def _get_fits_header(self, result, options):
"""Populate FITS Header keywords"""
super(WFIRSTInstrument, self)._get_fits_header(result, options)
pupil_hdr = fits.getheader(self.pupil)
apodizer_hdr = fits.getheader(self._apodizer_fname)
fpm_hdr = fits.getheader(self._fpm_fname)
lyotstop_hdr = fits.getheader(self._lyotstop_fname)
result[0].header.set('MODE', self.mode, comment='Observing mode')
result[0].header.set('CAMERA', self.camera, comment='Imager or IFS')
result[0].header.set('APODIZER', self.apodizer, comment='Apodizer')
result[0].header.set('APODTRAN', os.path.basename(self._apodizer_fname),
comment='Apodizer transmission')
result[0].header.set('PUPLSCAL', apodizer_hdr['PUPLSCAL'],
comment='Apodizer pixel scale in m/pixel')
result[0].header.set('PUPLDIAM', apodizer_hdr['PUPLDIAM'],
comment='Full apodizer array size, incl padding.')
result[0].header.set('FPM', self.fpm, comment='Focal plane mask')
result[0].header.set('FPMTRAN', os.path.basename(self._fpm_fname),
comment='FPM transmission')
result[0].header.set('FPMSCAL', fpm_hdr['PIXSCALE'], comment='FPM spatial sampling, arcsec/pix')
result[0].header.set('LYOTSTOP', self.lyotstop, comment='Lyot stop')
result[0].header.set('LSTRAN', os.path.basename(self._lyotstop_fname),
comment='Lyot stop transmission')
result[0].header.set('PUPLSCAL', lyotstop_hdr['PUPLSCAL'],
comment='Lyot stop pixel scale in m/pixel')
result[0].header.set('PUPLDIAM', lyotstop_hdr['PUPLDIAM'],
comment='Lyot stop array size, incl padding.')
def load_uvot_images(galaxy, band, imdir=''):
"""ugh:
:returns hdr:
:returns images:
List of images [cps, exposure, sensitivity]
:returns masks:
Dictionary of mask images, keyed by strings:
['bkg', 'reg', 'reg1', 'reg5', 'reg20']
"""
imroot = os.path.join(imdir, galaxy, galaxy)
info = {'root': imroot, 'band': band}
names = ['{root}_t{band}.fits', '{root}_t{band}_ex.fits', '{root}_t{band}_lss.fits']
#print(names[0].format(**info))
try:
hdr = pyfits.getheader(names[0].format(**info), 1)
except:
hdr = pyfits.getheader(names[0].format(**info), 0)
images = [pyfits.getdata(n.format(**info)) for n in names]
masknames = ['bkg', 'reg', 'reg1', 'reg5', 'reg20']
masks = [pyfits.getdata('{root}_{mask}.fits'.format(root=info['root'], mask=n))
for n in masknames]
return hdr, images, dict(zip(masknames, masks))
def logfile_sci(logfile, imlist, SaveSteps=False):
'''Add science images and info to logfile
logfile - Log file name
imlist - List of science files reduced
SaveSteps - Set to True if each step in the reduction is saved
as a new file
'''
imlist.sort()
nfiles = len(imlist)
log = open(logfile, 'a')
log.write('# Science Images\n')
if SaveSteps:
# if user is saving interim reduction steps to new files,
# include the extensions for each caliration type to header
log.write('# *.fits - raw image \n')
log.write('# *.bs.fits - bias-subtracted image \n')
log.write('# *.bs.ff.fits - bias-subtracted and flat-fielded \n')
log.write('# Image Object Exptime Filter\n')
# for each file, print the image name, object type, exptime, and filter
for i in range(nfiles):
imname = os.path.splitext(os.path.basename(imlist[i]))[0]
obj = fits.getheader(imlist[i])['OBJECT']
exptime = fits.getheader(imlist[i])['EXPTIME']
filt = fits.getheader(imlist[i])['FILTER']
log.write('%s %s %.3f %s \n' % (imname, obj, exptime, filt))
log.write('\n')
log.write('\n')
log.write('# Comments: \n')
log.write('\n')
log.close()
def hdr_get(gal,data_mode='12m'):
if isinstance(gal,Galaxy):
name = gal.name.lower()
elif isinstance(gal,str):
name = gal.lower()
else:
raise ValueError("'gal' must be a str or galaxy!")
if data_mode == '7m':
data_mode = '7m'
conbeam=None
print( 'WARNING: SFR maps come in 12m sizes only.') #(!!!) What about for all the new 15" maps?
print( 'WARNING: Convolution forcibly disabled.')
elif data_mode in ['12m','12m+7m']:
data_mode = '12m+7m'
hdr = None
hdr_found = False
if name=='m33':
for filename in [\
'notphangsdata/M33_14B-088_HI.clean.image.GBT_feathered.pbcov_gt_0.5_masked.peakvels.fits']:
if os.path.isfile(filename):
hdr = fits.getheader(filename)
hdr_found = True
else:
for filename in [\
'phangsdata/'+name+'_co21_'+data_mode+'+tp_mom0.fits',\
'phangsdata/'+name+'_co21_'+data_mode+'+tp_mom1.fits',\
'phangsdata/'+name+'_co21_'+data_mode+'+tp_tpeak.fits']:
if os.path.isfile(filename):
hdr = fits.getheader(filename)
hdr_found = True
if hdr_found == False:
print('WARNING: No header was found!')
hdr = None
return hdr
def get_file_list():
lista = sorted(glob.glob("0*.fits")) #will need work
try:
header0 = fits.getheader(lista[0])
this_night = night(header0["DATE"])
for i in lista:
#print("./" +i)
header = fits.getheader(i)
obstype = header["OBSTYPE"]
if obstype == "BIAS":
this_night.add_bias(i)
elif obstype == "FLAT":
grating = header["GRATING"]
if "NO GRATING" in grating:
this_night.add_flat_ng(i)
elif grating == "KOSI_600":
this_night.add_flat_wg(i)
else:
print("Unrecognized grating")
elif obstype == "OBJECT":
this_night.add_sci(i)
elif obstype == "COMP":
this_night.add_lamp(i)
return this_night
except IOError as err:
if str(err) == "Empty or corrupt FITS file":
print("Raised an IOError as ",err)
os.system("rem.csh")
sys.exit("Please run this script again")
else:
print(err)
sys.exit("Please correct the errors and try again")
def crossmatchtwofiles(img1, img2, radius=3):
''' This module is crossmatch two images:
It run sextractor transform the pixels position of the the sources in coordinates and crossmatch them
The output is a dictionary with the objects in common
'''
from numpy import array, argmin, min, sqrt
import agnkey
hd1 = pyfits.getheader(img1)
hd2 = pyfits.getheader(img2)
wcs1 = pywcs.WCS(hd1)
wcs2 = pywcs.WCS(hd2)
xpix1, ypix1, fw1, cl1, cm1, ell1, bkg1, fl1 = agnkey.agnastrodef.sextractor(img1)
xpix2, ypix2, fw2, cl2, cm2, ell2, bkg2, fl2 = agnkey.agnastrodef.sextractor(img2)
xpix1, ypix1, xpix2, ypix2 = array(xpix1, float), array(ypix1, float), array(xpix2, float), array(ypix2, float)
bb = wcs1.wcs_pix2sky(zip(xpix1, ypix1), 1) # transform pixel in coordinate
xra1, xdec1 = zip(*bb)
bb = wcs2.wcs_pix2sky(zip(xpix2, ypix2), 1) # transform pixel in coordinate
xra2, xdec2 = zip(*bb)
xra1, xdec1, xra2, xdec2 = array(xra1, float), array(xdec1, float), array(xra2, float), array(xdec2, float)
distvec, pos1, pos2 = agnkey.agnastrodef.crossmatch(xra1, xdec1, xra2, xdec2, radius)
# dict={}
dict = {'ra1': xra1[pos1], 'dec1': xdec1[pos1], 'ra2': xra2[pos2], 'dec2': xdec2[pos2],
'xpix1': xpix1[pos1], 'ypix1': ypix1[pos1], 'xpix2': xpix2[pos2], 'ypix2': ypix2[pos2]}
out = next(tempfile._get_candidate_names())+'.list'
np.savetxt(out, zip(xpix1[pos1], ypix1[pos1]), fmt='%10.10s\t%10.10s')
return out, dict
def read(cls, fname, sid, **kwargs):
ftype = kwargs.get('type', 'pdc').lower()
assert ftype in cls.allowed_types, 'Flux type must be either `sap` or `pdc`'
fkey = cls.fkeys[ftype]
try:
epic = int(re.findall('ktwo([0-9]+)-c', basename(fname))[0])
except:
epic = int(re.findall('C([0-9]+)_smear', basename(fname))[0][2:]) # for smear
data = pf.getdata(fname, 1)
phead = pf.getheader(fname, 0)
dhead = pf.getheader(fname, 1)
[h.remove('CHECKSUM') for h in (phead,dhead)]
[phead.remove(k) for k in 'CREATOR PROCVER FILEVER TIMVERSN'.split()]
return K2Data(epic,
time = data['time'],
cadence = data['cadenceno'],
quality = data['sap_quality'],
fluxes = data[fkey],
errors = data[fkey+'_err'],
x = data['pos_corr1'],
y = data['pos_corr2'],
primary_header = phead,
data_header = dhead,
campaign = phead['campaign'])
def image_limits(image, wispfield):
'''Find the '''
# read in combined image
hdr = fits.getheader(image)
hdr_wcs = pywcs.WCS(hdr)
# list of images that were combined
ims = hdr['IMCMB*']
Nim = len(ims)
xx = np.zeros((Nim,2), dtype=int)
yy = np.zeros((Nim,2), dtype=int)
for i in range(Nim):
if os.path.splitext(ims[i])[1] != '.fits':
ims[i] = ims[i] + '.fits'
hd = fits.getheader(os.path.join(wispfield,ims[i]))
nx1 = hd['NAXIS1']
nx2 = hd['NAXIS2']
hd_wcs = pywcs.WCS(hd)
# find WCS coordinates of input image corners
coo = hd_wcs.wcs_pix2sky([[1,1],[1,nx1-1],[nx2-1,1],[nx2-1,nx1-1]],1)
# find corresponding pixel coords in combined image
pix = hdr_wcs.wcs_sky2pix(coo,1)
xx[i,0] = pix[0][1]
xx[i,1] = pix[1][1]
yy[i,0] = pix[0][0]
yy[i,1] = pix[2][0]
xmin,xmax = np.max(xx[:,0]), np.min(xx[:,1])
ymin,ymax = np.max(yy[:,0]), np.min(yy[:,1])
return xmin,xmax,ymin,ymax
def filter_combo(Palim, UVISims):
'''Return the filter combination that should be used for
determination of the color term.
'''
# get Palomar SDSS filters
hdr = fits.getheader(Palim[0])
Pal_filt = hdr['FILTER'].rstrip("'")
# get UVIS filters
UVIS_filt = []
for im in UVISims:
hdr = fits.getheader(im)
UVIS_filt.append(hdr['FILTER'])
# are both UVIS filters available?
if len(UVIS_filt) > 1:
print '\nNOTE: Both UVIS filters -- %s -- are available for '+\
'this field\n' % [x for x in UVIS_filt]
# possible filter combinations for photometric calibration
if Pal_filt == 'g':
use_filt = [x for x in UVIS_filt if x == 'F600LP' or x == 'F814W']
use_im = [x for x in UVISims if \
os.path.basename(x).split('_')[0] == 'F600LP']
if Pal_filt == 'i':
use_filt = [x for x in UVIS_filt if x == 'F475X' or x == 'F606W']
use_im = [x for x in UVISims if \
os.path.basename(x).split('_')[0] == 'F475X']
use_filt.append(Pal_filt)
use_filt.sort()
return use_filt, use_im
def GetImage(image, mask):
Passed = 0
try:
imageData = fits.getdata(reducedpath+date+'.{:0>3}.reduced.fits'.format(image))
hdr = fits.getheader(reducedpath+date+'.{:0>3}.reduced.fits'.format(image))
w = WCS(reducedpath+date+'.{:0>3}.reduced.fits'.format(image))
Passed = 1
except:
print('Trying a different file name.')
Passed = 0
pass
if Passed == 0:
try:
imageData = fits.getdata(reducedpath+date+'.f{:0>3}.reduced.fits'.format(image))
hdr = fits.getheader(reducedpath+date+'.f{:0>3}.reduced.fits'.format(image))
w = WCS(reducedpath+date+'.f{:0>3}.reduced.fits'.format(image))
except: raise OSError('We do not know that filename: %s'%(reducedpath+date+'.f{:0>3}.reduced.fits'.format(image)))
# Parse the header to get the object name
ObjName = hdr['OBJECT'].split(',')[0].split(' ')[0]
band = hdr['FILTER2']
# Create the masked Image Data
imageDataM = np.ma.array(imageData, mask=mask)
# Computed the background levels
mean, median, std = stats.sigma_clipped_stats(imageData, mask=mask, sigma=3.0)
print('mean', 'median', 'std', 'BACKGROUND')
print(mean, median, std)
# Remove the background
imageDataRed = imageDataM - median
return imageDataRed, median, ObjName, band, std, hdr, w
def crossmatchtwofiles(img1, img2, radius=3):
''' This module is crossmatch two images:
It run sextractor transform the pixels position of the the sources in coordinates and crossmatch them
The output is a dictionary with the objects in common
'''
import lsc
from astropy.wcs import WCS
from numpy import array, argmin, min, sqrt
hd1 = fits.getheader(img1)
hd2 = fits.getheader(img2)
wcs1 = WCS(hd1)
wcs2 = WCS(hd2)
xpix1, ypix1, fw1, cl1, cm1, ell1, bkg1, fl1 = lsc.lscastrodef.sextractor(img1)
xpix2, ypix2, fw2, cl2, cm2, ell2, bkg2, fl2 = lsc.lscastrodef.sextractor(img2)
xpix1, ypix1, xpix2, ypix2 = array(xpix1, float), array(ypix1, float), array(xpix2, float), array(ypix2, float)
bb = wcs1.wcs_pix2world(zip(xpix1, ypix1), 1) # transform pixel in coordinate
xra1, xdec1 = zip(*bb)
bb = wcs2.wcs_pix2world(zip(xpix2, ypix2), 1) # transform pixel in coordinate
xra2, xdec2 = zip(*bb)
xra1, xdec1, xra2, xdec2 = array(xra1, float), array(xdec1, float), array(xra2, float), array(xdec2, float)
distvec, pos1, pos2 = lsc.lscastrodef.crossmatch(xra1, xdec1, xra2, xdec2, radius)
#dict={}
dict = {'ra1': xra1[pos1], 'dec1': xdec1[pos1], 'ra2': xra2[pos2], 'dec2': xdec2[pos2], \
'xpix1': xpix1[pos1], 'ypix1': ypix1[pos1], 'xpix2': xpix2[pos2], 'ypix2': ypix2[pos2]}
np.savetxt('substamplist', zip(xpix1[pos1], ypix1[pos1]), fmt='%10.10s\t%10.10s')
return 'substamplist', dict
def extract():
if os.path.isdir('database'):
shutil.rmtree('database')
scilist = glob.glob('AGN*.fits')
for img in scilist:
hds = pyfits.getheader(img)
_gain = hds['HIERARCH ESO DET OUT1 GAIN']
_ron = hds['HIERARCH ESO DET OUT1 RON']
name = img.rsplit('.fits',1)[0]+'_EX'
iraf.specred.apall(img,output=name,interactive='no',find='no',recenter='no',resize='no',edit='no',trace='yes',fittrace='no',extract='yes',extras='yes',review='no',line=600,nsum=20,lower=-5.,upper=5.,b_function='chebyshev',b_order=1,b_sample='-20:-10,10:20',b_naverage=3.,b_niterate=0,b_low_reject=3.,b_high_reject=3.,b_grow=0,width=10.,radius=10.,nfind=1,background='fit',clean='yes',readnoise=_ron,gain=_gain)
stdlist = glob.glob('STD*.fits')
lamplist = glob.glob('LAMP*.fits')
sllist = stdlist + lamplist
sci = scilist[0]
ref = sci.rsplit('.fits',1)[0]
for img in sllist:
hdl = pyfits.getheader(img)
_gain = hdl['HIERARCH ESO DET OUT1 GAIN']
_ron = hdl['HIERARCH ESO DET OUT1 RON']
dsum = 10
name = img.rsplit('.fits',1)[0]+'_EX'
iraf.apsum(img,output=name,references=ref,nsum=dsum,interactive='no',find='no',edit='no',trace='no',fittrace='no',extract='yes',extras='yes',review='no',clean='yes',readnoise=_ron,gain=_gain)
def get_header_info(pipeline, cat_name, frame):
"""
Get the exposure time, airmass, and gain from a DECam fits header
Parameters
----------
pipeline: :class:`.Pipeline`
Object that contains parameters about a decam pipeline
cat_name: str
Name of a catalog that contains a list of sources found using decamtoyz
frame: int
CCD number of the header to load
Returns
-------
exptime: float
Exposure time of the image
airmass: float
Airmass (sec(zenith distance))
gain: float
Gain in electrons/adu
"""
from astropyp import index
from astropy.io import fits
import numpy as np
expnum = os.path.basename(cat_name).split('-')[0]
sql = "select * from decam_files where EXPNUM={0} and PRODTYPE='image'".format(int(expnum))
files = index.query(sql, pipeline.idx_connect_str)
header = fits.getheader(os.path.join(pipeline.paths['decam'], files.iloc[0].filename), ext=0)
exptime = header['exptime']
airmass = 1/np.cos(np.radians(header['ZD']))
header = fits.getheader(os.path.join(pipeline.paths['decam'], files.iloc[0].filename), ext=frame)
gain = header['arawgain']
return exptime, airmass, gain
def test_adding_overscan_apogee_u9(make_overscan_test_files):
original_dir = getcwd()
apogee = ApogeeAltaU9()
print(getcwd())
oscan_dir, has_oscan, has_no_oscan = make_overscan_test_files
print(getcwd())
chdir(path.join(_test_dir, oscan_dir))
# first, does requesting *not* adding overscan actually leave it alone?
ph.patch_headers(dir='.', new_file_ext='', overwrite=True, purge_bad=False,
add_time=False, add_apparent_pos=False,
add_overscan=False, fix_imagetype=False)
header_no_oscan = fits.getheader(has_no_oscan)
assert 'biassec' not in header_no_oscan
assert 'trimsec' not in header_no_oscan
# Now add overscan
ph.patch_headers(dir='.', new_file_ext='', overwrite=True, purge_bad=False,
add_time=False, add_apparent_pos=False,
add_overscan=True, fix_imagetype=False)
print(_test_dir)
header_no_oscan = fits.getheader(has_no_oscan)
# This image had no overscan, so should be missing the relevant keywords.
assert 'biassec' not in header_no_oscan
assert 'trimsec' not in header_no_oscan
header_yes_oscan = fits.getheader(has_oscan)
# This one as overscan, so should include both of the overscan keywords.
assert header_yes_oscan['biassec'] == apogee.useful_overscan
assert header_yes_oscan['trimsec'] == apogee.trim_region
print(getcwd())
chdir(original_dir)
print(getcwd())
def load(filepath, pos_filepath):
"""
:param filepath: fits image path
:return: an Image
"""
# load image data information
data = pyfits.getdata(filepath, hdu=0)
primary = Header(pyfits.getheader(filepath, 0))
headers = [primary]
extcount = int(primary.get("NEXTEND", 0))
for idx in range(1, extcount):
ext = Header(pyfits.getheader(filepath, idx))
headers.append(ext)
# load position information
pos_primary = Header(pyfits.getheader(pos_filepath, 0))
pos_headers = [pos_primary]
pos_extcount = int(pos_primary.get("NEXTEND", 0))
for idx in range(1, pos_extcount):
ext = Header(pyfits.getheader(pos_filepath, idx))
pos_headers.append(ext)
return Image(array(data), headers, pos_headers)
def test_GetHeaderConvienceFunction(self, filename, ext, naxis1, naxis2):
"""Test the getheader convience function in both the fits and
stpyfits namespace."""
if ext is None:
hd = stpyfits.getheader(self.data(filename))
hd1 = fits.getheader(self.data(filename))
else:
hd = stpyfits.getheader(self.data(filename), ext)
hd1 = fits.getheader(self.data(filename), ext)
assert hd['NAXIS'] == 2
assert hd1['NAXIS'] == 0
assert hd['NAXIS1'] == naxis1
assert hd['NAXIS2'] == naxis2
for k in ('NAXIS1', 'NAXIS2'):
with pytest.raises(KeyError):
hd1[k]
for k in ('NPIX1', 'NPIX2'):
with pytest.raises(KeyError):
hd[k]
assert hd1['NPIX1'] == naxis1
assert hd1['NPIX2'] == naxis2
def setup(self, SE_only, no_conv):
"""Return lists of all images and filters used in this Par.
We will use the unrotated images for use with a single psf
Image filenames:
ParXXX/DATA/UVIS/IRtoUVIS/FYYY_UVIS_sci.fits
ParXXX/DATA/UVIS/IRtoUVIS/FYYY_UVIS_rms.fits
"""
images = glob(os.path.join(self.imdir, 'F*_UVIS_sci.fits'))
# dictionary of zero points
zps = self.get_zp(images[0])
# build table
t = Table(data=None,
names=['filt','image','convim','rms','wht','exptime','zp'],
dtype=['S10', 'S60', 'S60', 'S60', 'S60', float, float])
for image in images:
filt = fits.getheader(image)['FILTER']
# weight map
wht = image.split('_sci.fits')[0] + '_wht.fits'
# clean image for convolution
tmp = os.path.splitext(image)[0] + '_cln.fits'
image_cln = os.path.join(self.outdir, os.path.basename(tmp))
if SE_only is False:
print 'Cleaning %s' % os.path.basename(image)
if no_conv:
clean_image(image, image_cln, cln_by_wht=False, whtim=wht)
else:
clean_image(image, image_cln, cln_by_wht=True, whtim=wht)
# names of convolved images
if filt == self.reddest_filt:
convim = image_cln
else:
check = re.search('\d+', self.reddest_filt)
rf = check.group(0)
convim = os.path.join(self.outdir,'%s_convto%s.fits'%(filt,rf))
# replace zeros with 1.e9 in rms analysis maps
rms0 = image.split('_sci.fits')[0] + '_rms.fits'
tmp = os.path.splitext(rms0)[0] + '_analysis.fits'
rms_analysis = os.path.join(self.outdir, os.path.basename(tmp))
self.fix_rms_map(rms0, rms_analysis, value=1.e10,rmstype='analysis')
# for detection image, create detection RMS map as well
if filt == self.detect_filt:
tmp2 = os.path.splitext(rms0)[0] + '_detection.fits'
rms_detect = os.path.join(self.outdir, os.path.basename(tmp2))
self.fix_rms_map(rms0, rms_detect, value=0.01,
rmstype='detection', whtim=wht)
exptime = fits.getheader(image)['EXPTIME']
zp = zps[filt]
t.add_row([filt, image_cln, convim, rms_analysis, wht, exptime, zp])
# set detection RMS map
self.detect_rms = rms_detect
return t
def time_calibration(input_file):
"""
Obtain the calibration for time (hjd) by pyraf and the airmass for each image. Include in the header all information.
