def loadlns(fname: Union[str, Path],
mode: str = 'r',
strip: bool = True) -> List[str]:
checkInputFile(fname)
with open(fname, mode) as f:
lns = f.readlines()
lns = smap(lns, lambda x: x.rstrip('\n'))
if strip: lns = drop(lns, lambda x: x.strip() == '')
return lns
def onload(self, removefile: bool = False) -> Table:
if not isinstance(self._dmatx, np.memmap): logging.warning('Table not offloaded, skip'); return self
checkInputFile(self._memmap.file)
mdmatx = np.memmap(self._memmap.file, dtype = self._memmap.dtype, mode = 'r', shape = self._memmap.shape)
self._dmatx = np.array(mdmatx)
del mdmatx
if removefile: self._memmap.file.unlink()
self._memmap = None
return self
def load(fname: Union[str, Path], *, mode: str = 'r',
skips: Optional[int] = None, comment: Optional[str] = None, strip: bool = False, **kwargs: Any) -> List[List[str]]:
checkInputFile(fname)
with open(fname, mode) as f:
if available(skips) and skips > 0:
for _ in range(skips): next(f)
tb = csv.reader(f, **kwargs)
if strip: tb = drop(tb, lambda x: len(x) == 0 or (len(x) == 1 and x[0].strip() == ''))
if available(comment): tb = drop(tb, lambda x: x[0].startswith(comment))
tb = l(tb)
return tb
def loadhdf(cls, fname: Union[str, Path]) -> Table:
checkInputFile(fname)
hdf = ptb.open_file(fname, mode = 'r')
darr = hdf.root.DataMatx.read()
if darr.dtype.kind == 'S': darr = np.array(darr, dtype = str)
meta = [(n, getattr(hdf.root.DataMatx.attrs, n)) for n in hdf.root.DataMatx.attrs._f_list('user')]
rnam = np.array(hdf.root.RowNames.read(), dtype = str) if hasattr(hdf.root, 'RowNames') else None
cnam = np.array(hdf.root.ColNames.read(), dtype = str) if hasattr(hdf.root, 'ColNames') else None
ridx = StructuredArray.fromhtable(hdf.root.RowIndex) if hasattr(hdf.root, 'RowIndex') else None
cidx = StructuredArray.fromhtable(hdf.root.ColIndex) if hasattr(hdf.root, 'ColIndex') else None
hdf.close()
return Table(darr, rownames = rnam, colnames = cnam, rowindex = ridx, colindex = cidx, metadata = meta)
def loadrdata(cls, fname: Union[str, Path], dataobj: str, *,
ridxobj: Optional[str] = None, cidxobj: Optional[str] = None, transposed: bool = True) -> Table:
if missing(rw): raise RuntimeError('RWrapper not available for this installation')
checkInputFile(fname)
rw.r.load(fname)
dm, rn, cn = np.array(rw.r[dataobj]), rw.run(f'rownames({dataobj})'), rw.run(f'colnames({dataobj})') # stupid numpy conversion
rn = None if rn is rw.null else np.array(rn)
cn = None if cn is rw.null else np.array(cn)
def _parseidx(iname):
idx = rw.r[iname]
return zip(idx.dtype.names, zip(*idx))
ri = _parseidx(ridxobj) if available(ridxobj) else None
ci = _parseidx(cidxobj) if available(cidxobj) else None
if transposed: dm, rn, cn, ri, ci = dm.T, cn, rn, ci, ri
ntab = Table(dm, rownames = rn, colnames = cn, rowindex = ri, colindex = ci)
return ntab
def load(fname: Union[str, Path], mode: str = 'r') -> str:
checkInputFile(fname)
with open(fname, mode) as f:
txt = f.read()
return txt
def loadhdf(cls, fname: Union[str, Path]) -> StructuredArray:
checkInputFile(fname)
with tb.open_file(fname, mode='r') as hdf:
arr = cls.fromhtable(hdf.root.StructuredArray)
return arr