Deep Vacuum Cleaner
Radio telescope deconvolution based using a Conditional Generative Adversarial Deep Network.
Based on pix2pix-tensorflow
Whch is based on pix2pix by Isola et al.
Article about this implemention
preparations
You probably want to download a pretrained model.
download:
http://repo.kernsuite.info/vacuum/model.tar.xz
And extract to share/vacuum/model.
Setup
$ pip install vacuum-cleaner
or if you want to try the GPU accelerated version:
$ pip install "vacuum-cleaner[gpu]"
But the tensorflow-gpu package is not the most portable package available.
Usage
$ vacuum-clean dirty.fits psf.fits
Training
Have a look at vacuum-train --help or at the source. Intended to be trained with
spiel as training data generator.
1 maintainer
Metadata
1 maintainer
Release files for vacuum-cleaner 0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vacuum-cleaner-0.3.tar.gz | 14.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vacuum_cleaner-0.3-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 45.4 kB
Release files / vacuum-cleaner-0.3.tar.gz
| Download URL | vacuum-cleaner-0.3.tar.gz |
|---|---|
| Size | 14.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / vacuum_cleaner-0.3-py2.py3-none-any.whl
| Download URL | vacuum_cleaner-0.3-py2.py3-none-any.whl |
|---|---|
| Size | 31.5 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.1.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.5
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