np.zeros((len(mzaxis), len(dtaxis)))
if len(self.data.rawdata[:, 2]) == len(np.ravel(intgrid)):
intgrid = self.data.rawdata[:, 2].reshape((len(mzaxis), len(dtaxis)))
else:
for x, y, z in self.data.rawdata:
intgrid[np.where(mzaxis == x)[0][0], np.where(dtaxis == y)[0][0]] = z
self.data.rawdata = np.transpose([mzaxis, np.sum(intgrid, axis=1)])
mzaxis, dtaxis = np.meshgrid(mzaxis, dtaxis, sparse=False, indexing='ij')
self.data.rawdata3 = np.transpose([np.ravel(mzaxis), np.ravel(dtaxis), np.ravel(intgrid)])
self.data.data3 = self.data.rawdata3
else:
self.config.imflag = 0
self.data.data2 = self.data.rawdata
self.config.procflag = 0
# Change paths to unidecfiles folder
dirnew = self.config.outfname + "_unidecfiles"
if "clean" in kwargs and kwargs["clean"] and os.path.isdir(dirnew):
shutil.rmtree(dirnew)
if not os.path.isdir(dirnew):
os.mkdir(dirnew)
os.chdir(dirnew)
self.config.udir = os.getcwd()
if self.config.imflag == 0:
newname = os.path.join(os.getcwd(), self.config.outfname + "_rawdata.txt")
else:
newname = os.path.join(os.getcwd(), self.config.outfname + "_imraw.txt")
if not os.path.isfile(newname):
shutil.copy(file_directory, newname)
# Initialize Config
if os.path.isfile(self.config.confname) == True:
self.load_config(self.config.confname)
tend = time.clock()
if "silent" not in kwargs or not kwargs["silent"]:
print "Loading Time: %.2gs" % (tend - tstart)
def raw_process(self, dirname, inflag=False, binsize=1):
"""
Processes Water's Raw files into .txt using external calls to:
self.config.rawreaderpath for MS
self.config.cdcreaderpath for IM-MS
Default files are created with the header of the .raw file plus:
_rawdata.txt for MS
_imraw.txt for IM-MS
:param dirname: .raw directory name
:param inflag: If True, it will put the output .txt file inside the existing .raw directory. If False, it will
put the file in the same directory that contains the .raw directory
:param binsize: Parameter for IM-MS to specify the m/z bin size for conversion. If binsize=0, the conversion
will be at full resolution (which is huge), so the default is every 1 m/z.
:return: self.config.filename, self.config.dirname (name and location of created file)
"""
self.config.dirname = dirname
self.config.filename = os.path.split(self.config.dirname)[1]
self.config.outfname = os.path.splitext(self.config.filename)[0]
print "Openening: ", self.config.filename
if os.path.splitext(self.config.filename)[1] == ".zip":
print "Can't open zip, try Load State."
return None, None
if os.path.splitext(self.config.filename)[1] == ".raw" and self.config.system == "Windows":
self.config.outfname = os.path.splitext(self.config.filename)[0]
newfilename = self.config.outfname + "_rawdata.txt"
if self.config.imflag == 1:
newfilename = self.config.outfname + "_imraw.txt"
if inflag:
newfilepath = os.path.join(self.config.dirname, newfilename)
else:
newfilepath = os.path.join(os.path.dirname(self.config.dirname), newfilename)
if os.path.isfile(newfilepath):
self.config.filename = newfilename
print "Data already converted"
else:
if self.config.system == "Windows":
if self.config.imflag == 0:
result = subprocess.call(
[self.config.rawreaderpath, "-i", self.config.dirname, "-o", newfilepath])
self.config.filename = newfilename
else:
call = [self.config.cdcreaderpath, '-r', self.config.dirname, '-m',
newfilepath[:-10] + "_msraw.txt", '-i', newfilepath, '--ms_bin', binsize,
"--ms_smooth_window", "0", "--ms_number_smooth", "0", "--im_bin", binsize, "--sparse",
"1"]
result = subprocess.call(call)
self.config.filename = newfilename
if result == 0 and os.path.isfile(newfilepath):
print "Converted data from raw to txt"
else:
print "Failed conversion to txt file. ", result, newfilepath
return None, None
else:
print "Sorry. Waters Raw converter only works on windows. Convert to txt file first."
return None, None
return self.config.filename, self.config.dirname
pass
def process_data(self, **kwargs):
"""
Process data according to parameters in config.
Checks certain parameters to make sure the limits make sense.
Will accept silent=True kwarg to suppress printing.
