def calc_optimize_FrFZ(iteration, F_r, Fintra_r, rho0, i_Q, Q, Iincoh_Q, r,
rmin):
"""Function to calculate the F(r) optimization (eq 47, 48, 49).
Parameters
----------
iteration : int
number of iterations
F_r : numpy array
F(r)
rho0 : float
atomic density
i_Q : numpy array
i(Q)
Q : numpy array
momentum transfer (nm^-1)
Sinf : float
value of S(Q) for Q->inf
J_Q : numpy array
J(Q)
r : numpy array
atomic distance (nm)
rmin : float
r cut-off value (nm)
plot_iter : string
flag to plot the F(r) iterations
Returns
-------
F_r : numpy array
optimazed F(r)
deltaF_r : numpy array
difference between the last F(r) and its theoretical value
"""
for i in range(iteration):
deltaF_r = Optimization.calc_deltaFr(F_r, Fintra_r, r, rho0)
i_Q = calc_iQiFZ(i_Q, Q, Iincoh_Q, deltaF_r, r, rmin)
i_Q[0] = 0.0
F_r = MainFunctions.calc_Fr(r, Q, i_Q)
return (F_r, deltaF_r)
def Optimization(self):
"""Function to optimize and plot F(r)"""
#-------------------Intra-molecular components-----------------------------
# numAtoms, element, x, y, z = Utility.read_xyz_file(self.XYZFilePath)
numAtoms, element, x, y, z = Utility.read_xyz_file(
"/Users/ciccio/work/ID27/LASDiA/xyzFiles/Ar.xyz")
iintra_Q = Optimization.calc_iintra(self.Q, self.fe_Q, self.Ztot,
self.ui.QmaxIntegrate.value(),
self.ui.maxQ.value(),
self.elementList, element, x, y, z,
self.elementParameters)
iintradamp_Q = UtilityAnalysis.calc_iintradamp(iintra_Q,
self.dampingFunct)
Qiintradamp_Q = self.Q * iintradamp_Q
rintra, Fintra_r = MainFunctions.calc_Fr(
self.Q[self.Q <= self.ui.QmaxIntegrate.value()],
Qiintradamp_Q[self.Q <= self.ui.QmaxIntegrate.value()])
scaleFactor = self.ui.scaleFactorValue.value()
density0 = self.ui.densityValue.value()
# ----------------------First scale minimization---------------------------
scaleStep = 0.05
# sth = 0.008
# s0th = 0.006
sth = 0.0
s0th = 0.0
phi_matrix = 0.0
thickness_sampling = 0.0
scaleFactor = Minimization.OptimizeScale(
self.Q, self.I_Q, self.Ibkg_Q, self.J_Q, self.Iincoh_Q, self.fe_Q,
self.ui.maxQ.value(), self.ui.minQ.value(),
self.ui.QmaxIntegrate.value(), self.Ztot, density0,
scaleFactor, self.Sinf, self.ui.smoothingFactor.value(),
self.ui.rmin.value(), self.dampingFunct, Fintra_r,
self.ui.iterations.value(), scaleStep, sth, s0th,
thickness_sampling, phi_matrix, "n")
# ----------------------First density minimization-------------------------
densityStep = density0 / 50
densityStepEnd = density0 / 250
density = Minimization.OptimizeDensity(
self.Q, self.I_Q, self.Ibkg_Q, self.J_Q, self.Iincoh_Q, self.fe_Q,
