haddock3-traceback

setup.py

@@ -81,6 +81,7 @@ def read(*names, **kwargs):
             'haddock3-score = haddock.clis.cli_score:maincli',
             'haddock3-unpack = haddock.clis.cli_unpack:maincli',
             'haddock3-analyse = haddock.clis.cli_analyse:maincli',
+            'haddock3-traceback = haddock.clis.cli_traceback:maincli',
             ]
         },
     # cmdclass={'build_ext': optional_build_ext},

src/haddock/clis/cli_traceback.py

@@ -0,0 +1,241 @@
+"""
+Traces back PDB files from a HADDOCK run directory.
+
+Given an input run directory, haddock3-traceback traces back each model to the
+initial input molecules used, providing the rank of each intermediate model.
+
+USAGE::
+
+    haddock3-traceback -r <run_dir>
+
+"""
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from haddock import log
+from haddock.libs.libontology import ModuleIO, PDBFile
+from haddock.modules import get_module_steps_folders
+
+
+TRACK_FOLDER = "traceback"  # name of the traceback folder
+
+ANA_MODULES = ["caprieval",
+               "seletop",
+               "topoaa",
+               "rmsdmatrix",
+               "clustrmsd",
+               "clustfcc"]
+
+
+def get_ori_names(n: int, pdbfile: PDBFile, max_topo_len: int):
+    """
+    Get the original name(s) of the PDB file.
+
+    Parameters
+    ----------
+    n : int
+        Step number.
+    pdbfile : PDBFile
+        PDBFile object.
+    max_topo_len : int
+        Maximum length of the topologies found so far.
+
+    Returns
+    -------
+    ori_names : list
+        List of original names.
+    max_topo_len : int
+        Maximum length of the topologies found so far.
+    """
+    if n != 0:  # not the first step, ori_name should be defined
+        ori_names = [pdbfile.ori_name]
+    else:  # first step, we get topology files instead of ori_name
+        # topology can either be a list of topologies or a single
+        # topology
+        if isinstance(pdbfile.topology, list):
+            ori_names = [el.file_name for el in pdbfile.topology]
+            if len(pdbfile.topology) > max_topo_len:
+                max_topo_len = len(pdbfile.topology)
+        else:
+            ori_names = [pdbfile.topology.file_name]
+            max_topo_len = 1
+    return ori_names, max_topo_len
+
+
+def traceback_dataframe(data_dict: dict,
+                        rank_dict: dict,
+                        sel_step: list,
+                        max_topo_len: int):
+    """
+    Create traceback dataframe by combining together ranks and data.
+
+    Parameters
+    ----------
+    data_dict : dict
+        Dictionary containing the data to be traced back.
+    rank_dict : dict
+        Dictionary containing the ranks of the data to be traced back.
+    sel_step : list
+        List of selected steps.
+    max_topo_len : int
+        Maximum length of the topologies.
+
+    Returns
+    -------
+    df_ord : pandas.DataFrame
+        Dataframe containing the traceback data.
+    """
+    # get last step of the workflow
+    last_step = sel_step[-1]
+    # data dict to dataframe
+    df_data = pd.DataFrame.from_dict(data_dict, orient="index")
+    df_data.reset_index(inplace=True)
+    # assign columns
+    data_cols = [el for el in reversed(sel_step)]
+    data_cols.extend([f"00_topo{i+1}" for i in range(max_topo_len)])
+    df_data.columns = data_cols
+
+    # same for the rank_dict
+    df_ranks = pd.DataFrame.from_dict(rank_dict, orient="index")
+    df_ranks.reset_index(inplace=True)
+    ranks_col = [last_step]  # the key to merge the dataframes
+    ranks_col.extend([f"{el}_rank" for el in reversed(sel_step)])
+    df_ranks.columns = ranks_col
+
+    # merging the data and ranks dataframes
+    df_merged = pd.merge(df_data, df_ranks, on=last_step)
+    ordered_cols = sorted(df_merged.columns)
+    df_ord = df_merged[ordered_cols]
+    # last thing: substituting unk records with - in the last step
+    unk_records = df_ord[f'{last_step}'].str.startswith('unk')
+    df_ord.loc[unk_records, last_step] = "-"
+    return df_ord
+
+
+# Command line interface parser
+ap = argparse.ArgumentParser(
+    prog="haddock3-traceback",
+    description=__doc__,
+    formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+
+ap.add_argument(
+    "-r",
+    "--run-dir",
+    help="The input run directory.",
+    required=True,
+    )
+
+
+def _ap():
+    return ap
+
+
+def load_args(ap):
+    """Load argument parser args."""
+    return ap.parse_args()
+
+
+def cli(ap, main):
+    """Command-line interface entry point."""
+    cmd = vars(load_args(ap))
+    main(**cmd)
+
+
+def maincli():
+    """Execute main client."""
+    cli(ap, main)
+
+
+def main(run_dir):
+    """
+    Analyse CLI.
+
+    Parameters
+    ----------
+    run_dir : str or Path
+        Path to the original run directory.
+    """
+    log.level = 20
+    log.info(f"Running haddock3-traceback on {run_dir}")
+
+    outdir = Path(run_dir, TRACK_FOLDER)
+    try:
+        outdir.mkdir(exist_ok=False)
+        log.info(f"Created directory: {str(outdir.resolve())}")
+    except FileExistsError:
+        log.warning(f"Directory {str(outdir.resolve())} already exists.")
+
+    # Reading steps
+    log.info("Reading input run directory")
+    # get the module folders from the run_dir input
+    all_steps = get_module_steps_folders(Path(run_dir))
+    log.info(f"All_steps: {', '.join(all_steps)}")
+    sel_step = [st for st in all_steps if st.split("_")[1] not in ANA_MODULES]
+    log.info(f"Steps to trace back: {', '.join(sel_step)}")
+
+    data_dict, rank_dict = {}, {}
+    unk_idx, max_topo_len = 0, 0
+    # this cycle goes through the steps in reverse order
+    for n in range(len(sel_step) - 1, -1, -1):
+        delta = len(sel_step) - n - 1  # how many steps have we gone back?
+        log.info(f"Tracing back step {sel_step[n]}")
+        # loading the .json file
+        json_path = Path(run_dir, sel_step[n], "io.json")
+        io = ModuleIO()
+        io.load(json_path)
+        # list all the values in the data_dict
+        ls_values = [x for val in data_dict.values() for x in val]
+        # getting and sorting the ranks for the current step folder
+        ranks = [pdbfile.score for pdbfile in io.output]
+        ranks_argsort = np.argsort(ranks)
+
+        # iterating through the pdbfiles to fill data_dict and rank_dict
+        for i, pdbfile in enumerate(io.output):
+            rank = np.where(ranks_argsort == i)[0][0] + 1
+            # getting the original names
+            ori_names, max_topo_len = get_ori_names(n, pdbfile, max_topo_len)
+            if n != len(sel_step) - 1:
+                if pdbfile.file_name not in ls_values:
+                    # this is the first step in which the pdbfile appears.
+                    # This means that it was discarded for the subsequent steps
+                    # We need to add the pdbfile to the data_dict
+                    key = f"unk{unk_idx}"
+                    data_dict[key] = ["-" for el in range(delta - 1)]
+                    data_dict[key].append(pdbfile.file_name)
+                    rank_dict[key] = ["-" for el in range(delta)]
+                    unk_idx += 1
+                else:
+                    # we've already seen this pdb before.
+                    idx = ls_values.index(pdbfile.file_name)
+                    key = list(data_dict.keys())[idx // delta]
+
+                # assignment
+                for el in ori_names:
+                    data_dict[key].append(el)
+                rank_dict[key].append(rank)
+            else:  # last step of the workflow
+                data_dict[pdbfile.file_name] = [oname for oname in ori_names]
+                rank_dict[pdbfile.file_name] = [rank]
+
+        # print(f"rank_dict {rank_dict}")
+        # print(f"data_dict {data_dict}, maxtopo {max_topo_len}")
+    # dumping the data into a dataframe
+    df_output = traceback_dataframe(data_dict,
+                                    rank_dict,
+                                    sel_step,
+                                    max_topo_len)
+    # dumping the dataframe
+    track_filename = Path(run_dir, TRACK_FOLDER, "traceback.tsv")
+    log.info(f"Output dataframe {track_filename} "
+             f"created with shape {df_output.shape}")
+    df_output.to_csv(track_filename, sep="\t", index=False)
+    return
+
+
+if __name__ == "__main__":
+    sys.exit(maincli())

