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vcfnp.pyx
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vcfnp.pyx
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# cython: profile = False
# cython: boundscheck = False
# cython: wraparound = False
# cython: embedsignature = True
"""
Utility functions to extract data from a VCF file and load into a numpy array.
"""
# 1.10.1
# 1.10.2
# 1.11
__version__ = '1.11.1'
import sys
import re
from itertools import chain
import numpy as np
cimport numpy as np
from vcflib cimport (PyVariantCallFile, VariantCallFile, Variant,
VariantFieldType, FIELD_FLOAT, FIELD_INTEGER,
FIELD_STRING, FIELD_BOOL, FIELD_UNKNOWN,
ALLELE_NUMBER, GENOTYPE_NUMBER)
from libcpp cimport bool
from libcpp.string cimport string
from libcpp.vector cimport vector
from libcpp.map cimport map
from libc.stdlib cimport atoi, atol, atof
from cython.operator cimport dereference as deref
import time
from itertools import islice
import os
from datetime import datetime
cdef size_t npos = -1
cdef extern from "split.h":
# split a string on a single delimiter character (delim)
vector[string]& split(const string &s, char delim, vector[string] &elems)
vector[string] split(const string &s, char delim)
# split a string on any character found in the string of delimiters (delims)
vector[string]& split(const string &s, const string& delims, vector[string] &elems)
vector[string] split(const string &s, const string& delims)
TYPESTRING2KEY = {
'Float': FIELD_FLOAT,
'Integer': FIELD_INTEGER,
'String': FIELD_STRING,
'Flag': FIELD_BOOL,
}
# these are the possible fields in the variants array
STANDARD_VARIANT_FIELDS = (
'CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER',
'num_alleles', 'is_snp', 'svlen'
)
# default dtypes for the variants array fields
DEFAULT_VARIANT_DTYPE = {
'CHROM': 'a12',
'POS': 'i4',
'ID': 'a12',
'REF': 'a12',
'ALT': 'a12',
'QUAL': 'f4',
'num_alleles': 'u1',
'is_snp': 'b1',
'svlen': 'i4',
}
# default arities for the variants array fields
DEFAULT_VARIANT_ARITY = {
'CHROM': 1,
'POS': 1,
'ID': 1,
'REF': 1,
'ALT': 1, # default assume biallelic (1 alt allele)
'QUAL': 1,
'num_alleles': 1,
'is_snp': 1,
'svlen': 1, # default assume biallelic
}
# default fill values for the variants fields if values are missing
DEFAULT_VARIANT_FILL = {
'CHROM': '',
'POS': 0,
'ID': '',
'REF': '',
'ALT': '',
'QUAL': 0,
'num_alleles': 0,
'is_snp': False,
'svlen': 0,
}
# default mapping from VCF field types to numpy dtypes
DEFAULT_TYPE_MAP = {
FIELD_FLOAT: 'f4',
FIELD_INTEGER: 'i4',
FIELD_STRING: 'a12',
FIELD_BOOL: 'b1',
FIELD_UNKNOWN: 'a12' # leave as string
}
# default mapping from VCF field types to fill values for missing values
DEFAULT_FILL_MAP = {
FIELD_FLOAT: 0.,
FIELD_INTEGER: 0,
FIELD_STRING: '',
FIELD_BOOL: False,
FIELD_UNKNOWN: ''
}
# default dtypes for some known INFO fields where lower precision is acceptable in most cases
DEFAULT_INFO_DTYPE = {
'ABHet': 'f2',
'ABHom': 'f2',
'AC': 'u2',
'AF': 'f2',
'AN': 'u2',
'BaseQRankSum': 'f2',
'ClippingRankSum': 'f2',
'Dels': 'f2',
'FS': 'f2',
'HRun': 'u1',
'HaplotypeScore': 'f2',
'InbreedingCoeff': 'f2',
'VariantType': 'a12',
'MLEAC': 'u2',
'MLEAF': 'f2',
'MQ': 'f2',
'MQ0Fraction': 'f2',
'MQRankSum': 'f2',
'OND': 'f2',
'QD': 'f2',
'RPA': 'u2',
'RU': 'a12',
'ReadPosRankSum': 'f2',
}
DEFAULT_TRANSFORMER = dict()
STANDARD_CALLDATA_FIELDS = ('is_called', 'is_phased', 'genotype')
DEFAULT_CALLDATA_DTYPE = {
'is_called': 'b1',
'is_phased': 'b1',
'genotype': 'i1',
# set some lower precision defaults for known FORMAT fields
'AD': 'u2',
'DP': 'u2',
'GQ': 'u1',
'MLPSAC': 'u1',
'MLPSAF': 'f2',
'MQ0': 'u2',
'PL': 'u2',
}
DEFAULT_CALLDATA_FILL = {
'is_called': False,
'is_phased': False,
'genotype': -1,
}
DEFAULT_CALLDATA_ARITY = {
'is_called': 1,
'is_phased': 1,
# N.B., set genotype arity to ploidy
'AD': 2, # default to biallelic
}
cdef char SEMICOLON = ';'
cdef string DOT = '.'
