model = QENSmodels.sqwDeltaLorentz(x, q, scale, center, A0, hwhm)
return np.convolve(model, resolution / resolution.sum(), mode='same')
# Fit
M = []
# First dataset: wavelength=5 Angstrom
for i in range(len(q)):
# Bumps fitting model
Mq = bmp.Curve(model_convol,
hw,
sqw[:, i],
err[:, i],
q=q[i],
scale=1000,
center=0.0,
A0=0.5,
hwhm=0.01,
resolution=res[:, i])
Mq.scale.range(0, 1e5)
Mq.center.range(-0.1, 0.1)
Mq.A0.range(0, 1)
Mq.hwhm.range(0, 2)
# Q-independent parameters
if i == 0:
QA0 = Mq.A0
else:
Mq.A0 = QA0
M.append(Mq)
x = x[valid[1]]
if len(valid[1]) != len(x):
print(i, "truncate to make vectors symmetric with respect to max")
data = data[valid]
error = error[valid]
resol = resol[valid]
# Teixeira model
Mq = bmp.Curve(model_convol,
x,
data,
error,
q=q_5A[i],
scale=20,
center=0.0,
A0=0.0,
A1=0.9,
hwhm1=0.05,
hwhm2=0.3,
resolution=resol)
# Fitted parameters
Mq.scale.range(1.e-12, 20)
Mq.center.range(-0.1, 0.1)
Mq.A0.range(0, 0.05)
Mq.A1.range(0, 1)
Mq.hwhm1.range(0, 0.5)
Mq.hwhm2.range(0, 3)
# Q-independent parameters
resol = res_5A[idx_5A, i]
# Select only valid data (error = -1 for Q, w points not accessible)
valid = np.where(error > 0.0)
x = x[valid[1]]
data = data[valid]
error = error[valid]
resol = resol[valid]
# model
Mq = bmp.Curve(model_convol,
x,
data,
error,
q=q_5A[i],
scale=15,
center=0.0,
hwhm=0.1,
radius=1.1,
DR=1.,
resolution=resol)
# Fitted parameters
Mq.scale.range(0, 1e2)
Mq.center.range(-0.1, 0.1)
Mq.hwhm.range(0., 1)
Mq.radius.range(0.9, 1.1)
Mq.DR.range(0.01, 5)
# Q-independent parameters
if i == 0:
resol = res_5A[idx_5A, i]
# Select only valid data (error = -1 for Q, w points not accessible)
valid = np.where(error > 0.0)
x = x[valid[1]]
data = data[valid]
error = error[valid]
resol = resol[valid]
# Teixeira model
Mq = bmp.Curve(model_convol,
x,
data,
error,
q=q_5A[i],
scale=10,
center=0.0,
D=0.13,
resTime=0.1,
radius=1.0,
DR=0.3,
resolution=resol)
# Fitted parameters
Mq.scale.range(0, 1e2)
Mq.center.range(-0.1, 0.1)
Mq.D.range(0.05, 0.25)
Mq.resTime.range(0, 1)
Mq.radius.range(0.9, 1.1)
Mq.DR.range(0, 1)
# Q-independent parameters
dict_physical_units = {
'omega': "1/ps",
'q': "1/Angstrom",
'D': "ps.Angstrom^2",
'L': "Angstrom",
'scale': "unit_of_signal/ps",
'center': "1/ps"
}
M = []
for i in range(len(q)):
# Bumps fitting model
Mq = bmp.Curve(QENSmodels.sqwChudleyElliotDiffusion,
xx,
chudley_elliot_noisy[i],
q[i],
scale=1,
center=0,
D=0.2,
L=0.7)
Mq.scale.range(0.1, 1e5)
Mq.center.range(-0.1, 0.1)
Mq.D.range(0.1, 1)
Mq.L.range(0.1, 3)
# Q-independent parameters
if i == 0:
QD = Mq.D
QL = Mq.L
else:
Mq.D = QD
model = QENSmodels.sqwJumpTranslationalDiffusion(x, q, scale, center, D,
resTime)
return np.convolve(model, resolution / resolution.sum(), mode='same')
# Fit
M = []
for i in range(len(q)):
# Bumps fitting model
Mq = bmp.Curve(model_convol,
hw,
sqw[:, i],
err[:, i],
q=q[i],
scale=1000,
center=0.0,
D=0.1,
resTime=0.5,
resolution=res[:, i])
Mq.scale.range(0, 1e5)
Mq.center.range(-0.1, 0.1)
Mq.D.range(0, 1)
Mq.resTime.range(0, 5)
# Q-independent parameters
if i == 0:
QD = Mq.D
QT = Mq.resTime
else:
Mq.D = QD
print(f"""The names and units of `w` (`x`axis) and `q` are:
{str(unit_w[0], 'utf-8')} and {str(unit_q[0], 'utf-8')}, respectively.""")
# Fit
M = []
# First dataset: wavelength=5 Angstrom
for i in range(len(q)):
# Bumps fitting model
Mq = bmp.Curve(model_convol,
hw,
sqw[:, i],
err[:, i],
q=q[i],
scale=1000,
center=0.0,
radius=1.0,
DR=0.1,
resolution=res[:, i])
Mq.scale.range(0, 1e5)
Mq.center.range(-0.1, 0.1)
Mq.radius.range(0, 3)
Mq.DR.range(0, 2)
# Q-independent parameters
if i == 0:
QR = Mq.radius
QDR = Mq.DR
else:
Mq.radius = QR