"""
original_path = os.getcwd()
save_path = input_file['save_path']
#change to save data reduction directory
os.chdir(save_path)
print '\n Reading the list of images ....\n'
planet = input_file['exoplanet'] #set exoplanet name
images = sorted(glob.glob('AB'+planet+'*.fits'))
print images
#include de RA,DEC and epoch of the exoplanet
RA,DEC,epoch = input_file['RA'],input_file['DEC'],input_file['epoch']
#obtain ST JD using iraf task and introduce in the header
for i in range(len(images)):
hdr = fits.getheader(images[i])
if int(split(hdr['UT'],':')[0]) < int(hdr['timezone']):
new_date = use.yesterday(hdr['date-obs'])
#print images[i], new_date
else:
new_date = hdr['date-obs']
year,month,day = split(new_date,'-')
iraf.asttimes(year=year,month=month,day=day,time=hdr['loctime'],obs=input_file['observatory'])
JD = iraf.asttimes.jd #obtain julian date
LMST = iraf.asttimes.lmst #obtain the sideral time
LMST = use.sexagesimal_format(LMST) #convert sideral time in sexagesimal format
iraf.hedit(images[i],'ST',LMST,add='yes',verify='no',show='no',update='yes') #create the ST keyword in the header
iraf.ccdhedit(images[i],'LMST',LMST,type='string') #include the mean sideral time in the header
iraf.ccdhedit(images[i],'JD',JD,type='string') #include de julian date in the header
#include RA, and DEC of the object in your header
iraf.ccdhedit(images[i],"RA",RA,type="string") #include right ascention in the header
iraf.ccdhedit(images[i],"DEC",DEC,type="string") #include declination in the header
iraf.ccdhedit(images[i],"epoch",epoch,type="string") #include epoch in the header
# use.update_progress((i+1.)/len(images))
print '\n Setting airmass ....\n'
for i in range(len(images)):
print '# ',images[i]
#iraf.hedit(images[i],'airmass',airmass,add='yes')
#iraf.hedit(images[i],'HJD',HJD,add='yes')
iraf.setairmass.observatory = input_file['observatory']
iraf.setairmass(images[i])
iraf.setjd.time = 'ut'
iraf.setjd(images[i])
print '\n.... done.\n'
#export information
hjd, jd, airmass, st = [],[],[],[]
for i in range(len(images)):
hdr = fits.getheader(images[i])
hjd.append(hdr['HJD'])
jd.append(hdr['JD'])
airmass.append(hdr['airmass'])
st.append(hdr['st'])
#saving the data
data = DataFrame([list(hjd),list(jd),list(st),list(airmass)]).T
data.columns = ['HJD','JD','ST','Airmass']
data.to_csv('results_iraf_calibrations.csv')
#change to workings directory
os.chdir(original_path)
return
def find_best_flat(flt_fits, verbose=True):
"""
Find the most recent PFL file in $IREF for the filter used for the
provided FLT image. Doesn't do any special check on USEAFTER date, just
looks for the most-recently modified file.
"""
import glob
import os.path
import time
IREF = os.environ["iref"]
the_filter = fits.getheader(flt_fits,0).get('FILTER')
pfls = glob.glob(IREF+'*pfl.fits')
latest = 0
best_pfl = None
for pfl in pfls:
head = fits.getheader(pfl)
if head.get('FILTER') != the_filter:
continue
this_created = os.path.getmtime(pfl)
if this_created > latest:
best_pfl = pfl
latest = this_created
if verbose:
print '%s %s %s' %(pfl, the_filter, time.ctime(latest))
return best_pfl #, the_filter, time.ctime(latest)
def testGetHeaderConvienceFunction(self):
"""Test the getheader convience function in both the fits and
stpyfits namespace."""
hd = stpyfits.getheader(self.data('cdva2.fits'))
hd1 = fits.getheader(self.data('cdva2.fits'))
assert_equal(hd['NAXIS'], 2)
assert_equal(hd1['NAXIS'], 0)
assert_equal(hd['NAXIS1'], 10)
assert_equal(hd['NAXIS2'], 10)
assert_raises(KeyError, lambda: hd1['NAXIS1'])
assert_raises(KeyError, lambda: hd1['NAXIS2'])
assert_raises(KeyError, lambda: hd['NPIX1'])
assert_raises(KeyError, lambda: hd['NPIX2'])
assert_equal(hd1['NPIX1'], 10)
assert_equal(hd1['NPIX2'], 10)
hd = stpyfits.getheader(self.data('o4sp040b0_raw.fits'), 2)
hd1 = fits.getheader(self.data('o4sp040b0_raw.fits'), 2)
assert_equal(hd['NAXIS'], 2)
assert_equal(hd1['NAXIS'], 0)
assert_equal(hd['NAXIS1'], 62)
assert_equal(hd['NAXIS2'], 44)
assert_raises(KeyError, lambda: hd1['NAXIS1'])
assert_raises(KeyError, lambda: hd1['NAXIS2'])
assert_raises(KeyError, lambda: hd['NPIX1'])
assert_raises(KeyError, lambda: hd['NPIX2'])
assert_equal(hd1['NPIX1'], 62)
assert_equal(hd1['NPIX2'], 44)
def SExtractorDualImage(drPath,tract,bands,patches,prefix,sexdir,dotsex,zps):
'''
Run SExtractor in dual image mode for each patch,band using chi2 image for detection
'''
os.chdir(sexdir)
for band in bands:
for patch in patches:
# Move Variance extension to new file for SExtractor
imname = drPath+'deepCoadd/'+band+'/'+tract+'/'+patch+'/'+\
'calexp-'+band+'-'+tract+'-'+patch[0]+patch[-1]
varHead = fits.getheader(imname+'.fits','VARIANCE')
maskHead = fits.getheader(imname+'.fits','MASK')
im = fits.open(imname+'.fits')
fits.writeto(imname+'_var.fits',im['VARIANCE'].data,varHead,clobber=True)
fits.writeto(imname+'_mask.fits',im['MASK'].data.astype(float),maskHead,clobber=True)
inImage = imname+'.fits[1]'
outCat = drPath+'deepCoadd/'+band+'/'+tract+'/'+patch+'/'+band+'-'+tract+'-'+patch[0]+patch[-1]+'-chi2.cat'
chi2Image = drPath+'deepCoadd/'+prefix+'chi2/'+tract+'/'+patch+'/'+prefix+'chi2-'+tract+'-'+patch+'.fits'
# Run SExtractor
os.system('sex '+chi2Image+','+inImage+' -c '+dotsex+' -CATALOG_NAME '+outCat+' -WEIGHT_IMAGE None,'+\
imname+'_var.fits -WEIGHT_TYPE NONE,MAP_VAR -MAG_ZEROPOINT '+str(zps[bands.index(band)]))
# Add flags to catalog
os.system('./venice -m '+imname+'_mask.fits -cat '+outCat+ ' -f all -xcol 2 -ycol 3 -o '+\
drPath+'deepCoadd/'+band+'/'+tract+'/'+patch+'/'+band+'-'+tract+'-'+patch[0]+patch[-1]+'-chi2-flags.cat')
os.system('rm '+imname+'_var.fits')
os.system('rm '+imname+'_mask.fits')
def marxpars_from_asol(conf, asolfile, evt2file):
'''Set MARX parameters from asol and evt file.
This function parses the header of a fits file and uses the
information in the header to set as many marx parameters as possible.
Parameters
----------
conf : `~ConfigParser.ConfigParser` instance
The configuration file contains the initialization code for CIAO.
asolfile : string
Path and name of an asol file
evt2file : string
Path and name of an evt2 file
Returns
-------
marx_pars : dict
Dictionary with marx parameters as far as they can be extracted from
the data in the asol file.
'''
asol = fits.getheader(asolfile, 1)
evt = fits.getheader(evt2file, 1)
marx_pars = OrderedDict()
marx_pars['RA_Nom'] = asol['RA_NOM']
marx_pars['Dec_Nom'] = asol['DEC_NOM']
marx_pars['Roll_Nom'] = asol['ROLL_NOM']
marx_pars['GratingType'] = asol['GRATING']
marx_pars['ExposureTime'] = asol['TSTOP'] - asol['TSTART']
marx_pars['DitherModel'] = 'FILE'
marx_pars['DitherFile'] = asolfile
marx_pars['TStart'] = asol['TSTART']
# ACIS Exposure time (might vary for sub-array read-out)
if evt['INSTRUME'][0:4] == 'ACIS':
marx_pars['ACIS_Exposure_Time'] = evt['EXPTIME']
# Target coordiantes
# ra, dec = target_coos_from_asol(asolfile)
marx_pars['SourceRA'] = evt['RA_TARG']
marx_pars['SourceDEC'] = evt['DEC_TARG']
# DetectorType
det = detectorfromkeyword(evt['DETNAM'])
if det != '':
marx_pars['DetectorType'] = det
# Find offsets from nominal pointing
aimpts = Table.read(os.path.join(conf.get('marx', 'path'), 'share', 'marx', 'data', 'caldb', 'telD1999-07-23aimptsN0002.fits'))
det2aimpts = {'ACIS-I': 'AI2', 'ACIS-S': 'AS1', 'HRC-I': 'HI1', 'HRC-S': 'HS1'}
aimp = np.array([n.strip() for n in aimpts['AIMPOINT_NAME']])
ind = aimp == det2aimpts[det]
marx_pars['DetOffsetX'] = evt['SIM_X'] - aimpts[ind]['AIMPOINT'][0][0]
# No Y offset possible
marx_pars['DetOffsetZ'] = evt['SIM_Z'] - aimpts[ind]['AIMPOINT'][0][2]
else:
warn('detector {0} not understood. No DetectorType or aimpoint offset set.'.format(asol['DETNAM']))
return marx_pars
def OutputFitsFileExtensions(column_dicts, template, outfilename, mode="append", headers_info=[], primary_header={}):
"""
Function to output a fits file
column_dict is a dictionary where the key is the name of the column
and the value is a np array with the data. Example of a column
would be the wavelength or flux at each pixel
template is the filename of the template fits file. The header will
be taken from this file and used as the main header
mode determines how the outputted file is made. Append will just add
a fits extension to the existing file (and then save it as outfilename)
"new" mode will create a new fits file.
header_info takes a list of lists. Each sub-list should have size 2 where the first element is the name of the new keyword, and the second element is the corresponding value. A 3rd element may be added as a comment
primary_header takes a dictionary with keywords to insert into the primary fits header (and not each extension)
"""
# Get header from template. Use this in the new file
if mode == "new":
header = pyfits.getheader(template)
if not isinstance(column_dicts, list):
column_dicts = [column_dicts, ]
if len(headers_info) < len(column_dicts):
for i in range(len(column_dicts) - len(headers_info)):
headers_info.append([])
if mode == "append":
hdulist = pyfits.open(template)
elif mode == "new":
header = pyfits.getheader(template)
pri_hdu = pyfits.PrimaryHDU(header=header)
hdulist = pyfits.HDUList([pri_hdu, ])
if len(primary_header.keys()) > 0:
for key in primary_header:
hdulist[0].header[key] = primary_header[key]
for i in range(len(column_dicts)):
column_dict = column_dicts[i]
header_info = headers_info[i]
columns = []
for key in column_dict.keys():
columns.append(pyfits.Column(name=key, format="D", array=column_dict[key]))
cols = pyfits.ColDefs(columns)
tablehdu = pyfits.BinTableHDU.from_columns(cols)
# Add keywords to extension header
num_keywords = len(header_info)
header = tablehdu.header
for i in range(num_keywords):
info = header_info[i]
if len(info) > 2:
header.set(info[0], info[1], info[2])
elif len(info) == 2:
header.set(info[0], info[1])
hdulist.append(tablehdu)
hdulist.writeto(outfilename, clobber=True, output_verify='ignore')
hdulist.close()
def use_correlation(self):
"""
Use correlation data-cube.
"""
import numpy
from astropy.io.fits import getdata, getheader, writeto
from glob import glob
from os.path import splitext
from sys import stdout
self.print("\n A correlation cube will be used.")
self.print(" Looking for an existing correlation data-cube in the current folder.")
candidates = glob("*.fits")
corr_cube = None
for candidate in candidates:
if 'CORRFROM' in getheader(candidate):
if getheader(candidate)['CORRFROM'] == self.input_file:
self.print(" Correlation cube to be used: %s" % candidate)
return candidate
if corr_cube is None:
self.print(" Correlation cube not found. Creating a new one.")
data = getdata(self.input_file)
corr_cube = numpy.empty_like(data)
x = numpy.arange(self.width)
y = numpy.arange(self.height)
x, y = numpy.meshgrid(x, y)
x, y = numpy.ravel(x), numpy.ravel(y)
for i in range(x.size):
s = data[:, y[i], x[i]]
s = s / s.max() # Normalize
s = s - s.mean() # Remove mean to avoid triangular shape
s = numpy.correlate(s, self.ref_s, mode='same')
corr_cube[:, y[i], x[i]] = s
temp = ((i + 1) * 100.00 / x.size)
stdout.write('\r %2d%% ' % temp)
stdout.write(self.loading[int(temp * 10 % 5)])
stdout.flush()
self.print(" Done.")
corr_name = splitext(self.input_file)[0] + '--corrcube.fits'
self.print(" Saving correlation cube to %s" % corr_name)
corr_hdr = self.header.copy()
corr_hdr.set('CORRFROM', self.input_file, 'Cube used for corrcube.')
corr_hdr.set('', '', before='CORRFROM')
corr_hdr.set('', '--- Correlation cube ---', before='CORRFROM')
writeto(corr_name, corr_cube, corr_hdr, clobber=True)
del corr_hdr
del corr_cube
return corr_name
def reduce_all_cubes_for_map(mapname, lowhigh='high', **kwargs):
for dataset,maps in make_apex_cubes.datasets_2014.items():
if mapname in maps:
date = dataset[-10:]
both_directions = True
try:
make_apex_cubes.build_cube_2014(mapname,
lowhigh=lowhigh,
posang=[50,70],
datasets=[dataset],
extra_suffix='_cal{0}_lscans'.format(date),
**kwargs)
except IndexError:
both_directions = False
try:
make_apex_cubes.build_cube_2014(mapname,
lowhigh=lowhigh,
posang=[140,160],
datasets=[dataset],
extra_suffix='_cal{0}_bscans'.format(date),
**kwargs)
except IndexError:
both_directions = False
if both_directions:
fileb = os.path.join(outdir, 'APEX_H2CO_2014_{1}_{2}_cal{0}_bscans.fits'.format(date, mapname, lowhigh))
filel = os.path.join(outdir, 'APEX_H2CO_2014_{1}_{2}_cal{0}_lscans.fits'.format(date, mapname, lowhigh))
cubeb = fits.getdata(fileb)
cubel = fits.getdata(filel)
if cubeb.shape != cubel.shape:
header = FITS_tools.fits_overlap(fileb, filel)
hdb = fits.getheader(fileb)
# Add back 3rd dimension... HACK
for key in hdb:
if key[0] == 'C' and key.strip()[-1] == '3':
header[key] = hdb[key]
FITS_tools.regrid_fits_cube(fileb, outheader=header, outfilename=fileb, clobber=True)
FITS_tools.regrid_fits_cube(filel, outheader=header, outfilename=filel, clobber=True)
cubeb = fits.getdata(fileb)
cubel = fits.getdata(filel)
if cubeb.shape != cubel.shape:
log.fatal("Cube shapes don't match: {0}, {1}".format(cubeb.shape,cubel.shape))
raise ValueError
cube_comb = plait.plait_cube([cubeb,cubel], angles=[0, 90], scale=5)
cube_comb_naive = (cubeb+cubel)/2.
header = fits.getheader(fileb)
fits.PrimaryHDU(data=cube_comb,
header=header).writeto(os.path.join(outdir, '{1}_{2}_cal{0}_plait.fits'.format(date, mapname, lowhigh)),
clobber=True)
def identify_wcs1d_fits(origin, *args, **kwargs):
# check if file can be opened with this reader
# args[0] = filename
return (isinstance(args[0], str) and
os.path.splitext(args[0].lower())[1] == '.fits' and
# check if number of axes is one
fits.getheader(args[0])['NAXIS'] == 1 and
# check if CTYPE1 kep is in the header
'CTYPE1' in fits.getheader(args[0])
)
def imsubtract( image1, image2, outfile=None,
clobber=False, verbose=False, debug=False):
"""
Construct a simple subtraction: image2 - image1. Guards against
different sized data arrays by assuming that the lower left pixel
(0,0) is the anchor point. (i.e. the second image will be
trimmed or extended if needed.)
"""
import os
import exceptions
if debug : import pdb; pdb.set_trace()
if outfile :
if os.path.isfile( outfile ) and not clobber :
print("%s exists. Not clobbering."%outfile)
return( outfile )
# read in the images
if not os.path.isfile( image1 ) :
raise exceptions.RuntimeError(
"The image file %s is not valid."%image1 )
im1head = pyfits.getheader( image1 )
im1data = pyfits.getdata( image1 )
if not os.path.isfile( image2 ) :
raise exceptions.RuntimeError(
"The image file %s is not valid."%image2 )
im2head = pyfits.getheader( image2 )
im2data = pyfits.getdata( image2 )
# sometimes multidrizzle drops a pixel. Unpredictable.
nx2,ny2 = im2data.shape
nx1,ny1 = im1data.shape
if nx2>nx1 or ny2>ny1 :
im2data = im2data[:min(nx1,nx2),:min(ny1,ny2)]
im1data = im1data[:min(nx1,nx2),:min(ny1,ny2)]
elif nx2<nx1 or ny2<ny1 :
im1data = im1data[:min(nx1,nx2),:min(ny1,ny2)]
im2data = im2data[:min(nx1,nx2),:min(ny1,ny2)]
diffim = im2data - im1data
if not outfile :
return( diffim )
else :
im2head["SRCIM1"] = (image1,"First source image = template for subtraction")
im2head["SRCIM2"] = (image2,"Second source image = search epoch image")
outdir = os.path.split( outfile )[0]
if outdir and not os.path.isdir(outdir):
os.makedirs( outdir )
pyfits.writeto( outfile, diffim,
header=im2head,
clobber=clobber )
return( outfile )
def db_ingest(filepath, filename, force=False):
'''Read an image header and add a row to the database'''
global telescopeids, instrumentids
if '-en' in filename:
table = 'speclcoraw'
db_to_hdrkey = speclcoraw_to_hdrkey
else:
table = 'photlcoraw'
db_to_hdrkey = photlcoraw_to_hdrkey
fileindb = lsc.mysqldef.getfromdataraw(conn, table, 'filename', filename, column2='filepath')
if fileindb:
filepath = fileindb[0]['filepath'] # could be marked as bad
if not fileindb or force:
if filename[-3:] == '.fz':
hdr = fits.getheader(filepath + filename, 1)
else:
hdr = fits.getheader(filepath + filename)
groupidcode, targetid = get_groupidcode(hdr)
dbdict = {'filename': filename,
'filepath': filepath,
'groupidcode': groupidcode,
'targetid': targetid}
for dbcol, hdrkey in db_to_hdrkey.items():
if hdrkey in hdr and hdr[hdrkey] not in lsc.util.missingvalues:
if hdrkey in ['RA', 'CAT-RA']:
dbdict[dbcol] = Angle(hdr[hdrkey], u.hourangle).to_string(u.deg, decimal=True, precision=7)
elif hdrkey in ['DEC', 'CAT-DEC']:
dbdict[dbcol] = Angle(hdr[hdrkey], u.deg).to_string(decimal=True, precision=7)
elif hdrkey == 'DATE-OBS':
dbdict[dbcol] = hdr['DATE-OBS'].split('T')[0]
elif hdrkey == 'UTSTART':
dbdict[dbcol] = hdr['UTSTART'].split('.')[0]
else:
dbdict[dbcol] = hdr[hdrkey]
if hdr['TELESCOP'] not in telescopeids:
logger.info('{} not recognized. Adding to telescopes table.'.format(hdr['TELESCOP']))
lsc.mysqldef.insert_values(conn, 'telescopes', {'name': hdr['TELESCOP']})
telescopes = lsc.mysqldef.query(['select id, name from telescopes'], conn)
telescopeids = {tel['name']: tel['id'] for tel in telescopes}
dbdict['telescopeid'] = telescopeids[hdr['TELESCOP']]
if hdr['INSTRUME'] not in instrumentids:
logger.info('{} not recognized. Adding to instruments table.'.format(hdr['INSTRUME']))
lsc.mysqldef.insert_values(conn, 'instruments', {'name': hdr['INSTRUME']})
instruments = lsc.mysqldef.query(['select id, name from instruments'], conn)
instrumentids = {inst['name']: inst['id'] for inst in instruments}
dbdict['instrumentid'] = instrumentids[hdr['INSTRUME']]
if fileindb:
lsc.mysqldef.query(["delete from " + table + " where filename='" + filename + "'"], conn)
logger.info('ingesting {}'.format(filename))
lsc.mysqldef.insert_values(conn, table, dbdict)
else:
dbdict = {}
logger.info('{} already ingested'.format(filename))
return dbdict
def project_data_into_region(from_data_fn, to_region = "SC_241"):
if to_region == "SC_241":
to_region_fn = "/Volumes/DataDavy/GALFA/SC_241/LAB_corrected_coldens.fits"
to_region_hdr = fits.getheader(to_region_fn)
from_data_hdr = fits.getheader(from_data_fn)
from_data_data = fits.getdata(from_data_fn)
new_image, footprint = reproject_interp((from_data_data, from_data_hdr), to_region_hdr)
return new_image
def getJD(header, rootdir="./"):
"""
Use the header of a co-added file to determine the julian date
in the middle of the total observation
"""
# First, get the filenames that were used
fileList = [k for k in header.keys() if "FILE" in k]
fileheader = fits.getheader(rootdir + header[fileList[0]])
firstjd = fileheader['jd']
fileheader = fits.getheader(rootdir + header[fileList[-1]])
lastjd = fileheader['jd'] + fileheader['exptime'] / (24.0 * 3600.0)
return (firstjd + lastjd) / 2.0
def detect_sources(exp, sex_config, sex_io_dir, dual_exp=None,
delete_created_files=True, label='hugs',
original_fn=None):
"""
Source detection using SExtractor.
Parameters
----------
exp : lsst.afw.image.ExposureF
Exposure object to run sextractor on.
sex_config : dict
SExtractor configuration that is is different for its default.
sex_io_dir : str
Input/output directory for files needed to run SExtractor.
dual_exp : lsst.afw.image.ExposureF, optional
Dual exposure for forced photometry.
delete_created_files : bool, optional
If True, remove all files created for and by SExtractor.
label : str
Label for this run.
Returns
-------
cat : astropy.table.Table
SExtractor catalog.
