Page History

...

Image peak finding

Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html

...

pixels = pixel_array.reshape(1480,388)
np.save("pixel_pedestal_file.npy", pixels )

CSPad tile arrangement

To get a rough picture of the full detector, here's an example of how XtcExplorer/src/cspad.py does it:

• For each Quadrant:

  Code Block
  none none
  def get_quad_image( self, data3d, qn) : """get_quad_image Get an image for this quad (qn) @param data3d 3d data array (row vs. col vs. section) @param qn quad number """ # Construct one image for each quadrant, each with 8 sections # from a data3d = 3 x 2*194 x 185 data array # +---+---+-------+ # | | | 6 | # + 5 | 4 +-------+ # | | | 7 | # +---+---+---+---+ # | 2 | | | # +-------+ 0 | 1 | # | 3 | | | # +-------+---+---+ # # each section read from the event has "landscape" orientation # with 185 rows (first index) and 2*194 columns (2nd index) # - Sections 0,1: "portrait" orientation / tilted 90 degrees counter clockwise: # first index increases from left to right, 2nd index increases from bottom to top, # - Sections 2,3: "landscape" orientation / as is: # first index increases from top to bottom, 2nd index increases from left to right # - Sections 4,5: "portrait" orientation / tilted 90 degrees clockwise: # first index increases from right to left, 2nd index increases from top to bottom, # - Sections 6,7: "landscape" orientation / as is: # first index increases from top to bottom, 2nd index increases from left to right # Again, the orientations of the Sections for quadrant 1 are rotated 90 degrees clockwise pairs = [] for i in range (8) : # 1) insert gap between asics in the 2x1 asics = np.hsplit( data3d[i], 2) gap = np.zeros( (185,3), dtype=data3d.dtype ) # # gap should be 3 pixels wide pair = np.hstack( (asics[0], gap, asics[1]) ) # all sections are originally 185 (rows) x 388 (columns) # Re-orient each section in the quad if i==0 or i==1 : pair = pair[:,::-1].T # reverse columns, switch columns to rows. if i==4 or i==5 : pair = pair[::-1,:].T # reverse rows, switch rows to columns pairs.append( pair ) if self.small_angle_tilt : pair = scipy.ndimage.interpolation.rotate(pair,self.tilt_array[qn][i]) # make the array for this quadrant quadrant = np.zeros( (self.npix_quad, self.npix_quad), dtype=data3d.dtype ) # insert the 2x1 sections according to for sec in range (8): nrows, ncols = pairs[sec].shape # colp,rowp are where the top-left corner of a section should be placed rowp = self.npix_quad - self.sec_offset[0] - (self.section_centers[0][qn][sec] + nrows/2) colp = self.npix_quad - self.sec_offset[1] - (self.section_centers[1][qn][sec] + ncols/2) quadrant[rowp:rowp+nrows, colp:colp+ncols] = pairs[sec][0:nrows,0:ncols] if (rowp+nrows > self.npix_quad) or (colp+ncols > self.npix_quad) : print "ERROR" print rowp, ":", rowp+nrows, ", ", colp, ":",colp+ncols return quadrant

Then combine all four quadrant images into the full detector image:

        self.image = np.zeros((2*self.npix_quad+100, 2*self.npix_quad+100 ), dtype="float64")
        for quad in xrange (4):

            quad_image = self.get_quad_image( self.pixels[quad], quad )
            self.qimages[quad] = quad_image

            if quad>0:
                # reorient the quad_image as needed
                quad_image = np.rot90( quad_image, 4-quad)

            qoff_x = self.quad_offset[0,quad]
            qoff_y = self.quad_offset[1,quad]
            self.image[qoff_x:qoff_x+self.npix_quad, qoff_y:qoff_y+self.npix_quad]=quad_image

        return self.image

Fine tuning

Notice that the code snippets above make use of some predefined quantities which it reads in from "calibration files". The files contains calibrated numerical values for individual sections' and quads' rotations and shifts. All of these files are located in the experiment's 'calib' folder, but is not generated automatically. The XtcExplorer currently has a local version which is not correct but which is close enough to display a reasonable image. For how the files have been read in, you can take a look at XtcExplorer/src/cspad.py's read_alignment function.

CSPad alignment

Saving data arrays

...