https://github.com/jiwoncpark/node-to-joy
Modeling the external convergence from photometric catalogs
https://github.com/jiwoncpark/node-to-joy
graph-convolutional-network uncertainty-quantification
Last synced: 8 months ago
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Modeling the external convergence from photometric catalogs
- Host: GitHub
- URL: https://github.com/jiwoncpark/node-to-joy
- Owner: jiwoncpark
- License: mit
- Created: 2020-02-04T18:51:17.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-05-02T23:34:49.000Z (about 3 years ago)
- Last Synced: 2023-07-31T13:56:56.711Z (almost 3 years ago)
- Topics: graph-convolutional-network, uncertainty-quantification
- Language: Python
- Homepage:
- Size: 4.69 MB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
=========================================================================
Node to Joy - Modeling the external convergence from photometric catalogs
=========================================================================
.. image:: https://readthedocs.org/projects/node-to-joy/badge/?version=latest
:target: https://node-to-joy.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
This package contains functionality to
* postprocess the coarse convergence values of an existing simulation to introduce finer fluctuations at galaxy-galaxy lensing scales
* train a Bayesian graph neural network to infer convergence given photometric measurements of galaxies around a line of sight
* hierarchically infer the mean and standard deviation of convergence in the population
.. image:: plots/gallery_opaque.png
Installation
============
0. Virtual environments are strongly recommended, to prevent dependencies with conflicting versions. Create a conda virtual environment and activate it:
::
$conda create -n n2j python=3.8 -y
$conda activate n2j
1. Clone the repo and install.
::
$git clone https://github.com/jiwoncpark/node-to-joy.git
$cd node-to-joy
$pip install -e . -r requirements.txt
2. (Optional) To run the notebooks, add the Jupyter kernel.
::
$python -m ipykernel install --user --name n2j --display-name "Python (n2j)"