def slitmask(tslits_dict, pad=None, binning=None):
"""
Generic routine ton construct a slitmask image from a tslits_dict. Children of this class can
overload this function to implement instrument specific slitmask behavior, for example setting
where the orders on an echelle spectrograph end
Parameters
-----------
tslits_dict: dict
Trace slits dictionary with slit boundary information
Optional Parameters
pad: int or float
Padding of the slit boundaries
binning: tuple
Spectrograph binning in spectral and spatial directions
Returns
-------
slitmask: ndarray int
Image with -1 where there are no slits/orders, and an integer where there are slits/order with the integer
indicating the slit number going from 0 to nslit-1 from left to right.
"""
# These lines are always the same
pad = tslits_dict['pad'] if pad is None else pad
slitmask = pixels.slit_pixels(tslits_dict['lcen'],
tslits_dict['rcen'],
tslits_dict['nspat'],
pad=pad)
return slitmask
def slitmask(self, tslits_dict, pad=None):
"""
Generic routine ton construct a slitmask image from a tslits_dict. Children of this class can
overload this function to implement instrument specific slitmask behavior, for example setting
where the orders on an echelle spectrograph end
Parameters
-----------
tslits_dict: dict
Trace slits dictionary with slit boundary information
Optional Parameters
pad: int or float
Padding of the slit boundaries
binning: tuple
Spectrograph binning in spectral and spatial directions
Returns
-------
slitmask: ndarray int
Image with -1 where there are no slits/orders, and an integer where there are slits/order with the integer
indicating the slit number going from 0 to nslit-1 from left to right.
"""
# These lines are always the same
pad = tslits_dict['pad'] if pad is None else pad
slitmask = pixels.slit_pixels(tslits_dict['lcen'],
tslits_dict['rcen'],
tslits_dict['nspat'],
pad=pad)
spec_img = np.outer(
np.arange(tslits_dict['nspec'], dtype=int),
np.ones(tslits_dict['nspat'],
dtype=int)) # spectral position everywhere along image
nslits = tslits_dict['lcen'].shape[1]
if nslits != self.norders:
msgs.error(
'There is a problem with your slit bounadries. You have nslits={:d} orders, whereas NIR has norders={:d}'
.format(nslits, self.norders))
# These are the order boundaries determined by eye by JFH. 2025 is used as the maximum as the upper bit is not illuminated
order_max = [
1467, 1502, 1540, 1580, 1620, 1665, 1720, 1770, 1825, 1895, 1966,
2000, 2000, 2000, 2000, 2000
]
order_min = [
420, 390, 370, 345, 315, 285, 248, 210, 165, 115, 63, 10, 0, 0, 0,
0
]
for islit in range(nslits):
orderbad = (slitmask == islit) & ((spec_img < order_min[islit]) |
(spec_img > order_max[islit]))
slitmask[orderbad] = -1
return slitmask
def slitmask(tslits_dict, pad=None, binning=None):
"""
Generic routine ton construct a slitmask image from a tslits_dict. Children of this class can
overload this function to implement instrument specific slitmask behavior, for example setting
where the orders on an echelle spectrograph end
Parameters
-----------
tslits_dict: dict
Trace slits dictionary with slit boundary information
Optional Parameters
pad: int or float
Padding of the slit boundaries
binning: tuple
Spectrograph binning in spectral and spatial directions
Returns
-------
slitmask: ndarray int
Image with -1 where there are no slits/orders, and an integer where there are slits/order with the integer
indicating the slit number going from 0 to nslit-1 from left to right.
"""
# These lines are always the same
pad = tslits_dict['pad'] if pad is None else pad
slitmask = pixels.slit_pixels(tslits_dict['lcen'],
tslits_dict['rcen'],
tslits_dict['nspat'],
pad=pad)
spec_img = np.outer(
np.arange(tslits_dict['nspec'], dtype=int),
np.ones(tslits_dict['nspat'],
dtype=int)) # spectral position everywhere along image
nslits = tslits_dict['lcen'].shape[1]
# These are the order boundaries determined by eye by JFH. 2025 is used as the maximum as the upper bit is not illuminated
order_max = [
1476, 1513, 1551, 1592, 1687, 1741, 1801, 1864, 1935, 2007, 2025,
2025, 2025, 2025, 2025, 2025
]
order_min = [
418, 385, 362, 334, 303, 268, 230, 187, 140, 85, 26, 0, 0, 0, 0, 0
]
# TODO add binning adjustments to these
for islit in range(nslits):
orderbad = (slitmask == islit) & ((spec_img < order_min[islit]) |
(spec_img > order_max[islit]))
slitmask[orderbad] = -1
return slitmask
def slitmask(self, tslits_dict, pad=None):
"""
Generic routine ton construct a slitmask image from a tslits_dict. Children of this class can
overload this function to implement instrument specific slitmask behavior, for example setting
where the orders on an echelle spectrograph end
Parameters
-----------
tslits_dict: dict
Trace slits dictionary with slit boundary information
Optional Parameters
pad: int or float
Padding of the slit boundaries
binning: tuple
Spectrograph binning in spectral and spatial directions
Returns
-------
slitmask: ndarray int
Image with -1 where there are no slits/orders, and an integer where there are slits/order with the integer
indicating the slit number going from 0 to nslit-1 from left to right.
