DL1 Data Handler
================
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:target: https://zenodo.org/badge/latestdoi/72042185
:alt: DOI
.. image:: https://anaconda.org/ctlearn-project/dl1_data_handler/badges/version.svg
:target: https://anaconda.org/ctlearn-project/dl1_data_handler/
:alt: Anaconda-Server Badge
.. image:: https://img.shields.io/pypi/v/dl1-data-handler
:target: https://pypi.org/project/dl1-data-handler/
:alt: Latest Release
.. image:: https://github.com/cta-observatory/dl1-data-handler/actions/workflows/python-package-conda.yml/badge.svg
:target: https://github.com/cta-observatory/dl1-data-handler/actions/workflows/python-package-conda.yml
:alt: Continuos Integration
A package of utilities for reading, and applying image processing to `Cherenkov Telescope Array (CTA) <https://www.ctao.org/>`_ R0/R1/DL0/DL1 data in a standardized format. Created primarily for testing machine learning image analysis techniques on IACT data.
Currently supports ctapipe v6.0.0 data format.
Previously named image-extractor (v0.1.0 - v0.6.0). Currently under development, intended for internal use only.
Installation
------------
The following installation method (for Linux) is recommended:
Installing as a conda package
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To install dl1-data-handler as a conda package, first install Anaconda by following the instructions here: https://www.anaconda.com/distribution/.
The following command will set up a conda virtual environment, add the
necessary package channels, and install dl1-data-handler specified version and its dependencies:
.. code-block:: bash
DL1DH_VER=0.12.0
wget https://raw.githubusercontent.com/cta-observatory/dl1-data-handler/v$DL1DH_VER/environment.yml
conda env create -n [ENVIRONMENT_NAME] -f environment.yml
conda activate [ENVIRONMENT_NAME]
conda install -c ctlearn-project dl1_data_handler=$DL1DH_VER
This should automatically install all dependencies (NOTE: this may take some time, as by default MKL is included as a dependency of NumPy and it is very large).
Dependencies
------------
The main dependencies are:
* PyTables >= 3.8
* NumPy >= 1.20.0
* ctapipe == 0.21.2
Also see setup.py.
Usage
-----
ImageMapper
^^^^^^^^^^^
The ImageMapper class transforms the hexagonal input pixels into a 2D Cartesian output image. The basic usage is demonstrated in the `ImageMapper tutorial <https://github.com/cta-observatory/dl1-data-handler/blob/master/notebooks/test_image_mapper.ipynb>`_. It requires `ctapipe-extra <https://github.com/cta-observatory/ctapipe-extra>`_ outside of the dl1-data-handler. See this publication for a detailed description: `arXiv:1912.09898 <https://arxiv.org/abs/1912.09898>`_
Links
-----
* `Cherenkov Telescope Array (CTA) <https://www.ctao.org/>`_ - Homepage of the CTA Observatory
* `CTLearn <https://github.com/ctlearn-project/ctlearn/>`_ and `GammaLearn <https://gitlab.lapp.in2p3.fr/GammaLearn/GammaLearn>`_ - Repository of code for studies on applying deep learning to IACT analysis tasks. Maintained by groups at Columbia University, Universidad Complutense de Madrid, Barnard College (CTLearn) and LAPP (GammaLearn).
* `ctapipe <https://cta-observatory.github.io/ctapipe/>`_ - Official documentation for the ctapipe analysis package (in development)
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