loz = 0.0 - (numzs-1)/2 * ldz
rs = N.arange(numrs) * ldr + lor
zs = N.arange(numzs) * ldz + loz
if harmnum==1:
rs0, zs0 = rs[:], zs[:]
lo_file_r = int(rs.min()) - 1000
hi_file_r = int(rs.max()) + 1000
# Read and normalize the raw spectrum
infile.seek(lo_file_r * 8, 0)
fftamps = fread(infile, hi_file_r-lo_file_r+1, 'F')
fftpows = spectralpower(fftamps)
pownorm = 1.0 / (1.442695 * N.median(fftpows))
fftamps *= sqrt(pownorm)
ffd = ffdot_plane(fftamps, lor-lo_file_r, ldr, numrs, loz, ldz, numzs)
ffd_pows = (ffd * ffd.conj()).real
ffdps.append(ffd_pows)
if (harmnum==1):
sumpows = N.zeros_like(ffd_pows)
sumpows += ffd_pows
argmax = ffd_pows.argmax()
maxvals.append(ffd_pows.max())
maxargs.append((argmax / numrs, argmax % numrs))
print " Maximum power for harmonic %d = %.2f"%(harmnum, maxvals[-1])
if (convals.max() < 1.5): # Using relative contours
print "Using relative contours.."
pow_contours = convals * maxvals[-1]
else:
#r = N/4.0 + 0.5 # average freq over "observation"
rint = num.floor(r)
dr = 1.0/32.0
dz = 0.18
np = 512 # number of pixels across for f-fdot image
z = 10.0 # average fourier f-dot
w = -40.0 # fourier freq double deriv
noise = 0.0
noise = 1.0*norm(N)
us = num.arange(N, dtype=num.float64) / N # normalized time coordinate
r0 = r - 0.5 * z + w / 12.0 # Make symmetric for all z and w
z0 = z - 0.5 * w
phss = 2.0 * num.pi * (us * (us * (us * w/6.0 + z0/2.0) + r0))
ft = presto.rfft(num.cos(phss)+noise)
ffdot = presto.ffdot_plane(ft, rint-np/2*dr, dr, np, 0.0-np/2*dz, dz, np)
pffdot = presto.spectralpower(ffdot.flat)
theo_max_pow = N**2.0/4.0
frp = max(pffdot) / theo_max_pow # Fraction of recovered power
print "Fraction of recovered signal power = %f" % frp
a = time.clock()
[maxpow, rmax, zmax, rd] = presto.maximize_rz(ft, r+norm(1)[0]/5.0,
z+norm(1)[0], norm=1.0)
print "Time for rz:", time.clock()-a
print r, rmax, z, zmax, theo_max_pow, maxpow
a = time.clock()
[maxpow, rmax, zmax, wmax, rd] = presto.maximize_rzw(ft, r+norm(1)[0]/5.0,
z+norm(1)[0],
w+norm(1)[0]*5.0,
norm=1.0)
print "Time for rzw:", time.clock()-a
#r = N/4.0 + 0.5 # average freq over "observation"
rint = num.floor(r)
dr = 1.0 / 32.0
dz = 0.18
np = 512 # number of pixels across for f-fdot image
z = 0.0 # average fourier f-dot
w = 0.0 # fourier freq double deriv
noise = 0.0
#noise = 4*num.random.standard_normal(N)
us = num.arange(N, dtype=num.float64) / N # normalized time coordinate
r0 = r - 0.5 * z + w / 12.0 # Make symmetric for all z and w
z0 = z - 0.5 * w
phss = 2.0 * num.pi * (us * (us * (us * w / 6.0 + z0 / 2.0) + r0))
ft = presto.rfft(num.cos(phss) + noise)
ffdot = presto.ffdot_plane(ft, rint - np / 2 * dr, dr, np, 0.0 - np / 2 * dz,
dz, np)
pffdot = presto.spectralpower(ffdot.flat)
theo_max_pow = N**2.0 / 4.0
frp = max(pffdot) / theo_max_pow # Fraction of recovered power
print("Fraction of recovered signal power = %f" % frp)
# print "Raw power should be ~%.2e" % theo_max_pow
pffdot = pffdot / theo_max_pow
pffdot.shape = (np, np)
rs = num.arange(np) * dr - np / 2 * dr
zs = num.arange(np) * dz - np / 2 * dz
rgx = num.asarray([rs[0], rs[np - 1]])
rgy = num.asarray([zs[0], zs[np - 1]])
freqcut = pffdot[np / 2, :]
fdotcut = pffdot[:, np / 2]
image = 'antirainbow'
loz = 0.0 - (numzs - 1) / 2 * ldz
rs = N.arange(numrs) * ldr + lor
zs = N.arange(numzs) * ldz + loz
if harmnum == 1:
rs0, zs0 = rs[:], zs[:]
lo_file_r = int(rs.min()) - 1000
hi_file_r = int(rs.max()) + 1000
# Read and normalize the raw spectrum
infile.seek(lo_file_r * 8, 0)
fftamps = N.fromfile(infile, 'F', hi_file_r - lo_file_r + 1)
fftpows = spectralpower(fftamps)
pownorm = 1.0 / (1.442695 * N.median(fftpows))
fftamps *= sqrt(pownorm)
ffd = ffdot_plane(fftamps, lor - lo_file_r, ldr, numrs, loz, ldz, numzs)
ffd_pows = (ffd * ffd.conj()).real
ffdps.append(ffd_pows)
if (harmnum == 1):
sumpows = N.zeros_like(ffd_pows)
sumpows += ffd_pows
argmax = ffd_pows.argmax()
maxvals.append(ffd_pows.max())
maxargs.append((argmax / numrs, argmax % numrs))
print " Maximum power for harmonic %d = %.2f" % (harmnum, maxvals[-1])
if (convals.max() < 1.5): # Using relative contours
print "Using relative contours.."
pow_contours = convals * maxvals[-1]
else:
