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xaesa_exafs_class.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Feb 13 11:55:12 2018
@author: akali
"""
import sys
import os
from numpy import arange, argmin, argmax, argsort, asarray, concatenate, copy, delete, \
gradient, log, logical_and, multiply, newaxis, pi, power, \
sin, cos, sqrt, sum, where, zeros, unique, ones, convolve, \
isnan, polyfit, polyval, r_
from scipy.optimize import curve_fit
from scipy.interpolate import Rbf, InterpolatedUnivariateSpline, UnivariateSpline
from scipy.signal import symiirorder1
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from .xaesa_constants_formulas import victoreen, windowGauss10, \
me, hbar
from .xaesa_ft import FT, BFT, GETPHASE, BFTWindow
from interpolation_k2 import LSQ_ZeroLine, extend_zeroline
from scipy.interpolate import make_lsq_spline
#import xalglib
class xaesa_exafs_class():
def __init__(self, init_raw_data_type):
self.raw_data_type = init_raw_data_type #0 for transmission, 1 for fluorescence, 2 for Mju, 3 for EXAFS
self.name = "" #name
#raw data loaded from experimantal file
self.energy = [] #1D array
self.energyRebined = []
self.energyOriginal = []
self.i0 = [] #1D array
self.i1 = [] #1D array
self.i2 = [] #1D array
self.ifluo = [] #2D array
#Mju datasets
self.mju = []
self.mjuRebined = []
self.mjuOriginal = []
self.mjuDerivative= []
self.victoreen = []
self.mjuMinusVictoreen = []
self.mju0 = []
self.mjuRef = []
#EXAFS datasets
self.k = []
self.exafs = []
self.exafsZeroLine = []
self.exafsZLC = []
self.exafsZLCdeglitch = []
# FT datasets
self.window = []
self.exafsTimesWindow = []
self.exafsZLCTimesWindow = []
self.r = []
self.fr = []
self.fi = []
self.efr = []
self.efi = []
self.rZLC = []
self.frZLC = []
self.fiZLC = []
self.efrZLC = []
self.efiZLC = []
# BFT datasets
self.bftWindow = []
self.bftk = []
self.bftefr = []
self.bftefi = []
self.bftAmp = []
self.bftPha = []
self.bftEXAFS = []
self.bftefrWindow = []
self.bftefiWindow = []
#Rebin parameters
self.dE1 = 3
self.dE2 = 0.1
self.dE3 = 1
#extraction params
self.Es = 0
self.E0 = 0
self.E1 = 0
self.E2 = 0
self.E3 = 0
self.energyShift = 0
self.kPower = 2
self.zeroLineCorr = 0
self.mju0PolinomialDegree = 4
self.bckgrPolinomialDegree = -1
self.normalizationMode = 0 #0 for mju0 normalization, 1 for value normalization at given energy
self.normalizationEnergy = 0
self.reduceK = 0
self.c = 0
#FT params
self.kMin = 0.5
self.kMax = 18
self.dk = 0.05
self.rMin = 0
self.rMax = 6
self.dr = 0.02
#BFT params
self.rMinBft = 0
self.rMaxBft = 6
self.bftWindowParam = 0.1
# Deglitching params and result
self.deglitchIntervals = []
#fit params and results
self.isFitted = 0
self.fitKMin = 0.5
self.fitKMax = 15
self.fitdk = 0.05
self.fitNShels = 1
self.fitParams = []
self.fitAmps = []
self.fitPhas = []
self.fitK = []
self.fitExafs = []
#rdf params and results
self.isRdfed = 0
self.rdfKMin = 0.5
self.rdfKMax = 15
self.rdfdk = 0.05
self.rdfRMin = 0.5
self.rdfRMax = 2.8
self.rdfdr = 0.01
self.rdfMaxIterations = 20
self.rdfAmpK = []
self.rdfAmp = []
self.rdfPhaK = []
self.rdfPha = []
self.rdfAmpFile = ''
self.rdfPhaFile = ''
self.rdfK = []
self.rdfExafs = []
self.rdfR = []
self.rdf = []
def calculateMjuTransmission(self):
self.mju = log(self.i0 / self.i1)
try:
self.mjuRef = log(self.i1 / self.i2)
except:
pass
def calculateMjuFluorescence(self):
self.mju = self.ifluo.sum(axis=0) / self.i0
print(self.mju)
def estimateEnergyValues(self):
