def npy_npz_reader(filename, format='auto', auto_merge=False, **kwargs):
"""
Read in a Numpy structured array saved to a .npy or .npz file.
Parameters
----------
source: str
The pathname to the Numpy save file.
"""
import numpy as np
data = np.load(filename)
if isinstance(data, np.ndarray):
data = {None: data}
groups = []
for groupname in sorted(data):
d = Data(label=groupname)
arr = data[groupname]
if arr.dtype.names is None:
comp = Component.autotyped(arr)
d.add_component(comp, label='array')
else:
for name in arr.dtype.names:
comp = Component.autotyped(arr[name])
d.add_component(comp, label=name)
groups.append(d)
return groups
def npy_npz_reader(filename, format='auto', auto_merge=False, **kwargs):
"""
Read in a Numpy structured array saved to a .npy or .npz file.
Parameters
----------
source: str
The pathname to the Numpy save file.
"""
import numpy as np
data = np.load(filename)
if isinstance(data, np.ndarray):
data = {None: data}
groups = []
for groupname in sorted(data):
d = Data(label=groupname)
arr = data[groupname]
if arr.dtype.names is None:
comp = Component.autotyped(arr)
d.add_component(comp, label='array')
else:
for name in arr.dtype.names:
comp = Component.autotyped(arr[name])
d.add_component(comp, label=name)
groups.append(d)
return groups
def astropy_tabular_data(*args, **kwargs):
"""
Build a data set from a table. We restrict ourselves to tables
with 1D columns.
All arguments are passed to
astropy.table.Table.read(...).
"""
result = Data()
table = astropy_table_read(*args, **kwargs)
result.meta = table.meta
# Loop through columns and make component list
for column_name in table.columns:
c = table[column_name]
u = c.unit if hasattr(c, 'unit') else c.units
if table.masked:
# fill array for now
try:
c = c.filled(fill_value=np.nan)
except (ValueError, TypeError): # assigning nan to integer dtype
c = c.filled(fill_value=-1)
nc = Component.autotyped(c, units=u)
result.add_component(nc, column_name)
return result
def _nddata_to_glue_data(ndd, data_label):
if ndd.data.ndim != 2:
raise ValueError(f'Imviz cannot load this NDData with ndim={ndd.data.ndim}')
for attrib in ['data', 'mask', 'uncertainty']:
arr = getattr(ndd, attrib)
if arr is None:
continue
comp_label = attrib.upper()
cur_label = f'{data_label}[{comp_label}]'
cur_data = Data(label=cur_label)
cur_data.meta.update(ndd.meta)
if ndd.wcs is not None:
cur_data.coords = ndd.wcs
raw_arr = arr
if attrib == 'data':
bunit = ndd.unit or ''
elif attrib == 'uncertainty':
raw_arr = arr.array
bunit = arr.unit or ''
else:
bunit = ''
component = Component.autotyped(raw_arr, units=bunit)
cur_data.add_component(component=component, label=comp_label)
yield cur_data, cur_label
def astropy_tabular_data(*args, **kwargs):
"""
Build a data set from a table. We restrict ourselves to tables
with 1D columns.
All arguments are passed to
astropy.table.Table.read(...).
"""
result = Data()
table = astropy_table_read(*args, **kwargs)
result.meta = table.meta
# Loop through columns and make component list
for column_name in table.columns:
c = table[column_name]
u = c.unit if hasattr(c, 'unit') else c.units
if table.masked:
# fill array for now
try:
c = c.filled(fill_value=np.nan)
except (ValueError, TypeError): # assigning nan to integer dtype
c = c.filled(fill_value=-1)
nc = Component.autotyped(c, units=u)
result.add_component(nc, column_name)
return result
def npz_reader(filename, format='auto', auto_merge=False, **kwargs):
"""
Read in a Numpy structured array saved to a .npy or .npz file.
Parameters
----------
source: str
The pathname to the Numpy save file.
"""
import numpy as np
npy_data = np.load(filename)
groups = []
for groupname in sorted(npy_data.files):
d = Data(label=groupname)
arr = npy_data[groupname]
if not hasattr(arr.dtype, 'names'):
raise ValueError("Numpy save file loading currently only supports structured"
" arrays, e.g., with specified names.")
for name in arr.dtype.names:
comp = Component.autotyped(arr[name])
d.add_component(comp, label=name)
groups.append(d)
return groups
def _ndarray_to_glue_data(arr, data_label):
if arr.ndim != 2:
raise ValueError(f'Imviz cannot load this array with ndim={arr.ndim}')
data = Data(label=data_label)
component = Component.autotyped(arr)
data.add_component(component=component, label='DATA')
yield data, data_label
def hdf5_reader(filename, format='auto', auto_merge=False, **kwargs):
"""
Read in all datasets from an HDF5 file
Parameters
----------
source: str or HDUList
The pathname to the FITS file.
