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        "author": "Gijs Molenaar",
        "author_email": "gijs@pythonic.nl",
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            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Astronomy"
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        "description": "# Deep Vacuum Cleaner\n\nRadio telescope deconvolution based using a Conditional Generative Adversarial Deep Network.\n\nBased on [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow)\n\nWhch is based on [pix2pix](https://phillipi.github.io/pix2pix/) by Isola et al.\n\n[Article about this implemention](https://affinelayer.com/pix2pix/)\n\n\n## preparations\n\nYou probably want to download a pretrained model.\n\ndownload:\n\nhttp://repo.kernsuite.info/vacuum/model.tar.xz\n\nAnd extract to `share/vacuum/model`.\n\n\n## Setup\n\n```\n$ pip install vacuum-cleaner\n\n```\n\nor if you want to try the GPU accelerated version:\n\n```\n$ pip install \"vacuum-cleaner[gpu]\"\n\n```\nBut the tensorflow-gpu package is not the most portable package available.\n\n## Usage\n\n```\n$ vacuum-clean dirty.fits psf.fits\n```       \n\n\n\n## Training\n\nHave a look at `vacuum-train --help` or at the source. Intended to be trained with\n[spiel](https://github.com/gijzelaerr/spiel/) as training data generator.\n\n\n\n\n",
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