# try:
# spl = Rbf( self.energy, self.mju,
# function = 'multiquadric',
# epsilon = 3, #epsilon 3 woks fine
# smooth = 5 )
# except:
# print("spline error")
# self.mju = spl(self.energy)
#remove 10% of the range if other edges appears at the end of the spectrum
lastPoint = int(len(self.mju) - len(self.mju)*0.1)
self.mjuDerivative = gradient(self.mju)
maxderiv = argmax(self.mjuDerivative[0:lastPoint])
energyMaxDerivValue = self.energy[0:lastPoint][maxderiv]
print("max deriv at ", energyMaxDerivValue)
self.E0 = self.energy[maxderiv] + 0.001
self.E1 = self.energy[maxderiv] - (energyMaxDerivValue - self.energy[0])/5
self.E2 = self.energy[maxderiv] + (self.energy[-1] - energyMaxDerivValue )/5
self.E3 = self.energy[-1]
self.Es = self.energy[0]
self.kMax = sqrt( (2*me/hbar**2) * (self.E3-self.E0) * 1.602*10**-19 ) *10**-10 - 0.5
def removeBackground(self):
if self.Es == 0:
energyStart = self.energy[0]
else:
energyStart = self.Es
whereE1 = where( logical_and(self.energy > energyStart, self.energy < self.E1) )
Xb = self.energy[whereE1]
yb = self.mju[whereE1]
popt, pcov = curve_fit(victoreen, Xb, yb)
self.victoreen = victoreen(self.energy, popt[0], popt[1])
self.mjuMinusVictoreen = self.mju - self.victoreen
def removeBackgroundPoly(self):
if self.Es == 0:
energyStart = self.energy[0]
else:
energyStart = self.Es
whereE1 = where( logical_and(self.energy > energyStart, self.energy < self.E1) )
Xb = self.energy[whereE1]
yb = self.mju[whereE1]
p = polyfit(Xb, yb, self.bckgrPolinomialDegree)
self.victoreen = polyval(p,self.energy)
self.mjuMinusVictoreen = self.mju - self.victoreen
def findMju0(self):
whereE2E3 = where( logical_and(self.energy > self.E2, self.energy < self.E3) )
if self.mju0PolinomialDegree >= 0:
energyToFit = self.energy[whereE2E3]
mjuToFit = self.mjuMinusVictoreen[whereE2E3]
polynomial_features = PolynomialFeatures(self.mju0PolinomialDegree,
include_bias=True)
linear_regression = LinearRegression()
pipeline = Pipeline([("polynomial_features", polynomial_features),
("linear_regression", linear_regression)])
pipeline.fit(energyToFit[:, newaxis], mjuToFit)
self.mju0 = pipeline.predict(self.energy[:, newaxis])
else:
whereE0E3 = where( logical_and(self.energy > self.E0, self.energy < self.E3) )
energyToFit = self.energy[whereE2E3]
mjuToFit = self.mjuMinusVictoreen[whereE2E3]
l = len(energyToFit)
mid = int(l/2)
left = int(l/4)
right = int(l/4 *3)
t = [energyToFit[left], energyToFit[mid], energyToFit[right]]
k = abs(self.mju0PolinomialDegree)
t = r_[(energyToFit[0],)*(k+1),
t,
(energyToFit[-1],)*(k+1)]
w = power((energyToFit - energyToFit[0]) + 1, 3/2)
spl = make_lsq_spline(energyToFit, mjuToFit, t, k, w=w)
self.mju0 = spl(self.energy[whereE0E3])
def calculateEXAFS(self):
whereE0E3 = where( logical_and(self.energy > self.E0, self.energy < self.E3) )
energyE0E3 = self.energy[whereE0E3]
mjuE0E3 = self.mjuMinusVictoreen[whereE0E3]
if self.mju0PolinomialDegree >= 0: mju0E0E3 = self.mju0[whereE0E3]
if self.mju0PolinomialDegree < 0: mju0E0E3 = self.mju0
if self.normalizationMode==0: #mju0 normalization
# mju0E0E3 = mju0[whereE0E3]
# mju0E0E3_1 = self.mju0[whereE0E3]
mju0E0E3_1 = mju0E0E3
if self.normalizationMode==1:
idx = argmin(abs(self.energy - self.normalizationEnergy))
mju0E0E3_1 = zeros(len(energyE0E3)) + self.mju0[idx]
self.k = sqrt( (2*me/hbar**2) * (energyE0E3-self.E0) * 1.602*10**-19 ) *10**-10
self.exafs = (mjuE0E3 - mju0E0E3)/mju0E0E3_1 * power(self.k, self.kPower)
### test auto reduce points
if self.reduceK :
sys.path.append(os.path.join('.', 'lib'))
