class _ALLC:
def __init__(self, path, region):
self.f = TabixFile(path)
try:
self.f_region = self.f.fetch(region)
except ValueError:
self.f_region = TabixIterator()
def readline(self):
return self.f_region.next()
def close(self):
self.f.close()
class IndexedBedFile(DataSource):
name = "indexed_bedfile"
version = "0.1.0"
container = "dataframe"
partition_access = False
description = "A bgzipped and indexed bedfile"
def __init__(self, urlpath, include_unmapped=True, metadata=None):
self._urlpath = urlpath
self._include_unmapped = include_unmapped
self._dataset = None
self._dtype = None
self._chroms = None
super(IndexedBedFile, self).__init__(metadata=metadata)
def _open_dataset(self):
self._dataset = TabixFile(self._urlpath)
def _get_schema(self):
if self._dataset is None:
self._open_dataset()
self._chroms = list(self._dataset.contigs)
rec = next(self._dataset.fetch(self._chroms[0], parser=asTuple()))
num_fields = len(rec)
chrom_coord_dtype = np.int64
dtypes = {
"chrom": pd.CategorialDtype(self._chroms + ["NULL"], ordered=True),
"start": chrom_coord_dtype,
"end": chrom_coord_dtype,
"name": str,
"score": np.float32,
"strand": bool,
}
self._dtype = {
key: dtypes[key]
for key in list(dtypes.keys())[:num_fields]
}
return Schema(
datashape=None,
dtype=self._dtype,
shape=(None, len(self._dtype)),
npartitions=len(self._chroms),
extra_metadata={},
)
def _get_partition(self, i):
chrom = self._chroms[i]
columns = list(self._dtype.keys())
return pd.DataFrame(list(self._dataset.fetch(chrom, parser=asTuple())),
columns=columns).astype(self._dtype)
def read(self):
self._load_metadata()
return pd.concat(
[self.read_partition(i) for i in range(self.npartitions)],
ignore_index=True)
def _close(self):
# close any files, sockets, etc
if self._dataset is not None:
self._dataset.close()