:return: None
"""
tstart = time.clock()
self.export_config()
try:
float(self.config.minmz)
except ValueError:
self.config.minmz = np.amin(self.data.rawdata[:, 0])
try:
float(self.config.maxmz)
except ValueError:
self.config.maxmz = np.amax(self.data.rawdata[:, 0])
if self.config.imflag == 1:
try:
float(self.config.mindt)
except ValueError:
self.config.mindt = np.amin(self.data.rawdata3[:, 1])
try:
float(self.config.maxdt)
except ValueError:
self.config.maxdt = np.amax(self.data.rawdata3[:, 1])
if self.check_badness() == 1:
print "Badness found, aborting data prep"
return 1
if self.config.imflag == 0:
self.data.data2 = ud.dataprep(self.data.rawdata, self.config)
ud.dataexport(self.data.data2, self.config.infname)
else:
tstart2 = time.clock()
mz, dt, i3 = IM_func.process_data_2d(self.data.rawdata3[:, 0], self.data.rawdata3[:, 1],
self.data.rawdata3[:, 2],
self.config)
tend = time.clock()
if "silent" not in kwargs or not kwargs["silent"]:
print "Time: %.2gs" % (tend - tstart2)
self.data.data3 = np.transpose([np.ravel(mz), np.ravel(dt), np.ravel(i3)])
self.data.data2 = np.transpose([np.unique(mz), np.sum(i3, axis=1)])
ud.dataexport(self.data.data3, self.config.infname)
pass
self.config.procflag = 1
tend = time.clock()
if "silent" not in kwargs or not kwargs["silent"]:
print "Data Prep Done. Time: %.2gs" % (tend - tstart)
# self.get_spectrum_peaks()
pass
def run_unidec(self, silent=False, efficiency=False):
"""
Runs UniDec.
Checks that everything is set to go and then places external call to:
self.config.UniDecPath for MS
self.config.UniDecIMPath for IM-MS
If successful, calls self.unidec_imports()
If not, prints the error code.
:param silent: If True, it will suppress printing the output from UniDec
:param efficiency: Passed to self.unidec_imports()
:return: out (stdout from external UniDec call)
"""
# Check to make sure everything is in order
if self.config.procflag == 0:
print "Need to process data first..."
self.process_data()
if self.check_badness() == 1:
print "Badness found, aborting UniDec run"
return 1
# Export Config and Call
self.export_config()
tstart = time.clock()
out = ud.unidec_call(self.config, silent=silent)
tend = time.clock()
self.config.runtime = (tend - tstart)
if not silent:
print "UniDec run %.2gs" % self.config.runtime
# Import Results if Successful
if out == 0:
self.unidec_imports(efficiency)
if not silent:
print "File Name: ", self.config.filename, "R Sqaured: ", self.config.error
return out
else:
print "UniDec Run Error:", out
return out
def unidec_imports(self, efficiency=False):
"""
Imports files output from the UniDec core executable into self.data.
:param efficiency: If True, it will ignore the larger files to speed up the run.
:return: None
"""
# Import Results
self.pks = peakstructure.Peaks()
self.data.massdat = np.loadtxt(self.config.outfname + "_mass.txt")
self.data.ztab = np.arange(self.config.startz, self.config.endz + 1)
self.config.massdatnormtop = np.amax(self.data.massdat[:, 1])
if not efficiency:
self.data.massgrid = np.fromfile(self.config.outfname + "_massgrid.bin", dtype=float)
self.data.fitdat = np.fromfile(self.config.outfname + "_fitdat.bin", dtype=float)
try:
if self.config.aggressiveflag != 0:
self.data.baseline = np.fromfile(self.config.outfname + "_baseline.bin", dtype=float)
else:
self.data.baseline = np.array([])
except Exception, e:
self.data.baseline = np.array([])
pass
if self.config.imflag == 1:
self.data.fitdat2d = deepcopy(self.data.data3)
self.data.fitdat2d[:, 2] = self.data.fitdat
self.data.fitdat = np.sum(self.data.fitdat.reshape(
(len(np.unique(self.data.data3[:, 0])), len(np.unique(self.data.data3[:, 1])))), axis=1)
runstats = np.genfromtxt(self.config.outfname + "_error.txt", dtype='str')
if self.config.imflag == 0:
# Calculate Error
sse = float(runstats[0, 2])
mean = np.mean(self.data.data2[:, 1])
self.config.error = 1 - sse / np.sum((self.data.data2[:, 1] - mean) ** 2)
if not efficiency:
# Import Grid
self.data.mzgrid = np.fromfile(self.config.outfname + "_grid.bin", dtype=float)