import xaslib as xl
self.k, self.exafs = xl.kPointsReduction(self.k, self.exafs)
def calculateEXAFSZLC(self):
if self.zeroLineCorr == 0:
self.exafsZeroLine = zeros( len(self.k))
if self.zeroLineCorr > 0:
### test code
# sys.path.append(os.path.join('.', 'lib'))
# import xaslib as xl
# nk, ne = xl.kPointsReduction(self.k, self.exafs)
# spl = Rbf( nk, ne,
# function = 'multiquadric',
# epsilon = 3, #epsilon 3 woks fine
# smooth = self.zeroLineCorr )
### end test code
spl = Rbf( self.k, self.exafs,
function = 'multiquadric',
epsilon = 3, #epsilon 3 woks fine
smooth = self.zeroLineCorr )
self.exafsZeroLine = spl(self.k)
self.exafsZLC = self.exafs - self.exafsZeroLine
def ftEXAFS(self):
self.window = windowGauss10(self.k, self.kMin, self.kMax)
self.exafsTimesWindow = self.exafs * self.window
self.r, self.fr, self.fi = FT(self.k, self.exafsTimesWindow, self.rMin, self.rMax, self.dr)
self.efr = zeros(len( self.r))
sqrt(self.fr*self.fr + self.fi*self.fi, self.efr)
self.efi = self.fi * (-1)
def ftEXAFSZLC(self):
self.window = windowGauss10(self.k, self.kMin, self.kMax)
self.exafsZLCTimesWindow = self.exafsZLC * self.window
self.rZLC, self.frZLC, self.fiZLC = FT(self.k, self.exafsZLCTimesWindow, self.rMin, self.rMax, self.dr)
self.efrZLC = zeros(len( self.r))
sqrt(self.frZLC*self.frZLC + self.fiZLC*self.fiZLC, self.efrZLC)
self.efiZLC = self.fiZLC * (-1)
def bftEXAFSZLC(self):
self.bftWindow = BFTWindow(self.rZLC, self.rMinBft, self.rMaxBft, self.bftWindowParam)
forbftim = self.fiZLC * self.bftWindow
forbftre = self.frZLC * self.bftWindow
self.bftefrWindow = self.efrZLC * self.bftWindow
self.bftefiWindow = self.efiZLC * self.bftWindow
self.bftk, self.bftefr, self.bftefi = BFT(self.rZLC, forbftre, forbftim, self.kMin, self.kMax, self.dk)
wind = windowGauss10(self.bftk, self.kMin, self.kMax)
self.bftAmp = sqrt( self.bftefr*self.bftefr / (wind**self.kPower) +
self.bftefi*self.bftefi / (wind**self.kPower) )
self.bftPha = GETPHASE(self.bftefr, self.bftefi)
self.bftEXAFS = self.bftAmp * sin(self.bftPha)
def changeToRebinedMjuAveraging(self, rebinType = 'spline', dE1 = 1, dE2 = 0.1, dK = 0.01, s = 0):
# rebinType possible values:
# 'spline'
# 'rbf-smooth'
self.energyOriginal = copy(self.energy)
self.mjuOriginal = copy(self.mju)
# sort array in there are decreasing points
sortIndexes = self.energy.argsort()
sortedEnergy = self.energy[sortIndexes]
sortedMju = self.mju[sortIndexes]
dE = dE1
newE = []
newMju = []
#take data before E1
E1Region = where( logical_and(sortedEnergy > self.Es ,sortedEnergy < self.E1) )
E1Energy = sortedEnergy[E1Region]
E1Mju = sortedMju[E1Region]
newE.append(E1Energy[0])
newMju.append(E1Mju[0])
eCenter = E1Energy[0] + dE / 2
while eCenter + dE/2 < self.E1:
reg = where( logical_and(sortedEnergy > eCenter-dE/2, sortedEnergy < eCenter+dE/2) )
valE = sortedEnergy[reg].sum() / len(sortedEnergy[reg])
val = sortedMju[reg].sum() / len(sortedMju[reg])
newE.append(valE)
newMju.append(val)
eCenter = eCenter + dE
dE = dE2
#take data before E0+50
E0Region = where( logical_and(sortedEnergy > self.E1, sortedEnergy < self.E0+50) )
E0Energy = sortedEnergy[E0Region]
E0Mju = sortedMju[E0Region]
newE.append(E0Energy[0])
newMju.append(E0Mju[0])
eCenter = E0Energy[0] + dE / 2
while eCenter + dE/2 < self.E0+50:
reg = where( logical_and(sortedEnergy > eCenter-dE/2, sortedEnergy < eCenter+dE/2) )
# print("reg", len(reg), reg)
# if reg==[]:
# eCenter = eCenter + dE
if len(reg[0])==1:
valE = sortedEnergy[reg][0]
val = sortedMju[reg][0]
newE.append(valE)
