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bit Xplor-NIH VMD-XPLOR
Pydoc: module pasd.peakTools
 
pasd.peakTools		 index


 

 
Functions
       
analyzePeaks(pasdPot, refFilename='', violCutoff=0.5, longRangeCutoff=5, minLikelihood=-1, maxLikelihood=2, description='initialMatch', verbose=False)
Given a <pasdPot>.PASDPot term, generate statistics, and a set of
informational messages.
 
  minLikelihood     - perform analysis on peakAssignments whose likelihoods
  maxLikelihood       are between minLikelihood and maxLikelihood.
  longRangeCutoff   - difference in residue numner, a value greater than
                      which signifies a ``long range'' peakAssignment.
  description       - string descibing which stage of the PASD protocol is
                      calling this function.
 
  refFilename       - name of reference file (if available) used to annotate
                      whether peak assignments are good or bad.
  violCutoff        - distance violation threshold to determine if peak
                     is good (based on reference structure).
  verbose           - set to True for debugging messages.
 
Returns a string containing analysis results.
findSolventLine(shifts, peaks, minNumPeaks=500, shiftRange=(4.5, 4.9), binWidth=0.01, cutoff=0.2, useTo=False)
fracBadLRInfo(peaks, violCutoff=0.5, useGoodBadFlag=False, longRangeCutoff=6, minLikelihood=-1, maxLikelihood=2, minFiltersFailed=-1, maxFiltersFailed=9999, numIters=1, useLikelihoods=False)
Determine the fraction of the longrange forces that are good, based
either on current coords or on the good/bad flag of each PeakAssignment
(if called with -useGoodBadFlag).
 
By default, all peakAssignments are presumed to be active,  but if
-useLikelihoods is set, then a random set of active peakAssignments is
generated using the previousLikelihoods, and  fbadlong is calculated
using those active peakAssignments.
 
Setting -numIterations changes the number of such random activation sets
that are generated, and their mean fbadlong is returned.
 
If min/maxPreviousLikelihood is set, then activation is limited to
peakAssignments with previousLikelihoods < min and/or > max.  
 
Note that peakAssignments with no value for the previousLikelihood are
always be presumed to be active, so that for scripts that don't
calculate previousLikelihoods, all peakAssignments will be assumed to be
active.
markGoodPeakAssignments(peaks, violCutoff)
Set the official "good/bad" flag for each peakAssignment. Return a
remarks string.
nefPeaks(nef, noePot, fromProtonDim=None, toProtonDim=None, fromHeavyatomDim=None, toHeavyatomDim=None, name=None, verbose=True, tclOutput=False)
    Read NEF peak table.
 
    The from/toProtonDim and from/toHeavyatomDim are used to
    manually specify appropriate columns for chemical shift values.
 
    Specify the name of the NEF saveSet where the experimental data is located,
    and the identities of labels for the dimensions of the experiment.
 
    If _nef_nmr_spectrum.XplorNIH_experiment_type is set to one of
    CNH, NCH, CCH or NNH, then a pseudo 4D spectrum is read and the
    returned dictionary's 'isPseudo4D' value is set to True. See:
 
f      Journal of Biomolecular NMR, 15: 177–180, 1999.
removeDiagonalPeaks(pasdPot, tol=0.001, heavyTol=-1)

 FIX: Note that this is incomplete.  It doesn't work if the diagonal
has been folded.
removeLowIntensityPeaks(pasdPot, threshold)
Remove peaks whose (absolute value of) intensity is less than threshold.
removeSolventPeaks(pasdPot, dimension, tol=0.05, cutoff=0.2, autoDetect=True, autoRange=(4.5, 4.9), solventShift=4.76)
If autoDetect=True, no peaks are removed if the solvent line cannot be
detected.
 
The dimension argument must be "fromProton" or "toProton"