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.
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. 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, particularly the 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
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
. venv.sh
For subsequent use, without reinstalling, use
. /tmp/venv/bin/activate
If you want to install in an existing environment, instead of
using venv.sh, use this:
pip install -e .
Release files for droulette 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| droulette-0.2.1.tar.gz | 18.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| droulette-0.2.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:39.2 kB
Release files / droulette-0.2.1.tar.gz
| Download URL | droulette-0.2.1.tar.gz |
|---|---|
| Size | 18.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Yes |
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Transparency logRelease files / droulette-0.2.1-py3-none-any.whl
| Download URL | droulette-0.2.1-py3-none-any.whl |
|---|---|
| Size | 20.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
54d19aecab54ab13bc832075c9b74ad6417731e0f7ae2fdaf3a529f8fbbc0883
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 23, 2026.
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