def load_allele_model(allele, length):
"""Returns the SYFPEITHI pssm for a given allele"""
allele_model = "%s_%i" % (allele, length)
try:
return matrix_max(
getattr(
__import__("Fred2.Data.pssms.syfpeithi" + ".mat." +
allele_model,
fromlist=[allele_model]), allele_model))
except ImportError:
return None
# Calculate the maximum attainable score for each allele
converted_alleles = dict(zip(alleles, predictor.convert_alleles(alleles)))
max_score_by_allele = {(allele, length):
load_allele_model(converted_alleles[allele], length)
for length in predictor.supportedLength
for allele in alleles}
# Run predictions and output results
print 'Peptide\tAllele\tSyfpeithiRawScore\tSyfpeithiNormScore'
for pep_len, peptides in peptides_by_length.items():
for allele in alleles:
if (allele, pep_len
) in max_score_by_allele and max_score_by_allele[allele, pep_len]:
results = predictor.predict(peptides, alleles=[allele])
for index, row in results.iterrows():
print '{}\t{}\t{}\t{}'.format(
str(index[0]), allele, row[allele],
