ctlearn

Deep Learning for IACT Event Reconstruction

https://github.com/ctlearn-project/ctlearn

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

○
CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 3 DOI reference(s) in README
✓
Academic publication links
Links to: zenodo.org
✓
Committers with academic emails
2 of 13 committers (15.4%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (16.4%) to scientific vocabulary

Last synced: 6 months ago · JSON representation

Repository

Deep Learning for IACT Event Reconstruction

Basic Info

Host: GitHub
Owner: ctlearn-project
License: bsd-3-clause
Language: Python
Default Branch: main
Homepage:
Size: 82.6 MB

Statistics

Stars: 54
Watchers: 16
Forks: 46
Open Issues: 26
Releases: 19

Created over 9 years ago · Last pushed 7 months ago

Metadata Files

Readme Contributing License Codemeta Zenodo

README.rst

CTLearn: Deep Learning for IACT Event Reconstruction
====================================================

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3342952.svg
   :target: https://doi.org/10.5281/zenodo.3342952
   :alt: DOI

.. image:: https://img.shields.io/pypi/v/ctlearn
    :target: https://pypi.org/project/ctlearn/
    :alt: Latest Release

.. image:: https://github.com/ctlearn-project/ctlearn/actions/workflows/python-package-conda.yml/badge.svg
    :target: https://github.com/ctlearn-project/ctlearn/actions/workflows/python-package-conda.yml
    :alt: Continuos Integration
    
.. image:: images/CTLearnTextCTinBox_WhiteBkgd.png
   :target: images/CTLearnTextCTinBox_WhiteBkgd.png
   :alt: CTLearn Logo


CTLearn is a package under active development to run deep learning models to analyze data from all major current and future arrays of imaging atmospheric Cherenkov telescopes (IACTs). CTLearn can load R1/DL0/DL1 data from `CTAO `_ (Cherenkov Telescope Array Observatory), `FACT `_\ , `H.E.S.S. `_\ , `LST-1 `_\ , `MAGIC `_\ , and `VERITAS `_ telescopes reduced by `ctapipe `_ and processed by `DL1DataHandler `_.

* Code, feature requests, bug reports, pull requests: https://github.com/ctlearn-project/ctlearn
* Documentation: https://ctlearn.readthedocs.io
* License: BSD-3

Installation for users
----------------------

Download and install `Anaconda `_\ , or, for a minimal installation, `Miniconda `_.

The following command will set up a conda virtual environment, add the
necessary package channels, and install CTLearn specified version and its dependencies:

.. code-block:: bash

   CTLEARN_VER=0.10.3
   wget https://raw.githubusercontent.com/ctlearn-project/ctlearn/v$CTLEARN_VER/environment.yml
   conda env create -n [ENVIRONMENT_NAME] -f environment.yml
   conda activate [ENVIRONMENT_NAME]
   pip install ctlearn==$CTLEARN_VER
   ctlearn -h


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).

See the documentation for further information like `installation instructions for developers `_, `package usage `_, and `dependencies `_ among other topics.

Citing this software
--------------------

Please cite the corresponding version using the `DOIs from Zenodo `_ if this software package is used to produce results for any publication.

Team
----

.. list-table::
   :header-rows: 1

   * - .. image:: https://github.com/aribrill.png?size=100
        :target: https://github.com/aribrill
        :alt: Ari Brill
     
     - .. image:: https://github.com/bryankim96.png?size=100
        :target: https://github.com/bryankim96
        :alt: Bryan Kim
     
     - .. image:: https://github.com/TjarkMiener.png?size=100
        :target: https://github.com/TjarkMiener
        :alt: Tjark Miener
     
     - .. image:: https://github.com/nietootein.png?size=100
        :target: https://github.com/nietootein
        :alt: Daniel Nieto
     
   * - `Ari Brill `_
     - `Bryan Kim `_
     - `Tjark Miener `_
     - `Daniel Nieto `_


