else:
ats_tmp = range(len(sequences_full[0]))
sequences, ats = sca.makeATS(sequences_full, ats_tmp, s_tmp, i_ref, options.truncate)
except:
sys.exit("Error!! Can't find reference sequence...")
# filtering sequences and positions, calculations of effective number of seqs
print_("Conducting sequence and position filtering: alignment size is {:d} seqs, {:d} pos".format(len(sequences),
len(sequences[0])))
if options.pdbid is not None:
print_("ATS and distmat size - ATS: {:d}, distmat: {:d} x {:d}".format(len(ats), len(dist_pdb), len(dist_pdb[0])))
else:
print_("ATS should also have {:d} positions - ATS: {:d}".format(len(sequences[0]), len(ats)))
if i_ref is not None:
alg0, seqw0, seqkeep = sca.filterSeq(sequences, i_ref, max_fracgaps=options.parameters[1],
min_seqid=options.parameters[2], max_seqid=options.parameters[3])
else:
alg0, seqw0, seqkeep = sca.filterSeq(sequences, max_fracgaps=options.parameters[1], min_seqid=options.parameters[2],
max_seqid=options.parameters[3])
headers = [headers_full[s] for s in seqkeep]
alg1, iposkeep = sca.filterPos(alg0, seqw0, options.parameters[0])
ats = [ats[i] for i in iposkeep]
if options.pdbid is not None:
distmat = dist_pdb[np.ix_(iposkeep, iposkeep)]
effseqsprelimit = int(seqw0.sum())
Nseqprelimit = len(alg1)
print_("After filtering: alignment size is {:d} seqs, {:d} effective seqs, {:d} pos".format(len(alg1), effseqsprelimit,
len(alg1[0])))
# Limitation of total sequences to [1.5 * # ofeffective sequences] if Nselect is set to True
except:
sys.exit("Error!! Can't find reference sequence...")
# filtering sequences and positions, calculations of effective number of seqs
print(
"Conducting sequence and position filtering: alignment size is %i seqs, %i pos"
% (len(sequences), len(sequences[0])))
if options.pdbid is not None:
print("ATS and distmat size - ATS: %i, distmat: %i x %i" \
% (len(ats), len(dist_pdb), len(dist_pdb[0])))
else:
print("ATS should also have %i positions - ATS: %i" %
(len(sequences[0]), len(ats)))
if i_ref is not None:
alg0, seqw0, seqkeep = sca.filterSeq(sequences, i_ref, max_fracgaps=options.parameters[1], min_seqid=options.parameters[2], \
max_seqid=options.parameters[3])
else:
alg0, seqw0, seqkeep = sca.filterSeq(sequences, max_fracgaps=options.parameters[1], min_seqid=options.parameters[2], \
max_seqid=options.parameters[3])
headers = [headers_full[s] for s in seqkeep]
alg1, iposkeep = sca.filterPos(alg0, seqw0, options.parameters[0])
ats = [ats[i] for i in iposkeep]
if options.pdbid is not None:
distmat = dist_pdb[np.ix_(iposkeep, iposkeep)]
effseqsprelimit = int(seqw0.sum())
Nseqprelimit = len(alg1)
print(
"After filtering: alignment size is %i seqs, %i effective seqs, %i pos"
% (len(alg1), effseqsprelimit, len(alg1[0])))
print("Index of reference sequence: {}".format(reference_index))
sequences, ats = sca.makeATS(position_filtered_sequences, pdb_ats,
pdb_sequence, reference_index,
options.truncate)
# filtering sequences and positions, calculations of effective number of seqs
print(
"Conducting sequence and position filtering: alignment size is {} seqs, {} pos"
.format(len(sequences), len(sequences[0])))
print(f'ATS size: {len(ats)}')
print(
f'Dim Distance Matrix: {len(pdb_distances)} x {len(pdb_distances[0])}')
alg0, seqw0, seqkeep = sca.filterSeq(sequences,
reference_index,
max_fracgaps=PARAMETERS[1],
min_seqid=PARAMETERS[2],
max_seqid=PARAMETERS[3])
headers = [standard_headers[s] for s in seqkeep]
alg1, iposkeep = sca.filterPos(alg0, seqw0, PARAMETERS[0])
ats = [ats[i] for i in iposkeep]
distance_matrix = pdb_distances[np.ix_(iposkeep, iposkeep)]
effseqsprelimit = int(seqw0.sum())
Nseqprelimit = len(alg1)
print(
"After filtering: alignment size is {} seqs, {} effective seqs, {} pos"
.format(len(alg1), effseqsprelimit, len(alg1[0])))
alg = alg1
hd = headers