Installation

Installing via pip

juliet can be easily installed using pip:

pip install juliet

The core of juliet is comprised of the transit (batman, starry), radial-velocity (radvel) and Gaussian Process (george, celerite) modelling tools, as well as of the Nested Sampling algorithms (MultiNest via pymultinest, dynesty) that it uses. However, by default the ``juliet`` installation will force `dynesty` as the main sampler to be installed, and will not install `pymultinest`. This is because the pymultinest installation can involve a couple of extra steps, which we really recommend following, as pymultinest might be faster for problems involving less than about 20 free parameters (see below).

Installing from source

The source code for juliet can be downloaded from GitHub by running

git clone https://github.com/nespinoza/juliet.git

Once cloned, simply enter the juliet folder and do

python setup.py install

To install the latest version of the code.

Installing pymultinest

As described above, we really recommend installyng pymultinest. The full instructions on how to install pymultinest can be found in the project’s documentation. We repeat here the main steps. First, install it via pip:

pip install pymultinest

Then, you need to build and compile MultiNest. For this, do:

git clone https://github.com/JohannesBuchner/MultiNest
cd MultiNest/build
cmake ..
make

This will create a file libmultinest.so or libmultinest.dylib under MultiNest/lib: that is the one that will allow us to use pymultinest. To not move that file around in your system, you can include the MultiNest/lib folder in your LD_LIBRARY_PATH (e.g., in your ~/.bash_profile or ~/.bashrc file). In my case, the library is under /Users/nespinoza/github/MultiNest/lib, so I added the following line to my ~/.bash_profile file:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Users/nespinoza/github/MultiNest/lib

Dependencies

The above installation instructuins for juliet assume you have a Python installation. juliet, in turn, depends on the following libraries/packages, all of which will be installed automatically if you follow the instructions above:

  1. NumPy,
  2. SciPy,
  3. batman,
  4. radvel,
  5. george,
  6. celerite,
  7. dynesty,
  8. pymultinest (optional),
  9. matplotlib (optional), and
  10. seaborn (optional).

The last are only needed for certain plotting functions within juliet. The pymultinest installation is optional, but highly recommended.