newMju.append(val)
if len(reg[0])>1:
valE = sortedEnergy[reg].sum() / len(sortedEnergy[reg])
val = sortedMju[reg].sum() / len(sortedMju[reg])
newE.append(valE)
newMju.append(val)
eCenter = eCenter + dE
dk = dK
#take data after E0
E3Region = where( logical_and(sortedEnergy > self.E0+50, sortedEnergy < self.E3) )
E3Energy = sortedEnergy[E3Region]
E3Mju = sortedMju[E3Region]
newE.append(E3Energy[0])
newMju.append(E3Mju[0])
# kExafsMin = sqrt( (2*me/hbar**2) * (self.E0+50-self.E0) * 1.602*10**-19 ) *10**-10
# kExafsMax = sqrt( (2*me/hbar**2) * (self.E3-self.E0) * 1.602*10**-19 ) *10**-10
kScale = sqrt( (2*me/hbar**2) * (E3Energy-self.E0) * 1.602*10**-19 ) *10**-10
# eScale = kScale**2 * 10**20 / (1.602*10**-19 * (2*me/hbar**2)) + self.E0
kCenter = kScale[0] + dk / 2
while kCenter + dk/2 < kScale[-1]:
reg = where( logical_and(kScale > kCenter-dk/2, kScale < kCenter+dk/2) )
# print("reg", len(reg), reg)
# if reg==[]:
# eCenter = eCenter + dE
if len(reg[0])==1:
valE = E3Energy[reg][0]
val = E3Mju[reg][0]
newE.append(valE)
newMju.append(val)
if len(reg[0])>1:
valE = E3Energy[reg].sum() / len(E3Energy[reg])
val = E3Mju[reg].sum() / len(E3Mju[reg])
newE.append(valE)
newMju.append(val)
kCenter = kCenter + dk
newE = asarray(newE)
newMju = asarray(newMju)
kExafsMin = sqrt( (2*me/hbar**2) * (self.E0+50-self.E0) * 1.602*10**-19 ) *10**-10
kExafsMax = sqrt( (2*me/hbar**2) * (self.E3-self.E0) * 1.602*10**-19 ) *10**-10
kScale = arange(kExafsMin, kExafsMax, dk, dtype='float64')
eScale = kScale**2 * 10**20 / (1.602*10**-19 * (2*me/hbar**2)) + self.E0
a1 = arange(self.Es, self.E1, dE1, dtype='float64')
a2 = arange(self.E1, self.E0+50, dE2, dtype='float64')
self.energyRebined = concatenate((a1, a2, eScale.astype('float64')))
if rebinType == 'spline':
uniqueEnergy, uniqueIndexes = unique(newE, return_index=True)
newE = newE[uniqueIndexes]
newMju = newMju[uniqueIndexes]
newE = newE[~isnan(newE)]
newMju = newMju[~isnan(newMju)]
spl = InterpolatedUnivariateSpline(newE, newMju, k=1)
if rebinType == 'rbf-smooth':
spl = Rbf( newE, newMju,
function = 'multiquadric',
epsilon = 3, #epsilon 3 woks fine
smooth = s )
self.mjuRebined = spl(self.energyRebined)
self.mjuRebined = self.mjuRebined[1:-2]
self.energyRebined = self.energyRebined[1:-2]
self.energy = copy(self.energyRebined)
self.mju = copy(self.mjuRebined)
self.mjuDerivative = gradient(self.mju)
self.redoExtraction()
def changeToRebinedMjuAveraging1(self):
self.energyOriginal = copy(self.energy)
self.mjuOriginal = copy(self.mju)
# sort array in there are decreasing points
sortIndexes = self.energy.argsort()
sortedEnergy = self.energy[sortIndexes]
sortedMju = self.mju[sortIndexes]
dE = 5
newE = []
newMju = []
#take data before E1
E1Region = where( sortedEnergy < self.E1 )
E1Energy = sortedEnergy[E1Region]
E1Mju = sortedMju[E1Region]
newE.append(E1Energy[0])
newMju.append(E1Mju[0])
eCenter = E1Energy[0] + dE / 2
while eCenter + dE/2 < self.E1:
reg = where( logical_and(sortedEnergy > eCenter-dE/2, sortedEnergy < eCenter+dE/2) )
valE = sortedEnergy[reg].sum() / len(sortedEnergy[reg])
val = sortedMju[reg].sum() / len(sortedMju[reg])
newE.append(valE)
newMju.append(val)
eCenter = eCenter + dE
dE = 0.1
#take data before E0+50
E0Region = where( logical_and(sortedEnergy > self.E1, sortedEnergy < self.E0+50) )
E0Energy = sortedEnergy[E0Region]
E0Mju = sortedMju[E0Region]
newE.append(E0Energy[0])
newMju.append(E0Mju[0])
eCenter = E0Energy[0] + dE / 2
while eCenter + dE/2 < self.E0+50:
reg = where( logical_and(sortedEnergy > eCenter-dE/2, sortedEnergy < eCenter+dE/2) )
# print("reg", len(reg), reg)
# if reg==[]:
# eCenter = eCenter + dE
if len(reg[0])==1:
valE = sortedEnergy[reg][0]
val = sortedMju[reg][0]
newE.append(valE)
newMju.append(val)
if len(reg[0])>1:
valE = sortedEnergy[reg].sum() / len(sortedEnergy[reg])
val = sortedMju[reg].sum() / len(sortedMju[reg])
newE.append(valE)
newMju.append(val)
eCenter = eCenter + dE
dk = 0.01
#take data after E0
E3Region = where( logical_and(sortedEnergy > self.E0+50, sortedEnergy < self.E3) )
E3Energy = sortedEnergy[E3Region]
E3Mju = sortedMju[E3Region]
newE.append(E3Energy[0])
newMju.append(E3Mju[0])
# kExafsMin = sqrt( (2*me/hbar**2) * (self.E0-self.E0) * 1.602*10**-19 ) *10**-10
# kExafsMax = sqrt( (2*me/hbar**2) * (self.E3-self.E0) * 1.602*10**-19 ) *10**-10
kScale = sqrt( (2*me/hbar**2) * (E3Energy-self.E0) * 1.602*10**-19 ) *10**-10
# eScale = kScale**2 * 10**20 / (1.602*10**-19 * (2*me/hbar**2)) + self.E0
kCenter = kScale[0] + dk / 2
while kCenter + dk/2 < kScale[-1]:
reg = where( logical_and(kScale > kCenter-dE/2, kScale < kCenter+dE/2) )
# print("reg", len(reg), reg)
# if reg==[]:
# eCenter = eCenter + dE
if len(reg[0])==1:
valE = E3Energy[reg][0]
val = E3Mju[reg][0]
newE.append(valE)
newMju.append(val)
if len(reg[0])>1:
valE = E3Energy[reg].sum() / len(E3Energy[reg])
val = E3Mju[reg].sum() / len(E3Mju[reg])
newE.append(valE)
newMju.append(val)
kCenter = kCenter + dk
self.energy = copy(asarray(newE))
self.mju = copy(asarray(newMju))
self.energyRebined = copy(asarray(newE))
self.mjuRebined = copy(asarray(newMju))
self.mjuDerivative = gradient(self.mju)
self.redoExtraction()
def changeToOriginalMju(self):
self.energy = copy(self.energyOriginal)
self.mju = copy(self.mjuOriginal)
self.mjuDerivative = gradient(self.mju)
self.energyRebined = []
self.mjuRebined = []
self.redoExtraction()
def processExpData(self):
if self.raw_data_type == 0: #0 for transmission,
self.calculateMjuTransmission()
self.estimateEnergyValues()
self.removeBackground()
self.findMju0()
self.calculateEXAFS()
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
if self.raw_data_type == 1: # 1 for fluorescence
self.calculateMjuFluorescence()
self.estimateEnergyValues()
self.removeBackground()
self.findMju0()
self.calculateEXAFS()
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
if self.raw_data_type == 2: # 2 for Mju
print("processing", self.raw_data_type)
self.estimateEnergyValues()
self.removeBackground()
self.findMju0()
self.calculateEXAFS()
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
if self.raw_data_type == 3: # 3 for EXAFS
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
def changeKPower(self, newKPower):
actual_k_pow = self.kPower
new_k_pow = newKPower
self.exafs = self.exafs / self.k ** (actual_k_pow)
self.exafs = self.exafs * self.k ** (new_k_pow)
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
self.kPower = newKPower
def redoExtraction(self):
if self.raw_data_type in [0,1,2]: # 2 for Mju
if self.bckgrPolinomialDegree == -1: #use vectoreen
self.removeBackground()
if self.bckgrPolinomialDegree >=0 and self.bckgrPolinomialDegree <9:
self.removeBackgroundPoly()
self.findMju0()
self.calculateEXAFS()
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
if self.raw_data_type == 3: # 3 for EXAFS
self.calculateEXAFSZLC()
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
def redoFtBft(self):
self.ftEXAFS()
self.ftEXAFSZLC()
self.bftEXAFSZLC()
def redoBft(self):
self.bftEXAFSZLC()