xv, yv = np.meshgrid(self.data.ztab, self.data.data2[:, 0])
xv = np.c_[np.ravel(yv), np.ravel(xv)]
self.data.mzgrid = np.c_[xv, self.data.mzgrid]
else:
# Calculate Error
self.config.error = float(runstats[1])
self.data.ccsdata = np.loadtxt(self.config.outfname + "_ccs.txt")
if not efficiency:
# Import Grids and Reshape
masslen = len(self.data.massdat)
ccslen = len(self.data.ccsdata)
zlen = len(self.data.ztab)
self.data.massccs = np.fromfile(self.config.outfname + "_massccs.bin", dtype=float)
self.data.ccsz = np.fromfile(self.config.outfname + "_ccsz.bin", dtype=float)
self.data.mztgrid = np.fromfile(self.config.outfname + "_mzgrid.bin", dtype=float)
self.data.massccs = self.data.massccs.reshape((masslen, ccslen))
self.data.ccsz = self.data.ccsz.reshape((zlen, ccslen))
self.data.mztgrid = np.clip(self.data.mztgrid, 0.0, np.amax(self.data.mztgrid))
self.data.mztgrid = self.data.mztgrid.reshape(
(len(np.unique(self.data.data3[:, 0])), len(np.unique(self.data.data3[:, 1])), zlen))
self.data.mzgrid = np.sum(self.data.mztgrid, axis=1)
xv, yv = np.meshgrid(self.data.ztab, np.unique(self.data.data3[:, 0]))
xv = np.c_[np.ravel(yv), np.ravel(xv)]
self.data.mzgrid = np.c_[xv, np.ravel(self.data.mzgrid)]
def pick_peaks(self):
"""
Detect, Normalize, and Output Peaks
:return: None
"""
self.export_config()
# Detect Peaks and Normalize
peaks = ud.peakdetect(self.data.massdat, self.config)
if self.config.peaknorm == 1:
norm = np.amax(peaks[:, 1]) / 100.
peaks[:, 1] = peaks[:, 1] / norm
self.data.massdat[:, 1] = self.data.massdat[:, 1] / norm
elif self.config.peaknorm == 2:
norm = np.sum(peaks[:, 1]) / 100.
peaks[:, 1] = peaks[:, 1] / norm
self.data.massdat[:, 1] = self.data.massdat[:, 1] / norm
else:
norm = np.amax(peaks[:, 1]) / self.config.massdatnormtop
peaks[:, 1] = peaks[:, 1] / norm
self.data.massdat[:, 1] = self.data.massdat[:, 1] / norm
self.pks = peakstructure.Peaks()
self.pks.add_peaks(peaks, massbins=self.config.massbins)
self.pks.default_params(cmap=self.config.peakcmap)
ud.dataexport(peaks, self.config.peaksfile)
# Generate Intensities of Each Charge State for Each Peak
mztab = ud.make_peaks_mztab(self.data.mzgrid, self.pks, self.config.adductmass)
#Calculate errors for peaks with FWHM
ud.peaks_error_FWHM(self.pks, self.data.massdat)
ud.peaks_error_mean(self.pks, self.data.massgrid, self.data.ztab, self.data.massdat, self.config)
if self.config.batchflag == 0:
ud.make_peaks_mztab_spectrum(self.data.mzgrid, self.pks, self.data.data2, mztab)
self.export_config()
def convolve_peaks(self):
"""
Convolve Peaks with Peak Shape
:return: None
"""
ud.makeconvspecies(self.data.data2, self.pks, self.config)
def autorun(self):
self.process_data()
self.get_auto_peak_width()
self.run_unidec()
self.pick_peaks()
self.autointegrate()
self.export_params(0)
def autocorrelation(self, massdat=None):
"""
Performs autocorrelation on mass data. Result is stored as self.data.autocorr.
Picks peaks greater than 0 using peak detection parameters in config file.
Peaks are stored as a peak structure at self.autopeaks
:param massdat: Data on which to run autocorrelation. Default is None, in which case self.data.massdat is used.
:return: float. First peak in autocorrelation.
"""
if massdat is None:
massdat = self.data.massdat
# corr=np.correlate(self.data.massdat[:,1],self.data.massdat[:,1],mode="same")
self.data.autocorr, cpeaks = ud.autocorr(massdat, self.config)
self.autopeaks = peakstructure.Peaks()
self.autopeaks.add_peaks(cpeaks, massbins=self.config.massbins)
self.autopeaks.default_params()
print "Autocorrelation:", [p.mass for p in self.autopeaks.peaks]
return self.autopeaks.peaks[0].mass
def kendrick_peaks(self, kmass=None, centermode=1):
"""
Run Kendrick analysis on peaks (self.pks object)
:param kmass: Kendrick mass. Default is prior kendrick mass if it exists and is >0.
Otherwise, default is oligomer mass (self.config.molig)
:param centermode: Set range for normalization 1=(0,1),0=(-0.5,0.5)
:return: Array of [mass,defect] for each peak in self.pks.
