def correct_files(hdu,tilt=0):
"""For a given input file, apply corrections for wavelength,
distortion, and bad pixels
Parameters
----------
input_file: astropy.io.fits.HDUList
tilt: (float)
change in row from col = 0 to cols
"""
cbin, rbin = [int(x) for x in hdu[0].header['CCDSUM'].split(" ")]
beams, rows, cols = hdu[1].data.shape
#temporary cludge
thdu = fits.HDUList([fits.PrimaryHDU(), fits.ImageHDU(hdu[1].data[0])])
thdu[0].header = hdu[0].header
thdu[1].name = 'SCI'
rpix_oc = read_wollaston(thdu, wollaston_file=datadir+"wollaston.txt")
drow_oc = (rpix_oc-rpix_oc[:,cols/2][:,None])/rbin
drow_oc += -tilt*(np.arange(cols) - cols/2)/cols
for i in range(1, len(hdu)):
for o in range(beams):
if hdu[i].name == 'BPM' :
tdata = hdu[i].data[o].astype('float')
else:
tdata = hdu[i].data[o]
tdata = correct_wollaston(tdata, -drow_oc[o])
if hdu[i].name == 'BPM' :
hdu[i].data[o] = (tdata > 0.1).astype('uint')
else:
hdu[i].data[o] = tdata
return hdu
def correct_files(hdu, tilt=0):
"""For a given input file, apply corrections for wavelength,
distortion, and bad pixels
Parameters
----------
input_file: astropy.io.fits.HDUList
tilt: (float)
change in row from col = 0 to cols
"""
cbin, rbin = [int(x) for x in hdu[0].header['CCDSUM'].split(" ")]
beams, rows, cols = hdu[1].data.shape
#temporary cludge
thdu = fits.HDUList([fits.PrimaryHDU(), fits.ImageHDU(hdu[1].data[0])])
thdu[0].header = hdu[0].header
thdu[1].name = 'SCI'
rpix_oc = read_wollaston(thdu, wollaston_file=datadir + "wollaston.txt")
drow_oc = (rpix_oc - rpix_oc[:, cols / 2][:, None]) / rbin
drow_oc += -tilt * (np.arange(cols) - cols / 2) / cols
for i in range(1, len(hdu)):
for o in range(beams):
if hdu[i].name == 'BPM':
tdata = hdu[i].data[o].astype('float')
else:
tdata = hdu[i].data[o]
tdata = correct_wollaston(tdata, -drow_oc[o])
if hdu[i].name == 'BPM':
hdu[i].data[o] = (tdata > 0.1).astype('uint')
else:
hdu[i].data[o] = tdata
return hdu
def specpolwavmap(infilelist,
linelistlib="",
automethod='Matchlines',
function='legendre',
order=3,
debug=False,
logfile='salt.log'):
obsdate = os.path.basename(infilelist[0])[7:15]
with logging(logfile, debug) as log:
log.message('Pysalt Version: ' + pysalt.verno, with_header=False)
log.message('specpolwavmap version: 20180804', with_header=False)
# group the files together
config_dict = list_configurations(infilelist, log)
usesaltlinelist = (len(linelistlib) > 0)
for config in config_dict:
if len(config_dict[config]['arc']) == 0:
log.message('No Arc for this configuration:',
with_header=False)
continue
isdualarc = len(config_dict[config]['arc']) > 1
iarc = config_dict[config]['arc'][0]
hduarc = pyfits.open(iarc)
image_id = str(image_number(iarc))
rows, cols = hduarc[1].data.shape
grating = hduarc[0].header['GRATING'].strip()
grang = hduarc[0].header['GR-ANGLE']
artic = hduarc[0].header['CAMANG']
filter = hduarc[0].header['FILTER'].strip()
lamp = hduarc[0].header['LAMPID'].strip().replace(' ', '')
if lamp == 'NONE': lamp = 'CuAr'
cbin, rbin = [
int(x) for x in hduarc[0].header['CCDSUM'].split(" ")
]
if isdualarc:
iarc2 = config_dict[config]['arc'][1]
hduarc2 = pyfits.open(iarc2)
ratio21 = np.percentile(hduarc2[1].data, 99.9) / np.percentile(
hduarc[1].data, 99.9)
image_id = image_id + '_' + str(image_number(iarc2))
hduarc[1].data += hduarc2[1].data / ratio21
lamp2 = hduarc2[0].header['LAMPID'].strip().replace(' ', '')
log.message(('\nDual Arcs: ' + lamp + ' + ' + lamp2 +
'/ %8.4f' % ratio21),
with_header=False)
# need this for the distortion correction
rpix_oc = read_wollaston(hduarc,
wollaston_file=datadir + "wollaston.txt")
#split the arc into the two beams
hduarc, splitrow = specpolsplit(hduarc,
splitrow=None,
wollaston_file=datadir +
"wollaston.txt")
rows = 2 * hduarc['SCI'].data.shape[
