The file read in two SQLite databases, the data file and the map file.

In the ADCdf table, each of the entry is the signal trace for each of the 253 channel of the chamber.

The first preliminary filter is that for each signal trace, there are two threshold, 1. must be larger than the 20% of the larget amplitude; 2 must be larger than 20. Each of the time bin must be larger than both of the threshold.

A list of positions are extract from the traces (R,z), R is radial of the centroid of the pad , z is the number of the time bin.

The four quadrants are used to construct two images using each opposite pairs. The overlapping score (a overlapping on the edge (y=0 or 300) yield larger score than a overlapping in the center (y=150) ) is calculated to determine the direction of aligning the two images.

A enlarged reconstructed image is presented below

Besides that, as the image is fully contaminated by noisy data and disconnected points. The prepossessing steps consist GaussianBlur, threshold, erode and dilate.

__init__(self,data_path,map_path): initialization of DataFactory

data_path (str): the relative path to the data file
map_path (str): the relative path to the ATTPC map file

this module loads the ADC table into pandas spreadsheet. Then the function iterate through all channels to see at which time bin the signal amplitude is above threshold and store all the filtered signals into t3.

ConstructImage(self,EID):

EID (int): the EventID of the event for constructing the image

This function takes the spreadsheet t3 from __init__ function, and then produce an image using the positions for each “EventID”.

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 is messy, this function take the connected convexHull to clean up everything outside this hull.

image (numpy 2d uint8 array): the original image

GetEventPositions(pic,debug_mode=0, center_width = 12, quadrant_thresh=100, center_thresh=300, err_thresh =12, spread_thresh=6 ): get all the three tip points and also the vertex point

pic (numpy 2d uint8 array): the original image
debug_mode (bool): plot some debug features, this should be turned off in batch mode
center_width (float): not used for now
quadrant_thresh (float): threshold for number of pixel in the reaction product part
center_thresh (float): threshold for the beam part
err_thresh (float):  threshold for average distance to the fit
spread_thresh (float): threshold for x,y spread out

 

AveDist(x,y,k,b): calculate the average distance from (x,y) to a straight line with (k,b) parameters.

x (numpy float array): the x positions
y (numpy float array): the y positions
k (float): the slope of the line
b (float): the y-intercept of the line

return (float): average distance from (x,y) to the line (k,b)

r2(x,y,k,b): just to calculate the r2 score of the fitting

parameters are the same to function above

return (float): r2 score

 

VertexPos(fits,y0): using all fitting results and the y position from the right most tip point to estimate the vertex position

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.

fits ([int,float,float,float]*3): fit results for three parts of the image

return (float,float): (x,y)

 

tbjcfit(xs,ys): use SVD to calculate the least square DISTANCE (not y) fitting

xs (numpy float array): the x positions
ys (numpy float array): the y positions

return (int,float,float,float):(number of pixels, k,b,average distance)

 

GetFit(image_, part_thresh=60, err_thresh =1.2,spread_thresh=6): 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.

image_ (numpy 2d uint8 array): the part of the image you want to obtain a line fitting
part_thresh (float): if the number of pixels in the image is large enough for a fitting
err_thresh (float):the average distance to the fitting line of all the points
spread_thresh (float):the threshold for requiring a spread out distributed data on either x or y axis

return (numpy 2d uint8 array): a copy of filtered image

 

 

 

GetLineInfo(p1,p2, L_thre = -5): calculate the length and angle between two points

p1 (float,float): the tip point
p2 (float,float): the vertex point
L_thre = -5: not used for now

GetEventInfo(points,p0): calculate the length and angle for between each pair of the tip point and the vertex point

points ((float,float)*3): the positions of the tip points
p0 (float,float): the position of the vertex point

return ((float,float)*3,float): (theta,length) for each pair, and the reaction range