"""
if kmass is not None:
self.config.kendrickmass = kmass
if not self.config.kendrickmass > 0:
self.config.kendrickmass = self.config.molig
if self.config.kendrickmass > 0:
self.pks.get_mass_defects(self.config.kendrickmass, mode=centermode)
return np.array([[p.mass, p.kendrickdefect] for p in self.pks.peaks])
else:
print "Need non-zero Kendrick mass"
return None
def kendrick_continuous(self, ref_mass=None, centermode=0, nbins=50, transformmode=0, xaxistype=1):
"""
Runs continuous Kendrick analysis on self.data.massdat
:param ref_mass: Kendrick mass. Default is self.config.kendrickmass if it is already set and >0.
Otherwise, default is oligomer mass (self.config.molig)
:param centermode: Set range for normalization 0=(0,1),1=(-0.5,0.5). Default is 0.
:param nbins: Set mass defect axis density. Default is 50 bins.
:param transformmode: Set type of transformation. 0=Interpolation. 1=Integration. Default is 0.
:param xaxistype: Set x-axis dimensions. 0=Kendrick Mass Number, 1=Mass Number * Kendrick Mass. Default is 1.
:return: mass grid, mass defect grid, intensity grid. All with shape (len(self.data.massdat),nbins)
"""
if ref_mass is not None:
self.config.kendrickmass = ref_mass
if not self.config.kendrickmass > 0:
self.config.kendrickmass = self.config.molig
if self.config.kendrickmass > 0:
data1, data2, m1grid, m2grid, igrid = ud.kendrick_analysis(self.data.massdat, self.config.kendrickmass,
centermode=centermode, nbins=nbins,
transformmode=transformmode,
xaxistype=xaxistype)
# Write outputs
outfile2 = os.path.join(self.config.outfname + "_2D_Mass_Defects.txt")
outfile1 = os.path.join(self.config.outfname + "_1D_Mass_Defects.txt")
np.savetxt(outfile2, data2)
np.savetxt(outfile1, data1)
print "Saved Kendrick:", outfile2, outfile1
return m1grid, m2grid, igrid
else:
print "Need non-zero Kendrick mass"
return None, None, None
def mass_grid_to_f_grid(self):
"""
Convert the mass vs charge grid to a mass vs charge offset grid.
Calculates the charge offset for each (mass,charge) point, creates a new axis of regularly spaced charge
offsets (oaxis), and the interpolates a new grid of (mass, offset) from oaxis, which is output as outgrid.
:return: oxais, outgrid: offset axis (N) and offset grid (M x N)
"""
mgrid, zgrid = np.meshgrid(self.data.massdat[:, 0], np.array(self.data.ztab), indexing="ij")
ogrid = ud.get_z_offset(mgrid, zgrid)
oaxis = np.arange(np.amin(ogrid), np.amax(ogrid), 0.5)
mgrid2, ogrid2 = np.meshgrid(self.data.massdat[:, 0], oaxis, indexing="ij")
massgrid = self.data.massgrid.reshape((len(self.data.massdat[:, 0]), len(self.data.ztab)))
outgrid = ud.mergedata2d(mgrid2, ogrid2, mgrid, ogrid, massgrid)
outgrid -= np.amin(outgrid)
outgrid /= np.amax(outgrid)
return oaxis, outgrid
def integrate(self, limits, data=None):
"""
Trapezoid ntegrate data between limits[0] and limits[1]
:param limits: [min,max] list of lower and upper bounds on integration
:param data: N x 2 array of data (mass, intensity)
If data is None (default), self.data.massdat is used.
:return: None
"""
if data is None:
massdat = self.data.massdat
else:
massdat = np.transpose([self.data.massdat[:, 0], data])
integral, intdat = ud.integrate(massdat, limits[0], limits[1])
return integral, intdat
def autointegrate(self, ztab=None):
"""
Perform automatic integration of peaks.
If self.config.integrateup is empty, the upperbound becomes self.config.peakwindow.
If self.config.integratelb is empty, the lowerbound becomes -self.config.peakwindow.
Integral range for each peak is set to peak.integralrange.
Integral value is set to peak.integral.
If ztab parameter is set to a list of charge states, it will integrate the mass vs charge grid at each
individual charge state. Otherwise, this is ignored.
:param ztab: List of charge states (default = None)
:return: zarea: P x Z array where P is the number of peaks and Z is the number of charge states.
Each value of the array is the integral of peak P at charge state Z.