If an HDUList is passed in, simply use that.
"""
import h5py
from astropy.table import Table
# Open file
file_handle = h5py.File(filename, 'r')
# Define function to read
# Read in all datasets
datasets = extract_hdf5_datasets(file_handle)
label_base = os.path.basename(filename).rpartition('.')[0]
if not label_base:
label_base = os.path.basename(filename)
data_by_shape = {}
groups = OrderedDict()
for key in datasets:
label = '{0}[{1}]'.format(label_base, key)
if datasets[key].dtype.kind in ('f', 'i'):
if auto_merge and datasets[key].value.shape in data_by_shape:
data = data_by_shape[datasets[key].value.shape]
else:
data = Data(label=label)
data_by_shape[datasets[key].value.shape] = data
groups[label] = data
data.add_component(component=datasets[key].value, label=key)
else:
table = Table.read(datasets[key], format='hdf5')
data = Data(label=label)
groups[label] = data
for column_name in table.columns:
column = table[column_name]
if column.ndim == 1:
component = Component(column, units=column.unit)
data.add_component(component=component, label=column_name)
else:
warnings.warn(
"HDF5: Ignoring vector column {0}".format(column_name))
# Close HDF5 file
file_handle.close()
return [groups[idx] for idx in groups]
def hdf5_reader(filename, auto_merge=False, memmap=True, **kwargs):
"""
Read in all datasets from an HDF5 file
Parameters
----------
filename : str
The filename of the HDF5 file
memmap : bool, optional
Whether to use memory mapping
"""
from astropy.table import Table
# Read in all datasets
datasets = extract_hdf5_datasets(filename, memmap=memmap)
label_base = os.path.basename(filename).rpartition('.')[0]
if not label_base:
label_base = os.path.basename(filename)
data_by_shape = {}
groups = OrderedDict()
for key in datasets:
label = '{0}[{1}]'.format(label_base, key)
array = datasets[key]
if isinstance(array, Table):
data = Data(label=label)
groups[label] = data
for column_name in array.columns:
column = array[column_name]
if column.ndim == 1:
component = Component.autotyped(column, units=column.unit)
data.add_component(component=component, label=column_name)
else:
warnings.warn(
"HDF5: Ignoring vector column {0}".format(column_name))
else:
if auto_merge and array.shape in data_by_shape:
data = data_by_shape[datasets[key].shape]
else:
data = Data(label=label)
data_by_shape[array.shape] = data
groups[label] = data
data.add_component(component=datasets[key], label=key[1:])
return [groups[idx] for idx in groups]
def hdf5_reader(filename, auto_merge=False, memmap=True, **kwargs):
"""
Read in all datasets from an HDF5 file
Parameters
----------
filename : str
The filename of the HDF5 file
memmap : bool, optional
Whether to use memory mapping
"""
from astropy.table import Table
# Read in all datasets
datasets = extract_hdf5_datasets(filename, memmap=memmap)
label_base = os.path.basename(filename).rpartition('.')[0]
if not label_base:
label_base = os.path.basename(filename)
data_by_shape = {}
groups = OrderedDict()
for key in datasets:
label = '{0}[{1}]'.format(label_base, key)
array = datasets[key]
if isinstance(array, Table):
data = Data(label=label)
groups[label] = data
for column_name in array.columns:
column = array[column_name]
if column.ndim == 1:
component = Component.autotyped(column, units=column.unit)
data.add_component(component=component,
label=column_name)
else:
warnings.warn("HDF5: Ignoring vector column {0}".format(column_name))
else:
if auto_merge and array.shape in data_by_shape:
data = data_by_shape[datasets[key].shape]
else:
data = Data(label=label)
data_by_shape[array.shape] = data
groups[label] = data
data.add_component(component=datasets[key], label=key[1:])
return [groups[idx] for idx in groups]
def _hdu2data(hdu, data_label, hdulist, include_wcs=True):