self.ui.maxQ.value(), self.ui.minQ.value(),
self.ui.QmaxIntegrate.value(), self.Ztot, density0,
scaleFactor, self.Sinf, self.ui.smoothingFactor.value(),
self.ui.rmin.value(), self.dampingFunct, Fintra_r,
self.ui.iterations.value(), densityStep, densityStepEnd, sth, s0th,
thickness_sampling, phi_matrix, "n")
# print("density0, density", density0, density)
numLoopIteration = 0
while 1:
if np.abs(density - density0) > density / 25:
# print("First")
scaleStep = 0.006
densityStep = density / 10
WSamplestep = 0.0008
WRefstep = 0.0008
elif np.abs(density - density0) > density / 75:
# print("Second")
scaleStep = 0.0006
densityStep = density / 100
WSamplestep = 0.0002
WRefstep = 0.0002
else:
# print("Third")
scaleStep = 0.00006
densityStep = density / 1000
WSamplestep = 0.0001
WRefstep = 0.0001
scaleFactor = Minimization.OptimizeScale(
self.Q, self.I_Q, self.Ibkg_Q, self.J_Q, self.Iincoh_Q,
self.fe_Q, self.ui.maxQ.value(), self.ui.minQ.value(),
self.ui.QmaxIntegrate.value(), self.Ztot, density,
scaleFactor, self.Sinf, self.ui.smoothingFactor.value(),
self.ui.rmin.value(), self.dampingFunct, Fintra_r,
self.ui.iterations.value(), scaleStep, sth, s0th,
thickness_sampling, phi_matrix, "n")
density0 = density
density = Minimization.OptimizeDensity(
self.Q, self.I_Q, self.Ibkg_Q, self.J_Q, self.Iincoh_Q,
self.fe_Q, self.ui.maxQ.value(), self.ui.minQ.value(),
self.ui.QmaxIntegrate.value(), self.Ztot, density0,
scaleFactor, self.Sinf, self.ui.smoothingFactor.value(),
self.ui.rmin.value(), self.dampingFunct, Fintra_r,
self.ui.iterations.value(), densityStep, density / 250, sth,
s0th, thickness_sampling, phi_matrix, "n")
numLoopIteration += 1
# print("numLoopIteration", numLoopIteration, scaleFactor, density)
if (np.abs(density - density0) > np.abs(
density / 2500)) and (numLoopIteration <= 30):
continue
else:
break
# print("final scale", scaleFactor, "final density", density)
self.ui.scaleFactorValue.setValue(scaleFactor)
self.ui.densityValue.setValue(density)
Isample_Q = MainFunctions.calc_IsampleQ(self.I_Q, scaleFactor,
self.Ibkg_Q)
alpha = MainFunctions.calc_alpha(
self.J_Q[self.Q <= self.ui.QmaxIntegrate.value()], self.Sinf,
self.Q[self.Q <= self.ui.QmaxIntegrate.value()],
Isample_Q[self.Q <= self.ui.QmaxIntegrate.value()],
self.fe_Q[self.Q <= self.ui.QmaxIntegrate.value()], self.Ztot,
density)
Icoh_Q = MainFunctions.calc_Icoh(alpha, Isample_Q, self.Iincoh_Q)
S_Q = MainFunctions.calc_SQ(Icoh_Q, self.Ztot, self.fe_Q, self.Sinf,
self.Q, self.ui.minQ.value(),
self.ui.QmaxIntegrate.value(),
self.ui.maxQ.value())
Ssmooth_Q = UtilityAnalysis.calc_SQsmoothing(
self.Q, S_Q, self.Sinf, self.ui.smoothingFactor.value(),
self.ui.minQ.value(), self.ui.QmaxIntegrate.value(),
self.ui.maxQ.value())
SsmoothDamp_Q = UtilityAnalysis.calc_SQdamp(Ssmooth_Q, self.Sinf,
self.dampingFunct)
i_Q = MainFunctions.calc_iQ(SsmoothDamp_Q, self.Sinf)
Qi_Q = self.Q * i_Q
r, F_r = MainFunctions.calc_Fr(
self.Q[self.Q <= self.ui.QmaxIntegrate.value()],
Qi_Q[self.Q <= self.ui.QmaxIntegrate.value()])
Fopt_r, deltaFopt_r = Optimization.calc_optimize_Fr(
self.ui.iterations.value(), F_r, Fintra_r, density,
i_Q[self.Q <= self.ui.QmaxIntegrate.value()],
self.Q[self.Q <= self.ui.QmaxIntegrate.value()], self.Sinf,
self.J_Q[self.Q <= self.ui.QmaxIntegrate.value()], r,
self.ui.rmin.value(), "n")
Scorr_Q = MainFunctions.calc_SQCorr(Fopt_r, r, self.Q, self.Sinf)
self.ui.distfuncPlot.canvas.ax.plot(r,
Fopt_r,
"g",
label=r"$F_{opt}(r)$")
self.ui.distfuncPlot.canvas.ax.legend()
self.ui.distfuncPlot.canvas.draw()
self.ui.factorPlot.canvas.ax.plot(self.Q,
Scorr_Q,
"g",
label=r"$S_{opt}(Q)$")
self.ui.factorPlot.canvas.ax.legend()
self.ui.factorPlot.canvas.draw()
# -------------------Preliminary calculation-------------------------------
fe_Q, Ztot = MainFunctions.calc_eeff(elementList, Q, elementParameters)
Iincoh_Q = MainFunctions.calc_Iincoh(elementList, Q, elementParameters)
J_Q = MainFunctions.calc_JQ(Iincoh_Q, Ztot, fe_Q)
Sinf = MainFunctions.calc_Sinf(elementList, fe_Q, Q, Ztot,
elementParameters)
dampingFunction = UtilityAnalysis.calc_dampingFunction(
Q, variables.dampingFactor, variables.QmaxIntegrate,
variables.typeFunction)
# ------------------Intra-molecular components-----------------------------
iintra_Q = Optimization.calc_iintra(Q, fe_Q, Ztot, variables.QmaxIntegrate,
variables.maxQ, elementList, element,
x, y, z, elementParameters)
iintradamp_Q = UtilityAnalysis.calc_iintradamp(iintra_Q, dampingFunction)
Qiintradamp_Q = Q * iintradamp_Q
rintra, Fintra_r = MainFunctions.calc_Fr(
Q[Q <= variables.QmaxIntegrate],
Qiintradamp_Q[Q <= variables.QmaxIntegrate])
# _, Fintra_r = UtilityAnalysis.rebinning(rintra, Fintra_r, np.amin(Fintra_r),
# np.amax(Fintra_r), 8192)
# _, dampingFunction = UtilityAnalysis.rebinning(Q, dampingFunction, 0.0,
# variables.maxQ, variables.NumPoints)
# ---------------------Geometrical correction------------------------------
def Kaplow_method(Q, I_Q, Ibkg_Q, J_Q, fe_Q, Iincoh_Q, Sinf, Ztot, scaleFactor,
density, Fintra_r, r, minQ, QmaxIntegrate, maxQ,
smoothFactor, dampFactor, iteration, rmin):
"""Function to apply the Kaplow method.
Parameters
----------
variables : module
input variables setted by the user
Q : numpy array
momentum transfer (nm^-1)
I_Q : numpy array
measured scattering intensity
Ibkg_Q : numpy array
background scattering intensity
J_Q : numpy array
J(Q)
fe_Q : numpy array
effective electric form factor
Iincoh_Q : numpy array
incoherent scattering intensity
Sinf : float
value of S(Q) for Q->inf
Ztot : int
total Z number
scaleFactor : float
scale factor
density : float
average atomic density
Fintra_r : numpy array
intramolecular contribution of F(r)
r : numpy array
atomic distance (nm)
Returns
-------
chi2 : float
chi2 value
SsmoothDamp_Q : numpy array
smoothed and damped structure factor
Fopt_r : numpy array
optimized F(r)
"""
Isample_Q = MainFunctions.calc_IsampleQ(I_Q, scaleFactor, Ibkg_Q)
alpha = MainFunctions.calc_alpha(J_Q[Q <= QmaxIntegrate], Sinf,
Q[Q <= QmaxIntegrate],
Isample_Q[Q <= QmaxIntegrate],
fe_Q[Q <= QmaxIntegrate], Ztot, density)
Icoh_Q = MainFunctions.calc_Icoh(alpha, Isample_Q, Iincoh_Q)
S_Q = MainFunctions.calc_SQ(Icoh_Q, Ztot, fe_Q, Sinf, Q, minQ,
QmaxIntegrate, maxQ)
Ssmooth_Q = UtilityAnalysis.calc_SQsmoothing(Q, S_Q, Sinf, smoothFactor,
minQ, QmaxIntegrate, maxQ)
SsmoothDamp_Q = UtilityAnalysis.calc_SQdamp(Ssmooth_Q, Q, Sinf,
QmaxIntegrate, dampFactor)
i_Q = MainFunctions.calc_iQ(SsmoothDamp_Q, Sinf)
F_r = MainFunctions.calc_Fr(r, Q[Q <= QmaxIntegrate],
i_Q[Q <= QmaxIntegrate])
Fopt_r, deltaFopt_r = Optimization.calc_optimize_Fr(
iteration, F_r, Fintra_r, density, i_Q[Q <= QmaxIntegrate],
Q[Q <= QmaxIntegrate], Sinf, J_Q[Q <= QmaxIntegrate], r, rmin, "n")
chi2 = simps(deltaFopt_r[r < rmin]**2, r[r < rmin])
return (chi2, SsmoothDamp_Q, F_r, Fopt_r)
# plt.plot(Qbkg, Ibkg_Q)
# plt.show
fe_Q, Ztot = MainFunctions.calc_eeff(elementList, Q, elementParameters)
Iincoh_Q = MainFunctions.calc_Iincoh(elementList, Q, elementParameters)
J_Q = MainFunctions.calc_JQ(Iincoh_Q, Ztot, fe_Q)
Sinf = MainFunctions.calc_Sinf(elementList, fe_Q, Q, Ztot,
elementParameters)
dampingFunction = UtilityAnalysis.calc_dampingFunction(
Q, inputVariables["dampingFactor"], inputVariables["QmaxIntegrate"],
inputVariables["typeFunction"])
#-------------------Intra-molecular components-----------------------------
iintra_Q = Optimization.calc_iintra(
Q, fe_Q, Ztot, inputVariables["QmaxIntegrate"], inputVariables["maxQ"],
elementList, elementPosition["element"], elementPosition["x"],
elementPosition["y"], elementPosition["z"], elementParameters)
iintradamp_Q = UtilityAnalysis.calc_iintradamp(iintra_Q, dampingFunction)
Qiintradamp_Q = Q * iintradamp_Q
rintra, Fintra_r = MainFunctions.calc_Fr(
Q[Q <= inputVariables["QmaxIntegrate"]],
Qiintradamp_Q[Q <= inputVariables["QmaxIntegrate"]])
# ---------------------Geometrical correction------------------------------
# global phi_matrix
# global thickness_sampling
if inputVariables["mccFlag"].lower() == "y":
