Web resources of EM techniques

Top hat filter

The top_hat filter can be used to detect the relatively small edges/peaks superimposed on large background signals. The concept came from the EELS workshop during IMC19. Thanks to Prof. Nestor J. Zaluzec. -- // Using Top_hat digital filter to detect the relatively small edges // superimposed on large background signals. // // ref: Ultramicroscopy 18 (1985) 185-190 // Digital Filters for Application to Data Analysis in EELS // by Nestor J. ZALUZEC // Parameters: // win_s: signal window (default:3) // win_b: background window (default:3) // a_s : amplitude of signal (fixed value) // a_b : amplitude of background (fixed value) // Renfong 2018/10/11 // Main function image Top_Hat_Filter(image img, number win_s, number win_b) { // read image string fname=img.GetName() number sx,sy img.getsize(sx,sy) // filter image img2 := imageclone(img)*0 //the area between...

MLLS in matlab

MLLS stands for multiple linear least squares fitting, which is the common strategy for the solving EELS edge overlapping and which is also built-in the GMS software. The target spectrum Y and the reference spectrum X Y = A * X Assuming Y is 1*256 matrix and we have three reference spectrums, ie, X is 3*256 matrix. So A is 1*3 matrix. The target is to solve A. If Y and X are n*n matrices, we can use the simple formula Y * inv(X) = A * X * inv(X), ie., A = Y * inv(X). However, Y and X are not n*n matrices, it is necessary to have some trick to solve it. We can multiply the transpose matrix to produce n*n matrix. Y * X' = A * X * X' (ps X' means the transpose matrix of X) so A = Y * X' * inv(X * X') Here is the Matlab code: ========= % create target spectrum x=0:256; c=[90,120,155]; sig=[5,10,8]; int=[5,10,8]; xn=zeros(size(x)); ref=zeros(length(c),length(x)); factor=rand(size(c))'; for i=1:length(c) xn=xn+int(i)*ex...

HyperSpy - read the calibration information in a dm3/dm4 file

Some example of dm3 file reading by using Python HyperSpy package, which can read the detail information of the dm file. -- # import packages import numpy as np import hyperspy.api as hs # load file sp=hs.load('sp.dm3') # Read the axis information # Print all the calibration detail print(sp.axes_manager) ''' <Axes manager, axes: (272|2042)> Name | size | index | offset | scale | units ================ | ======= | ====== | ======= | ======= | ====== x | 272 | 0 | -0 | 0.0025 | µm --------------- | ------ | ----- | ------ | ------- | ------ Energy loss | 2042 | | 3.2e+02 | 1 | eV...

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