"""
sw = sextractor.Wrapper(sex_config, sex_io_dir)
#########################################################
# write exposure for sextractor input and run
#########################################################
detect_band = exp.getFilter().getName().lower()
# some bands have numbers --> get the relevant letter
detect_band = [b for b in detect_band if b in 'gri'][0]
exp_fn = sw.get_io_dir('exp-{}-{}.fits'.format(label, detect_band))
# HACK: work around strange new bug related to SEXtractor
if original_fn is None:
exp.writeFits(exp_fn)
else:
header = fits.getheader(original_fn, ext=1)
fits.writeto(exp_fn, exp.getImage().getArray(), header, overwrite=True)
if dual_exp is not None:
meas_band = dual_exp.getFilter().getName().lower()
meas_band = [b for b in meas_band if b in 'gri'][0]
dual_fn = sw.get_io_dir('exp-{}-{}.fits'.format(label, meas_band))
# HACK: work around strange new bug related to SEXtractor
if original_fn is None:
dual_exp.writeFits(dual_fn)
else:
fn = original_fn.replace('HSC-'+detect_band.upper(),
'HSC-'+meas_band.upper())
header = fits.getheader(fn, ext=1)
fits.writeto(dual_fn, dual_exp.getImage().getArray(), header,
overwrite=True)
run_fn = exp_fn+'[1],'+dual_fn+'[1]'
cat_label = 'sex-{}-{}-{}'.format(label, detect_band, meas_band)
else:
meas_band = detect_band
cat_label = 'sex-{}-{}'.format(label, detect_band)
run_fn = exp_fn+'[1]'
cat_fn = sw.get_io_dir(cat_label+'.cat')
#########################################################
# run SExtactor and get catalog
#########################################################
sw.run(run_fn, cat_fn=cat_fn)
cat = sextractor.read_cat(sw.get_io_dir(cat_fn))
if len(cat)>0:
#########################################################
# only save positions from the primary detection band
#########################################################
detect_band_only = [
'X_IMAGE', 'Y_IMAGE', 'ALPHA_J2000', 'DELTA_J2000', 'FLAGS',
'PETRO_RADIUS', 'THETA_IMAGE', 'A_IMAGE', 'B_IMAGE',
'ELLIPTICITY', 'KRON_RADIUS'
]
ebv = dustmap.ebv(cat['ALPHA_J2000'], cat['DELTA_J2000'])
if meas_band==detect_band:
x0, y0 = exp.getXY0()
cat['X_IMAGE'] -= 1
cat['Y_IMAGE'] -= 1
cat['X_HSC'] = cat['X_IMAGE'] + x0
cat['Y_HSC'] = cat['Y_IMAGE'] + y0
detect_band_only.append('X_HSC')
detect_band_only.append('Y_HSC')
else:
cat.remove_columns(detect_band_only)
#########################################################
# rename columns, change units of flux_radius and
# fwhm_image to arcsec, add extinction params
#########################################################
cat.rename_column('MAG_APER', 'MAG_APER_0')
cat.rename_column('MAGERR_APER', 'MAGERR_APER_0')
cat.rename_column('FLUX_RADIUS', 'FLUX_RADIUS_0')
for i, diam in enumerate(sex_config['PHOT_APERTURES'].split(',')):
cat.rename_column('MAG_APER_'+str(i), 'mag_ap'+str(i))
cat.rename_column('MAGERR_APER_'+str(i), 'magerr_ap'+str(i))
for i, frac in enumerate(sex_config['PHOT_FLUXFRAC'].split(',')):
frac = str(int(100 * float(frac)))
cat.rename_column('FLUX_RADIUS_'+str(i), 'flux_radius_'+frac)
cat['flux_radius_'+frac] *= utils.pixscale
cat['FWHM_IMAGE'] = cat['FWHM_IMAGE']*utils.pixscale
cat.rename_column('FWHM_IMAGE', 'FWHM')
for name in cat.colnames:
if name not in detect_band_only:
cat.rename_column(name, name.lower()+'_'+meas_band)
else:
cat.rename_column(name, name.lower())
if meas_band==detect_band:
cat.rename_column('alpha_j2000', 'ra')
cat.rename_column('delta_j2000', 'dec')
cat['ebv'] = ebv
cat['A_'+meas_band] = ebv*getattr(utils.ext_coeff, meas_band)
#########################################################
# delete files created by and for sextractor
#########################################################
if delete_created_files:
if dual_exp is not None:
os.remove(dual_fn)
os.remove(exp_fn)
os.remove(cat_fn)
return cat
mprocPool.close()
return wav_chip, flux_conv_res
def save_new_data(fname, flux, hdr):
fits.writeto(fname, flux, hdr, overwrite=True)
if __name__ == "__main__":
filepaths = np.loadtxt("filelist.dat", dtype=str)
wavelength_range = np.loadtxt("lines.rdb", skiprows=2)
R = 50000
for fname in np.arange(len(filepaths)):
wav, flux = read_data(fname)
hdr = fits.getheader(fname)
wav_limits = [wav[0], wav[-1]]
convolved_wav, convolved_flux = ip_convolution(wav,
flux,
wav_limits,
R,
fwhm_lim=5.0)
newname = fname.replace(".fits", "") + "new_resolution.fits"
save_new_data(newname, convolved_flux, hdr)
def __init__(self,
xarr,
grid_vwidth=1.0,
grid_vwidth_scale=False,
texgrid=None,
taugrid=None,
hdr=None,
path_to_texgrid='',
path_to_taugrid='',
temperature_gridnumber=3,
debug=False,
verbose=False,
modelfunc=None,
**kwargs):
"""
Use a grid of RADEX-computed models to make a model line spectrum
The RADEX models have to be available somewhere.
OR they can be passed as arrays. If as arrays, the form should be:
texgrid = ((minfreq1,maxfreq1,texgrid1),(minfreq2,maxfreq2,texgrid2))
xarr must be a SpectroscopicAxis instance
xoff_v, width are both in km/s. With is 'sigma'
grid_vwidth is the velocity assumed when computing the grid in km/s
this is important because tau = modeltau / width (see, e.g.,
Draine 2011 textbook pgs 219-230)
grid_vwidth_scale is True or False: False for LVG, True for Sphere
A modelfunc must be specified. Model functions should take an xarr and
a series of keyword arguments corresponding to the line parameters
(Tex, tau, xoff_v, and width (gaussian sigma, not FWHM))
"""
self.modelfunc = modelfunc
if self.modelfunc is None:
raise ValueError(
"Must specify a spectral model function. See class help for form."
)
if texgrid is None and taugrid is None:
if path_to_texgrid == '' or path_to_taugrid == '':
raise IOError("Must specify model grids to use.")
else:
self.taugrid = [pyfits.getdata(path_to_taugrid)]
self.texgrid = [pyfits.getdata(path_to_texgrid)]
hdr = pyfits.getheader(path_to_taugrid)
self.yinds, self.xinds = np.indices(self.taugrid[0].shape[1:])
self.densityarr = (xinds + hdr['CRPIX1'] -
1) * hdr['CD1_1'] + hdr[
'CRVAL1'] # log density
self.columnarr = (yinds + hdr['CRPIX2'] -
1) * hdr['CD2_2'] + hdr[
'CRVAL2'] # log column
self.minfreq = (4.8, )
self.maxfreq = (5.0, )
elif len(taugrid) == len(texgrid) and hdr is not None:
self.minfreq, self.maxfreq, self.texgrid = zip(*texgrid)
self.minfreq, self.maxfreq, self.taugrid = zip(*taugrid)
self.yinds, self.xinds = np.indices(self.taugrid[0].shape[1:])
self.densityarr = (xinds + hdr['CRPIX1'] -
1) * hdr['CD1_1'] + hdr['CRVAL1'] # log density
self.columnarr = (yinds + hdr['CRPIX2'] -
1) * hdr['CD2_2'] + hdr['CRVAL2'] # log column
else:
raise Exception
# Convert X-units to frequency in GHz
self.xarr = copy.copy(xarr)
self.xarr.convert_to_unit('Hz', quiet=True)
#tau = modelgrid.line_params_2D(gridval1,gridval2,densityarr,columnarr,taugrid[temperature_gridnumber,:,:])
#tex = modelgrid.line_params_2D(gridval1,gridval2,densityarr,columnarr,texgrid[temperature_gridnumber,:,:])
if debug:
import pdb
pdb.set_trace()
t = Table()
sector_folders = np.sort(glob.glob(args.Folder + 's00*/'))
for sector in sector_folders:
sn = int(sector[-3:-1])
color_print('\nSECTOR %d' % sn, 'cyan')
for cam in range(1,5):
for ccd in range(1,5):
imgs = glob.glob(sector + 'tess*-%d-%d-*_ffic.fits' % (cam, ccd))
for img in imgs:
try:
hdr = fits.getheader(img, 1)
w = WCS(hdr)
T = np.transpose([w.all_pix2world(i,0,0) for i in range(44,2092,10)])
R = np.transpose([w.all_pix2world(2091,i,0) for i in range(1,2048,10)])
B = np.transpose([w.all_pix2world(i,2047,0) for i in range(2091,43,-10)])
L = np.transpose([w.all_pix2world(44,i,0) for i in range(2046,0,-10)])
TOP = SkyCoord(T[0], T[1], unit='deg').transform_to('barycentrictrueecliptic')
RIGHT = SkyCoord(R[0], R[1], unit='deg').transform_to('barycentrictrueecliptic')
BOTTOM = SkyCoord(B[0], B[1], unit='deg').transform_to('barycentrictrueecliptic')
LEFT = SkyCoord(L[0], L[1], unit='deg').transform_to('barycentrictrueecliptic')
color_print('CAM %d, CCD %d' % (cam, ccd), 'lightgreen')
print('\t', TOP[0].lon.degree, TOP[0].lat.degree)
print('\t', RIGHT[0].lon.degree, RIGHT[0].lat.degree)
def main(args):
log = get_logger()
nspec = 500 #- Hardcode! Number of DESI fibers per spectrograph
#- Sanity check that all spectra are represented
fibers = set()
for filename in args.files:
x = fits.getdata(filename, 'FIBERMAP')
fibers.update(set(x['FIBER']))
if len(fibers) != nspec:
msg = "Input files only have {} instead of {} spectra".format(
len(fibers), nspec)
if args.force:
log.warn(msg)
else:
log.fatal(msg)
sys.exit(1)
#- Read a file to get basic dimensions
w = fits.getdata(args.files[0], 'WAVELENGTH')
nwave = len(w)
R1 = fits.getdata(args.files[0], 'RESOLUTION')
ndiag = R1.shape[1]
hdr = fits.getheader(args.files[0])
camera = hdr['CAMERA'].lower() #- b0, r1, .. z9
spectrograph = int(camera[1])
fibermin = spectrograph * nspec
#- Output arrays to fill
flux = np.zeros((nspec, nwave))
ivar = np.zeros((nspec, nwave))
R = np.zeros((nspec, ndiag, nwave))
fibermap = desispec.io.empty_fibermap(nspec, specmin=fibermin)
mask = np.zeros((nspec, nwave), dtype=np.uint32)
chi2pix = np.zeros((nspec, nwave))
#- Fill them!
for filename in args.files:
fx = fits.open(filename)
xhdr = fx[0].header
xflux = fx['FLUX'].data
xivar = fx['IVAR'].data
xR = fx['RESOLUTION'].data
xfibermap = fx['FIBERMAP'].data
xmask = fx['MASK'].data
xchi2pix = fx['CHI2PIX'].data
fx.close()
ii = xfibermap['FIBER'] % nspec
flux[ii] = xflux
ivar[ii] = xivar
R[ii] = xR
fibermap[ii] = xfibermap
mask[ii] = xmask
chi2pix[ii] = xchi2pix
#- Write it out
print("Writing", args.output)
frame = Frame(w,
flux,
ivar,
mask=mask,
resolution_data=R,
spectrograph=spectrograph,
meta=hdr,
fibermap=fibermap,
chi2pix=chi2pix)
desispec.io.write_frame(args.output, frame)
#- Scary! Delete input files
if args.delete:
for filename in args.files:
os.remove(filename)
def get_all_phoenix():
all_sed = []
# define the top directory
SEDfile_dir = os.path.join(top_pysynphot_data_dir, dir_nostar,
dir_submodels[4])
filelist1 = os.listdir(SEDfile_dir + '/phoenixm00')
filelist2 = os.listdir(SEDfile_dir + '/phoenixm05')
filelist3 = os.listdir(SEDfile_dir + '/phoenixm10')
filelist4 = os.listdir(SEDfile_dir + '/phoenixm15')
filelist5 = os.listdir(SEDfile_dir + '/phoenixm20')
filelist6 = os.listdir(SEDfile_dir + '/phoenixm25')
filelist7 = os.listdir(SEDfile_dir + '/phoenixm30')
filelist8 = os.listdir(SEDfile_dir + '/phoenixm35')
filelist9 = os.listdir(SEDfile_dir + '/phoenixm40')
filelist10 = os.listdir(SEDfile_dir + '/phoenixp03')
filelist11 = os.listdir(SEDfile_dir + '/phoenixp05')
filelist1_group = [
os.path.join('phoenixm00', f) for f in filelist1 if f.endswith('.fits')
]
filelist2_group = [
os.path.join('phoenixm05', f) for f in filelist2 if f.endswith('.fits')
]
filelist3_group = [
os.path.join('phoenixm10', f) for f in filelist3 if f.endswith('.fits')
]
filelist4_group = [
os.path.join('phoenixm15', f) for f in filelist4 if f.endswith('.fits')
]
filelist5_group = [
os.path.join('phoenixm20', f) for f in filelist5 if f.endswith('.fits')
]
filelist6_group = [
os.path.join('phoenixm25', f) for f in filelist6 if f.endswith('.fits')
]
filelist7_group = [
os.path.join('phoenixm30', f) for f in filelist7 if f.endswith('.fits')
]
filelist8_group = [
os.path.join('phoenixm35', f) for f in filelist8 if f.endswith('.fits')
]
filelist9_group = [
os.path.join('phoenixm40', f) for f in filelist9 if f.endswith('.fits')
]
filelist10_group = [
os.path.join('phoenixp03', f) for f in filelist10
if f.endswith('.fits')
]
filelist11_group = [
os.path.join('phoenixp05', f) for f in filelist11
if f.endswith('.fits')
]
filelist_group=filelist1_group + filelist2_group + filelist3_group + filelist4_group + filelist5_group+ \
filelist6_group + filelist7_group + filelist8_group + filelist9_group +filelist10_group+filelist11_group
fits_files = filelist_group
obj_headers = []
obj_files = []
for filename in fits_files:
index = 0
if re.search('fits', filename): #example of filename filter
index += 1
fullfilename = os.path.join(SEDfile_dir, filename)
hdr = fits.getheader(fullfilename)
obj_headers.append(hdr)
obj_files.append(filename)
obj_temperatures = []
obj_log_z_all = []
index = 0
for hdr in obj_headers:
obj_temp = float(obj_headers[index]['TEFF'])
obj_logz = float(obj_headers[index]['LOG_Z'])
obj_temperatures.append(obj_temp)
obj_log_z_all.append(obj_logz)
index += 1
obj_names2 = []
index = 0
for thefile in fits_files:
#thenames=re.findall('^bk_([a-z][0-9]+).fits$',thefile)
thenames = re.findall('([a-z].+_[0-9].+).fits$', thefile)
if (len(thenames) > 0):
obj_names2.append(thenames[0])
else:
print 'bad file ', thefile
index += 1
obj_names = obj_names2
obj_files = filelist_group
#objames_and_objfiles = zip(obj_names, obj_files)
#objames_and_objtemp = zip(obj_names, obj_temperatures)
objtemp_and_objlogz = zip(obj_temperatures, obj_log_z_all)
#all_logg=np.array([0.0,0.5,1.,1.5,2.,2.5,3.,3.5,4.,4.5])
#all_logg=np.array([0.0,1.,2.,3.,4.])
all_logg = np.array([0.0])
index = 0
for temp, logz in objtemp_and_objlogz:
if index % 100 == 0:
print 'phoenix star : T=', temp, ' metal=', logz
for logg in all_logg:
#Icat(model,temp,logz,logg)
sed = S.Icat('phoenix', temp, logz, logg)
sed.convert('flam') # to be sure every spectrum is in flam unit
all_sed.append(sed)
index += 1
return all_sed
'UTSTTIME', 'UTNDTIME', 'RA', 'DEC', 'PAP', 'PAD', 'IMRA', 'AIRMASS'
])
allRows = []
displayCount = 0
displayInt = 10
# Go through files and extract headers
for filename in allFilenames:
if displayCount == displayInt:
print('Extracting header from ' + filename)
displayCount = 0
else:
displayCount = displayCount + 1
try:
hdr = fits.getheader(filename, 1)
except:
print("File ignored, couldn't load FITS header")
continue
try:
UTSTTIME = hdr['UTSTTIME']
UTNDTIME = hdr['UTNDTIME']
RA = hdr['RA']
DEC = hdr['DEC']
PAP = hdr['PAP']
PAD = hdr['PAD']
IMRA = hdr['IMRA']
AIRMASS = hdr['AIRMASS']
except:
print("File ignored, couldn't find all keywords.")
def separate_period(base_dir):
"""Separate observations in the base dir into needed folders.
Parameters
----------
base_dir, str
directory containing darks and biases to be split.
"""
print('Separating', base_dir)
all_files = glob.glob(os.path.join(base_dir, 'o*_raw.fits'))
if not len(all_files):
print("nothing to move")
return
mjd_times = np.array([fits.getval(item, 'EXPSTART', ext=1)
for item in all_files])
month_begin = mjd_times.min()
month_end = mjd_times.max()
print('All data goes from', month_begin, ' to ', month_end)
select_gain = {'WK' : 1,
'BIWK' : 4}
for file_type, mode in zip(['BIAS', 'DARK', 'BIAS'],
['WK', 'WK', 'BIWK']):
gain = select_gain[mode]
obs_list = []
for item in all_files:
with fits.open(item) as hdu:
if (hdu[0].header['TARGNAME'] == file_type) and (hdu[0].header['CCDGAIN'] == gain):
obs_list.append(item)
if not len(obs_list):
print('{} No obs to move. Skipping'.format(mode))
continue
else:
print(file_type, mode, len(obs_list), 'files to move, ', 'gain = ', gain)
N_days = int(month_end - month_begin)
N_periods = figure_number_of_periods(N_days, mode)
week_lengths = functions.figure_days_in_period(N_periods, N_days)
#--Add remainder to end
week_lengths[-1] += (month_end - month_begin) - N_days
#-- Translate to MJD
anneal_weeks = []
start = month_begin
end = start + week_lengths[0]
anneal_weeks.append((start, end))
for item in week_lengths[1:]:
start = end
end += item
anneal_weeks.append((start, end))
print()
print(file_type, mode, 'will be broken up into %d periods as follows:'%(N_periods))
print('\tWeek start, Week end')
for a_week in anneal_weeks:
print('\t', a_week)
print()
for period in range(N_periods):
begin, end = anneal_weeks[period]
# weeks from 1-4, not 0-3
week = str(period + 1)
while len(week) < 2:
week = '0' + week
output_path = base_dir
if file_type == 'BIAS':
output_path = os.path.join(output_path,
'biases/%d-1x1/%s%s/'%(gain,
mode.lower(),
week))
elif file_type == 'DARK':
output_path = os.path.join(output_path,
'darks/%s%s/'%(mode.lower(), week))
else:
print('File Type not recognized')
print(output_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
print('week goes from: ', begin, end)
obs_to_move = [item for item in obs_list if
(begin <= fits.getval(item, 'EXPSTART', ext=1) <= end)]
if not len(obs_to_move):
raise ValueError('error, empty list to move')
for item in obs_to_move:
print('Moving ', item, ' to:', output_path)
shutil.move(item, output_path)
if not 'IMPHTTAB' in fits.getheader(os.path.join(output_path,
item.split('/')[-1]), 0):
###Dynamic at some point
fits.setval(os.path.join(output_path, item.split('/')[-1]),
'IMPHTTAB',
ext=0,
value='oref$x9r1607mo_imp.fits')
obs_list.remove(item)
all_files.remove(item)
def match_IPS_keywords(stsci_pipe_ready_file, ips_file, cmd_line_args=None):
"""
This function performs the change of keyword values for the STScI pipeline-ready file to match the values in the
IPS file.
