def analyze(dmax, alpha, n1, n2):
global sasrec
sasrec = saxs.Sasrec(Iq[n1:n2],
dmax,
qc=qc,
r=None,
alpha=alpha,
ne=nes)
Icinterp = np.interp(sasrec.q, sasrec.qc, np.abs(sasrec.Ic))
res = np.log10(np.abs(sasrec.I)) - np.log10(Icinterp)
I_l2.set_data(sasrec.qc[:n2], sasrec.Ic[:n2])
Ires_l1.set_data(sasrec.q, res)
P_l2.set_data(sasrec.r, sasrec.P)
axrg.set_text("Rg = " + str(round(sasrec.rg, 2)) + " +- " +
str(round(sasrec.rgerr, 2)))
axI0.set_text("I(0) = " + str(round(sasrec.I0, 2)) + " +- " +
str(round(sasrec.I0err, 2)))
axVp.set_text("Vp = " + str(round(sasrec.Vp, 2)) + " +- " +
str(round(sasrec.Vperr, 2)))
axVpmw.set_text("Vp MW = " + str(round(sasrec.mwVp, 2)) + " +- " +
str(round(sasrec.mwVperr, 2)))
axVcmw.set_text("Vc MW = " + str(round(sasrec.mwVc, 2)) + " +- " +
str(round(sasrec.mwVcerr, 2)))
axlc.set_text("Lc = " + str(round(sasrec.lc, 2)) + " +- " +
str(round(sasrec.lcerr, 2)))
def update(val):
dmax = sdmax.val
alpha = salpha.val
#nes = int(snes.val)
global sasrec
sasrec = saxs.Sasrec(Iq, dmax, qc=qc, r=None, alpha=alpha, ne=nes)
I_l2.set_data(sasrec.qc, sasrec.Ic)
res = np.log10(np.abs(sasrec.I)) - np.log10(
np.interp(sasrec.q, sasrec.qc, np.abs(sasrec.Ic)))
Ires_l1.set_data(sasrec.q, res)
P_l2.set_data(sasrec.r, sasrec.P)
axrg.set_text("Rg = " + str(round(sasrec.rg, 2)) + " +- " +
str(round(sasrec.rgerr, 2)))
axI0.set_text("I(0) = " + str(round(sasrec.I0, 2)) + " +- " +
str(round(sasrec.I0err, 2)))
axVp.set_text("Vp = " + str(round(sasrec.Vp, 2)) + " +- " +
str(round(sasrec.Vperr, 2)))
axVpmw.set_text("Vp MW = " + str(round(sasrec.mwVp, 2)) + " +- " +
str(round(sasrec.mwVperr, 2)))
axVcmw.set_text("Vc MW = " + str(round(sasrec.mwVc, 2)) + " +- " +
str(round(sasrec.mwVcerr, 2)))
axlc.set_text("Lc = " + str(round(sasrec.lc, 2)) + " +- " +
str(round(sasrec.lcerr, 2)))
#axI.set_ylim([0.9*np.min(sasrec.Ic),1.1*np.max(sasrec.Ic)])
#axP.set_xlim([0,1.1*np.max(sasrec.r)])
fig.canvas.draw_idle()
def analyze(dmax, alpha, n1, n2, extrapolate):
global sasrec
sasrec = saxs.Sasrec(Iq_orig[n1:n2],
dmax,
qc=qc,
r=r,
nr=args.nr,
alpha=alpha,
ne=nes,
extrapolate=extrapolate)
Icinterp = np.interp(sasrec.q_data, sasrec.qc, np.abs(sasrec.Ic))
res = sasrec.I_data / sasrec.Ierr_data - Icinterp / sasrec.Ierr_data
ridx = np.where((sasrec.q_data < sasrec.qc.max()))
I_l1.set_data(sasrec.q_data, sasrec.I_data)
I_l2.set_data(sasrec.qc, sasrec.Ic)
I_l3.set_data(sasrec.qn, sasrec.In)
Ires_l1.set_data(sasrec.q_data[ridx], res[ridx])
P_l2.set_data(sasrec.r, sasrec.P)
axI0.set_text("$I(0)$ = %.2e $\pm$ %.2e" %
(sasrec.I0, sasrec.I0err))
axrg.set_text("$R_g$ = %.2e $\pm$ %.2e" %
(sasrec.rg, sasrec.rgerr))
axrav.set_text("$\overline{r}$ = %.2e $\pm$ %.2e" %
(sasrec.avgr, sasrec.avgrerr))
axVp.set_text("$V_p$ = %.2e $\pm$ %.2e" %
(sasrec.Vp, sasrec.Vperr))
axVc.set_text("$V_c$ = %.2e $\pm$ %.2e" %
(sasrec.Vc, sasrec.Vcerr))
axlc.set_text("$\ell_c$ = %.2e $\pm$ %.2e" %
(sasrec.lc, sasrec.lcerr))
def analyze(dmax, alpha, n1, n2):
global sasrec
sasrec = saxs.Sasrec(Iq[n1:n2],
dmax,
qc=qc,
