| Previous changeset 0:de29b4f35536 (2020-03-27) Next changeset 2:f6f9b47d02b6 (2020-04-13) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/chemicaltoolbox/transfs commit ec28658e0e9cd92edff6e1f86427c6e80c69d572" |
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modified:
transfs.py transfs.xml |
| b |
| diff -r de29b4f35536 -r 8d9c8ba2ec86 transfs.py --- a/transfs.py Fri Mar 27 09:18:53 2020 -0400 +++ b/transfs.py Wed Apr 08 07:28:49 2020 -0400 |
| [ |
| @@ -210,14 +210,14 @@ return "{0}{1}{2}".format(work_dir, os.path.sep, predict_file_name) -def run_predictions(): +def run_predictions(model): global types_file_name global predict_file_name global work_dir # python3 scripts/predict.py -m resources/dense.prototxt -w resources/weights.caffemodel -i work_0/test_set.types >> work_0/caffe_output/predictions.txt cmd1 = ['python3', '/train/fragalysis_test_files/scripts/predict.py', '-m', '/train/fragalysis_test_files/resources/dense.prototxt', - '-w', '/train/fragalysis_test_files/resources/weights.caffemodel', + '-w', os.path.sep.join(['/train/fragalysis_test_files/resources', model]), '-i', os.path.sep.join([work_dir, types_file_name]), '-o', os.path.sep.join([work_dir, predict_file_name])] log("CMD:", cmd1) @@ -286,13 +286,13 @@ sdf_file.close() -def execute(ligands_sdf, protein, outfile, distance, mock=False): +def execute(ligands_sdf, protein, outfile, distance, model='weights.caffemodel', mock=False): write_inputs(protein, ligands_sdf, distance) if mock: mock_predictions() else: - run_predictions() + run_predictions(model) scores = read_predictions() patch_scores_sdf(outfile, scores) @@ -307,6 +307,7 @@ parser.add_argument('-d', '--distance', type=float, default=2.0, help="Cuttoff for removing waters") parser.add_argument('-o', '--outfile', default='output.sdf', help="File name for results") parser.add_argument('-w', '--work-dir', default=".", help="Working directory") + parser.add_argument('-m', '--model', default="weights.caffemodel", help="Model to use for predictions") parser.add_argument('--mock', action='store_true', help='Generate mock scores rather than run on GPU') args = parser.parse_args() @@ -314,7 +315,7 @@ work_dir = args.work_dir - execute(args.input, args.receptor, args.outfile, args.distance, mock=args.mock) + execute(args.input, args.receptor, args.outfile, args.distance, model=args.model, mock=args.mock) if __name__ == "__main__": |
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| diff -r de29b4f35536 -r 8d9c8ba2ec86 transfs.xml --- a/transfs.xml Fri Mar 27 09:18:53 2020 -0400 +++ b/transfs.xml Wed Apr 08 07:28:49 2020 -0400 |
| [ |
| @@ -1,11 +1,11 @@ -<tool id="xchem_transfs_scoring" name="XChem TransFS pose scoring" version="0.2.0"> +<tool id="xchem_transfs_scoring" name="XChem TransFS pose scoring" version="0.3.0"> <description>using deep learning</description> <requirements> <!--requirement type="package" version="3.0.0">openbabel</requirement--> <!--requirement type="package" version="3.7">python</requirement--> <!-- many other requirements are needed --> - <container type="docker">informaticsmatters/deep-app-ubuntu-1604:0.9</container> + <container type="docker">informaticsmatters/deep-app-ubuntu-1604:1.2</container> </requirements> <command detect_errors="exit_code"><![CDATA[ @@ -24,7 +24,7 @@ ## mkdir -p ligands && cd ../ && - python '$__tool_directory__/transfs.py' -i ./workdir/ligands.sdf -r ./workdir/receptor.pdb -d $distance -w /train/fragalysis_test_files/workdir && + python '$__tool_directory__/transfs.py' -i ./workdir/ligands.sdf -r ./workdir/receptor.pdb -d $distance -w /train/fragalysis_test_files/workdir --model '$model' && ls -l && ls -l workdir && sudo -u ubuntu cp ./workdir/output.sdf '$output' && @@ -44,6 +44,12 @@ <param type="data" name="receptor" format="pdb" label="Receptor" help="Select a receptor (pdb format)."/> <param type="data" name="ligands" format="sdf,mol" label="Ligands" help="Ligands (docked poses) in SDF format)"/> <param name="distance" type="float" value="2.0" min="1.0" max="5.0" label="Distance to waters" help="Remove waters closer than this distance to any ligand heavy atom"/> + <param name="model" type="select" label="Model for predictions"> + <option value="weights.caffemodel">No threshold (original model)</option> + <option value="10nm.0_iter_50000.caffemodel">10nM threshold</option> + <option value="50nm.0_iter_50000.caffemodel">50nM threshold</option> + <option value="200nm.0_iter_50000.caffemodel">200nM threshold</option> + </param> <param type="hidden" name="mock" value="" label="Mock calculations" help="Use random numbers instead of running on GPU"/> </inputs> <outputs> |