The Timepix detector (Llopart et al. 2007) is capable of measuring and visualising ionising radiation in a 256-by-256 grid of pixels, representing the 14mm-by14-mm silicon wafer of its sensor element. This repository contains code for analysing, visualising and sorting clusters of "hit" pixels by their cluster properties. Further information can be found in (Whyntie et al. 2015)

• This code dates from 2015. While every attempt has been made to ensure that it is usable, some work may be required to get it running on your own particular system. We recommend using a GridPP CernVM; please refer to this guide for further instructions. Unfortunately CERN@school cannot guarantee further support for this code. Please proceed at your own risk.
• This repository is now deprecated, and remains here for legacy purposes. For future work regarding CERN@school, please refer to the Institute for Research in Schools (IRIS) GitHub repository. Please also feel free to fork and modify this code as required for your own research.

To get the code, create a working directory on your CernVM and clone it from GitHub with the following command:

$ git clone https://github.com/CERNatschool/cluster-sorter.git

To prepare for running, run the setup.sh script with the following command:

$ source setup.sh

Note: if you are not using a GridPP CernVM, the setup.sh script will not work as you won't have access to the CERN@school CVMFS repository and will have to source your own version of the Python libraries such as matplotlib via e.g. the Anaconda Python distribution.

To get you started, we've supplied some data to process with the cluster finding code. The processing script, process-frames.py, runs over the data in the inputPath directory and writes the results to the outputPath directory. You need to create the latter directory. Let's do this now:

$ cd cluster-sorter
$ mkdir tmp

You can then run the processing script with:

$ python process-frames.py testdata/crookes/ tmp/

After a while (depending on the speed of your computer), you should see the following output from the terminal:

*
*======================================*
* CERN@school - local frame processing *
*======================================*
*
* Input path          : 'testdata/crookes/'
* Output path         : 'tmp/'
* Gamma candidate clusters WILL NOT be processed.
*

The directory tmp should now be full of results, which you can check with the following command:

$ ls tmp/
clusters  frames  frames.json  klusters.json

The code has extracted the properties of the data frames and clusters into the frames.json and klusters.json JSON files (more on JSON files here) - so we don't have to run the processing code again - and created image files of all the frames and the clusters. so you can view them with any standard image viewer. On the GridPP CernVM, for example, you can use the Eye of Gnome viewer:

$ sudo yum install eog
[... say 'yes' to everything and type your password when asked ...]
$ eog tmp/frames/ &
$ eog tmp/clusters/ &

You can then view each image by pressing the left or right arrow keys.

We can also visualise the different properties of the frames and clusters by making plots of the different numbers we've used our code to extract. Again, we've done this for you to start with - try:

$ python make-plots.py tmp/ tmp/
*
*==============================*
* CERN@school - make the plots *
*==============================*
*
* Input path          : 'tmp/'
* Output path         : 'tmp/'
*
*
* Plotting complete.
* View your results by opening:
* 'tmp/frameplots/index.html' or 'tmp/clusterplots/index.html'
* in a browser, e.g.
* $ firefox tmp/frameplots/index.html &
* $ firefox tmp/clusterplots/index.html &

Follow those instructions and you should be able to see the two webpages - one for the frames, one for the clusters - displaying the different plots for the Crookes dataset.

Now that we've plotted the frame and cluster properties, let's try sorting the clusters into different particle types.

The sort-clusters.py Python script sorts the clusters that you've just extracted from the test data. You can run it with the following command:

$ python sort-clusters.py tmp/ types.json tmp/

...and you should get the following output:

===============================
  CERN@school - Sort Clusters  
===============================
*
* Input path          : 'tmp/'
* Particle JSON       : 'types.json'
* Output file         : 'tmp/'
*
*
* Sorted 149 clusters!
*
* Sorting complete.
* View your results by opening 'tmp/sorted/index.html' in a browser, e.g.
* firefox tmp/sorted/index.html &

The webpage that appears in the Firefox browser should present you with a list of clickable particle types, each representing a particle type that the code has sorted the clusters into. Try clicking on each and looking at the different types. Can you spot the patterns? Once you start to look "under the hood" of the code, you'll see how the different types are decided upon.

Good luck!

The data in the testdata folder is taken from the Crookes dataset, a sample set of measurements made at the Royal Institution of Great Britain during the BIG SCIENCE event of 18th June 2013. The data were obtained by placing a CERN@school MX-10 detector next to the notebook of William Crookes, the discoverer of Thallium. You can find an image of the experimental setup here.

CERN@school was supported by the UK Science and Technology Facilities Council (STFC) via grant numbers ST/J000256/1 and ST/N00101X/1, as well as a Special Award from the Royal Commission for the Exhibition of 1851.

• Setting up a GridPP CernVM;
• Whyntie et al. 2015 - the CERN@school Contemporary Physics paper featuring a discussion of cluster properties and their use in particle identification;
• The Crookes dataset on FigShare;
• The Institute for Research in Schools (IRIS) homepage;
• The IRIS CERN@school website;
• The Official IRIS GitHub Organization.