Skip to main content

LikelihoodCombiner combines DM-related likelihoods from different experiments.

Project description

DOI Anaconda-Server Badge Anaconda-Server Badge Anaconda-Server Badge Anaconda-Server Badge
Gloryduck logo

LikelihoodCombiner is a package under active development to combine likelihoods from different experiments. The main target of this package is the Gloryduck project. This project joint analysis of gamma-ray data from *Fermi*-LAT, HAWC, H.E.S.S., MAGIC and VERITAS to search for gamma-ray signals from dark matter annihilation in dwarf satellite galaxies.

Install LikelihoodCombiner

Clone Repository with Git

Clone the LikelihoodCombiner repository:

cd </installation/path>
git clone https://github.com/TjarkMiener/likelihood_combiner

Install Package with Anaconda

Next, download and install Anaconda, or, for a minimal installation, Miniconda. Create a new conda environment that includes all the dependencies for LikelihoodCombiner:

conda env create -f </installation/path>/likelihood_combiner/environment.yml

Finally, install LikelihoodCombiner into the new conda environment with pip:

conda activate lklcom
cd </installation/path>/likelihood_combiner
pip install --upgrade .

NOTE for developers: If you wish to fork/clone the respository and make changes to any of the LikelihoodCombiner modules, the package must be reinstalled for the changes to take effect.

Installing as a conda package

To install it as a conda package, first install Anaconda by following the instructions here: https://www.anaconda.com/distribution/.

Then, create and enter a new Python 3.8 environment with:

conda create -n [ENVIRONMENT_NAME] python=3.8
source activate [ENVIRONMENT_NAME]

From the environment, add the necessary channels for all dependencies:

conda config --add channels conda-forge
conda config --add channels menpo

Install the package:

conda install -c tmiener likelihood_combiner

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).

If you want to import any functionality from LikelihoodCombiner into your own Python scripts, then you are all set. However, if you wish to make use of any of the scripts in likelihood_combiner/scripts (like {local/cluster}.py), you should also clone the repository locally and checkout the corresponding tag (i.e. for version v0.4.1):

git clone https://github.com/TjarkMiener/likelihood_combiner
git checkout v0.4.1

LikelihoodCombiner should already have been installed in your environment by Conda, so no further installation steps (i.e. with setuptools or pip) are necessary and you should be able to run scripts/{local/cluster}.py directly.

Dependencies

  • Python 3.8.X

  • NumPy

  • SciPy

  • Pandas

  • PyTables

  • PyYAML

  • Matplotlib

Run the Combiner

Run LikelihoodCombiner from the command line:

LikelihoodCombiner_dir=</installation/path>/likelihood_combiner
python $LikelihoodCombiner_dir/scripts/{local|cluster}.py $LikelihoodCombiner_dir/config/example_config.yml

Mock data

The data you can find in the LikelihoodCombiner, where produced with gLike using the mock data. These txt files don’t correspond to IACT observations of Segue 1 or Ursa Major II and are only included for testing the code framework.

Uninstall LikelihoodCombiner

Remove Anaconda Environment

First, remove the conda environment in which LikelihoodCombiner is installed and all its dependencies:

conda remove --name lklcom --all

Remove LikelihoodCombiner

Next, completely remove LikelihoodCombiner from your system:

rm -rf </installation/path>/likelihood_combiner

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lklcom-0.4.1.post33.tar.gz (119.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lklcom-0.4.1.post33-py3-none-any.whl (147.7 kB view details)

Uploaded Python 3

File details

Details for the file lklcom-0.4.1.post33.tar.gz.

File metadata

  • Download URL: lklcom-0.4.1.post33.tar.gz
  • Upload date:
  • Size: 119.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.2 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.9.1

File hashes

Hashes for lklcom-0.4.1.post33.tar.gz
Algorithm Hash digest
SHA256 c9552fcd4f5b51323dbd97fff5d29df90a38d327f13b2d9617f30aacdadaf0bb
MD5 e042a95496f60200f9365d8cf55f665e
BLAKE2b-256 9a0e6bcc16ce4c074cd59094d61a4777bb3c9249152a5315095c75a1bae0a80e

See more details on using hashes here.

File details

Details for the file lklcom-0.4.1.post33-py3-none-any.whl.

File metadata

  • Download URL: lklcom-0.4.1.post33-py3-none-any.whl
  • Upload date:
  • Size: 147.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.1.2 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.9.1

File hashes

Hashes for lklcom-0.4.1.post33-py3-none-any.whl
Algorithm Hash digest
SHA256 74e0f0ecc3e00af811d860db0c9c3cb3f83042f42c88cdd273eddffab16e8c04
MD5 51c72ef95950192474806d99d05db549
BLAKE2b-256 f05f597a39122cbffd2298b70b7174531b49f00ba0aff7762983257f69bb6c0a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page