def test_correctionFactor_dimensionality():
# We need some lines
L1 = LorentzianLine(name="Lorentzian1",
domain=(-16.0, 16.0),
x0=-1.0,
width=0.4,
c=0.0,
weight=1)
# Contruct a SqE model
sqe = SqE(lines=(L1, ), lam=6.0, dlam=0.12, lSD=3.43, T=20)
# Instantiate a detector efficiency corr factor
decf = DetectorEfficiencyCorrectionFactor(sqe)
# Instantiate a energy cutoff corr factor
eccf = EnergyCutOffCorrectionFactor(sqe)
ne = 15
nlam = 5
lam = 6.0 * linspace(1 - 0.12 * 1.01, 1 + 0.12 * 1.01, nlam)
lams = tile(lam, (ne, 1))
a = -0.99999 * energy_from_lambda(lam)
b = 15.0 + a
es = linspace(a, b, ne)
print(lams)
print(es)
print(decf.calc(es, lams))
print(eccf.calc(es, lams))
def test_EnergyCutOffCorrectionFactor_vs_arg():
# We need some lines
L1 = LorentzianLine(name="Lorentzian1",
domain=(-16.0, 16.0),
x0=-1.0,
width=0.4,
c=0.0,
weight=1)
# Contruct a SqE model
sqe = SqE(lines=(L1, ), lam=6.0, dlam=0.12, lSD=3.43, T=20)
# Instantiate a energy cutoff corr factor
eccf = EnergyCutOffCorrectionFactor(sqe)
ne = 15000
nlam = 20
lam = 6.0 * linspace(1 - 0.12 * 1.01, 1 + 0.12 * 1.01, nlam)
lams = tile(lam, (ne, 1))
a = -0.99999 * energy_from_lambda(lam)
b = 15.0 + a
es = linspace(a, b, ne)
test_eccf_vals = arg.CutFac_Eint(arg.lorentzian, -1.0, 0.4, 15000, 20, 6.0,
0.12, 1, 0.0, 1.0, 20, 0.5, 0.00001, 350.)
eccf_vals = eccf.correction(es, lams)
# print(test_eccf_vals.shape)
print(test_eccf_vals)
# print(eccf_vals.shape)
print(eccf_vals)
def test_export_load():
### Creating a SqE model for transformation
# We need some lines
L1 = LorentzianLine("Lorentzian1", (-5.0, 5.0),
x0=0.0,
width=0.00005,
c=0.0,
weight=0.1)
L2 = F_ILine("FI1", (-energy_from_lambda(6.0), 15),
x0=-0.01,
width=0.008,
A=350.0,
q=0.02,
kappa=0.01,
c=0.0,
weight=0.9)
# Contruct a SqE model
sqe = SqE(lines=(L1, L2), lam=6.0, dlam=0.12, lSD=3.43, T=628)
# Add the detector efficiency correction
decf = DetectorEfficiencyCorrectionFactor(sqe, ne=500, nlam=20)
# Add the energycutoff correction
eccf = EnergyCutOffCorrectionFactor(sqe, ne=500, nlam=20)
### Instantiate a transformer
sqt = SqtTransformer(sqe,
corrections=(decf, eccf),
nlam=20,
ne=500,
lSD=3.43,
integ_mode="adaptive")
### Export
sqt_dict = sqt.export_to_dict()
pprint(sqt_dict["corrections"])
sqt.export_to_jsonfile(
f"{testdir}/resources/test_transformer_export_load_file.json")
print("",
"SqE's of the: ",
f"- sqe: {sqe}",
f"- decf: {decf.sqe}",
f"- eccf: {eccf.sqe}",
f"- sqt: {sqt.sqemodel}",
sep="\n")
### Loading
sqt_from_dict = sqt.load_from_dict(**sqt_dict)
print("", "Loading successful: ", sqt, sqt_from_dict, sep='\n')
sqt_from_file = sqt.load_from_jsonfile(
f"{testdir}/resources/test_transformer_export_load_file.json")
print("Loading successful: ", sqt, sqt_from_file, sep='\n')
print("",
"SqE's of the: ",
f"- sqe: {sqe}",
f"- decf: {sqt_from_dict.corrections[0].sqe}",
f"- eccf: {sqt_from_dict.corrections[1].sqe}",
f"- sqt: {sqt_from_dict.sqemodel}",
sep="\n")
print("\n\nThis is the sqt loaded from dict")