:param stsci_pipe_ready_file: string, path and name of the STScI pipeline-ready file
:param ips_file: string, path and name of the IPS file
:param cmd_line_args: dictionary, keywords and corresponding values indicated in the command line
:return: nothing; the input fits file with the modified keyword values
"""
# get the headers from the IPS file
primary_ext_ips_keywd_dict = fits.getheader(ips_file, 0)
header_ext_ips_keywd_dict = fits.getheader(ips_file, extname='header')
# get the header from the STScI pipeline-ready file
st_pipe_ready_dict = fits.getheader(stsci_pipe_ready_file, 0)
# iterate over the map of STScI to IPS keywords dictionary (defined in this script)
for key2modify, val2modify in stsci2ips_dict.items():
if val2modify == 'N/A':
# for a non applicable (not too important) keyword for the simulations, set value to N/A
st_pipe_ready_dict[key2modify] = 'N/A'
if 'primary_ext' in val2modify:
# look for the same keyword in IPS file in the primary extension header
ips_key = val2modify.split(':')[-1]
if 'SUBARRAY' in key2modify:
set_subarray_and_size_keywds(primary_ext_ips_keywd_dict,
st_pipe_ready_dict,
stsci_pipe_ready_file)
else:
change_keyword2ips_value(primary_ext_ips_keywd_dict,
st_pipe_ready_dict, ips_key,
key2modify, stsci_pipe_ready_file)
if 'header_ext' in val2modify:
# look for the same keyword in IPS file in the header extension
ips_key = val2modify.split(':')[-1]
change_keyword2ips_value(header_ext_ips_keywd_dict,
st_pipe_ready_dict, ips_key, key2modify,
stsci_pipe_ready_file)
if 'set_to_given_string' in val2modify:
# change the keyword value to that given in the command line - this is optional
if cmd_line_args is None:
continue
else:
if key2modify in cmd_line_args:
print('Modified keyword: ', key2modify, ' old_value=',
st_pipe_ready_dict[key2modify], ' new_value=',
cmd_line_args[key2modify])
fits.setval(stsci_pipe_ready_file,
key2modify,
value=cmd_line_args[key2modify])
else:
print('Value for keyword=', key2modify,
' not provided with line command.')
continue
if 'calculation' in val2modify:
print('Value for keyword ', key2modify, ' will be calculated...')
if key2modify == 'EXPSTART':
# put the dates/times values into the time stamp format to do operations
dateobs_string = st_pipe_ready_dict['DATE-OBS'].replace(
'T', ' ')
dateobs = datetime.timestamp(
datetime.fromisoformat(dateobs_string))
timeobs_string = st_pipe_ready_dict['TIME-OBS']
timeobs_string = dateobs_string + ' ' + timeobs_string
timeobs = datetime.timestamp(
datetime.fromisoformat(timeobs_string))
visitstart_string = st_pipe_ready_dict['VSTSTART'].replace(
'T', ' ')
visitstart = datetime.timestamp(
datetime.fromisoformat(visitstart_string))
# this the calculation follows the JWST keyword dictionary calculation:
# https://mast.stsci.edu/portal/Mashup/Clients/jwkeywords/
# expstart = input('DATE-OBS') + 'T' + input('TIME-OBS') / UTC exposure start time (MJD)
# However, we are using the visit start time instead of the last argument since we do not have an
# actual exposure start time, and because the exposures are 1 in this case
new_val = dateobs + timeobs / visitstart
# change value in dictionary for use within the script
st_pipe_ready_dict['EXPSTART'] = new_val
if key2modify == 'DURATION':
# this the calculation follows the JWST keyword dictionary calculation:
# https://mast.stsci.edu/portal/Mashup/Clients/jwkeywords/
# duration = TFRAME*((NGROUPS*NFRAMES+(NGROUPS-1)*GROUPGAP+DROPFRAMES1*NINTS)) where DROPFRAMES1 is
# a lookup in the PRD DataModes table.
# However, NIRSpec only drops frames in TA, hence the calculation simplifies to:
# duration = TFRAME*((NGROUPS*NFRAMES))
new_val = st_pipe_ready_dict['TFRAME'] * st_pipe_ready_dict[
'NGROUPS'] * st_pipe_ready_dict['NFRAMES']
# change value in dictionary for use within the script
st_pipe_ready_dict['DURATION'] = new_val
if key2modify == 'EXPEND':
new_val = st_pipe_ready_dict['EXPSTART'] + st_pipe_ready_dict[
'DURATION']
# change value in dictionary for use within the script
st_pipe_ready_dict['EXPEND'] = new_val
if key2modify == 'EXPMID':
new_val = st_pipe_ready_dict['EXPSTART'] + (
st_pipe_ready_dict['DURATION'] / 2.0)
# change value in dictionary for use within the script
st_pipe_ready_dict['EXPMID'] = new_val
if key2modify == 'TSAMPLE':
# this the calculation follows the JWST keyword dictionary calculation:
# https://mast.stsci.edu/portal/Mashup/Clients/jwkeywords/
# tsample = readout pattern lookup
# table taken from:
# https://jwst-docs.stsci.edu/near-infrared-spectrograph/nirspec-instrumentation/nirspec-detectors/nirspec-detector-readout-modes-and-patterns
readout_patterns = {
'NRSRAPID': 10.737, # frames=1
'NRSRAPIDD1': 21.474, # frames=1
'NRSRAPIDD2': 32.210, # frames=1
'NRSRAPIDD6': 75.159, # frames=1
'NRS': 42.947, # frames=4
'NRSIRS2RAPID': 14.589, # frames=1
'NRSIRS2': 72.944
} # frames=5
new_val = readout_patterns[st_pipe_ready_dict['READPATT']]
# change value in dictionary for use within the script
st_pipe_ready_dict['TSAMPLE'] = new_val
if key2modify == 'TGROUP':
# this the calculation follows the JWST keyword dictionary calculation:
# https://mast.stsci.edu/portal/Mashup/Clients/jwkeywords/
# tgroup = (GROUPGAP+NFRAMES)*TFRAME
# However, NIRSpec only drops frames in TA, hence the calculation simplifies to:
# tgroup = NFRAMES*TFRAME
# reference for GROUPGAP: http://www.stsci.edu/~tumlinso/nirspec_ocd_v6_DRAFT.pdf
new_val = st_pipe_ready_dict['NFRAMES'] * st_pipe_ready_dict[
'TFRAME']
# change value in dictionary for use within the script
st_pipe_ready_dict['TGROUP'] = new_val
print('Modified keyword: ', key2modify, ' old_value=',
st_pipe_ready_dict[key2modify], ' new_value=', new_val)
print(' * WARNING: This calculation needs to be verified ')
fits.setval(stsci_pipe_ready_file, key2modify, value=new_val)
elif 'specific_string' in val2modify:
# now set all the other keyword whose string value will not change from simulation from simulation, this
# is the case for the 'specific_string' in the map of STScI to IPS keywords dictionary
new_val = val2modify.split(':')[-1]
print('Modified keyword: ', key2modify, ' old_value=',
st_pipe_ready_dict[key2modify], ' new_value=', new_val)
fits.setval(stsci_pipe_ready_file, key2modify, value=val2modify)
def which_band_is_file(filename):
"""This resuts the band of the given file if it is a
stella one"""
if not is_stella_file(filename):
return None
return pf.getheader(filename, ext=1).get("FILTER")
def get_offsets(path0, mylogger=None, silent=True, verbose=False):
'''
Function to get offsets from FITS header and write it to ASCII file
Parameters
----------
path0 : str
Path to FITS file. Must include '/' at the end
silent : boolean
Turns off stdout messages. Default: False
verbose : boolean
Turns on additional stdout messages. Default: True
Returns
-------
tab0 : astropy.table.Table
Astropy ASCII table written to [path0]+'sci_offsets.tbl'
Notes
-----
Created by Chun Ly, 30 May 2017
Modified by Chun Ly, 10 December 2017
- Implement glog logging, allow mylogger keyword input
Modified by Chun Ly, 17 December 2017
- Minor fix: log -> clog
- Call dir_check with mylogger input
'''
# + on 10/12/2017
if type(mylogger) == type(None):
mylog, clog = 0, log
else:
mylog, clog = 1, mylogger
if silent == False: clog.info('### Begin get_offsets : ' + systime())
# Mod on 17/12/2017
if not mylog:
dir_list, list_path = dir_check.main(path0,
silent=silent,
verbose=verbose)
else:
dir_list, list_path = dir_check.main(path0,
silent=silent,
verbose=verbose,
mylogger=clog)
for path in list_path:
outfile = path + 'sci_offsets.tbl'
if exists(outfile):
# Mod on 10/12/2017
clog.warning('File exists : ' + outfile)
clog.warning('Not over-writing!!! ')
else:
fits_files = np.loadtxt(path + 'obj.lis', dtype=type(str))
fits_files = [path + file0 for file0 in fits_files] # Bug fix
n_files = len(fits_files)
names0 = ('filename', 'xoffset', 'yoffset', 'poffset', 'qoffset')
dtype0 = ('S20', 'f8', 'f8', 'f8', 'f8')
tab0 = Table(names=names0, dtype=dtype0)
for nn in xrange(n_files):
basename = os.path.basename(fits_files[nn])
if verbose == True: log.info('## Reading : ' + basename)
h0 = fits.getheader(fits_files[nn])
vec0 = [
basename, h0['XOFFSET'], h0['YOFFSET'], h0['POFFSET'],
h0['QOFFSET']
]
tab0.add_row(vec0)
if silent == False: clog.info('Writing : ' + outfile)
asc.write(tab0, outfile, format='fixed_width_two_line')
#endelse
#endfor
if silent == False: clog.info('### End get_offsets : ' + systime())
def align_to_reference(ROOT_DIRECT, ALIGN_IMAGE, fitgeometry="shift",
clean=True, verbose=False, ALIGN_EXTENSION=0, toler=3, skip_swarp=False,
align_sdss_ds9=False, catalog=None):
"""
xshift, yshift, rot, scale, xrms, yrms = align_to_reference()
"""
import os
import glob
import shutil
from pyraf import iraf
from iraf import stsdas,dither
import threedhst
from threedhst import catIO
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
#### Clean slate
rmfiles = ['SCI.fits','WHT.fits','align.cat','direct.cat'
'align.map','align.match','align.reg','align.xy',
'direct.reg','direct.xy','ds9_align.tsv']
for file in rmfiles:
try:
os.remove(file)
except:
pass
if catalog is not None:
align_sdss_ds9 = True
#### Get only images that overlap from the ALIGN_IMAGE list
if not align_sdss_ds9:
align_img_list = find_align_images_that_overlap(ROOT_DIRECT+'_drz.fits', ALIGN_IMAGE, ALIGN_EXTENSION=ALIGN_EXTENSION)
if not align_img_list:
print('threedhst.shifts.align_to_reference: no alignment images overlap.')
return 0,0
#### Use swarp to combine the alignment images to the same image
#### dimensions as the direct mosaic
if (not skip_swarp) & (not align_sdss_ds9):
try:
os.remove(ROOT_DIRECT+'_align.fits')
except:
pass
matchImagePixels(input=align_img_list,
matchImage=ROOT_DIRECT+'_drz.fits',
output=ROOT_DIRECT+'_align.fits', match_extension = 1,
input_extension=ALIGN_EXTENSION)
#### Run SExtractor on the direct image, with the WHT
#### extension as a weight image
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = 'WHT.fits'
se.options['FILTER'] = 'Y'
## Detect thresholds (default = 1.5)
THRESH = 10
if align_sdss_ds9:
if 'Vizier' not in REFERENCE_CATALOG:
THRESH = 20
se.options['DETECT_THRESH'] = '%d' %(THRESH)
se.options['ANALYSIS_THRESH'] = '%d' %(THRESH)
se.options['MAG_ZEROPOINT'] = str(threedhst.options['MAG_ZEROPOINT'])
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
iraf.imcopy(ROOT_DIRECT+'_drz.fits[1]',"SCI.fits")
iraf.imcopy(ROOT_DIRECT+'_drz.fits[2]',"WHT.fits")
status = se.sextractImage('SCI.fits')
## Read the catalog
directCat = threedhst.sex.mySexCat('direct.cat')
if align_sdss_ds9:
### Use ds9 SDSS catalog to refine alignment
import threedhst.dq
import pywcs
import threedhst.catIO as catIO
wcs = pywcs.WCS(pyfits.getheader('SCI.fits', 0))
#wcs = pywcs.WCS(pyfits.getheader('Q0821+3107-F140W_drz.fits', 1))
if 'Vizier' in REFERENCE_CATALOG:
#### Use (unstable) astroquery Vizier search
#### CFHTLS-Deep: 'Vizier.II/317'
VIZIER_CAT = REFERENCE_CATALOG.split('Vizier.')[1]
print('Align to Vizier catalog: http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=%s' %(VIZIER_CAT))
import astroquery
if astroquery.__version__ < '0.0.dev1078':
from astroquery import vizier
query = {}
query["-source"] = VIZIER_CAT
#query["-out"] = ["_r", "CFHTLS", "rmag"]
query["-out"] = ["_RAJ2000", "_DEJ2000"] ### Just RA/Dec.
#### Center position and query radius
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1/2., wcs.naxis2/2.]], 1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = np.sqrt((r0-rll)**2*np.cos(d0/360.*2*np.pi)**2+(d0-dll)**2)*60.*1.5
h = query["-c"] = "%.6f %.6f" %(r0, d0)
query["-c.rm"] = "%.3f" %(corner_radius) ### xxx check image size
#### Run the query
vt = vizier.vizquery(query)
else:
#### Newer astroquery
from astroquery.vizier import Vizier
import astropy.coordinates as coord
import astropy.units as u
Vizier.ROW_LIMIT = -1
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1/2., wcs.naxis2/2.]], 1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = np.sqrt((r0-rll)**2*np.cos(d0/360.*2*np.pi)**2+(d0-dll)**2)*60.*1.5
#
c = coord.ICRSCoordinates(ra=r0, dec=d0, unit=(u.deg, u.deg))
#### something with astropy.coordinates
# c.icrs.ra.degree = c.icrs.ra.degrees
# c.icrs.dec.degree = c.icrs.dec.degrees
#
vt = Vizier.query_region(c, width=u.Quantity(corner_radius, u.arcminute), catalog=[VIZIER_CAT])[0]
#### Make a region file
ra_list, dec_list = vt['RAJ2000'], vt['DEJ2000']
print('Vizier, found %d objects.' %(len(ra_list)))
fp = open('%s.vizier.reg' %(ROOT_DIRECT),'w')
fp.write('# %s, r=%.1f\'\nfk5\n' %(VIZIER_CAT, corner_radius))
for ra, dec in zip(ra_list, dec_list):
fp.write('circle(%.6f, %.6f, 0.5")\n' %(ra, dec))
#
fp.close()
else:
#### Use DS9 catalog
ds9 = threedhst.dq.myDS9()
ds9.set('file SCI.fits')
#ds9.set('file Q0821+3107-F140W_drz.fits')
ds9.set('catalog %s' %(REFERENCE_CATALOG))
### Can't find XPA access point for "copy to regions"
ds9.set('catalog export tsv ds9_align.tsv')
lines = open('ds9_align.tsv').readlines()
ra_list, dec_list = [], []
for line in lines[1:]:
spl = line.split()
ra, dec = float(spl[0]), float(spl[1])
ra_list.append(ra)
dec_list.append(dec)
#
del(ds9)
x_image, y_image = [], []
for ra, dec in zip(ra_list, dec_list):
x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
x_image.append(x)
y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat['X_IMAGE'] = np.array(x_image)
alignCat['Y_IMAGE'] = np.array(y_image)
else:
## alignment image
se.options['CATALOG_NAME'] = 'align.cat'
status = se.sextractImage(ROOT_DIRECT+'_align.fits')
alignCat = threedhst.sex.mySexCat('align.cat')
xshift = 0
yshift = 0
rot = 0
scale = 1.
xrms = 2
yrms = 2
NITER = 5
IT = 0
while (IT < NITER):
IT = IT+1
#### Get x,y coordinates of detected objects
## direct image
fp = open('direct.xy','w')
for i in range(len(directCat.X_IMAGE)):
fp.write('%s %s\n' %(directCat.X_IMAGE[i],directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open('align.xy','w')
for i in range(len(alignCat.X_IMAGE)):
fp.write('%s %s\n' %(np.float(alignCat.X_IMAGE[i])+xshift,
np.float(alignCat.Y_IMAGE[i])+yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler*1.
try:
os.remove('align.match')
except:
pass
status1 = iraf.xyxymatch(input="direct.xy", reference="align.xy",
output="align.match",
tolerance=2**pow, separation=0, verbose=yes, Stdout=1)
nmatch = 0
while status1[-1].startswith('0') | (nmatch < 10):
pow+=1
os.remove('align.match')
status1 = iraf.xyxymatch(input="direct.xy", reference="align.xy",
output="align.match",
tolerance=2**pow, separation=0, verbose=yes, Stdout=1)
#
nmatch = 0
for line in open('align.match'): nmatch += 1
if verbose:
for line in status1:
print(line)
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(input="align.match", database="align.map",
fitgeometry=fitgeometry, interactive=no,
xmin=INDEF, xmax=INDEF, ymin=INDEF, ymax=INDEF,
maxiter = 10, reject = 2.0, Stdout=1)
if verbose:
for line in status2:
print(line)
#fp = open(root+'.iraf.log','a')
#fp.writelines(status1)
#fp.writelines(status2)
#fp.close()
#### Parse geomap.output
fp = open("align.map","r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
#os.system('wc align.match')
print('Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' %(IT, xshift, yshift, rot, scale, xrms, yrms))
im = pyfits.open('SCI.fits')
shutil.copy('align.map',ROOT_DIRECT+'_align.map')
shutil.copy('align.match',ROOT_DIRECT+'_align.match')
#### Cleanup
if clean:
rmfiles = ['SCI.fits','WHT.fits','align.cat',
'align.map','align.match','align.reg','align.xy',
'direct.cat','direct.reg','direct.xy',
'drz_sci.fits','drz_wht.fits','bg.fits']
for file in rmfiles:
try:
os.remove(file)
except:
pass
return xshift, yshift, rot, scale, xrms, yrms
def formaldehyde_mm_radex(xarr,
temperature=25,
column=13,
density=4,
xoff_v=0.0,
width=1.0,
grid_vwidth=1.0,
texgrid=None,
taugrid=None,
hdr=None,
path_to_texgrid='',
path_to_taugrid='',
debug=False,
verbose=False,
**kwargs):
"""
Use a grid of RADEX-computed models to make a model line spectrum
The RADEX models have to be available somewhere.
OR they can be passed as arrays. If as arrays, the form should be:
texgrid = ((minfreq1,maxfreq1,texgrid1),(minfreq2,maxfreq2,texgrid2))
xarr must be a SpectroscopicAxis instance
xoff_v, width are both in km/s
Parameters
----------
grid_vwidth : float
the velocity assumed when computing the grid in km/s
this is important because tau = modeltau / width (see, e.g.,
Draine 2011 textbook pgs 219-230)
density : float
Density!
"""
if texgrid is None and taugrid is None:
if path_to_texgrid == '' or path_to_taugrid == '':
raise IOError("Must specify model grids to use.")
else:
taugrid = [pyfits.getdata(path_to_taugrid)]
texgrid = [pyfits.getdata(path_to_texgrid)]
hdr = pyfits.getheader(path_to_taugrid)
zinds, yinds, xinds = np.indices(taugrid[0].shape)
if 'CD1_1' in hdr:
cd11 = 'CD1_1'
cd22 = 'CD2_2'
else:
cd11 = 'CDELT1'
cd22 = 'CDELT2'
densityarr = (xinds + hdr['CRPIX1'] -
1) * hdr[cd11] + hdr['CRVAL1'] # log density
columnarr = (yinds + hdr['CRPIX2'] -
1) * hdr[cd22] + hdr['CRVAL2'] # log column
temparr = (zinds + hdr['CRPIX3'] -
1) * hdr['CDELT3'] + hdr['CRVAL3'] # lin temperature
minfreq = (218., )
maxfreq = (219., )
elif len(taugrid) == len(texgrid) and hdr is not None:
minfreq, maxfreq, texgrid = zip(*texgrid)
minfreq, maxfreq, taugrid = zip(*taugrid)
zinds, yinds, xinds = np.indices(taugrid[0].shape)
if 'CD1_1' in hdr:
cd11 = 'CD1_1'
cd22 = 'CD2_2'
else:
cd11 = 'CDELT1'
cd22 = 'CDELT2'
densityarr = (xinds + hdr['CRPIX1'] -
1) * hdr[cd11] + hdr['CRVAL1'] # log density
columnarr = (yinds + hdr['CRPIX2'] -
1) * hdr[cd22] + hdr['CRVAL2'] # log column
temparr = (zinds + hdr['CRPIX3'] -
1) * hdr['CDELT3'] + hdr['CRVAL3'] # lin temperature
else:
raise Exception
# Convert X-units to frequency in GHz
xarr = xarr.as_unit('Hz', quiet=True)
#tau_nu_cumul = np.zeros(len(xarr))
gridval1 = np.interp(density, densityarr[0, 0, :], xinds[0, 0, :])
gridval2 = np.interp(column, columnarr[0, :, 0], yinds[0, :, 0])
gridval3 = np.interp(temperature, temparr[:, 0, 0], zinds[:, 0, 0])
if np.isnan(gridval1) or np.isnan(gridval2) or np.isnan(gridval3):
raise ValueError("Invalid column/density")
if scipyOK:
# this is mostly a trick for speed: slice so you only have two thin layers to interpolate
# between
#slices = [density_gridnumber] + [slice(np.floor(gv),np.floor(gv)+2) for gv in (gridval2,gridval1)]
slices = [
slice(np.floor(gridval3),
np.floor(gridval3) + 2),
slice(np.floor(gridval2),
np.floor(gridval2) + 2),
slice(np.floor(gridval1),
np.floor(gridval1) + 2)
]
tau = [
scipy.ndimage.map_coordinates(tg[slices],
np.array([[gridval3 % 1],
[gridval2 % 1],
[gridval1 % 1]]),
order=1) for tg in taugrid
]
tex = [
scipy.ndimage.map_coordinates(tg[slices],
np.array([[gridval3 % 1],
[gridval2 % 1],
[gridval1 % 1]]),
order=1) for tg in texgrid
]
else:
raise ImportError(
"Couldn't import scipy, therefore cannot interpolate")
#tau = modelgrid.line_params_2D(gridval1,gridval2,densityarr,columnarr,taugrid[temperature_gridnumber,:,:])
#tex = modelgrid.line_params_2D(gridval1,gridval2,densityarr,columnarr,texgrid[temperature_gridnumber,:,:])
if verbose:
for ta, tk in zip(tau, tex):
print(
"density %20.12g temperature %20.12g column %20.12g: tau %20.12g tex %20.12g"
% (density, temperature, column, ta, tk))
if debug:
import pdb
pdb.set_trace()
spec = np.sum([(formaldehyde_mm_vtau(xarr,
Tex=float(tex[ii]),
tau=float(tau[ii]),
xoff_v=xoff_v,
width=width,
**kwargs) *
(xarr.as_unit('GHz').value > minfreq[ii]) *
(xarr.as_unit('GHz').value < maxfreq[ii]))
for ii in xrange(len(tex))],
axis=0)
return spec
def cutout_id_chem_map(yslice=slice(367,467), xslice=slice(114,214),
vrange=[51,60]*u.km/u.s, sourcename='e2',
filelist=glob.glob(paths.dpath('12m/cutouts/*e2e8*fits')),
source=None, radius=None,
molecular_database=ch3oh,
radiative_transitions=rt,
frqs=frqs,
chem_name='CH3OH',
shape=None, # check that shape matches slice
):
assert filelist
maps = {}
map_error = {}
energies = {}
degeneracies = {}
frequencies = {}
indices = {}
# sanity check
#shapes = [fits.getdata(fn).shape for fn in filelist]
#assert len(set(shapes)) == 1
for ii,fn in enumerate(ProgressBar(filelist)):
if chem_name not in fn:
log.debug("Skipping {0} because it doesn't have {1}".format(fn, chem_name))
continue
if 'temperature' in fn or 'column' in fn:
continue
if 'moment0' in fn:
