Skip to main content

Practice of dm-script build-in function: icol & calibration information

Using icol function to create peak.

 Gaussian peak
I=A*exp((x-mu)^2/(2*sigma^2)
where mu is the peak center
           sigma is the divergence of the peak

 --

 image img=RealImage("peak",4,1024,1)

 //  peak 1 parameters
// N_K edge
number int1=30
number mu1=300
number sig1=5

 // peak 2 parameters
// O_K edge
number int2=150
number mu2=432
number sig2=0.9

 number int3=50
number mu3=438
number sig3=2.5

 // add noise
number noise=0.5

 // EELS signal creation
img=int1*exp(-1*(icol-mu1)**2/(2*sig1**2))
img+=int2*exp(-1*(icol-mu2)**2/(2*sig2**2))
img+=int3*exp(-1*(icol-mu3)**2/(2*sig3**2))
img+=0.05*exp(10-icol/100)+noise*int1*random()

 // Write calibration
img.ImageSetDimensionOrigin(0,100)
img.ImageSetDimensionScale(0,1)
img.ImageSetDimensionUnitString(0, "eV" )
img.ImageSetIntensityUnitString( "e-" )
img.setname("Test Spec + " + noise*100 + "% noise")
img.showimage()

 --

 Result
Labels:

Comments

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more