def cone_p_noll(N, j, XX, YY, ZZ):
'''
Same as before, but input coordinates are not factorized
'''
n, m = aotools.zernIndex(j)
cj = cone_jacobi(N, n, ZZ)
zz = zk.zernike_noll(j, XX, YY)
return cj * zz
def cone_polynomial_noll(N, j, X, Y, t):
'''
same as before, but Noll index instead of (n,m)
'''
n, m = aotools.zernIndex(j)
cj = cone_jacobi(N, n, t)
zz = zk.zernike_noll(j, X, Y)
return np.outer(cj, zz).flatten()
def compute_j_list(nmax, mmax_in):
'''
Construct a list of Noll indices with n<=nmax, abs(m)<=mmax
'''
l = []
mmax = np.min((mmax_in, nmax))
(n, m) = (0, 0)
j = 1
while (n <= nmax):
(n, m) = aotools.zernIndex(j)
if (abs(m) <= mmax and n <= nmax):
l.append(j)
j += 1
return (l)
def compute_j_N_list(nmax, mmax):
'''
Computes the list of quantum numbers N,n,m such that
abs(m)<=min(n,mmax)
0<=n<=N<=nmax
'''
ll = []
for n in range(nmax + 1):
jlistn = zk.compute_j_list_n(n, mmax)
# For each element of jlistn corresponding to a different m, we have
# nmax+1-n cone jacobi terms
for j in jlistn:
n, m = aotools.zernIndex(j)
for N in range(n, nmax + 1):
ll.append((j, N))
return ll
def zernike_noll(j, X, Y):
(n, m) = aotools.zernIndex(j)
return zernike_nm(n, m, X, Y)
for ilayer in range(nLayers):
plt.plot(windSpeedFunction[ilayer])
plt.plot(windDirectionFunction[ilayer])
plt.plot(r0Function[ilayer])
plt.draw()
raw_input()
# telescope
zerns = aotools.zernikeArray(nZerns,npix)
pupil = zerns[0]
subapertureMap = aotools.pupil.circle(nsubx/2,nsubx)
subapertureMap -= aotools.pupil.circle(2,nsubx)
# magic WFS
magicSlopes=MagicSHslopes(npix,nsubx,pupil,subapertureMap)
gammas = aotools.makegammas(aotools.zernIndex(nZerns)[0])
subGrads = zernikeSubapSlopes(zerns,gammas[0][:nZerns,:nZerns],gammas[1][:nZerns,:nZerns],nsubx)
z=subGrads.reshape(nZerns,2*nsubx*nsubx)
zmat=numpy.linalg.pinv(z).transpose()
# initiate arrays
phsLayers = numpy.zeros((nLayers,npix,npix),numpy.float)
coeffs = numpy.zeros((zerns.shape[0]),numpy.float)
centroidArray = numpy.zeros((nsteps,2*int(subapertureMap.sum())),numpy.float)
totalMoved = numpy.zeros((nLayers))
demandMoved = numpy.zeros((nLayers))
DMphs = numpy.zeros((npix,npix))
longImg = numpy.zeros((2*npix,2*npix))
longResImg = numpy.zeros((2*npix,2*npix))
# initiate lists