r=None,
alpha=alpha,
ne=nes)
Icinterp = np.interp(sasrec.q, sasrec.qc, np.abs(sasrec.Ic))
res = np.log10(np.abs(sasrec.I)) - np.log10(Icinterp)
I_l2.set_data(sasrec.qc[:n2], sasrec.Ic[:n2])
I_l3.set_data(sasrec.qn, sasrec.In)
Ires_l1.set_data(sasrec.q, res)
P_l2.set_data(sasrec.r, sasrec.P)
axI0.set_text("I(0) = %.2e $\pm$ %.2e" % (sasrec.I0, sasrec.I0err))
axrg.set_text("Rg = %.2e $\pm$ %.2e" % (sasrec.rg, sasrec.rgerr))
axrav.set_text("$\overline{r}$ = %.2e $\pm$ %.2e" %
(sasrec.avgr, sasrec.avgrerr))
axVp.set_text("Vp = %.2e $\pm$ %.2e" % (sasrec.Vp, sasrec.Vperr))
axVc.set_text("Vc = %.2e $\pm$ %.2e" % (sasrec.Vc, sasrec.Vcerr))
axlc.set_text("Lc = %.2e $\pm$ %.2e" % (sasrec.lc, sasrec.lcerr))
if args.max_alpha is None:
args.max_alpha = 10.
q = Iq[:, 0]
qmin = np.min(q[0])
dq = q[1] - q[0]
nq = int(qmin / dq)
qc = np.concatenate(([0.0], np.arange(nq) * dq + (qmin - nq * dq), q))
#Icerr = np.concatenate((np.ones(nq+1)*Iq[0,2],Iq[:,2]))
if args.qfile is not None:
qc = np.loadtxt(args.qfile, usecols=(0, ))
Icerr = np.interp(qc, q, Iq[:, 2])
sasrec = saxs.Sasrec(Iq, D, qc=qc, r=None, alpha=alpha, ne=nes)
if args.plot:
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
#fig, (axI, axP) = plt.subplots(1, 2, figsize=(12,6))
fig = plt.figure(0, figsize=(12, 6))
axI = plt.subplot2grid((3, 2), (0, 0), rowspan=2)
axR = plt.subplot2grid((3, 2), (2, 0))
axP = plt.subplot2grid((3, 2), (0, 1), rowspan=3)
plt.subplots_adjust(left=0.068, bottom=0.25, right=0.98, top=0.95)
I_l1, = axI.plot(sasrec.q, sasrec.I, 'k.')
I_l2, = axI.plot(sasrec.qc, sasrec.Ic, 'r-', lw=2)
if args.log: axI.semilogy()
for i in range(waittime + 1):
sys.stdout.write(
"\rTo cancel, press CTRL-C in the next %d seconds. " %
(waittime - i))
sys.stdout.flush()
time.sleep(1)
print()
except KeyboardInterrupt:
print("Canceling...")
exit()
#calculate chi2 when alpha=0, to get the best possible chi2 for reference
sasrec = saxs.Sasrec(Iq[n1:n2],
D,
qc=qc,
r=r,
nr=args.nr,
alpha=0.0,
extrapolate=args.extrapolate)
ideal_chi2 = sasrec.calc_chi2()
if args.alpha is None:
al = []
chi2 = []
#here, alphas are actually the exponents, since the range can
#vary from 10^-10 upwards of 10^20. This should cover nearly all likely values
alphas = np.arange(-20, 20.)
i = 0
nalphas = len(alphas)
for alpha in alphas:
i += 1
n1 = 0
else:
n1 = args.n1
if args.n2 is None:
n2 = len(Iq[:, 0])
else:
n2 = args.n2
Iq = Iq[n1:n2]
if args.dmax is None:
#estimate dmax directly from data
D, sasrec = saxs.estimate_dmax(Iq, clean_up=True)
else:
D = args.dmax
sasrec = saxs.Sasrec(Iq, D, alpha=0.0)
if args.max_dmax is None:
args.max_dmax = 2. * D
if args.extrapolate:
qe = np.linspace(1.0 * Iq[-1, 0], 3.0 * Iq[-1, 0], 1001)
qsph = qe[qe > Iq[-1, 0]]
#simulate scattering of sphere of Rg of particle
Isph = saxs.sphere(q=qsph, R=3. / 5. * sasrec.rg**0.5)
Isph += np.random.normal(Isph * 1e-2, size=len(Isph))
idx = np.where(Isph > 0)
qsph = qsph[idx]
Isph = Isph[idx]
#scale extrapolated intensity to match data
Isph *= Iq[-50:, 1].mean() / Isph[:20].mean()