/
spectrum_evaluation.py
508 lines (460 loc) · 19.8 KB
/
spectrum_evaluation.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Mar 29 10:26:57 2019
Handbook of X-Ray Spectrometry, 2nd Ed., ISBN: 0-8247-0600-5
Chapter 2, Spectrum Evalutaion, P. van Espen
Fortran code converted into Python
@author: windover
"""
import numpy as np
import scipy as sp
import scipy.signal as signal
from os import walk
import bruker_io as bruker_io
import hyperspy as hs
import copy
import pandas as pd
# Savitsky and Golay Poly Smoothing (pg 315 in Fortran)
#
# Input: Y Original Spectrum
# NCHAN Number of channels in the spectrum
# ICH1,ICH2 First and last channel number to be smoothed
# IWID Width of the filter (2m+1), IWDI<52
# Output: S Smoothed spectrum, only defined between ICH1 & ICH2
def SGSMITH(Y, NCHAN, ICH1, ICH2, IWID):
#calculate filter coefficients
#
IW = np.min([IWID, 51])
C = np.zeros(IW) #changed this to the width of the filter
M = np.int((IW-1)/2) # needed to add a -1 term from Fortran
SUM = (2*M-1)*(2*M+1)*(2*M+3) #no change
for j in np.arange(IW): #index from 0 to 2M instead of -M to M
C[j] = 3*(3*M**2 + 3*M-1-5*(j-M)**2) #need to subtract M
#convolute spectrum with filter
#
JCH1 = np.max([ICH1, M])
JCH2 = np.min([ICH2, NCHAN-1-M])
S = np.zeros(NCHAN) #initialize and empty filtered spectrum
for i in np.arange(JCH1, JCH2): #this is a subset of the full spectra
for j in np.arange(IW): # this is 0 to 2m
S[i] = S[i] + C[j]*Y[i+(j-M)] # need to subtract M
S[i] = S[i]/SUM
return S
# Peak stripping - SNIP algorithm (pg 319 in Fortran)
#
# Input: Y Spectrum
# NCHAN Number of channels
# ICH1,ICH2 First and Last channels of region for continuum calc
# FWHM Width parame for smoothing &stripping algorithm
# set to ave. FWHM of peaks (typical 8)
# NITER Number of iterations for SNIP algorithm
# Output: YBACK Caltculated continuum over ICH1 to ICH2
# comment: uses subroutine SGSMITH
def SNIPBG(Y, NCHAN, ICH1, ICH2, FWHM, NREDUC, NITER):
#Smooth spectrum
IW = np.int(FWHM)
I1 = np.max([ICH1-IW, 0])
I2 = np.min([ICH2+IW, NCHAN-1])
YBACK = SGSMITH(Y, NCHAN, I1, I2, IW)
zeros = np.zeros(NCHAN)
test = np.zeros(NCHAN)
#Square root transformation over region
YBACK = np.sqrt(np.maximum(YBACK, zeros))
#for i in np.arange(I1,I2):
# YBACK[i] = np.sqrt(np.max([YBACK[i],0]))
#YBACK[i] = np.log(np.log(np.sqrt(np.max([YBACK[i],0]) +1)+1)+1)
#Peak stripping
REDFAC = 1
for n in np.arange(0, NITER):
if n+1 > NITER-NREDUC:
REDFAC = REDFAC/np.sqrt(2)
IW = np.max([np.int(REDFAC*FWHM), 1])
for i in np.arange(ICH1, ICH2):
I1 = np.int(np.max([i-IW, 0]))
I2 = np.int(np.min([i+IW, NCHAN-1]))
test[i] = 0.5*(YBACK[I1]+YBACK[I2])
