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Mask

To create mask use Mask Editor command med or launch it through the calibman.

Geometry center

ix_cent, iy_cent = det.point_indexes(runnum)

879, 871 (then x and y are swapped to Cartesian system).

Equatorial mask

Mask parameters

Wedge  871  879  454  387  148  196 1 k False False
Wedge  871  879  454  387  -27   21 1 k False False
Wedge parameters
t   = pars[0]              # figure type, ex. 'Wedge'
x   = float(pars[1])       # x coordinate of the wedge center
y   = float(pars[2])       # y coordinate of the wedge center
r   = float(pars[3])       # 1-st radius of the wedge 
w   = float(pars[4])       # radial width of the wedge 
t1  = float(pars[5])       # 1-st angle
t2  = float(pars[6])       # 2-nd angle
lw  = int(pars[7])         # line width
col = str(pars[8])         # color
s   = self.dicBool[pars[9].lower()]   # isSelected boolean parameter
rem = self.dicBool[pars[10].lower()]  # isRemoved - helper parameter

Involved sensors in adopted cxif5315 geometry: 0, 1,    9,15,    16,17,   25,31

Quads numeration in cxif5315 geometry
---------
| 1 | 0 |
----+----
| 2 | 3 |
---------

Arc mask

Wedge  871  879  454   26  -174  186 1 k False False

Involved sensors in the top part of the image in adopted cxif5315 geometry: 0, (1), 7,   8, (9), 15

Equatorial and arc combined mask

 

Masks for segments

Code below shows how to generate mask n-d arrays for particular set of segments (2x1s)

shape_cspad = (32,185,388)
seg1 = np.ones((185,388))
mask_winds_all = np.zeros(shape_cspad, dtype=np.int16)
mask_winds_equ = np.zeros(shape_cspad, dtype=np.int16)
mask_winds_arc = np.zeros(shape_cspad, dtype=np.int16)

mask_winds_all[(0,1,7,8,9,15,16,17,23,24,25,31),:,:] = seg1
mask_winds_equ[(0,1,9,15,16,17,25,31),:,:] = seg1
mask_winds_arc[(0,7,8,15),:,:] = seg1

Only listed segments are highlighted on plots:

 

Background

 

Radial background subtraction

For some reason polarization correction does not work well in this experiment for entire image.

Comparison of the 2-d interpolated radial background subtraction 

  • nbins (rad:500, phi:1)

  • nbins (rad:500, phi:32)

SIngle angular bin still works fine in our ROI defined by both masks.

Background subtraction examples

Subtract background shape evaluated in run 192

Radial background subtraction

Image corrected up to 80mm, 40<phi< 320:

 

Image corrected up to 80mm, rings span for entire phi

 

OLD IMAGE PROCESSING

Subtract background shape evaluated in run 192. 

Background shape was evaluated WITH common mode correction. Central 2x1s got offset due to non-uniform water background shape.

Averaged Fraser- transformed image using angles from fit to 2 arc peaks


References

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