def _parse_args(self, *args, silence_errors=False, **kwargs):
"""
Parses an args list into data-header pairs.
args can contain any mixture of the following entries:
* tuples of data,header
* data, header not in a tuple
* data, wcs object in a tuple
* data, wcs object not in a tuple
* filename, as a str or pathlib.Path, which will be read
* directory, as a str or pathlib.Path, from which all files will be read
* glob, from which all files will be read
* url, which will be downloaded and read
* lists containing any of the above.
Examples
--------
self._parse_args(data, header,
(data, header),
['file1', 'file2', 'file3'],
'file4',
'directory1',
'*.fits')
"""
# Account for nested lists of items
args = expand_list(args)
# Sanitise the input so that each 'type' of input corresponds to a different
# class, so single dispatch can be used later
nargs = len(args)
i = 0
while i < nargs:
arg = args[i]
if isinstance(arg, SUPPORTED_ARRAY_TYPES):
# The next two items are data and a header
data = args.pop(i)
header = args.pop(i)
args.insert(i, (data, header))
nargs -= 1
elif isinstance(arg, str) and is_url(arg):
# Repalce URL string with a Request object to dispatch on later
args[i] = Request(arg)
elif possibly_a_path(arg):
# Repalce path strings with Path objects
args[i] = pathlib.Path(arg)
i += 1
# Parse the arguments
# Note that this list can also contain GenericMaps if they are directly given to the factory
data_header_pairs = []
for arg in args:
try:
data_header_pairs += self._parse_arg(arg, **kwargs)
except NoMapsInFileError as e:
if not silence_errors:
raise
warn_user(
f"One of the arguments failed to parse with error: {e}")
return data_header_pairs
def _sanitise_args(self, args):
"""
Sanitise a list of args so that a single argument corresponds to either:
- (data, header, units) tuple.
- path-like `pathlib.Path` (e.g. a filename, directory, glob etc.).
- `urllib.request.Request`.
- `GenericTimeSeries`.
"""
# Account for nested lists of items. Simply outputs a single list of
# items, nested lists are expanded to element level.
args = expand_list(args)
# Sanitise the input so that each 'type' of input corresponds to a different
# class, so single dispatch can be used later
i = 0
while i < len(args):
arg = args[i]
if isinstance(arg, (np.ndarray, Table, pd.DataFrame)):
# Extract data and metadata
# The next item is data
data = args[i]
meta = MetaDict()
units = OrderedDict()
if isinstance(data, Table):
# We have an Astropy Table:
data, new_meta, new_units = self._from_table(data)
units.update(new_units)
meta.update(new_meta)
elif isinstance(data, np.ndarray):
# We have a numpy ndarray. We assume the first column is a dt index
data = pd.DataFrame(data=data[:, 1:],
index=Time(data[:, 0]))
# The next two could be metadata or units
for _ in range(2):
j = i + 1
if j < len(args):
arg = args[j]
if self._is_units(arg):
units.update(arg)
args.pop(j)
elif self._is_metadata(arg):
meta.update(self._parse_meta(arg))
args.pop(j)
args[i] = (data, meta, units)
elif isinstance(arg, str) and is_url(arg):
args[i] = Request(arg)
elif possibly_a_path(arg):
args[i] = pathlib.Path(arg)
i += 1
return args