def chi2Func(params,nmax,im,nm,order=['beta0','beta1','phi','yc','xc'],set_beta=[None,None],set_phi=None,set_xc=[None,None],xx=None,yy=None):
"""Function which is to be minimized in the chi^2 analysis for Cartesian shapelets
params = [beta0, beta1, phi, xc, yc] or some subset
beta0: characteristic size of shapelets, fit parameter
beta1: characteristic size of shapelets, fit parameter
phi: rotation angle of shapelets, fit parameter
yc: y centroid of shapelets, fit parameter
xc: x centroid of shapelets, fit parameter
nmax: number of coefficents to use in the Hermite polynomials
im: observed image
nm: noise map
order: order of parameters
fixed parameters: set_beta, set_phi, set_xc
xx: X position grid, array of im.shape, not required if xc and yc being fit
yy: Y position grid, array of im.shape, not required if xc and yc being fit
"""
#determine which parameters are being fit for, and which are not
betaY=set_beta[0]
betaX=set_beta[1]
phi=set_phi
yc=set_xc[0]
xc=set_xc[1]
fitParams={'beta':False,'phi':False,'xc':False}
for pid,paramName in enumerate(order):
if paramName=='beta0':
betaY=params[pid]
fitParams['beta']=True
elif paramName=='beta1':
betaX=params[pid]
fitParams['beta']=True
elif paramName=='phi':
phi=params[pid]
fitParams['phi']=True
elif paramName=='xc':
xc=params[pid]
fitParams['xc']=True
elif paramName=='yc':
yc=params[pid]
fitParams['xc']=True
#if betaX<0.:
# print 'warning: beta going negative, setting to 0.0'
# betaX=0.
#if betaY<0.:
# print 'warning: beta going negative, setting to 0.0'
# betaY=0.
if betaX<0.:
print 'warning: beta going negative, taking absolute value'
betaX = np.abs(betaY)
if betaY<0.:
print 'warning: beta going negative, taking absolute value'
betaY = np.abs(betaX)
print 'beta: (%f,%f)\t phi: %f\txc: (%f,%f)'%(betaX,betaY,phi,xc,yc)
#update noise map
nm=img.makeNoiseMap(nm.shape,np.mean(nm),np.std(nm))
size=im.shape
if fitParams['xc'] or xx is None:
#shift the (0,0) point to the centroid
ry=np.array(range(0,size[0]),dtype=float)-yc
rx=np.array(range(0,size[1]),dtype=float)-xc
yy,xx=shapelet.xy2Grid(ry,rx)
bvals=genBasisMatrix([betaY,betaX],nmax,phi,yy,xx)
coeffs=solveCoeffs(bvals,im)
mdl=img.constructModel(bvals,coeffs,size)
return np.sum((im-mdl)**2 / nm**2)/(size[0]*size[1])
def chi2PolarFunc(params,nmax,im,nm,order=['beta0','beta1','phi','yc','xc'],set_beta=[None,None],set_phi=None,set_xc=[None,None],r=None,th=None):
"""Function which is to be minimized in the chi^2 analysis for Polar shapelets
params = [beta0, beta1, phi, xc, yc] or some subset
beta0: characteristic size of shapelets, fit parameter
beta1: characteristic size of shapelets, fit parameter
phi: rotation angle of shapelets, fit parameter
yc: y centroid of shapelets, fit parameter
xc: x centroid of shapelets, fit parameter
nmax: number of coefficents to use in the Laguerre polynomials
im: observed image
nm: noise map
order: order of parameters
fixed parameters: set_beta, set_phi, set_xc
r: radius from centroid, array of im.shape, not required if xc and yc being fit
th: angle from centroid, array of im.shape, not required if xc and yc being fit
"""
#determine which parameters are being fit for, and which are not
beta0=set_beta[0]
beta1=set_beta[1]
phi=set_phi
yc=set_xc[0]
xc=set_xc[1]
fitParams={'beta':False,'phi':False,'xc':False}
for pid,paramName in enumerate(order):
if paramName=='beta0':
beta0=params[pid]
fitParams['beta']=True
elif paramName=='beta1':
beta1=params[pid]
fitParams['beta']=True
elif paramName=='phi':
phi=params[pid]
fitParams['phi']=True
elif paramName=='xc':
xc=params[pid]
fitParams['xc']=True
elif paramName=='yc':
yc=params[pid]
fitParams['xc']=True
#if beta0<0.:
# print 'warning: beta going negative, setting to 0.0'
# beta0=0.
#if beta1<0.:
# print 'warning: beta going negative, setting to 0.0'
# beta1=0.
if beta0<0.:
print 'warning: beta going negative, taking absolute value'
beta0 = np.abs(beta0)
if beta1<0.:
print 'warning: beta going negative, taking absolute value'
beta1 = np.abs(beta1)
print 'beta: (%f,%f)\t phi: %f\txc: (%f,%f)'%(beta0,beta1,phi,xc,yc)
#update noise map
nm=img.makeNoiseMap(nm.shape,np.mean(nm),np.std(nm))
size=im.shape
if fitParams['xc'] or r is None:
r,th=shapelet.polarArray([yc,xc],size) #the radius,theta pairs need to updated if fitting for the xc centre or if not using the r,th inputs
bvals=genPolarBasisMatrix([beta0,beta1],nmax,phi,r,th)
coeffs=solveCoeffs(bvals,im)
mdl=np.abs(img.constructModel(bvals,coeffs,size))
return np.sum((im-mdl)**2 / nm**2)/(size[0]*size[1])
xc=img.maxPos(subim)
rx=np.array(range(0,subim.shape[0]),dtype=float)-xc[0]
ry=np.array(range(0,subim.shape[1]),dtype=float)-xc[1]
xx,yy=shapelet.xy2Grid(rx,ry)
mCart=genBasisMatrix(beta0,[5,5],phi0,xx,yy)
coeffs=solveCoeffs(mCart,subim)
print coeffs
if write_files: fileio.writeHermiteCoeffs('testHermite.pkl',coeffs,xc,subim.shape,beta0,0.,5,pos=[hdr['ra'],hdr['dec'],hdr['dra'],hdr['ddec']],info='Test Hermite coeff file')
except:
print 'Test failed (%i):'%tc, sys.exc_info()[0]
te+=1
beta0,phi0=initBetaPhi(subim,mode='fit')
xc=img.maxPos(subim)
mean,std=img.estimateNoise(subim,mode='basic')
nm=img.makeNoiseMap(subim.shape,mean,std)
nmax=[10,10]
#chi2PolarFunc(params,nmax,im,nm):
tc+=1
try:
print chi2PolarFunc([beta0[0],beta0[1],phi0,xc[0],xc[1]],nmax,subim,nm,order=['beta0','beta1','phi','xc','yc']) #fit: all
print chi2PolarFunc([beta0[0],beta0[1]],nmax,subim,nm,order=['beta0','beta1'],set_phi=phi0,set_xc=xc) #fit: beta
print chi2PolarFunc([phi0],nmax,subim,nm,order=['phi'],set_beta=beta0,set_xc=xc) #fit: phi
print chi2PolarFunc([xc[0],xc[1]],nmax,subim,nm,order=['xc','yc'],set_beta=beta0,set_phi=phi0) #fit: xc
print chi2PolarFunc([beta0[0],beta0[1],phi0],nmax,subim,nm,order=['beta0','beta1','phi'],set_xc=xc) #fit: beta, phi
print chi2PolarFunc([beta0[0],beta0[1],xc[0],xc[1]],nmax,subim,nm,order=['beta0','beta1','xc','yc'],set_phi=phi0) #fit: beta, xc
print chi2PolarFunc([phi0,xc[0],xc[1]],nmax,subim,nm,order=['phi','xc','yc'],set_beta=beta0) #fit: phi, xc
except:
print 'Test failed (%i):'%tc, sys.exc_info()[0]
te+=1
def chi2Func(params,
nmax,
im,
nm,
order=['beta0', 'beta1', 'phi', 'yc', 'xc'],
set_beta=[None, None],
set_phi=None,
set_xc=[None, None],
xx=None,
yy=None):
"""Function which is to be minimized in the chi^2 analysis for Cartesian shapelets
params = [beta0, beta1, phi, xc, yc] or some subset
beta0: characteristic size of shapelets, fit parameter
beta1: characteristic size of shapelets, fit parameter
phi: rotation angle of shapelets, fit parameter
yc: y centroid of shapelets, fit parameter
xc: x centroid of shapelets, fit parameter
nmax: number of coefficents to use in the Hermite polynomials
im: observed image
nm: noise map
order: order of parameters
fixed parameters: set_beta, set_phi, set_xc
xx: X position grid, array of im.shape, not required if xc and yc being fit
yy: Y position grid, array of im.shape, not required if xc and yc being fit
"""
#determine which parameters are being fit for, and which are not
betaY = set_beta[0]
betaX = set_beta[1]
phi = set_phi
yc = set_xc[0]
xc = set_xc[1]
fitParams = {'beta': False, 'phi': False, 'xc': False}
for pid, paramName in enumerate(order):
if paramName == 'beta0':
betaY = params[pid]
fitParams['beta'] = True
elif paramName == 'beta1':
betaX = params[pid]
fitParams['beta'] = True
elif paramName == 'phi':
phi = params[pid]
fitParams['phi'] = True
elif paramName == 'xc':
xc = params[pid]
fitParams['xc'] = True
elif paramName == 'yc':
yc = params[pid]
fitParams['xc'] = True
#if betaX<0.:
