| Name | dl1-data-handler JSON |
| Version |
0.13.0
JSON |
| download |
| home_page | None |
| Summary | A package of utilities for reading, and applying image processing to Cherenkov Telescope Array (CTA) R1/DL0/DL1 data in a standardized format. Created primarily for testing machine learning image analysis techniques on IACT data. |
| upload_time | 2025-02-11 12:42:40 |
| maintainer | None |
| docs_url | None |
| author | Ari Brill, Bryan Kim, Tjark Miener |
| requires_python | >=3.10 |
| license | BSD-3-Clause |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
DL1 Data Handler
================
.. image:: https://zenodo.org/badge/72042185.svg
: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=0.13.0
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).
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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"description": "DL1 Data Handler\n================\n\n\n.. image:: https://zenodo.org/badge/72042185.svg\n :target: https://zenodo.org/badge/latestdoi/72042185\n :alt: DOI\n\n\n.. image:: https://anaconda.org/ctlearn-project/dl1_data_handler/badges/version.svg\n :target: https://anaconda.org/ctlearn-project/dl1_data_handler/\n :alt: Anaconda-Server Badge\n\n\n.. image:: https://img.shields.io/pypi/v/dl1-data-handler\n :target: https://pypi.org/project/dl1-data-handler/\n :alt: Latest Release\n\n\n.. image:: https://github.com/cta-observatory/dl1-data-handler/actions/workflows/python-package-conda.yml/badge.svg\n :target: https://github.com/cta-observatory/dl1-data-handler/actions/workflows/python-package-conda.yml\n :alt: Continuos Integration\n\nA 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.\n\nCurrently supports ctapipe v6.0.0 data format. \n\nPreviously named image-extractor (v0.1.0 - v0.6.0). Currently under development, intended for internal use only.\n\n\nInstallation\n------------\n\nThe following installation method (for Linux) is recommended:\n\nInstalling as a conda package\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nTo install dl1-data-handler as a conda package, first install Anaconda by following the instructions here: https://www.anaconda.com/distribution/.\n\nThe following command will set up a conda virtual environment, add the\nnecessary package channels, and install dl1-data-handler specified version and its dependencies:\n\n.. code-block:: bash\n\n DL1DH_VER=0.13.0\n wget https://raw.githubusercontent.com/cta-observatory/dl1-data-handler/v$DL1DH_VER/environment.yml\n conda env create -n [ENVIRONMENT_NAME] -f environment.yml\n conda activate [ENVIRONMENT_NAME]\n conda install -c ctlearn-project dl1_data_handler=$DL1DH_VER\n\nThis 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).\n\n\nLinks\n-----\n\n\n* `Cherenkov Telescope Array (CTA) <https://www.ctao.org/>`_ - Homepage of the CTA Observatory \n* `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).\n* `ctapipe <https://cta-observatory.github.io/ctapipe/>`_ - Official documentation for the ctapipe analysis package (in development)\n\n",
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