1] # allow for odd number of input rows
if usesaltlinelist: # if linelistlib specified, use salt-supplied
with open(linelistlib) as fd:
linelistdict = dict(line.strip().split(None, 1)
for line in fd)
lampfile = iraf.osfn("pysalt$data/linelists/" +
linelistdict[lamp])
if isdualarc:
lamp2file = iraf.osfn("pysalt$data/linelists/" +
linelistdict[lamp2])
else: # else, use line lists in polarimetry area for 300l
if grating == "PG0300":
linelistlib = datadir + "linelistlib_300.txt"
lib_lf = list(
np.loadtxt(linelistlib, dtype=str,
usecols=(0, 1, 2))) # lamp,filter,file
linelistdict = defaultdict(dict)
for ll in range(len(lib_lf)):
linelistdict[lib_lf[ll][0]][int(
lib_lf[ll][1])] = lib_lf[ll][2]
filter_l = np.sort(np.array(linelistdict[lamp].keys()))
usefilter = filter_l[np.where(
int(filter[-5:-1]) < filter_l)[0][0]]
lampfile = datadir + linelistdict[lamp][usefilter]
if isdualarc:
lamp2file = datadir + linelistdict[lamp2][usefilter]
else:
linelistlib = datadir + "linelistlib.txt"
with open(linelistlib) as fd:
linelistdict = dict(line.strip().split(None, 1)
for line in fd)
lampfile = iraf.osfn("pysalt$data/linelists/" +
linelistdict[lamp])
if isdualarc:
lamp2file = iraf.osfn("pysalt$data/linelists/" +
linelistdict[lamp2])
if isdualarc:
lamp_dl = np.loadtxt(lampfile, usecols=(0, 1), unpack=True)
lamp2_dl = np.loadtxt(lamp2file, usecols=(0, 1), unpack=True)
duallamp_dl = np.sort(np.hstack((lamp_dl, lamp2_dl)))
np.savetxt('duallamp.txt', duallamp_dl.T, fmt='%10.3f %8i')
lampfile = 'duallamp.txt'
lamp = lamp + ',' + lamp2
# some housekeeping for bad keywords
if hduarc[0].header['MASKTYP'].strip(
) == 'MOS': # for now, MOS treated as single, short 1 arcsec longslit
hduarc[0].header['MASKTYP'] = 'LONGSLIT'
hduarc[0].header['MASKID'] = 'P001000P99'
del hduarc['VAR']
del hduarc['BPM']
# log the information about the arc
log.message('\nARC: image '+image_id+' GRATING '+grating\
+' GRANG '+("%8.3f" % grang)+' ARTIC '+("%8.3f" % artic)+' LAMP '+lamp, with_header=False)
log.message(' Split Row: ' + ("%4i " % splitrow),
with_header=False)
# set up the correction for the beam splitter
drow_oc = (rpix_oc - rpix_oc[:, cols / 2][:, None]) / rbin
wavmap_orc = pol_wave_map(hduarc,
image_id,
drow_oc,
rows,
cols,
lampfile=lampfile,
function=function,
order=order,
automethod=automethod,
log=log,
logfile=logfile)
# if image not already cleaned,
# use upper outlier quartile fence of 3 column subarray across normalized configuration
# or 10-sigma spike to cull cosmic rays. Normalize by rows
images = len(config_dict[config]['object'])
historylist = list(
pyfits.open(
config_dict[config]['object'][0])[0].header['HISTORY'])
cleanhistory = next((x for x in historylist if x[:7] == "CRCLEAN"),
"None")
iscr_irc = np.zeros((images, rows, cols), dtype='bool')
if cleanhistory == 'CRCLEAN: None':
historyidx = historylist.index(cleanhistory)
upperfence = 4.0
lowerfence = 1.5
sigmaveto = 2.0
sci_irc = np.zeros((images, rows, cols))
var_irc = np.zeros((images, rows, cols))
for (i, image) in enumerate(config_dict[config]['object']):
hdulist = pyfits.open(image)
okbin_rc = (hdulist['BPM'].data[:rows, :] == 0)
sci_irc[i][okbin_rc] = hdulist['SCI'].data[:rows, :][
okbin_rc]
var_irc[i][okbin_rc] = hdulist['VAR'].data[:rows, :][
okbin_rc]
okrow_r = okbin_rc.any(axis=1)
for r in np.where(okrow_r)[0]:
rowmean = sci_irc[i, r][okbin_rc[r]].mean()
sci_irc[i, r] /= rowmean
var_irc[i, r] /= rowmean**2
sci_ijrc = np.zeros((images, 3, rows, cols))
for j in range(3):
sci_ijrc[:, j, :, 1:-1] = sci_irc[:, :, j:cols + j - 2]
sci_Irc = sci_ijrc.reshape((-1, rows, cols))
sci_Irc.sort(axis=0)
firstmthird_rc = sci_Irc[-1] - sci_Irc[-3]
q1_rc, q3_rc = np.percentile(sci_Irc, (25, 75),
axis=0,
overwrite_input=True)
dq31_rc = q3_rc - q1_rc
okq_rc = (dq31_rc > 0.)
