HealpixML 0.2.0

Package adapted for Machine Learning on the Healpix grid.

HealpixML

the concept

The HealpixML genesis has been built to synthesise data (2D or Healpix) using Cross Scattering Transform. For a detailed method description please refer to https://arxiv.org/abs/2207.12527. This algorithm could be effectively usable for component separation (e.g. denoising).

A demo package for this process can be found at https://github.com/jmdelouis/HEALPIXML_DEMO.

usage

Short tutorial

https://github.com/IAOCEA/demo-HealpixML-pangeo-eosc/blob/main/Demo_Synthesis.ipynb

HEALPIXML_DEMO

The python scripts demo.py included in this package demonstrate how to use the HealpixML library to generate synthetic fields that have patterns with the same statistical properties as a specified image.

Install HealpixML library

Before installing, make sure you have python installed in your enviroment. The last version of the HealpixML library can be installed using PyPi:

pip install HealpixML

Load the HEALPIXML_DEMO package from github.

git clone https://github.com/jmdelouis/HEALPIXML_DEMO.git

Recommended installing procedures for mac users

It is recomended to use python=3.9*.

micromamba create -n HEALPIXML
micromamba install -n HEALPIXML ‘python==3.9*’
micromamba activate HEALPIXML
pip install HealpixML
git clone https://github.com/jmdelouis/HEALPIXML_DEMO.git

Recommended installing procedures HPC users

It is recomended to install tensorflow in advance. For DATARMOR for using GPU ;

micromamba create -n HEALPIXML
micromamba install -n HEALPIXML ‘python==3.9*’
micromamba install -n HEALPIXML ‘tensorflow==2.11.0’
micromamba activate HEALPIXML
pip install HealpixML
git clone https://github.com/jmdelouis/HEALPIXML_DEMO.git