示例#1
0
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])
示例#2
0
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])
示例#3
0
        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
示例#4
0
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])
示例#5
0
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])
示例#6
0
                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,