oksig_rc = (var_irc.sum(axis=0) > 0.)
sigma_rc = np.zeros_like(q1_rc)
sigma_rc[oksig_rc] = np.sqrt(
var_irc.sum(axis=0)[oksig_rc] /
((var_irc > 0).sum(axis=0)[oksig_rc]))
dq31_rc = np.maximum(
dq31_rc, 1.35 *
sigma_rc) # avoid impossibly low dq from fluctuations
iscr1_irc = np.zeros((images, rows, cols), dtype=bool)
iscr2_irc = np.zeros((images, rows, cols), dtype=bool)
iscr1_irc[:, okq_rc] = (sci_irc[:, okq_rc] >
(q3_rc + upperfence * dq31_rc)[okq_rc]
) # above upper outlier fence
iscr2_irc[:,okbin_rc] = ((sci_irc[:,okbin_rc]==sci_irc[:,okbin_rc].max(axis=0)) & \
(firstmthird_rc[okbin_rc] > 10*sigma_rc[okbin_rc])) # or a 10-sigma spike
iscr_irc = (iscr1_irc | iscr2_irc)
notcr3_irc = (iscr_irc & (iscr_irc.sum(axis=0) > 2)
) # but >2 CR's in one place are bogus
notcr4_irc = (iscr_irc & (firstmthird_rc < sigmaveto * dq31_rc)
) # seeing/guiding errors, not CR
iscr_irc &= (np.logical_not(notcr3_irc | notcr4_irc))
isnearcr_irc = np.zeros((images, rows + 2, cols + 2),
dtype=bool)
for dr, dc in np.ndindex(3, 3): # lower fence on neighbors
isnearcr_irc[:, dr:rows + dr, dc:cols + dc] |= iscr_irc
isnearcr_irc = isnearcr_irc[:, 1:-1, 1:-1]
iscr_irc[isnearcr_irc] |= (
okq_rc & (sci_irc >
(q3_rc + lowerfence * dq31_rc)))[isnearcr_irc]
log.message('CR culling with upper quartile fence\n',
with_header=False)
elif cleanhistory == 'None':
log.message(
'CR clean history unknown, none applied (suggest rerunning imred)',
with_header=False)
else:
log.message('CR cleaning already done: ' + cleanhistory,
with_header=False)
# for images using this arc,save split data along third fits axis,
# add wavmap extension, save as 'w' file
hduwav = pyfits.ImageHDU(data=wavmap_orc.astype('float32'),
header=hduarc['SCI'].header,
name='WAV')
for (i, image) in enumerate(config_dict[config]['object']):
hdu = pyfits.open(image)
if cleanhistory == 'CRCLEAN: None':
hdu['BPM'].data[:rows, :][iscr_irc[i]] = 1
hdu[0].header['HISTORY'][historyidx] = \
('CRCLEAN: upper= %3.1f, lower= %3.1f, sigmaveto= %3.1f' % (upperfence,lowerfence,sigmaveto))
hdu, splitrow = specpolsplit(hdu, splitrow=splitrow)
hdu['BPM'].data[:rows, :][wavmap_orc == 0.] = 1
hdu.append(hduwav)
for f in ('SCI', 'VAR', 'BPM', 'WAV'):
hdu[f].header['CTYPE3'] = 'O,E'
hdu.writeto('w' + image, overwrite='True')
log.message('Output file ' + 'w' + image + ' crs: ' +
str(iscr_irc[i].sum()),
with_header=False)
return
def specpolwavmap(infilelist, linelistlib="", automethod='Matchlines',
function='legendre', order=3, logfile='salt.log'):
obsdate=os.path.basename(infilelist[0])[7:15]
with logging(logfile, debug) as log:
log.message('Pysalt Version: '+pysalt.verno, with_header=False)
# group the files together
config_dict = list_configurations(infilelist, log)
for config in config_dict:
if len(config_dict[config]['arc']) == 0:
log.message('No Arc for this configuration:', with_header=False)
continue
#set up some information needed later
iarc = config_dict[config]['arc'][0]
hduarc = pyfits.open(iarc)
image_no = image_number(iarc)
rows, cols = hduarc[1].data.shape
grating = hduarc[0].header['GRATING'].strip()
grang = hduarc[0].header['GR-ANGLE']
artic = hduarc[0].header['CAMANG']
cbin, rbin = [int(x) for x in hduarc[0].header['CCDSUM'].split(" ")]
