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Image peak finding

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

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

• For each Quadrant (cspad_layout.txt):

  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

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