def main(fin, fin_gcode, fin_taxa, fout):
"""
@param fin: interleaved phylip codon alignment file open for reading
@param fin_gcode: open file for reading the genetic code
@param fin_taxa: optional open file for defining taxon subset and order
@param fout: open file for writing the integer ndarray as text
"""
# read the description of the genetic code
arr = list(csv.reader(fin_gcode, delimiter='\t'))
indices, aminos, codons = zip(*arr)
if [int(x) for x in indices] != range(len(indices)):
raise ValueError
# read the interleaved phylip alignment
taxon_names = None
cols = []
for col in phylip.read_interleaved_codon_alignment(fin):
if taxon_names is None:
taxon_names = col
else:
cols.append(col)
# define the ndarray of integers
M_full = design.get_pattern_array(codons, cols)
if fin_taxa is None:
M = M_full
else:
# read the ordered taxon subset
arr = list(csv.reader(fin_taxa, delimiter='\t'))
indices, requested_taxa = zip(*arr)
if [int(x) for x in indices] != range(len(indices)):
raise ValueError
# init the pattern ndarray with unknown codon states
M = np.empty((len(cols), len(requested_taxa)), dtype=int)
M.fill(-1)
# construct the inverse map of the default taxon ordering
name_to_phlip_index = dict((x, i) for i, x in enumerate(taxon_names))
# Redefine the columns according to the user ordering and subsetting.
# In this code we are pretending to be a database software.
for i, name in enumerate(requested_taxa):
phylip_index = name_to_phlip_index.get(name, None)
if phylip_index is not None:
M[:, i] = M_full[:, phylip_index]
# write the ndarray of integers
np.savetxt(fout, M, fmt='%d', delimiter='\t')
def main(args):
# read the description of the genetic code
with open(args.code) as fin_gcode:
arr = list(csv.reader(fin_gcode, delimiter='\t'))
indices, aminos, codons = zip(*arr)
if [int(x) for x in indices] != range(len(indices)):
raise ValueError
# read the input
with fileutil.open_or_stdin(args.i) as fin:
paragraphs = list(gen_paragraphs(fin))
human_header = paragraphs[1][0]
human_lines = paragraphs[1][1:]
chimp_header = paragraphs[2][0]
chimp_lines = paragraphs[2][1:]
if human_header != 'Human_Horai':
raise ValueError
if chimp_header != 'Chimp_Horai':
raise ValueError
human_dna = ''.join(human_lines)
human_codons = [human_dna[i:i+3] for i in range(0, len(human_dna), 3)]
chimp_dna = ''.join(chimp_lines)
chimp_codons = [chimp_dna[i:i+3] for i in range(0, len(chimp_dna), 3)]
codon_alignment_columns = zip(*(human_codons, chimp_codons))
patterns = design.get_pattern_array(codons, codon_alignment_columns)
ncodons = len(codons)
counts = np.zeros((ncodons, ncodons), dtype=int)
for i, j in patterns:
counts[i, j] += 1
# write the (ncodons, ncodons) array of counts of human to chimp changes
with fileutil.open_or_stdout(args.counts_out, 'w') as fout:
np.savetxt(fout, counts, fmt='%g', delimiter='\t')