"""
if self.config.integrateub == "":
ub = self.config.peakwindow
else:
ub = self.config.integrateub
if self.config.integratelb == "":
lb = -self.config.peakwindow
else:
lb = self.config.integratelb
zarea = []
for p in self.pks.peaks:
p.integralrange = [p.mass + lb, p.mass + ub]
p.integral = self.integrate(p.integralrange)[0]
zlist = []
if ztab is not None:
for i in xrange(0, len(ztab)):
integral = self.integrate(p.integralrange,
data=np.reshape(self.data.massgrid, (len(self.data.massdat), len(ztab)))[
:, i])[0]
zlist.append(integral)
zarea.append(zlist)
self.normalize_peaks()
return np.array(zarea)
def export_params(self, e):
"""
Export a number of different parameters about the peaks into different text files.
:param e: if e is "PostFit", it will output mass fit parameters as well
:return: None
"""
if self.pks.plen > 0:
# Export Peaks Height by Charge Grid
mztab = np.array([p.mztab for p in self.pks.peaks])
ud.dataexport(mztab[:, :, 1], self.config.outfname + "_chargedata.dat")
print "Exported data to " + self.config.outfname + "_chargedata.dat"
# Export Peaks Integral by Charge Grid
if self.config.batchflag == 0:
try:
chargeareas = self.autointegrate(ztab=self.data.ztab)
ud.dataexport(chargeareas, self.config.outfname + "_chargedata_areas.dat")
except (IndexError, ValueError, AttributeError, ZeroDivisionError):
print "Unable to autointegrate"
# Get Params
peaks = np.array([[p.mass, p.height] for p in self.pks.peaks])
try:
self.autointegrate()
areas = [p.integral for p in self.pks.peaks]
except (IndexError, ValueError, AttributeError, ZeroDivisionError):
areas = peaks[:, 1]
print "Failed to integrate. Substituting heights for areas."
peakparams = []
for i in range(0, len(peaks)):
avg = np.average(self.data.ztab, weights=mztab[i, :, 1])
std = np.sqrt(np.average((np.array(self.data.ztab) - avg) ** 2, weights=mztab[i, :, 1]))
if e == "PostFit":
peakparams.append(
[peaks[i, 0], self.config.mzsig * avg, avg, std, peaks[i, 1] / np.sum(peaks[:, 1]),
self.massfit[i, 1], self.massfit[i, 2] / np.sum(self.massfit[:, 2])])
else:
peakparams.append([peaks[i, 0], self.config.mzsig * avg, avg, std, peaks[i, 1], areas[i]])
self.peakparams = np.array(peakparams)
print "Mass MassStdGuess AvgCharge StdDevCharge Height Area"
np.set_printoptions(precision=2, formatter={'float': '{: 0.2f}'.format})
print self.peakparams
np.set_printoptions()
outfile = self.config.outfname + "_peakparam.dat"
ud.dataexport(self.peakparams, outfile)
print "Peak Parameters (Saved To", outfile, ")"
else:
print "Pick Peaks First"
# TODO: Streamline to remove multiple integration steps
# TODO: Rework params into peakstructure
# TODO: Better docstring
def process_mass_data(self):
"""
Apply the same parameters used to process the data to process the mass distribution. Linearization parameters
are ignored, but smoothing, baseline subtraction, normalization, and intensity threshold all apply.
:return: None
"""
self.pks = peakstructure.Peaks()
if self.config.smooth > 0:
self.data.massdat = ud.gsmooth(self.data.massdat, self.config.smooth)
# Baseline Subtraction
buff = abs(self.config.subbuff)
subtype = self.config.subtype
if subtype == 1 and buff != 0:
self.data.massdat = ud.datasimpsub(self.data.massdat, buff)
elif subtype == 2 and buff != 0:
self.data.massdat = ud.datacompsub(self.data.massdat, buff)
elif subtype == 0 and buff != 0:
self.data.massdat[:, 1] = self.data.massdat[:, 1] - np.amin(self.data.massdat[:, 1])
# Normalization
self.data.massdat = ud.normalize(self.data.massdat)
# Intensity Threshold
self.data.massdat = ud.intensitythresh(self.data.massdat, self.config.intthresh) # thresh
def center_of_mass(self, data=None, limits=None):
"""
Return the center of mass and weighted standard deviation for data within some limits. If data is None,
self.data.massdat is used. If limits is None, the whole range is used.
:param data: mass data to determine center of mass
:param limits: limits to restrict the calculation
:return: com, std (center of mass, weighted standard deviation)
"""
if data is None:
data = self.data.massdat
if limits is None:
com = np.average(data[:, 0], weights=data[:, 1])
std = ud.weighted_std(data[:, 0], data[:, 1])
else:
com, std = ud.center_of_mass(data, limits[0], limits[1])
return com, std
def fit_all_masses(self):
"""
Fit all masses to a series of peaks, with initial guesses defined by the peak parameters.
