Installation

There are two distinct installations of ltu-ili which provide access to different inference engines, namely the pytorch (sbi and lampe) and the tensorflow (pydelfi) installations. This codebase provides simultaneous support for both of these backends, but they cannot (yet!) be installed at the same time due to dependency issues. As a result, we recommend you install them separately in two distinct virtual environments.

Note: The pydelfi backend requires tensorflow==1.15, which fails on Mac OS with M1/M2 chips.

Basic installation

First, create a virtual environment with an appropriate Python version for your choice of backend (i.e. Python>=3.7 for sbi/lampe or Python==3.6 for pydelfi). Then, install ltu-ili from source or PyPI.

Install from source (recommended)

# Clone and install with pytorch backend (sbi/lampe)
conda create -n ili-torch python=3.10 
conda activate ili-torch
pip install --upgrade pip
git clone https://github.com/maho3/ltu-ili.git
cd ltu-ili
pip install ".[pytorch]"

OR

# Clone and install with tensorflow backend (pydelfi)
conda create -n ili-tf python=3.6
conda activate ili-tf
pip install --upgrade pip
git clone https://github.com/maho3/ltu-ili.git
cd ltu-ili
pip install ".[tensorflow]"

Install from PyPI

# Install base package
pip install ltu-ili

# Then install your preferred backend
pip install "ltu-ili[pytorch]"  # for sbi/lampe
# OR
pip install "ltu-ili[tensorflow]"  # for pydelfi

Legacy installation (deprecated)

The old installation method using pip from git will continue to work but is deprecated:

pip install -e git+https://github.com/maho3/ltu-ili#egg=ltu-ili

Verify installation

Toy example

You can verify that the installation is working by running the toy example

   cd ltu-ili/examples
   
   # sbi backend
   python toy_sbi.py
   
   # pydelfi backend 
   python toy_pydelfi.py

After the script completes, you should be able to find some metric summaries and plots in the examples/toy folder.

Quijote example

You can also download pre-processed Quijote two point correlation function (TPCF) summaries and run example cosmological inference. Quijote TPCF summaries can be found in the LtU Open Storage Network data repository (https://sdsc.osn.xsede.org/learningtheuniverse). Contact Matt Ho (matthew.annam.ho@gmail) for access.

Store this data in a subfolder called ltu-ili-data/ and run the inference using

   cd examples
   
   # sbi backend
   python quijote_sbi.py 

   # pydelfi backend
   python3 quijote_pydelfi.py

In case you want to use a mixture density network as density estimator instead of a normalizing flow, then execute

   python quijote_sbi.py --cfginfer configs/infer/quijote_sbi_MDN.yaml

Jupyter Interface

You can also interact with ltu-ili through an iPython environment such as jupyter. See the notebooks folder for a comprehensive guide to the jupyter interface.