Skip to main content

Mask creation

// A practice to create mask by built-in function
// iradius, itheta, tert

image img:=RealImage("Mask",4,256,256)

// for STEM equivalent detector
image BF, ABF, LAADF, HAADF
BF=img*0
BF=(iradius<10)
BF.SetName("BF")

ABF=img*0
ABF=(iradius>=10 & iradius<20)
ABF.SetName("ABF")

LAADF=img*0
LAADF=(iradius>=60 & iradius <80)
LAADF.SetName("LAADF")

HAADF=img*0
HAADF=(iradius>=100 & iradius<135)
HAADF.SetName("HAADF")

BF.ShowImage()
ABF.ShowImage()
LAADF.ShowImage()
HAADF.ShowImage()


// for DPC detector, 4 segment
// set rotate angle
number ang=15
if(ang>=90)
{
ang=0
}
ang=pi()*ang/180

// Note that the itheta is varied counterclockwise from 0 to -pi() when y>0 
//                                                        and clockwise from 0 to +pi() when y<0 


image seg1, seg2, seg3, seg4
seg1=img*0
seg1=(iradius>10 & iradius<100 & itheta>=ang & itheta<=ang+pi()/2)
seg1.SetName("Segment_1")
seg2=img*0
seg2+=tert((iradius>10 & iradius<100 & itheta>=ang+pi()/2 & itheta<=pi()),1,0)
seg2+=tert((iradius>10 & iradius<100 & itheta>=-1*pi() & itheta<=ang-pi()),1,0)
seg2.SetName("Segment_2")
seg3=img*0
seg3=(iradius>10 & iradius<100 & itheta>=ang-pi() & itheta<=ang-pi()/2)
seg3.SetName("Segment_3")
seg4=img*0
seg4=(iradius>10 & iradius<100 & itheta>=ang-pi()/2 & itheta<=ang)
seg4.SetName("Segment_4")

seg1.ShowImage()
seg2.ShowImage()
seg3.ShowImage()
seg4.ShowImage()


Comments

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...

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...

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...