def qtest_pocs(st_rec, st_orginal, alpharange, irange):
"""
Runs the selected method in a certain range of parameters (iterations and alpha), returns a table of Q values ,defined as:
Q = 10 * log( || d_org || ^2 _2 / || d_org - d_rec || ^2 _2 )
The highest Q value is the one to be chosen.
"""
Qall = []
dmethod = 'reconstruct'
method = 'linear'
st_org = st_orginal.copy()
data_org= stream2array(st_org, normalize=True)
for alpha in alpharange:
print("##################### CURRENT ALPHA %f #####################\n" % alpha )
for i in irange:
print('POCS RECON WITH %i ITERATIONS' % i, end="\r")
sys.stdout.flush()
srs = st_rec.copy()
st_pocsrec = pocs_recon(srs, maxiter=int(i), method=method, dmethod=dmethod, alpha=alpha)
drec = stream2array(st_pocsrec, normalize=True)
Q_tmp = np.linalg.norm(data_org,2)**2. / np.linalg.norm(data_org - drec,2)**2.
Q = 10.*np.log(Q_tmp)
Qall.append([alpha, i, Q])
Qmax = [0,0,0]
for i in Qall:
if i[2] > Qmax[2]: Qmax = i
return Qall
DOMETHOD = 'recon'
for alpha in alphalist:
name1 = 'pocs_' + str(noiselevellist[i]) + '-noise_' + '{:01.2}'.format(alpha) + '-alpha_' + 'linear' + '.png'
name2 = 'pocs_' + str(noiselevellist[i]) + '-noise_' + '{:01.2}'.format(alpha) + '-alpha_' + 'exp' + '.png'
picpath1 = PICPATH + name1
picpath2 = PICPATH + name2
plotnameQall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'lin'
plotnameQmbwall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'mask_bw'
plotnameQmtaperall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'mask_taper'
plotnameQSSAall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'SSA'
print("##################### CURRENT ALPHA %f #####################\n" % alpha )
for maxiter in maxiterlist:
print('POCS RECON WITH %i ITERATIONS' % maxiter, end="\r")
sys.stdout.flush()
data_org = d0.copy()
srs = array2stream(data.copy())
st_rec = pocs_recon(srs, maxiter, dmethod='recon', method='linear', alpha=alpha)
drec = stream2array(st_rec, normalize=True)
Q = np.linalg.norm(data_org,2)**2. / np.linalg.norm(data_org - drec,2)**2.
Qlinall.append([alpha, maxiter, 10.*np.log(Q)])
savepath = plotnameQall + '.dat'
np.savetxt(savepath, Qlinall)
picpath2 = PICPATH + name2
plotnameQall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'lin'
plotnameQmbwall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'mask_bw'
plotnameQmtaperall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + DOMETHOD + 'mask_taper'
plotnameQmvaryall = PICPATH + 'pocs_' + str(noiselevellist[i]) + '{:01.2}'.format(alpha) + '-alpha' + 'norm' + 'mask_vary'
print("##################### CURRENT ALPHA %f #####################\n" % alpha )
for maxiter in maxiterlist:
print('POCS RECON WITH %i ITERATIONS' % maxiter, end="\r")
sys.stdout.flush()
print('LINEAR\n')
data_org = d0.copy()
srs = array2stream(data.copy())
st_rec = pocs_recon(srs, maxiter, method=DOMETHOD, dmethod='linear', alpha=alpha)
drec = stream2array(st_rec, normalize=True)
Q = np.linalg.norm(data_org,2)**2. / np.linalg.norm(data_org - drec,2)**2.
Qlin = 10.*np.log(Q)
Qlinall.append([alpha, maxiter, Qlin])
for bws in bwlist:
print('USING BW, %i, MASK' % int(bws), end="\r")
sys.stdout.flush()
data_org = d0.copy()
srs = array2stream(data.copy())
st_rec = pocs_recon(srs, maxiter, method=DOMETHOD, dmethod='linear', alpha=alpha, beta=None, peaks=peaks)
drecmask = stream2array(st_rec, normalize=True)
Q = np.linalg.norm(data_org,2)**2. / np.linalg.norm(data_org - drecmask,2)**2.
Qbwmaskall.append([alpha, maxiter, 10.*np.log(Q), bws])