def readAndGroupTable(infile, options):
"""read table from infile and group.
"""
fields, table = CSV.readTable(infile,
with_header=options.has_headers,
as_rows=True)
options.columns = getColumns(fields, options.columns)
assert options.group_column not in options.columns
converter = float
new_fields = [fields[options.group_column]
] + [fields[x] for x in options.columns]
if options.group_function == "min":
f = min
elif options.group_function == "max":
f = max
elif options.group_function == "sum":
f = lambda z: reduce(lambda x, y: x + y, z)
elif options.group_function == "mean":
f = scipy.mean
elif options.group_function == "cat":
f = lambda x: ";".join([y for y in x if y != ""])
converter = str
elif options.group_function == "uniq":
f = lambda x: ";".join([y for y in set(x) if y != ""])
converter = str
elif options.group_function == "stats":
f = lambda x: str(Stats.DistributionalParameters(x))
# update headers
new_fields = [fields[options.group_column]]
for c in options.columns:
new_fields += list([
"%s_%s" % (fields[c], x)
for x in Stats.DistributionalParameters().getHeaders()
])
# convert values to floats (except for group_column)
# Delete rows with unconvertable values and not in options.columns
new_table = []
for row in table:
skip = False
new_row = [row[options.group_column]]
for c in options.columns:
if row[c] == options.missing_value:
new_row.append(row[c])
else:
try:
new_row.append(converter(row[c]))
except ValueError:
skip = True
break
if not skip:
new_table.append(new_row)
table = new_table
new_rows = CSV.groupTable(table, group_column=0, group_function=f)
options.stdout.write("\t".join(new_fields) + "\n")
for row in new_rows:
options.stdout.write("\t".join(map(str, row)) + "\n")
def readAndGroupTable(infile, options):
"""read table from infile and group.
"""
fields, table = CSV.readTable(
infile, with_header=options.has_headers, as_rows=True)
options.columns = getColumns(fields, options.columns)
assert options.group_column not in options.columns
converter = float
new_fields = [fields[options.group_column]] + [fields[x]
for x in options.columns]
if options.group_function == "min":
f = min
elif options.group_function == "max":
f = max
elif options.group_function == "sum":
f = lambda z: reduce(lambda x, y: x + y, z)
elif options.group_function == "mean":
f = scipy.mean
elif options.group_function == "cat":
f = lambda x: ";".join([y for y in x if y != ""])
converter = str
elif options.group_function == "uniq":
f = lambda x: ";".join([y for y in set(x) if y != ""])
converter = str
elif options.group_function == "stats":
f = lambda x: str(Stats.DistributionalParameters(x))
# update headers
new_fields = [fields[options.group_column]]
for c in options.columns:
new_fields += list(map(lambda x: "%s_%s" %
(fields[c], x), Stats.DistributionalParameters().getHeaders()))
# convert values to floats (except for group_column)
# Delete rows with unconvertable values and not in options.columns
new_table = []
for row in table:
skip = False
new_row = [row[options.group_column]]
for c in options.columns:
if row[c] == options.missing_value:
new_row.append(row[c])
else:
try:
new_row.append(converter(row[c]))
except ValueError:
skip = True
break
if not skip:
new_table.append(new_row)
table = new_table
new_rows = CSV.groupTable(table,
group_column=0,
group_function=f)
options.stdout.write("\t".join(new_fields) + "\n")
for row in new_rows:
options.stdout.write("\t".join(map(str, row)) + "\n")
