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class
jade.rosetta_jade.ScoreFiles.
ScoreFile
(filename)[source]¶ -
get_Dataframe
(scoreterms=None, order_by='total_score', top_n=-1, reverse=True)[source]¶ Get data as a pandas dataframe. Definitely preferred now. :param scoreterms: list :param order_by: str :param top_n: int :param reverse: bool :rtype: pandas.DataFrame
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get_ordered_decoy_list
(scoreterm, decoy_names=None, top_n=-1, reverse=False)[source]¶ Get an ordered tuple of [[score, decoy_name], …] Will automatically order some known scoreterms (hbonds_int, dSASA_int)
Return type: list[list]
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jade.rosetta_jade.ScoreFiles.
get_scorefiles
(indir='/home/docs/checkouts/readthedocs.org/user_builds/bio-jade/checkouts/latest/docs')[source]¶ Get Score files from a directory. Walk through all directories in directory. :param indir: str :rtype: list
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jade.rosetta_jade.ScoreFiles.
plot_score_vs_rmsd
(df, title, outpath, score='total_score', rmsd='looprms', top_p=0.95, reverse=True)[source]¶ Plot a typical Score VS RMSD using matplotlib, save it somewhere. Return the axes. By default, plot the top 95% :param df: pandas.DataFrame :param outpath: str :param score: str :param rmsd: str :rtype: matplotlib.Axes
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jade.rosetta_jade.ScoreFiles.
pymol_session_on_top_df
(df, outdir, decoy_dir=None, scoreterm='total_score', top_n=10, decoy_column='decoy', native_path=None, out_prefix_override=None, ab_structure=False, superimpose=False, run_pymol=True)[source]¶ Make a PyMol session (or setup a scripter) on top X using a dataframe. Return the scripter for extra control.
df should have an attribute of ‘name’ or out_prefix_override should be set.
Parameters: - df – pandas.DataFrame
- outdir – str
- decoy_dir – str
- scoreterm – str
- top_n – int
- decoy_column – str
- native_path – str
- out_prefix_override – str
- ab_structure – boolean
- superimpose – boolean
Return type: