UniDec
Universal Deconvolution of Mass and Ion Mobility Spectra
Functions | Variables
UniDec.unidec_modules.fft_test Namespace Reference

Functions

def MALDI_baseline (data, w)
 
def calc_medians (data, start, stop, w)
 
def fit_medians (data, start, stop, w)
 
def old_cwt_2d
 
def cwt_2d
 
def fitfun (x, data, recon)
 
def optimize_overlap (data, recon)
 

Variables

string __author__ = 'michael.marty'
 
string datfile = "C:\\NDData\\PG25\\CG_07\\150820_CG_07_ramp90.txt"
 
tuple data = np.loadtxt(datfile)
 
float mzbinsize = 1.0
 
int baseline = 30
 
tuple background = np.sin((data[:, 0] - data[0, 0]) / (data[len(data) - 1, 0] - data[0, 0]) * np.pi)
 
int width = 20
 
int num = 10
 
int cutoff = 5000
 
string wf = "dog"
 
int param = 2
 
int hpfilt = 1
 
tuple lpfilt = stats.norm.cdf(widths, cutoff, cutoff * 0.01)
 
 filt = lpfilt
 
tuple filtmat = np.transpose([filt for d in data])
 
tuple recon = mlpy.wavelet.icwt(cwt * filtmat, mzbinsize, widths, wf=wf, p=param)
 
tuple reconscale = optimize_overlap(data, recon)
 
tuple fdat = np.average(cwt, axis=1)
 
list boo1 = data[:, 0]
 
tuple mindat = min(data[:, 0])
 
tuple maxdat = max(data[:, 0])
 
tuple mzdata = linearize(data, 0.05, 3)
 
tuple xvals = np.arange(mindat, maxdat, 500)
 
tuple results = np.array([windowed_fft(mzdata, x, 2000) for x in xvals])
 
list intdat = results[:, :, 1]
 
tuple yvals = np.unique(results[:, :, 0])
 

Function Documentation

def UniDec.unidec_modules.fft_test.calc_medians (   data,
  start,
  stop,
  w 
)

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def UniDec.unidec_modules.fft_test.cwt_2d (   data,
  N = 20,
  wf = "dog",
  param = 2 
)
def UniDec.unidec_modules.fft_test.fit_medians (   data,
  start,
  stop,
  w 
)

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def UniDec.unidec_modules.fft_test.fitfun (   x,
  data,
  recon 
)
def UniDec.unidec_modules.fft_test.MALDI_baseline (   data,
  w 
)

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def UniDec.unidec_modules.fft_test.old_cwt_2d (   data,
  N = 20 
)

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def UniDec.unidec_modules.fft_test.optimize_overlap (   data,
  recon 
)

Variable Documentation

string UniDec.unidec_modules.fft_test.__author__ = 'michael.marty'
tuple UniDec.unidec_modules.fft_test.background = np.sin((data[:, 0] - data[0, 0]) / (data[len(data) - 1, 0] - data[0, 0]) * np.pi)
int UniDec.unidec_modules.fft_test.baseline = 30
list UniDec.unidec_modules.fft_test.boo1 = data[:, 0]
int UniDec.unidec_modules.fft_test.cutoff = 5000
list UniDec.unidec_modules.fft_test.data = np.loadtxt(datfile)
string UniDec.unidec_modules.fft_test.datfile = "C:\\NDData\\PG25\\CG_07\\150820_CG_07_ramp90.txt"
tuple UniDec.unidec_modules.fft_test.fdat = np.average(cwt, axis=1)
UniDec.unidec_modules.fft_test.filt = lpfilt
tuple UniDec.unidec_modules.fft_test.filtmat = np.transpose([filt for d in data])
int UniDec.unidec_modules.fft_test.hpfilt = 1
list UniDec.unidec_modules.fft_test.intdat = results[:, :, 1]
tuple UniDec.unidec_modules.fft_test.lpfilt = stats.norm.cdf(widths, cutoff, cutoff * 0.01)
tuple UniDec.unidec_modules.fft_test.maxdat = max(data[:, 0])
tuple UniDec.unidec_modules.fft_test.mindat = min(data[:, 0])
float UniDec.unidec_modules.fft_test.mzbinsize = 1.0
tuple UniDec.unidec_modules.fft_test.mzdata = linearize(data, 0.05, 3)
int UniDec.unidec_modules.fft_test.num = 10
int UniDec.unidec_modules.fft_test.param = 2
tuple UniDec.unidec_modules.fft_test.recon = mlpy.wavelet.icwt(cwt * filtmat, mzbinsize, widths, wf=wf, p=param)
tuple UniDec.unidec_modules.fft_test.reconscale = optimize_overlap(data, recon)
tuple UniDec.unidec_modules.fft_test.results = np.array([windowed_fft(mzdata, x, 2000) for x in xvals])
string UniDec.unidec_modules.fft_test.wf = "dog"
int UniDec.unidec_modules.fft_test.width = 20
tuple UniDec.unidec_modules.fft_test.xvals = np.arange(mindat, maxdat, 500)
tuple UniDec.unidec_modules.fft_test.yvals = np.unique(results[:, :, 0])