src/haddock/modules/analysis/seletopclusts/__init__.py

@@ -87,6 +87,7 @@ def _run(self):
                 # changing attributes
                 name_path = Path(name)
                 name_path.write_text(model.rel_path.read_text())
+                model.ori_name = model.file_name
                 model.file_name = name
                 model.full_name = name
                 model.rel_path = Path('..', Path(self.path).name, name)

src/haddock/modules/refinement/emref/__init__.py

@@ -85,6 +85,10 @@ def _run(self):
                     model, idx, ".", "emref"
                     )
                 expected_pdb.restr_fname = ambig_fname
+                try:
+                    expected_pdb.ori_name = model.file_name
+                except AttributeError:
+                    expected_pdb.ori_name = None
                 self.output_models.append(expected_pdb)
 
                 job = CNSJob(inp_file, out_file, envvars=self.envvars)

src/haddock/modules/refinement/flexref/__init__.py

@@ -87,6 +87,10 @@ def _run(self):
                     model, idx, ".", "flexref"
                     )
                 expected_pdb.restr_fname = ambig_fname
+                try:
+                    expected_pdb.ori_name = model.file_name
+                except AttributeError:
+                    expected_pdb.ori_name = None
                 self.output_models.append(expected_pdb)
 
                 job = CNSJob(inp_file, out_file, envvars=self.envvars)

src/haddock/modules/refinement/mdref/__init__.py

@@ -85,6 +85,10 @@ def _run(self):
                     model, idx, ".", "mdref"
                     )
                 expected_pdb.restr_fname = ambig_fname
+                try:
+                    expected_pdb.ori_name = model.file_name
+                except AttributeError:
+                    expected_pdb.ori_name = None
                 self.output_models.append(expected_pdb)
 
                 job = CNSJob(inp_file, out_file, envvars=self.envvars)