def walk(adj_file, k, path_file, strand_specific=False, cov_gradient=0.05, dist_file=None):
# parse adj
adj_graph, largest_cid = parse_adj(adj_file, k, strand_specific=strand_specific)
graph = adj_graph.graph
log('ADJ: %d vertices, %d edges' % (graph.vcount(), graph.ecount()))
# Descending order of mean kmer coverage
vidx_mkc_tuple_list = []
if strand_specific:
for vidx, mkc in enumerate(adj_graph.mkcs):
vidx_mkc_tuple_list.append((vidx, mkc))
#endfor
else:
for vidx, mkc in enumerate(adj_graph.mkcs):
vidx_mkc_tuple_list.append((vidx*2, mkc))
#endfor
#endif
vidx_mkc_tuple_list.sort(key=itemgetter(1), reverse=True)
# initialize the path id
pathid = largest_cid+1
cid_partners_dict = None
if dist_file:
cid_partners_dict = parse_dist(dist_file)
#endif
with open(path_file, 'w') as fh_path:
for seed_index, mkc in vidx_mkc_tuple_list:
# extend a path from a seed
path = extend_seed(seed_index, mkc, adj_graph, cov_gradient=cov_gradient)
if len(path) > 0 and cid_partners_dict is not None:
path = extend_path_with_paired_support(path, adj_graph, cid_partners_dict)
#endif
if len(path) > 1:
path_as_cids = []
for index in path:
path_as_cids.append(graph.vs[index][GRAPH_ATT_NAME])
#endfor
fh_path.write(str(pathid) + '\t' + ' '.join(path_as_cids) + '\n')
pathid += 1
#endif
#endfor
#endwith
#return the number of paths walked
return pathid - largest_cid - 1
def unbraid(adj_file, k, path_file, err_cid_file, strand_specific=False, cov_gradient=0.05, length_diff_tolerance=1):
# parse adj
adj_graph, largest_cid = parse_adj(adj_file, k, strand_specific=strand_specific)
graph = adj_graph.graph
log('ADJ: %d vertices, %d edges' % (graph.vcount(), graph.ecount()))
# Descending order of mean kmer coverage
vidx_mkc_tuple_list = []
if strand_specific:
for vidx, mkc in enumerate(adj_graph.mkcs):
vidx_mkc_tuple_list.append((vidx, mkc))
#endfor
else:
for vidx, mkc in enumerate(adj_graph.mkcs):
vidx_mkc_tuple_list.append((vidx*2, mkc))
#endfor
#endif
vidx_mkc_tuple_list.sort(key=itemgetter(1), reverse=True)
# initialize the path id
pathid = largest_cid+1
num_errors = 0
with open(path_file, 'w') as fh_path:
for seed_index, mkc in vidx_mkc_tuple_list:
# extend a path from a seed
path = extend_seed(seed_index, mkc, adj_graph, cov_gradient=cov_gradient)
if len(path) > 1:
path_as_cids = []
for index in path:
path_as_cids.append(graph.vs[index][GRAPH_ATT_NAME])
#endfor
fh_path.write(str(pathid) + '\t' + ' '.join(path_as_cids) + '\n')
# walk along the path to remove erroneous branches
find_erroneous_branches(path, adj_graph, k, length_diff_tolerance=length_diff_tolerance)
pathid += 1
#endif
#endfor
with open(err_cid_file, 'w') as fh_err:
if strand_specific:
for idx, val in enumerate(adj_graph.states):
if val == REMOVE_VERTEX_STATE:
num_errors += 1
name = graph.vs[idx][GRAPH_ATT_NAME]
cid = get_cid(name)
fh_path.write(str(cid) + '\n')
fh_err.write(str(cid) + '\n')
#endif
#endfor
else:
for idx, val in enumerate(adj_graph.states):
if val == REMOVE_VERTEX_STATE:
num_errors += 1
name = graph.vs[idx*2][GRAPH_ATT_NAME]
cid = get_cid(name)
fh_path.write(str(cid) + '\n')
fh_err.write(str(cid) + '\n')
#endif
#endfor
#endif
#endwith
#endwith
#return the number of paths walked, number vertices marked for removal
return pathid - largest_cid - 1, num_errors