This page is devoted to the full installation guide for the SimpleMC cosmological inference code. SimpleMC is a Python package designed for cosmological parameter estimation and model comparison using Bayesian inference, optimization techniques, and machine learning algorithms. It also provides a collection of useful tools for cosmological data analysis, including cosmological calculators, plotting utilities, and various statistical analysis tools.
The cosmological models currently implemented in SimpleMC mainly focus on dark energy scenarios in which the cosmic expansion history plays the central role. In this tutorial, you will learn how to install SimpleMC on Linux-based systems, macOS, and Windows. The guide is written for beginners and includes step-by-step instructions to help you set up a fully working environment for cosmological analyses.
Acknowledgement: This tutorial is based on the publicly available SimpleMC cosmological parameter estimation framework. The original code was developed by J. Alberto Vazquez and Isidro Gómez-Vargas. Readers are encouraged to consult the official repository and documentation for the latest updates, examples, and advanced features.
Original Repository: SimpleMC GitHub Repository
Official Documentation: SimpleMC Official Documentation
First, the computer needs to install essential libraries and compilers.
1. Ubuntu
Install Compiler
sudo apt update && sudo apt upgrade
sudo apt install nano
sudo apt install wget
sudo apt install git -y
sudo apt install liblapack-dev
sudo apt install libcfitsio-dev
sudo apt install build-essential
sudo apt-get install openmpi-bin openmpi-doc libopenmpi-dev
Install Python and Librareis, We recommd you to install Miniconda for better manage Python evironment.
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh
source ~/miniconda3/bin/activate
2. MacOS
For Apple Silicon chip
mkdir -p ~/miniconda3
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh
source ~/miniconda3/bin/activate
For Intel chip
mkdir -p ~/miniconda3
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh
source ~/miniconda3/bin/activate
Installing SimpleMC
To install SimpleMC, the system architecture does not matter significantly. The code can be installed on both x86_64 and arm64 systems by following the methods below.
In this tutorial, I will use my personal GitHub repository for convenience and testing purposes. However, please always cite and acknowledge the original SimpleMC repository appropriately in scientific work.
1. Create a Conda Environment
Before installing SimpleMC, it is recommended to create a clean Conda environment:
conda create -n simpleMC_env python=3.10 -y
conda activate simpleMC_env
2. Clone the repository:
git clone https://github.com/1729Him/SimpleMC
cd SimpleMC
Install Required Python Packages
Using pip
3. Install the core required packages:
pip install numpy==1.26.4
pip install scipy==1.11.4
pip install scikit-learn==1.3.2
pip install pandas matplotlib numdifftools mpi4py
4. Using conda (Recommended)
For better compatibility and easier dependency management, we recommend using conda:
conda install numpy=1.26.4
conda install scipy=1.11.4
conda install scikit-learn=1.3.2
conda install -c conda-forge pandas matplotlib numdifftools mpi4py
5. Install SimpleMC
For editable/development mode:
pip install -e .
or for a standard installation:
pip install .
In the next page, we will explore the basic structure of SimpleMC and learn how to add a new dark energy model by considering the JBP (Jassal–Bagla–Padmanabhan) as an example.