YBACK[i] = np.minimum.reduce([YBACK[i], test[i]])
YBACK = np.square(YBACK)
#for i in np.arange(I1,I2):
# YBACK[i] = np.square(YBACK[i])
#YBACK[i] = np.square(np.exp(np.exp(YBACK[i])-1)-1)-1
#YBACK[i] = np.square(np.max([YBACK[i],0]))
return YBACK
# TOPHAT TOPHAT filtering protram (pg 323)
#
# Input: IN Spectrum
# NCHAN Number of channels
# IFIRST First channel of region for top hat filter
# ILAST Last channel of region for top hat filter
# IWIDTH Width parameter for tophat region
# MODE 0 or !0 to set filtered spectra of weights
# OUTPUT OUT Output spectrum or weights
# Has two modes :
# Mode = 0 (calculates the filtered spectrum)
# Mode ne 0 (calculates weights)
def TOPHAT(IN, NCHAN, IFIRST, ILAST, IWIDTH, MODE):
# Mode = 0 (calculate filtered spctrum)
# Mode != 0 (calculate weights)
# Calculate filter constants
#print("mode: ", MODE)
OUT = np.zeros(NCHAN)
IW = IWIDTH
# makes sure IW is odd by adding 1 to any even IW
if np.mod(IW, 2) == 0: IW = IW + 1
FPOS = 1./np.float(IW)
#print("FPOS: ", FPOS)
KPOS = np.int(IW/2 -0.5)
IV = 2*np.int(IW/2 - 0.5)
FNEG = -1./np.float(2*IV)
#print("FNEG: ", FNEG)
KNEG1 = np.int(IW/2 +1)
KNEG2 = np.int(IW/2 +IV)
N = 0
#loop over all requested channels
for channel in np.arange(IFIRST, ILAST, 1):
#central positive part
YPOS = 0
YNEG = 0
for xPOS in np.arange(-KPOS, KPOS +1, 1):
IK = np.min([np.max([channel+xPOS, 1]), NCHAN])
YPOS = YPOS + IN[IK]
# left and right negagive part
for xNEG in np.arange(KNEG1, KNEG2+1, 1):
IK = np.min([np.max([channel-xNEG, 1]), NCHAN])
YNEG = YNEG + IN[IK]
IK = np.min([np.max([channel+xNEG, 1]), NCHAN])
YNEG = YNEG + IN[IK]
#calc filtered spectra
if MODE == 0:
OUT[channel] = FPOS*YPOS + FNEG*YNEG
#calc variance of the spectra
else:
VAR = FPOS*FPOS*YPOS + FNEG*FNEG*YNEG
OUT[channel] = 1/np.max([VAR, 1])
N = N +1
return OUT
# TOPHATFAST TOPHAT filtering protram (Mofiied by DW 20190403)
#
# Input: IN Spectrum
# NCHAN Number of channels
# IFIRST First channel of region for top hat filter
# ILAST Last channel of region for top hat filter
# IWIDTH Width parameter for tophat region
# MODE 0 or !0 to set filtered spectra of weights
# OUTPUT OUT Output spectrum or weights
# Has two modes :
# Mode = 0 (calculates the filtered spectrum)
# Mode ne 0 (calculates weights)
def TOPHATFAST(IN, NCHAN, IWIDTH, MODE):
if np.mod(IWIDTH, 2) == 0: IWIDTH = IWIDTH + 1
ones = np.ones(NCHAN)
KNEG = np.int(IWIDTH/2 -0.5)
#print('KNEG: ', KNEG)
KPOS = np.int(IWIDTH -1)
#print('KPOS: ', KPOS)
hatpos = np.zeros(KPOS) +1/KPOS
hatneg = np.zeros(KNEG) -1/(2*KNEG)
tophat = np.hstack((hatneg, hatpos, hatneg))
#print('tophat: ',tophat)
hatpos_var = np.zeros(KPOS) +(1/KPOS)**2
hatneg_var = np.zeros(KNEG) + (-1/(2*KNEG))**2
tophat_var = np.hstack((hatneg_var, hatpos_var, hatneg_var))
#print('tophat_var: ',tophat_var)
#print('tophat: ', np.sum(tophat))
#print(tophat)
# call to scipy.signal for S.G. filter
IN = signal.savgol_filter(IN, IWIDTH, 2)
if MODE == 0:
OUT = signal.convolve(IN, tophat, mode='same', method = 'direct')
#calc variance of the spectra
else:
OUT = signal.convolve(IN, tophat_var, mode='same', method = 'direct')
OUT = 1/np.maximum(OUT, ones)
return OUT
# Peak stripping - SNIP algorithm (Modified by DW 20190403)
# NOTE: instead of altering YBACK as it is used in each loop, we convolve
# a +/-IW 1 function with the YBACK and find the min of this with YBACK
# this removes the asymmetric bias in the Pg 319 implementation of acting
# on YBACK during its used in a NCHAN for loop. This method requires
# more loops for stripping, but is much faster 10ms verus 3s
#
# Input: Y Spectrum
# NCHAN Number of channels
# ICH1,ICH2 First and Last channels of region for continuum calc
# FWHM Width parame for smoothing &stripping algorithm
# set to ave. FWHM of peaks (typical 8)
# NITER Number of iterations for SNIP algorithm
# Output: YBACK Caltculated continuum over ICH1 to ICH2
# comment: uses subroutine SGSMITH
def SNIPFAST (Y, NCHAN, FWHM, NREDUC, NITER):
REDFAC = 1
if np.mod(FWHM,2) == 0: FWHM = FWHM + 1
#Smooth spectrum using scipy.signal function of S.G.
YBACK = Y
#YBACK = signal.savgol_filter(Y, FWHM, 2 )
#initialize two NCHAN arrays for calulation of sums
zeros = np.zeros(NCHAN)
YBACK_sum = np.zeros(NCHAN)
# we are using a non linear square/square root scaling circa Van espen
YBACK = np.sqrt(np.maximum(YBACK, zeros))
#alternative scaling not used
#YBACK = np.log(np.log(np.sqrt(np.maximum(YBACK,zeros) +1) +1) +1)
# for loop for number of times we wish to strip background
for n in np.arange(0, NITER):
#after a suffiicient number of loops, reduce 'FWHM' by sqrt(2) until = 1
if n+1 > NITER-NREDUC:
REDFAC = REDFAC/np.sqrt(2)
#Allow for a reduction in 'FWHM' over loops with a minimum IW of 1
IW = np.max([np.int(REDFAC*FWHM),1])
#make a function [1,0,0,0,...1] width 2W+1 for convolve
straddle = np.zeros(2*IW+1)
straddle[0]=1
straddle[-1]=1
# use scipy.signal.convolve to determine +/- average for test
YBACK_sum = 0.5*signal.convolve(YBACK, straddle, mode='same')
YBACK = np.minimum(YBACK, YBACK_sum)
# we are using a non linear square/square root scaling circa Van espen
YBACK = np.square(YBACK)
#alternative scaling not used
#YBACK = np.square(np.exp(np.exp(YBACK)-1)-1)-1
return YBACK
def pulse_pileup_removal(fittingdata):
"""Removal tool for first-order pulse-pileups in XRF data.