# need this for the distortion correction
rpix_oc = read_wollaston(hduarc, wollaston_file=datadir+"wollaston.txt")
#split the arc into the two beams
hduarc, splitrow = specpolsplit(hduarc, splitrow=None, wollaston_file=datadir+"wollaston.txt")
# set up the linelamp to be used
if len(linelistlib) ==0:
linelistlib=datadir+"linelistlib.txt"
if grating=="PG0300":
linelistlib=datadir+"linelistlib_300.txt"
with open(linelistlib) as fd:
linelistdict = dict(line.strip().split(None, 1) for line in fd)
#set up the lamp to be used
lamp=hduarc[0].header['LAMPID'].strip().replace(' ', '')
if lamp == 'NONE': lamp='CuAr'
lampfile=iraf.osfn("pysalt$data/linelists/"+linelistdict[lamp])
# some housekeeping for bad keywords
if hduarc[0].header['MASKTYP'].strip() == 'MOS': # for now, MOS treated as single, short 1 arcsec longslit
hduarc[0].header.update('MASKTYP','LONGSLIT')
hduarc[0].header.update('MASKID','P001000P99')
del hduarc['VAR']
del hduarc['BPM']
# log the information about the arc
log.message('\nARC: image '+str(image_no)+' GRATING '+grating\
+' GRANG '+("%8.3f" % grang)+' ARTIC '+("%8.3f" % artic)+' LAMP '+lamp, with_header=False)
log.message(' Split Row: '+("%4i " % splitrow), with_header=False)
# set up the correction for the beam splitter
drow_oc = (rpix_oc-rpix_oc[:,cols/2][:,None])/rbin
wavmap_orc = pol_wave_map(hduarc, image_no, drow_oc, rows, cols,
lampfile=lampfile, function=function, order=order,
automethod=automethod, log=log, logfile=logfile)
# for images using this arc,save split data along third fits axis,
# add wavmap extension, save as 'w' file
hduwav = pyfits.ImageHDU(data=wavmap_orc.astype('float32'), header=hduarc['SCI'].header, name='WAV')
for image in config_dict[config]['object']:
hdu = pyfits.open(image)
hdu, splitrow = specpolsplit(hdu, splitrow=splitrow)
hdu['BPM'].data[wavmap_orc==0.] = 1
hdu.append(hduwav)
for f in ('SCI','VAR','BPM','WAV'): hdu[f].header.update('CTYPE3','O,E')
hdu.writeto('w'+image,clobber='True')
log.message('Output file '+'w'+image, with_header=False)
return
def specpolwavmap(infilelist, linelistlib="", automethod='Matchlines',
function='legendre', order=3, debug=False, logfile='salt.log'):
obsdate=os.path.basename(infilelist[0])[7:15]
with logging(logfile, debug) as log:
log.message('Pysalt Version: '+pysalt.verno, with_header=False)
log.message('specpolwavmap version: 20171226', with_header=False)
# group the files together
config_dict = list_configurations(infilelist, log)
usesaltlinelist = (len(linelistlib)>0)
for config in config_dict:
if len(config_dict[config]['arc']) == 0:
log.message('No Arc for this configuration:', with_header=False)
continue
#set up some information needed later
iarc = config_dict[config]['arc'][0]
hduarc = pyfits.open(iarc)
image_no = image_number(iarc)
rows, cols = hduarc[1].data.shape
grating = hduarc[0].header['GRATING'].strip()
grang = hduarc[0].header['GR-ANGLE']
artic = hduarc[0].header['CAMANG']
filter = hduarc[0].header['FILTER'].strip()
cbin, rbin = [int(x) for x in hduarc[0].header['CCDSUM'].split(" ")]
# need this for the distortion correction
rpix_oc = read_wollaston(hduarc, wollaston_file=datadir+"wollaston.txt")
#split the arc into the two beams
hduarc, splitrow = specpolsplit(hduarc, splitrow=None, wollaston_file=datadir+"wollaston.txt")
