Z = float(f[2:])
if f[:3] == 'it=':
it = int(f[3:])
Ef = float(Ef)
print 'Z =', Z, 'Ef =', Ef, 'it =', it
testname = 'data/sim/denssim-Z=%g-N=500-Nfa=3.6-Ef=%g-it=%d.npz' %(Z,Ef,it+1)
if os.path.isfile(testname):
sys.exit('THIS IS NOT THE FINAL ITERATION!')
Emax = Emax0
Emin = Emin0
Espace0 = np.linspace(Emin,Emax,N0)
Espace = np.array(Espace0)
dos0 = odedirac.doscalc(Espace0, r, U, mlist)
dos = np.array(dos0)
ipeak = dos0[itest,:].argmax()
while (Emax - Emin)/N0 > threshold:
if ipeak > 0:
Emin = Espace[ipeak - 1]
else: Emin = Espace[0]
if ipeak < len(Espace) - 1:
Emax = Espace[ipeak + 1]
else: Emax = Espace[-1]
Espace = np.linspace(Emin,Emax,N0)
dos = odedirac.doscalc(Espace, r, U, mlist)
ipeak = dos[itest,:].argmax()
if abs(Emin-Emin0)<1e-7 or abs(Emax-Emax0)<1e-7:
for i in range(0, len(its)):
Epeak = peaks[i]
Ef = Efs[i]
it = its[i]
simdata = np.load(
'data/sim/denssim-Z=%g-N=500-Nfa=3.6-Ef=%g-it=%d.npz' %
(Z, Ef, it))
U = simdata['U']
r = simdata['r']
itest = abs(r - rtest).argmin()
height = heights[i][itest]
normE = np.linspace(0.1, 4.0, 1000)
trueE = normE * Epeak
normdos = odedirac.doscalc(trueE, r, U, mlist)[itest, :] / height
print 'Z =', Z, 'Ef =', Ef
bnds = []
bnds.append((0.9, 1.1)) # peak amplitude
bnds.append((0.9, 1.1)) # peak position
bnds.append((0.1, 1.5)) # peak width
bnds.append((0.2, 1.2)) # envelope E scaling
bnds.append((0.01, 2.0 * normdos[-1])) # bg height
bnds.append((0.5, 5.0)) # bg E scaling
bnds = tuple(bnds)
start = [[1.0, 1.0, 1.0, 0.5, 1.5 * normdos[-1], 1.5]]
fit, params = lfit.peakfit(normE, normdos, 1, bnds, start)
hts = []
hfs = []
for i in range(0, len(its)):
Epeak = peaks[i]
Ef = Efs[i]
it = its[i]
simdata = np.load("data/sim/denssim-Z=%g-N=500-Nfa=3.6-Ef=%g-it=%d.npz" % (Z, Ef, it))
U = simdata["U"]
r = simdata["r"]
itest = abs(r - rtest).argmin()
height = heights[i][itest]
normE = np.linspace(0.1, 4.0, 1000)
trueE = normE * Epeak
normdos = odedirac.doscalc(trueE, r, U, mlist)[itest, :] / height
print "Z =", Z, "Ef =", Ef
bnds = []
bnds.append((0.9, 1.1)) # peak amplitude
bnds.append((0.9, 1.1)) # peak position
bnds.append((0.1, 1.5)) # peak width
bnds.append((0.2, 1.2)) # envelope E scaling
bnds.append((0.01, 2.0 * normdos[-1])) # bg height
bnds.append((0.5, 5.0)) # bg E scaling
bnds = tuple(bnds)
start = [[1.0, 1.0, 1.0, 0.5, 1.5 * normdos[-1], 1.5]]
fit, params = lfit.peakfit(normE, normdos, 1, bnds, start)