def genBasisMatrix(beta,nmax,rx,ry):
"""Generate the n x k matrix of basis functions(k) for each pixel(n)
nmax: maximum decompisition order
beta: characteristic size of the shapelet
rx: range of x values to evaluate basis functions
ry: range of y values to evaluate basis functions
"""
bvals=[]
for x in range(nmax[0]):
for y in range(nmax[1]):
bf=shapelet.dimBasis2d(x,y,beta=beta)
bvals.append(shapelet.computeBasis2d(bf,rx,ry).flatten())
bm=np.array(bvals)
return bm.transpose()
def genBasisMatrix(beta,nmax,phi,yy,xx,fourier=False):
"""Generate the n x k matrix of basis functions(k) for each pixel(n)
nmax: maximum decompisition order
beta: characteristic size of the shapelet
phi: rotation angle
yy: y values to evaluate basis functions
xx: x values to evaluate basis functions
fourier: return a FOurer transformed version of the basis functions
"""
bvals=[]
if type(nmax) is int: nmax=[nmax,nmax]
for ny in range(nmax[0]):
for nx in range(nmax[1]):
bf=shapelet.dimBasis2d(ny,nx,beta=beta,phi=phi,fourier=fourier)
bvals.append(shapelet.computeBasis2d(bf,yy,xx).flatten())
bm=np.array(bvals)
return bm.transpose()
def genBasisMatrix(beta, nmax, phi, yy, xx, fourier=False):
"""Generate the n x k matrix of basis functions(k) for each pixel(n)
nmax: maximum decompisition order
beta: characteristic size of the shapelet
phi: rotation angle
yy: y values to evaluate basis functions
xx: x values to evaluate basis functions
fourier: return a FOurer transformed version of the basis functions
"""
bvals = []
if type(nmax) is int: nmax = [nmax, nmax]
for ny in range(nmax[0]):
for nx in range(nmax[1]):
bf = shapelet.dimBasis2d(ny,
nx,
beta=beta,
phi=phi,
fourier=fourier)
bvals.append(shapelet.computeBasis2d(bf, yy, xx).flatten())
bm = np.array(bvals)
return bm.transpose()
