def in_out_kappa(self):
df = pd.read_csv(self.train_fpi, sep='\t', index_col=0)
df = df[df['y'] == 0]
seqs = list(df['Sequence'])
for seq in seqs:
ms = motif_seq.LcSeq(seq, self.k, self.lca, 'lca')
in_seq, out_seq = ms.seq_in_motif()
SeqOb = SequenceParameters(in_seq)
print(SeqOb.get_kappa())
seqOb = SequenceParameters(out_seq)
print(seqOb.get_kappa())
print('')
def get_features_charge(seq):
"""Return dictionary of all features associated with charge."""
SeqOb = SequenceParameters(seq)
return {'FCR': FCR(seq), 'NCPR': NCPR(seq),
'net_charge': net_charge(seq), 'net_charge_P': net_charge_P(seq),
'RK_ratio': RK_ratio(seq), 'ED_ratio': ED_ratio(seq),
'kappa': SeqOb.get_kappa(), 'omega': SeqOb.get_Omega(), 'SCD': SeqOb.get_SCD()}
def feat_charge(seq):
SeqOb = SequenceParameters(seq)
return {
'FCR': FCR(seq),
'NCPR': NCPR(seq),
'net_charge': net_charge(seq),
'net_charge_P': net_charge_P(seq),
'RK_ratio': RK_ratio(seq),
'ED_ratio': ED_ratio(seq),
'kappa': SeqOb.get_kappa(),
'omega': SeqOb.get_Omega(),
'SCD': SeqOb.get_SCD()
}
def get_kappa(sequence):
####-CREATE A SEQUENCEOBJECT FROM THE AMINO ACID SEQUENCE-##############################################################
SeqOb = SequenceParameters(sequence)
####-KAPPA RANGES: 0 < K < 1 --------------- LOW KAPPA:EXTENDED ---- HIGH KAPPA:COMPACTED --------------################
kappa = SeqOb.get_kappa()
return kappa