![\"\"](\"http:\/\/www.jianping-lai.com\/wp-content\/uploads\/Physics\/ATTPC\/VertexAnalyzor\/VertexAnalyzor_flow-e1507559481268.png\")
<\/p>\nThe overall functions should be used in the sequence below<\/p>\n
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Each of the functions calls sub functions in the flow presented in the graph below<\/p>\n
![\"\"](\"http:\/\/www.jianping-lai.com\/wp-content\/uploads\/Physics\/ATTPC\/VertexAnalyzor\/VertexAnalyzor_functions-e1507563086293.png\")
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An example of the original image is shown below,<\/p>\n
![\"\"](\"http:\/\/www.jianping-lai.com\/wp-content\/uploads\/Physics\/ATTPC\/VertexAnalyzor\/original_image.png\")
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FilterBackground(image):<\/strong> as the image is messy, this function take the connected convexHull to clean up everything outside this hull.<\/p>\n image (numpy 2d uint8 array): the original image<\/p>\n GetEventPositions(pic,debug_mode=0, center_width = 12, quadrant_thresh=100, center_thresh=300, err_thresh =12, spread_thresh=6 ):<\/strong>\u00a0get all the three tip points and also the vertex point<\/p>\n pic (numpy 2d uint8 array): the original image <\/p>\n AveDist(x,y,k,b):\u00a0<\/strong>calculate the average distance from (x,y) to a straight line with (k,b) parameters.<\/p>\n x (numpy float array): the x positions return (float): average distance from (x,y) to the line (k,b)<\/p>\n r2(x,y,k,b):\u00a0<\/strong>just to calculate the r2 score of the fitting<\/p>\n parameters are the same to function above<\/p>\n return (float): r2 score<\/p>\n <\/p>\n VertexPos(fits,y0): <\/strong>using all fitting results and the y position from the right most tip point to estimate the vertex position<\/p>\n The function divide the calculation to 2 scenarios. 1. you have 2 or 3 fitted lines, then you just pick the parameters of the first two lines for the calculation. 2. if you have only 1 line and this one will not be you center line (because of previous fitting condition), you assume the center line is straight on y0.<\/p>\n fits ([int,float,float,float]*3): fit results for three parts of the image<\/p>\n return (float,float): (x,y)<\/p>\n <\/p>\n tbjcfit(xs,ys):\u00a0<\/strong>use SVD to calculate the least square DISTANCE (not y) fitting<\/p>\n xs (numpy float array): the x positions return (int,float,float,float):(number of pixels, k,b,average distance)<\/p>\n <\/p>\n GetFit(image_, part_thresh=60, err_thresh =1.2,spread_thresh=6):<\/strong> this function extract the x,y positions of each pixel above 0 value. Then fit the x,y points using tbjcfit. The results will be filtered through a few conditions to see if the fitting is good, like if the average distance from the points to the line is within the err_thresh and if the scattered positions does give a reasonable line shape distribution.<\/p>\n image_ (numpy 2d uint8 array): the part of the image you want to obtain a line fitting return (numpy 2d uint8 array): a copy of filtered image<\/p>\n <\/p>\n <\/p>\n <\/p>\n GetLineInfo(p1,p2, L_thre = -5):\u00a0<\/strong>calculate the length and angle between two points<\/p>\n p1 (float,float): the tip point GetEventInfo(points,p0):\u00a0<\/strong>calculate the length and angle for between each pair of the tip point and the vertex point<\/p>\n points ((float,float)*3): the positions of the tip points return ((float,float)*3,float): (theta,length) for each pair, and the reaction range<\/p>\n Distance(contours,n1,n2):<\/strong> calculate the minimum distance between two contour<\/p>\n contours [numpy.array (n,1,2)]:all the contours return (float): the minimum distance<\/p>\n Groups(contours):\u00a0<\/strong>combine all adjacent contours<\/p>\n contours [numpy.array (n,1,2)]:all the contours<\/p>\n return ((float)*n, (float)*n): grouped contours<\/p>\n convexHull(thresh, debug_mode = 0):\u00a0<\/strong>calculated the convexHull using the largest grouped contour<\/p>\n thresh (numpy 2d uint8 array): image after preliminary processing<\/p>\n return ((float,float)*n): the hull points<\/p>\n MaxEnclosedTriangle(hull):\u00a0<\/strong>calculate the maximum enclosed triangle using the hull points<\/p>\n hull ((float,float)*n):\u00a0the hull points<\/p>\n return (int, int, int): index of the hull points to form the maximum enclosed triangle<\/p>\n TipFinder(thresh, debug_mode = 0):\u00a0<\/strong>return the position of the tip on the\u00a0maximum enclosed triangle<\/p>\n thresh (numpy 2d uint8 array): image after preliminary processing<\/p>\n return ((float,float)*3):\u00a0the position of the tip on the\u00a0maximum enclosed triangle<\/p>\n","protected":false},"excerpt":{"rendered":" The overall functions should be used in the sequence below Each of the functions calls sub functions in the flow presented in the graph below An example of the original image is shown below, FilterBackground(image): as the image … Continue reading ![\"\"](\"http:\/\/www.jianping-lai.com\/wp-content\/uploads\/Physics\/ATTPC\/VertexAnalyzor\/filtered_image-1.png\")
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\ndebug_mode (bool): plot some debug features, this should be turned off in batch mode
\ncenter_width (float): not used for now
\nquadrant_thresh (float): threshold for number of pixel in the reaction product part
\ncenter_thresh (float):\u00a0threshold for the beam part
\nerr_thresh (float):\u00a0 threshold for average distance to the fit
\nspread_thresh (float): threshold for x,y spread out<\/p>\n![\"\"](\"http:\/\/www.jianping-lai.com\/wp-content\/uploads\/Physics\/ATTPC\/VertexAnalyzor\/post_image.png\")
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\ny (numpy float array): the y positions
\nk (float): the slope of the line
\nb (float): the y-intercept of the line<\/p>\n
\nys (numpy float array): the y positions<\/p>\n
\npart_thresh (float): if the number of pixels in the image is large enough for a fitting
\nerr_thresh (float):the average distance to the fitting line of all the points
\nspread_thresh (float):the threshold for requiring a spread out distributed data on either x or y axis<\/p>\n
\np2\u00a0(float,float): the vertex point
\nL_thre = -5: not used for now<\/p>\n
\np0 (float,float): the position of the vertex point<\/p>\n
\nn1 (int):\u00a0index of the first contour
\nn2 (int): index of the second contour<\/p>\n