create libfcc.py

src/haddock/clis/re/clustfcc.py

@@ -1,9 +1,7 @@
 """haddock3-re clustfcc subcommand."""
 
-from pathlib import Path
 import shutil
-from fcc.scripts import cluster_fcc
-
+from pathlib import Path
 
 from haddock import log
 from haddock.core.defaults import INTERACTIVE_RE_SUFFIX
@@ -17,8 +15,9 @@
     rank_clusters,
     write_structure_list,
     )
-from haddock.libs.libontology import ModuleIO
+from haddock.libs.libfcc import read_matrix
 from haddock.libs.libinteractive import look_for_capri, rewrite_capri_tables
+from haddock.libs.libontology import ModuleIO
 from haddock.modules.analysis.clustfcc.clustfcc import (
     get_cluster_centers,
     iterate_clustering,
@@ -32,72 +31,72 @@ def add_clustfcc_arguments(clustfcc_subcommand):
     clustfcc_subcommand.add_argument(
         "clustfcc_dir",
         help="The clustfcc directory to recluster.",
-        )
+    )
 
     clustfcc_subcommand.add_argument(
         "-f",
         "--clust_cutoff",
         help="Minimum fraction of common contacts to be considered in a cluster.",  # noqa: E501
         required=False,
         type=float,
-        )
+    )
 
     clustfcc_subcommand.add_argument(
         "-s",
         "--strictness",
         help="Strictness factor.",
         required=False,
         type=float,
-        )
-    
+    )
+
     clustfcc_subcommand.add_argument(
         "-t",
         "--min_population",
         help="Clustering population threshold.",
         required=False,
         type=int,
-        )
-    
+    )
+
     clustfcc_subcommand.add_argument(
         "-p",
         "--plot_matrix",
         help="Generate the matrix plot with the clusters.",
         required=False,
         default=False,
-        action='store_true',
-        )
-    
+        action="store_true",
+    )
+
     return clustfcc_subcommand
 
 
 def reclustfcc(
-        clustfcc_dir: str,
-        clust_cutoff: Union[bool, float] = None,
-        strictness: Union[bool, float] = None,
-        min_population: Union[bool, int] = None,
-        plot_matrix : bool = True,
-        ) -> Path:
+    clustfcc_dir: str,
+    clust_cutoff: Union[bool, float] = None,
+    strictness: Union[bool, float] = None,
+    min_population: Union[bool, int] = None,
+    plot_matrix: bool = True,
+) -> Path:
     """
     Recluster the models in the clustfcc directory.
-    
+
     Parameters
     ----------
     clustfcc_dir : str
         Path to the clustfcc directory.
-    
+
     clust_cutoff : Union[bool, float]
         Fraction of common contacts to not be considered a singleton model.
-    
+
     strictness : Union[bool, float]
         Fraction of common contacts to be considered to be part of the same
          cluster.
-    
+
     min_population : Union[bool, int]
         Minimum cluster population.
-    
+
     plot_matrix : bool
         Should the corresponding matrix plot be generated.
-    
+
     Returns
     -------
     outdir : Path
@@ -121,8 +120,8 @@ def reclustfcc(
 
     # load the original clustering parameters via json
     clustfcc_params = read_config(Path(clustfcc_dir, "params.cfg"))
-    key = list(clustfcc_params['final_cfg'].keys())[0]
-    clustfcc_params = clustfcc_params['final_cfg'][key]
+    key = list(clustfcc_params["final_cfg"].keys())[0]
+    clustfcc_params = clustfcc_params["final_cfg"][key]
     log.info(f"Previous clustering parameters: {clustfcc_params}")
 
     # adjust the parameters
@@ -135,18 +134,17 @@ def reclustfcc(
     clustfcc_params["plot_matrix"] = plot_matrix
 
     # load the fcc matrix
-    pool = cluster_fcc.read_matrix(
+    pool = read_matrix(
         Path(clustfcc_dir, "fcc.matrix"),
-        clustfcc_params['clust_cutoff'],
-        clustfcc_params['strictness'],
-        )
-    
+        clustfcc_params["clust_cutoff"],
+        clustfcc_params["strictness"],
+    )
+
     # iterate clustering until at least one cluster is found
     clusters, min_population = iterate_clustering(
-        pool,
-        clustfcc_params['min_population']
-        )
-    clustfcc_params['min_population'] = min_population
+        pool, clustfcc_params["min_population"]
+    )
+    clustfcc_params["min_population"] = min_population
     log.info(f"Updated clustering parameters: {clustfcc_params}")
 
     # Prepare output and read the elements
@@ -160,20 +158,20 @@ def reclustfcc(
         _score_dic, sorted_score_dic = rank_clusters(clt_dic, min_population)
 
         output_models = add_cluster_info(sorted_score_dic, clt_dic)
-        
+
         # Write unclustered structures
-        write_structure_list(models,
-                             output_models,
-                             out_fname=Path(outdir, "clustfcc.tsv"))
-        
+        write_structure_list(
+            models, output_models, out_fname=Path(outdir, "clustfcc.tsv")
+        )
+
         write_clustfcc_file(
             clusters,
             clt_centers,
             clt_dic,
             clustfcc_params,
             sorted_score_dic,
-            output_fname=Path(outdir, 'clustfcc.txt')
-            )
+            output_fname=Path(outdir, "clustfcc.txt"),
+        )
 
         save_config(clustfcc_params, Path(outdir, "params.cfg"))
 
@@ -183,7 +181,7 @@ def reclustfcc(
         if caprieval_folder:
             log.info("Rewriting capri tables")
             rewrite_capri_tables(caprieval_folder, clt_dic, outdir)
-    
+
     else:
         output_models = models
 
@@ -194,25 +192,23 @@ def reclustfcc(
         final_order_idx, labels, cluster_ids = [], [], []
         for pdb in output_models:
             final_order_idx.append(models.index(pdb))
-            labels.append(pdb.file_name.replace('.pdb', ''))
+            labels.append(pdb.file_name.replace(".pdb", ""))
             cluster_ids.append(pdb.clt_id)
         # Get custom cluster data
-        matrix_cluster_dt, cluster_limits = get_cluster_matrix_plot_clt_dt(
-            cluster_ids
-            )
+        matrix_cluster_dt, cluster_limits = get_cluster_matrix_plot_clt_dt(cluster_ids)
         # Define output filename
-        html_matrix_basepath = Path(outdir, 'fcc_matrix')
+        html_matrix_basepath = Path(outdir, "fcc_matrix")
         # Plot matrix
         html_matrixpath = plot_cluster_matrix(
             Path(clustfcc_dir, "fcc.matrix"),
             final_order_idx,
             labels,
-            dttype='FCC',
+            dttype="FCC",
             diag_fill=1,
             output_fname=html_matrix_basepath,
             matrix_cluster_dt=matrix_cluster_dt,
             cluster_limits=cluster_limits,
-            )
+        )
         log.info(f"Plotting matrix in {html_matrixpath}")
-            
+
     return outdir