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import numpy as np
import pywcs
import pyfits
from hstwcs import wcsutil
from numpy import sqrt, arctan2
def output_wcs(list_of_wcsobj, ref_wcs=None, outwcs=None):
fra_dec = np.vstack([w.footprint for w in list_of_wcsobj])
"""
ra_min = np.array(fra_dec[:,0]).min()
dec_min = np.array(fra_dec[:,1]).min()
ra_max = np.array(fra_dec[:,0]).max()
dec_max = np.array(fra_dec[:,1]).max()
output_footprint=np.zeros(shape=(4,2),dtype=np.float64)
output_footprint[0,0]=ra_min
output_footprint[0,1]=dec_min
output_footprint[1,0]=ra_min
output_footprint[1,1]=dec_max
output_footprint[2,0]=ra_max
output_footprint[2,1]=dec_max
output_footprint[3,0]=ra_max
output_footprint[3,1]=dec_min
"""
if outwcs is None:
if ref_wcs == None:
ref_wcs = list_of_wcsobj[0]
outwcs = undistortWCS(ref_wcs)
outwcs.wcs.crpix = ref_wcs.wcs.crpix
outwcs.wcs.crval = ref_wcs.wcs.crval
outwcs.pscale = sqrt(outwcs.wcs.cd[0,0]**2 + outwcs.wcs.cd[1,0]**2)*3600.
outwcs.orientat = arctan2(outwcs.wcs.cd[0,1],outwcs.wcs.cd[1,1]) * 180./np.pi
else:
outwcs.pscale = sqrt(outwcs.wcs.cd[0,0]**2 + outwcs.wcs.cd[1,0]**2)*3600.
outwcs.orientat = arctan2(outwcs.wcs.cd[0,1],outwcs.wcs.cd[1,1]) * 180./np.pi
#out_px = outwcs.wcs.s2p_fits(output_footprint)['pixcrd']
out_px = outwcs.wcs.s2p_fits(fra_dec)['pixcrd']
outwcs.naxis1 = int(np.ceil(out_px[:,0].max()) - np.floor(out_px[:,0].min()))
outwcs.naxis2 = int(np.ceil(out_px[:,1].max()) - np.floor(out_px[:,1].min()))
outwcs.recenter()
return outwcs
def undistortWCS(wcsobj):
assert isinstance(wcsobj, pywcs.WCS)
#if wcsobj.idcmodel == None:
# return
# get the sip coefficients and the first order IDC coeffs
# reconstruct the idc model
# apply the idc model
from hstwcs.distortion import coeff_converter
cx, cy = coeff_converter.sip2idc(wcsobj)
crpix1 = wcsobj.wcs.crpix[0]
crpix2 = wcsobj.wcs.crpix[1]
xy = np.array([(crpix1,crpix2),(crpix1+1.,crpix2),(crpix1,crpix2+1.)],dtype=np.double)
offsets = np.array([wcsobj.ltv1, wcsobj.ltv2])
px = xy + offsets
order = wcsobj.sip.a_order
pscale = wcsobj.idcscale
#pixref = np.array([wcsobj.sip.SIPREF1, wcsobj.sip.SIPREF2])
tan_pix = apply_idc(px, cx, cy, wcsobj.wcs.crpix, pscale, order)
xc = tan_pix[:,0]
yc = tan_pix[:,1]
am = xc[1] - xc[0]
bm = xc[2] - xc[0]
cm = yc[1] - yc[0]
dm = yc[2] - yc[0]
cd_mat = np.array([[am,bm],[cm,dm]],dtype=np.double)
# Check the determinant for singularity
_det = (am * dm) - (bm * cm)
if ( _det == 0.0):
print 'Singular matrix in updateWCS, aborting ...'
return
#lin_wcsobj = wcsutil.HSTWCS(instrument=wcsobj.instrument)
lin_wcsobj = pywcs.WCS() #instrument=wcsobj.instrument)
cd_inv = np.linalg.inv(cd_mat)
print 'inv_cd', cd_inv
lin_wcsobj.wcs.cd = np.dot(wcsobj.wcs.cd, cd_inv)
lin_wcsobj.orientat = arctan2(lin_wcsobj.wcs.cd[0,1],lin_wcsobj.wcs.cd[1,1]) * 180./np.pi
lin_wcsobj.pscale = sqrt(lin_wcsobj.wcs.cd[0,0]**2 + lin_wcsobj.wcs.cd[1,0]**2)*3600.