# there is a slight danger of off-by-one-pixel errors with the
# cropping used here. Ideally, we'd reproject...
m0_full = fits.getdata(fn)
#stddev = fits.getdata(fn.replace("moment0","madstd"))
header = fits.getheader(fn)
cutout = Cutout2D(m0_full, source, 2*radius, wcs=wcs.WCS(header))
m0 = cutout.data
#cutout_std = Cutout2D(stddev, source, 2*radius, wcs=wcs.WCS(header))
#stddev = cutout_std.data
try:
beam = radio_beam.Beam.from_fits_header(header)
jtok = beam.jtok(header['RESTFRQ']*u.Hz).value
except:
jtok = 222. # approximated 0.32x0.34 at 225 GHz
m0 = m0 * jtok
# stddev = error in a single channel... not very accurate
#stddev = stddev * jtok
print("stddev for {0}".format(fn)) # this debug statement prevents abort traps
stddev = mad_std(m0_full[np.isfinite(m0_full)]) * jtok
header = cutout.wcs.to_header()
else:
cube_ = SpectralCube.read(fn)
if shape:
# have to have same shapes, otherwise pixel slices don't make sense
assert cube_.shape == shape
cube = cube_[:,yslice,xslice]
bm = cube.beams[0]
#jtok = bm.jtok(cube.wcs.wcs.restfrq*u.Hz)
cube = cube.to(u.K, bm.jtok_equiv(cube.wcs.wcs.restfrq*u.Hz))
slab = cube.spectral_slab(*vrange)
cube.beam_threshold = 1
#contguess = cube.spectral_slab(0*u.km/u.s, 40*u.km/u.s).percentile(50, axis=0)
#contguess = cube.spectral_slab(70*u.km/u.s, 100*u.km/u.s).percentile(50, axis=0)
mask = (cube.spectral_axis<40*u.km/u.s) | (cube.spectral_axis > 75*u.km/u.s)
contguess = cube.with_mask(mask[:,None,None]).percentile(30, axis=0)
stddev = cube.with_mask(mask[:,None,None]).apply_numpy_function(mad_std, axis=0)
slabsub = (slab-contguess)
slabsub.beam_threshold = 0.15
m0 = slabsub.moment0()
header = m0.hdu.header
label = linere.search(fn).groups()[0]
frq = name_to_freq[label]
closest_ind = np.argmin(np.abs(frqs - frq))
closest_key = list(radiative_transitions.keys())[closest_ind]
closest_rt = radiative_transitions[closest_key]
upperstate = molecular_database.data['States'][closest_rt.UpperStateRef]
upperen = u.Quantity(float(upperstate.StateEnergyValue),
unit=upperstate.StateEnergyUnit)
maps[label] = m0
map_error[label] = stddev
energies[label] = upperen
degeneracies[label] = int(upperstate.TotalStatisticalWeight)
indices[label] = closest_ind
frequencies[label] = frq
# make sure the dict indices don't change order
energy_to_key = {v:k for k,v in energies.items()}
order = sorted(energy_to_key.keys())
keys = [energy_to_key[k] for k in order]
cube = np.empty((len(maps),)+maps[label].shape)
ecube = np.empty_like(cube)
xaxis = u.Quantity([energies[k] for k in keys])
xaxis = xaxis.to(u.erg, u.spectral()).to(u.K, u.temperature_energy())
for ii,key in enumerate(keys):
# divide by degeneracy
cube[ii,:,:] = maps[key]
ecube[ii,:,:] = map_error[key]
frequencies = u.Quantity([frequencies[k] for k in keys])
indices = [indices[k] for k in keys]
degeneracies = [degeneracies[k] for k in keys]
assert xaxis.size == cube.shape[0]
return xaxis,cube,ecube,maps,map_error,energies,frequencies,indices,degeneracies,header
def process(P, F, stamp=False, plot=False):
'''P is an int refering to the Par being processed
F is the int 110 or 160; it sets G to 102 or 141, respectively
if stamp is True, stamps of all the bright objects will be cut and put in a directory called stamps
if plot is True, a pdf showing the steps of the processing will be created and put in a directory called stages'''
if plot:
logging.info('Plotting enabled')
if F == 110:
G = 102
elif F == 160:
G = 141
else:
raise ValueError('F==%i; should be 110 or 160' % F)
imageDir = 'Par%i/DATA/DIRECT_GRISM' % P
stampDir = '%s/F%i_stamps' % (imageDir, F)
grismDir = 'Par%i/G%i_DRIZZLE' % (P, G)
subgrismDir = '%s/subtracted' % grismDir
modelDir = '%s/contaminationModels' % subgrismDir
stageDir = '%s/processingStages' % subgrismDir
detailsDir = '%s/detailedPlots' % subgrismDir
if not os.path.exists(stampDir):
os.mkdir(stampDir)
if not os.path.exists(subgrismDir):
os.mkdir(subgrismDir)
if not os.path.exists(modelDir):
os.mkdir(modelDir)
if not os.path.exists(stageDir):
os.mkdir(stageDir)
if not os.path.exists(detailsDir):
os.mkdir(detailsDir)
#load image data
image = '%s/F%iW_sci.fits' % (imageDir, F)
img = fits.getdata(image, 0)
header = fits.open(image)[0].header
catalog = getCatalog('%s/fin_F%i.cat' % (imageDir, F))
distortionFactor = .08 / 0.128254
#separate list of galaxies by brightness cut-off
magcutFaint = 24 #these are too faint to bother with
magcutBright = 20 #point sources (stars) require extra-large stamps
faint = catalog[catalog['MAG_F1153W'] > magcutFaint]
stars = catalog[(catalog['MAG_F1153W'] <= magcutBright)
& (catalog['CLASS_STAR'] > 0.1)]
bright = catalog[(catalog['MAG_F1153W'] <= magcutFaint)
& ((catalog['MAG_F1153W'] > magcutBright)
| (catalog['CLASS_STAR'] <= 0.1))]
#sb = np.hstack((bright, stars))
#cut stamps
if stamp or not os.path.exists('%s/stamp1.fits' % stampDir):
print 'Cutting stamps'
for entry in stars:
cutStamp(img, header, stampDir, entry, catalog, 2)
for entry in np.hstack((bright, faint)):
cutStamp(img, header, stampDir, entry, catalog)
for entry in bright:
#find all entries that might overlap
startTime = time.time()
grism = '%s/aXeWFC3_G%i_mef_ID%i.fits' % (grismDir, G, entry['NUMBER'])
if not os.path.exists(grism):
print 'Skipping object %i; file not found: %s' % (entry['NUMBER'],
grism)
else:
print 'grism %i' % entry['NUMBER']
logging.info('grism %i' % entry['NUMBER'])
gimg = fits.getdata(grism, 1)
gerr = fits.getdata(grism, 2)
gheader = fits.getheader(grism, 1)
gdx = gheader['NAXIS1']
gdy = gheader['NAXIS2']
gyRef = gheader['CRPIX2']
#find contaminants to have own profiles calculated
cond1 = entry['NUMBER'] != bright['NUMBER']
cond2 = entry['Y_IMAGE'] + gdy / 2. >= bright['Y_IMAGE'] - bright[
'A_IMAGE']
cond3 = entry['Y_IMAGE'] - gdy / 2. <= bright['Y_IMAGE'] + bright[
'A_IMAGE']
cond4 = entry['X_IMAGE'] - gdx / 2. <= bright['X_IMAGE'] + gdx / 2.
cond5 = bright['X_IMAGE'] + gdx / 2. <= entry['X_IMAGE'] + gdx / 2.
cond6 = entry['X_IMAGE'] - gdx / 2. <= bright['X_IMAGE'] - gdx / 2.
cond7 = bright['X_IMAGE'] - gdx / 2. <= entry['X_IMAGE'] + gdx / 2.
contams = bright[cond1 & cond2 & cond3 & ((cond4 & cond5) |
(cond6 & cond7))]
print 'contaminants: %s' % contams['NUMBER']
logging.info('contaminants: %s' % contams['NUMBER'])
#get main profile
stamp = '%s/stamp%i.fits' % (stampDir, entry['NUMBER'])
simg = fits.getdata(stamp, 0)
sheader = fits.getheader(stamp, 0)
sdx = sheader['NAXIS1']
sdy = sheader['NAXIS2']
stripe = [] #linear profile starting from bottom of stamp
for row in simg:
val = 0
for x, col in enumerate(row):
if sdx / 3. <= x <= sdx * 2 / 3.: #use middle third of profile
val += col
stripe.append(val)
#extend stripe to height of grism
speakLocationY = (np.where(stripe == max(stripe))[0][0])
profile = np.zeros(gdy)
profile[gyRef - speakLocationY:gyRef - speakLocationY +
sdy] = stripe
#normalize and interpolate the profile
profileInterp = interpolate.interp1d(xrange(gdy),
profile / gdy,
kind='linear',
bounds_error=False,
fill_value=0)
pRange = np.array(xrange(100 * (gdy - 1) + 1)) / 100.
profile = profileInterp(pRange)
#get contaminating profiles and relative x range
c_bounds, c_profiles, c_profileInterps = {}, {}, {}
for contam in contams:
c_stamp = '%s/stamp%i.fits' % (stampDir, contam['NUMBER'])
c_simg = fits.getdata(c_stamp, 0)
c_sheader = fits.getheader(c_stamp, 0)
c_sdx = c_sheader['NAXIS1']
c_sdy = c_sheader['NAXIS2']
c_xoffset = (contam['X_IMAGE'] -
entry['X_IMAGE']) * distortionFactor
c_yoffset = (contam['Y_IMAGE'] -
entry['Y_IMAGE']) * distortionFactor
c_stripe = [] #linear profile starting from bottom of stamp
for row in c_simg:
val = 0
for x, col in enumerate(row):
if c_sdx / 3. <= x <= c_sdx * 2 / 3.: #use middle third of profile
val += col
c_stripe.append(val)
#make the stripe a spline to allow subpixelling
c_spline = interpolate.interp1d(xrange(c_sdy),
c_stripe,
kind='linear',
bounds_error=False,
fill_value=0)
c_fineRange = np.array(xrange(100 * c_sdy)) / 100.
c_splineVals = c_spline(c_fineRange)
#extend stripe to height of grism
c_speakLocationY = (np.where(c_stripe == max(c_stripe))[0][0]
) #use data, not interpolation
c_profile = np.zeros(100 * gdy)
left = int(round((gyRef - c_speakLocationY + c_yoffset) * 100))
right = int(
round(
(gyRef - c_speakLocationY + c_sdy + c_yoffset) * 100))
c_profile[max(left, 0):min(right, gdy * 100)] = c_splineVals[
-min(left, 0):min(right, gdy * 100) - left]
#interpolate the profile
c_profileInterp = interpolate.interp1d(
np.arange(len(c_profile)) / 100.,
c_profile / gdy,
kind='linear',
bounds_error=False,
fill_value=0)
c_profile = c_profileInterp(pRange)
#determine to where in grism the contamination may extend
#assumes tophat prior; use grism response curve eventually
cdx = [
max(0, c_xoffset - contam['A_IMAGE']),
min(c_xoffset + gimg.shape[1] + contam['A_IMAGE'], gdx - 1)
]
c_bounds[contam['NUMBER']] = cdx
c_profiles[contam['NUMBER']] = c_profile
c_profileInterps[contam['NUMBER']] = c_profileInterp
#code to compress whole grism
gimgMasked = np.ma.masked_array(gimg,
mask=(gerr == 0),
fill_value=np.NaN)
gerrMasked = np.ma.masked_array(gerr,
mask=(gerr == 0),
fill_value=np.NaN)
gColumnTotal = np.ma.average(gimgMasked, axis=-1)
gErrColumnTotal = np.sqrt(
np.ma.average(np.square(gerrMasked), axis=-1))
gColumnTotalFine = interpolate.interp1d(xrange(gdy),
gColumnTotal,
kind='linear',
bounds_error=False,
fill_value=0)(pRange)
gErrColumnTotalFine = interpolate.interp1d(xrange(gdy),
gErrColumnTotal,
kind='linear',
bounds_error=False,
fill_value=0)(pRange)
gColumnTotalFine = np.ma.masked_array(
gColumnTotalFine,
mask=(gErrColumnTotalFine == 0),
fill_value=np.NaN)
gErrColumnTotalFine = np.ma.masked_array(
gErrColumnTotalFine,
mask=(gErrColumnTotalFine == 0),
fill_value=np.NaN)
#list all the profiles
profiles = [profileInterp]
for c in c_profileInterps:
profiles.append(c_profileInterps[c])
#find the pixel offsets for the whole object
weights = [1, 0] * len(
profiles) #both amplitudes and dy pixel offsets can vary
b = [(0, 10), (-2, 2)] * len(
profiles
) #bound amplitudes to be nonnegative, pixel offset within 2
st = time.time()
minimization = optimize.minimize(residualVaryOffset,
weights,
args=(gColumnTotalFine,
gErrColumnTotalFine,
profiles, gdy),
bounds=b)
et = time.time()
logging.info(minimization)
logging.info('Time to optimize: %f' % (et - st))
offsets = minimization['x'][1::2]
if not minimization['success']:
print 'Overall minimization unsuccessful'
#plot the data and models
if plot:
plotDir = '%s/%i' % (detailsDir, entry['NUMBER'])
if not os.path.exists(plotDir):
os.mkdir(plotDir)
totalSuccess = minimization['success']
wpa = makeModels(profiles, minimization['x'][0::2], offsets,
gdy)
plotCompressedGrism(gimg, pRange, gColumnTotalFine, wpa,
minimization, plotDir)
#determine contributions due to each object via chi^2 minimization
#each column point is the mean of the three points centered around its index
contamGimg = np.ma.masked_array(np.zeros(gimg.shape),
mask=np.ma.getmask(gimgMasked))
subtractGimg = np.ma.copy(gimgMasked)
for x in xrange(gdx):
if x == 0: #nothing left of first index
temp = np.ma.masked_array(
[gimgMasked.T[x], gimgMasked.T[x + 1]])
tempErr = np.ma.masked_array(
[gerrMasked.T[x], gerrMasked.T[x + 1]])
elif x == gdx - 1: #nothing right of last index
temp = np.ma.masked_array(
[gimgMasked.T[x - 1], gimgMasked.T[x]])
tempErr = np.ma.masked_array(
[gerrMasked.T[x - 1], gerrMasked.T[x]])
else:
temp = np.ma.masked_array([
gimgMasked.T[x - 1], gimgMasked.T[x],
gimgMasked.T[x + 1]
])
tempErr = np.ma.masked_array([
gerrMasked.T[x - 1], gerrMasked.T[x],
gerrMasked.T[x + 1]
])
gColumn = np.ma.average(temp.T, axis=-1)
gErrColumn = np.sqrt(np.ma.average(tempErr.T, axis=-1))
gColumnFine = interpolate.interp1d(xrange(gdy),
gColumn,
kind='linear',
bounds_error=False,
fill_value=0)(pRange)
gErrColumnFine = interpolate.interp1d(xrange(gdy),
gErrColumn,
kind='linear',
bounds_error=False,
fill_value=0)(pRange)
gColumnFine = np.ma.masked_array(gColumnFine,
mask=(gErrColumnFine == 0),
fill_value=np.NaN)
gErrColumnFine = np.ma.masked_array(gErrColumnFine,
mask=(gErrColumnFine == 0),
fill_value=np.NaN)
#determine which contaminants may be contaminating the grism
profiles = [profileInterp]
for c in contams['NUMBER']:
if c_bounds[c][0] <= x <= c_bounds[c][1]:
profiles.append(c_profileInterps[c])
#determine the weights of each profile and calculate their contributions
weights = [1] * len(
profiles) #both amplitudes and dy pixel offsets can vary
b = [(0, 10)] * len(
profiles
) #bound amplitudes to be nonnegative, pixel offset within 2
minimization = optimize.minimize(residualConstOffset,
weights,
args=(offsets, gColumnFine,
gErrColumnFine,
profiles, gdy),
bounds=b)
if not minimization['success']:
print 'Minimization unsuccessful at x=%i' % x
logging.info('Minimization unsuccessful at x=%i' % x)
#raise Warning(minimization)
weights = minimization['x']
weightedProfArrays = makeModels(profiles, weights, offsets,
gdy)
logging.info('At x = %i: weights = %s, success = %s' %
(x, weights, minimization['success']))
#plot the data and models
if plot and not x % (gdx / 10): #only plot 10 x values
plotIndividualWavelengths(pRange, gColumnFine,
weightedProfArrays, minimization,
x, plotDir)
#subtract each contaminating profile from the grism
for y, ySlice in enumerate(gimg):
for profNum, prof in enumerate(weightedProfArrays):
if profNum and gimg[y, x]:
subtractGimg[y, x] -= prof[::100][y]
#creates a contamination model
for y, val in enumerate(gimg):
for profNum, prof in enumerate(weightedProfArrays):
if gimg[y, x]:
contamGimg[y, x] += prof[::100][y]
#save the new grism
subtractFile = '%s/aXeWFC3_G%i_mef_ID%i_subtracted.fits' % (
subgrismDir, G, entry['NUMBER'])
fits.writeto(subtractFile,
data=subtractGimg.data,
header=gheader,
clobber=True)
if plot:
contamFile = '%s/contam%i.fits' % (modelDir, entry['NUMBER'])
fits.writeto(contamFile,
data=contamGimg.data,
header=gheader,
clobber=True)
plotFinalResults(entry, img, totalSuccess, profile, c_profiles,
pRange, gimgMasked, gColumnTotalFine,
contamGimg, wpa, subtractGimg, stageDir)
endTime = time.time()
logging.info('Total time for object %i: %f' %
(entry['NUMBER'], endTime - startTime))
def refine_shifts(ROOT_DIRECT='f160w',
ALIGN_IMAGE='../../ACS/h_sz*drz_img.fits',
fitgeometry='shift', clean=True,
ALIGN_EXTENSION=0, shift_params=None,
toler=3, maxtoler=5, align_sdss_ds9=False,
verbose=False):
"""
refine_shifts(ROOT_DIRECT='f160w',
ALIGN_IMAGE='../../ACS/h_sz*drz_img.fits',
fitgeometry='shift', clean=True)
Refine shifts by catalog matching an input multidrizzle image,
ROOT_DIRECT+'_drz.fits' to one or more alignment images
"""
run = threedhst.prep_flt_files.MultidrizzleRun(ROOT_DIRECT.upper())
## radius for match is 2**toler. Make it larger if fit comes out bad
#toler, maxtoler = 3, 5
iter, MAXIT = 0, 5
xrms, yrms = 100, 100
if shift_params is not None:
xshift, yshift, rot, scale = shift_params
threedhst.showMessage('Using specified DRZ-frame shifts: %f %f %f %f' %(xshift, yshift, rot, scale))
else:
threedhst.showMessage('Aligning WCS to %s (%s)' %(threedhst.options['ALIGN_IMAGE'], fitgeometry))
while ((xrms > 1) | (yrms > 1)) & (toler <= maxtoler) & (iter < MAXIT):
iter = iter + 1
xshift, yshift, rot, scale, xrms, yrms = threedhst.shifts.align_to_reference(
ROOT_DIRECT,
ALIGN_IMAGE,
fitgeometry=fitgeometry, clean=clean,
ALIGN_EXTENSION=ALIGN_EXTENSION,
toler=toler, skip_swarp=(toler > 3),
align_sdss_ds9=align_sdss_ds9, verbose=verbose)
toler+=1
#### shifts measured in DRZ frame. Translate to FLT frame
drz = pyfits.open(ROOT_DIRECT+'_drz.fits')
#alpha = (180.-drz[1].header['PA_APER'])/360.*2*np.pi
#### Get reference angle from first image in the ASN file
asn = threedhst.utils.ASNFile(ROOT_DIRECT+'_asn.fits')
alpha = (180.-pyfits.getheader(asn.exposures[0]+'_flt.fits',1)['PA_APER'])/360.*2*np.pi
xsh = (xshift*np.cos(alpha)-yshift*np.sin(alpha))*np.float(run.scl)
ysh = (xshift*np.sin(alpha)+yshift*np.cos(alpha))*np.float(run.scl)
print('Final shift:', xsh, ysh, drz[1].header['PA_APER'])
fp = open(ROOT_DIRECT+'_align.info','w')
fp.write('%s %8.3f %8.3f %8.3f\n' %(ALIGN_IMAGE, xsh, ysh, rot))
fp.close()
#### Read the shiftfile
shiftF = threedhst.shifts.ShiftFile(ROOT_DIRECT+'_shifts.txt')
#### Apply the alignment shifts to the shiftfile
shiftF.xshift = list(np.array(shiftF.xshift)-xsh)
shiftF.yshift = list(np.array(shiftF.yshift)-ysh)
shiftF.rotate = list((np.array(shiftF.rotate)+rot) % 360)
shiftF.scale = list(np.array(shiftF.scale)*scale)
shiftF.write(ROOT_DIRECT+'_shifts.txt')
def get_many_k93model():
SEDfile_dir = os.path.join(top_pysynphot_data_dir, dir_nostar,
dir_submodels[8])
all_sed = []
filelist1 = os.listdir(SEDfile_dir + '/km01')
filelist2 = os.listdir(SEDfile_dir + '/km02')
filelist3 = os.listdir(SEDfile_dir + '/km03')
filelist4 = os.listdir(SEDfile_dir + '/km05')
filelist5 = os.listdir(SEDfile_dir + '/km10')
filelist6 = os.listdir(SEDfile_dir + '/km20')
filelist7 = os.listdir(SEDfile_dir + '/km25')
filelist8 = os.listdir(SEDfile_dir + '/km30')
filelist9 = os.listdir(SEDfile_dir + '/km35')
filelist10 = os.listdir(SEDfile_dir + '/km40')
filelist11 = os.listdir(SEDfile_dir + '/km45')
filelist12 = os.listdir(SEDfile_dir + '/km50')
filelist13 = os.listdir(SEDfile_dir + '/kp00')
filelist14 = os.listdir(SEDfile_dir + '/kp01')
filelist15 = os.listdir(SEDfile_dir + '/kp02')
filelist16 = os.listdir(SEDfile_dir + '/kp03')
filelist17 = os.listdir(SEDfile_dir + '/kp05')
filelist18 = os.listdir(SEDfile_dir + '/kp10')
filelist1.remove('AA_README')
filelist2.remove('AA_README')
filelist3.remove('AA_README')
filelist4.remove('AA_README')
filelist5.remove('AA_README')
filelist6.remove('AA_README')
filelist7.remove('AA_README')
filelist8.remove('AA_README')
filelist9.remove('AA_README')
filelist10.remove('AA_README')
filelist11.remove('AA_README')
filelist12.remove('AA_README')
filelist13.remove('AA_README')
filelist14.remove('AA_README')
filelist15.remove('AA_README')
filelist16.remove('AA_README')
filelist17.remove('AA_README')
filelist18.remove('AA_README')
filelist=filelist1 + filelist2 + filelist3 + filelist4 + filelist5+ filelist6 + filelist7 + filelist8 + filelist9 + \
filelist10 + filelist11 + filelist12 + filelist13 + filelist14 + filelist15+ filelist16 + filelist17 + filelist18
filelist1_group = [
os.path.join('km01', f) for f in filelist1 if f.endswith('.fits')
]
filelist2_group = [
os.path.join('km02', f) for f in filelist2 if f.endswith('.fits')
]
filelist3_group = [
os.path.join('km03', f) for f in filelist3 if f.endswith('.fits')
]
filelist4_group = [
os.path.join('km05', f) for f in filelist4 if f.endswith('.fits')
]
filelist5_group = [
os.path.join('km10', f) for f in filelist5 if f.endswith('.fits')
]
filelist6_group = [
os.path.join('km20', f) for f in filelist6 if f.endswith('.fits')
]
filelist7_group = [
os.path.join('km25', f) for f in filelist7 if f.endswith('.fits')
]
filelist8_group = [
os.path.join('km30', f) for f in filelist8 if f.endswith('.fits')
]
filelist9_group = [
os.path.join('km35', f) for f in filelist9 if f.endswith('.fits')
]
filelist10_group = [
