def process(args, raw_info_lines, input_headers, output_headers):
info_lines = Util.get_stripped_lines(raw_info_lines)
# extract info from the .csv file
rows = list(csv.reader(info_lines))
# the number of columns should be consistent among rows
if len(set(len(row) for row in rows)) != 1:
msg = 'the number of columns should be consistent among rows'
raise ValueError(msg)
# break the list of rows into a header row and data rows
header, data_rows = rows[0], rows[1:]
# account for missing input data
if args.star_missing_in:
data_rows = [[None if v == '*' else v for v in r] for r in data_rows]
elif args.NULL_missing_in:
data_rows = [[None if v == 'NULL' else v for v in r]
for r in data_rows]
# define the renamed input headers
if len(input_headers) < len(header):
msg = 'each input header should be explicitly (re)named'
raise ValueError(msg)
if len(header) < len(input_headers):
msg = 'more renamed headers than input headers'
raise ValueError(msg)
for h in input_headers:
if not Carbone.is_valid_header(h):
msg = 'invalid column header: %s' % h
raise ValueError(msg)
# force IC prefix for non-missing elements in the first column if requested
if args.clean_isolates:
data_rows = Carbone.clean_isolate_table(data_rows)
# define the ordered output headers
bad_output_headers = set(output_headers) - set(input_headers)
if bad_output_headers:
msg_a = 'unrecognized output column headers: '
msg_b = ', '.join(bad_output_headers)
raise ValueError(msg_a + msg_b)
# define the order of the output data columns
h_to_i = dict((h, i) for i, h in enumerate(input_headers))
# build the output data rows by reordering the columns
data_rows = [[row[h_to_i[h]] for h in output_headers] for row in data_rows]
# deal with missing data by skipping rows or replacing elements
table = []
for row in data_rows:
if args.remove_missing_out and (None in row):
continue
elif args.NA_missing_out:
row = ['NA' if x is None else x for x in row]
table.append(row)
# add row index labels for R compatibility if requested
if args.add_indices:
table = [[i + 1] + row for i, row in enumerate(table)]
# begin writing the R table
out = StringIO()
# write the table header
print >> out, '\t'.join(output_headers)
# write the table
for row in table:
print >> out, '\t'.join(str(x) for x in row)
# return the table
return out.getvalue()
def process(args, raw_info_lines, raw_input_headers, raw_output_headers):
info_lines = Util.get_stripped_lines(raw_info_lines)
rows = [line.split() for line in info_lines]
# the number of columns should be consistent among rows
if len(set(len(row) for row in rows)) != 1:
msg = 'the number of columns should be consistent among rows'
raise ValueError(msg)
# break the list of rows into a header row and data rows
header, data_rows = rows[0], rows[1:]
# account for missing input data
if args.star_missing_in:
data_rows = [[None if v=='*' else v for v in r] for r in data_rows]
elif args.NULL_missing_in:
data_rows = [[None if v=='NULL' else v for v in r] for r in data_rows]
# define the renamed input headers
input_headers = Util.get_stripped_lines(raw_input_headers)
if len(input_headers) < len(header):
msg = 'each input header should be explicitly (re)named'
raise ValueError(msg)
if len(header) < len(input_headers):
msg = 'more renamed headers than input headers'
raise ValueError(msg)
for h in input_headers:
if not Carbone.is_valid_header(h):
msg = 'invalid column header: %s' % h
raise ValueError(msg)
# force IC prefix for non-missing elements in the first column if requested
if args.clean_isolates:
data_rows = Carbone.clean_isolate_table(data_rows)
# define the ordered output headers
output_headers = Util.get_stripped_lines(raw_output_headers)
bad_output_headers = set(output_headers) - set(input_headers)
if bad_output_headers:
msg_a = 'unrecognized output column headers: '
msg_b = ', '.join(bad_output_headers)
raise ValueError(msg_a + msg_b)
# define the order of the output data columns
h_to_i = dict((h, i) for i, h in enumerate(input_headers))
# build the output data rows by reordering the columns
data_rows = [[row[h_to_i[h]] for h in output_headers] for row in data_rows]
# deal with missing data by skipping rows or replacing elements
table = []
for row in data_rows:
if args.remove_missing_out and (None in row):
continue
elif args.NA_missing_out:
row = ['NA' if x is None else x for x in row]
table.append(row)
# add row index labels for R compatibility if requested
if args.add_indices:
table = [[i+1] + row for i, row in enumerate(table)]
# begin writing the R table
out = StringIO()
# write the table header
print >> out, '\t'.join(output_headers)
# write the table
for row in table:
print >> out, '\t'.join(str(x) for x in row)
# return the table
return out.getvalue()