"""
# These lines are always the same
pad = tslits_dict['pad'] if pad is None else pad
nslits = tslits_dict['lcen'].shape[1]
if nslits != self.norders:
msgs.error('Not all the orders were identified!')
slitmask = pixels.slit_pixels(tslits_dict['lcen'],
tslits_dict['rcen'],
tslits_dict['nspat'],
pad=pad)
spec_img = np.outer(
np.arange(tslits_dict['nspec'], dtype=int),
np.ones(tslits_dict['nspat'],
dtype=int)) # spectral position everywhere along image
binning = tslits_dict['binning']
binspectral, binspatial = parse.parse_binning(binning)
# These are the order boundaries determined by eye by JFH.
order_max = np.asarray([4000] * 14 + [3000]) // binspectral
order_min = np.asarray([2000, 1000] + [0] * 13) // binspectral
# TODO add binning adjustments to these
for iorder in range(self.norders):
orderbad = (slitmask == iorder) & ((spec_img < order_min[iorder]) |
(spec_img > order_max[iorder]))
slitmask[orderbad] = -1
return slitmask
file_list=pixflat_image_files,
det=det,
par=par['calibrations']['pixelflatframe'],
msbias=msbias)
flatimg = flatField.build_pixflat()
# Read in the tilts
tiltsfile = masterpath + 'MasterTilts_A_' + sdet + '_aa.fits'
mstilts = fits.getdata(tiltsfile)
# Read in the tslits_dict
traceslitsroot = masterpath + 'MasterTrace_A_' + sdet + '_aa'
Tslits = traceslits.TraceSlits.from_master_files(traceslitsroot)
tslits_dict = {}
tslits_dict['lcen'] = Tslits.lcen
tslits_dict['rcen'] = Tslits.rcen
tslits_dict['slitpix'] = pixels.slit_pixels(tslits_dict['lcen'],
tslits_dict['rcen'],
flatimg.shape,
Tslits.par['pad'])
elif type == 'ESI':
flatfile = '/Users/joe/REDUX/esi_redux/Mar_2008/Flats/FlatECH10_1x1_D.fits.gz'
piximgfile = '/Users/joe/REDUX/esi_redux/Mar_2008/Final/f_esi1044.fits.gz'
waveimgfile = '/Users/joe/REDUX/esi_redux/Mar_2008/Arcs/ArcECH10_1x1IMG.fits.gz'
sedg_file = '/Users/joe/REDUX/esi_redux/Mar_2008/Flats/SEdgECH10_1x1.fits.gz'
flatimg = fits.getdata(flatfile)
(nspec, nspat) = flatimg.shape
piximg = fits.getdata(piximgfile, 3)
mstilts = piximg / nspec
slit_edges = fits.getdata(sedg_file, 0)
tslits_dict = {}
tslits_dict['lcen'] = slit_edges[0, :, :].T
tslits_dict['rcen'] = slit_edges[1, :, :].T
tslits_dict['slitpix'] = pixels.slit_pixels(tslits_dict['lcen'],
#slit_righ = readsav(user + '/Dropbox/hires_fndobj/GNIRS/J021514.76+004223.8/right_edge_J0215.sav',python_dict=False)['right_edge'].T
#slit_left = readsav(user + '/Dropbox/hires_fndobj/GNIRS/J005424.45+004750.2/left_edge_J0054.sav',python_dict=False)['left_edge'].T
#slit_righ = readsav(user + '/Dropbox/hires_fndobj/GNIRS/J005424.45+004750.2/right_edge_J0054.sav',python_dict=False)['right_edge'].T
slit_left = readsav(user + 'Dropbox/hires_fndobj/GNIRS/J002407.02-001237.2/left_edge_J0024.sav',python_dict=False)['left_edge'].T
slit_righ = readsav(user + 'Dropbox/hires_fndobj/GNIRS/J002407.02-001237.2/right_edge_J0024.sav',python_dict=False)['right_edge'].T
plate_scale = 0.15
#Use std. Telluric directory. Extension 4 is the object structure, and you want xpos and ypos. Xpos is the spatial trace that you want
# to pass in. ypos is just np.arange(nspec)
hdu_std =fits.open(user + 'Dropbox/hires_fndobj/GNIRS/J002407.02-001237.2/Science/HIP117774_5/tel-N20171006S0228-231.fits')
std_xpos = hdu_std[4].data['XPOS'].T
std_trace = np.zeros(np.shape(slit_left))
for i in range(6):
std_trace[:,i] = std_xpos[:,2*i]
ordermask = pixels.slit_pixels(slit_left, slit_righ, objminsky.shape[1])
#viewer, ch = ginga.show_image(objminsky)
