def test_update_line_params():
L = LorentzianLine(name="Lorentzian",
domain=(-5.0, 5.0),
x0=-0.5,
width=0.4,
c=0.2)
print(L.export_to_dict())
tdict = dict(x0=1.0, c=0.0)
L.update_line_params(**tdict)
print("After update: ", L.export_to_dict())
def test_Lines():
l1 = Line("LineBase", (-15., 15), x0=0, FWHM=0.5)
# assert isinstance(l1, Line)
# try:
# l1.check_params()
# except KeyError:
# print("KeyError was caught and 'handled' in this test.")
l2 = LorentzianLine("Lorentzian1", (-15., 15.), x0=0.2, width=0.3)
# l2.check_params()
l2.update_line_params(c=0.2)
e = np.linspace(-1.5, 1.5, 16)
print("Returned by 'l1.calc': ", l1.calc(e, **l1.line_params))
print("Returned by 'l2(e)': ", l2(e))
def test_Lines():
l1 = Line("LineBase", (-15., 15), x0=0, FWHM=0.5)
# assert isinstance(l1, Line)
# try:
# l1.check_params()
# except KeyError:
# print("KeyError was caught and 'handled' in this test.")
l2 = LorentzianLine("Lorentzian1", (-15., 15.), x0=0.2, width=0.3)
# l2.check_params()
l2.update_line_params(c=0.2)
l3 = F_cLine("FcLine1", (-15, 15),
x0=0.0,
width=0.02,
A=350.0,
q=0.023,
c=0.0,
weight=1.0)
l3.update_line_params(width=0.1)
l3.update_domain((-1.0, 1.0))
l4 = F_ILine("FILine1", (-15, 15),
x0=0.0,
width=0.02,
A=350.0,
q=0.023,
kappa=0.01,
c=0.0,
weight=1.0)
l4.update_line_params(kappa=0.015)
e = np.linspace(-1.5, 1.5, 16)
print("Returned by 'l1.calc': ", l1.calc(e, **l1.line_params))
print("Returned by 'l2(e)': ", l2(e))
print("Returned by 'F_cLine': ", l3(e))
print("Returned by 'F_ILine': ", l4(e))
def slow_vis_fitting():
### Creating a SqE model for transformation
# We need some lines
# L1 = LorentzianLine("Lorentzian1", (-5.0, 5.0), x0=0.0, width=0.4, c=0.0, weight=1)
L2 = LorentzianLine(name="Lorentzian2",
domain=(-5.0, 5.0),
x0=1.5,
width=0.4,
c=0.0,
weight=2)
L3 = F_ILine("FI1", (-energy_from_lambda(6.0), 15),
x0=0.0,
width=0.008,
A=350.0,
q=0.02,
kappa=0.01,
c=0.0,
weight=1)
# Contruct a SqE model
sqe1 = SqE((L2, L3), lam=6.0, dlam=0.12, lSD=3.43, T=20)
### Instantiate a transformer
# Consider detector efficiency
decf = DetectorEfficiencyCorrectionFactor(sqe1)
sqt1 = SqtTransformer(sqe1,
corrections=(decf, ),
ne=10000,
nlam=20,
lSD=3.43)
### Creating a logger for debugging:
import logging
# Create and configure logger
logging.basicConfig(filename=f"{testdir}/resources/fit.log",
level=logging.INFO,
filemode="w")
logger = logging.getLogger()
### Values for transformation
taus = logspace(-6, -1, 101)
datataus = array([
2.0e-6, 6.0e-6, 4.1e-5, 2.5e-4, 5.5e-4, 1.0e-3, 1.32e-3, 1.7e-3,
2.2e-3, 2.63e-3, 2.77e-3, 3.3e-3, 4.27e-3, 5.11e-3, 6.77e-3, 8.96e-3,
2.0e-2
])
x = MIEZE_DeltaFreq_from_time(datataus * 1.0e-9, 3.43, 6.0)
longx = MIEZE_DeltaFreq_from_time(taus * 1.0e-9, 3.43, 6.0)
### TRANSFORM!!
y = array([sqt1(freq) for freq in x])
sqt1vals = array([sqt1(freq) for freq in longx])
yerr = 0.03 * random.randn(len(y))
