def test_get_adaptive_integration_grid():
### Lines
LL1 = LorentzianLine("LL1", (-energy_from_lambda(6.0), 15),
x0=0.048,
width=0.04,
c=0.0,
weight=0.0)
F_c = F_cLine("F_c1", (-energy_from_lambda(6.0), 15),
x0=0.0,
width=0.02,
A=350.0,
q=0.02,
c=0.0,
weight=1)
F_I = F_ILine("F_I1", (-energy_from_lambda(6.0), 15),
x0=-0.02,
width=0.008,
A=350.0,
q=0.02,
kappa=0.01,
c=0.0,
weight=1)
### CalcStrategy
inel = InelasticCalcStrategy(610)
quasi = QuasielasticCalcStrategy()
def test_get_param_names():
L1 = LorentzianLine(name="Lorentzian",
domain=(-5.0, 5.0),
x0=-0.5,
width=0.4,
c=0.2)
F_c1 = F_cLine(name="Fc1", domain=(-5.0, 5.0), x0=-0.0, width=0.4, c=0.2)
print(L1.get_param_names())
print(F_c1.get_param_names())
def visualize_Lines():
Lorentzian = LorentzianLine("Lorentzian1", (-energy_from_lambda(6.0), 15),
x0=-0.3,
width=0.5,
c=0.0)
F_c = F_cLine("F_c1", (-energy_from_lambda(6.0), 15),
x0=-0.3,
width=0.5,
A=350.0,
q=0.02,
c=0.0,
weight=1)
F_I = F_ILine("F_I1", (-energy_from_lambda(6.0), 15),
x0=-0.3,
width=0.008,
A=350.0,
q=0.02,
kappa=0.01,
c=0.0,
weight=1)
print(F_I.integrate())
F_I2 = F_ILine("F_I2", (-energy_from_lambda(6.0), 15),
x0=-0.3,
width=0.1,
A=367.0,
q=0.124,
kappa=0.065,
c=0.0,
weight=1)
print(F_I2.integrate())
e = np.linspace(-0.5, 0.0, 1201)
plt.plot(e, Lorentzian(e))
plt.plot(e, F_c(e) * F_c.normalize(), ls="--", lw=2.0)
plt.plot(e, F_I(e), ls="dotted", lw=4.0)
plt.plot(e, F_I2(e), ls="-.", lw=1.0)
plt.show()
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 test_domainenforcement():
Lorentzian = LorentzianLine("Lorentzian1", (-5., 5),
x0=-0.3,
width=0.5,
c=0.2)
F_c = F_cLine("FcLine1", (-15, 15),
x0=0.0,
width=0.02,
A=350.0,
q=0.023,
c=0.0,
weight=1.0)
e_in_domain = np.linspace(-5.0, 5.0, 11)
e_beyond_domain = np.linspace(-10.0, 10.0, 21)
pL1 = Lorentzian(e_in_domain)
pL2 = Lorentzian(e_beyond_domain)
pF1 = F_c(e_in_domain)
pF2 = F_c(e_beyond_domain)
assert np.all(pL1 == pL2[Lorentzian.within_domain(e_beyond_domain)])
assert np.all(pF1 == pF2[Lorentzian.within_domain(e_beyond_domain)])
def test_normalization():
Lorentzian = LorentzianLine("Lorentzian1", (-5., 5),
x0=0.0,
width=0.5,
c=0.0)
n = Lorentzian.normalize()
from scipy.integrate import quad
to_integrate = lambda x: Lorentzian(x)
val, err = quad(to_integrate, min(Lorentzian.domain),
max(Lorentzian.domain))
print(
f"Integration value: {val:.5f} +- {err:.5f} | normalization factor: {n:.5f}"
)
print(f"Normalized Line area: {val*n}")
# - - - - - - - - - - - - - - - - - - - -
F_c = F_cLine("FcLine1", (-15, 15),
x0=0.0,
width=0.02,
A=350.0,
q=0.023,
c=0.0,
weight=1.0)
nfc = F_c.normalize()
ifc = F_c.integrate()
x = np.linspace(-1000, 1000, 5000000)
y = F_c.calc(x, **F_c.line_params)
