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The easiest way to analyze Fermi-LAT data

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<p align="left" width="100%">
 <img width="100%" height="100%" src="/docs/source/logo.jpg">
 <h1>easyfermi</h1>
</p>

<a href="https://easyfermi.readthedocs.io/en/latest/">
 
![easyfermiDocsBadge](https://img.shields.io/badge/docs-easyfermi-green?style=for-the-badge&logo=googledocs&logoColor=white&labelColor=gray&color=blue)

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[![CI](https://github.com/ranieremenezes/easyfermi/actions/workflows/ci.yml/badge.svg)](https://github.com/ranieremenezes/easyfermi/actions/workflows/ci.yml)

The easiest way to analyze Fermi-LAT data.

`easyfermi` is a solution to facilitate Fermi-LAT data analysis by providing an intuitive graphical interface to interact with the Fermi science tools.

If you would like to support `easyfermi`'s maintenance, consider buying us a coffee:

# Requirements

- Linux OS / Windows with <a href="https://learn.microsoft.com/en-us/windows/wsl/install">WSL (Windows Subsystem for Linux)</a>
- [Miniconda 3](https://docs.conda.io/projects/miniconda/en/latest/),
  [Anaconda 3](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) or [Miniforge](https://github.com/conda-forge/miniforge) (recommended) distribution.

# Installation

The following instructions assume an installation of `conda` or `mamba` (i.e. a faster version of `conda`).

### Mamba-based installation

In the terminal, run:
<pre><code>mamba create --name easyfermi -c conda-forge -c fermi python=3.9 "fermitools>=2.2.0" "healpy=1.16.1" "gammapy=1.1" "scipy=1.10.1" "astropy=5.3.3" "pyqt=5.15.9" "astroquery=0.4.6" "psutil=5.9.8" "emcee=3.1.4" "corner=2.2.2" "matplotlib=3.8.2" </code></pre>

This will create the virtual environment and install all dependencies. Then activate the environment and install _fermipy_ and _easyfermi_:
<pre><code>mamba activate easyfermi
pip install fermipy easyfermi</code></pre>

- (ONLY FOR WINDOWS) Install the `libgl1` package:
<pre><code>sudo apt-get install libgl1</code></pre>

- If you want, you can set _easyfermi_ as an environmental variable. For instance, if you use a Bash shell environment, you can open the `.bashrc` file in your home and set:
<pre><code>alias easyfermi="mamba activate easyfermi && python -c 'import easyfermi'"</code></pre>
substituting _miniforge_ and _mamba_ by e.g. _anaconda_ and _conda_ if needed. This line of command depends on which distribution of Python you installed and how you set up the _mamba/conda_ environment.

### Installation for developers

Another option to install `easyfermi` is by cloning the GitHub repository:
<pre><code>git clone https://github.com/ranieremenezes/easyfermi.git</code></pre>

Then change to the `easyfermi` directory and create the environment:
<pre><code>cd easyfermi
mamba env create -f environment.yml
mamba activate easyfermi</code></pre>

Install `easyfermi` in editable mode:
<pre><code>pip install -e .</code></pre>

# Upgrading

You can check your currently installed version of `easyfermi` with _pip show_:
<pre><code>pip show easyfermi</code></pre>
   
If you have `easyfermi 2.0.X` installed, upgrade your installation to the latest version by running the following command in the _easyfermi_ environment:
<pre><code>pip install easyfermi --upgrade --no-deps</code></pre>

If instead, you have `easyfermi 1.X.X` installed, please install easyfermi V2 following section **Installation**.

# Uninstalling

In the terminal, run:
<pre><code>mamba deactivate</code></pre>
<pre><code>mamba env remove --name easyfermi</code></pre>

# Running

If you defined the variable _easyfermi_ in your shell environment (see **Installation**), simply type the following in the terminal:
<pre><code>easyfermi</code></pre>

Otherwise, type:
<pre><code>mamba activate easyfermi</code></pre>
<pre><code>python -c "import easyfermi"</code></pre>

Substituting _mamba_ by _conda_ if this is the case for you.

The main window of _easyfermi_ looks like this:
<p align="center" width="100%">
 <img width="100%" height="100%" src="/src/easyfermi/resources/images/easyFermiWindow.png">
</p>

# Tutorials and Documentation

Check our tutorials on the `easyfermi` YouTube channel:

![youtube](https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white)

</a>

- [Learn how to analyze Fermi-LAT data under 10 min.](https://www.youtube.com/watch?v=utwvFsl67_4&t=5s&ab_channel=easyFermi)
- [Saving time using an external ltcube.](https://www.youtube.com/watch?v=jaNKL3jUfy8&ab_channel=easyFermi)
- [Loading a previous analysis.](https://www.youtube.com/watch?v=EnbeujVh6wA&ab_channel=easyFermi)
- [Selecting specific time intervals when building the SED](https://www.youtube.com/watch?v=BG3ldxJv7t4&ab_channel=easyFermi)
- [Going to the sensitivity limit of Fermi-LAT](https://www.youtube.com/watch?v=2TpgRcXf24M&ab_channel=easyFermi)

The documentation of `easyfermi` can be found [here](https://easyfermi.readthedocs.io/en/latest/index.html).

# Acknowledgments

To acknowledge `easyfermi` in a publication, please cite  [de Menezes, R (2022)](https://ui.adsabs.harvard.edu/abs/2022arXiv220611272D/abstract) and [de Menezes et al. (2025)](https://doi.org/10.5281/zenodo.17135824).

Since _easyfermi_ relies on _fermipy_, _gammapy_, _astropy_, _astroquery_, and _emcee_, please also cite [Wood et al. 2017](https://ui.adsabs.harvard.edu/abs/2017ICRC...35..824W/abstract), [Donath et al. 2023](https://ui.adsabs.harvard.edu/abs/2023A%26A...678A.157D/abstract), [Astropy Collaboration et al. 2018](https://ui.adsabs.harvard.edu/abs/2018AJ....156..123A/abstract), [Ginsburg et al. 2019](https://ui.adsabs.harvard.edu/abs/2019AJ....157...98G/abstract), and [Foreman-Mackey et al. 2013](https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F/abstract).

The EBL models adopted in _easyfermi_ are from:
 - Franceschini et al. 2008 (http://adsabs.harvard.edu/abs/2008A%26A...487..837F)
 - Finke et al. 2010 (http://adsabs.harvard.edu/abs/2009arXiv0905.1115F)
 - Dominguez et al. 2011 (http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1007.1459)
 - Franceschini & Rodighiero 2017 (https://ui.adsabs.harvard.edu/abs/2017A%26A...603A..34F/abstract)
 - Saldana-Lopez et al. 2021 (https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.5144S/abstract)

If you make use of EBL correction via _easyfermi_ in your publication, please cite the papers corresponding to the adopted EBL models. The EBL data files in this repository were collected from the _gammapy_ repository at https://github.com/gammapy/gammapy-data/tree/main/ebl

I want to thank Clodomir Vianna for helping me with the design of _easyfermi_, for making the _easyfermi_ logo, and for the several hours of discussion about this project. Clodomir is the one responsible for making _easyfermi_ user-friendly. Thanks to Fabio Cafardo, Lucas Costa Campos, Raí Menezes, and Michele Peresano for their help and strong support to this project. A big thanks to Alessandra Azzollini, Douglas Carlos, Kaori Nakashima, Lucas Siconato, Matt Pui, and Romana Grossova, the first users/testers of _easyfermi_.

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