Collaborators
-------------

.. list-table::
   :header-rows: 1

   * - .. image:: https://github.com/qi-feng.png?size=100
        :target: https://github.com/qi-feng
        :alt: Qi Feng

     - .. image:: https://github.com/rlopezcoto.png?size=100
        :target: https://github.com/rlopezcoto
        :alt: Ruben Lopez-Coto

   * - `Qi Feng `_
     - `Ruben Lopez-Coto `_


Alumni
------

.. list-table::
   :header-rows: 1

   * - .. image:: https://github.com/Jsevillamol.png?size=100
        :target: https://github.com/Jsevillamol
        :alt: Jaime Sevilla
     
     - .. image:: https://github.com/hrueda25.png?size=100
        :target: https://github.com/hrueda25
        :alt: Héctor Rueda
     
     - .. image:: https://github.com/jredondopizarro.png?size=100
        :target: https://github.com/jredondopizarro
        :alt: Juan Redondo Pizarro
     
     - .. image:: https://github.com/LucaRomanato.png?size=100
        :target: https://github.com/LucaRomanato
        :alt: LucaRomanato
     
     - .. image:: https://github.com/sahilyadav27.png?size=100
        :target: https://github.com/sahilyadav27
        :alt: Sahil Yadav
     
     - .. image:: https://github.com/sgh14.png?size=100
        :target: https://github.com/sgh14
        :alt: Sergio García Heredia
     
   * - `Jaime Sevilla `_
     - `Héctor Rueda `_
     - `Juan Redondo Pizarro `_
     - `Luca Romanato `_
     - `Sahil Yadav `_
     - `Sergio García Heredia `_

Owner

Name: CTLearn
Login: ctlearn-project
Kind: organization

Repositories: 5
Profile: https://github.com/ctlearn-project

Deep learning for IACT data analysis

CodeMeta (codemeta.json)

