Content

Analysis of cxif5315

Comments and references to code

2015-05-04 from Kartik Ayyer:

Regarding my code, I have made a git repo public here:
The code is in C. It is a complete mess and is not optimized as I have been tinkering with it. The main file is peakalign.c, and the functions you asked for are calc_phi(), calc_beta() and calc_merge().
My workflow has been Cheetah -> cxi file output -> Align -> Signal and background RZ merge -> Subtract background from signal. I have been posting my results on DESY confluence, which is unfortunately private, but Meng has access to it.

 

2015-05-04 from Meng:

 

fraser1.m - matlab code for doing the reciprocal space mapping.

 

2015-04-30 Meng's link for the CFEL confluence  that has the data analysis summary

 

 

Fraser transformation in python

Menng's matlab code fraser1.m,  fraser2.m for Fraser transformation is implemented in python fraser.py (download).

Assumptions in implemented algorithm:

  • detector plane is transverse to the beam
  • by default image center (sizex/2, sizey/2) coincides with origin for transformation. Non-default center can be set to different point.

 

Example of diffraction and transformed images: 

 

Angles phi and beta using Kartik's code

Using Kartik's formulas for angles phi and beta kartik-2-peak-ev-angles.py.txt one can get distributions:

 

In summary:

  • angle phi is evaluated using two peak positions and sample-to-detector distance
  • angle beta is evaluated using one !!! peak position and angle phi.
  • peak and event selection need to be improved
  • it could be nice to have explanation to this formalism...

Angles phi and beta from fit

Solving equation for tan(theta) we may get functional dependence between x and y peak position for angles phi and theta,

funy(x,phi,beta)

(warning) this function presents a solution of quadratic equation which has two roots. The root is selected depending on sign of theta angle...(question)

This function is catheterized in python script fiber_angles.py and can be plotted for different angles:


Solutions: y1=-B+sqtr(B^2-C)   and y2=-B-sqtr(B^2-C):

 

Similar function is used in data processing to fit peak (x,y) positions in equatorial region and use two angles as a fit parameters.

from scipy.optimize import curve_fit

def funcy(x, phi_deg, bet_deg) :
    # ATTENTION! x is treated as numpy array..., 
    # so function should also return numpy array of the same size
    <some parameterization>

def procEventEqu() :
    # summary for event
    if sp.count_equ_pks_sel > 1 :
        yn = [-line[4] / L for line in sp.lst_equ_evt_peaks]
        xn = [ line[5] / L for line in sp.lst_equ_evt_peaks]
        #? yn.append(0)
        #? xn.append(0)
        en = np.ones_like(xn)
        p0 = [-3.1,-5.1]
        popt, pcov = curve_fit(funcy, xn, yn, p0, en)

results of the fit to peaks found in each event gives distributions for two angles:

  • No labels