def sharpen_iracpsf(psfname, mag, interpolant='sinc'):
"""
Make PSF sharper by shrinking pixels!
mag: pixel ratio (mag > 1 means shrinking pixels and make PSF sharper)
"""
newpsf = os.path.splitext(psfname)[0] + '_mag%.2f.fits' % mag
if os.path.exists(newpsf):
os.remove(newpsf)
oldpsf_array = pyfits.getdata(psfname)
ncols, nrows = oldpsf_array.shape
iraf.imlintran(input=psfname,
output=newpsf,
xrotation=0.,
yrotation=0.,
xmag=mag,
ymag=mag,
ncols=ncols,
nlines=nrows,
interpolant=interpolant)
def oversample_all(self, factor=10., interpolant='sinc'):
"""
For each star, perform centroid finding and over-sample the images by
a factor supplied in the method call.
"""
self.factor = factor
self.interpolant = interpolant
f = open('psflist', 'wb')
for i in range(len(self.c)):
imgname = 'obj%d_%s.fits' % (self.c.number[i], self.band)
starimg = pyfits.getdata(imgname)
pm = image_moments.moments(starimg)
pm.firstorder()
mag = 1. / factor # for use in iraf.imlintran
# Now over-sample the image
shape0 = starimg.shape
shape1 = (np.array(shape0) * factor).astype('int')
imgname_oversample = os.path.splitext(
imgname)[0] + '_mag%.2f.fits' % (mag)
# xin = self.x_image_list[i] - self.xmin_list[i]
# yin = self.y_image_list[i] - self.ymin_list[i]
xin = pm.y1
yin = pm.x1
iraf.imlintran(
input=imgname,
output=imgname_oversample,
xrotation=0.,
yrotation=0.,
xmag=mag,
ymag=mag,
xin=xin,
yin=yin,
# xin=pm.x1+1, yin=pm.y1+1,
# xout=shape1[0]/2., yout=shape1[1]/2.,
ncols=shape1[1],
nlines=shape1[0],
interpolant=interpolant)
f.write('%s\n' % imgname_oversample)
f.close()
def plotWithHST(rotateFITS=True):
# Load up the OSIRIS image
cubeFile = datadir + cuberoot + '_img.fits'
cube, cubehdr = pyfits.getdata(cubeFile, header=True)
paCube = cubehdr['PA_SPEC']
scaleCube = 0.05
# Load up the NIRC2 image
kFile = '/u/jlu/data/m31/05jul/combo/m31_05jul_kp.fits'
# kFile = '/u/jlu/data/m31/09sep/combo/mag09sep_m31_kp.fits'
k, khdr = pyfits.getdata(kFile, header=True)
m31K = np.array([751.986, 716.989])
paK = 0.0
scaleK = 0.00995
f330Root = '/u/jlu/data/m31/hst/m31_acshrc_f330w_j9am01030'
f435Root = '/u/jlu/data/m31/hst/m31_acshrc_f435w_j9am01010'
f555Root = '/u/jlu/data/m31/hst/m31_wfpc2pc1_f555w_u2lg020at'
f814Root = '/u/jlu/data/m31/hst/m31_wfpc2pc1_f814w_u2lg020bt'
##########
# Rotate all the HST images to PA=0, scale=NIRC2
##########
if rotateFITS == True:
# Load up the HST ACS image (330 nm)
f330File = f330Root + '.fits'
f330FileRot = f330Root + '_rot.fits'
f330, f330hdr = pyfits.getdata(f330File, 1, header=True)
m31F330 = np.array([547.0, 623.2])
paF330 = f330hdr['ORIENTAT']
scaleF330 = 0.025
# Load up the HST ACS image (435 nm)
f435File = f435Root + '.fits'
f435FileRot = f435Root + '_rot.fits'
f435, f435hdr = pyfits.getdata(f435File, 1, header=True)
m31F435 = np.array([546.9, 623.5])
paF435 = f435hdr['ORIENTAT']
scaleF435 = 0.025
# Load up the HST ACS image (555 nm)
f555File = f555Root + '.fits'
f555FileRot = f555Root + '_rot.fits'
f555, f555hdr = pyfits.getdata(f555File, 1, header=True)