{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "@type": "SoftwareSourceCode",
  "license": "https://spdx.org/licenses/BSD-3-Clause",
  "codeRepository": "https://github.com/ctlearn-project/ctlearn",
  "contIntegration": "https://github.com/ctlearn-project/ctlearn/actions",
  "datePublished": "2023-05-08",
  "dateModified": "2025-03-21",
  "downloadUrl": "https://zenodo.org/record/5947837/files/ctlearn-project/ctlearn-v0.10.2.zip",
  "issueTracker": "https://github.com/ctlearn-project/ctlearn/issues",
  "name": "CTLearn: Deep learning for imaging atmospheric Cherenkov telescopes event reconstruction",
  "readme": "https://ctlearn.readthedocs.io",
  "version": "0.10.2",
  "softwareVersion": "0.10.2",
  "identifier": "10.5281/zenodo.3342952",
  "description": "CTLearn is a high-level Python package providing a backend for training deep learning models for the reconstruction of imaging atmospheric Cherenkov telescope events using TensorFlow.",
  "applicationCategory": "Astronomy",
  "funding": "ESCAPE 824064",
  "developmentStatus": "active",
  "isPartOf": "https://github.com/ctlearn-project",
  "referencePublication": "https://doi.org/10.5281/zenodo.3342952",
  "funder": {
    "@type": "Organization",
    "name": "European Union's Horizon 2020 research and innovation programme"
  },
  "keywords": [
    "Imaging atmospheric Cherenkov telescopes",
    "Deep learning",
    "High energy physics",
    "Event reconstruction"
  ],
  "programmingLanguage": [
    "Python 3"
  ],
  "operatingSystem": [
    "Linux",
    "MacOS"
  ],
  "softwareRequirements": [
    "Python 3",
    "TensorFlow 2",
    "astropy",
    "scikit-learn",
    "ctapipe",
    "DL1DataHandler",
    "numba",
    "NumPy",
    "PyYAML",
    "PyDot",
    "Pandas"
  ],
  "author": [
    {
      "@type": "Person",
      "@id": "https://orcid.org/0000-0003-1821-7964",
      "givenName": "Tjark",
      "familyName": "Miener",
      "affiliation": {
        "@type": "Organization",
        "name": "Dpartement de Physique Nuclaire et Corpusculaire, Univertit de Genve, Genve, Switzerland"
      }
    },
    {
      "@type": "Person",
      "@id": "https://orcid.org/0000-0003-3343-0755",
      "givenName": "Daniel",
      "familyName": "Nieto",
      "email": "d.nieto@ucm.es",
      "affiliation": {
        "@type": "Organization",
        "name": "Instituto de Fsica de Partculas y del Cosmos, Departamento de EMFTEL, Universidad Complutense de Madrid, Madrid, Spain "
      }
    },
    {
      "@type": "Person",
      "@id": " https://orcid.org/ 0000-0002-6208-5244",
      "givenName": "Ari",
      "familyName": "Brill"
    },
    {
      "@type": "Person",
      "givenName": "Bryan",
      "familyName": "Kim",
      "affiliation": {
        "@type": "Organization",
        "name": "University of California Los Angeles, Division of Astronomy and Astrophysics, Los Angeles, CA, USA"
      }
    },
    {
      "@type": "Person",
      "@id": " https://orcid.org/0000-0001-6674-4238",
      "givenName": "Qi",
      "familyName": "Feng",
      "affiliation": {
        "@type": "Organization",
        "name": "Barnard College, Columbia University, New York, NY, USA"
      }
    }
  ],
  "contributor": [
    {
      "@type": "Person",
      "@id": " https://orcid.org/0009-0007-1566-9507",
      "givenName": "Alexander",
      "familyName": "Cervio Cortnez",
      "affiliation": {
        "@type": "Organization",
        "name": "Instituto de Fsica de Partculas y del Cosmos, Departamento de EMFTEL, Universidad Complutense de Madrid, Madrid, Spain"
      }
    },
    {
      "@type": "Person",
      "@id": " https://orcid.org/0000-0002-4454-1146",
      "givenName": "Jaime",
      "familyName": "Sevilla",
      "affiliation": {
        "@type": "Organization",
        "name": "Facultad de Ingeniera Informtica, Universidad Complutense de Madrid, Madrid, Spain"
      }
    }
  ],
  "maintainer": [
    {
      "@type": "Person",
      "@id": "https://orcid.org/0000-0003-3343-0755",
      "givenName": "Daniel",
      "familyName": "Nieto",
      "email": "d.nieto@ucm.es",
      "affiliation": {
        "@type": "Organization",
        "name": "Instituto de Fsica de Partculas y del Cosmos, Departamento de EMFTEL, Universidad Complutense de Madrid, Madrid, Spain "
      }
    },
    {
      "@type": "Person",
      "@id": "https://orcid.org/0000-0003-1821-7964",
      "givenName": "Tjark",
      "familyName": "Miener",
      "affiliation": {
        "@type": "Organization",
        "name": "Dpartement de Physique Nuclaire et Corpusculaire, Univertit de Genve, Genve, Switzerland"
      }
    }
  ]
}

GitHub Events

Total

Create event: 37
Release event: 3
Issues event: 21
Watch event: 2
Delete event: 29
Member event: 3
Issue comment event: 28
Push event: 338
Pull request review comment event: 7
Pull request review event: 46
Pull request event: 51
Fork event: 8

Last Year

Create event: 37
Release event: 3
Issues event: 21
Watch event: 2
Delete event: 29
Member event: 3
Issue comment event: 28
Push event: 338
Pull request review comment event: 7
Pull request review event: 46
Pull request event: 51
Fork event: 8

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 976
Total Committers: 13
Avg Commits per committer: 75.077
Development Distribution Score (DDS): 0.705