# print 'warning: beta going negative, setting to 0.0'
# betaX=0.
#if betaY<0.:
# print 'warning: beta going negative, setting to 0.0'
# betaY=0.
if betaX < 0.:
print 'warning: beta going negative, taking absolute value'
betaX = np.abs(betaY)
if betaY < 0.:
print 'warning: beta going negative, taking absolute value'
betaY = np.abs(betaX)
print 'beta: (%f,%f)\t phi: %f\txc: (%f,%f)' % (betaX, betaY, phi, xc, yc)
#update noise map
nm = img.makeNoiseMap(nm.shape, np.mean(nm), np.std(nm))
size = im.shape
if fitParams['xc'] or xx is None:
#shift the (0,0) point to the centroid
ry = np.array(range(0, size[0]), dtype=float) - yc
rx = np.array(range(0, size[1]), dtype=float) - xc
yy, xx = shapelet.xy2Grid(ry, rx)
bvals = genBasisMatrix([betaY, betaX], nmax, phi, yy, xx)
coeffs = solveCoeffs(bvals, im)
mdl = img.constructModel(bvals, coeffs, size)
return np.sum((im - mdl)**2 / nm**2) / (size[0] * size[1])
def chi2PolarFunc(params,
nmax,
im,
nm,
order=['beta0', 'beta1', 'phi', 'yc', 'xc'],
set_beta=[None, None],
set_phi=None,
set_xc=[None, None],
r=None,
th=None):
"""Function which is to be minimized in the chi^2 analysis for Polar shapelets
params = [beta0, beta1, phi, xc, yc] or some subset
beta0: characteristic size of shapelets, fit parameter
beta1: characteristic size of shapelets, fit parameter
phi: rotation angle of shapelets, fit parameter
yc: y centroid of shapelets, fit parameter
xc: x centroid of shapelets, fit parameter
nmax: number of coefficents to use in the Laguerre polynomials
im: observed image
nm: noise map
order: order of parameters
fixed parameters: set_beta, set_phi, set_xc
r: radius from centroid, array of im.shape, not required if xc and yc being fit
th: angle from centroid, array of im.shape, not required if xc and yc being fit
"""
#determine which parameters are being fit for, and which are not
beta0 = set_beta[0]
beta1 = set_beta[1]
phi = set_phi
yc = set_xc[0]
xc = set_xc[1]
fitParams = {'beta': False, 'phi': False, 'xc': False}
for pid, paramName in enumerate(order):
if paramName == 'beta0':
beta0 = params[pid]
fitParams['beta'] = True
elif paramName == 'beta1':
beta1 = params[pid]
fitParams['beta'] = True
elif paramName == 'phi':
phi = params[pid]
fitParams['phi'] = True
elif paramName == 'xc':
xc = params[pid]
fitParams['xc'] = True
elif paramName == 'yc':
yc = params[pid]
fitParams['xc'] = True
#if beta0<0.:
# print 'warning: beta going negative, setting to 0.0'
# beta0=0.
#if beta1<0.:
# print 'warning: beta going negative, setting to 0.0'
# beta1=0.
if beta0 < 0.:
print 'warning: beta going negative, taking absolute value'
beta0 = np.abs(beta0)
if beta1 < 0.:
print 'warning: beta going negative, taking absolute value'
beta1 = np.abs(beta1)
print 'beta: (%f,%f)\t phi: %f\txc: (%f,%f)' % (beta0, beta1, phi, xc, yc)
#update noise map
nm = img.makeNoiseMap(nm.shape, np.mean(nm), np.std(nm))
size = im.shape
if fitParams['xc'] or r is None:
r, th = shapelet.polarArray(
[yc, xc], size
) #the radius,theta pairs need to updated if fitting for the xc centre or if not using the r,th inputs
bvals = genPolarBasisMatrix([beta0, beta1], nmax, phi, r, th)
coeffs = solveCoeffs(bvals, im)
mdl = np.abs(img.constructModel(bvals, coeffs, size))
return np.sum((im - mdl)**2 / nm**2) / (size[0] * size[1])
coeffs,
xc,
subim.shape,
beta0,
0.,
5,
pos=[hdr['ra'], hdr['dec'], hdr['dra'], hdr['ddec']],
info='Test Hermite coeff file')
except:
print 'Test failed (%i):' % tc, sys.exc_info()[0]
te += 1
beta0, phi0 = initBetaPhi(subim, mode='fit')
xc = img.maxPos(subim)
mean, std = img.estimateNoise(subim, mode='basic')
nm = img.makeNoiseMap(subim.shape, mean, std)
nmax = [10, 10]
#chi2PolarFunc(params,nmax,im,nm):
tc += 1
try:
print chi2PolarFunc([beta0[0], beta0[1], phi0, xc[0], xc[1]],
nmax,
subim,
nm,
order=['beta0', 'beta1', 'phi', 'xc',
'yc']) #fit: all
print chi2PolarFunc([beta0[0], beta0[1]],
nmax,
subim,
nm,