print("Start matrix calculation or reading")
thickness_sampling, phi_matrix = Geometry.check_phi_matrix(
#--------------------Preliminary calculation-------------------------------
# Q, I_Q, Qbkg, Ibkg_Q = UtilityAnalysis.check_data_length(Q, I_Q, Qbkg, Ibkg_Q, \
# variables.minQ, variables.maxQ)
fe_Q, Ztot = MainFunctions.calc_eeff(elementList, Q, elementParameters)
Iincoh_Q = MainFunctions.calc_Iincoh(elementList, Q, elementParameters)
J_Q = IgorFunctions.calc_JQ(Iincoh_Q, fe_Q)
Sinf = MainFunctions.calc_Sinf(elementList, fe_Q, Q, Ztot, elementParameters)
dampingFunction = UtilityAnalysis.calc_dampingFunction(Q, variables.dampingFactor,
variables.QmaxIntegrate, variables.typeFunction)
#-------------------Intra-molecular components-----------------------------
iintra_Q = Optimization.calc_iintra(Q, fe_Q, Ztot, variables.QmaxIntegrate,
variables.maxQ, elementList, element, x, y, z, elementParameters)
iintradamp_Q = UtilityAnalysis.calc_iintradamp(iintra_Q, Q, variables.QmaxIntegrate,
dampingFunction)
rintra, Fintra_r = IgorFunctions.calc_FFT_QiQ(Q, Q*iintradamp_Q, variables.QmaxIntegrate)
_, Fintra_r = UtilityAnalysis.rebinning(rintra, Fintra_r, np.amin(rintra),
np.amax(rintra), 8192)
_, dampingFunction = UtilityAnalysis.rebinning(Q, dampingFunction, 0.0,
variables.maxQ, variables.NumPoints)
# ---------------------Geometrical correction------------------------------
absCorrFactor = IgorFunctions.absorption(Q)
I_Q = I_Q/absCorrFactor
Ibkg_Q = Ibkg_Q/absCorrFactor
def OptimizeScale(Q, I_Q, Ibkg_Q, J_Q, Iincoh_Q, fe_Q, minQ, QmaxIntegrate,
maxQ, Ztot, density, scaleFactor, Sinf, smoothingFactor,
rmin, dampingFunction, Fintra_r, iterations, scaleStep, sth,
s0th, mccFlag, thickness_sampling, phi_matrix):
"""Function for the scale factor optimization.
Q : numpy array
momentum transfer (nm^-1)
I_Q : numpy array
measured scattering intensity
Ibkg_Q : numpy array
background scattering intensity
J_Q : numpy array
J(Q)
Iincoh_Q : numpy array
incoherent scattering intensity
fe_Q : numpy array
effective electric form factor
minQ : float
minimum Q value
QmaxIntegrate : float
maximum Q value for the intagrations
maxQ : float
maximum Q value
Ztot : int
total Z number
density : float
average atomic density
scaleFactor : float
scale factor
Sinf : float
value of S(Q) for Q->inf
smoothingFactor : float
smoothing factor
rmin : float
r cut-off value (nm)
dampingFunction : numpy array
damping function
Fintra_r : numpy array
intramolecular contribution of F(r)
iterations : int
number of iterations
scaleStep
sth
s0th
MCC_flag
"""
numSample = 23
if mccFlag.lower() == "y":
T_MCC_sth, T_MCC_corr_factor_bkg = Geometry.MCC_correction(
sth, s0th, thickness_sampling, phi_matrix)