#ginga.show_slits(viewer,ch, slit_left, slit_righ)
#yvec = np.outer(np.ones(norders), spec_vec)
#tset_left = pydl.xy2traceset(yvec, slit_left.T, ncoeff=ncoeff)
#slit_left_fit = tset_left.yfit.T
#tset_righ = pydl.xy2traceset(yvec, slit_righ.T, ncoeff=ncoeff)
#slit_righ_fit = tset_righ.yfit.T
#ginga.show_slits(viewer,ch, slit_left_fit, slit_righ_fit)
slit_mid = (slit_left + slit_righ)/2.0
mask = (ivar > 0.0)
#slit_left = readsav('/Users/feige/Dropbox/hires_fndobj/left_edge.sav', python_dict=False)['left_edge'].T
#slit_righ = readsav('/Users/feige/Dropbox/hires_fndobj/right_edge.sav', python_dict=False)['right_edge'].T
#slit_left = readsav('/Users/feige/Dropbox/hires_fndobj/GNIRS/J021514.76+004223.8/left_edge_J0215.sav',python_dict=False)['left_edge'].T
#slit_righ = readsav('/Users/feige/Dropbox/hires_fndobj/GNIRS/J021514.76+004223.8/right_edge_J0215.sav',python_dict=False)['right_edge'].T
#slit_left = readsav('/Users/feige/Dropbox/hires_fndobj/GNIRS/J005424.45+004750.2/left_edge_J0054.sav',python_dict=False)['left_edge'].T
#slit_righ = readsav('/Users/feige/Dropbox/hires_fndobj/GNIRS/J005424.45+004750.2/right_edge_J0054.sav',python_dict=False)['right_edge'].T
slit_left = readsav(
'/Users/feige/Dropbox/hires_fndobj/GNIRS/J002407.02-001237.2/left_edge_J0024.sav',
python_dict=False)['left_edge'].T
slit_righ = readsav(
'/Users/feige/Dropbox/hires_fndobj/GNIRS/J002407.02-001237.2/right_edge_J0024.sav',
python_dict=False)['right_edge'].T
plate_scale = 0.15
ordermask = pixels.slit_pixels(slit_left, slit_righ, objminsky.shape, 0)
#viewer, ch = ginga.show_image(objminsky)
#ginga.show_slits(viewer,ch, slit_left, slit_righ)
#yvec = np.outer(np.ones(norders), spec_vec)
#tset_left = pydl.xy2traceset(yvec, slit_left.T, ncoeff=ncoeff)
#slit_left_fit = tset_left.yfit.T
#tset_righ = pydl.xy2traceset(yvec, slit_righ.T, ncoeff=ncoeff)
#slit_righ_fit = tset_righ.yfit.T
#ginga.show_slits(viewer,ch, slit_left_fit, slit_righ_fit)
slit_mid = (slit_left + slit_righ) / 2.0
spectro_name = 'keck_lris_red'
spectrograph = load_spectrograph(spectrograph=spectro_name)
par = spectrograph.default_pypeit_par()
flatField = flatfield.FlatField(spectrograph, file_list=pixflat_image_files,det=det, par=par['calibrations']['pixelflatframe']
, msbias = msbias)
flatimg = flatField.build_pixflat()
# Read in the tilts
tiltsfile = masterpath + 'MasterTilts_A_' + sdet + '_aa.fits'
mstilts = fits.getdata(tiltsfile)
# Read in the tslits_dict
traceslitsroot = masterpath + 'MasterTrace_A_' + sdet + '_aa'
Tslits = traceslits.TraceSlits.from_master_files(traceslitsroot)
tslits_dict = {}
tslits_dict['lcen']=Tslits.lcen
tslits_dict['rcen']=Tslits.rcen
tslits_dict['slitpix'] = pixels.slit_pixels(tslits_dict['lcen'],tslits_dict['rcen'], flatimg.shape, Tslits.par['pad'])
elif type == 'ESI':
flatfile = '/Users/joe/REDUX/esi_redux/Mar_2008/Flats/FlatECH10_1x1_D.fits.gz'
piximgfile = '/Users/joe/REDUX/esi_redux/Mar_2008/Final/f_esi1044.fits.gz'
waveimgfile = '/Users/joe/REDUX/esi_redux/Mar_2008/Arcs/ArcECH10_1x1IMG.fits.gz'
sedg_file = '/Users/joe/REDUX/esi_redux/Mar_2008/Flats/SEdgECH10_1x1.fits.gz'
flatimg = fits.getdata(flatfile)
(nspec, nspat) = flatimg.shape
piximg = fits.getdata(piximgfile, 3)
mstilts = piximg/nspec
slit_edges = fits.getdata(sedg_file,0)
tslits_dict = {}
tslits_dict['lcen']=slit_edges[0,:,:].T
tslits_dict['rcen']=slit_edges[1,:,:].T
tslits_dict['slitpix'] = pixels.slit_pixels(tslits_dict['lcen'],tslits_dict['rcen'], flatimg.shape, 0)
