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 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 _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'])