:return: self.massfitdat, self.massfit (fit to data, fit parameters)
"""
self.massfitdat, self.massfit = MassFitter.MassFitter(self.data.massdat, self.peakparams,
self.config.psfun).perform_fit("nonorm", "sort")
return self.massfitdat, self.massfit
def get_charge_peaks(self):
"""
Determines total charge distribution. Imports each charge state as a peak in self.pks.
Will overwrite mass peaks.
:return: cpeaks (Z x 2 array of (charge state, intensity))
"""
if not ud.isempty(self.data.mzgrid):
dat = self.data.mzgrid
c = dat[:, 2]
xlen = len(np.unique(dat[:, 0]))
ylen = len(np.unique(dat[:, 1]))
newgrid = np.reshape(c, (xlen, ylen))
cint = np.sum(newgrid, axis=0)
if self.config.peaknorm == 1:
cint = cint / np.amax(cint) * 100.
elif self.config.peaknorm == 2:
cint = cint / np.sum(cint) * 100.
cpeaks = np.transpose([self.data.ztab, cint])
np.savetxt(self.config.outfname + "_chargepeaks.txt", cpeaks)
# com, std = self.center_of_mass(data=cpeaks)
self.pks = peakstructure.Peaks()
self.pks.add_peaks(cpeaks, massbins=1)
self.pks.default_params(self.config.peakcmap)
for i, p in enumerate(self.pks.peaks):
p.stickdat = newgrid[:, i]
p.label = str(int(self.data.ztab[i]))
return cpeaks
else:
print "Error: no m/z grid."
return None
def save_state(self, file_name):
ud.zip_folder(file_name)
def load_state(self, load_path):
"""
Load UniDec state from a zip save file.
Note: save_state is located under unidectools (ud.savestate)
:param load_path: .zip file to load
:return: True is successful, False if failed
"""
# Set up extensions
extension = "_rawdata."
extension2 = "_imraw."
# In zip file, search for correct files
zipf = zipfile.ZipFile(load_path)
imfile = None
msfile = None
for file_path in zipf.namelist():
if fnmatch.fnmatch(file_path, '*' + extension + "*") or fnmatch.fnmatch(file_path, '*' + extension2 + "*"):
if fnmatch.fnmatch(file_path, '*' + extension + "*"):
msfile = file_path
elif fnmatch.fnmatch(file_path, '*' + extension2 + "*"):
imfile = file_path
# Set file and extension
header = None
if imfile is not None:
if msfile is None or self.config.imflag == 1:
header = imfile[:-8]
extension = "_imraw."
elif msfile is not None:
if imfile is None or self.config.imflag == 0:
header = msfile[:-10]
extension = "_rawdata."
else:
print "Broken Save File. Unable to find _rawdata or _imraw"
return False
# Get directory, filename, and header
self.config.dirname = os.path.split(load_path)[0]
self.config.outfname = string.rsplit(header, sep="_", maxsplit=1)[0]
print "Header:", self.config.outfname
# Setup default file names, unidecfile directory, and extract there
self.config.default_file_names()
os.chdir(self.config.dirname)
dirnew = os.path.join(self.config.dirname, self.config.outfname + "_unidecfiles")
flag = os.path.isdir(dirnew)
if not flag:
os.mkdir(dirnew)
os.chdir(dirnew)
zipf.extractall(dirnew)
# Copy data file from unidecfiles to directory above it
file_name = self.config.outfname + extension + "txt"
# if not os.path.isfile(file_name):
# file_name = self.config.outfname + extension + "dat"
filename2 = self.config.outfname + ".txt"
load_path = os.path.join(self.config.dirname, filename2)
print "Data file:", file_name, load_path
shutil.copy(file_name, load_path)
# Open File
self.open_file(filename2, self.config.dirname)
# Import Processed Data
if os.path.isfile(self.config.infname):
if self.config.imflag == 0:
self.data.data2 = np.loadtxt(self.config.infname)
else:
self.data.data3 = np.loadtxt(self.config.infname)
i3 = self.data.data3[:, 2].reshape(
(len(np.unique(self.data.data3[:, 0])), len(np.unique(self.data.data3[:, 1]))))
self.data.data2 = np.transpose([np.unique(self.data.data3[:, 0]), np.sum(i3, axis=1)])
self.config.procflag = 1
else:
self.config.procflag = 0
# Import UniDec Results
if os.path.isfile(self.config.outfname + "_error.txt"):
self.unidec_imports()
# Import Peaks
if os.path.isfile(self.config.peaksfile):
self.pick_peaks()