cdef string GT_DELIMS = '/|'
cdef string FIELD_NAME_CHROM = 'CHROM'
cdef string FIELD_NAME_POS = 'POS'
cdef string FIELD_NAME_ID = 'ID'
cdef string FIELD_NAME_REF = 'REF'
cdef string FIELD_NAME_ALT = 'ALT'
cdef string FIELD_NAME_QUAL = 'QUAL'
cdef string FIELD_NAME_FILTER = 'FILTER'
cdef string FIELD_NAME_INFO = 'INFO'
cdef string FIELD_NAME_NUM_ALLELES = 'num_alleles'
cdef string FIELD_NAME_IS_SNP = 'is_snp'
cdef string FIELD_NAME_SVLEN = 'svlen'
cdef string FIELD_NAME_IS_CALLED = 'is_called'
cdef string FIELD_NAME_IS_PHASED = 'is_phased'
cdef string FIELD_NAME_GENOTYPE = 'genotype'
cdef string FIELD_NAME_GT = 'GT'
def _variants_fields(fields, exclude_fields, info_ids):
if fields is None:
fields = STANDARD_VARIANT_FIELDS + info_ids
else:
for f in fields:
if f not in STANDARD_VARIANT_FIELDS and f not in info_ids:
# support extracting INFO even if not declared in header, but warn...
print >>sys.stderr, 'WARNING: no INFO definition found for field %s' % f
if exclude_fields is not None:
fields = [f for f in fields if f not in exclude_fields]
return tuple(fields)
def _variants_arities(fields, arities, info_counts):
if arities is None:
arities = dict()
for f, vcf_count in zip(fields, info_counts):
if f == 'FILTER':
arities[f] = 1 # one value
elif f not in arities:
if f in STANDARD_VARIANT_FIELDS:
arities[f] = DEFAULT_VARIANT_ARITY[f]
elif vcf_count == ALLELE_NUMBER:
# default to 1 (biallelic)
arities[f] = 1
elif vcf_count <= 0:
# catch any other cases of non-specific arity
arities[f] = 1
else:
arities[f] = vcf_count
arities = tuple(arities[f] for f in fields)
return arities
def _variants_fills(fields, fills, info_types):
if fills is None:
fills = dict()
for f, vcf_type in zip(fields, info_types):
if f == 'FILTER':
fills[f] = False
elif f not in fills:
if f in STANDARD_VARIANT_FIELDS:
fills[f] = DEFAULT_VARIANT_FILL[f]
else:
fills[f] = DEFAULT_FILL_MAP[vcf_type]
fills = tuple(fills[f] for f in fields)
return fills
def _info_transformers(fields, transformers):
if transformers is None:
transformers = dict()
for f in fields:
if f not in transformers:
transformers[f] = DEFAULT_TRANSFORMER.get(f, None)
return tuple(transformers[f] for f in fields)
def _variants_dtype(fields, dtypes, arities, filter_ids, flatten_filter, info_types):
dtype = list()
for f, n, vcf_type in zip(fields, arities, info_types):
if f == 'FILTER' and flatten_filter:
# represent FILTER as multiple boolean fields
for flt in filter_ids:
nm = 'FILTER_' + flt
dtype.append((nm, 'b1'))
elif f == 'FILTER' and not flatten_filter:
# represent FILTER as a structured datatype
t = [(flt, 'b1') for flt in filter_ids]
dtype.append((f, t))
else:
if dtypes is not None and f in dtypes:
# user overrides default dtype
t = dtypes[f]
elif f in STANDARD_VARIANT_FIELDS:
t = DEFAULT_VARIANT_DTYPE[f]
elif f in DEFAULT_INFO_DTYPE:
# known INFO field
t = DEFAULT_INFO_DTYPE[f]
else:
t = DEFAULT_TYPE_MAP[vcf_type]
if n == 1:
dtype.append((f, t))
else:
dtype.append((f, t, (n,)))
return dtype
def _filenames_from_arg(filename):
if isinstance(filename, basestring):
filenames = [filename]
elif isinstance(filename, (list, tuple)):
filenames = filename
else:
raise Exception('filename argument must be basestring, list or tuple')
for fn in filenames:
if not os.path.exists(fn):
raise Exception('file not found: %s' % fn)
return filenames
def _setup_variants(filename,
region,
fields,
exclude_fields,
arities,
fills,
transformers,
vcf_types,
flatten_filter):
filenames = _filenames_from_arg(filename)
# extract definitions from VCF header
vcf = PyVariantCallFile(filenames[0])
# FILTER definitions
filter_ids = vcf.filterIds
_warn_duplicates(filter_ids)
filter_ids = sorted(set(filter_ids))
if 'PASS' not in filter_ids:
filter_ids.append('PASS')
filter_ids = tuple(filter_ids)
# INFO definitions
_warn_duplicates(vcf.infoIds)
info_ids = tuple(sorted(set(vcf.infoIds)))
info_types = vcf.infoTypes
info_counts = vcf.infoCounts
# determine fields to extract
fields = _variants_fields(fields, exclude_fields, info_ids)
# determine if we need to parse the INFO field
parse_info = any([f not in STANDARD_VARIANT_FIELDS for f in fields])
# support for working around VCFs with bad INFO headers
for f in fields:
if f not in STANDARD_VARIANT_FIELDS and f not in info_ids:
# fall back to unary string; can be overridden with vcf_types, dtypes and arities args
info_types[f] = FIELD_STRING
info_counts[f] = 1
if vcf_types is not None and f in vcf_types:
# override type declared in VCF header
info_types[f] = TYPESTRING2KEY[vcf_types[f]]
info_types = tuple(info_types[f] if f in info_types else -1 for f in fields)
info_counts = tuple(info_counts[f] if f in info_counts else -1 for f in fields)
# determine expected number of values for each field
arities = _variants_arities(fields, arities, info_counts)
# determine fill values to use where number of values is less than expectation
fills = _variants_fills(fields, fills, info_types)
# initialise INFO field transformers
transformers = _info_transformers(fields, transformers)
return filenames, region, fields, arities, fills, info_types, transformers, parse_info, filter_ids, flatten_filter
def log(logstream, *msg):
print >>logstream, '[vcfnp] ' + str(datetime.now()) + ' :: ' + ' '.join([str(m) for m in msg])
sys.stderr.flush()
def variants(filename,
region=None,
fields=None,
exclude_fields=None,
dtypes=None,
arities=None,
fills=None,
transformers=None,
vcf_types=None,
count=None,
progress=0,
logstream=sys.stderr,
condition=None,
slice=None,
flatten_filter=False,
verbose=False,
cache=False,
cachedir=None,
skip_cached=False,
):
"""
Load an numpy structured array with data from the fixed fields of a VCF file
(including INFO).
Parameters
----------
filename: string or list
Name of the VCF file or list of file names
region: string
Region to extract, e.g., 'chr1' or 'chr1:0-100000'
fields: list or array-like
List of fields to extract from the VCF
exclude_fields: list or array-like
Fields to exclude from extraction
dtypes: dict or dict-like
Dictionary cotaining dtypes to use instead of the default inferred ones
arities: dict or dict-like
Dictinoary containing field:integer mappings used to override the number
of values to expect
fills: dict or dict-like
Dictionary containing field:fillvalue mappings used to override the
defaults used for missing values
transformers: dict or dict-like
Dictionary containing field:function mappings used to preprocess
any values prior to loading into array
vcf_types: dict or dict-like
Dictionary containing field:string mappings used to override any
bogus type declarations in the VCF header (e.g., MQ0Fraction declared
as Integer)
count: int
Attempt to extract a specific number of records
progress: int
If greater than 0, log parsing progress
logstream: file or file-like object
Stream to use for logging progress
condition: array
Boolean array defining which rows to load
slice: tuple or list
Slice of the underlying iterator, e.g., (0, 1000, 10) takes every 10th row from the first 1000
flatten_filter: bool
Return FILTER as multiple boolean fields, e.g., FILTER_PASS, FILTER_LowQuality, etc.
verbose: bool
Log more messages.
cache: bool
If True, save the resulting numpy array to disk, and load from the cache if present rather than rebuilding
from the VCF.
cachedir: string
Manually specify the directory to use to store cache files.
skip_cached: bool
If True and cache file is fresh, do not load and return None.
