Metadata-Version: 2.4
Name: droulette
Version: 0.1.0
Summary: Estimate roulette amplitudes with machine learning
Project-URL: Homepage, https://github.com/nicolopinci/droulette
Project-URL: Issues, https://github.com/nicolopinci/droulette/issues
Author-email: Nicolò Pinciroli <nicolooreste.pinciroli@polimi.it>, Hans Georg Schaathun <georg+github@schaathun.net>
License-Expression: GPL-3.0
Keywords: cosmology,gravitational lensing,machine learning
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.11
Requires-Dist: cosmosim==3.1
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: toml
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: tqdm
Description-Content-Type: text/markdown

# DeepRoulette

**droulette** is a deep learning framework for estimating local gravitational
lensing coefficients (Roulette parameters) directly from simulated image data.
The goal of this project is to provide a standardised experimental protocol,
using simulated images from [CosmoSim](https://github.com/CosmoAI-AES/CosmoSim).

The current version is closed source.  We plan to release it open source when
we first publish results based on the work.

This version is based on
[nicolopinci/droulette](https://github.com/nicolopinci/droulette).
The original version history has been squashed to get rid of BLOBs.
It is still work in progress.

For user documentation, see the
[CosmoAI web page](https://cosmoai-aes.github.io/),
particularly the
[Pipeline](https://cosmoai-aes.github.io/docs/ML/Pipeline) for
roulette parameter recovery.

If you want to use the tool, please get in touch with me to
discuss colaboration.

+ **Initial Prototype** by Nicolò Pinciroli 
+ **Current developer** 
  [Hans Georg Schaathun](https://www.ntnu.no/ansatte/hasc)

## Installation

The library is not yet published on PyPI and has to be installed
from the working directory.

The `venv.sh` script creates a virtual environment under `/tmp`
and installs the library.  If you want to use this, run
```sh
. venv.sh
```
For subsequent use, without reinstalling, use
```sh
. /tmp/venv/bin/activate
```

If you want to install in an existing environment, instead of
using `venv.sh`, use this:
```sh
pip install -e .
```