pprint(sqt_from_file.export_to_dict())
def test_update_params():
### 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=2)
L2 = LorentzianLine(name="Lorentzian2",
domain=(-5.0, 5.0),
x0=-1.0,
width=0.4,
c=0.0,
weight=1)
L3 = F_ILine("FI1", (-energy_from_lambda(6.0), 15),
x0=-0.1,
width=0.008,
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=10000, nlam=20)
# Add the energycutoff correction
eccf = EnergyCutOffCorrectionFactor(sqe, ne=10000, nlam=20)
### Instantiate a transformer
sqt = SqtTransformer(sqe,
corrections=(decf, eccf),
nlam=20,
ne=10000,
lSD=3.43)
print("\n\nBefore update:")
pprint(sqt.export_to_dict())
tdict = dict(T=30,
lam=8.0,
x0_Lorentzian2=-0.5,
width_Lorentzian2=0.025,
weight_Lorentzian1=5,
nlam=55,
kappa_FI1=1.0,
some_wired_param=True)
sqt.update_params(**tdict)
print("\n\nAfter update:")
pprint(sqt.export_to_dict())
def test_EnergyCutoffCorrectionFactor():
# We need some lines
L1 = LorentzianLine("Lorentzian1", (-5.0, 5.0),
x0=0.0,
width=0.4,
c=0.0,
weight=2)
L2 = LorentzianLine(name="Lorentzian2",
domain=(-5.0, 5.0),
x0=-1.0,
width=0.4,
c=0.02,
weight=1)
# Contruct a SqE model
sqe = SqE(lines=(L1, L2), lam=6.0, dlam=0.12, lSD=3.43, T=20)
new_domain = (-1 * energy_from_lambda(6.0), UPPER_INTEGRATION_LIMIT)
sqe.update_domain(new_domain)
# init energycutoff
eccf = EnergyCutOffCorrectionFactor(sqe, ne=10000, nlam=20)
ne = 10000
nlam = 5
lam = 6.0 * linspace(1 - 0.12 * 1.01, 1 + 0.12 * 1.01, nlam)
lams = tile(lam, (ne, 1))
a = -0.99999 * energy_from_lambda(lam)
b = 15.0 + a
es = linspace(a, b, ne)
### Calculate the trapz integral over the S(q,E)
# Only over domain (interval length: 15 meV)
I_over_dom_only = trapz(sqe(es[:, 2]), es[:, 2])
print("Trapz integration over the domain.")
print(f"Interval {a[2]:.4f} - {b[2]:.4f} -> length {b[2]-a[2]:.4f} meV.")
print(f"#Steps = {ne}")
print(f"Integral value: {I_over_dom_only:.4f}")
# plt.plot(es[:,2], sqe(es[:,2]), label="Over domain only")
# plt.show()
# Beyond domain same array length
es_same_length = linspace(-UPPER_INTEGRATION_LIMIT,
UPPER_INTEGRATION_LIMIT, ne)
I_beyond_dom_same_length = trapz(sqe(es_same_length), es_same_length)
print("\nTrapz integration beyond the domain with varrying stepsize.")
print(
f"Interval {-UPPER_INTEGRATION_LIMIT} - {UPPER_INTEGRATION_LIMIT} -> length {30.0} meV."
)
print(f"#Steps = {ne}")
print(f"Integral value: {I_beyond_dom_same_length:.4f}")
# plt.plot(es_same_length, sqe(es_same_length), ls="--", label="Beyond domain ne=10000")
# plt.show()
# Beyond domain same step size
es_same_stepsize = arange(-UPPER_INTEGRATION_LIMIT,
UPPER_INTEGRATION_LIMIT + 0.001, 15e-3)
I_beyond_dom_same_stepsize = trapz(sqe(es_same_stepsize), es_same_stepsize)
print("\nTrapz integration beyond the domain with varrying stepsize.")
print(
f"Interval {-UPPER_INTEGRATION_LIMIT} - {UPPER_INTEGRATION_LIMIT} -> length {30.0} meV."
)
print(f"#Steps = {30.0 / 0.015}")
print(f"Integral value: {I_beyond_dom_same_stepsize:.4f}")