Examples
--------
>>> from vcfnp import variants
>>> V = variants('fixture/sample.vcf')
>>> V
array([ ('19', 111, '.', 'A', 'C', 9.600000381469727, (False, False, False), 2, True, 0, '.', 0, 0.0, 0, False, 0, False, 0),
('19', 112, '.', 'A', 'G', 10.0, (False, False, False), 2, True, 0, '.', 0, 0.0, 0, False, 0, False, 0),
('20', 14370, 'rs6054257', 'G', 'A', 29.0, (False, False, True), 2, True, 0, '.', 0, 0.5, 0, True, 14, True, 3),
('20', 17330, '.', 'T', 'A', 3.0, (True, False, False), 2, True, 0, '.', 0, 0.017000000923871994, 0, False, 11, False, 3),
('20', 1110696, 'rs6040355', 'A', 'G', 67.0, (False, False, True), 3, True, 0, 'T', 0, 0.3330000042915344, 0, True, 10, False, 2),
('20', 1230237, '.', 'T', '.', 47.0, (False, False, True), 2, False, 0, 'T', 0, 0.0, 0, False, 13, False, 3),
('20', 1234567, 'microsat1', 'G', 'GA', 50.0, (False, False, True), 3, False, 1, 'G', 3, 0.0, 6, False, 9, False, 3),
('20', 1235237, '.', 'T', '.', 0.0, (False, False, False), 2, False, 0, '.', 0, 0.0, 0, False, 0, False, 0),
('X', 10, 'rsTest', 'AC', 'A', 10.0, (False, False, True), 3, False, -1, '.', 0, 0.0, 0, False, 0, False, 0)],
dtype=[('CHROM', 'S12'), ('POS', '<i4'), ('ID', 'S12'), ('REF', 'S12'), ('ALT', 'S12'), ('QUAL', '<f4'), ('FILTER', [('q10', '?'), ('s50', '?'), ('PASS', '?')]), ('num_alleles', 'u1'), ('is_snp', '?'), ('svlen', '<i4'), ('AA', 'S12'), ('AC', '<u2'), ('AF', '<f4'), ('AN', '<u2'), ('DB', '?'), ('DP', '<i4'), ('H2', '?'), ('NS', '<i4')])
>>> V['QUAL']
array([ 9.60000038, 10. , 29. , 3. ,
67. , 47. , 50. , 0. , 10. ], dtype=float32)
>>> V['FILTER']['PASS']
array([False, False, True, False, True, True, True, False, True], dtype=bool)
>>> V['AF']
array([ 0. , 0. , 0.5 , 0.017, 0.333, 0. , 0. , 0. , 0. ], dtype=float32)
"""
if cache:
if isinstance(filename, (list, tuple)):
raise Exception('caching only supported when loading from a single VCF file')
cache_fn = _mk_cache_fn(filename, array_type='variants', region=region, cachedir=cachedir)
if not os.path.exists(cache_fn) or os.path.getmtime(filename) > os.path.getmtime(cache_fn):
if verbose:
log(logstream, 'no cache file found or cache out of date')
A = _build_variants(filename,
region=region,
fields=fields,
exclude_fields=exclude_fields,
dtypes=dtypes,
arities=arities,
fills=fills,
transformers=transformers,
vcf_types=vcf_types,
count=count,
progress=progress,
logstream=logstream,
condition=condition,
slice=slice,
flatten_filter=flatten_filter,
verbose=verbose)
if verbose:
log(logstream, 'saving to cache', cache_fn)
np.save(cache_fn, A)
return A
else:
if skip_cached:
if verbose:
log(logstream, 'skipping from cache', cache_fn)
return None
else:
if verbose:
log(logstream, 'loading from cache', cache_fn)
A = np.load(cache_fn)
return A
else:
A = _build_variants(filename,
region=region,
fields=fields,
exclude_fields=exclude_fields,
dtypes=dtypes,
arities=arities,
fills=fills,
transformers=transformers,
vcf_types=vcf_types,
count=count,
progress=progress,
logstream=logstream,
condition=condition,
slice=slice,
flatten_filter=flatten_filter,
verbose=verbose)
return A
cachedir_suffix = '.vcfnp_cache'
def _mk_cache_fn(vcf_fn, array_type, region=None, cachedir=None):
if cachedir is None:
cachedir = vcf_fn + cachedir_suffix
if not os.path.exists(cachedir):
os.makedirs(cachedir)
else:
assert os.path.isdir(cachedir), 'unexpected error, cache directory is not a directory: %s' % cachedir
if region is None:
cache_fn = os.path.join(cachedir, '%s.npy' % array_type)
else:
region = region.replace(':', '_').replace('-', '_')
cache_fn = os.path.join(cachedir, '%s.%s.npy' % (array_type, region))
