def grab_ions(match_list, conf):
'''Takes peptides and returns a sorted list of their predicted ions'''
output = []
for match in enumerate(match_list):
consts = conf["other_constants"]
masses = match[1]["orderedMasses"]
bIons = PeptideDB.coarsen(PeptideDB.returnIons(masses, consts["B+"]))
yIons = PeptideDB.coarsen(PeptideDB.returnIons(masses[::-1], consts["Y+"]))
for bIon in bIons:
output.append([int(bIon), int((match[0]*2)+1)])
for yIon in yIons:
output.append([int(yIon), int(match[0])*2])
return sorted(output, key=lambda entry: entry[0])
else:
peptides[(ions[match_index][1]+1)/2]["bCount"] += 1
peptides[(ions[match_index][1]+1)/2]["bSum"] += int(peak[1])
match_index += 1
if match_index == len(ions):
break
output = []
for entry in peptides:
entry["spec"] = spectra["name"]
output.append(entry)
return output
configurations = configs.readConfigs("mascotStyle.cfg")
file_confs = configurations["data"]
peptide_set = PeptideDB.returnPeptides(configurations)
print "[+]"+str(len(peptide_set))+" mass entries generated."
spectra_file = file_confs["spectra_file"]
MGFFile = open(spectra_file, "rb")
spectra_gen = MGFParse.MGFReader(MGFFile, configurations)
score_mod = scorer.Score(configurations)
top_scores = []
for spectrum in spectra_gen:
print ("[+]Searching %s. \r" % (spectrum["name"])),
tol = configurations["search_options"]["initial_tolerance"]*spectrum["trueMass"]
m1Matches = match_masses(peptide_set, spectrum["trueMass"], tol)
counter = count_matches(m1Matches, spectrum, configurations)
