Redoing the r13221-13223 merge in the actual trunk now. This updates trunk to the setup_refactoring branch (however, coords, pysynphot, and pywcs are still being pulled from the astrolib setup_refactoring branch. Will have to do that separately then update the svn:externals)

git-svn-id: http://svn.stsci.edu/svn/ssb/stsci_python/stsci_python/trunk/stwcs@13225 fe389314-cf27-0410-b35b-8c050e845b92

1 files changed, 0 insertions, 204 deletions

diff --git a/distortion/utils.py b/distortion/utils.py
deleted file mode 100644
index a99e227..0000000
--- a/distortion/utils.py
+++ /dev/null
@@ -1,204 +0,0 @@
-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, owcs=None, undistort=True):
-    """
-    Create an output WCS.
-    
-    Parameters
-    ----------
-    list_of_wcsobj: Python list
-                    a list of HSTWCS objects
-    ref_wcs: an HSTWCS object 
-             to be used as a reference WCS, in case outwcs is None.
-             if ref_wcs is None (default), the first member of the list 
-             is used as a reference
-    outwcs:  an HSTWCS object
-             the tangent plane defined by this object is used as a reference
-    undistort: boolean (default-True)
-              a flag whether to create an undistorted output WCS
-    """
-    fra_dec = np.vstack([w.calcFootprint() for w in list_of_wcsobj])
-    
-    # This new algorithm may not be strictly necessary, but it may be more 
-    # robust in handling regions near the poles or at 0h RA.
-    crval1,crval2 = computeFootprintCenter(fra_dec) 
-
-    crval = np.array([crval1,crval2], dtype=np.float64) # this value is now zero-based
-    if owcs 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 = owcs.deepcopy()
-        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, 0)['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.], dtype=np.float64)
-    outwcs.wcs.crpix = crpix
-    outwcs.wcs.set()
-    tanpix = outwcs.wcs.s2p(fra_dec, 0)['pixcrd']
-
-    # shift crpix to take into account (floating-point value of) position of
-    # corner pixel relative to output frame size: no rounding necessary... 
-    newcrpix = np.array([crpix[0]+tanpix[:,0].min(), crpix[1]+
-                         tanpix[:,1].min()])
-
-    newcrval = outwcs.wcs.p2s([newcrpix], 1)['world'][0]
-    outwcs.wcs.crval = newcrval
-    outwcs.wcs.set()
-    return outwcs
-
-def computeFootprintCenter(edges):
-    """ Geographic midpoint in spherical coords for points defined by footprints.
-        Algorithm derived from: http://www.geomidpoint.com/calculation.html
-        
-        This algorithm should be more robust against discontinuities at the poles. 
-    """
-    alpha = fileutil.DEGTORAD(edges[:,0])
-    dec = fileutil.DEGTORAD(edges[:,1])
-    
-    xmean = np.mean(np.cos(dec)*np.cos(alpha))
-    ymean = np.mean(np.cos(dec)*np.sin(alpha))
-    zmean = np.mean(np.sin(dec))
-    
-    crval1 = fileutil.RADTODEG(np.arctan2(ymean,xmean))%360.0
-    crval2 = fileutil.RADTODEG(np.arctan2(zmean,np.sqrt(xmean*xmean+ymean*ymean)))
-    
-    return crval1,crval2
-
-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)
-    cd = np.dot(wcsobj.wcs.cd, cd_inv).astype(np.float64)
-    lin_wcsobj.wcs.cd = cd
-    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.ctype = ['RA---TAN', 'DEC--TAN']
-    lin_wcsobj.wcs.set()
-    return lin_wcsobj
-
-def apply_idc(pixpos, cx, cy, pixref, pscale= None, order=None):
-    """
-    Apply the IDCTAB polynomial distortion model to pixel positions.
-    pixpos must be already corrected for ltv1/2.
-    
-    Parameters
-    ----------
-    pixpos: a 2D numpy array of (x,y) pixel positions to be distortion corrected
-    cx, cy: IDC model distortion coefficients
-    pixref: reference opixel position
-    
-    """
-    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
-