def get_slitmask(self, filename):
hdu = fits.open(filename)
corners = np.array([
hdu['BluSlits'].data['slitX1'], hdu['BluSlits'].data['slitY1'],
hdu['BluSlits'].data['slitX2'], hdu['BluSlits'].data['slitY2'],
hdu['BluSlits'].data['slitX3'], hdu['BluSlits'].data['slitY3'],
hdu['BluSlits'].data['slitX4'], hdu['BluSlits'].data['slitY4']
]).T.reshape(-1, 4, 2)
self.slitmask = SlitMask(corners,
slitid=hdu['BluSlits'].data['dSlitId'])
return self.slitmask
def get_slitmask(self, filename):
"""
Parse the slitmask data from a DEIMOS file into a
:class:`pypeit.spectrographs.slitmask.SlitMask` object.
Args:
filename (:obj:`str`):
Name of the file to read.
"""
# Open the file
hdu = fits.open(filename)
# Build the object data
# - Find the index of the object IDs in the slit-object
# mapping that match the object catalog
mapid = hdu['SlitObjMap'].data['ObjectID']
catid = hdu['ObjectCat'].data['ObjectID']
indx = index_of_x_eq_y(mapid, catid)
# - Pull out the slit ID, object ID, and object coordinates
objects = np.array([
hdu['SlitObjMap'].data['dSlitId'][indx].astype(float),
catid.astype(float), hdu['ObjectCat'].data['RA_OBJ'],
hdu['ObjectCat'].data['DEC_OBJ']
]).T
# - Only keep the objects that are in the slit-object mapping
objects = objects[mapid[indx] == catid]
# Match the slit IDs in DesiSlits to those in BluSlits
indx = index_of_x_eq_y(hdu['DesiSlits'].data['dSlitId'],
hdu['BluSlits'].data['dSlitId'],
strict=True)
# Instantiate the slit mask object and return it
self.slitmask = SlitMask(
np.array([
hdu['BluSlits'].data['slitX1'], hdu['BluSlits'].data['slitY1'],
hdu['BluSlits'].data['slitX2'], hdu['BluSlits'].data['slitY2'],
hdu['BluSlits'].data['slitX3'], hdu['BluSlits'].data['slitY3'],
hdu['BluSlits'].data['slitX4'], hdu['BluSlits'].data['slitY4']
]).T.reshape(-1, 4, 2),
slitid=hdu['BluSlits'].data['dSlitId'],
align=hdu['DesiSlits'].data['slitTyp'][indx] == 'A',
science=hdu['DesiSlits'].data['slitTyp'][indx] == 'P',
onsky=np.array([
hdu['DesiSlits'].data['slitRA'][indx],
hdu['DesiSlits'].data['slitDec'][indx],
hdu['DesiSlits'].data['slitLen'][indx],
hdu['DesiSlits'].data['slitWid'][indx],
hdu['DesiSlits'].data['slitLPA'][indx]
]).T,
objects=objects)
return self.slitmask