def format(self, new_file_name=None):
fin = open(self.orig_file)
csvReader = csv.reader(fin)
headers = map(lambda x: x.strip(), csvReader.next())
new_headers = self.convert_headers(headers)
spec_desc = new_headers.index('Spectrum Description')
mz = new_headers.index('Experimental mz')
charge = new_headers.index('Charge')
seq = new_headers.index('Peptide Sequence')
var_mods = new_headers.index('Variable Modifications')
new_data = []
for prerow in csvReader:
row = map(lambda x: x.strip(), prerow)
row[spec_desc] = self.convert_spectrum(row[spec_desc])
row[mz] = self.convert_mass(row[mz],row[charge])
(the_seq,the_mods) = self.convert_seq(row[seq])
row[seq] = the_seq
row[var_mods] = the_mods
new_data.append( row )
fin.close()
dir_split = os.path.split(self.orig_file)
if not new_file_name:
new_file_name = os.path.join(dir_split[0], "mz_" + dir_split[1])
report = mz.Report.writer(new_file_name, columns = new_headers)
for data in new_data:
report.write(data)
report.close()
def format_XML(xTandem_file, save_file, rev_mods=None, db_regex=None):
'''Create a report from the xTandem output (which is an XML file)'''
rows = xTandemArray(xTandem_file, rev_mods, db_regex)
if os.path.exists(save_file):
os.remove(save_file)
report = mz.Report.writer(save_file, columns=['Expect'],
default_columns=True)
for row in rows:
report.write(row)
report.close()
def format(self, new_file_name=None):
fh = open(self.orig_file)
#Change header names
header_line = fh.readline()
headers = header_line.strip().split('\t')
new_headers = multiplierz.mzReport.default_columns[:]
new_headers.extend(('MS2 Time', 'Protein Coverage'))
new_headers.remove('Peptide Rank')
new_headers.remove('Query')
new_headers.extend(h for h in headers if h not in self.reps)
if 'Unused' in new_headers:
new_headers.remove('Unused')
if 'Contrib' in new_headers:
new_headers.remove('Contrib')
if 'Sc' in new_headers:
new_headers.remove('Sc')
rows = []
protein_matches = defaultdict(int)
for line in fh:
new_data = dict((h,None) for h in new_headers)
data = dict(zip(headers,line[:-1].split('\t')))
protein_matches[data['Accessions']] += 1
for h in data:
if h in self.reps and self.reps[h] in new_data:
new_data[self.reps[h]] = data[h]
elif h in new_data:
new_data[h] = data[h]
elif h not in ('Unused','Contrib','Sc'):
logger_message(10, 'Missing key: %s' % h)
if 'Modifications' in data:
new_data['Variable Modifications'] = self.convert_var_mod(data['Modifications'])
if 'Cleavages' in data:
new_data['Missed Cleavages'] = data['Cleavages'].count('missed')
if 'Time' in data and 'Spectrum' in data:
new_data['Spectrum Description'] = self.convert_spectrum(data['Spectrum'], data['Time'])
rows.append(new_data)
fh.close()
for row in rows:
row['Protein Matches'] = protein_matches[row['Accession Number']]
dir_split = os.path.split(self.orig_file)
if not new_file_name:
new_file_name = os.path.join(dir_split[0], "mz_" + dir_split[1])
report = mz.Report.writer(new_file_name, columns=new_headers)
for row in rows:
report.write(row)
report.close()