return True
# TODO: Import Matches, others things in state?
def cross_validate(self, numcrosstot=5):
"""
Experimental function to perform cross validation
:param numcrosstot: Number of cross validation routines to perform
:return: mean, stddtev (mean and standard deviaition of mass distribution following cross validation)
"""
data2archive = deepcopy(self.data.data2)
tstart = time.clock()
massdatavg = []
peakdatavg = []
toppeaks = ud.peakdetect(self.data.massdat, self.config)
for j in xrange(2, numcrosstot + 1):
numcross = j
for i in xrange(0, numcross):
# Delete one of k-fold
traindata = ud.dataprep(np.delete(self.data.rawdata, np.s_[i::numcross], 0), self.config)
# Select one of k-fold
# testdata = ud.dataprep(self.data.rawdata[i::numcross], self.config)
ud.dataexport(traindata, self.config.infname)
ud.unidec_call(self.config, silent=True)
massdat = np.loadtxt(self.config.outfname + "_mass.txt")
try:
peaks = ud.peakdetect(massdat, self.config)
peaks = ud.mergepeaks(toppeaks, peaks, self.config.peakwindow)
peakdatavg.append(peaks)
except (ValueError, TypeError, IndexError, ZeroDivisionError):
print "No peaks selected"
pass
massdat = ud.mergedata(self.data.massdat, massdat)
massdatavg.append(massdat[:, 1])
tend = time.clock()
mean = np.mean(np.array(massdatavg), axis=0)
stddev = np.std(np.array(massdatavg), axis=0)
print j, "Total CV Time:", (tend - tstart), "STD:", np.mean(stddev)
self.data.data2 = deepcopy(data2archive)
ud.dataexport(self.data.data2, self.config.infname)
try:
peaksvert = []
for peak in toppeaks:
i = np.where(self.data.massdat[:, 0] == peak[0])
peaksvert.append([peak[0], mean[i], stddev[i], stddev[i] / mean[i] * 100])
peaksvert = np.array(peaksvert)
print "\nIntensity Variation at Fixed Mass: "
print "Mass Int.Mean Int.Std Int.%Std"
print peaksvert
ud.dataexport(peaksvert, self.config.outfname + "_peakcvinterr.dat")
peakdatavg = np.array(peakdatavg)
peakmean = np.array(
[np.mean(peakdatavg[:, i][peakdatavg[:, i, 1] != 0], axis=0) for i in range(0, len(toppeaks))])
peakstd = np.array(
[np.std(peakdatavg[:, i][peakdatavg[:, i, 1] != 0], axis=0) for i in range(0, len(toppeaks))])
peaks = [peakmean[:, 0], peakstd[:, 0], peakstd[:, 0] / peakmean[:, 0] * 100., peakmean[:, 1],
peakstd[:, 1], peakstd[:, 1] / peakmean[:, 1] * 100.]
# Output format: Mass: Mean, Std Dev, % Std Dev Intensity: Mean, Std Dev, %Std Dev
peaks = np.transpose(np.array(peaks))
print "\nMass and Intensity Variation for Fresh Peaks Each Round:"
print "MassMean MassStd Mass%Std Int.Mean Int.Std Int.%Std"
print peaks
ud.dataexport(peaks, self.config.outfname + "_peakcverr.dat")
except (IndexError, ValueError, ZeroDivisionError, TypeError, AttributeError):
print "No peaks in cross validation..."
return mean, stddev
def normalize_peaks(self):
"""
Noamlize everything in the peaks accoring to the type set in self.config.peaknorm
0 = No normalization
1 = Normalize the max value to 1
2 = Normalize the sum to 1
:return: None
"""
integrals = np.array([p.integral for p in self.pks.peaks])
heights = np.array([p.height for p in self.pks.peaks])
corrints = np.array([p.corrint for p in self.pks.peaks])
fitareas = np.array([p.fitarea for p in self.pks.peaks])
if self.config.peaknorm == 1:
inorm = np.amax(integrals) / 100.
hnorm = np.amax(heights) / 100.
cnorm = np.amax(corrints) / 100.
fnorm = np.amax(fitareas) / 100.
elif self.config.peaknorm == 2:
inorm = np.sum(integrals) / 100.
hnorm = np.sum(heights) / 100.
cnorm = np.sum(corrints) / 100.
fnorm = np.sum(fitareas) / 100.
else:
inorm = 1.
hnorm = 1.
cnorm = 1.
fnorm = 1.
if inorm != 0:
for p in self.pks.peaks:
p.integral /= inorm
if hnorm != 0:
for p in self.pks.peaks:
p.height /= hnorm
if cnorm != 0:
for p in self.pks.peaks:
p.corrint /= cnorm
p.correrr /= cnorm
if fnorm != 0:
for p in self.pks.peaks:
p.fitarea /= fnorm
p.fitareaerr /= fnorm
def align_peaks(self, pmasses=None, x_range=None, window=None, norm=False):
if x_range is None:
if window is None:
window = self.config.peakwindow * 1.