### FIT!
m = FitModelCreator.from_transformer(
sqt1,
x,
abs(y + yerr),
yerr, [-1.5, 0.2, 0.0, 3.0, 0.01, 350.0, 0.02, 0.015, 0.0, 1.0],
logger=logger)
# m.fixed["c_Lorentzian1"] = True
m.fixed["c_Lorentzian2"] = True
print("fcn for m:\n", m.fcn)
m2 = FitModelCreator.from_Minuit(
minuitobj=m,
# limit_weight_Lorentzian1=(0, None),
# limit_width_Lorentzian1=(0, None),
limit_weight_Lorentzian2=(0, None),
limit_width_Lorentzian2=(0, None),
limit_weight_FI1=(0.0, None),
limit_kappa_FI1=(0.0, None),
# fix_c_Lorentzian1=True,
fix_c_Lorentzian2=True,
# fix_weight_Lorentzian1=True
fix_c_FI1=True,
fix_weight_FI1=True,
fix_A_FI1=True,
fix_q_FI1=True)
print("fcn for m2:\n", m2.fcn)
# fmin, res = m.migrad(ncall=1000)
# print(fmin)
# print(res)
fmin, res = m2.migrad(ncall=1000)
print(fmin)
print(res)
### Gather Fit results
# dL1res1 = {
# "x0" : 0.0,
# "width" : res[0].value,
# "c" : res[1].value,
# "weight" : res[2].value
# }
# dL2res1 = {
# "x0" : res[3].value,
# "width" : res[4].value,
# "c" : res[5].value,
# "weight" : res[6].value
# }
dL2res1 = {
"x0": res[0].value,
"width": res[1].value,
"c": res[2].value,
"weight": res[3].value
}
dFI1res = {
"width": res[4].value,
"A": res[5].value,
"q": res[6].value,
"kappa": res[7].value,
"c": res[8].value,
"weight": res[9].value
}
# L1.update_line_params(**dL1res1)
L2.update_line_params(**dL2res1)
L3.update_line_params(**dFI1res)
sqtres1vals = array([sqt1(freq) for freq in longx])
# ### Gather Fit results
# dL1res2 = {
# "x0" : 0.0,
# "width" : res2[0].value,
# "c" : res2[1].value,
# "weight" : res2[2].value
# }
# dL2res2 = {
# "x0" : res2[3].value,
# "width" : res2[4].value,
# "c" : res2[5].value,
# "weight" : res2[6].value
# }
# L1.update_line_params(**dL1res2)
# L2.update_line_params(**dL2res2)
# sqtres2vals = array([sqt1(freq) for freq in longx])
### Calculate init guess
# dL1ig = {
# "x0" : 0.0,
# "width" : 0.6,
# "c" : 0,
# "weight" : 1.0
# }
# dL2ig = {
# "x0" : -1.5,
# "width" : 0.2,
# "c" : 0.0,
# "weight" : 3.0
# }
dL2ig = {"x0": -1.5, "width": 0.2, "c": 0.0, "weight": 3.0}
dFI1ig = {
"x0": 0.0,
"width": 0.01,
"A": 350,
"q": 0.02,
"kappa": 0.015,
"c": 0.0,
"weight": 1.0
}
# L1.update_line_params(**dL1ig)
L2.update_line_params(**dL2ig)
L3.update_line_params(**dFI1ig)
sqtigvals = array([sqt1(freq) for freq in longx])
plt.plot(taus, sqt1vals, label="orig. curve", ls="--", color="C0")
plt.plot(taus, sqtres1vals, label="fit curve 1", ls="-", color="C1")
# plt.plot(taus, sqtres2vals, label="fit curve 2", ls=":", color="C4")
plt.plot(taus, sqtigvals, label="init guess", ls="-.", color="C2")
plt.errorbar(datataus,
abs(y + yerr),
yerr,
label="data",
ls="",
marker="o",
color="C0")
plt.xscale("log")
plt.xlabel("$\\tau_{MIEZE}$ [ns]", fontsize=16.)
plt.ylabel("S(q,t) [arb. u.]", fontsize=16.)
plt.legend()
plt.show()