def test_find_ramp():
p_ramp = .025
L = 128
sig_rms = 5.0
def iid_nz(scale):
return np.random.normal(loc=0, scale=scale, size=L)
fov_win = np.zeros((L, ), 'i')
fov_win[L / 2 - 40:L / 2 + 40] = 1
sig_phs = np.arange(L) * p_ramp + np.random.randn(1)
sig_mag = iid_nz(sig_rms)
sig = np.abs(sig_mag) * np.exp(1j * sig_phs)
# attenutate the signal according to a simulated object pattern
atten = np.zeros(L)
segs = np.unique(np.random.randint(0, high=10, size=5))
segs = map(lambda s: int(round(s * (L - 1) / 10.)), segs)
segs.sort()
segs = np.insert(segs, len(segs), L)
segs = np.insert(segs, 0, 0)
s_val = [.1, 1]
vi = 1
for i in xrange(len(segs) - 1):
atten[segs[i]:segs[i + 1]] = s_val[vi]
vi = vi ^ 1
sig *= atten
# now add a small amount of noise to real/imag channels
sig.real += iid_nz(sig_rms / 50.)
sig.imag += iid_nz(sig_rms / 50.)
m = util.find_ramp(np.angle(sig))
print m
def test_find_ramp():
p_ramp = .025
L = 128
sig_rms = 5.0
def iid_nz(scale):
return np.random.normal(loc=0, scale=scale, size=L)
fov_win = np.zeros((L,), 'i')
fov_win[L/2-40:L/2+40] = 1
sig_phs = np.arange(L) * p_ramp + np.random.randn(1)
sig_mag = iid_nz(sig_rms)
sig = np.abs(sig_mag) * np.exp(1j*sig_phs)
# attenutate the signal according to a simulated object pattern
atten = np.zeros(L)
segs = np.unique(np.random.randint(0, high=10, size=5))
segs = map(lambda s: int(round(s*(L-1)/10.)), segs)
segs.sort()
segs = np.insert(segs, len(segs), L)
segs = np.insert(segs, 0, 0)
s_val = [.1, 1]
vi = 1
for i in xrange(len(segs)-1):
atten[segs[i]:segs[i+1]] = s_val[vi]
vi = vi ^ 1
sig *= atten
# now add a small amount of noise to real/imag channels
sig.real += iid_nz(sig_rms/50.)
sig.imag += iid_nz(sig_rms/50.)
m = util.find_ramp(np.angle(sig))
print m
def simple_unbal_phase_ramp(rdata,
nramp,
nflat,
pref_polarity,
fov_lim=None,
mask_noise=True,
debug=False):
N1 = rdata.shape[-1]
rdata[:, :nramp + 1] = 0.
rdata[:, nflat + 1:] = 0.
irdata = util.ifft1(rdata, shift=True)
if pref_polarity == 1:
# [(pos - neg), (neg - pos)]
ref_funcs = irdata[:2] * irdata[1:3].conj()
else:
# [(pos - neg), (neg - pos)]
ref_funcs = irdata[:2].conj() * irdata[1:3]
ref = ref_funcs[0] * ref_funcs[1].conjugate()
ref_funcs_phs = np.angle(ref_funcs)
pos_neg_diff = ref_funcs_phs[0]
neg_pos_diff = ref_funcs_phs[1]
ref_peak = np.abs(ref).max()
# if an FOV limit is requested, then make sure the rest of the algorithm
# only considers data within the limit
if fov_lim:
fov_max, fov = fov_lim
dx = fov / N1
x_grid = np.arange(-N1 / 2, N1 / 2) * dx
fov_q1_mask = np.where(np.abs(x_grid) > fov_max, 0, 1)
else:
fov_q1_mask = np.ones((N1, ), 'i')
ref_peak = np.abs(ref * fov_q1_mask).max()
if mask_noise:
thresh = 0.1
nz_q1_mask = np.where(np.abs(ref) > thresh * ref_peak, 1, 0)
while nz_q1_mask.sum() < 10:
thresh *= 0.5
print 'relaxing threshold to include more points in the fit:', thresh
nz_q1_mask = np.where(np.abs(ref) > thresh * ref_peak, 1, 0)
else:
nz_q1_mask = np.ones((N1, ), 'i')
ref_phs = (pos_neg_diff - neg_pos_diff)
q1_mask = nz_q1_mask & fov_q1_mask
# only do the unwrapping in the method if noisy patches have been rejected
m = util.find_ramp(ref_phs / 4.,
mask=q1_mask,
do_unwrap=mask_noise,
debug=debug)
## m,b,r = util.lin_regression(ref_phs/4, mask=nz_q1_mask & fov_q1_mask)
## m = m[0]; b = b[0]
return m
def simple_unbal_phase_ramp(rdata, nramp, nflat, pref_polarity,
fov_lim=None, mask_noise=True,
debug=False):
N1 = rdata.shape[-1]
rdata[:,:nramp+1] = 0.
rdata[:,nflat+1:] = 0.
irdata = util.ifft1(rdata, shift=True)
if pref_polarity==1:
# [(pos - neg), (neg - pos)]
ref_funcs = irdata[:2] * irdata[1:3].conj()
else:
# [(pos - neg), (neg - pos)]
ref_funcs = irdata[:2].conj()*irdata[1:3]
ref = ref_funcs[0]*ref_funcs[1].conjugate()
ref_funcs_phs = np.angle(ref_funcs)
pos_neg_diff = ref_funcs_phs[0]
neg_pos_diff = ref_funcs_phs[1]
ref_peak = np.abs(ref).max()
# if an FOV limit is requested, then make sure the rest of the algorithm
# only considers data within the limit
if fov_lim:
fov_max, fov = fov_lim
dx = fov/N1
x_grid = np.arange(-N1/2, N1/2) * dx
fov_q1_mask = np.where(np.abs(x_grid)>fov_max,0,1)
else:
fov_q1_mask = np.ones((N1,), 'i')
ref_peak = np.abs(ref*fov_q1_mask).max()
if mask_noise:
thresh = 0.1
nz_q1_mask = np.where(np.abs(ref) > thresh*ref_peak, 1, 0)
while nz_q1_mask.sum() < 10:
thresh *= 0.5
print 'relaxing threshold to include more points in the fit:', thresh
nz_q1_mask = np.where(np.abs(ref) > thresh*ref_peak, 1, 0)
else:
nz_q1_mask = np.ones((N1,), 'i')
ref_phs = (pos_neg_diff-neg_pos_diff)
q1_mask = nz_q1_mask & fov_q1_mask
# only do the unwrapping in the method if noisy patches have been rejected
m = util.find_ramp(ref_phs/4., mask=q1_mask, do_unwrap=mask_noise,
debug=debug)
## m,b,r = util.lin_regression(ref_phs/4, mask=nz_q1_mask & fov_q1_mask)
## m = m[0]; b = b[0]
return m