x_range = [-window, window]
if pmasses is None:
pmasses = [p.mass for p in self.pks.peaks]
# xaxis = np.arange(x_range[0], x_range[1], self.config.massbins)
aligned = []
for i, pm in enumerate(pmasses):
x = self.data.massdat[:, 0] - pm
boo1 = x > x_range[0]
boo2 = x < x_range[1]
boo3 = np.all([boo1, boo2], axis=0)
y = self.data.massdat[boo3, 1]
x = x[boo3]
# x2 = self.data.massdat[boo3, 0]
if norm:
y /= np.amax(y)
dat = np.transpose([x, y])
if i == 0:
aligned.append(dat)
else:
dat1 = deepcopy(dat)
if len(aligned[0]) < len(dat):
f = interp1d(aligned[0][:, 0], aligned[0][:, 1], fill_value=0, bounds_error=False)
aligned[0] = np.transpose([dat[:, 0], f(dat[:, 0])])
# TODO: Problem when len (aligned[[0]) < len (dat) (Fixed?)
corr = np.correlate(dat[:, 1], aligned[0][:, 1], mode="same")
move = np.argmax(corr) - np.argmax(dat[:, 1])
y = np.roll(self.data.massdat[:, 1], -move)[boo3]
if norm:
y /= np.amax(y)
dat = np.transpose([x, y])
'''
print move
import matplotlib.pyplot as plt
plt.figure()
plt.plot(dat1[:,0],dat1[:,1])
#plt.plot(dat1[:,0],corr)
plt.plot(dat[:,0],dat[:,1])
plt.plot(aligned[0][:,0],aligned[0][:,1])
plt.show()
'''
aligned.append(ud.mergedata(aligned[0], dat))
aligned = np.array(aligned)
combined, aligned = ud.broaden(aligned)
'''
# Realign on combined
aligned=[]
for i,pm in enumerate(pmasses):
x=self.data.massdat[:,0]-pm
boo1=x>x_range[0]
boo2=x<x_range[1]
boo3=np.all([boo1,boo2],axis=0)
y=self.data.massdat[boo3,1]
x=x[boo3]
x2=self.data.massdat[boo3,0]
if norm:
y=y/np.amax(y)
dat=np.transpose([x,y])
corr=signal.correlate(dat[:,1],combined[:,1],mode="same")
move=np.argmax(corr)-np.argmax(dat[:,1])
y=np.roll(self.data.massdat[:,1],-move)[boo3]
if norm:
y=y/np.amax(y)
dat=np.transpose([x,y])
aligned.append(ud.mergedata(combined,dat))
'''
return np.array(aligned), combined
def correlate_intensities(self, pmasses=None, x_range=None, window=None, ci=0.99, **kwargs):
aligned, combined = self.align_peaks(pmasses=pmasses, x_range=x_range, window=window, norm=False)
corrs = np.array([ud.correlation_integration(combined, spec, alpha=(1 - ci), **kwargs) for spec in aligned])
cmax = np.amax(corrs[:, 0])
norm = np.amax(self.data.massdat[:, 1]) / cmax
if pmasses is None:
self.get_peaks_scores(window=window, x_range=x_range, ci=ci)
for i, p in enumerate(self.pks.peaks):
plvl = corrs[i, 4]
if plvl < (1 - ci):
p.corrint = corrs[i, 0] * norm
p.correrr = p.tval * corrs[i, 3] / np.sqrt(p.score) * norm
else:
p.corrint = 0
p.correrr = 0
return corrs
def get_peaks_scores(self, window=None, x_range=None, ci=0.99, **kwargs):
if x_range is None:
if window is None:
window = self.config.peakwindow * 1.
x_range = [-window, window]
zarr = np.reshape(self.data.massgrid, (len(self.data.massdat), len(self.data.ztab)))
zarr = zarr / np.amax(np.sum(zarr, axis=1)) * np.amax(self.data.massdat[:, 1])
for i, p in enumerate(self.pks.peaks):
boo1 = self.data.massdat[:, 0] < p.mass + x_range[1]
boo2 = self.data.massdat[:, 0] > p.mass + x_range[0]
boo3 = np.all([boo1, boo2], axis=0)
top = self.data.massdat[boo3]
mztabi = []
peakmasses = []
for j, z in enumerate(self.data.ztab):
spec = np.transpose([top[:, 0], zarr[boo3, j]])
corr = ud.correlation_integration(top, spec, alpha=(1 - ci), **kwargs)
if corr[4] < (1 - ci):
mztabi.append([corr[0], corr[3]])
peakmasses.append(top[np.argmax(spec[:, 1]), 0])
else:
mztabi.append([0, 0])
p.mztabi = np.array(mztabi)
p.peakmasses = np.array(peakmasses)