Parameters
----------
energy_scale : array of MCA energy at each channel (in eVs)
channels : array from MCA giving counts in each channel
shaping_time : events per second that can be processed
Examples
--------
"""
pos_channels = \
fittingdata.channels[np.nonzero(fittingdata.energy_scale>0)[0][0]:-1]
pos_channels_per_s = pos_channels/(fittingdata.life_time_in_ms/1000)
pileup_sum = np.zeros(len(pos_channels))
for i in np.arange(100,len(pos_channels)):
forward = pos_channels_per_s[0:i]
reverse = np.flip(forward, axis=0)
shape_factor = (0.006/fittingdata.shaping_time)
pileup = shape_factor*(forward)*(reverse)
pileup_sum[i] = sum(pileup)
fittingdata.channels[np.nonzero(fittingdata.energy_scale>0)[0][0]:-1] = \
(pos_channels_per_s - pileup_sum) * (fittingdata.life_time_in_ms/1000)
return
def SCALEDSNIP(fittingdata):
pos_energy_scale = \
fittingdata.energy_scale[np.nonzero(fittingdata.energy_scale>0)[0][0]:-1]
pos_channels = \
fittingdata.channels[np.nonzero(fittingdata.energy_scale>0)[0][0]:-1]
spectrum_function = \
sp.interpolate.interp1d(np.sqrt(pos_energy_scale),
pos_channels, kind = 'linear',
fill_value = (0,0), bounds_error = False )
energy_scale_sqrt = np.arange(0,np.sqrt(max(pos_energy_scale)),
np.sqrt(max(pos_energy_scale))
/len(pos_channels))
channels_sqrt = spectrum_function(energy_scale_sqrt)
data_bg = SNIPFAST(channels_sqrt, len(channels_sqrt), 13, 10, 1000)
#data_bg = SNIPBG(channels_sqrt, len(channels_sqrt), 0, len(channels_sqrt)-1, 13, 10, 1000)
new_data_corr = channels_sqrt - data_bg
spectrum_function_squared = \
sp.interpolate.interp1d(np.square(energy_scale_sqrt),
new_data_corr, kind = 'linear',
fill_value = (0,0), bounds_error = False )
channels_corr = spectrum_function_squared(pos_energy_scale)
channels_corr = channels_corr.clip(min=0)
fittingdata.channels[np.nonzero(fittingdata.energy_scale>0)[0][0]:-1] = \
channels_corr
return
def polycap_remove(fittingdata):
number_of_points = 40 #changed from 40 on 20190725 dw
scaling = int(4000/number_of_points)
bg_energy_scale = np.zeros(number_of_points)
bg_channels = np.zeros(number_of_points)
for i in np.arange(bg_energy_scale.shape[0]):
bg_energy_scale[i] = fittingdata.energy_scale[i*scaling + 100]
bg_channels[i] = np.min(fittingdata.channels[i*scaling + 50:(i+1)*scaling+50])
bg_function = \
sp.interpolate.interp1d(bg_energy_scale, bg_channels, kind = 'cubic',
fill_value = (0,0), bounds_error = False)
bg_intensity = bg_function(fittingdata.energy_scale)
bg_intensity = bg_intensity.clip(min=0)
bg_corrected = fittingdata.channels - bg_intensity
fittingdata.channels = bg_corrected.clip(min=0)
return
def spectra_fit(directory_path, fitter, method, elements):
files = []
spx_files =[]
roi_data = elements
model_data = elements
print(elements)
life_time_in_ms = []
for (dirpath, dirnames, filenames) in walk(directory_path):
files.extend(filenames)
break
for file in files:
if '.spx' in file:
spx_files.append(file)
for file in spx_files:
print(file)
spx_file = file