os.path.join('km40', f) for f in filelist10 if f.endswith('.fits')
]
filelist11_group = [
os.path.join('km45', f) for f in filelist11 if f.endswith('.fits')
]
filelist12_group = [
os.path.join('km50', f) for f in filelist12 if f.endswith('.fits')
]
filelist13_group = [
os.path.join('kp00', f) for f in filelist13 if f.endswith('.fits')
]
filelist14_group = [
os.path.join('kp01', f) for f in filelist14 if f.endswith('.fits')
]
filelist15_group = [
os.path.join('kp02', f) for f in filelist15 if f.endswith('.fits')
]
filelist16_group = [
os.path.join('kp03', f) for f in filelist16 if f.endswith('.fits')
]
filelist17_group = [
os.path.join('kp05', f) for f in filelist17 if f.endswith('.fits')
]
filelist18_group = [
os.path.join('kp10', f) for f in filelist18 if f.endswith('.fits')
]
filelist_group=filelist1_group + filelist2_group + filelist3_group + filelist4_group + filelist5_group+ \
filelist6_group + filelist7_group + filelist8_group + filelist9_group + filelist10_group + filelist11_group + filelist12_group + filelist5_group+ \
filelist13_group + filelist14_group + filelist15_group + filelist16_group + filelist17_group + filelist18_group
fits_files = filelist_group
obj_headers = []
obj_files = []
for filename in fits_files:
index = 0
if re.search('fits', filename): #example of filename filter
index += 1
fullfilename = os.path.join(SEDfile_dir, filename)
hdr = fits.getheader(fullfilename)
obj_headers.append(hdr)
obj_files.append(filename)
obj_temperatures = []
obj_log_z_all = []
index = 0
for hdr in obj_headers:
obj_temp = obj_headers[index]['TEFF']
obj_logz = obj_headers[index]['LOG_Z']
obj_temperatures.append(obj_temp)
obj_log_z_all.append(obj_logz)
index += 1
obj_names2 = []
index = 0
obj_temp = []
for thefile in fits_files:
#thenames=re.findall('^bk_([a-z][0-9]+).fits$',thefile)
thenames = re.findall('([a-z].+_[0-9].+).fits$', thefile)
temp = re.findall('([a-z].+_[0-9].+).fits$', thefile)
if (len(thenames) > 0):
obj_names2.append(thenames[0])
else:
print 'bad file ', thefile
index += 1
obj_names = obj_names2
obj_files = filelist_group
objames_and_objfiles = zip(obj_names, obj_files)
objames_and_objtemp = zip(obj_names, obj_temperatures)
objtemp_and_objlogz = zip(obj_temperatures, obj_log_z_all)
#all_logg=np.array([0.0,1.,2.,3.,4.]) # gravity
all_logg = np.array([0.0]) # gravity
for temp, logz in objtemp_and_objlogz:
#Icat(model,temp,logz,logg)
for logg in all_logg:
sed = S.Icat('k93models', temp, logz, logg)
sed.convert('flam') # to be sure every spectrum is in flam unit
all_sed.append(sed)
return all_sed
def telescope(img):
import string
from astropy.io import fits as pyfits
telescopelist = [
'ekar', 'pennar', 'TNG', 'NOT', 'ACAM', 'WHT', 'lo', 'wise', 'ca',
'FORS1', 'FORS2', 'NTT', 'lp', 'wise', 'rem', 'CI', 'sub', 'mag',
'FTN', 'danish', 'SOFI', 'NICS', 'ctio', 'montsec', 'hct', 'trp',
'k61', 'b155', 'DFOT', 'ST'
]
geth = pyfits.getheader(img)
try:
_telescope = geth['TELESCOP']
if string.find(_telescope, 'Ekar') != -1:
_telescope = 'ekar'
elif string.find(_telescope, 'Reflector') != -1:
_telescope = 'ekar'
elif string.find(_telescope, 'Schmidt') != -1:
_telescope = 'sch'
elif string.find(_telescope, 'bigelow') != -1:
_telescope = 'b155'
elif string.find(_telescope, 'Kuiper') != -1:
_telescope = 'k61'
elif string.find(_telescope, 'ASIAGO') != -1:
_telescope = 'pennar'
elif string.find(_telescope, 'TNG') != -1:
if string.find(geth['INSTRUME'], 'NICS') != -1:
_telescope = 'NICS'
else:
_telescope = 'TNG'
elif string.find(_telescope, 'NOT') != -1:
_telescope = 'NOT'
elif string.find(_telescope, 'Danish') != -1:
_telescope = 'danish'
elif string.find(_telescope, 'WHT') != -1:
_instrum = geth['INSTRUME']
if _instrum == 'ACAM':
_telescope = 'ACAM'
else:
_telescope = 'WHT'
elif string.find(_telescope, 'Orzale') != -1:
_telescope = 'lo'
elif string.find(_telescope, '40 INCH') != -1:
_telescope = 'wise'
elif string.find(_telescope, 'CA-2.2') != -1:
_telescope = 'ca'
elif _telescope == 'ca':
_telescope = 'ca'
elif string.find(_telescope, 'Subaru') != -1:
_telescope = 'sub'
elif string.find(_telescope, 'K.A.I.T.') != -1:
_telescope = 'kait'
elif string.find(_telescope, 'Clay_Mag') != -1:
_telescope = 'mag'
elif string.find(_telescope, 'ESO-VLT-U1') != -1:
_telescope = 'FORS2'
elif string.find(_telescope, 'ESO-VLT-U2') != -1:
_telescope = 'FORS1'
elif string.find(_telescope, 'ESO-NTT') != -1:
if string.find(geth['INSTRUME'], 'SOFI') != -1:
_telescope = 'SOFI'
else:
_telescope = 'NTT'
elif string.find(_telescope, 'Wendelstein') != -1:
_telescope = 'wend'
elif string.find(_telescope, 'Liverpool') != -1:
_telescope = 'lp'
elif string.find(_telescope, 'REM') != -1:
_telescope = 'rem'
elif string.find(_telescope, 'AZT-24') != -1:
_telescope = 'CI'
elif string.find(_telescope, 'Prompt') != -1:
_telescope = 'prompt'
elif string.find(_telescope, 'Faulkes') != -1:
if string.find(_telescope, 'North') != -1:
_instrume = geth['instrume']
if _instrume == 'EM01':
_telescope = 'mer'
else:
_telescope = 'FTN'
elif string.find(_telescope, 'South') != -1:
_telescope = 'FTS'
elif string.find(_telescope, 'PS1') != -1:
_telescope = 'PS1'
elif string.find(_telescope, 'SWIFT') != -1:
_telescope = 'swift'
elif string.find(_telescope, 'Dutch') != -1:
_telescope = 'dutch'
elif string.find(_telescope, 'ct13m') != -1:
_telescope = 'ctio'
elif string.find(_telescope, 'Montsec') != -1:
_telescope = 'montsec'
elif string.find(_telescope, 'TRAPPIST') != -1:
_telescope = 'trp'
elif string.find(_telescope, 'Sampur') != -1:
_telescope = 'sampur'
elif string.find(_telescope, 'other') != -1:
_telescope = 'other'
elif _telescope in ['1m0-04', '1m0-05', '1m0-08', '1m0-09']:
_telescope = 'lsc'
elif _telescope in ['0m4-14', '0m4-15', '0m4-12', '0m4-13']:
_telescope = 'lco0m4'
elif _telescope in ['DFOT']:
_telescope = 'DFOT'
elif _telescope in ['ST']:
_telescope = 'ST'
elif _telescope in telescopelist:
pass
else:
try:
_telescope = geth['OBSERVAT']
if string.find(_telescope, 'Prompt') != -1:
_telescope = 'prompt'
elif string.find(_telescope, 'Meckering') != -1:
_telescope = 'Meckering'
else:
_telescope = ''
except:
_telescope = ''
except:
_telescope = ''
if _telescope == '':
try:
_instrume = geth['INSTRUME']
if string.find(_instrume, 'HFOSC') != -1:
_telescope = 'hct'
else:
_telescope = ''
except:
_telescope = ''
if _telescope == '':
try:
_telescope = geth['QUBATELE']
if string.find(_telescope, 'other') != -1:
_telescope = 'other'
else:
_telescope = ''
except:
if pyfits.open(img)[0].header.get('PSCAMERA') == 'GPC1':
_telescope = 'PS1'
else:
_telescope = ''
if _telescope == '':
print 'WARNING: Telescope not defined !!!'
return _telescope
def get_all_calspec_hd():
SEDfile_dir = os.path.join(top_pysynphot_data_dir, 'calspec')
filelist = os.listdir(SEDfile_dir)
fits_files = [f for f in os.listdir(SEDfile_dir) if f.endswith('.fits')]
# extract header and filename
star_header = []
star_file_calspec = []
for filename in filelist:
index = 0
if re.search('fits',
filename) and re.search('hd', filename) and re.search(
'stis', filename): #example of filename filter
index += 1
fullfilename = os.path.join(SEDfile_dir, filename)
hdr = fits.getheader(fullfilename)
star_header.append(hdr)
star_file_calspec.append(filename)
# extract starname
star_names = []
index = 0
for hdr in star_header:
# print index
if index != 433:
star_name = star_header[index]['TARGETID']
star_names.append(star_name)
index += 1
else:
print '>>>>>> skip file # ', index, 'BAD HEADER'
print '>>>>>> filename = ', filelist[index]
print hdr
index += 1
# sort filename
star_names_sorted = sorted(star_names, key=star_names.count, reverse=True)
star_names_sorted_upper = map(lambda s: s.upper(), star_names_sorted)
star_names_set = set(star_names_sorted_upper)
# build dictionnary of filename (several files per star name)
StarDict = {}
for star in star_names_set:
print star, ': \n'
star_set_of_file = []
tag_upper = '^' + star.upper() + '*'
tag_lower = '^' + star.lower() + '*'
for thefile in fits_files:
if re.search(tag_upper, thefile) or re.search(tag_lower, thefile):
star_set_of_file.append(thefile)
#StarDict[star]=sorted(star_set_of_file,key=star_names.count,reverse=True)
StarDict[star] = sorted(star_set_of_file, key=star_names.count)
print StarDict[star], '\n'
# SED
all_sed = []
for keystar in StarDict:
the_files = StarDict[keystar]
if (len(the_files)) > 0 and keystar != 'SUN_REFERENCE':
#print sorted(the_files,reverse=True)
selected_file = the_files[0]
selected_fullfile = os.path.join(SEDfile_dir, selected_file)
sed = S.FileSpectrum(selected_fullfile)
if (max(sed.flux) >
0): # remove empty fluxes because of bad parameters
all_sed.append(sed)
return all_sed
def poet_dataread(event, type=0, log=None):
"""
This function reads a set of IRAC AORS, (or IRAC Subarray AORS),
sorting by dither position, if any.
Parameters:
----------
event : An event object.
type : integer
Specifies the type of data to read.
0 = data, 1 = precalibration data, 2 = postcalibration data.
log : A logedit object that keeps the log.
Outputs:
-------
data : [maxnimpos, nx, ny, npos] float array containing the data
frames, sorted into dither positions.
head : header data
uncd : [maxnimpos, nx, ny, npos] float array, uncertainties
bdmskd: [maxnimpos, nx, ny, npos] int array, per-pixel data flag
nimpos: array like
array containing the number of frames in each position.
fp: FrameParameters object containing [npos, maxnimpos] double arrays
of per-frame parameters.
Example:
-------
Modification History:
--------------------
Written by: Joseph Harrington, Cornell.
2005-09-16 [email protected]
2005-10-26 jh Fixed frame times.
2005-10-27 jh Moved calculation of some constants out of the
routine. Filled in header. Corrected some
array datatypes.
2005-11-25 jh Converted to using FP array.
2006-01-04 jh Header tweak.
2006-03-20 jh Added zodi, ism, cib header values.
2007-03-07 khorning Adapted program to use for non-subarray data
2007-07-15 jh Made nimpos be a long, not integer, array.
2010-08-24 patricio Converted to python.
2010-10-27 patricio Comments added.
"""
# General variables
dpref = event.dpref # data directory preffix
expadj = event.expadj # id number of fisrt image
ndcenum = event.ndcenum # number of dcenum
npos = event.npos # number of position
nnod = event.nnod # number of nodding positions
#fpref = event.fpref # file names prefix
pipev = event.pipev # spitzer pipeline version
bcddir = event.inst.bcddir # directory containing bcd files
bcdsuf = event.inst.bcdsuf # bcd file suffix
buncsuf = event.inst.buncsuf # uncertainities file preffix
#bdmsksuf = event.inst.bdmsksuf # badpixelmask file suffix
brmsksuf = event.inst.brmsksuf # badpixelmask file suffix
masksuf = event.masksuf # badpixelmask file suffix
nx = event.nx #
ny = event.ny #
nz = event.nz # number of subarrays in datafile
nh = event.nh #
framtime = event.framtime #
bcdlist = event.bcdfiles # lists of files
# AORs/cal AORs variables
aorname = event.aorname[np.where(event.aortype == type)]
if type != 0:
naor = event.calnaor
nexpid = event.calnexpid
maxnimpos = event.calmaxnimpos
nmcyc = event.calnmcyc
else:
naor = event.naor
nexpid = event.nexpid
maxnimpos = event.maxnimpos
nmcyc = event.nmcyc
nscyc = event.nscyc
# Allocate space for returned arrays
headerdtype = 'a' + str(nh * 81)
#head = np.zeros( (maxnimpos / nz, npos), dtype=headerdtype)
print(maxnimpos / nz)
maxnimpos = int(maxnimpos)
print(maxnimpos / nz)
print(npos)
print(nz)
#nz = int(nz)
#npos = int(npos)
#print(headerdtype)
tmp = int(maxnimpos / nz)
head = np.zeros((tmp, npos), dtype=headerdtype)
data = np.zeros((maxnimpos, ny, nx, npos), dtype=float)
uncd = np.zeros((maxnimpos, ny, nx, npos), dtype=float)
bdmskd = np.zeros((maxnimpos, ny, nx, npos), dtype=int)
brmskd = np.zeros((maxnimpos, ny, nx, npos), dtype=int)
# Allocate space for the frame paramaters
fp = FrameParameters()
fpsize = np.zeros((npos, maxnimpos))
fp.frmobs = np.copy(fpsize) # sequential frame number
fp.pos = np.copy(fpsize) # position number
fp.aor = np.copy(fpsize) # sequential AOR number
fp.expid = np.copy(fpsize) # EXPosure ID
fp.dce = np.copy(fpsize) # Data Collection Event
fp.subarn = np.copy(fpsize) # subarray frame number
fp.time = np.copy(fpsize) # frame mid-time, seconds J2000.0
fp.bmjd = np.copy(fpsize) # frame mid-time, BJD-2400000.5
fp.zodi = np.copy(fpsize) # zodiacal light estimate, see header comment
fp.ism = np.copy(fpsize) # interstellar medium estimate,see head comment
fp.cib = np.copy(fpsize) # cosmic infrared background,see header comment
fp.afpat2b = np.copy(fpsize) # temperatures, K, see header comment
fp.afpat2e = np.copy(fpsize)
fp.ashtempe = np.copy(fpsize)
fp.atctempe = np.copy(fpsize)
fp.acetempe = np.copy(fpsize)
fp.apdtempe = np.copy(fpsize)
fp.acatmp1e = np.copy(fpsize)
fp.acatmp2e = np.copy(fpsize)
fp.acatmp3e = np.copy(fpsize)
fp.acatmp4e = np.copy(fpsize)
fp.acatmp5e = np.copy(fpsize)
fp.acatmp6e = np.copy(fpsize)
fp.acatmp7e = np.copy(fpsize)
fp.acatmp8e = np.copy(fpsize)
# mips frame parameters
fp.cmd_t_24 = np.copy(fpsize)
fp.ad24tmpa = np.copy(fpsize)
fp.ad24tmpb = np.copy(fpsize)
fp.acsmmtmp = np.copy(fpsize)
fp.aceboxtm = np.copy(fpsize)
fp.pxscl2 = np.copy(fpsize)
fp.pxscl1 = np.copy(fpsize)
fp.heady = np.copy(fpsize)
fp.headx = np.copy(fpsize)
fp.filename = np.zeros((npos, maxnimpos), dtype='S150')
nimpos = np.zeros(npos, np.long)
# conveniences
salist = np.arange(nz)
sadind = np.arange(nz, dtype=np.double)
# position of the star
sky = [[event.ra * 180 / np.pi, event.dec * 180 / np.pi]]
# dictionary to get position in MIPS
mirind = {
1929.: 0,
2149.5: 1,
1907.5: 2,
2128.: 3,
1886.: 4,
2106.5: 5,
1864.5: 6
}
# Write to log first line
if log != None:
log.writelog(' aor expid dcenum pos')
else:
print(' aor expid dcenum pos')
# pattern to find expid dcenum
pattern = re.compile("_([0-9]{4})_([0-9]{4})_")
# Obtain data
for aor in np.arange(naor):
direc = dpref + aorname[aor] + bcddir
bcd = bcdlist[aor]
for i in np.arange(len(bcd)):
# Read data
try:
dataf = pf.getdata(direc + bcd[i])
bcdhead = pf.getheader(direc + bcd[i])
except: # If a file doesn't exist, skip to next file.
log.writelog(direc + bcd[i] + " File not found!")
continue
try: # Read uncertainity and mask files
# Replace suffix in bcd file to get the corresponding file.
uncfile = re.sub(bcdsuf, buncsuf, direc + bcd[i])
uncf = pf.getdata(uncfile)
mskfile = re.sub(bcdsuf, masksuf, direc + bcd[i])
bdmskf = pf.getdata(mskfile)
except:
bdmskf = np.ones((nz, ny, nx), np.long)
try: # Mips
brmskfile = re.sub(bcdsuf, brmsksuf, direc + bcd[i])
brmskf = pf.getdata(brmskfile)
except:
brmskf = -np.ones((nz, ny, nx), np.long)
# Obtain expid and dcenum
index = pattern.search(bcd[i])
expid = int(index.group(1))
dcenum = int(index.group(2))
# Do I really need this?
if np.size(bdmskf) == 1:
bdmskf = -np.ones((nz, ny, nx), np.long)
if np.size(brmskf) == 1:
brmskf = -np.ones((nz, ny, nx), np.long)
# Find dither position
try:
pos = bcdhead['DITHPOS'] - 1
except:
pos = 0 # No dither position in stare data
if event.inst.name == 'irs':
pos = expid % npos
elif event.inst.name == 'mips':
nod = expid % nnod
pos = nod * nscyc + mirind[bcdhead['CSM_PRED']]
be = nimpos[pos] # begining
en = nimpos[pos] + nz # end
# Store data
data[be:en, :, :, pos] = dataf.reshape((nz, ny, nx))
uncd[be:en, :, :, pos] = uncf.reshape((nz, ny, nx))
bdmskd[be:en, :, :, pos] = bdmskf.reshape((nz, ny, nx))
brmskd[be:en, :, :, pos] = brmskf.reshape((nz, ny, nx))
# All the single numbers per frame that we care about
fp.frmobs[pos, be:en] = np.sum(nimpos) + salist
fp.pos[pos, be:en] = pos
fp.aor[pos, be:en] = aor
fp.expid[pos, be:en] = expid
fp.dce[pos, be:en] = dcenum
fp.subarn[pos, be:en] = salist
fp.bmjd[pos,
be:en] = bcdhead['BMJD_OBS'] + framtime * (sadind +
0.5) / 86400.
# ccampo 2011/3/18: changed to UTC from SCLK to avoid timing inconsistencies
try:
fp.time[
pos,
be:en] = bcdhead['UTCS_OBS'] + framtime * (sadind + 0.5)
except:
pass
try:
fp.zodi[pos, be:en] = bcdhead['ZODY_EST']
fp.ism[pos, be:en] = bcdhead['ISM_EST']
fp.cib[pos, be:en] = bcdhead['CIB_EST']
fp.afpat2b[pos, be:en] = bcdhead['AFPAT2B']
fp.afpat2e[pos, be:en] = bcdhead['AFPAT2E']
fp.ashtempe[pos, be:en] = bcdhead['ASHTEMPE'] + 273.0
fp.atctempe[pos, be:en] = bcdhead['ATCTEMPE'] + 273.0
fp.acetempe[pos, be:en] = bcdhead['ACETEMPE'] + 273.0
fp.apdtempe[pos, be:en] = bcdhead['APDTEMPE'] + 273.0
fp.acatmp1e[pos, be:en] = bcdhead['ACATMP1E']
fp.acatmp2e[pos, be:en] = bcdhead['ACATMP2E']
fp.acatmp3e[pos, be:en] = bcdhead['ACATMP3E']
fp.acatmp4e[pos, be:en] = bcdhead['ACATMP4E']
fp.acatmp5e[pos, be:en] = bcdhead['ACATMP5E']
fp.acatmp6e[pos, be:en] = bcdhead['ACATMP6E']
fp.acatmp7e[pos, be:en] = bcdhead['ACATMP7E']
fp.acatmp8e[pos, be:en] = bcdhead['ACATMP8E']
except:
pass
try:
fp.pxscl2[pos, be:en] = np.abs(bcdhead['PXSCAL2'])
fp.pxscl1[pos, be:en] = np.abs(bcdhead['PXSCAL1'])
fp.acatmp5e[pos, be:en] = bcdhead['CMD_T_24']
fp.acatmp6e[pos, be:en] = bcdhead['AD24TMPA']
fp.acatmp6e[pos, be:en] = bcdhead['AD24TMPB']
fp.acatmp5e[pos, be:en] = bcdhead['ACSMMTMP']
fp.acatmp6e[pos, be:en] = bcdhead['ACEBOXTM'] + 273.0
except:
pass
# Store filename
fp.filename[pos, be:en] = direc + bcd[i]
# Store header
head[np.int(nimpos[pos] / nz), pos] = np.str(bcdhead)
# Header position of the star:
bcdhead["NAXIS"] = 2
wcs = pw.WCS(bcdhead, naxis=2)
pix = wcs.wcs_world2pix(sky, 0)
fp.headx[pos, be:en] = pix[0][0]
fp.heady[pos, be:en] = pix[0][1]
# Print to log and screen:
if log != None:
log.writelog('%4d' % aor + '%7d' % expid + '%7d' % dcenum +
'%7d' % pos)
else:
print('%4d' % aor + '%7d' % expid + '%7d' % dcenum +
'%7d' % pos)
nimpos[pos] += nz
# frame tags in fp
# where there exist data
fp.exist = np.zeros((npos, maxnimpos), np.long)
for pos in np.arange(npos):
fp.exist[pos, 0:nimpos[pos]] = 1
fp.im = np.copy(fpsize) # Frame within position
for pos in np.arange(npos):
fp.im[pos, 0:nimpos[pos]] = np.arange(nimpos[pos], dtype=np.double)
if event.inst.name != 'mips':
fp.cycpos = np.trunc(fp.frmobs / (npos * nmcyc * nz)) # Cycle number
fp.visobs = np.trunc(fp.frmobs /
(nmcyc * nz)) # Visit number within obs. set
fp.frmvis = fp.im % (nmcyc * nz) # Frame within visit
else:
fp.cycpos = np.trunc(fp.frmobs / (2 * ndcenum)) # Cycle number
fp.visobs = np.trunc(fp.frmobs /
ndcenum) # Visit number within obs. set
fp.frmvis = np.trunc(fp.frmobs % ndcenum) # Frame within visit
# Image scale:
for pos in np.arange(npos):
last = nimpos[pos]
if np.all(fp.pxscl1[pos, 0:last] == fp.pxscl1[pos, 0]):
event.posscl[1, pos] = np.abs(fp.pxscl1[pos, 0])
if np.all(fp.pxscl2[pos, 0:last] == fp.pxscl2[pos, 0]):
event.posscl[0, pos] = np.abs(fp.pxscl2[pos, 0])
# Update event
event.data = data
event.uncd = uncd
event.bdmskd = bdmskd
event.brmskd = brmskd
event.head = head
event.fp = fp
event.nimpos = nimpos
return
import subprocess
conf = json.load(open('config.json'))
root = conf['root']
os.chdir(root)
#date_list = glob.glob('20190402')
date_list = glob.glob('{}/{}/{}/'.format(conf['fakes'], conf['field'], conf['date']))
date_list = filter(lambda x: not x.endswith('gz'), date_list)
for i in date_list:
os.chdir(root+i)
#field_list = glob.glob('A0b')
field_list = glob.glob('.')