return lin_wcsobj
def apply_idc(pixpos, cx, cy, pixref, pscale= None, order=None):
#pixpos must be already corrected for ltv1/2
if cx == None:
return pixpos
if order is None:
print 'Unknown order of distortion model \n'
return pixpos
if pscale is None:
print 'Unknown model plate scale\n'
return pixpos
# Apply in the same way that 'drizzle' would...
_cx = cx/pscale
_cy = cy/ pscale
_p = pixpos
# Do NOT include any zero-point terms in CX,CY here
# as they should not be scaled by plate-scale like rest
# of coeffs... This makes the computations consistent
# with 'drizzle'. WJH 17-Feb-2004
_cx[0,0] = 0.
_cy[0,0] = 0.
dxy = _p - pixref
# Apply coefficients from distortion model here...
c = _p * 0.
for i in range(order+1):
for j in range(i+1):
c[:,0] = c[:,0] + _cx[i][j] * pow(dxy[:,0],j) * pow(dxy[:,1],(i-j))
c[:,1] = c[:,1] + _cy[i][j] * pow(dxy[:,0],j) * pow(dxy[:,1],(i-j))
return c
def sip2idc(wcs):
"""
Converts SIP style coefficients to IDCTAB coefficients.
:Parameters:
`wcs`: pyfits.Header or pywcs.WCS object
"""
if isinstance(wcs,pyfits.Header):
ocx10 = wcs.get('OCX10', None)
ocx11 = wcs.get('OCX11', None)
ocy10 = wcs.get('OCY10', None)
ocy11 = wcs.get('OCY11', None)
order = hdr.get('A_ORDER', None)
sipa, sipb = read_sip_kw(header)
if sipa == None or sipb == None:
print 'SIP coefficients are not available.\n'
print 'Cannot convert SIP to IDC coefficients.\n'
return
elif isinstance(wcs,pywcs.WCS):
try:
ocx10 = wcs.ocx10
ocx11 = wcs.ocx11
ocy10 = wcs.ocy10
ocy11 = wcs.ocy11
except AttributeError:
print 'First order IDCTAB coefficients are not available.\n'
print 'Cannot convert SIP to IDC coefficients.\n'
return
try:
sipa = wcs.sip.a
sipb = wcs.sip.b
except AttributeError:
print 'SIP coefficients are not available.\n'
print 'Cannot convert SIP to IDC coefficients.\n'
return
else:
print 'Input to sip2idc must be a PyFITS header or a wcsutil.HSTWCS object\n'
return
try:
order = wcs.sip.a_order
except AttributeError:
print 'SIP model order unknown, exiting ...\n'
return
if None in [ocx10, ocx11, ocy10, ocy11]:
print 'First order IDC coefficients not found, exiting ...\n'
return
idc_coeff = np.array([[ocx11, ocx10], [ocy11, ocy10]])
cx = np.zeros((order+1,order+1), dtype=np.double)
cy = np.zeros((order+1,order+1), dtype=np.double)
for n in range(order+1):
for m in range(order+1):
if n >= m and n>=2:
sipval = np.array([[sipa[m,n-m]],[sipb[m,n-m]]])
idcval = np.dot(idc_coeff, sipval)
cx[n,m] = idcval[0]
cy[n,m] = idcval[1]
cx[1,0] = ocx10
cx[1,1] = ocx11
cy[1,0] = ocy10
cy[1,1] = ocy11
return cx, cy
def read_sip_kw(header):
"""
Reads SIP header keywords and returns an array of coefficients.
If no SIP header keywords are found, None is returned.
"""
if header.has_key("A_ORDER"):
if not header.has_key("B_ORDER"):
raise ValueError(
"A_ORDER provided without corresponding B_ORDER "
"keyword for SIP distortion")
m = int(header["A_ORDER"])
a = np.zeros((m+1, m+1), np.double)
for i in range(m+1):
for j in range(m-i+1):
a[i, j] = header.get("A_%d_%d" % (i, j), 0.0)
m = int(header["B_ORDER"])
b = np.zeros((m+1, m+1), np.double)
for i in range(m+1):
for j in range(m-i+1):
b[i, j] = header.get("B_%d_%d" % (i, j), 0.0)
elif header.has_key("B_ORDER"):
raise ValueError(
"B_ORDER provided without corresponding A_ORDER "
"keyword for SIP distortion")
else:
a = None
b = None
return a , b
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