if 'BUNIT' in hdu.header and _validate_bunit(hdu.header['BUNIT'], raise_error=False):
bunit = hdu.header['BUNIT']
else:
bunit = ''
comp_label = f'{hdu.name.upper()},{hdu.ver}'
new_data_label = f'{data_label}[{comp_label}]'
data = Data(label=new_data_label)
if include_wcs:
data.coords = WCS(hdu.header, hdulist)
component = Component.autotyped(hdu.data, units=bunit)
data.add_component(component=component, label=comp_label)
return data, new_data_label
def _hdu2data(hdu, data_label, hdulist, include_wcs=True):
if 'BUNIT' in hdu.header:
bunit = _validate_bunit(hdu.header['BUNIT'], raise_error=False)
else:
bunit = ''
comp_label = f'{hdu.name.upper()},{hdu.ver}'
new_data_label = f'{data_label}[{comp_label}]'
data = Data(label=new_data_label)
if hdulist is not None and hdu.name != 'PRIMARY' and 'PRIMARY' in hdulist:
data.meta.update(dict(hdulist['PRIMARY'].header))
data.meta.update(dict(hdu.header))
if include_wcs:
data.coords = WCS(hdu.header, hdulist)
component = Component.autotyped(hdu.data, units=bunit)
data.add_component(component=component, label=comp_label)
return data, new_data_label
def _jwst2data(file_obj, ext, data_label):
comp_label = ext.upper()
new_data_label = f'{data_label}[{comp_label}]'
data = Data(label=new_data_label)
unit_attr = f'bunit_{ext}'
try:
# This is very specific to JWST pipeline image output.
with AsdfInFits.open(file_obj) as af:
dm = af.tree
dm_meta = af.tree["meta"]
if (unit_attr in dm_meta and
_validate_bunit(dm_meta[unit_attr], raise_error=False)):
bunit = dm_meta[unit_attr]
else:
bunit = ''
# This is instance of gwcs.WCS, not astropy.wcs.WCS
if 'wcs' in dm_meta:
data.coords = dm_meta['wcs']
imdata = dm[ext]
component = Component.autotyped(imdata, units=bunit)
# Might have bad GWCS. If so, we exclude it.
try:
data.add_component(component=component, label=comp_label)
except Exception: # pragma: no cover
data.coords = None
data.add_component(component=component, label=comp_label)
# TODO: Do not need this when jwst.datamodels finally its own package.
# This might happen for grism image; fall back to FITS loader without WCS.
except Exception:
if ext == 'data':
ext = 'sci'
hdu = file_obj[ext]
return _hdu2data(hdu, data_label, file_obj, include_wcs=False)
return data, new_data_label
def casalike_cube(filename, **kwargs):
"""
This provides special support for 4D CASA FITS - like cubes,
which have 2 spatial axes, a spectral axis, and a stokes axis
in that order.
Each stokes cube is split out as a separate component
"""
from astropy.io import fits
result = Data()
if 'ignore_missing_end' not in kwargs:
kwargs['ignore_missing_end'] = True
with fits.open(filename, mode='denywrite', **kwargs) as hdulist:
array = hdulist[0].data
header = hdulist[0].header
result.coords = coordinates_from_header(header)
for i in range(array.shape[0]):
units = header.get('BUNIT')
component = Component.autotyped(array[[i]], units=units)
result.add_component(component, label='STOKES %i' % i)
return result
def gridded_data(filename, format='auto', **kwargs):
result = Data()
# Try and automatically find the format if not specified
if format == 'auto':
format = file_format(filename)
# Read in the data
if is_fits(filename):
from astropy.io import fits
arrays = extract_data_fits(filename, **kwargs)
header = fits.getheader(filename)
result.coords = coordinates_from_header(header)
elif is_hdf5(filename):
arrays = extract_data_hdf5(filename, **kwargs)
else:
raise Exception("Unkonwn format: %s" % format)
for component_name in arrays:
comp = Component.autotyped(arrays[component_name])
result.add_component(comp, component_name)
return result
def casalike_cube(filename, **kwargs):
"""
This provides special support for 4D CASA FITS - like cubes,
which have 2 spatial axes, a spectral axis, and a stokes axis
in that order.
