comparison vkmz.py @ 5:04079c34452a draft

planemo upload for repository https://github.com/HegemanLab/VKMZ commit 3e37abe007f126480b9b748ad5322755445ac95b-dirty

4:1c2fe3a17065

  inputSubparser.add_argument('--error',    '-e', nargs='?', type=float, required=True, help='Mass error of mass spectrometer in PPM')
  inputSubparser.add_argument('--database', '-d', nargs='?', default='databases/bmrb-light.tsv', help='Select database.')
  inputSubparser.add_argument('--directory', nargs='?', default='', type=str, help='Define directory of tool.')
  inputSubparser.add_argument('--polarity', '-p', choices=['positive','negative'], help='Force polarity mode. Ignore variables in input file.')
  inputSubparser.add_argument('--no-adjustment', '-na', action='store_true', help='Use flag to turn off polarity based mass adjustment. This flag should always be used if reprocessing data generated by VKMZ.')
  inputSubparser.add_argument('--multiprocessing', '-m', action='store_true', help='Use flag to turn on multiprocessing.')
  inputSubparser.add_argument('--plottype', '-t', nargs='?', default='scatter-2d', choices=['scatter-2d', 'scatter-3d'], help='Select plot type.')
  inputSubparser.add_argument('--size',     '-s', nargs='?', default=5, type=int, help='Set maxium size of plot symbols.')
  inputSubparser.add_argument('--size-algorithm', '-a', nargs='?', default=0, type=int, choices=[0,1], help="Symbol size algorithm selector. Algorithm 0 sets all symbols to the maxium size. Algorithm 2 determines a features symbol size by it's log intensity.")
args = parser.parse_args()

# read inputs, arguments and define globals
vkError = getattr(args, "error")

vkPolarity = getattr(args, "polarity")

vkMultiprocessing = getattr(args, "multiprocessing")

vkNoAdjustment = getattr(args, "no_adjustment")

  uncertainty = mass * vkError / 1e6
  prediction = predict(mass, uncertainty, 0, vkMaxIndex)
  if prediction != -1:
    feature[2] = mass
    predictions = predictNeighbors(mass, uncertainty, prediction)
    feature.append(predictions) # feature[5]
    predictionClosest = predictions[0]
    formula = predictionClosest[1]
    formulaList = re.findall('[A-Z][a-z]?|[0-9]+', formula)
    formulaDictionary = {'C':0,'H':0,'O':0,'N':0}

5:04079c34452a

  inputSubparser.add_argument('--error',    '-e', nargs='?', type=float, required=True, help='Mass error of mass spectrometer in PPM')
  inputSubparser.add_argument('--database', '-d', nargs='?', default='databases/bmrb-light.tsv', help='Select database.')
  inputSubparser.add_argument('--directory', nargs='?', default='', type=str, help='Define directory of tool.')
  inputSubparser.add_argument('--polarity', '-p', choices=['positive','negative'], help='Force polarity mode. Ignore variables in input file.')
  inputSubparser.add_argument('--no-adjustment', '-na', action='store_true', help='Use flag to turn off polarity based mass adjustment. This flag should always be used if reprocessing data generated by VKMZ.')
  inputSubparser.add_argument('--unique', '-u', action='store_true', help='Set flag to only output features which have a single match.')
  inputSubparser.add_argument('--multiprocessing', '-m', action='store_true', help='Set flag to turn on multiprocessing.')
  inputSubparser.add_argument('--plottype', '-t', nargs='?', default='scatter-2d', choices=['scatter-2d', 'scatter-3d'], help='Select plot type.')
  inputSubparser.add_argument('--size',     '-s', nargs='?', default=5, type=int, help='Set maxium size of plot symbols.')
  inputSubparser.add_argument('--size-algorithm', '-a', nargs='?', default=0, type=int, choices=[0,1], help="Symbol size algorithm selector. Algorithm 0 sets all symbols to the maxium size. Algorithm 2 determines a features symbol size by it's log intensity.")
args = parser.parse_args()

# read inputs, arguments and define globals
vkError = getattr(args, "error")

vkPolarity = getattr(args, "polarity")

vkUnique = getattr(args, "unique")

vkMultiprocessing = getattr(args, "multiprocessing")

vkNoAdjustment = getattr(args, "no_adjustment")

  uncertainty = mass * vkError / 1e6
  prediction = predict(mass, uncertainty, 0, vkMaxIndex)
  if prediction != -1:
    feature[2] = mass
    predictions = predictNeighbors(mass, uncertainty, prediction)
    if vkUnique and len(predictions) > 1:
      return
    feature.append(predictions) # feature[5]
    predictionClosest = predictions[0]
    formula = predictionClosest[1]
    formulaList = re.findall('[A-Z][a-z]?|[0-9]+', formula)
    formulaDictionary = {'C':0,'H':0,'O':0,'N':0}