Code for unbinned likelihood analysis of astroparticle physics data, created by @robertdstein.

Both time-dependent and time-independent analyses can be performed, as well as a "flare-search" algorithm to find event clustering in time as well as space.

Performs single point source analyses, as well as the stacking of sources according to predefined weighting. Also performs stacking analyses where the signal strength of each source is fit individually.

The easiest way to start using Flarestack is to play with the introductory ipython notebooks, which can be opened with the following link:

The notebooks themselves are found under examples/ipython_notebooks/.

The "Binder" provides a pre-built Docker image containing all necessary dependencies, so you can simply click and play. It avoids the need for local installation, and should provide understanding of how the code works.

The answer to this question depends on how lazy you're feeling, and how much of the backend you want to deal with.

In that case:

pip install flarestack

The entire package can simply be pip installed, and this will automatically install all dependencies.

Now you will need a couple of extra code lines:

git clone [email protected]:IceCubeOpenSource/flarestack.git
export PYTHONPATH=/path/to/flarestack

This will give you the very latest copy of the code, update the installed version if you git pull or modify scripts yourself, and still enable you to import flarestack.

Flarestack is compatible with python 3.6, 3.7 and 3.8, and requires the following packages:

• numpy
• scipy
• astropy
• healpy=1.10.1
• matplotlib
• numexpr

All required dependencies can be found using the IceCube py3-v4 environment. They can collectively be installed with pip install -r requirements.txt, if you don't want to install flarestack via pip.

You can get started with flarestack immediatly using public IceCube datasets provided as part of the code. You can simply run scripts such as those under /flarestack/analyses/, and do your science!

You can optionally set custom directorioes for datasets, and for storing data calculated with the code.

If you are running on WIPAC or DESY, you do not need to specify a dataset directory, as IceCube data will be found automatically. Otherwise, you can add:

export FLARESTACK_DATASET_DIR=/path/to/datasets

to point the code to local copies of Icecube datasets.

Flarestack will produce many files that do not need to be version-controlled. The principle is that everything within this directory can be reproduced by the code, so does not need to be backed up. By default, these files will be saved in a separate within the user home directory, but it might be preferrable to save them elsewhere, such as a scratch directory. You can specify the parent directory:

export FLARESTACK_SCRATCH_DIR=/path/to/scratch

A folder flarestack__data will be created in that directory. This is where you will find plots, pickle files and other files produced by the code.

Is flarestack actually working? If you've already run the precomputation, you can check the functionality of flarestack with unit tests. There are a suite of unit tests to cover flarestack functionality, which can be run from the base flarestack directory with:

python -m unittest discover tests/

If you want to contribute to flarestack, please remember to add new tests!

Flarestack runs with Travis CI, a Continuous Integration Service (https://travis-ci.org/). After each commit, the suite of tests is run, to ensure that the commit did not break anything. You can see the results of these tests at:

• Federica Bradascio @fbradascio
• Simone Garrappa @simonegarrappa
• Jannis Necker @JannisNe
• Robert Stein @robertdstein