m31F555 = np.array([973.0, 961.0])
paF555 = f555hdr['ORIENTAT']
scaleF555 = 0.1
# Load up the HST ACS image (814 nm)
f814File = f814Root + '.fits'
f814FileRot = f814Root + '_rot.fits'
f814, f814hdr = pyfits.getdata(f814File, 1, header=True)
m31F814 = np.array([975.0, 962.0])
paF814 = f814hdr['ORIENTAT']
scaleF814 = 0.1
print('scaleK = ', scaleK)
print('scaleF330 = ', scaleF330)
print('scaleF435 = ', scaleF435)
print('scaleF555 = ', scaleF555)
print('scaleF814 = ', scaleF814)
print('paK = ', paK)
print('paF330 = ', paF330)
print('paF435 = ', paF435)
print('paF555 = ', paF555)
print('paF814 = ', paF814)
gcutil.rmall([f330FileRot, f435FileRot, f555FileRot, f814FileRot])
from pyraf import iraf as ir
ir.unlearn('imlintran')
ir.imlintran.boundary = 'constant'
ir.imlintran.constant = 0
ir.imlintran.interpolant = 'spline3'
ir.imlintran.fluxconserve = 'yes'
# 330
ir.imlintran.xin = m31F330[0]
ir.imlintran.yin = m31F330[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f330File + '[1]', f330FileRot, paF330, paF330,
scaleK / scaleF330, scaleK / scaleF330)
# 435
ir.imlintran.xin = m31F435[0]
ir.imlintran.yin = m31F435[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f435File + '[1]', f435FileRot, paF435, paF435,
scaleK / scaleF435, scaleK / scaleF435)
# 555
ir.imlintran.xin = m31F555[0]
ir.imlintran.yin = m31F555[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f555File + '[1]', f555FileRot, paF555, paF555,
scaleK / scaleF555, scaleK / scaleF555)
# 814
ir.imlintran.xin = m31F814[0]
ir.imlintran.yin = m31F814[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f814File + '[1]', f814FileRot, paF814, paF814,
scaleK / scaleF814, scaleK / scaleF814)
f330 = pyfits.getdata(f330Root + '_rot.fits')
f435 = pyfits.getdata(f435Root + '_rot.fits')
img = np.zeros((k.shape[0], k.shape[1], 3), dtype=float)
img[:, :, 0] = img_scale.linear(k, scale_min=300, scale_max=2400)
img[:, :, 1] = img_scale.linear(f435, scale_min=0.45, scale_max=1.8)
img[:, :, 2] = img_scale.linear(f330, scale_min=0, scale_max=0.16)
# Axes
xaxis = (np.arange(img.shape[0], dtype=float) - m31K[0]) * 0.00995
yaxis = (np.arange(img.shape[1], dtype=float) - m31K[1]) * 0.00995
py.clf()
py.imshow(img,
aspect='equal',
extent=[xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]])
#py.plot([m31K[0]], [m31K[1]], 'ko')
#py.axis([625, 825, 650, 850])
py.plot([0], [0], 'k+')
py.axis([-1.5, 1.5, -1.5, 1.5])
py.xlabel('X Offset from M31* (arcsec)')
py.ylabel('Y Offset from M31* (arcsec)')
py.title('Blue = F330W, Green = F435W, Red = Kband')
py.savefig(workdir + 'plots/hst_nirc2_rgb.png')
py.show()
def make_iracpsf(psfname,
oversample=10,
interp='sinc',
iracpix=0.6,
radius=3.0):
"""
Make an oversample IRAC PSF given the PSF image in the IRAC pixel scale,
for use in TFIT.
psfname: the input IRAC PSF
oversample: the oversampling factor (default is 10)
interp: the algorithm of interpolation (default is linear)
other options are nearest, poly3, poly5, spline3, sinc, lsinc,
and drizzle.