return cache_fn
def _build_variants(filename,
region=None,
fields=None,
exclude_fields=None,
dtypes=None,
arities=None,
fills=None,
transformers=None,
vcf_types=None,
count=None,
progress=0,
logstream=sys.stderr,
condition=None,
slice=None,
flatten_filter=False,
verbose=False,
cache=False,
):
if verbose:
log(logstream, 'loading variants from', filename)
filenames, region, fields, arities, fills, infoTypes, transformers, parseInfo, filterIds, flatten_filter = _setup_variants(filename,
region,
fields,
exclude_fields,
arities,
fills,
transformers,
vcf_types,
flatten_filter)
# zip up field parameters
fieldspec = zip(fields, arities, fills, infoTypes, transformers)
# create a numpy dtype
dtype = _variants_dtype(fields, dtypes, arities, filterIds, flatten_filter, infoTypes)
# set up iterator
if condition is not None:
it = itervariants_with_condition(filenames, region, fieldspec, filterIds, flatten_filter, parseInfo, condition)
else:
it = itervariants(filenames, region, fieldspec, filterIds, flatten_filter, parseInfo)
# slice?
if slice:
it = islice(it, *slice)
# build an array from the iterator
return _fromiter(it, dtype, count, progress, logstream)
def _fromiter(it, dtype, count, long progress=0, logstream=sys.stderr):
if progress > 0:
it = _iter_withprogress(it, progress, logstream)
if count is not None:
a = np.fromiter(it, dtype=dtype, count=count)
else:
a = np.fromiter(it, dtype=dtype)
return a
def _iter_withprogress(iterable, long progress, logstream):
cdef long i, n
before_all = time.time()
before = before_all
for i, o in enumerate(iterable):
yield o
n = i+1
if n % progress == 0:
after = time.time()
log(logstream, '%s rows in %.2fs; batch in %.2fs (%d rows/s)' % (n, after-before_all, after-before, progress/(after-before)))
before = after
after_all = time.time()
log(logstream, '%s rows in %.2fs (%d rows/s)' % (n, after_all-before_all, n/(after_all-before_all)))
def itervariants(filenames,
region,
list fieldspec,
tuple filterIds,
bint flatten_filter,
parseInfo):
cdef VariantCallFile *variantFile
cdef Variant *var
for current_filename in filenames:
variantFile = new VariantCallFile()
variantFile.open(current_filename)
variantFile.parseInfo = parseInfo
variantFile.parseSamples = False
if region is not None:
region_set = variantFile.setRegion(region)
if not region_set:
raise StopIteration
var = new Variant(deref(variantFile))
while _get_next_variant(variantFile, var):
yield _mkvrow(var, fieldspec, filterIds, flatten_filter)
del variantFile
del var
def itervariants_with_condition(filenames,
region,
list fieldspec,
tuple filter_ids,
bint flatten_filter,
parse_info,
condition,
):
cdef VariantCallFile *variantFile
cdef Variant *var
cdef long i = 0
cdef long n = len(condition)
for current_filename in filenames:
variantFile = new VariantCallFile()
variantFile.open(current_filename)
variantFile.parseInfo = parse_info
variantFile.parseSamples = False
if region is not None:
region_set = variantFile.setRegion(region)
if not region_set:
raise StopIteration
var = new Variant(deref(variantFile))
while i < n and _get_next_variant(variantFile, var):
if condition[i]:
yield _mkvrow(var, fieldspec, filter_ids, flatten_filter)
i += 1
del variantFile
del var
cdef inline bool _get_next_variant(VariantCallFile *variantFile, Variant *var):
# break this out into a separate function so we can profile it
return variantFile.getNextVariant(deref(var))
cdef inline object _mkvrow(Variant *var,
list fieldspec,
tuple filter_ids,
bint flatten_filter):
out = list()
for f, arity, fill, vcf_type, transformer in fieldspec:
val = _mkvval(var, f, arity, fill, vcf_type, transformer, filter_ids)