# print(thickness_sampling)
# plt.contour(phi_matrix)
# plt.show()
# x = np.linspace(0, len(T_MCC_sth), len(T_MCC_sth), endpoint=True)
# _, T_MCC_sth = UtilityAnalysis.rebinning(x, T_MCC_sth, np.amin(T_MCC_sth),
# np.amax(T_MCC_sth), len(I_Q))
# plt.plot(T_MCC_sth)
# plt.show()
I_Q = I_Q / T_MCC_sth
Ibkg_Q = Ibkg_Q * T_MCC_corr_factor_bkg / (T_MCC_sth)
flag = 0
# Loop for the range shifting
while 1:
# print(type(scaleFactor))
# print(type(scaleStep))
scaleArray = UtilityAnalysis.makeArrayLoop(scaleFactor, scaleStep)
chi2Array = np.zeros(numSample)
flag += 1
print("iter flag ", flag)
for i in range(numSample):
print("sample ", i, scaleArray[i])
# ------------------Kaplow method for scale--------------------
Isample_Q = MainFunctions.calc_IsampleQ(I_Q, scaleArray[i], Ibkg_Q)
alpha = MainFunctions.calc_alpha(J_Q[Q <= QmaxIntegrate], Sinf,
Q[Q <= QmaxIntegrate],
Isample_Q[Q <= QmaxIntegrate],
fe_Q[Q <= QmaxIntegrate], Ztot,
density)
Icoh_Q = MainFunctions.calc_Icoh(alpha, Isample_Q, Iincoh_Q)
S_Q = MainFunctions.calc_SQ(Icoh_Q, Ztot, fe_Q, Sinf, Q, minQ,
QmaxIntegrate, maxQ)
Ssmooth_Q = UtilityAnalysis.calc_SQsmoothing(
Q, S_Q, Sinf, smoothingFactor, minQ, QmaxIntegrate, maxQ)
SsmoothDamp_Q = UtilityAnalysis.calc_SQdamp(
Ssmooth_Q, Sinf, dampingFunction)
i_Q = MainFunctions.calc_iQ(SsmoothDamp_Q, Sinf)
Qi_Q = Q * i_Q
r, F_r = MainFunctions.calc_Fr(Q[Q <= QmaxIntegrate],
Qi_Q[Q <= QmaxIntegrate])
Fopt_r, deltaFopt_r = Optimization.calc_optimize_Fr(
iterations, F_r, Fintra_r, density, i_Q[Q <= QmaxIntegrate],
Q[Q <= QmaxIntegrate], Sinf, J_Q[Q <= QmaxIntegrate], r, rmin,
"n")
deltaFopt_r[np.where(r >= rmin)] = 0.0 # Igor version
chi2Array[i] = np.mean(deltaFopt_r**2) # Igor version
# Fth_r = Fintra_r - 4*np.pi*r*density
# plt.plot(r, Fth_r)
# plt.show()
# print(Fth_r)
# print(Fintra_r)
# chi2Array[i] = simps(deltaFopt_r[np.where((r>0)&(r<rmin))]**2)
# --------------------Range shifting selection --------------------
plt.scatter(scaleArray, chi2Array)
plt.grid(True)
plt.show()
maxLimit = 10**5
if np.amax(chi2Array) >= maxLimit:
scaleArray = scaleArray[0:np.argmax(chi2Array >= maxLimit)]
chi2Array = chi2Array[0:np.argmax(chi2Array >= maxLimit)]
if np.argmin(chi2Array) < 6:
scaleFactor -= scaleStep * 10
if np.argmin(chi2Array) > 16:
scaleFactor += scaleStep * 10
if (np.argmin(chi2Array) >= 6 and np.argmin(chi2Array) <= 16):
break
else:
continue
# ------------------------chi2 curve fit for scale-------------------------
# plt.ioff()
xFit, yFit, scaleFactor, chi2 = IgorFunctions.chi2Fit(
scaleFactor, scaleArray, chi2Array)
print("final scale factor", scaleFactor)
plt.scatter(scaleArray, chi2Array)
plt.plot(xFit, yFit)
plt.grid(True)
plt.show()
return scaleFactor
def OptimizeThicknessRef(Q, I_Q, Ibkg_Q, J_Q, Iincoh_Q, fe_Q, maxQ, minQ,
QmaxIntegrate, Ztot, density, scaleFactor, sth, s0th,
Sinf, smoothingFactor, rmin, dampingFunction,
Fintra_r, iterations, s0thStep, ws1, ws2, r1, r2, d,
phi_matrix_flag):
"""Function for the thickness optimization.