#set up the lamp to be used
lamp=hduarc[0].header['LAMPID'].strip().replace(' ', '')
if lamp == 'NONE': lamp='CuAr'
# set up the linelist to be used
if usesaltlinelist: # if linelistlib specified, use salt-supplied
with open(linelistlib) as fd:
linelistdict = dict(line.strip().split(None, 1) for line in fd)
lampfile=iraf.osfn("pysalt$data/linelists/"+linelistdict[lamp])
else: # else, use line lists in polarimetry area for 300l
if grating=="PG0300":
linelistlib=datadir+"linelistlib_300.txt"
lib_lf = list(np.loadtxt(linelistlib,dtype=str,usecols=(0,1,2))) # lamp,filter,file
linelistdict = defaultdict(dict)
for ll in range(len(lib_lf)):
linelistdict[lib_lf[ll][0]][int(lib_lf[ll][1])] = lib_lf[ll][2]
filter_l = np.sort(np.array(linelistdict[lamp].keys()))
usefilter = filter_l[np.where(int(filter[-5:-1]) < filter_l)[0][0]]
lampfile = datadir+linelistdict[lamp][usefilter]
else:
linelistlib=datadir+"linelistlib.txt"
with open(linelistlib) as fd:
linelistdict = dict(line.strip().split(None, 1) for line in fd)
lampfile=iraf.osfn("pysalt$data/linelists/"+linelistdict[lamp])
# some housekeeping for bad keywords
if hduarc[0].header['MASKTYP'].strip() == 'MOS': # for now, MOS treated as single, short 1 arcsec longslit
hduarc[0].header['MASKTYP'] = 'LONGSLIT'
hduarc[0].header['MASKID'] = 'P001000P99'
del hduarc['VAR']
del hduarc['BPM']
# log the information about the arc
log.message('\nARC: image '+str(image_no)+' GRATING '+grating\
+' GRANG '+("%8.3f" % grang)+' ARTIC '+("%8.3f" % artic)+' LAMP '+lamp, with_header=False)
log.message(' Split Row: '+("%4i " % splitrow), with_header=False)
# set up the correction for the beam splitter
drow_oc = (rpix_oc-rpix_oc[:,cols/2][:,None])/rbin
wavmap_orc = pol_wave_map(hduarc, image_no, drow_oc, rows, cols,
lampfile=lampfile, function=function, order=order,
automethod=automethod, log=log, logfile=logfile)
# if image not already cleaned,
# use upper outlier quartile fence of 3 column subarray across normalized configuration
# or 10-sigma spike to cull cosmic rays. Normalize by rows
images = len(config_dict[config]['object'])
historylist = list(pyfits.open(config_dict[config]['object'][0])[0].header['HISTORY'])
cleanhistory = next((x for x in historylist if x[:7]=="CRCLEAN"),"None")
iscr_irc = np.zeros((images,rows,cols),dtype='bool')
if cleanhistory == 'CRCLEAN: None':
historyidx = historylist.index(cleanhistory)
upperfence = 4.0
lowerfence = 1.5
sigmaveto = 2.0
sci_irc = np.zeros((images,rows,cols))
var_irc = np.zeros((images,rows,cols))
for (i,image) in enumerate(config_dict[config]['object']):
hdulist = pyfits.open(image)
okbin_rc = (hdulist['BPM'].data == 0)
sci_irc[i][okbin_rc] = hdulist['SCI'].data[okbin_rc]
var_irc[i][okbin_rc] = hdulist['VAR'].data[okbin_rc]
okrow_r = okbin_rc.any(axis=1)
for r in np.where(okrow_r)[0]:
rowmean = sci_irc[i,r][okbin_rc[r]].mean()
sci_irc[i,r] /= rowmean
var_irc[i,r] /= rowmean**2
sci_ijrc = np.zeros((images,3,rows,cols))
for j in range(3):
sci_ijrc[:,j,:,1:-1] = sci_irc[:,:,j:cols+j-2]
sci_Irc = sci_ijrc.reshape((-1,rows,cols))
sci_Irc.sort(axis=0)
firstmthird_rc = sci_Irc[-1] - sci_Irc[-3]
q1_rc,q3_rc = np.percentile(sci_Irc,(25,75),axis=0,overwrite_input=True)
dq31_rc = q3_rc - q1_rc
okq_rc = (dq31_rc > 0.)