spx = bruker_io.FittingData(directory_path + '/' + spx_file)
bruker_io.bruker_spx_import(spx)
pulse_pileup_removal(spx)
SCALEDSNIP(spx)
polycap_remove(spx)
hsEDS = hs.signals.EDSSEMSpectrum(spx.channels)
hsEDS.set_microscope_parameters(50000)
hsEDS.axes_manager[0].name = 'XRF spectra'
hsEDS.axes_manager[0].offset = spx.calibration_abs
hsEDS.axes_manager[0].scale = spx.calibration_lin
hsEDS.axes_manager[0].units = 'eV'
hsEDS.add_elements(elements)
hsEDS.add_lines()
line_names = hsEDS.metadata.Sample.xray_lines
#print(line_names)
mod = hsEDS.create_model()
mod.remove('background_order_6')
new_roi = np.zeros(len(elements))
new_model = np.zeros(len(elements))
mod.fit(fitter= fitter, method= method)
for i in np.arange(len(elements)):
new_roi[i] = np.float(hsEDS.get_lines_intensity([line_names[i]])[0].data[0])
model_call = ''.join(['mod.components.',line_names[i],'.A.value'])
new_model[i] = eval(model_call)
#test_param[i] = ''.join(['mod.components.', line_names[i], '.A.value'])
#new_model[i]= np.float(test_param)
#Hf_La_model.append(np.float())
#Si_Ka_model.append(np.float(mod.components.Si_Ka.A.value))
#Hf_Si_ratio.append(np.float(mod.components.Hf_La.A.value)/np.float(mod.components.Si_Ka.A.value))
life_time_in_ms.append(spx.life_time_in_ms)
roi_data = np.vstack((roi_data,new_roi))
model_data = np.vstack((model_data,new_model))
roi_data = roi_data[1:,:]
model_data = model_data[1:,:]
#spx_files = np.array(spx_files)
#print(spx_files)
#spx_files = spx_files[np.newaxis]
#spx_files = spx_files.transpose
#spx_files.shape
#element_data.shape
#columns_df = ['filename'].append(line_names)
#data_df = np.hstack((spx_files, element_data))
roi_df = pd.DataFrame(data=roi_data, columns = line_names)
roi_df.insert(0,'filename', spx_files)
roi_df['life time in ms'] = life_time_in_ms
model_df = pd.DataFrame(data=model_data, columns = line_names)
model_df.insert(0,'filename', spx_files)
model_df['life time in ms'] = life_time_in_ms
return roi_df, model_df
#def model_lookup(mod, line_name):
# model_call = {'N_Ka': mod.components.N_Ka.A.value,
# 'O_Ka': mod.components.O_Ka.A.value,
# 'F_Ka': mod.components.F_Ka.A.value,
# 'Ne_Ka': mod.components.Ne_Ka.A.value,
# 'Na_Ka': mod.components.Na_Ka.A.value,
# 'Mg_Ka': mod.components.Mg_Ka.A.value,
# 'Al_Ka': mod.components.Al_Ka.A.value,
# 'Si_Ka': mod.components.Si_Ka.A.value,
# 'P_Ka': mod.components.P_Ka.A.value,
# 'S_Ka': mod.components.S_Ka.A.value,
# 'Cl_Ka': mod.components.Cl_Ka.A.value,
# 'Ar_Ka': mod.components.Ar_Ka.A.value,
# 'K_Ka': mod.components.K_Ka.A.value,
# 'Ca_Ka': mod.components.Ca_Ka.A.value,
# 'Sc_Ka': mod.components.Sc_Ka.A.value,
# 'Ti_Ka': mod.components.Ti_Ka.A.value,
# 'V_Ka': mod.components.V_Ka.A.value,
# 'Cr_Ka': mod.components.Cr_Ka.A.value,
# 'Mn_Ka': mod.components.Mn_Ka.A.value,
# 'Fe_Ka': mod.components.Fe_Ka.A.value,
# 'Co_Ka': mod.components.Co_Ka.A.value,
# 'Ni_Ka': mod.components.Ni_Ka.A.value,
# 'Cu_Ka': mod.components.Cu_Ka.A.value,
# 