From f7c24eddaf8397a48efb0dde69a3f17c61a9b874 Mon Sep 17 00:00:00 2001 From: Svetlana Kutuzova Date: Thu, 26 Oct 2023 10:51:38 +0200 Subject: [PATCH] chore: remove AAE and lint --- vamb/__main__.py | 56 ++++++++++++++++-------------------------------- 1 file changed, 19 insertions(+), 37 deletions(-) diff --git a/vamb/__main__.py b/vamb/__main__.py index 56549741..3122e17f 100755 --- a/vamb/__main__.py +++ b/vamb/__main__.py @@ -2220,38 +2220,34 @@ def main(): VAMB = "basic" TAXVAMB = "taxvamb" AVAMB = "avamb" - AAE = "aae" RECLUSTER = "recluster" predict_parser = subparsers.add_parser( - TAXOMETER, - help= - ''' + TAXOMETER, + help=""" refines taxonomic classification of any metagenome binner. See the paper "Taxometer: deep learning improves taxonomic classification of contigs using binning features and a hierarchical loss" (link) - ''' + """, ) add_input_output_arguments(predict_parser) add_taxonomy_arguments(predict_parser, taxonomy_only=True) add_predictor_arguments(predict_parser) vaevae_parserbin_parser = subparsers.add_parser( - BIN, - help= - ''' + BIN, + help=""" VAMB, TaxVAMB, AVAMB binners - ''' + """, ) subparsers_model = vaevae_parserbin_parser.add_subparsers(dest="model_subcommand") vae_parser = subparsers_model.add_parser( - VAMB, - help= - ''' + VAMB, + help=""" default binner based on a variational autoencoder. See the paper "Improved metagenome binning and assembly using deep variational autoencoders" (https://www.nature.com/articles/s41587-020-00777-4) - ''' + """, ) add_input_output_arguments(vae_parser) add_vae_arguments(vae_parser) @@ -2259,13 +2255,12 @@ def main(): add_predictor_arguments(vae_parser) vaevae_parser = subparsers_model.add_parser( - TAXVAMB, - help= - ''' + TAXVAMB, + help=""" taxonomy informed binner based on a bi-modal variational autoencoder. See the paper "TaxVAMB: taxonomic annotations improve metagenome binning" (link) - ''' + """, ) add_input_output_arguments(vaevae_parser) add_vae_arguments(vaevae_parser) @@ -2274,37 +2269,24 @@ def main(): add_taxonomy_arguments(vaevae_parser) vaeaae_parser = subparsers_model.add_parser( - AVAMB, - help= - ''' + AVAMB, + help=""" ensemble model of an adversarial autoencoder and a variational autoencoder. See the paper "Adversarial and variational autoencoders improve metagenomic binning" (https://www.nature.com/articles/s42003-023-05452-3). WARNING: recommended use is through a Snakemake pipeline - ''' + """, ) add_input_output_arguments(vaeaae_parser) add_vae_arguments(vaeaae_parser) add_aae_arguments(vaeaae_parser) add_clustering_arguments(vaeaae_parser) - aae_parser = subparsers_model.add_parser( - AAE, - help= - ''' - adversarial autoencoder (helper method for AVAMB) - ''' - ) - add_input_output_arguments(aae_parser) - add_aae_arguments(aae_parser) - add_clustering_arguments(aae_parser) - recluster_parser = subparsers.add_parser( - RECLUSTER, - help= - ''' + RECLUSTER, + help=""" reclustering using single-copy genes for the binning results of VAMB, TaxVAMB or AVAMB - ''' + """, ) add_input_output_arguments(recluster_parser) add_reclustering_arguments(recluster_parser) @@ -2317,7 +2299,6 @@ def main(): elif args.subcommand == BIN: classes_map = { VAMB: VAEArguments, - AAE: AAEArguments, AVAMB: VAEAAEArguments, TAXVAMB: VAEVAEArguments, } @@ -2329,3 +2310,4 @@ def main(): if __name__ == "__main__": main() + \ No newline at end of file