oksig_rc = (var_irc.sum(axis=0) > 0.)
sigma_rc = np.zeros_like(q1_rc)
sigma_rc[oksig_rc] = np.sqrt(var_irc.sum(axis=0)[oksig_rc]/((var_irc > 0).sum(axis=0)[oksig_rc]))
dq31_rc = np.maximum(dq31_rc,1.35*sigma_rc) # avoid impossibly low dq from fluctuations
iscr1_irc = np.zeros((images,rows,cols),dtype=bool)
iscr2_irc = np.zeros((images,rows,cols),dtype=bool)
iscr1_irc[:,okq_rc] = (sci_irc[:,okq_rc] > (q3_rc + upperfence*dq31_rc)[okq_rc]) # above upper outlier fence
iscr2_irc[:,okbin_rc] = ((sci_irc[:,okbin_rc]==sci_irc[:,okbin_rc].max(axis=0)) & \
(firstmthird_rc[okbin_rc] > 10*sigma_rc[okbin_rc])) # or a 10-sigma spike
iscr_irc = (iscr1_irc | iscr2_irc)
notcr3_irc =(iscr_irc & (iscr_irc.sum(axis=0)>2)) # but >2 CR's in one place are bogus
notcr4_irc =(iscr_irc & (firstmthird_rc < sigmaveto*dq31_rc)) # seeing/guiding errors, not CR
iscr_irc &= (np.logical_not(notcr3_irc | notcr4_irc))
isnearcr_irc = np.zeros((images,rows+2,cols+2),dtype=bool)
for dr,dc in np.ndindex(3,3): # lower fence on neighbors
isnearcr_irc[:,dr:rows+dr,dc:cols+dc] |= iscr_irc
isnearcr_irc = isnearcr_irc[:,1:-1,1:-1]
iscr_irc[isnearcr_irc] |= (okq_rc & (sci_irc > (q3_rc + lowerfence*dq31_rc)))[isnearcr_irc]
log.message('CR culling with upper quartile fence\n', with_header=False)
elif cleanhistory == 'None':
log.message('CR clean history unknown, none applied (suggest rerunning imred)',with_header=False)
else:
log.message('CR cleaning already done: '+cleanhistory,with_header=False)
# for images using this arc,save split data along third fits axis,
# add wavmap extension, save as 'w' file
hduwav = pyfits.ImageHDU(data=wavmap_orc.astype('float32'), header=hduarc['SCI'].header, name='WAV')
for (i,image) in enumerate(config_dict[config]['object']):
hdu = pyfits.open(image)
if cleanhistory == 'CRCLEAN: None':
hdu['BPM'].data[iscr_irc[i]] = 1
hdu[0].header['HISTORY'][historyidx] = \
('CRCLEAN: upper= %3.1f, lower= %3.1f, sigmaveto= %3.1f' % (upperfence,lowerfence,sigmaveto))
hdu, splitrow = specpolsplit(hdu, splitrow=splitrow)
hdu['BPM'].data[wavmap_orc==0.] = 1
hdu.append(hduwav)
for f in ('SCI','VAR','BPM','WAV'): hdu[f].header['CTYPE3'] = 'O,E'
hdu.writeto('w'+image,overwrite='True')
log.message('Output file '+'w'+image+' crs: '+str(iscr_irc[i].sum()), with_header=False)
return
def specpolsplit(hdu, splitrow=None, wollaston_file=None):
""" Split the O and E beams
Parameters
----------
hdu: fits.HDUList
Polarimetric observations data
splitrow: None or int
Row to split the image. If None, the value will be calculated
wollaston_file: None or str
File containing the central position of the split O and E beams
Return
------
whdu: fits.HDUList
New header object with extensions split between the O and E beams
splitrow: float
Row at which to split the images
"""
rows, cols = hdu[1].data.shape
#determine the row to split the file from the estimated center
if splitrow is None and wollaston_file:
# use arc to make first-guess wavecal from model
# locate beamsplitter split point based on the center of the chips
# given in the wollaston file
cbin, rbin = [int(x) for x in hdu[0].header['CCDSUM'].split(" ")]
