lephare 0.1.5

LEPHARE photometric redshift estimator

lephare

IMPORTANT! This project is in an early development stage. If you wish to use and run LePHARE please download it from the official repository.

LePHARE (PHotometric Analysis for Redshift Estimation) is a Python package built on a complete rewrite in C++ of the Fortran code LePhare. LePHARE computes photometric redshifts and physical parameters by fitting spectral energy distributions (SED) to a dataset of photometric fluxes or apparent magnitudes.

Installation

The simplest way to install lephare is using pip:

pip install lephare

If you prefer to use binary executables from the command line you may wish to conduct a legacy installation.

Example usage

We provide a number of Jupyter notebooks demonstrating various aspects of the Python code.

Dev Guide - Getting Started

Before installing any dependencies or writing code, it's a great idea to create a virtual environment. LINCC-Frameworks engineers primarily use conda to manage virtual environments. If you have conda installed locally, you can run the following to create and activate a new environment.

>> conda create env -n <env_name> python=3.10
>> conda activate <env_name>

Once you have created a new environment, you can install this project for local development using the following commands:

>> git submodule update --init --recursive
>> pip install -e .'[dev]'
>> pre-commit install
>> conda install pandoc

Notes:

1. The single quotes around '[dev]' may not be required for your operating system.
2. pre-commit install will initialize pre-commit for this local repository, so that a set of tests will be run prior to completing a local commit. For more information, see the Python Project Template documentation on pre-commit
3. Install pandoc allows you to verify that automatic rendering of Jupyter notebooks into documentation for ReadTheDocs works as expected. For more information, see the Python Project Template documentation on Sphinx and Python Notebooks

Citation

If using this code in scientific research please cite the following papers:

This project was automatically generated using the LINCC-Frameworks python-project-template. For more information about the project template see the documentation.