for j in field_list:
#print(j)
os.chdir(root+i+'/'+j)
ccd_list = glob.glob(conf["ccd"])
#ccd_list = ['CCD62']
for k in ccd_list:
os.chdir(root+i+'/'+j+'/'+k)
print(root+i+'/'+j+'/'+k)
fits_list = [i.rstrip('diff.fits') for i in glob.glob('*.diff.fits')]
sub_fits = [i.rstrip('.diff.sub.fits') for i in glob.glob('*.diff.sub.fits')]
for l in fits_list:
print(l)
#zp = fits.getheader('{}.diff.fits'.format(l), 1)['MAGZERO']
zp = fits.getheader('{}s.diff.fits'.format(l), 0)['MAGZERO']
cat = '{}s.diff.cat'.format(l)
subimg = '{}s.diff.sub.fits'.format(l)
command = 'sex -DETECT_THRESH 5.0 -CATALOG_NAME {} -CHECKIMAGE_TYPE OBJECTS -CHECKIMAGE_NAME {} -MAG_ZEROPOINT {} {}s.diff.fits'.format(cat, subimg, zp, l)
p0 = subprocess.call(shlex.split(command))
master_flat_path = 'outputs/masterflat_20160708.fits'
master_dark_path = 'outputs/masterdark_20160708.fits'
flat = fits.getdata(master_flat_path)
dark = fits.getdata(master_dark_path)
from skimage.feature import peak_local_max
mid = image_shape[0] // 2
times = []
with ProgressBar(len(paths)) as bar:
for i, path in enumerate(paths):
raw_image = fits.getdata(path) / flat
times.append(fits.getheader(path)['JD'])
# mask, cleaned_image = detect_cosmics(raw_image)
#cleaned_image = raw_image
# smoothed_image = gaussian_filter(cleaned_image, 10)
coordinates = peak_local_max(raw_image,
min_distance=10,
num_peaks=1,
exclude_border=10)
y_mean = int(coordinates[:, 1].mean())
x_mean = int(coordinates[:, 0].mean())
# y, x = np.unravel_index(np.argmax(smoothed_image), smoothed_image.shape)
firstroll = np.roll(
raw_image,
_site=option.site
_force=option.force
if '-' not in str(epoch):
epoch0=datetime.date(int(epoch[0:4]),int(epoch[4:6]),int(epoch[6:8]))
listepoch=[re.sub('-','',str(epoch0))]
else:
epoch1,epoch2=string.split(epoch,'-')
start=datetime.date(int(epoch1[0:4]),int(epoch1[4:6]),int(epoch1[6:8]))
stop=datetime.date(int(epoch2[0:4]),int(epoch2[4:6]),int(epoch2[6:8]))
listepoch=[re.sub('-','',str(i)) for i in [start + datetime.timedelta(days=x) for x in range(0,(stop-start).days)]]
directory = agnkey.util.workingdirectory+'floydsraw2/'+_site
for epoch in listepoch:
path = directory+'/'+epoch+'/raw/'
imglist=glob.glob(path+'*fits')
print imglist
# ingest
#ingestfloyds(imglist,_force)
# make plot
for img in imglist:
hdr = pyfits.getheader(img)
if hdr.get('OBSTYPE')=='SPECTRUM':
html = makeplot(img,False)
print html
def fix_file(row, input_folder=None, check_corrector=None, force_version=None, tpf_rootdir=None):
logger = logging.getLogger(__name__)
fname = os.path.join(input_folder, row['lightcurve'])
fname_original = regex_fileend.sub('.original.fits.gz', fname)
if os.path.exists(fname_original):
raise RuntimeError(f"ORIGINAL exists: {fname_original}")
dataval = int(row['dataval'])
modification_needed = False
m = regex_filename.match(os.path.basename(fname))
if not m:
raise RuntimeError("RegEx doesn't match!")
starid = int(m.group(1))
sector = int(m.group(2))
camera = int(m.group(3))
ccd = int(m.group(4))
cadence = int(m.group(5))
datarel = int(m.group(6))
version = int(m.group(7))
corrector = m.group(8)
# Basic checks:
if starid != row['starid']:
raise RuntimeError("STARID")
if sector != row['sector']:
raise RuntimeError("SECTOR")
if camera != row['camera']:
raise RuntimeError("CAMERA")
if ccd != row['ccd']:
raise RuntimeError("CCD")
#if cadence != row['cadence']:
# raise RuntimeError("CADENCE")
if force_version is not None and version != force_version:
#modification_needed = True
raise RuntimeError("Version mismatch!")
if corrector != check_corrector:
raise RuntimeError("CORRECTOR")
# Do we really need to modify the FITS file?
openfile_needed = True # FORCE modification check!
fix_wcs = False
# We need to open if there is a dataval to add to the header
if dataval > 0:
openfile_needed = True
# Fix for bug with WCS being incorrect in TPF lightcurves
if cadence == 120 and version <= 5:
openfile_needed = True
fix_wcs = True
# Because of the problem with multiple MJD-OBS keywords
# in FITS headers, we have to check files in these cases.
# TODO: Modify this when we know the CAUSE of this SYMPTOM
if version <= 5:
openfile_needed = True
# We need to open the ensemble files to find the lightcurve dependencies:
if corrector == 'ens':
openfile_needed = True
# Find the starid of the TPF which was used to create this lightcurve:
if row['datasource'] == 'tpf':
dependency_tpf = row['starid']
elif row['datasource'].startswith('tpf:'):
dependency_tpf = int(row['datasource'][4:])
else:
dependency_tpf = None
# Placeholder for dependencies between lightcurves:
dependency_lc = None
# Damn, it looks like a modification is needed:
allow_change = []
if openfile_needed:
logger.debug("Opening FITS file: %s", fname)
modification_needed = False
if fix_wcs:
if tpf_rootdir is None:
raise RuntimeError("You need to provide a TPF_ROOTDIR")
# Find out what the
if dependency_tpf is None:
raise RuntimeError("We can't fix WCSs of FFI targets!")
# Find the original TPF file and extract the WCS from its headers:
tpf_file = find_tpf_files(tpf_rootdir, starid=dependency_tpf, sector=sector, camera=camera, ccd=ccd, cadence=cadence)
if len(tpf_file) != 1:
raise RuntimeError(f"Could not find TPF file: starid={dependency_tpf:d}, sector={sector:d}")
# Extract the FITS header with the correct WCS:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=FITSFixedWarning)
wcs_header = WCS(header=fits.getheader(tpf_file[0], extname='APERTURE'), relax=True).to_header(relax=True)
atomic_copy(fname, fname_original)
with fits.open(fname_original, mode='readonly', memmap=True) as hdu:
prihdr = hdu[0].header
# Check if the current DATAVAL matches what it should be:
current_dataval = prihdr.get('DATAVAL')
if current_dataval != dataval:
modification_needed = True
allow_change += ['DATAVAL']
if current_dataval is None:
# Insert DATAVAL keyword just before CHECKSUM:
prihdr.insert('CHECKSUM', ('DATAVAL', dataval, 'Data validation flags'))
else:
prihdr['DATAVAL'] = dataval
if corrector == 'ens' and version <= 5:
if hdu['ENSEMBLE'].header.get('TDISP2') == 'E':
logger.info("%s: Changing ENSEMBLE/TDISP2 header", fname)
modification_needed = True
allow_change += ['TDISP2']
hdu['ENSEMBLE'].header['TDISP2'] = 'E26.17'
if corrector == 'ens':
# Pick out the list of TIC-IDs used to build ensemble:
dependency_lc = list(hdu['ENSEMBLE'].data['TIC'])
if fix_wcs:
logger.info("%s: Changing WCS", fname)
modification_needed = True
allow_change += ['CRPIX1', 'CRPIX2']
mjdref_remove = ('MJDREF' not in hdu['APERTURE'].header)
hdu['APERTURE'].header.update(wcs_header)
hdu['SUMIMAGE'].header.update(wcs_header)
if mjdref_remove:
hdu['APERTURE'].header.remove('MJDREF', ignore_missing=True, remove_all=True)
hdu['SUMIMAGE'].header.remove('MJDREF', ignore_missing=True, remove_all=True)
# Fix bug with multiple MJD-OBS keywords in FITS headers:
if version <= 5: # TODO: Modify this when we know the CAUSE of this SYMPTOM
for extname in ('APERTURE', 'SUMIMAGE'):
if list(hdu[extname].header.keys()).count('MJD-OBS') > 1:
logger.info("%s: Multiple MJD-OBS in %s", fname, extname)
mjdobs = hdu[extname].header['MJD-OBS']
indx = hdu[extname].header.index('MJD-OBS')
hdu[extname].header.remove('MJD-OBS', remove_all=True)
hdu[extname].header.insert(indx, ('MJD-OBS', mjdobs, '[d] MJD at start of observation'))
allow_change += ['MJD-OBS']
modification_needed = True
if modification_needed:
hdu.writeto(fname, output_verify='exception', checksum=True, overwrite=True)
if modification_needed:
try:
if check_fits_changes(fname_original, fname, allow_header_value_changes=allow_change):
os.remove(fname_original)
else:
logger.error("File check failed: %s", fname)
raise RuntimeError(f"File check failed: {fname}")
except: # noqa: E722
logger.exception("Whoops: %s", fname)
if os.path.isfile(fname_original) and os.path.getsize(fname_original) > 0:
if os.path.exists(fname):
os.remove(fname)
os.rename(fname_original, fname)
raise
elif os.path.exists(fname_original):
os.remove(fname_original)
# Extract information from final file:
filesize = os.path.getsize(fname)
filehash = get_filehash(fname)
# Check that filesize is not zero:
if filesize == 0:
raise RuntimeError(f"File has zero size: {fname}")
return {
'priority': row['priority'],
'starid': row['starid'],
'sector': row['sector'],
'camera': row['camera'],
'ccd': row['ccd'],
'cbv_area': row['cbv_area'],
'cadence': cadence,
'lightcurve': row['lightcurve'],
'dataval': dataval,
'datarel': datarel,
'version': version,
'filesize': filesize,
'filehash': filehash,
'dependency_tpf': dependency_tpf,
'dependency_lc': dependency_lc
}
# ----------------------------------------------------------------------
# find one example of each extension
examples = find_examples(WORKSPACE)
# for each example we need to open the file and print to a string
lines = []
# loop around examples
for example in np.sort(list(examples.keys())):
# filename
example_filename = examples[example]
# add line to lines with which example this is
lines.append(' - {0}'.format(example))
lines.append('')
lines.append('```')
# open file
header = fits.getheader(example_filename)
# loop around header keys
for key in header:
if key.startswith(KEY_PREFIX):
largs = [key, header[key], header.comments[key]]
lines.append('{0:9s} {1} // {2}'.format(*largs))
lines.append('```')
lines.append('')
lines.append('')
# write to file
f = open(OUTPUTFILE, 'w')
for line in lines:
f.write(line + '\n')
f.close()
w1, f1 = d1
i1 = np.argmin(abs(w1 - center))
v1 = f1[i1]
w2, f2 = d2
i2 = np.argmin(abs(w2 - center))
v2 = f2[i2]
return min([v1])
if __name__ == '__main__':
regions = [[10000, 10100], [10130, 10230], [12200, 12300]]
lines = np.loadtxt('Felines.moog', usecols=(0, ))
wArcturus = get_wavelength(fits.getheader('ArcturusSummer.fits'))
fArcturus = fits.getdata('ArcturusSummer.fits')
w10Leo1 = get_wavelength(fits.getheader('10LeoYJ.fits'))
f10Leo1 = fits.getdata('10LeoYJ.fits')
w10Leo2 = get_wavelength(fits.getheader('10LeoH.fits'))
f10Leo2 = fits.getdata('10LeoH.fits')
w10Leo3 = get_wavelength(fits.getheader('10LeoK.fits'))
f10Leo3 = fits.getdata('10LeoK.fits')
f10Leo1, w10Leo1 = dopplerShift(w10Leo1, f10Leo1, -82.53)
f10Leo2, w10Leo2 = dopplerShift(w10Leo2, f10Leo2, -81.82)
f10Leo3, w10Leo3 = dopplerShift(w10Leo3, f10Leo3, -81.37)
for i, region in enumerate(regions):
if i != 1:
def get_segment_offset(segment_number, detector, library_list):
"""Convert vectors coordinates in the local segment control
coordinate system to NIRCam detector X and Y coordinates,
at least proportionally, in order to calculate the location
of the segment PSFs on the given detector.
Parameters
----------
segment : int
Segment ID, i.e 3
detector : str
Name of NIRCam detector
library_list : list
List of the names of the segment PSF library files
Returns
-------
x_arcsec
The x offset of the segment PSF in arcsec
y_arcsec
The y offset of the segment PSF in arcsec
"""
# Verify that the segment number in the header matches the index
seg_index = int(segment_number) - 1
header = fits.getheader(library_list[seg_index])
assert int(header['SEGID']) == int(segment_number), \
"Uh-oh. The segment ID of the library does not match the requested " \
"segment. The library_list was not assembled correctly."
xtilt = header['XTILT']
ytilt = header['YTILT']
segment = header['SEGNAME'][:2]
sm_piston = header.get('SMPISTON', 0)
# SM piston has, as one of its effects, adding tilt onto each segment,
# along with higher order WFE such as defocus. We model here the effect
# of SM piston onto the x and y offsets.
# Coefficients determined based on WAS influence function matrix, as
# derived from segment control geometries.
if segment.startswith('A'):
xtilt += sm_piston * 0.010502
elif segment.startswith('B'):
xtilt += sm_piston * -0.020093
elif segment.startswith('C'):
ytilt += sm_piston * 0.017761
# Next we work out the individual offsets from segment-level tilts
control_xaxis_rotations = {
'A1': 180,
'A2': 120,
'A3': 60,
'A4': 0,
'A5': -60,
'A6': -120,
'B1': 0,
'C1': 60,
'B2': -60,
'C2': 0,
'B3': -120,
'C3': -60,
'B4': -180,
'C4': -120,
'B5': -240,
'C5': -180,
'B6': -300,
'C6': -240
}
x_rot = control_xaxis_rotations[segment] # degrees
x_rot_rad = x_rot * np.pi / 180 # radians
# Note that y is defined as the x component and x is defined as the y component.
# This is because "xtilt" moves the PSF in the y direction, and vice versa.
tilt_onto_y = (xtilt * np.cos(x_rot_rad)) - (ytilt * np.sin(x_rot_rad))
tilt_onto_x = (xtilt * np.sin(x_rot_rad)) + (ytilt * np.cos(x_rot_rad))
umrad_to_arcsec = 1e-6 * (180. / np.pi) * 3600
x_arcsec = 2 * umrad_to_arcsec * tilt_onto_x
y_arcsec = 2 * umrad_to_arcsec * tilt_onto_y
# Secondary mirror tilts and translations also shift the apparent location of each PSF,
# often referred to as "changing the boresight".
# Coefficients for this are worked out by Randal Telfer in
# "JWST Secondary Mirror Influence Functions", doc #JWST-PRES-043631
# Values here are taken from Rev C of that document. They are given in units of NIRCam SW pixels per micro-unit of SM pose.
# We include just the first order terms, neglecting the small higher order terms
sm_xtilt = header.get('SMXTILT', 0)
sm_ytilt = header.get('SMYTILT', 0)
sm_xtrans = header.get('SMXTRANS', 0)
sm_ytrans = header.get('SMYTRANS', 0)
nrc_pixelscale = 0.0311 # arcsec/pixel
x_boresight_offset = (1.27777 * sm_ytilt -
0.71732 * sm_xtrans) * nrc_pixelscale
y_boresight_offset = (-1.27363 * sm_xtilt -
0.71571 * sm_ytrans) * nrc_pixelscale
x_arcsec += x_boresight_offset
y_arcsec += y_boresight_offset
# Optionally, arbitrary boresight offset may also be present in the FITS header metadata.
# If so, include that in the PSF too. Be careful about coordinate sign for the V2 axis!
try:
x_arcsec -= header[
'BSOFF_V2'] * 60 # BS offset values in header are in arcminutes
y_arcsec += header['BSOFF_V3'] * 60 #
except:
pass
return x_arcsec, y_arcsec
def main(path0, silent=False, verbose=True, overwrite=False):
'''
main() function to obtain information from FITS header and write to
ASCII file
Parameters
----------
path0 : str
Path to FITS file. Must include '/' at the end
silent : boolean
Turns off stdout messages. Default: False
verbose : boolean
Turns on additional stdout messages. Default: True
Returns
-------
tab0 : astropy.table.Table
Astropy ASCII table written to [path0]+'hdr_info.tbl'
Notes
-----
Created by Chun Ly, 4 March 2017
- Later re-organized to check for file first
Modified by Chun Ly, 5 March 2017
- File exists warning always printed out
- Include AIRMASS
Modified by Chun Ly, 23 March 2017
- Call dir_check.main() to handle multiple date directories
Modified by Chun Ly, 11 May 2017
- Handle longer filter1 and filter2 FITS values
Modified by Chun Ly, 8 December 2017
- Import glog and call for stdout and ASCII logging
- Pass mylogger to dir_check.main()
'''
# Moved up on 10/12/2017
logfile = path0 + 'hdr_info.log'
mylogger = glog.log0(logfile)._get_logger()
if silent == False: mylogger.info('### Begin main : ' + systime())
dir_list, list_path = dir_check.main(path0,
mylogger=mylogger,
silent=silent,
verbose=verbose)
# Mod on 23/03/2017
for path in list_path:
outfile = path + 'hdr_info.tbl'
# Mod later on 04/03/2017 to not overwrite file
# Mod on 05/03/2017 to always print out this warning
if overwrite == False and exists(outfile):
# Mod on 08/12/2017
mylogger.warning('File exists : ' + outfile)
mylogger.warning('Not over-writing!!! ')
else:
fits_files = glob.glob(path + 'N*fits')
n_files = len(fits_files)
# Mod on 05/03/2017 to include airmass
names0 = ('filename', 'datelabel', 'UT_date', 'obstype', 'object',
'exptime', 'airmass', 'grating', 'gratwave', 'filter1',
'filter2', 'slit')
dtype0 = ('S20', 'S30', 'S25', 'S8', 'S100', 'f8', 'f8', 'S15',
'f8', 'S20', 'S20', 'S20')
tab0 = Table(names=names0, dtype=dtype0)
for nn in xrange(n_files):
basename = os.path.basename(fits_files[nn])
if silent == False: mylogger.info('Reading : ' + basename)
h0 = fits.getheader(fits_files[nn])
# Mod on 05/03/2017 to include airmass
vec0 = [
basename, h0['DATALAB'], h0['DATE-OBS'] + 'T' + h0['UT'],
h0['OBSTYPE'], h0['OBJECT'], h0['EXPTIME'], h0['AIRMASS'],
h0['GRATING'], h0['GRATWAVE'], h0['FILTER1'],
h0['FILTER2'], h0['SLIT']
]
tab0.add_row(vec0)
if silent == False:
mylogger.info('Writing : ' + outfile) # Mod on 08/12/2017
asc.write(tab0, outfile, format='fixed_width_two_line')
#endelse
#endfor
if silent == False: mylogger.info('### End main : ' + systime())
}
while True:
current_session = get_session().strftime(nightfmt)
src_path = os.environ['DIRECTORY_TO_WATCH'] + current_session
copy_path = os.environ['FEROS_DATA_PATH'] + current_session + '/RAW/'
if not os.path.exists(copy_path):
os.makedirs(copy_path)
allimages = glob.glob(src_path + '/FEROS*.fits')
for image in allimages:
hdr = pf.getheader(image)
imagename = image.split('/')[-1]
if normal_obsmode['HIERARCH ESO DET READ CLOCK'] == hdr['HIERARCH ESO DET READ CLOCK'] and \
normal_obsmode['CDELT1'] == hdr['CDELT1'] and \
normal_obsmode['CDELT2'] == hdr['CDELT2'] and \
hdr['HIERARCH ESO TPL NAME'] in [HIERARCH_ESO_TPL_NAME['bias'],HIERARCH_ESO_TPL_NAME['flat'],HIERARCH_ESO_TPL_NAME['lamp']]:
cmd = 'rsync -avz %s %s' % (image, copy_path + imagename)
status = subprocess.call(cmd, shell=True)
print '---------------------------------------------------------------'
print 'Copy file are:'
print image
print copy_path + image.split('/')[-1]
print '---------------------------------------------------------------'
def batss_pointing_detect(obs_id, #should be BATSS_slew object?