if f == 'FILTER' and flatten_filter:
out.extend(val)
else:
out.append(val)
return tuple(out)
cdef inline object _mkvval(Variant *var, string field, int arity, object fill, int vcf_type, transformer, tuple filter_ids):
if field == FIELD_NAME_CHROM:
out = var.sequenceName
elif field == FIELD_NAME_POS:
out = var.position
elif field == FIELD_NAME_ID:
out = var.id
elif field == FIELD_NAME_REF:
out = var.ref
elif field == FIELD_NAME_ALT:
out = _mkaltval(var, arity, fill)
elif field == FIELD_NAME_QUAL:
out = var.quality
elif field == FIELD_NAME_FILTER:
out = _mkfilterval(var, filter_ids)
elif field == FIELD_NAME_NUM_ALLELES:
out = <int>(var.alt.size() + 1)
elif field == FIELD_NAME_IS_SNP:
out = _is_snp(var)
elif field == FIELD_NAME_SVLEN:
out = _svlen(var, arity, fill)
elif transformer is not None:
out = transformer(var.info[field])
elif vcf_type == FIELD_BOOL:
# ignore arity, this is a flag
out = (var.infoFlags.count(field) > 0)
else:
out = _mkval(var.info[field], arity, fill, vcf_type)
return out
cdef inline object _mkaltval(Variant *var, int arity, object fill):
if arity == 1:
if var.alt.size() == 0:
out = fill
else:
out = var.alt.at(0)
elif var.alt.size() == arity:
out = var.alt
out = tuple(out)
elif var.alt.size() > arity:
out = var.alt
out = tuple(out[:arity])
else:
out = var.alt
out += [fill] * (arity-var.alt.size())
out = tuple(out)
return out
cdef inline object _mkfilterval(Variant *var, tuple filter_ids):
filters = <list>split(var.filter, SEMICOLON)
out = [(id in filters) for id in filter_ids]
out = tuple(out)
return out
cdef inline object _is_snp(Variant *var):
cdef int i
cdef bytes alt
if var.ref.size() > 1:
return False
for i in range(var.alt.size()):
alt = var.alt.at(i)
if alt not in {'A', 'C', 'G', 'T'}:
return False
return True
cdef inline object _svlen(Variant *var, int arity, object fill):
if arity == 1:
return _svlen_single(var.ref, var.alt, fill)
else:
return _svlen_multi(var.ref, var.alt, arity, fill)
cdef inline object _svlen_single(string ref, vector[string]& alt, object fill):
if alt.size() > 0:
return <int>(alt.at(0).size() - ref.size())
return fill
cdef inline object _svlen_multi(string ref, vector[string]& alt, int arity, object fill):
cdef int i
out = list()
for i in range(arity):
if i < alt.size():
out.append(<int>(alt.at(i).size() - ref.size()))
else:
out.append(fill)
return out
def _warn_duplicates(fields):
visited = set()
for f in fields:
if f in visited:
print >>sys.stderr, 'WARNING: duplicate definition in header: %s' % f
visited.add(f)
cdef inline object _mkval(vector[string]& string_vals, int arity, object fill, int vcf_type):
if vcf_type == FIELD_FLOAT:
out = _mkval_double(string_vals, arity, fill)
elif vcf_type == FIELD_INTEGER:
out = _mkval_long(string_vals, arity, fill)
else:
# make strings by default
out = _mkval_string(string_vals, arity, fill)
return out
cdef inline object _mkval_string(vector[string]& string_vals, int arity, object fill):
if arity == 1:
if string_vals.size() > 0:
return string_vals.at(0)
else:
return fill
else:
return _mkval_string_multi(string_vals, arity, fill)
cdef inline object _mkval_string_multi(vector[string]& string_vals, int arity, object fill):
cdef int i
out = list()
for i in range(arity):
if i < string_vals.size():
out.append(string_vals.at(i))
else:
out.append(fill)
return out
cdef inline object _mkval_double(vector[string]& string_vals, int arity, object fill):
if arity == 1:
out = _mkval_double_single(string_vals, fill)
else:
out = _mkval_double_multi(string_vals, arity, fill)
return out
cdef inline object _mkval_double_single(vector[string]& string_vals, object fill):