Distance(contours,n1,n2): calculate the minimum distance between two contour

contours [numpy.array (n,1,2)]:all the contours
n1 (int): index of the first contour
n2 (int): index of the second contour

return (float): the minimum distance

Groups(contours): combine all adjacent contours

contours [numpy.array (n,1,2)]:all the contours

return ((float)*n, (float)*n): grouped contours

convexHull(thresh, debug_mode = 0): calculated the convexHull using the largest grouped contour

thresh (numpy 2d uint8 array): image after preliminary processing

return ((float,float)*n): the hull points

MaxEnclosedTriangle(hull): calculate the maximum enclosed triangle using the hull points

hull ((float,float)*n): the hull points

return (int, int, int): index of the hull points to form the maximum enclosed triangle

TipFinder(thresh, debug_mode = 0): return the position of the tip on the maximum enclosed triangle

thresh (numpy 2d uint8 array): image after preliminary processing

return ((float,float)*3): the position of the tip on the maximum enclosed triangle

Since most of the analysis work happens in the python code, it deserves a independent post for explaining all the code. So far the code will only works for two body reaction and regular kinematics, where both reaction products are forward focused.
DataFactory.py recontruct a 2D image using signal traces from each of the 253 channels and perform a first-order cleanup in this image
VertexAnalyzor.py contains functions to extract line features from the 2D image.
The package so far is producing reasonable results, which can be observed in the theta1 vs theta2 plot of the two reaction products.

Finally, I think I should write a instruction of using my code.

The code is basically divided into two parts. The C++ convertor, which is inherited from Yassid’s ATTPCroot and python analysis code. Between those two, there is also a python script converting the root tree data files into SQLite.

The convertor and all other pyhton code needs to be installed differently.

Convertor :

The C++ part of the convertor converts a Raw binary data file to a Root tree file; then you need the python convertor to convert the Root tree file to a SQLite database.

Installation Requirements:  Root>6.xx, Boost, CMake, Linux environment, Python (Anaconda 2.x is recommended)

Installation steps:

### C++ part ###

to build:
mkdir build
cd build
cmake ..

### python part ###

### download the Anaconda 2.x https://www.anaconda.com/download/#linux
bash Anaconda-2.x.x-Linux-x86[_64].sh

How to run:

There are two ways of running the program.

• single process: in the main folder, (the build/example will only run in the main folder)

./build/example <root file name> <Binary file name>  ## this convert binary data to root tree file
python tbjcConvertor/Convertor_sqlite.py <root file name> <SQLite database name> ## this convert the root tree file to SQLite database

• multi process:

python mult.py

 

Tested System:

Ubuntu 16.04.1 LTS
Red Hat Enterprise Linux Server release 7.4 (Maipo)

Multiprocess:

The C++ code is a single thread and process program, which can be paralleled (multiprocess) through the multi.py

In multi.py, there are three variables need to be changed, all of the variables are RELATIVE DIRECTORY

parentPath: the relative directory of the parent folder of the DATA FOLDERS of the raw binary

SQLpath: where you want to store all your converted SQLite databases

paths: all the data folders you want to convert

Analysis Part:

Ideally, the analysis part of the C++ code should be working, including ATHoughTask, ATPSATask, ATAnalysisTask and ATPhiRecoTask. However, it is not commonly used in my analysis work.

What’s different?

As mentioned in the ReadMe file, the FairRoot part and the Root TCloneArray, is completely faked. The fake classes only provide basic functions of iterating tasks and storing temporary variables. However, from my observation, all the analysis tasks should be functioning.

Analysis program :

Though most of the programs are unfinished works, but they should give good insights of what the data look like.

The most complete program is the VertexAnalyzer, which reconstruct the

Requirements:

besides built-in Anaconda packages, you will also need opencv2 and seaborn (mainly for visualizing, when debug option is on)

conda install -c menpo opencv
conda install seaborn

Tested System:

Ubuntu 16.04.1 LTS
Red Hat Enterprise Linux Server release 7.4 (Maipo)

Running:

under the main folder, run for the multi-process mode

python Multi.py

or run the jupyter notebook interactive mode

jupyter notebook

the Multi.py will produce a text file contains a list of ranges for reaction length.