Each stokes cube is split out as a separate component
"""
from astropy.io import fits
result = Data()
if 'ignore_missing_end' not in kwargs:
kwargs['ignore_missing_end'] = True
with fits.open(filename, **kwargs) as hdulist:
array = hdulist[0].data
header = hdulist[0].header
result.coords = coordinates_from_header(header)
for i in range(array.shape[0]):
units = header.get('BUNIT')
component = Component.autotyped(array[[i]], units=units)
result.add_component(component, label='STOKES %i' % i)
return result
def gridded_data(filename, format='auto', **kwargs):
result = Data()
# Try and automatically find the format if not specified
if format == 'auto':
format = file_format(filename)
# Read in the data
if is_fits(filename):
from astropy.io import fits
arrays = extract_data_fits(filename, **kwargs)
header = fits.getheader(filename)
result.coords = coordinates_from_header(header)
elif is_hdf5(filename):
arrays = extract_data_hdf5(filename, **kwargs)
else:
raise Exception("Unkonwn format: %s" % format)
for component_name in arrays:
comp = Component.autotyped(arrays[component_name])
result.add_component(comp, component_name)
return result
def fits_reader(source, auto_merge=False, exclude_exts=None, label=None):
"""
Read in all extensions from a FITS file.
Parameters
----------
source: str or HDUList
The pathname to the FITS file.
If an HDUList is passed in, simply use that.
auto_merge: bool
Merge extensions that have the same shape
and only one has a defined WCS.
exclude_exts: [hdu, ] or [index, ]
List of HDU's to exclude from reading.
This can be a list of HDU's or a list
of HDU indexes.
"""
from astropy.io import fits
from astropy.table import Table
exclude_exts = exclude_exts or []
if not isinstance(source, fits.hdu.hdulist.HDUList):
hdulist = fits.open(source)
hdulist.verify('fix')
else:
hdulist = source
groups = OrderedDict()
extension_by_shape = OrderedDict()
if label is not None:
label_base = label
else:
hdulist_name = hdulist.filename()
if hdulist_name is None:
hdulist_name = "HDUList"
label_base = basename(hdulist_name).rpartition('.')[0]
if not label_base:
label_base = basename(hdulist_name)
# Create a new image Data.
def new_data():
label = '{0}[{1}]'.format(label_base, hdu_name)
data = Data(label=label)
data.coords = coords
groups[hdu_name] = data
extension_by_shape[shape] = hdu_name
return data
for extnum, hdu in enumerate(hdulist):
hdu_name = hdu.name if hdu.name else "HDU{0}".format(extnum)
if (hdu.data is not None and hdu.data.size > 0
and hdu_name not in exclude_exts
and extnum not in exclude_exts):
if is_image_hdu(hdu):
shape = hdu.data.shape
coords = coordinates_from_header(hdu.header)
if not auto_merge or has_wcs(coords):
data = new_data()
else:
try:
data = groups[extension_by_shape[shape]]
except KeyError:
data = new_data()
data.add_component(component=hdu.data, label=hdu_name)
elif is_table_hdu(hdu):
# Loop through columns and make component list
table = Table(hdu.data)
label = '{0}[{1}]'.format(label_base, hdu_name)
data = Data(label=label)
groups[hdu_name] = data
for column_name in table.columns:
column = table[column_name]
component = Component(column, units=column.unit)
data.add_component(component=component, label=column_name)
return [groups[idx] for idx in groups]
def hdf5_reader(filename, auto_merge=True, memmap=True, **kwargs):
"""
Read in all datasets from an HDF5 file
Parameters
----------
filename : str or file-like
The path or file handle to the HDF5 file
auto_merge : bool
If all datasets have the same shape, and are at the base of the file,
assume they are a column-based table and merge them into a single dataset.
memmap : bool, optional
Whether to use memory mapping
"""
from astropy.table import Table
# Read in all datasets
datasets = extract_hdf5_datasets(filename, memmap=memmap)
label_base = os.path.basename(filename).rpartition('.')[0]
if not label_base:
label_base = os.path.basename(filename)
if len(datasets) == 0:
return
if not auto_merge or len(datasets) == 1 or any(
[isinstance(data, Table) for data in datasets.values()]):
merge_data = False
else:
reference_shape = list(datasets.values())[0].shape
merge_data = all([
data.shape == reference_shape and key.count('/') == 1
for key, data in datasets.items()
])
groups = OrderedDict()
data = None
for key in datasets:
label = '{0}[{1}]'.format(label_base, key)
array = datasets[key]
if isinstance(array, Table):
data = Data(label=label)
groups[label] = data
for column_name in array.columns:
column = array[column_name]
if column.ndim == 1:
component = Component.autotyped(column, units=column.unit)
data.add_component(component=component, label=column_name)
else:
warnings.warn(
"HDF5: Ignoring vector column {0}".format(column_name))
else:
if data is None and merge_data:
data = Data(label=label_base)
groups[label_base] = data
elif not merge_data:
data = Data(label=label)
groups[label] = data
data.add_component(component=datasets[key], label=key[1:])
return [groups[idx] for idx in sorted(groups)]
def fits_reader(source, auto_merge=False, exclude_exts=None, label=None):
"""
Read in all extensions from a FITS file.