cdef double v
if string_vals.size() > 0:
return atof(string_vals.at(0).c_str())
return fill
cdef inline object _mkval_double_multi(vector[string]& string_vals, int arity, object fill):
cdef int i
out = list()
for i in range(arity):
if i < string_vals.size():
out.append(atof(string_vals.at(i).c_str()))
else:
out.append(fill)
return out
cdef inline object _mkval_long(vector[string]& string_vals, int arity, object fill):
if arity == 1:
out = _mkval_long_single(string_vals, fill)
else:
out = _mkval_long_multi(string_vals, arity, fill)
return out
cdef inline object _mkval_long_single(vector[string]& string_vals, object fill):
if string_vals.size() > 0:
return atol(string_vals.at(0).c_str())
return fill
cdef inline object _mkval_long_multi(vector[string]& string_vals, int arity, object fill):
cdef int i
out = list()
for i in range(arity):
if i < string_vals.size():
out.append(atol(string_vals.at(i).c_str()))
else:
out.append(fill)
return out
def _calldata_fields(fields, exclude_fields, formatIds):
if fields is None:
fields = STANDARD_CALLDATA_FIELDS + formatIds
else:
for f in fields:
if f not in STANDARD_CALLDATA_FIELDS and f not in formatIds:
# support extracting FORMAT even if not declared in header, but warn...
print >>sys.stderr, 'WARNING: no definition found for field %s' % f
if exclude_fields is not None:
fields = [f for f in fields if f not in exclude_fields]
return tuple(fields)
def _calldata_arities(fields, arities, formatCounts, ploidy):
if arities is None:
arities = dict()
for f, vcf_count in zip(fields, formatCounts):
if f not in arities:
if f == 'genotype':
arities[f] = ploidy
elif f in DEFAULT_CALLDATA_ARITY:
arities[f] = DEFAULT_CALLDATA_ARITY[f]
elif vcf_count == ALLELE_NUMBER:
# default to 2 (biallelic)
arities[f] = 2
elif vcf_count == GENOTYPE_NUMBER:
# arity = (n + p - 1) choose p (n is number of alleles; p is ploidy)
# default to biallelic (n = 2)
arities[f] = ploidy + 1
elif vcf_count <= 0:
# catch any other cases of non-specific arity
arities[f] = 1
else:
arities[f] = vcf_count
return tuple(arities[f] for f in fields)
def _calldata_fills(fields, fills, formatTypes, ploidy):
if fills is None:
fills = dict()
for f, vcf_type in zip(fields, formatTypes):
if f not in fills:
if f == 'GT':
fills[f] = '/'.join(['.'] * ploidy)
elif f in DEFAULT_CALLDATA_FILL:
fills[f] = DEFAULT_CALLDATA_FILL[f]
else:
fills[f] = DEFAULT_FILL_MAP[vcf_type]
return tuple(fills[f] for f in fields)
def _calldata_dtype(fields, dtypes, format_types, arities, samples, ploidy):
# construct a numpy dtype for structured array cells
cell_dtype = list()
for f, vcf_type, n in zip(fields, format_types, arities):
if dtypes is not None and f in dtypes:
t = dtypes[f]
elif f == 'GT':
t = 'a%d' % ((ploidy*2)-1)
elif f in DEFAULT_CALLDATA_DTYPE:
# known field
t = DEFAULT_CALLDATA_DTYPE[f]
else:
t = DEFAULT_TYPE_MAP[vcf_type]
if n == 1:
cell_dtype.append((f, t))
else:
cell_dtype.append((f, t, (n,)))
# construct a numpy dtype for structured array
dtype = [(s, cell_dtype) for s in samples]
return dtype
def calldata(filename,
region=None,
samples=None,
ploidy=2,
fields=None,
exclude_fields=None,
dtypes=None,
arities=None,
fills=None,
vcf_types=None,
count=None,
progress=0,
logstream=sys.stderr,
condition=None,
slice=None,
verbose=False,
cache=False,
cachedir=None,
skip_cached=False,
):
"""
Load a numpy 1-dimensional structured array with data from the sample columns of a VCF
file.
Parameters
----------
filename: string or list
Name of the VCF file or list of file names
region: string
Region to extract, e.g., 'chr1' or 'chr1:0-100000'