"""
Flag = 0
NoPeak = 0
s0th = max(s0th - s0thStep * 11, 0.0)
s0thStepEnd = 0.0006
numSample = 23
# Loop for the range shifting
while 1:
s0thArray = IgorFunctions.makeArrayLoop(s0th, s0thStep)
chi2Array = np.zeros(numSample)
for i in range(numSample):
# ------------------Kaplow method for scale--------------------
T_MCC_sth, T_MCC_corr_factor_bkg = Geometry.MCCCorrection(
sth, s0thArray[i], thickness_sampling, phi_matrix)
I_Q = I_Q / T_MCC_sth
Ibkg_Q = Ibkg_Q * T_MCC_corr_factor_bkg / (T_MCC_sth)
Isample_Q = MainFunctions.calc_IsampleQ(I_Q, scaleFactor, Ibkg_Q)
alpha = MainFunctions.calc_alpha(J_Q[Q <= QmaxIntegrate], Sinf,
Q[Q <= QmaxIntegrate],
Isample_Q[Q <= QmaxIntegrate],
fe_Q[Q <= QmaxIntegrate], Ztot,
density)
Icoh_Q = MainFunctions.calc_Icoh(alpha, Isample_Q, Iincoh_Q)
S_Q = MainFunctions.calc_SQ(Icoh_Q, Ztot, fe_Q, Sinf, Q, minQ,
QmaxIntegrate, maxQ)
Ssmooth_Q = UtilityAnalysis.calc_SQsmoothing(
Q, S_Q, Sinf, smoothingFactor, minQ, QmaxIntegrate, maxQ)
SsmoothDamp_Q = UtilityAnalysis.calc_SQdamp(
Ssmooth_Q, Sinf, dampingFunction)
i_Q = MainFunctions.calc_iQ(SsmoothDamp_Q, Sinf)
Qi_Q = Q * i_Q
r, F_r = MainFunctions.calc_Fr(Q[Q <= QmaxIntegrate],
Qi_Q[Q <= QmaxIntegrate])
Fopt_r, deltaFopt_r = Optimization.calc_optimize_Fr(
iterations, F_r, Fintra_r, density, i_Q[Q <= QmaxIntegrate],
Q[Q <= QmaxIntegrate], Sinf, J_Q[Q <= QmaxIntegrate], r, rmin,
"n")
deltaFopt_r[np.where(r >= rmin)] = 0.0
chi2Array[i] = np.mean(deltaFopt_r**2)
# --------------------Range shifting selection --------------------
if np.amax(chi2Array) > 10**8:
s0thIdx = np.argmin(chi2Array[0:np.argmax(chi2Array)])
else:
s0thIdx = np.argmin(chi2Array)
s0th = s0thArray[s0thIdx] - s0thStep * 1.1
nearIdx, nearEl = UtilityAnalysis.find_nearest(s0thArray, s0th)
if nearIdx == 0:
print("out1")
s0th -= s0thStep * 10
s0thStep *= 10
NoPeak += 1
if nearIdx >= numSample - 2:
print("out2")
s0th += s0thStep * 10
s0thStep *= 10
NoPeak += 1
s0thStep /= 10
Flag += 1
if ((10 * s0thStep > s0thStepEnd) and (NoPeak < 5)):
continue
else:
break
# ------------------------chi2 curve fit for scale-------------------------
xFit, yFit, sth, chi2 = IgorFunctions.chi2Fit(s0th, s0thArray, chi2Array)
print("final sample thickness ref", s0th)
return s0th
def OptimizeDensity(Q, I_Q, Ibkg_Q, J_Q, Iincoh_Q, fe_Q, minQ, QmaxIntegrate,
maxQ, Ztot, density, scaleFactor, Sinf, smoothingFactor,
rmin, dampingFunction, Fintra_r, iterations, densityStep,
sth, s0th, mccFlag, thickness_sampling, phi_matrix):
"""Function for the density optimization.