iracpix: the pixel scale (in arcsec) of the input IRAC PSF (default is 0.6)
radius: the radius of the circularized PSF in arcsec (beyond which is set to 0)
"""
newscale = int(round(iracpix / oversample * 1000))
# the new pixel scale in milli-arcsec
interp = interp.replace('[', '_')
interp = interp.replace(']', '_')
psfroot = os.path.splitext(psfname)[0]
psfroot = psfroot + '_%dmas' % newscale
psfroot = psfroot + '_%s' % interp
size0 = pyfits.getdata(psfname).shape[0]
size1 = size0 * oversample
# First, run iraf.imlintran
factor = 1. / oversample
print "factor", factor
if os.path.exists('temp1.fits'):
os.remove('temp1.fits')
iraf.imlintran(psfname,
'temp1.fits',
0.,
0.,
factor,
factor,
ncols=size1,
nlines=size1,
interpolant=interp,
fluxconserve='yes')
# Second, circularize the PSF, assume that the input PSF has equal numbers
# of rows and columns
imgsize = pyfits.getdata('temp1.fits').shape[0]
imgcenter = (imgsize + 1) / 2
print "imgcenter", imgcenter
pixrad = radius / iracpix * oversample
pixrad = np.round(pixrad)
mathstr = 'if ((x-%d)**2 + (y-%d)**2) < %f**2 then im1 else 0' % (
imgcenter, imgcenter, pixrad)
print "mathstr", mathstr
if os.path.exists('temp2.fits'):
os.remove('temp2.fits')
iraf.imcalc('temp1.fits', 'temp2.fits', mathstr)
# Third, trim the borders; shed border with units of 100 pixels
nborder = ((imgsize - 2 * pixrad) / 2) / 100
print "nborder:", nborder
if nborder > 0:
begin = nborder * 100 + 1
end = imgsize - nborder * 100 - 1
iraf.imcopy('temp2.fits[%d:%d,%d:%d]' % (begin, end, begin, end),
psfroot + '.fits')
else:
os.system('mv temp2.fits %s' % (psfroot + '.fits'))
os.system('rm temp*.fits')
def plotWithHST(rotateFITS=True):
# Load up the OSIRIS image
cubeFile = datadir + cuberoot + "_img.fits"
cube, cubehdr = pyfits.getdata(cubeFile, header=True)
paCube = cubehdr["PA_SPEC"]
scaleCube = 0.05
# Load up the NIRC2 image
kFile = "/u/jlu/data/m31/05jul/combo/m31_05jul_kp.fits"
# kFile = '/u/jlu/data/m31/09sep/combo/mag09sep_m31_kp.fits'
k, khdr = pyfits.getdata(kFile, header=True)
m31K = np.array([751.986, 716.989])
paK = 0.0
scaleK = 0.00995
f330Root = "/u/jlu/data/m31/hst/m31_acshrc_f330w_j9am01030"
f435Root = "/u/jlu/data/m31/hst/m31_acshrc_f435w_j9am01010"
f555Root = "/u/jlu/data/m31/hst/m31_wfpc2pc1_f555w_u2lg020at"
f814Root = "/u/jlu/data/m31/hst/m31_wfpc2pc1_f814w_u2lg020bt"
##########
# Rotate all the HST images to PA=0, scale=NIRC2
##########
if rotateFITS == True:
# Load up the HST ACS image (330 nm)
f330File = f330Root + ".fits"
f330FileRot = f330Root + "_rot.fits"
f330, f330hdr = pyfits.getdata(f330File, 1, header=True)
m31F330 = np.array([547.0, 623.2])
paF330 = f330hdr["ORIENTAT"]
scaleF330 = 0.025
# Load up the HST ACS image (435 nm)
f435File = f435Root + ".fits"
f435FileRot = f435Root + "_rot.fits"
f435, f435hdr = pyfits.getdata(f435File, 1, header=True)
m31F435 = np.array([546.9, 623.5])
