Exemple #1
0
    def read_range(self):

        condition = self.condition_menu.condition()

        residue_range = (pyutil.string_to_int(self.range_variables[0].get()),
                         pyutil.string_to_int(self.range_variables[1].get()))
        if residue_range[0] == None or residue_range[1] == None:
            self.progress_report('Must select residue range.')
            return 0

        reslist = self.range_resonances(condition, residue_range)
        if len(reslist) == 0:
            self.progress_report('No residues in selected range.')
            return 0

        seq = sequence.molecule_sequence(condition.molecule)
        if seq == None:
            self.progress_report('Molecule sequence unknown.')
            return 0

        self.residue_range = residue_range
        self.resonances = reslist
        self.sequence = seq

        return 1
  def read_range(self):
    
    condition = self.condition_menu.condition()

    residue_range = (pyutil.string_to_int(self.range_variables[0].get()),
                     pyutil.string_to_int(self.range_variables[1].get()))
    if residue_range[0] == None or residue_range[1] == None:
      self.progress_report('Must select residue range.')
      return 0

    reslist = self.range_resonances(condition, residue_range)
    if len(reslist) == 0:
      self.progress_report('No residues in selected range.')
      return 0

    seq = sequence.molecule_sequence(condition.molecule)
    if seq == None:
      self.progress_report('Molecule sequence unknown.')
      return 0

    self.residue_range = residue_range
    self.resonances = reslist
    self.sequence = seq
    # Compute global ncIDP-based random coil chemical shifts
    self.RC_prediction = shiftstats_ncIDP.resonance_statistics(reslist)

    return 1
  def write_spectrum_table(self, asys, molecule, tolerances, temp_directory):

    table_path = os.path.join(temp_directory, 'table.aat')
    f = open(table_path, 'w')

    f.write('#\n' +
            '# %s table file for AutoAssign\n' % molecule.name +
            '#\n')
    f.write('Protein: %s\n' % molecule.name)
    f.write('\n')

    seq = sequence.molecule_sequence(molecule)
    f.write('Sequence: %d %s*\n' % (seq.first_residue_number,
                                    seq.one_letter_codes))
    f.write('\n')

    toltext = ''
    for atom_name in ('HN', 'N15', 'CA', 'CB', 'HA', 'CO'):
      toltext = toltext + ' %s %.3f' % (atom_name, tolerances[atom_name])
    f.write('Tolerances: %s\n' % toltext)
    f.write('\n')

    f.write('Spectra:\n')
    for asp in asys.assignable_spectra:
      self.write_spectrum_table_entry(asp, f)
    f.write('$\n')
    
    f.close()
  def read_range(self):
    
    condition = self.condition_menu.condition()

    residue_range = (pyutil.string_to_int(self.range_variables[0].get()),
                     pyutil.string_to_int(self.range_variables[1].get()))
    if residue_range[0] == None or residue_range[1] == None:
      self.progress_report('Must select residue range.')
      return 0

    reslist = self.range_resonances(condition, residue_range)
    if len(reslist) == 0:
      self.progress_report('No residues in selected range.')
      return 0

    seq = sequence.molecule_sequence(condition.molecule)
    if seq == None:
      self.progress_report('Molecule sequence unknown.')
      return 0

    self.residue_range = residue_range
    self.resonances = reslist
    self.sequence = seq

    return 1
def single_resonance_statistics(r):
  """
    Compute global, sequence-specific ncIDP-based chemical shifts using resonance list
  """
  seq = sequence.molecule_sequence(r.condition.molecule).one_letter_codes
  path = get_sparky_home()+'/Python'
  prediction = compute_RC(path+'/ncIDP-cs.tab', path+'/ncIDP-dev.tab', seq)
  return prediction
Exemple #6
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def assignable_spectrum_from_pattern(spectrum, tolerances,
                                     pattern_name, pattern_axes, stoppable):

  remember_pattern(spectrum, pattern_name, pattern_axes)
  seq = sequence.molecule_sequence(spectrum.molecule)
  pattern_list = expected_peak_descriptions[pattern_name]

  epeaks = []
  for pat in pattern_list:
    stoppable.progress_report(pattern_name + ' pattern ' + pat)
    patpeaks = expected_peaks_from_pattern(pat, pattern_axes, seq)
    epeaks = epeaks + patpeaks

  asp = assignable_spectrum(spectrum, seq, epeaks, tolerances)

  asp.expected_peak_pattern_name = pattern_name
  asp.pattern_axis_order = pattern_axes

  return asp