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from __future__ import division # confidence high
import datetime
import numpy as np
from numpy import linalg
from pytools import fileutil
from utils import diff_angles
import makewcs, dgeo
MakeWCS = makewcs.MakeWCS
DGEOCorr = dgeo.DGEOCorr
class TDDCorr(object):
"""
Apply time dependent distortion correction to distortion coefficients and basic
WCS keywords. This applies only to ACS/WFC data.
Parameters
----------
ext_wcs: HSTWCS object
An HSTWCS object to be modified
ref_wcs: HSTWCS object
A reference HSTWCS object
Notes
-----
Compute the ACS/WFC time dependent distortion terms
as described in [1]_.
The zero-point terms account for the skew accumulated between
2002.0 and 2004.5, when the latest IDCTAB was delivered.
alpha = 0.095 + 0.090*(rday-dday)/2.5
beta = -0.029 - 0.030*(rday-dday)/2.5
References
----------
.. [1] Jay Anderson, "Variation of the Distortion Solution of the WFC", ACS ISR 2007-08.
"""
def updateWCS(cls, ext_wcs, ref_wcs):
"""
- Calculates alpha and beta for ACS/WFC data.
- Writes 2 new kw to the extension header: TDDALPHA and TDDBETA
"""
alpha, beta = cls.compute_alpha_beta(ext_wcs)
cls.apply_tdd2idc(ref_wcs, alpha, beta)
cls.apply_tdd2idc(ext_wcs, alpha, beta)
ext_wcs.idcmodel.refpix['TDDALPHA'] = alpha
ext_wcs.idcmodel.refpix['TDDBETA'] = beta
ref_wcs.idcmodel.refpix['TDDALPHA'] = alpha
ref_wcs.idcmodel.refpix['TDDBETA'] = beta
newkw = {'TDDALPHA': alpha, 'TDDBETA':beta, 'OCX10':ext_wcs.idcmodel.cx[1,0],
'OCX11':ext_wcs.idcmodel.cx[1,1],'OCY10':ext_wcs.idcmodel.cy[1,0],
'OCY11':ext_wcs.idcmodel.cy[1,1],}
return newkw
updateWCS = classmethod(updateWCS)
def apply_tdd2idc(cls, hwcs, alpha, beta):
"""
Applies TDD to the idctab coefficients of a ACS/WFC observation.
This should be always the first correction.
"""
theta_v2v3 = 2.234529
mrotp = fileutil.buildRotMatrix(theta_v2v3)
mrotn = fileutil.buildRotMatrix(-theta_v2v3)
tdd_mat = np.array([[1+(beta/2048.), alpha/2048.],[alpha/2048.,1-(beta/2048.)]],np.float64)
abmat1 = np.dot(tdd_mat, mrotn)
abmat2 = np.dot(mrotp,abmat1)
xshape, yshape = hwcs.idcmodel.cx.shape, hwcs.idcmodel.cy.shape
icxy = np.dot(abmat2,[hwcs.idcmodel.cx.ravel(), hwcs.idcmodel.cy.ravel()])
hwcs.idcmodel.cx = icxy[0]
hwcs.idcmodel.cy = icxy[1]
hwcs.idcmodel.cx.shape = xshape
hwcs.idcmodel.cy.shape = yshape
apply_tdd2idc = classmethod(apply_tdd2idc)
def compute_alpha_beta(cls, ext_wcs):
"""
Compute the ACS time dependent distortion skew terms
as described in ACS ISR 07-08 by J. Anderson.
Jay's code only computes the alpha/beta values based on a decimal year
with only 3 digits, so this line reproduces that when needed for comparison
with his results.
rday = float(('%0.3f')%rday)
The zero-point terms account for the skew accumulated between
2002.0 and 2004.5, when the latest IDCTAB was delivered.