Parameters
----------
source: str or HDUList
The pathname to the FITS file.
If an HDUList is passed in, simply use that.
auto_merge: bool
Merge extensions that have the same shape
and only one has a defined WCS.
exclude_exts: [hdu, ] or [index, ]
List of HDU's to exclude from reading.
This can be a list of HDU's or a list
of HDU indexes.
"""
from astropy.io import fits
from astropy.table import Table
exclude_exts = exclude_exts or []
if isinstance(source, fits.hdu.hdulist.HDUList):
hdulist = source
close_hdulist = False
else:
hdulist = fits.open(source, ignore_missing_end=True)
hdulist.verify('fix')
close_hdulist = True
groups = OrderedDict()
extension_by_shape = OrderedDict()
if label is not None:
label_base = label
else:
hdulist_name = hdulist.filename()
if hdulist_name is None:
hdulist_name = "HDUList"
label_base = basename(hdulist_name).rpartition('.')[0]
if not label_base:
label_base = basename(hdulist_name)
# Create a new image Data.
def new_data(suffix=True):
if suffix:
label = '{0}[{1}]'.format(label_base, hdu_name)
else:
label = label_base
data = Data(label=label)
data.coords = coords
# We need to be careful here because some header values are special
# objects that we should convert to strings
for key, value in hdu.header.items():
if (key == 'COMMENT' or key == 'HISTORY'):
if key not in data.meta:
data.meta[key] = [str(value)]
else:
data.meta[key].append(str(value))
elif isinstance(value, string_types) or isinstance(value, (int, float, bool)):
data.meta[key] = value
else:
data.meta[key] = str(value)
groups[hdu_name] = data
extension_by_shape[shape] = hdu_name
return data
for extnum, hdu in enumerate(hdulist):
hdu_name = hdu.name if hdu.name else "HDU{0}".format(extnum)
if (hdu.data is not None and
hdu.data.size > 0 and
hdu_name not in exclude_exts and
extnum not in exclude_exts):
if is_image_hdu(hdu):
shape = hdu.data.shape
coords = coordinates_from_header(hdu.header)
if not auto_merge or has_wcs(coords):
data = new_data(suffix=len(hdulist) > 1)
else:
try:
data = groups[extension_by_shape[shape]]
except KeyError:
data = new_data(suffix=len(hdulist) > 1)
data.add_component(component=hdu.data,
label=hdu_name)
elif is_table_hdu(hdu):
# Loop through columns and make component list
table = Table.read(hdu, format='fits')
label = '{0}[{1}]'.format(label_base, hdu_name)
data = Data(label=label)
groups[hdu_name] = data
for column_name in table.columns:
column = table[column_name]
if column.ndim != 1:
warnings.warn("Dropping column '{0}' since it is not 1-dimensional".format(column_name))
continue
component = Component.autotyped(column, units=column.unit)
data.add_component(component=component,
label=column_name)
if close_hdulist:
hdulist.close()
return [groups[idx] for idx in groups]
def _load_component(rec, context):
if 'log' in rec:
return context.object(rec['log']).component(rec['log_item'])
return Component(data=context.object(rec['data']),
units=rec['units'])
def fits_reader(source, auto_merge=False, exclude_exts=None, label=None):
"""
Read in all extensions from a FITS file.
Parameters
----------
source: str or HDUList
The pathname to the FITS file.
If an HDUList is passed in, simply use that.
auto_merge: bool
Merge extensions that have the same shape
and only one has a defined WCS.
exclude_exts: [hdu, ] or [index, ]
List of HDU's to exclude from reading.
This can be a list of HDU's or a list
of HDU indexes.
"""
from astropy.io import fits
from astropy.table import Table
exclude_exts = exclude_exts or []
if not isinstance(source, fits.hdu.hdulist.HDUList):
hdulist = fits.open(source, ignore_missing_end=True)
hdulist.verify('fix')
else:
hdulist = source
groups = OrderedDict()
extension_by_shape = OrderedDict()
if label is not None:
label_base = label
else:
hdulist_name = hdulist.filename()
if hdulist_name is None:
hdulist_name = "HDUList"
label_base = basename(hdulist_name).rpartition('.')[0]
if not label_base:
label_base = basename(hdulist_name)