Top Committers

Name	Email	Commits
Ari Brill	a**l@g**m	288
TjarkMiener	t**2@g**m	215
Bryan Kim	b**3@c**u	190
Daniel Nieto	d**o@u**s	126
Bryan Kim	b**m@g**m	81
Jsevillamol	j**l@o**m	26
nieto@talos	n**o@g**s	18
qi-feng	s**0@g**m	12
Tjark Miener	3**r@u**m	9
Daniel Nieto	n**n@u**m	5
Ari Brill	a**l@c**u	3
Luca Romanato	l**1@c**t	2
sgh14	4**4@u**m	1

Committer Domains (Top 20 + Academic)

columbia.edu: 2 campus.unimib.it: 1 gae.ucm.es: 1 ucm.es: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 67
Total pull requests: 141
Average time to close issues: 9 months
Average time to close pull requests: about 2 months
Total issue authors: 17
Total pull request authors: 25
Average comments per issue: 2.1
Average comments per pull request: 0.72
Merged pull requests: 103
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 17
Pull requests: 61
Average time to close issues: 30 days
Average time to close pull requests: 8 days
Issue authors: 5
Pull request authors: 5
Average comments per issue: 1.06
Average comments per pull request: 0.44
Merged pull requests: 48
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

TjarkMiener (23)
nietootein (9)
aribrill (9)
Jsevillamol (8)
maxnoe (4)
BastienLacave (3)
rcervinoucm (2)
TheCakeIsNotALie (1)
evantkchong (1)
Olmichu22 (1)
bryankim96 (1)
mescobargodoy (1)
riwim (1)
jonpsy (1)
vuillaut (1)

Pull Request Authors

TjarkMiener (66)
rcervinoucm (41)
nietootein (10)
sahilyadav27 (4)
aribrill (4)
BastienLacave (4)
evantkchong (3)
vuillaut (2)
maxnoe (2)
Olmichu22 (2)
h3lio5 (2)
ayushvpaliwal (1)
Amanbhandula (1)
mrektor (1)
hrueda25 (1)

Top Labels

Issue Labels

enhancement (25) bug (8) maintenance (5) help wanted (4) keras (3) dependency (3) installation (2) docs (2) onnx (1) ctapipe (1) discussion (1) tensorflow2 (1) question (1)

Pull Request Labels

ready for review (17) bug (12) enhancement (11) dl1dh (8) ctapipe (7) docs (6) dependency (6) installation (4) tensorflow2 (4) maintenance (4) keras (3) discussion (2) onnx (1) aitrigger (1)

Packages

Total packages: 1
Total downloads:
- pypi 53 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 12
Total maintainers: 1

pypi.org: ctlearn

CTLearn is a package under active development to run deep learning models to analyze data from all major current and future arrays of imaging atmospheric Cherenkov telescopes (IACTs).

Documentation: https://ctlearn.readthedocs.io/
License: BSD-3-Clause
Latest release: 0.10.2
published 12 months ago

Versions: 12
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 53 Last month

Rankings

Forks count: 6.4%

Stargazers count: 9.5%

Dependent packages count: 10.1%

Average: 16.1%

Dependent repos count: 21.6%

Downloads: 33.0%

Maintainers (1)

tmiener

Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi

astropy *
dl1_data_handler *
matplotlib *
numpy *
pandas *
pip *
pyyaml *
scikit-learn *
tensorflow *
tf2onnx *

.github/workflows/python-package-conda.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite

.github/workflows/check_codemeta.yml actions

actions/checkout v2 composite

environment.yml conda

astropy
dl1_data_handler 0.10.10
matplotlib
numpy
pandas
pip
python 3.10.*
pyyaml
scikit-learn

setup.py pypi

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science

ctlearn

Science Score: 59.0%

Repository

Basic Info

Statistics

Metadata Files

README.rst

Owner

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: ctlearn

Rankings

Maintainers (1)

Dependencies