def chi2nmaxFunc(params,im,nm,betaY,betaX,phi,xc):
"""
params = [nmaxX,nmaxY]
nmax: number of coefficents to use in x,y
im: observed image
nm: noise map
betaY: characteristic size of shapelet
betaX: characteristic size of shapelet
phi: rotation angle of shapelets
xc: fit centroid position
"""
nmax=params
size=im.shape
#shift the (0,0) point to the centroid
ry=np.array(range(0,size[0]),dtype=float)-xc[0]
rx=np.array(range(0,size[1]),dtype=float)-xc[1]
yy,xx=shapelet.xy2Grid(ry,rx)
bvals=genBasisMatrix([betaY,betaX],[nmax,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 chi2nmaxFunc(params, im, nm, betaY, betaX, phi, xc):
"""
params = [nmaxX,nmaxY]
nmax: number of coefficents to use in x,y
im: observed image
nm: noise map
betaY: characteristic size of shapelet
betaX: characteristic size of shapelet
phi: rotation angle of shapelets
xc: fit centroid position
"""
nmax = params
size = im.shape
#shift the (0,0) point to the centroid
ry = np.array(range(0, size[0]), dtype=float) - xc[0]
rx = np.array(range(0, size[1]), dtype=float) - xc[1]
yy, xx = shapelet.xy2Grid(ry, rx)
bvals = genBasisMatrix([betaY, betaX], [nmax, 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 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])
coeffs=solveCoeffs(mPolar,subim)
print coeffs
if write_files: fileio.writeLageurreCoeffs('testLageurre.pkl',coeffs,xc,subim.shape,beta0,0.,[5,5],pos=[hdr['ra'],hdr['dec'],hdr['dra'],hdr['ddec']],info='Test Lageurre coeff file')
except:
print 'Test failed (%i):'%tc, sys.exc_info()[0]
te+=1
#genBasisMatrix(beta,nmax,rx,ry):
#solveCoeffs(m,im):
tc+=1
try:
beta0,phi0=initBetaPhi(subim,mode='fit')
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):
mean, std = estimateNoise(im, mode='basic')
nm = makeNoiseMap(im.shape, mean, std)
print nm.shape
except:
print 'Test failed (%i):' % tc, sys.exc_info()[0]
te += 1
#constructModel(bvals,coeffs,size):
tc += 1
try:
shapeDict = fileio.readLageurreCoeffs('../data/testHermite.pkl')
rx = np.array(range(0, shapeDict['size'][0]),
dtype=float) - shapeDict['xc'][0]
ry = np.array(range(0, shapeDict['size'][1]),
dtype=float) - shapeDict['xc'][1]
xx, yy = shapelet.xy2Grid(rx, ry)
bvals = decomp.genBasisMatrix(shapeDict['beta'], shapeDict['norder'],
shapeDict['phi'], xx, yy)
mdl = constructModel(bvals, shapeDict['coeffs'], shapeDict['size'])
print mdl.shape
except:
print 'Test failed (%i):' % tc, sys.exc_info()[0]
te += 1
#polarCoeffImg(coeffs,nmax):
tc += 1
try:
shapeDict = fileio.readLageurreCoeffs('../data/testLageurre.pkl')
cim = polarCoeffImg(shapeDict['coeffs'].real, shapeDict['norder'])
print cim
except:
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])