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()