HighResAnalysis

The current repository is in development and is not guaranteed to work The working version can be found https://github.com/diamondIPP/HighResAnalysis

Prerequisites

• python>=3.6
  • python=3.10 was used for the development
• cmake>=3.7
  • optionally cmake GUI, for example ccmake

Installation

• First, install mamba. If you do not yet have conda install then getting Mambaforge is the recommended way to get mamba. Here is the instruction for Linux.

curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh"
bash Mambaforge-Linux-x86_64.sh 

If this not the case for you. You can follow the instruction on mamba install page

• Next you can install root. In the root installation instructions you need to replace conda with mamba and skip the instructions about the environment, since the Mambaforge already created the default base environment.

mamba config --set channel_priority strict
mamba install root
mamba install -c conda-forge root

• Install the analysis code:

pip install HighResAnalysys

• Optionally install other useful python packages:

mamba install -c conda-forge scikit-learn numpy pandas 
mamba install pyarrow openpyxl xlrd pytables requests sqlalchemy
mamba install -c fastai nbdev
mamba install jupyterlab
mamba install jupyternotebook
mamba install ipython
mamba install notebook
mamba install voila

• For the installation of the software hosted on the GitHub it is useful to make a dedicated folder:

mkdir software
cd software

- And clone all the necessary packages there:

        git clone git@github.com:diamondIPP/DRS4-v5-shared.git
        git clone git@github.com:diamondIPP/proteus.git
        git clone git@github.com:diamondIPP/judith.git
        git clone git@github.com:diamondIPP/HVClient.git
        git clone git@github.com:diamondIPP/eudaq-2.git

• generate shh keys and copy them to login.phys.ethz.ch

        ssh-keygen
        ssh-copy-id username@login.phys.ethz.ch

• Clone the analysis setup from GitHub. It contains all the necessary config files:

git clone git@github.com:diamondIPP/setup-analysis.git HighResAnalysis
cd HighResAnalysis/

• To install the converters follow the instructions on the respective pages:
  • proteus
    • It will need Eigen3 and you will have to tell cmake the path to it.
  • judith (only for CERN data)
  • eudaq2 (only for DESY data)

Example analysis of the DESY data

the data need to be frist pre-converted:

>analyse --run=4

you will need to import a couple of libraries. Most of the tools are in src.dut_analysis. It will load the data and set all the cuts. The draw module from plotting library has some useful functions and presets that allow plotting histograms and graphs

from HighResAnalysis.src.dut_analysis import *
from HighResAnalysis.plotting.draw import *

Welcome to JupyROOT 6.28/00

Initialize the DUTAnalysis with run number, DUT number, and a string indicating the year and the month of the beam test

run4 = DUTAnalysis(4, 0, '201912')

--- Palette ------ 55

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
STARTING DUT ANALYSIS of D02, run 4 (Dec 2019), 2.50M ev |
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

************** Initing Converter *****************
*************** Initing PROTEUS ******************

A small function that allows inline plotting of ROOT histograms

def dc(): get_last_canvas().Draw()

Let’s plot a signal distribution

run4.draw_charge_distribution()
dc()

INFO:     10:34:18 --> Creating directory: /Users/hits/Documents/GitHub/HighResAnalysis/HighResAnalysis/results/201912
INFO:     10:34:18 --> saving plot: SignalDist
WARNING:  10:34:19 --> Diamond server is not mounted in /Users/hits/mounts/high-rate