public.antibody_benchmark_utils

RunRosettaBenchmarksMPI.py

This program runs Rosetta MPI locally or on a cluster using slurm or qsub. Relative paths are accepted.

usage: RunRosettaBenchmarksMPI.py [-h]

bm-RAbD_Jade.py

This program is a GUI used for benchmarking Rosetta Antibody Design.Before running this application, you will probably want to run ‘run_rabd_features_for_benchmarks.py to create the databases required.

usage: bm-RAbD_Jade.py [-h] [--main_dir MAIN_DIR] [--out_dir OUT_DIR] --jsons
                       [JSONS [JSONS ...]]

Named Arguments

--main_dir	Main working directory. Not Required. Default = PWD Default: “/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs”
--out_dir	Output data directory. Not Required. Default = pooled_data Default: “pooled_data”
--jsons, -j	Analysis JSONs to use. See RAbD_MB.AnalysisInfo for more on what is in the JSON.The JSON allows us to specify the final name, decoy directory, and features db associated with the benchmark as well as all options that went into it.

bm-calculate_graft_closure_rabd.py

Calculate the frequence of graft closures.

usage: bm-calculate_graft_closure_rabd.py [-h] [--dir DIR] [--outfile OUTFILE]
                                          [--use_ensemble]
                                          [--match_name MATCH_NAME]

Named Arguments

--dir, -i	Input directory
--outfile, -o	Path to outfile
--use_ensemble	Use ensembles in calculation Default: False
--match_name	Match a subexperiment in the file name such as relax

bm-calculate_recoveries_and_risk_ratios.py

Calculates and plots monte carlo acceptance values for antibody design benchmarking.

usage: bm-calculate_recoveries_and_risk_ratios.py [-h] --jsons
                                                  [JSONS [JSONS ...]]
                                                  [--data_outdir DATA_OUTDIR]

Named Arguments

--jsons, -j	Analysis JSONs to use. See RAbD_MB.AnalysisInfo for more on what is in the JSON.The JSON allows us to specify the final name, decoy directory, and features db associated with the benchmark as well as all options that went into it.
--data_outdir, -o
	Path to outfile. DEFAULT = data Default: “data”

bm-output_all_clusters.py

Calculates and plots monte carlo acceptance values for antibody design benchmarking.

usage: bm-output_all_clusters.py [-h] --jsons [JSONS [JSONS ...]]
                                 [--data_outdir DATA_OUTDIR]

Named Arguments

--jsons, -j	Analysis JSONs to use. See RAbD_MB.AnalysisInfo for more on what is in the JSON.The JSON allows us to specify the final name, decoy directory, and features db associated with the benchmark as well as all options that went into it.
--data_outdir, -o
	Path to outfile. DEFAULT = data Default: “data”

bm-plot_features.py

Calculates and plots monte carlo acceptance values for antibody design benchmarking.

usage: bm-plot_features.py [-h] --jsons [JSONS [JSONS ...]]
                           [--plot_outdir PLOT_OUTDIR]

Named Arguments

--jsons, -j	Analysis JSONs to use. See RAbD_MB.AnalysisInfo for more on what is in the JSON.The JSON allows us to specify the final name, decoy directory, and features db associated with the benchmark as well as all options that went into it.
--plot_outdir, -p
	DIR for plots. DEFAULT = plots Default: “plots”

bm-run_rabd_benchmarks.py

This program runs Rosetta MPI locally or on a cluster using slurm or qsub. Relative paths are accepted.

usage: bm-run_rabd_benchmarks.py [-h]