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from model import _Call
cdef _map(func, iterable, bad=['.', '']):
'''``map``, but make bad values None.'''
return [func(x) if x not in bad else None
for x in iterable]
INTEGER = 'Integer'
FLOAT = 'Float'
NUMERIC = 'Numeric'
def _parse_filter(filt_str):
'''Parse the FILTER field of a VCF entry into a Python list
NOTE: this method has a python equivalent and care must be taken
to keep the two methods equivalent
'''
if filt_str == '.':
return None
elif filt_str == 'PASS':
return []
else:
return filt_str.split(';')
def parse_samples(
list names, list samples, samp_fmt,
list samp_fmt_types, list samp_fmt_nums, site):
cdef char *name, *fmt, *entry_type, *sample
cdef int i, j
cdef list samp_data = []
cdef dict sampdict
cdef list sampvals
n_samples = len(samples)
n_formats = len(samp_fmt._fields)
for i in range(n_samples):
name = names[i]
sample = samples[i]
# parse the data for this sample
sampdat = [None] * n_formats
sampvals = sample.split(':')
for j in range(n_formats):
if j >= len(sampvals):
break
vals = sampvals[j]
# short circuit the most common
if samp_fmt._fields[j] == 'GT':
sampdat[j] = vals
continue
# genotype filters are a special case
elif samp_fmt._fields[j] == 'FT':
sampdat[j] = _parse_filter(vals)
continue
elif not vals or vals == '.':
sampdat[j] = None
continue
entry_type = samp_fmt_types[j]
# TODO: entry_num is None for unbounded lists
entry_num = samp_fmt_nums[j]
# we don't need to split single entries
if entry_num == 1:
if entry_type == INTEGER:
try:
sampdat[j] = int(vals)
except ValueError:
sampdat[j] = float(vals)
elif entry_type == FLOAT or entry_type == NUMERIC:
sampdat[j] = float(vals)
else:
sampdat[j] = vals
continue
vals = vals.split(',')
if entry_type == INTEGER:
try:
sampdat[j] = _map(int, vals)
except ValueError:
sampdat[j] = map(float, vals)
elif entry_type == FLOAT or entry_type == NUMERIC:
sampdat[j] = _map(float, vals)
else:
sampdat[j] = vals
# create a call object
call = _Call(site, name, samp_fmt(*sampdat))
samp_data.append(call)
return samp_data
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