alpha = 0.095 + 0.090*(rday-dday)/2.5
beta = -0.029 - 0.030*(rday-dday)/2.5
"""
if not isinstance(ext_wcs.date_obs,float):
year,month,day = ext_wcs.date_obs.split('-')
rdate = datetime.datetime(int(year),int(month),int(day))
rday = float(rdate.strftime("%j"))/365.25 + rdate.year
else:
rday = ext_wcs.date_obs
skew_coeffs = ext_wcs.idcmodel.refpix['skew_coeffs']
if skew_coeffs is None:
# Only print out warning for post-SM4 data where this may matter
if rday > 2009.0:
err_str = "------------------------------------------------------------------------ \n"
err_str += "WARNING: the IDCTAB geometric distortion file specified in the image \n"
err_str += " header did not have the time-dependent distortion coefficients. \n"
err_str += " The pre-SM4 time-dependent skew solution will be used by default.\n"
err_str += " Please update IDCTAB with new reference file from HST archive. \n"
err_str += "------------------------------------------------------------------------ \n"
print err_str
# Using default pre-SM4 coefficients
skew_coeffs = {'TDD_A':[0.095,0.090/2.5],
'TDD_B':[-0.029,-0.030/2.5],
'TDD_DATE':2004.5,'TDDORDER':1}
alpha = 0
beta = 0
# Compute skew terms, allowing for non-linear coefficients as well
for c in range(skew_coeffs['TDDORDER']+1):
alpha += skew_coeffs['TDD_A'][c]* np.power((rday-skew_coeffs['TDD_DATE']),c)
beta += skew_coeffs['TDD_B'][c]*np.power((rday-skew_coeffs['TDD_DATE']),c)
return alpha,beta
compute_alpha_beta = classmethod(compute_alpha_beta)
class VACorr(object):
"""
Apply velocity aberation correction to WCS keywords.
Notes
-----
Velocity Aberration is stored in the extension header keyword 'VAFACTOR'.
The correction is applied to the CD matrix and CRVALs.
"""
def updateWCS(cls, ext_wcs, ref_wcs):
if ext_wcs.vafactor != 1:
ext_wcs.wcs.cd = ext_wcs.wcs.cd * ext_wcs.vafactor
crval0 = ref_wcs.wcs.crval[0] + ext_wcs.vafactor*diff_angles(ext_wcs.wcs.crval[0],
ref_wcs.wcs.crval[0])
crval1 = ref_wcs.wcs.crval[1] + ext_wcs.vafactor*diff_angles(ext_wcs.wcs.crval[1],
ref_wcs.wcs.crval[1])
crval = np.array([crval0,crval1])
ext_wcs.wcs.crval = crval
ext_wcs.wcs.set()
else:
pass
kw2update={'CD1_1': ext_wcs.wcs.cd[0,0], 'CD1_2':ext_wcs.wcs.cd[0,1],
'CD2_1':ext_wcs.wcs.cd[1,0], 'CD2_2':ext_wcs.wcs.cd[1,1],
'CRVAL1':ext_wcs.wcs.crval[0], 'CRVAL2':ext_wcs.wcs.crval[1]}
return kw2update
updateWCS = classmethod(updateWCS)
class CompSIP(object):
"""
Compute SIP coefficients from IDC table coefficients.
Notes
-----
"""
def updateWCS(cls, ext_wcs, ref_wcs):
kw2update = {}
order = ext_wcs.idcmodel.norder
kw2update['A_ORDER'] = order
kw2update['B_ORDER'] = order
pscale = ext_wcs.idcmodel.refpix['PSCALE']
cx = ext_wcs.idcmodel.cx
cy = ext_wcs.idcmodel.cy
matr = np.array([[cx[1,1],cx[1,0]], [cy[1,1],cy[1,0]]], dtype=np.float64)
imatr = linalg.inv(matr)
akeys1 = np.zeros((order+1,order+1), dtype=np.float64)
bkeys1 = np.zeros((order+1,order+1), dtype=np.float64)
for n in range(order+1):
for m in range(order+1):
if n >= m and n>=2:
idcval = np.array([[cx[n,m]],[cy[n,m]]])
sipval = np.dot(imatr, idcval)
akeys1[m,n-m] = sipval[0]
bkeys1[m,n-m] = sipval[1]
Akey="A_%d_%d" % (m,n-m)
Bkey="B_%d_%d" % (m,n-m)
kw2update[Akey] = sipval[0,0] * ext_wcs.binned
kw2update[Bkey] = sipval[1,0] * ext_wcs.binned
kw2update['CTYPE1'] = 'RA---TAN-SIP'
kw2update['CTYPE2'] = 'DEC--TAN-SIP'
return kw2update
updateWCS = classmethod(updateWCS)
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