Spaceborne 2025.7.1

Code to compute the covariance matrix for the 3x2pt photometric survey in harmonic and real space.

Spaceborne

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Quickstart

For detailed instructions on how to install and use Spaceborne, please refer to the documentation.

To install the code, we recommend using a dedicated Conda environment. Clone the repository, then checkout the latest release (e.g. v2025.05.1) with

git checkout <latest_version_tag>

and run

conda env create -f environment.yaml
conda activate spaceborne
pip install .

As the code is evolving quite quickly at the moment, please make sure to check for new releases periodically

🐍 note: using mamba instead of conda in the first line will significantly speed up the environment creation. To install mamba, run conda install mamba in your base environment.

Spaceborne leverages julia for computationally intensive tasks. We recommend installing julia via juliaup:

curl -fsSL https://install.julialang.org | sh  # Install juliaup
juliaup default 1.10                           # Install Julia version 1.10

Then, install the required Julia packages:

julia -e 'using Pkg; Pkg.add("LoopVectorization"); Pkg.add("YAML"); Pkg.add("NPZ")'

Running the Code

All the available options and configurations can be found, along with their explanation, in the config.yaml file. To run Spaceborne with the configuration specified in the Spaceborne/config.yaml file, simply execute the following command:

python main.py

If you want to use a configuration file with a different name and/or location, you can instead run with

python main.py --config=<path_to_config_file>

for example:

python main.py --config="path/to/my/config/config.yaml"

To display the plots generated by the code, add the --show-plots flag:

python main.py --config="path/to/my/config/config.yaml" --show-plots

Additionally, in the config.yaml file you can set save_figs: True to save the figures in the <output_folder>/figs path.

Using Docker 🐋

If you find issues with the installation, you can happily disregard the above instructions and run the code through Docker. To do this, install Docker Desktop or the Docker engine; then, in the root of the cloned repository (again, remember to checkout the latest release), run:

docker-compose up --build

Note that the --build flag is only needed the first time you run the code (or if you modify the environment.yaml or Dockerfile, which is discouraged). For subsequent runs, simply do:

docker-compose up

Finally, to stop the container, do

docker-compose down

That's it! The container will use your local config.yaml file and output to the directories specified in your configuration, just as if you were running locally.

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