'Zn_Ka': mod.components.Zn_Ka.A.value,
# 'Ga_Ka': mod.components.Ga_Ka.A.value,
# 'Ge_Ka': mod.components.Ge_Ka.A.value,
# 'As_Ka': mod.components.As_Ka.A.value,
# 'Se_Ka': mod.components.Se_Ka.A.value,
# 'Br_Ka': mod.components.Br_Ka.A.value,
# 'Kr_Ka': mod.components.Kr_Ka.A.value,
# 'Rb_Ka': mod.components.Rb_Ka.A.value,
# 'Sr_Ka': mod.components.Sr_Ka.A.value,
# 'Y_Ka': mod.components.Y_Ka.A.value,
# 'Zr_Ka': mod.components.Zr_Ka.A.value,
# 'Nb_Ka': mod.components.Nb_Ka.A.value,
# 'Mo_Ka': mod.components.Mo_Ka.A.value,
# 'Tc_Ka': mod.components.Tc_Ka.A.value,
# 'Ru_Ka': mod.components.Ru_Ka.A.value,
# 'Pd_Ka': mod.components.Pd_Ka.A.value,
# 'Ag_Ka': mod.components.Ag_Ka.A.value,
# 'Cd_Ka': mod.components.Cd_Ka.A.value,
# 'In_Ka': mod.components.In_Ka.A.value,
# 'Sn_Ka': mod.components.Sn_Ka.A.value,
# 'Sb_Ka': mod.components.Sb_Ka.A.value,
# 'Te_Ka': mod.components.Te_Ka.A.value,
# 'I_Ka': mod.components.I_Ka.A.value,
# 'Xe_Ka': mod.components.Xe_Ka.A.value,
# 'Cs_Ka': mod.components.Cs_Ka.A.value,
# 'Ba_Ka': mod.components.Ba_Ka.A.value,
# 'La_Ka': mod.components.La_Ka.A.value,
# 'Hf_La': mod.components.Hf_La.A.value,
# 'Ta_La': mod.components.Ta_La.A.value,
# 'W_La': mod.components.W_La.A.value,
# 'Re_La': mod.components.Re_La.A.value,
# 'Os_La': mod.components.Os_La.A.value,
# 'Ir_La': mod.components.Ir_La.A.value,
# 'Pt_La': mod.components.Pt_La.A.value,
# 'Au_La': mod.components.Au_La.A.value,
# 'Hg_La': mod.components.Hg_La.A.value,
# 'Tl_La': mod.components.Tl_La.A.value,
# 'Pb_La': mod.components.Pb_La.A.value,
# 'Bi_La': mod.components.Bi_La.A.value,
# 'U_La': mod.components.U_La.A.value,
# 'xx_La': mod.components.xx_La.A.value}
# call = model_call[line_name]
# return call
#Work in progress - it performs a tophat on the fly, and is very
#confusing to read through 20190401 daw
## Peak Search - LOCPEAKS uses tophyhat filter
##
## Input: Y Spectrum
## NCHAN Number of channels
## R Peak search sensitivity factor (typcially 2 to 4)
## IWID Width of the filter, approx, FWHM of the peaks
## MAXP Maxiumum number of peaks allowed (sets max arrays)
## OUtput: NPEAK Number of peaks found
## IPOS Array of peak positions
#
#def LOCPEAKS (Y, NCHAN, IWID, R, MAXP):
# # Width of filter (number of channels in the tophat)
# # must be odd and at least 3
# NP = np.int(np.max([(IWID/2)*2 + 1, 3]))
# print ("NP: ", NP)
# NPEAKS = 0 #initiallize the number of peaks
# # Calculate the half width and start and stop channel
# N = np.int(NP/2)
# I1 = NP
# I2 = NCHAN - NP
# # INisitalize the running sums
# I = I1
# TOTAL = 0
# TOP = 0
# for i in np.arange(2*NP+1):
# TOTAL = TOTAL + Y[i]
# print("TOTAL: ", TOTAL)
# for i in np.arange(2*N+1):
# TOP = TOP + (i)
# print("TOP: ", TOP)
# #Loop ofver all channels
# LASTPOS = 0
# SENS = R**2
# FI = 0
# FNEXT = 0
# SNEXT = 0
# for i in np.arange(I2-I1):
# TOP = TOP - Y[i+I1 - N] + Y[i+I1 + N]
# TOTAL = TOTAL - Y[i+I1-NP] + Y[i+I1+NP]
# print("TOP: ", TOP)
# print("TOTAL: ", TOTAL)
# return