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
def __init__(self,
corners,
slitid=None,
align=None,
science=None,
onsky=None,
objects=None):
# TODO: Allow random input order and then fix
# TODO: Is counter-clockwise order more natural (the order in
# DEIMOS slitmasks is clockwise, which is why that was chosen
# here)
# Convert to a numpy array if it isn't already one
_corners = numpy.asarray(corners)
# Check the shape
if _corners.shape[-1] != 2 or _corners.shape[-2] != 4:
raise ValueError(
'Incorrect input shape. Must provide 4 corners with x and y for '
'each corner.')
# Assign corner attribute allowing for one slit on input
# TODO: Annoyingly, numpy.atleast_3d appends a dimension at the
# end instead of at the beginning, like what's done with
# numpy.atleast_2d.
self.corners = _corners.reshape(1, 4,
2) if _corners.ndim == 2 else _corners
# Assign the slit IDs
self.slitid = numpy.arange(self.corners.shape[0]) if slitid is None \
else numpy.atleast_1d(slitid)
# Check the numbers match
if self.slitid.size != self.corners.shape[0]:
raise ValueError('Incorrect number of slit IDs provided.')
if len(numpy.unique(self.slitid)) != len(self.slitid):
raise ValueError('Slit IDs must be unique!')
# Set the bitmask
self.mask = numpy.zeros(self.nslits,
dtype=self.bitmask.minimum_dtype())
if align is not None:
_align = numpy.atleast_1d(align)
if _align.size != self.nslits:
raise ValueError(
'Alignment flags must be provided for each slit.')
self.mask[_align] = self.bitmask.turn_on(self.mask[_align],
'ALIGN')
if science is not None:
_science = numpy.atleast_1d(science)
if _science.size != self.nslits:
raise ValueError(
'Science-target flags must be provided for each slit.')
self.mask[_science] = self.bitmask.turn_on(self.mask[_science],
'SCIENCE')
# On-sky coordinates of the slit center
self.onsky = None
if onsky is not None:
self.onsky = numpy.atleast_2d(onsky)
if self.onsky.shape != (self.nslits, 5):
raise ValueError(
'Must provide sky coordinates and slit length, width, and PA '
'for each slit.')
# Expected objects in each slit
self.objects = None
self.slitindx = None
if objects is not None:
self.objects = numpy.atleast_2d(objects)
if self.objects.shape[1] != 4:
raise ValueError(
'Must provide the slit ID and sky coordinates for each object.'
)
try:
self.slitindx = index_of_x_eq_y(self.slitid,
self.objects[:, 0].astype(int),
strict=True)
except:
# Should only fault if there are slit IDs in `objects`
# that are not in `slitid`. In that case, return a more
# sensible error message than what index_of_x_eq_y
# would provide.
raise ValueError('Some slit IDs in object list not valid.')
# Center coordinates
self.center = numpy.mean(self.corners, axis=1)
# Top and bottom (assuming the correct input order)
self.top = numpy.mean(numpy.roll(self.corners, 1, axis=1)[:, 0:2, :],
axis=1)
self.bottom = numpy.mean(self.corners[:, 1:3, :], axis=1)
# Length and width
self.length = numpy.absolute(
numpy.diff(self.corners[:, 0:2, 0], axis=1)).ravel()
self.width = numpy.absolute(numpy.diff(self.corners[:, 1:3, 1],
axis=1)).ravel()
# Position angle
self.pa = numpy.degrees(
numpy.arctan2(numpy.diff(self.corners[:, 0:2, 1], axis=1),
numpy.diff(self.corners[:, 0:2, 0],
axis=1))).ravel()
self.pa[self.pa < -90] += 180
self.pa[self.pa > 90] -= 180
def load_keck_deimoslris(filename:str, instr:str):
""" Load up the mask design info from the header
of the file provided
Args:
filename (str):
instr (str): Name of spectrograph
Allowed are keck_lris_xxx, keck_deimos
Returns:
[type]: [description]
"""
# Open the file
hdu = io.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)
objname = [item.strip() for item in hdu['ObjectCat'].data['OBJECT']]
# - Pull out the slit ID, object ID, name, object coordinates, top and bottom distance
objects = numpy.array([hdu['SlitObjMap'].data['dSlitId'][indx].astype(int),
catid.astype(int),
hdu['ObjectCat'].data['RA_OBJ'],
hdu['ObjectCat'].data['DEC_OBJ'],
objname,
hdu['ObjectCat'].data['mag'],
hdu['ObjectCat'].data['pBand'],
hdu['SlitObjMap'].data['TopDist'][indx],
hdu['SlitObjMap'].data['BotDist'][indx]]).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)
# PA corresponding to positive x on detector (spatial)
posx_pa = hdu['MaskDesign'].data['PA_PNT'][-1]
# Insure it is positive
posx_pa, _ = positive_maskpa(posx_pa)
# Instantiate the slit mask object and return it
try:
hdu['BluSlits'].data['slitX0']
indices = numpy.arange(4)
except KeyError:
indices = numpy.arange(4)+1
#indices = numpy.arange(4) if instr == 'keck_deimos' else numpy.arange(4)+1
slit_list = []
for index in indices:
for cdim in ['X', 'Y']:
slit_list.append(hdu['BluSlits'].data[f'slit{cdim}{index}'])
slitmask = SlitMask(numpy.array(slit_list).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=numpy.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,
#object_names=hdu['ObjectCat'].data['OBJECT'],
mask_radec=(hdu['MaskDesign'].data['RA_PNT'][0],
hdu['MaskDesign'].data['DEC_PNT'][0]),
posx_pa=posx_pa)
# Return
return slitmask