Q : numpy array
momentum transfer (nm^-1)
I_Q : numpy array
measured scattering intensity
Ibkg_Q : numpy array
background scattering intensity
J_Q : numpy array
J(Q)
Iincoh_Q : numpy array
incoherent scattering intensity
fe_Q : numpy array
effective electric form factor
minQ : float
minimum Q value
QmaxIntegrate : float
maximum Q value for the intagrations
maxQ : float
maximum Q value
Ztot : int
total Z number
density : float
average atomic density
scaleFactor : float
scale factor
Sinf : float
value of S(Q) for Q->inf
smoothingFactor : float
smoothing factor
rmin : float
r cut-off value (nm)
dampingFunction : numpy array
damping function
Fintra_r : numpy array
intramolecular contribution of F(r)
iterations : int
number of iterations
scaleStep
sth
s0th
thickness_sampling : float
phi_matrix
MCC_flag
"""
numSample = 23
if mccFlag.lower() == "y":
T_MCC_sth, T_MCC_corr_factor_bkg = Geometry.MCC_correction(
sth, s0th, thickness_sampling, phi_matrix)
I_Q = I_Q / T_MCC_sth
Ibkg_Q = Ibkg_Q * T_MCC_corr_factor_bkg / (T_MCC_sth)
flag = 0
# Loop for the range shifting
while 1:
flag += 1
print("iter flag ", flag)
densityArray = UtilityAnalysis.makeArrayLoop(density, densityStep)
chi2Array = np.zeros(numSample)
for i in range(numSample):
print("sample ", i, densityArray[i])
# ------------------Kaplow method for scale--------------------
Isample_Q = MainFunctions.calc_IsampleQ(I_Q, scaleFactor, Ibkg_Q)
alpha = MainFunctions.calc_alpha(J_Q[Q <= QmaxIntegrate], Sinf,
Q[Q <= QmaxIntegrate],
Isample_Q[Q <= QmaxIntegrate],
fe_Q[Q <= QmaxIntegrate], Ztot,
densityArray[i])
Icoh_Q = MainFunctions.calc_Icoh(alpha, Isample_Q, Iincoh_Q)
S_Q = MainFunctions.calc_SQ(Icoh_Q, Ztot, fe_Q, Sinf, Q, minQ,
QmaxIntegrate, maxQ)
Ssmooth_Q = UtilityAnalysis.calc_SQsmoothing(
Q, S_Q, Sinf, smoothingFactor, minQ, QmaxIntegrate, maxQ)
SsmoothDamp_Q = UtilityAnalysis.calc_SQdamp(
Ssmooth_Q, Sinf, dampingFunction)
i_Q = MainFunctions.calc_iQ(SsmoothDamp_Q, Sinf)
Qi_Q = Q * i_Q
r, F_r = MainFunctions.calc_Fr(Q[Q <= QmaxIntegrate],
Qi_Q[Q <= QmaxIntegrate])
# r, F_r = MainFunctions.calc_Fr(Q[Q<=QmaxIntegrate],
# Qi_Q[Q<=QmaxIntegrate])
Fopt_r, deltaFopt_r = Optimization.calc_optimize_Fr(
iterations, F_r, Fintra_r, densityArray[i],
i_Q[Q <= QmaxIntegrate], Q[Q <= QmaxIntegrate], Sinf,
J_Q[Q <= QmaxIntegrate], r, rmin, "n")
deltaFopt_r[np.where(r >= rmin)] = 0.0
chi2Array[i] = np.mean(deltaFopt_r**2)
# --------------------Range shifting selection --------------------
# densityIdx = np.argmin(chi2Array)
# density = densityArray[densityIdx] - densityStep*1.1
# nearIdx, nearEl = UtilityAnalysis.find_nearest(densityArray, density)
plt.scatter(densityArray, chi2Array)
plt.grid(True)
plt.show()
maxLimit = 10**5
if np.amax(chi2Array) >= maxLimit:
densityArray = densityArray[0:np.argmax(chi2Array >= maxLimit)]
chi2Array = chi2Array[0:np.argmax(chi2Array >= maxLimit)]
# print(np.argmin(chi2Array))
if np.argmin(chi2Array) < 6:
# print("bug")
density -= densityStep * 10
if np.argmin(chi2Array) > 16:
density += densityStep * 10
if (np.argmin(chi2Array) >= 6 and np.argmin(chi2Array) <= 16):
break
else:
continue
# ------------------------chi2 curve fit for scale-------------------------
#xFit, yFit, density, chi2Min = chi2Fit(densityArray, chi2Array)
xFit, yFit, density, chi2 = IgorFunctions.chi2Fit(density, densityArray,
chi2Array)
print("final density", density)
plt.scatter(densityArray, chi2Array)
plt.plot(xFit, yFit)
plt.grid(True)
plt.show()
return density