1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
|
from __future__ import division # confidence high
import os
import numpy as np
import pywcs
import pyfits
from stwcs import wcsutil
from numpy import sqrt, arctan2
from pytools import fileutil
def output_wcs(list_of_wcsobj, ref_wcs=None, outwcs=None, undistort=True):
fra_dec = np.vstack([w.calcFootprint() for w in list_of_wcsobj])
delta_fdec = (fra_dec[:,1].max()-fra_dec[:,1].min())
crval2 = fra_dec[:,1].min()+ delta_fdec/2.
delta_fra = (fra_dec[:,0].max()-fra_dec[:,0].min())
min_ind = fra_dec[:,0].argmin()
crval1 = (fra_dec[min_ind,0]+ (delta_fra/2.)*np.cos((fra_dec[min_ind,1]-crval2)*np.pi/180))
crval = np.array([crval1,crval2])
if outwcs is None:
if ref_wcs == None:
ref_wcs = list_of_wcsobj[0].deepcopy()
if undistort:
outwcs = undistortWCS(ref_wcs)
else:
outwcs = ref_wcs.deepcopy()
outwcs.wcs.crval = crval
outwcs.wcs.set()
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
tanpix = outwcs.wcs.s2p(fra_dec, 1)['pixcrd']
outwcs.naxis1 = int(np.ceil(tanpix[:,0].max() - tanpix[:,0].min()))
outwcs.naxis2 = int(np.ceil(tanpix[:,1].max() - tanpix[:,1].min()))
crpix = np.array([outwcs.naxis1/2., outwcs.naxis2/2.])
outwcs.wcs.crpix = crpix
outwcs.wcs.set()
return outwcs
def undistortWCS(wcsobj):
"""
Creates an undistorted linear WCS by applying the IDCTAB distortion model
to a 3-point square. The new ORIENTAT angle is calculated as well as the
plate scale in the undistorted frame.
"""
assert isinstance(wcsobj, pywcs.WCS)
import coeff_converter
cx, cy = coeff_converter.sip2idc(wcsobj)
# cx, cy can be None because either there is no model available
# or updatewcs was not run.
if cx == None or cy == None:
if foundIDCTAB(wcsobj.idctab):
m = """IDCTAB is present but distortion model is missing.
Run updatewcs() to update the headers or
pass 'undistort=False' keyword to output_wcs().\n
"""
raise RuntimeError, m
else:
print 'Distortion model is not available, using input reference image for output WCS.\n'
return wcsobj.copy()
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=1)
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 = pywcs.WCS()
cd_inv = np.linalg.inv(cd_mat)
lin_wcsobj.wcs.cd = np.dot(wcsobj.wcs.cd, cd_inv)
lin_wcsobj.wcs.set()
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.
lin_wcsobj.wcs.crval = np.array([0.,0.])
lin_wcsobj.wcs.crpix = np.array([0.,0.])
lin_wcsobj.wcs.set()
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 foundIDCTAB(idctab):
idctab_found = True
try:
idctab = fileutil.osfn(idctab)
if idctab == 'N/A' or idctab == "":
idctab_found = False
if os.path.exists(idctab):
idctab_found = True
else:
idctab_found = False
except KeyError:
idctab_found = False
return idctab_found
|