# Create a new image Data.
def new_data():
label = '{0}[{1}]'.format(
label_base,
hdu_name
)
data = Data(label=label)
data.coords = coords
groups[hdu_name] = data
extension_by_shape[shape] = hdu_name
return data
for extnum, hdu in enumerate(hdulist):
hdu_name = hdu.name if hdu.name else "HDU{0}".format(extnum)
if (hdu.data is not None and
hdu.data.size > 0 and
hdu_name not in exclude_exts and
extnum not in exclude_exts):
if is_image_hdu(hdu):
shape = hdu.data.shape
coords = coordinates_from_header(hdu.header)
if not auto_merge or has_wcs(coords):
data = new_data()
else:
try:
data = groups[extension_by_shape[shape]]
except KeyError:
data = new_data()
data.add_component(component=hdu.data,
label=hdu_name)
elif is_table_hdu(hdu):
# Loop through columns and make component list
table = Table.read(hdu, format='fits')
label = '{0}[{1}]'.format(
label_base,
hdu_name
)
data = Data(label=label)
groups[hdu_name] = data
for column_name in table.columns:
column = table[column_name]
if column.ndim != 1:
warnings.warn("Dropping column '{0}' since it is not 1-dimensional".format(column_name))
continue
component = Component.autotyped(column, units=column.unit)
data.add_component(component=component,
label=column_name)
return [groups[idx] for idx in groups]
def fits_reader(source, auto_merge=False, exclude_exts=None, label=None):
"""
Read in all extensions from a FITS file.
Parameters
----------
source: str or HDUList
The pathname to the FITS file.
If an HDUList is passed in, simply use that.
auto_merge: bool
Merge extensions that have the same shape
and only one has a defined WCS.
exclude_exts: [hdu, ] or [index, ]
List of HDU's to exclude from reading.
This can be a list of HDU's or a list
of HDU indexes.
"""
from astropy.io import fits
from astropy.table import Table
exclude_exts = exclude_exts or []
if isinstance(source, fits.hdu.hdulist.HDUList):
hdulist = source
close_hdulist = False
else:
hdulist = fits.open(source, ignore_missing_end=True)
hdulist.verify('fix')
close_hdulist = True
groups = OrderedDict()
extension_by_shape = OrderedDict()
if label is not None:
label_base = label
else:
hdulist_name = hdulist.filename()
if hdulist_name is None:
hdulist_name = "HDUList"
label_base = basename(hdulist_name).rpartition('.')[0]
if not label_base:
label_base = basename(hdulist_name)
# Create a new image Data.
def new_data(suffix=True):
if suffix:
label = '{0}[{1}]'.format(label_base, hdu_name)
else:
label = label_base
data = Data(label=label)
data.coords = coords
# We need to be careful here because some header values are special
# objects that we should convert to strings
for key, value in hdu.header.items():
if (key == 'COMMENT' or key == 'HISTORY'):
if key not in data.meta:
data.meta[key] = [str(value)]
else:
data.meta[key].append(str(value))
elif isinstance(value, string_types) or isinstance(value, (int, float, bool)):
data.meta[key] = value
else:
data.meta[key] = str(value)
groups[hdu_name] = data
extension_by_shape[shape] = hdu_name
return data
for extnum, hdu in enumerate(hdulist):
hdu_name = hdu.name if hdu.name else "HDU{0}".format(extnum)
if (hdu.data is not None and
hdu.data.size > 0 and
hdu_name not in exclude_exts and
extnum not in exclude_exts):
if is_image_hdu(hdu):
shape = hdu.data.shape
coords = coordinates_from_header(hdu.header)
units = hdu.header.get('BUNIT')
if not auto_merge or has_wcs(coords):
data = new_data(suffix=len(hdulist) > 1)
else:
try:
data = groups[extension_by_shape[shape]]
except KeyError:
data = new_data(suffix=len(hdulist) > 1)
component = Component.autotyped(hdu.data, units=units)
data.add_component(component=component,
label=hdu_name)
elif is_table_hdu(hdu):
# Loop through columns and make component list
table = Table.read(hdu, format='fits')
label = '{0}[{1}]'.format(label_base, hdu_name)
data = Data(label=label)
groups[hdu_name] = data
for column_name in table.columns:
column = table[column_name]
if column.ndim != 1:
warnings.warn("Dropping column '{0}' since it is not 1-dimensional".format(column_name))
continue
component = Component.autotyped(column, units=column.unit)
data.add_component(component=component,
label=column_name)
if close_hdulist:
hdulist.close()
return [groups[idx] for idx in groups]