ifctrapz = np.trapz(y, x)
ifcquadv, ifcquade = quad(lambda x: nfc * F_c(x), min(F_c.domain),
max(F_c.domain))
print(f"{F_c.line_params}")
print(f"Standard Integration value for 10000 steps: {ifc}")
print(
f"QUADPACK Integration value (after normal.): {ifcquadv} +- {ifcquade}"
)
print(f"TRAPEZOID Integration value -1e3 from 1e3 : {ifctrapz}")
# - - - - - - - - - - - - - - - - - - - -
F_I = 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)
nfI = F_I.normalize()
ifI = F_I.integrate()
x = np.linspace(-1000, 1000, 5000000)
y = F_I.calc(x, **F_I.line_params)
ifItrapz = np.trapz(y, x)
ifIquadv, ifIquade = quad(lambda x: F_I(x), min(F_I.domain),
max(F_I.domain))
print(f"{F_I.line_params}")
print(f"Standard Integration value for 10000 steps: {ifI}")
print(
f"QUADPACK Integration value : {ifIquadv} +- {ifIquade}"
)
print(f"TRAPEZOID Integration value -1e3 from 1e3 : {ifItrapz}")
def test_adaptive_vs_linear():
#
L1 = LorentzianLine("LL1", (-energy_from_lambda(6.0), 15),
x0=0.048,
width=0.04,
c=0.0,
weight=0.0)
L2 = F_cLine("F_c1", (-energy_from_lambda(6.0), 15),
x0=0.0,
width=0.0001,
A=350.0,
q=0.02,
c=0.0,
weight=1)
L3 = F_ILine("F_I1", (-energy_from_lambda(6.0), 15),
x0=-0.02,
width=0.01,
A=350.0,
q=0.02,
kappa=0.01,
c=0.0,
weight=1)
# Contruct a SqE model
sqe = SqE(lines=(L1, L2, L3), lam=6.0, dlam=0.12, lSD=3.43, T=20)
# Add the detector efficiency correction
decf = DetectorEfficiencyCorrectionFactor(sqe, ne=100, nlam=20)
### Construct the adaptive integration grid
ne = 100
nlam = 21
l = linspace(1 - sqe.model_params["dlam"], 1 + sqe.model_params["dlam"],
nlam) * sqe.model_params["lam"]
a = -0.99999 * energy_from_lambda(l)
ee = sqe.get_adaptive_integration_grid(ne, nlam)
ee = where(ee <= atleast_2d(a), atleast_2d(a), ee)
ne = ee.shape[0]
ll = tile(l, (ne, 1))
print(
f"ee: - Shape: {ee.shape}\n - ee[::50, {nlam//2}]: {ee[::50, nlam//2]}"
)
print(
f"ll: - Shape: {ll.shape}\n - ll[::50, {nlam//2}]: {ll[::50, nlam//2]}"
)
### Construct a standard linear integration grid
nelin = 10000
nlamlin = 21
eelin, lllin = energy_lambda_nrange(15.0, 6.0, 0.12, nelin, nlamlin)
print(
f"eelin: - Shape: {ee.shape}\n - ee[::{nelin//10}, {nlam//2}]: {eelin[::nelin//10, nlam//2]}"
)
print(
f"lllin: - Shape: {ll.shape}\n - ll[::{nelin//10}, {nlam//2}]: {lllin[::nelin//10, nlam//2]}"
)
### perform correction calculation
from time import time
startt = time()
adaptcorr = decf.calc(ee, ll)
intermedt = time()
lincorr = decf.calc(eelin, lllin)
stopt = time()
print(f"Adaptive integration took: {intermedt - startt:.6f}")
print(f"Adaptive grid correction value: {adaptcorr:.6f}")
print(f"Linear integration took : {stopt - intermedt:.6f}")
print(f"Linear grid correction value : {lincorr:.6f}")