paF435 = f435hdr["ORIENTAT"]
scaleF435 = 0.025
# Load up the HST ACS image (555 nm)
f555File = f555Root + ".fits"
f555FileRot = f555Root + "_rot.fits"
f555, f555hdr = pyfits.getdata(f555File, 1, header=True)
m31F555 = np.array([973.0, 961.0])
paF555 = f555hdr["ORIENTAT"]
scaleF555 = 0.1
# Load up the HST ACS image (814 nm)
f814File = f814Root + ".fits"
f814FileRot = f814Root + "_rot.fits"
f814, f814hdr = pyfits.getdata(f814File, 1, header=True)
m31F814 = np.array([975.0, 962.0])
paF814 = f814hdr["ORIENTAT"]
scaleF814 = 0.1
print "scaleK = ", scaleK
print "scaleF330 = ", scaleF330
print "scaleF435 = ", scaleF435
print "scaleF555 = ", scaleF555
print "scaleF814 = ", scaleF814
print "paK = ", paK
print "paF330 = ", paF330
print "paF435 = ", paF435
print "paF555 = ", paF555
print "paF814 = ", paF814
gcutil.rmall([f330FileRot, f435FileRot, f555FileRot, f814FileRot])
from pyraf import iraf as ir
ir.unlearn("imlintran")
ir.imlintran.boundary = "constant"
ir.imlintran.constant = 0
ir.imlintran.interpolant = "spline3"
ir.imlintran.fluxconserve = "yes"
# 330
ir.imlintran.xin = m31F330[0]
ir.imlintran.yin = m31F330[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f330File + "[1]", f330FileRot, paF330, paF330, scaleK / scaleF330, scaleK / scaleF330)
# 435
ir.imlintran.xin = m31F435[0]
ir.imlintran.yin = m31F435[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f435File + "[1]", f435FileRot, paF435, paF435, scaleK / scaleF435, scaleK / scaleF435)
# 555
ir.imlintran.xin = m31F555[0]
ir.imlintran.yin = m31F555[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f555File + "[1]", f555FileRot, paF555, paF555, scaleK / scaleF555, scaleK / scaleF555)
# 814
ir.imlintran.xin = m31F814[0]
ir.imlintran.yin = m31F814[1]
ir.imlintran.xout = m31K[0]
ir.imlintran.yout = m31K[1]
ir.imlintran.ncols = k.shape[1]
ir.imlintran.nlines = k.shape[0]
ir.imlintran(f814File + "[1]", f814FileRot, paF814, paF814, scaleK / scaleF814, scaleK / scaleF814)
f330 = pyfits.getdata(f330Root + "_rot.fits")
f435 = pyfits.getdata(f435Root + "_rot.fits")
img = np.zeros((k.shape[0], k.shape[1], 3), dtype=float)
img[:, :, 0] = img_scale.linear(k, scale_min=300, scale_max=2400)
img[:, :, 1] = img_scale.linear(f435, scale_min=0.45, scale_max=1.8)
img[:, :, 2] = img_scale.linear(f330, scale_min=0, scale_max=0.16)
# Axes
xaxis = (np.arange(img.shape[0], dtype=float) - m31K[0]) * 0.00995
yaxis = (np.arange(img.shape[1], dtype=float) - m31K[1]) * 0.00995
py.clf()
py.imshow(img, aspect="equal", extent=[xaxis[0], xaxis[-1], yaxis[0], yaxis[-1]])
# py.plot([m31K[0]], [m31K[1]], 'ko')
# py.axis([625, 825, 650, 850])
py.plot([0], [0], "k+")
py.axis([-1.5, 1.5, -1.5, 1.5])
py.xlabel("X Offset from M31* (arcsec)")
py.ylabel("Y Offset from M31* (arcsec)")
py.title("Blue = F330W, Green = F435W, Red = Kband")
py.savefig(workdir + "plots/hst_nirc2_rgb.png")
py.show()