ra, dec, # Source RA/Dec
eband_name, # Source energy band
err_rad): # Source error radius (arcmin, 90%)
'''
Run imaging and detection for BAT pointings before and after a given slew,
for given sky coordinates and energy band
'''
#Check input parameters
obs = [] # Initialize observation list
if isinstance(obs_id, list):
for obs_id0 in obs_id:
obs.append(BATSS_slew(obs_id0))
else:
obs.append(BATSS_slew(obs_id))
pos = SkyCoord(ra, dec, unit='deg')
coord_str = ('J'+pos.ra.to_string(unit='hour',pad=True,sep='',fields=2)
+(10*pos.dec).to_string(pad=True,sep='',fields=1,alwayssign=True))
coord_str_tex = coord_str[:5]+'$'+coord_str[5]+'$'+coord_str[6:] # TeX
eband = BATSS_eband(eband_name)
err_rad = err_rad * u.arcmin
# Input/Output directories
root = BATSS_dir.root
dataroot = './data/'
if not os.path.exists(dataroot):
os.makedirs(dataroot)
# Loop over BATSS observations
for obs0 in obs:
t0 = datetime.now()
##Time object with slew date
#obs_date = Time('20'+obs0.id[:2]+'-'+obs0.id[2:4]+'-'+obs0.id[4:6])
obs_date = datetime(int('20'+obs0.id[:2]), int(obs0.id[2:4]), int(obs0.id[4:6]))
print(f'{70*"="} {datetime.now():%c}')
print('BATSS Observation type and ID: ', obs0.type.upper(), obs0.id)
print('Coordinates to search (J2000): ',pos.to_string('hmsdms'))
print('Energy band: '+eband.name+' ('+eband.str_keV+')')
# Output directories
datadir = dataroot+obs0.type+'_'+obs0.id+'_'+coord_str+ '_'+eband.name+'/'
if not os.path.exists(datadir):
os.makedirs(datadir)
tempdir = datadir+'temp/'
if not os.path.exists(tempdir):
os.makedirs(tempdir)
# Initialize output txt file
txtfile = datadir+obs0.type+'_'+obs0.id+'_'+coord_str+ '_'+eband.name+'.txt'
f = open(txtfile, 'w')
f.write(f'{70*"="} {datetime.now():%c}\n')
f.write('BATSS Observation type and ID: '+obs0.type.upper()+' '+obs0.id+'\n')
f.write('Coordinates to search (J2000): '+pos.to_string('hmsdms')+'\n')
f.write('Energy band: '+eband.name+' ('+eband.str_keV+')'+'\n')
f.close()
#Input catalog file
catfile_in = tempdir+'batss.in.cat'
# CATNUM: Source number within catalog
# NAME: Source name
# RA_CAT/GLON_CAT: Catalogued source longitude
# DEC_CAT/GLAT_CAT: Catalogued source latitude
# RA_OBJ/GLON_OBJ: Source longitude (to be modified upon detection)
# DEC_OBJ/GLAT_OBJ: Source latitude (to be modified upon detection)
# ERR_RAD_BATSS: BATSS error radius (90%, deg)
cat_in_Table = Table(
{'CATNUM':[0],
'NAME':['BATSS_'+coord_str],
'RA_OBJ':[pos.ra.value] * pos.ra.unit,
'DEC_OBJ':[pos.dec.value] * pos.dec.unit,
'RA_CAT':[pos.ra.value] * pos.ra.unit,
'DEC_CAT':[pos.dec.value] * pos.ra.unit,
'ERR_RAD_BATSS':[err_rad.to_value(u.deg)] * u.deg},
names=('CATNUM','NAME','RA_OBJ','DEC_OBJ','RA_CAT','DEC_CAT',
'ERR_RAD_BATSS')) #Specifies column order
cat_in = fits.BinTableHDU(cat_in_Table, name='BAT_CATALOG')
cat_in.header.set('HDUNAME', 'BAT_CATALOG', 'Name of extension',
before='TTYPE1') #Necessary?
cat_in.header.set('HDUCLASS', 'CATALOG', 'Source catalog',
before='TTYPE1')
cat_in.header.comments['TTYPE1'] = 'Source number within catalog'
cat_in.header.set('TNULL1', -1, 'data null value', after='TFORM1')
cat_in.header.comments['TTYPE2'] = 'Source name'
cat_in.header.comments['TTYPE3'] = 'Detected source longitude'
cat_in.header.comments['TUNIT3'] = 'physical unit of field'
cat_in.header.set('TDISP3', 'F10.4', 'column display format',
after='TUNIT3')
cat_in.header.comments['TTYPE4'] = 'Detected source latitude'
cat_in.header.comments['TUNIT4'] = 'physical unit of field'
cat_in.header.set('TDISP4', 'F10.4', 'column display format',
after='TUNIT4')
cat_in.header.comments['TTYPE5'] = 'Catalogued source longitude'
cat_in.header.comments['TUNIT5'] = 'physical unit of field'
cat_in.header.set('TDISP5', 'F10.4', 'column display format',
after='TUNIT5')
cat_in.header.comments['TTYPE6'] = 'Catalogued source latitude'
cat_in.header.comments['TUNIT6'] = 'physical unit of field'
cat_in.header.set('TDISP6', 'F10.4', 'column display format',
after='TUNIT6')
cat_in.header.comments['TTYPE7'] = 'BATSS cat_in. error radius (90%)'
cat_in.header.comments['TUNIT7'] = 'physical unit of field'
cat_in.header.set('TDISP7', 'F6.4', 'column display format',
after='TUNIT7')
cat_in.writeto(catfile_in, overwrite=True)
# Get master FITS header for slew (archival by default)
flag_realtime = False
if os.path.exists(obs0.fitsfile):
hdrfile = obs0.fitsfile
hdrext = 0
else:
print('Warning: No archival master FITS file found for'
f' {obs0.type} {obs0.id}. Getting header info from queue file.')
if os.path.exists(obs0.queuefile):
hdrfile = obs0.queuefile
hdrext = obs0.type+'_'+obs0.id
else:
print('Warning: No archival queue file found for'
f' {obs0.type} {obs0.id}. Getting header info from'
f' real-time data')
flag_realtime = True
if os.path.exists(obs0.fitsfile_realtime):
hdrfile = obs0.fitsfile_realtime
hdrext = 0
else:
print('Warning: No real-time master FITS file found for'
f' {obs0.type} {obs0.id}. Getting header info from'
f' queue file.')
if os.path.exists(obs0.queuefile_realtime):
hdrfile = obs0.queuefile_realtime
hdrext = obs0.type+'_'+obs0.id
else:
raise IOError('Neither archival nor real-time files'
f' found for {obs0.type} {obs0.id}')
#fitsfile = obs0.fitsfile_realtime if flag_realtime else obs0.fitsfile
print('Header file: '+hdrfile)
print('Extension:')
print(hdrext)
try:
header = fits.getheader(hdrfile, hdrext)
except IOError as err:
raise IOError(err)
except:
print('Some other error! (hdrfile)')
# Partial coding map
pcfile = obs0.pcfile_realtime if flag_realtime else obs0.pcfile
try:
if not os.path.exists(pcfile):
# Try getting default partial coding map
print('Warning: Partial coding file ('
+('realtime' if flag_realtime else 'archival')
+') does not exist. Reading from default file.')
pcfile = BAT_pcfile_def()
print('Partial coding map file: '+pcfile)
pcmap, pchdr = fits.getdata(pcfile, header=True)
except IOError:
raise
except:
print('Some other error! (pcfile)')
else:
dims_pcmap = np.shape(pcmap)
# Attitude file
attfile = obs0.attfile_realtime if flag_realtime else obs0.attfile
try:
if not os.path.exists(attfile):
raise IOError('Attitude file ('+('realtime' if flag_realtime else 'archival')+') does not exist')
att = fits.getdata(obs0.attfile, 1)
except IOError:
raise
else:
flag_settled = 192 # (binary) FLAGS field for settled spacecraft
# Get time windows for preceding and following pointings
obs_t0 = header['BEG_SLEW'] #[MET]
gti_pre = {'start':0, 'stop':header['BEG_SLEW']} #[MET]
gti_pre_sod = {'start':0, 'stop':int(obs0.id[7:9])*3600 + int(obs0.id[10:12])*60 + int(obs0.id[13:15])} #[SOD]
gti_pos = {'start':header['END_SLEW'], 'stop':0} #[MET]
gti_pos_sod = {'start':gti_pre_sod['stop'] + int(obs0.id[17:20]), 'stop':0} #[SOD]
queuefile = obs0.queuefile_realtime if flag_realtime else obs0.queuefile
try:
with fits.open(queuefile) as queue_hdul:
w = np.array([hdu.name == 'SLEW_'+obs0.id for hdu in queue_hdul]).nonzero()[0]
assert len(w) == 1
except IOError:
raise
else:
w = w[0]
# Beginning of preceding pointing
if w == 1:
# Get slew from previous day
date_pre = obs_date - timedelta(days=1)
queuefile_pre = root + f'products/{date_pre.year:04}_{date_pre.month:02}/queue{"_realtime" if flag_realtime else ""}/queue_{date_pre.year % 100:02}{date_pre.month:02}{date_pre.day:02}_{obs0.type}.fits'
try:
with fits.open(queuefile_pre) as queue_pre_hdul:
wpre = len(queue_pre_hdul)
gti_pre_sod['start'] = -86400
except OSError:
print('File not found: '+queuefile_pre)
raise
else:
queuefile_pre = queuefile
queue_pre_hdul = queue_hdul
wpre = w-1
slew_id_pre = queue_pre_hdul[wpre].name[5:]
gti_pre['start'] = fits.getval(queuefile_pre, 'END_SLEW', ext=wpre)
gti_pre_sod['start'] += int(slew_id_pre[7:9])*3600 + int(slew_id_pre[10:12])*60 + int(slew_id_pre[13:15]) + int(slew_id_pre[17:20])
# End of following pointing
if w == len(queue_hdul):
# Get slew from following day
date_pre = obs_date + timedelta(days=1)
queuefile_pos = root + f'products/{date_pos.year:04}_{date_pos.month:02}/queue{"_realtime" if flag_realtime else ""}/queue_{date_pos.year % 100:02}{date_pos.month:02}{date_pos.day:02}_{obs0.type}.fits'
try:
with fits.open(queuefile_pos) as queue_pos_hdul:
wpos = len(queue_pos_hdul)
gti_pos_sod['stop'] = 86400
except OSError:
print('File not found: '+queuefile_pos)
raise
else:
queuefile_pos = queuefile
queue_pos_hdul = queue_hdul
wpos = w+1
slew_id_pos = queue_pos_hdul[wpos].name[5:]
gti_pos['stop'] = fits.getval(queuefile_pos, 'BEG_SLEW', ext=wpos)
gti_pos_sod['stop'] += (int(slew_id_pos[7:9])*3600 +
int(slew_id_pos[10:12])*60 + int(slew_id_pos[13:15]))
# Read AFST files for previous, current and following days
afst_obs_id = []
afst_yymmdd = []
afst_start_sod = []
afst_stop_sod = []
for d in [-1,0,1]:
date0 = obs_date + timedelta(days=d)
yymmdd = f'{date0.year % 100:02}{date0.month:02}{date0.day:02}'
afstfile = (root + f'products/{date0.year:04}_{date0.month:02}/'
f'afst/afst_{date0.year % 100:02}{date0.month:02}'
f'{date0.day:02}.html')
try:
with open(afstfile,'r') as f0:
afst_soup = BeautifulSoup(f0, features='lxml')
except OSError:
raise
tr = afst_soup.find_all('tr') #, features='lxml')
for tr0 in tr:
try:
afst_class = tr0['class'][0]
except KeyError:
continue
if afst_class == 'header':
continue
td0 = tr0.find_all('td')
start0 = td0[0].get_text(strip=True)
start_sod0 = (datetime(int(start0[:4]), int(start0[5:7]), int(start0[8:10])) - obs_date).days*86400 + int(start0[11:13])*3600 + int(start0[14:16])*60 + int(start0[17:19])
stop0 = td0[1].get_text(strip=True)
stop_sod0 = (datetime(int(stop0[:4]), int(stop0[5:7]), int(stop0[8:10])) - obs_date).days*86400 + int(stop0[11:13])*3600 + int(stop0[14:16])*60 + int(stop0[17:19])
afst_obs_id.append(td0[2].a.text.zfill(8) + td0[3].a.text.zfill(3))
afst_yymmdd.append(yymmdd)
afst_start_sod.append(start_sod0)
afst_stop_sod.append(stop_sod0)
point = Table({'obs_id':afst_obs_id, 'yymmdd':afst_yymmdd, 'start_sod':afst_start_sod, 'stop_sod':afst_stop_sod})
del afst_obs_id, afst_yymmdd, afst_start_sod, afst_stop_sod
# Get Observation IDs for preceding and following pointings
dt_pre = point['stop_sod'].clip(max=gti_pre_sod['stop']) - point['start_sod'].clip(min=gti_pre_sod['start'])
upre = np.argmax(dt_pre)
dt_pre = dt_pre[upre]
assert dt_pre > 0
obs_id_pre = point[upre]['obs_id']
yymmdd_pre = point[upre]['yymmdd']
dt_pos = point['stop_sod'].clip(max=gti_pos_sod['stop']) - point['start_sod'].clip(min=gti_pos_sod['start'])
upos = np.argmax(dt_pos)
dt_pos = dt_pos[upos]
assert dt_pos > 0
obs_id_pos = point[upos]['obs_id']
yymmdd_pos = point[upos]['yymmdd']
del point
# Save GTI files for preceding and following pointings
gtifile_pre = tempdir+obs0.type+'_'+obs0.id+'_pre.gti'
gti_pre_Table = Table({'START':[gti_pre['start']] * u.s, 'STOP':[gti_pre['stop']] * u.s}, names=('START','STOP'))
gtihdr_pre = BATSS_gtihdr(gti_pre_Table)
hdu_pre = fits.BinTableHDU(gti_pre_Table, header=gtihdr_pre)
hdu_pre.writeto(gtifile_pre, overwrite=True)
gtifile_pos = tempdir+obs0.type+'_'+obs0.id+'_pos.gti'
gti_pos_Table = Table({'START':[gti_pos['start']] * u.s, 'STOP':[gti_pos['stop']] * u.s}, names=('START','STOP'))
gtihdr_pos = BATSS_gtihdr(gti_pos_Table)
hdu_pos = fits.BinTableHDU(gti_pos_Table, header=gtihdr_pos)
hdu_pos.writeto(gtifile_pos, overwrite=True)
# Perform BATSURVEY analysis on preceding and following pointings
obs0.src_name = 'BATSS '+coord_str # Include BATSS source name
obs0.src_name_tex = 'BATSS '+coord_str_tex # TeX formatted
obs0.eband = eband
for flag_pre in [True, False]:
print(f'{70*"="} {datetime.now():%c}')
f = open(txtfile, 'a')
if flag_pre:
print('PRECEDING POINTING. ',end='')
f.write(f'\n{95*"="}\nPRECEDING POINTING. ')
prefix = 'pre'
gtifile = gtifile_pre
obs_id = obs_id_pre
yymmdd_point = yymmdd_pre
else:
print('FOLLOWING POINTING. ',end='')
f.write(f'\n{95*"="}\nFOLLOWING POINTING. ')
prefix = 'pos'
gtifile = gtifile_pos
obs_id = obs_id_pos
yymmdd_point = yymmdd_pos
yyyy_mm_point = '20'+yymmdd_point[:2]+'_'+yymmdd_point[2:4]
print(f'Observation ID: {obs_id}')
f.write(f'Observation ID: {obs_id}\n')
# Get coding fraction of source from attitude data
gti = fits.getdata(gtifile,1)
w = ((att['time'] >= gti['start'])
& (att['time'] <= gti['stop'])).nonzero()[0]
assert len(w) > 0
#print(f'Attitude records found within GTI: {len(w)}')
w0 = (att[w]['flags'] == flag_settled).nonzero()[0]
assert len(w0) > 0
w = w[w0]
#print(f'Settled records: {len(w)}')
w0 = (att[w]['obs_id'] == obs_id).nonzero()[0]
if len(w0) == 0:
str_out = ('WARNING: No settled attitude records found for'
f' Observation {obs_id}')
print(str_out)
f.write('\t'+str_out+'\n')
obs_id0, obs_id0_pos = np.unique(att[w]['obs_id'],
return_inverse=True)
obs_id0_cts = np.bincount(obs_id0_pos)
imax = obs_id0_cts.argmax()
str_out = (f'\tUsing most frequent Obs ID: {obs_id0[imax]}'
f' ({obs_id0_cts[imax]} records)')
print(str_out)
f.write(str_out+'\n')
obs_id = obs_id0[imax]
w0 = (obs_id0_pos == imax).nonzero()[0]
assert len(w0) > 0
del obs_id0, obs_id0_pos, obs_id0_cts, imax
w = w[w0]
w0 = w[len(w)//2]
ra0 = att[w0]['pointing'][0]
dec0 = att[w0]['pointing'][1]
roll0 = att[w0]['pointing'][2]
# Modify pchdr astrometry
pchdr = BAT_astrmod(pchdr, ra=ra0, dec=dec0, roll=roll0)
#fits.PrimaryHDU(pcmap, pchdr).writeto(datadir+'test_pchdr_'
# +prefix+'.fits', overwrite=True) #TEMP
pcwcs = wcs.WCS(pchdr)
pix = pcwcs.all_world2pix([[pos.ra.deg, pos.dec.deg]],
1)[0].round().astype(int)[::-1] # For [y,x] indexing!
pix = pix.clip(1, dims_pcmap) - 1
pcodefr0 = 100 * pcmap[pix[0], pix[1]]
str_out = f'Source coding fraction: {pcodefr0:6.2f}%. '
print(str_out, end='')
f.write(str_out)
if pcodefr0 == 0:
str_out = 'Pointing skipped'
print(str_out)
f.write(str_out+'\n')
if flag_pre:
obs0.cat_pre = []
else:
obs0.cat_pos = []
continue
print('Downloading pointing data... ', end='')
t1 = datetime.now()
obsdir = datadir+prefix+'_'+obs0.type+'_'+obs0.id+'/'
command = ['wget' # basic command
' -q' # turn off output
' -r -l0' # recursive retrieval (max depth 0)
' -nH' # no host-prefixed directories
' --cut-dirs=7' # also ignore 7 directories
' -np' # do not ascend to parent directory
f' --directory-prefix={obsdir}' # top directory for output
' --no-check-certificate' # don't check server certificate
' -c' # continue partial downloading
' -N' # use same timestamping as remote file
" -R'index*'" # reject all 'index*' files
' -erobots=off' # turn off Robots Exclusion Standard
' --retr-symlinks' # download symbolic links
' http://heasarc.gsfc.nasa.gov/FTP/swift/data/obs/'
f'{yyyy_mm_point}//{obs_id}/'+s for s in ['bat/','auxil/']]
for command0 in command:
subp.run(command0.split(' '))
str_out = f'({(datetime.now()-t1).seconds}s)'
print('done '+str_out)
f.write(f'Pointing data downloaded {str_out}\n')
f.close()
# Loop over DPH and SNAPSHOT imaging
datadir_in = obsdir
cat_tex = []
for flag_dph in [False, True]:
gti_ntries = 0
while gti_ntries < 2:
gti_ntries += 1
print(f'{70*"-"} {datetime.now():%c}')
print(('DPH' if flag_dph else 'SNAPSHOT')+' loop:')
print(f' GTI loop {gti_ntries}: '+
('Standard filtering' if gti_ntries == 1
else 'USERGTI filtering only'))
datadir_out = (obsdir+'results_'
+eband.name+('_dph' if flag_dph else '')+'/')
# BATSURVEY command
command = ['batsurvey',
datadir_in, datadir_out,
'energybins='+eband.str,
'elimits='+eband.str,
'incatalog='+catfile_in,
'ncleaniter=2', #Always clean DPH
# Apply DPH keyword
'timesep='+('DPH' if flag_dph else 'SNAPSHOT'),
'filtnames='+('all' if gti_ntries == 1
else ('global,pointing,filter_file,startracker,'
'st_lossfcn,data_flags,earthconstraints,'
'remove_midnight,occultation,usergti')),
'gtifile='+gtifile,
# Minimum exposure threshold
'expothresh=150.0']
print(' '.join(command))
subp.run(command)
# Find if master GTI file was created
gtifile_out = glob.glob(datadir_out+'gti/master.gti')
if len(gtifile_out) > 0:
if gti_ntries == 1:
gti_text = 'Standard'
elif gti_ntries == 2:
gti_text = 'Standard failed. USERGTI only'
break
else:
if gti_ntries == 1:
print('Standard GTI filtering failed. ', end='')
if flag_dph:
print('DPH binning does not work with '
'USERGTI. Aborting')
gti_text = ('Standard failed. DPH binning '
'does not work with USERGTI filtering')
break
else:
print('Standard GTI filtering failed.'
' Trying USERGTI only')
elif gti_ntries == 2:
print('Standard GTI and USERGTI filtering failed.'
' Aborting')
gti_text = 'Standard and USERGTI failed'
# Get output catalogs
cat_out = []
catfile_out = glob.glob(datadir_out+'point_*/point_*_2.cat')
catfile = (datadir+prefix+'_'+obs0.type+'_'+obs0.id
+'_'+coord_str+'_'+eband.name
+('_dph' if flag_dph else '')+'.cat')
if len(catfile_out) > 0:
print(('DPH' if flag_dph else 'SNAPSHOT')
+' catalogs found:', len(catfile_out))
else:
print('Warning: No '+('DPH' if flag_dph else 'SNAPSHOT')
+' catalogs found. Skipping')
for catfile_out0 in catfile_out:
print('Catalog file: '+catfile_out0)
t_ss = os.path.basename(catfile_out0).split('_')[1]
print(f' {t_ss[:4]}-{t_ss[4:7]}-{t_ss[7:9]}'
f':{t_ss[9:11]}...', end='')
cat0, hdr0 = fits.getdata(catfile_out0, 1, header=True)
cat0_name = cat0['name'].strip()
#cat0['name'] = cat0['name'].strip()
#cat0['rate'] /= 0.16 #[cts/cm2/sec]
#cat0['cent_rate'] /= 0.16
#cat0['rate_err'] /= 0.16
#cat0['bkg_var'] /= 0.16
w = (cat0_name == 'BATSS_'+coord_str).nonzero()[0]
if len(w) > 0:
cat0 = Table(cat0)
for w0 in w:
if len(cat_out) == 0:
cat0[w0]['CATNUM'] = 1
cat_out = Table(cat0[w0])
hdr_out = hdr0
hdr_out.remove('HISTORY', ignore_missing=True,
remove_all=True)
hdr_out['EXTNAME'] = 'BATSURVEY_CATALOG'
hdr_out['HDUNAME'] = 'BATSURVEY_CATALOG'
# Index for new sources in catalog
hdr_out['NEWSRCIN'] = 2
else:
cat0[w0]['CATNUM'] = hdr_out['NEWSRCIN']
hdr_out['NEWSRCIN'] += 1
cat_out.add_row(cat0[w0])
# Save catalog file
n_det = len(cat_out)
with open(txtfile,'a') as f:
f.write(f'\n{"DPH" if flag_dph else "SNAPSHOT"}'
' processing:\n')
f.write(f'GTI filtering: {gti_text}\n')
f.write(f'Detections: {n_det if n_det > 0 else "NONE"}\n')
if n_det > 0:
fits.BinTableHDU(cat_out, hdr_out).writeto(catfile,
overwrite=True)
print(f'Saved {n_det} detection(s) of'
f' BATSS_{coord_str} to file {catfile}')
f.write(' '.join([' #',
f'{"Time_start":23s}', f'{"Time_stop":23s}',
f'{"Exp[s]":7s}', f'{"CF[%]":6s}',
'S/N(pix)','S/N(fit)'])+'\n')
for cat0 in cat_out:
f.write(' '.join([f'{cat0["CATNUM"]:2}',
met2Time(cat0['TIME']).iso,
met2Time(cat0['TIME_STOP']).iso,
f'{cat0["EXPOSURE"]:7.1f}',
f'{100*cat0["PCODEFR"]:6.2f}',
f'{cat0["CENT_SNR"]:8.2f}',
f'{cat0["SNR"]:8.2f}'])
+'\n')
cat_tex.append({
'dt':cat0['TIME']-obs_t0,
'exp':cat0['EXPOSURE'],
'cf':100*cat0['PCODEFR'],
'cent_snr':cat0['CENT_SNR'],
'snr':cat0['SNR']
})
if flag_pre:
obs0.cat_pre = cat_tex
else:
obs0.cat_pos = cat_tex
str_out = ('\nDONE. Processing time: '
+str(datetime.now()-t0).split('.')[0])
print(str_out)
with open(txtfile, 'a') as f:
f.write(str_out+'\n')
print('Closed output text file: ', f.name)
return obs