Diffusion model for galaxy generation
Project description
Contains 4 generative diffusion models ScoreNet32 and ScoreNet64 for both the HSC and ZTF surveys. These are used to return the gradients of an arbitrary image with respect to a prior distribution of individual artifact free galaxy models. Current functions include ScoreNetXX(image) returns gradients as stated. Data transformatons are now done inside the package.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
galaxygrad-0.1.4.tar.gz
(24.7 MB
view details)
Built Distribution
File details
Details for the file galaxygrad-0.1.4.tar.gz.
File metadata
- Download URL: galaxygrad-0.1.4.tar.gz
- Upload date:
- Size: 24.7 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 24676116af0b98c4e8b3509a3ebd5f83cc13de87d7a87d53737851738df532c1 |
|
| MD5 | 6553983f8de3df938505de07fc5fea8e |
|
| BLAKE2b-256 | c73702f3bfa77231bf93d4634302bdeb8f2282841f08286d7c7ba1ebab8dfb29 |
File details
Details for the file galaxygrad-0.1.4-py3-none-any.whl.
File metadata
- Download URL: galaxygrad-0.1.4-py3-none-any.whl
- Upload date:
- Size: 24.7 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f978b5bdbfd865acd9274fc1ecdc77d94e60c68fc5970816dfd5b37e20ce0772 |
|
| MD5 | e5049b53630bbcac0cf38588f2d553cc |
|
| BLAKE2b-256 | 8edb5eaf60c9264d8f04eec77990562dee4fc8ffc8995cef5d876076e0c9ef8f |
