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CHECLabPy Build Status

Python scripts for reduction and analysis of CHEC lab data

These set of python scripts provide a standard approach for reading, plotting and reducing the CHEC tio files for lab testing and comissioning.

Refer to https://forge.in2p3.fr/projects/gct/wiki/Installing_CHEC_Software for intstructions for preparing your environment and installing the TARGET libraries. It is not a requirement to install the TARGET libraries to use this package (unless you wish to read R0/R1 tio files).

To set up TC_CONFIG_PATH for the Transfer Functions, download svn.in2p3.fr/cta/Sandbox/justuszorn/CHECDevelopment/CHECS/Operation to a directory, and export TC_CONFIG_PATH=....

The dl1 files are stored as a pandas.DataFrame in HDF5 format. A DataFrame is an object that acts as a table. It is compatible with numpy methods and allows easy category searching. Learn about pandas.DataFrame at: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe

There are also executables created to convert the DataFrame into other formats like csv and ROOT TTree.

See the examples/tutorials for instructions on the CHEC calibration and reduction flow and how to use the CHECLabPy software.

Installation

Downloading and updating

Prerequisites

It is recommended to use a conda environment running Python3.5 (or above). Instructions on how to setup such a conda environment can be found in https://forge.in2p3.fr/projects/gct/wiki/Installing_CHEC_Software. The required python packages for CHECLabPy (which can be installed using conda install ... or pip install ...) are:

  • astropy
  • scipy
  • numpy
  • matplotlib
  • tqdm
  • pandas

Non-contributor

If you wish to just use the software, and not contribute:

  • To Download: git clone https://github.com/cta-chec/CHECLabPy.git
  • To Update: git pull

Contributor

  1. Create a fork of https://github.com/cta-chec/CHECLabPy to your GitHub account
  2. git clone https://github.com/YOURGITHUBACCOUNT/CHECLabPy.git
  3. cd CHECLabPy
  4. git remote add upstream https://github.com/cta-chec/CHECLabPy.git
  • To Update: git fetch upstream && git checkout master && git merge upstream/master && git push origin master

Installing

To install, run python setup.py develop

Contributing

The "master" branch is meant to always be a clean, up-to-date copy of the cta-chec/CHECLabPy:master. You should always create a branch when developing some new code (unless it is a very small change). Generally make a new branch for each new feature, so that you can make pull-requests for each one separately and not mix code from each. Remember that git checkout switches between branches, git checkout -b creates a new branch, and git branch on it’s own will tell you which branches are available and which one you are currently on.

1. Create a new branch

git checkout -b branch_name

2. Make your additions to the code on that branch 3. Commit your changes locally

git add some_changed_file.py another_file.py
git commit

4. Push your changes to your GitHub account

git push -u origin branch_name

5. On GitHub, create a pull request into cta-chec/CHECLabPy:master

Layout

scripts

Contains the useful scripts for processessing the data. The most important script is extract_dl1.py, which reduces the waveforms into a TIO file to produce a dl1.h5 file, with parameters extracted per pixel and per event.

examples

Contains some examples of how to use functionality of CHECLabPy. There are also some tutorial Jupyter notebooks included in this directory.

CHECLabPy/core

This module contains the core funtionality for CHECLabPy, such as base classes and file io. It is advised not to change the contents of this directory, it should not be necessary.

CHECLabPy/waveform_reducers

This directory contains all the waveform reducer methods. See the tutorial on waveform reducers on how to contribute a new waveform reducer class.

CHECLabPy/spectrum_fitters

This directory contains all the fitting methods for SPE spectra.

CHECLabPy/plotting

Defines some classes for various plots, including a conveniant method for plotting camera images (which uses the TargetCalib mapping)

CHECLabPy/utils

Contains some useful functions and classes that may be used in multiple scripts to process the data and waveforms.