woll_pix = read_wollaston(hdu, wollaston_file)
# print woll_pix[:, cols/2]
axisrow_o = ((2052 + woll_pix[:, cols / 2]) / rbin).astype(int)
data_y = hdu[1].data.sum(axis=1)
top = axisrow_o[1] + np.argmax(
data_y[axisrow_o[1]:] < 0.5 * data_y[axisrow_o[1]])
bot = axisrow_o[0] - np.argmax(
data_y[axisrow_o[0]::-1] < 0.5 * data_y[axisrow_o[0]])
splitrow = 0.5 * (bot + top)
elif splitrow is None and wollaston_file is None:
splitrow = rows / 2.0
offset = int(splitrow -
rows / 2) # how far split is from center of detector
# split arc into o/e images
padbins = (np.indices((rows, cols))[0] < offset) | (np.indices(
(rows, cols))[0] > rows + offset)
image_rc = np.roll(hdu['SCI'].data, -offset, axis=0)
image_rc[padbins] = 0.
hdu['SCI'].data = image_rc.reshape((2, rows / 2, cols))
var_rc = np.roll(hdu['VAR'].data, -offset, axis=0)
var_rc[padbins] = 0.
hdu['VAR'].data = var_rc.reshape((2, rows / 2, cols))
bpm_rc = np.roll(hdu['BPM'].data, -offset, axis=0)
bpm_rc[padbins] = 1
bpm_orc = bpm_rc.reshape((2, rows / 2, cols))
hdu['BPM'].data = bpm_orc
return hdu, splitrow
def specpolsplit(hdu, splitrow=None, wollaston_file=None):
""" Split the O and E beams
Parameters
----------
hdu: fits.HDUList
Polarimetric observations data
splitrow: None or int
Row to split the image. If None, the value will be calculated
wollaston_file: None or str
File containing the central position of the split O and E beams
Return
------
whdu: fits.HDUList
New header object with extensions split between the O and E beams
splitrow: float
Row at which to split the images
"""
rows,cols = hdu[1].data.shape
#determine the row to split the file from the estimated center
if splitrow is None and wollaston_file:
# use arc to make first-guess wavecal from model
# locate beamsplitter split point based on the center of the chips
# given in the wollaston file
cbin, rbin = [int(x) for x in hdu[0].header['CCDSUM'].split(" ")]
woll_pix = read_wollaston(hdu, wollaston_file)
# print woll_pix[:, cols/2]
axisrow_o = ((2052 + woll_pix[:,cols/2])/rbin).astype(int)
data_y = hdu[1].data.sum(axis=1)
top = axisrow_o[1] + np.argmax(data_y[axisrow_o[1]:] < 0.5*data_y[axisrow_o[1]])
bot = axisrow_o[0] - np.argmax(data_y[axisrow_o[0]::-1] < 0.5*data_y[axisrow_o[0]])
splitrow = 0.5*(bot + top)
elif splitrow is None and wollaston_file is None:
splitrow = rows/2.0
offset = int(splitrow - rows/2) # how far split is from center of detector
# split arc into o/e images
padbins = (np.indices((rows,cols))[0]<offset) | (np.indices((rows,cols))[0]>rows+offset)
image_rc = np.roll(hdu['SCI'].data,-offset,axis=0)
image_rc[padbins] = 0.
hdu['SCI'].data = image_rc.reshape((2,rows/2,cols))
var_rc = np.roll(hdu['VAR'].data,-offset,axis=0)
var_rc[padbins] = 0.
hdu['VAR'].data = var_rc.reshape((2,rows/2,cols))
bpm_rc = np.roll(hdu['BPM'].data,-offset,axis=0)
bpm_rc[padbins] = 1
bpm_orc = bpm_rc.reshape((2,rows/2,cols))
hdu['BPM'].data = bpm_orc
return hdu, splitrow