[WIP] Add structural analysis to the SepTop protocol#1982
[WIP] Add structural analysis to the SepTop protocol#1982hannahbaumann wants to merge 14 commits into
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hannahbaumann
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This is what it would look like for the complex right now. 
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## main #1982 +/- ##
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- Coverage 94.94% 90.58% -4.36%
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No API break detected ✅ |
| u_top = mda.Universe(pdb_file) | ||
| for state_idx in range(n_lambda): | ||
| universe = create_universe_single_state(u_top._topology, ds, state=state_idx) | ||
| prot = universe.select_atoms(protein_selection) |
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Not sure if its possible or faster but could we do this selection once on the u_top to get the indices of the atoms and then just indexing to get the atoms in each loop?
| trj_file: pathlib.Path, | ||
| output_directory: pathlib.Path, | ||
| dry: bool, | ||
| simtype: str, |
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Use a literal type here with complex and solvent
| n_frames = len(range(0, ds.dimensions["iteration"].size, ds.PositionInterval)) | ||
| else: | ||
| n_frames = ds.dimensions["iteration"].size | ||
| skip = max(n_frames // 500, 1) |
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Is there a reason why we want to keep the analysis to only 500 frames, should this be exposed?
| if simtype == "complex": | ||
| np.savez_compressed( | ||
| npz_file, | ||
| ligand_A_RMSD=np.asarray(data["ligand_A_RMSD"], dtype=np.float32), | ||
| ligand_B_RMSD=np.asarray(data["ligand_B_RMSD"], dtype=np.float32), | ||
| ligand_A_COM_drift=np.asarray(data["ligand_A_COM_drift"], dtype=np.float32), | ||
| ligand_B_COM_drift=np.asarray(data["ligand_B_COM_drift"], dtype=np.float32), | ||
| protein_2D_RMSD=np.asarray(data["protein_2D_RMSD"], dtype=np.float32), | ||
| time_ps=np.asarray(data["time_ps"], dtype=np.float32), | ||
| ) | ||
| else: | ||
| np.savez_compressed( | ||
| npz_file, | ||
| ligand_A_RMSD=np.asarray(data["ligand_A_RMSD"], dtype=np.float32), | ||
| ligand_B_RMSD=np.asarray(data["ligand_B_RMSD"], dtype=np.float32), | ||
| time_ps=np.asarray(data["time_ps"], dtype=np.float32), | ||
| ) |
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What about building a dict of shared data that will be saved in both cases so the time_ps and ligand_A/B_RMSD data and then if the simtype=="complex" then add the extra data to the dict then you can have single np.savez_compressed call?
| ligand_B_indices: list[int], | ||
| rdmol_A: Chem.Mol, | ||
| rdmol_B: Chem.Mol, | ||
| protein_selection: str = "protein and name CA", |
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Do you want to expose this to users, and does it make sense to remove the protein_selection default from the private functions so that if you do update the default, you only have to do it in a single place, i.e single source of truth on the public function.
| selection_indices = np.array(setup.outputs["selection_indices"]) | ||
| ligand_A_indices = np.where(np.isin(selection_indices, setup.outputs["ligand_A_indices"]))[ | ||
| 0 | ||
| ].tolist() | ||
| ligand_B_indices = np.where(np.isin(selection_indices, setup.outputs["ligand_B_indices"]))[ | ||
| 0 | ||
| ].tolist() |
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I got lost trying to follow this through but could this go wrong if the user accidentally changes the settings to only save the protein and water by mistake?
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