Metadata-Version: 1.1
Name: galaxy2galaxy
Version: 0.0.1rc2
Summary: Galaxy2Galaxy
Home-page: http://github.com/ml4astro/galaxy2galaxy
Author: ML4Astro Contributors
Author-email: UNKNOWN
License: MIT
Description: # Galaxy2Galaxy
        
        Galaxy2Galaxy, or G2G for short, is a library of models, datasets, and utilities to build generative models for astronomical images, based on the Tensor2Tensor library. Similarly to T2T, the goal of this project is to accelerate research in machine
        learning models applied to astronomical image processing problems.
        
        ## Install
        
        G2G can easily be installed using pip:
        ```
        $ pip install galaxy2galaxy
        ```
        
        Should the install fail because of the GalSim dependency, check the GalSim [install
        guide]( https://github.com/GalSim-developers/GalSim/blob/releases/2.1/INSTALL.md) to fix it.
        
        ## Usage
        
        To generate the COSMOS 25.2 sample at native pixel scale and stamp size:
        
        ```bash
        $ g2g-datagen --problem=img2img_cosmos --data_dir=data/img2img_cosmos
        ```
        This uses GalSim to draw postage stamps and save them in TFRecord format which can then be used for training.
        
        To  train an autoencoder with this data:
        ```bash
        $ g2g-trainer --data_dir=data/img2img_cosmos --output_dir=training/cosmos_ae   --problem=img2img_cosmos --model=continuous_autoencoder_basic  --train_steps=2000  --eval_steps=100 --hparams_set=continuous_autoencoder_basic
        ```
        
Keywords: astronomy machine learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Astronomy
