Linting.

clusttraj/distmat.py

@@ -195,7 +195,9 @@ def compute_distmat_line(
                 # whereins = np.where(
                 #     np.isin(np.arange(natoms), reorderexcl[soluexcl]) is True
                 # )
-                whereins = np.where(np.atleast_1d(np.isin(np.arange(natoms), reorderexcl[soluexcl])))
+                whereins = np.where(
+                    np.atleast_1d(np.isin(np.arange(natoms), reorderexcl[soluexcl]))
+                )
                 Psolu = np.insert(
                     Pview,
                     [x - whereins[0].tolist().index(x) for x in whereins[0]],

clusttraj/io.py

@@ -544,9 +544,11 @@ def parse_args(args: argparse.Namespace) -> ClustOptions:
 
     options_dict = {
         "solute_natoms": args.natoms_solute,
-        "reorder_excl": np.asarray([x - 1 for x in args.reorder_exclusions], np.int32)
-        if args.reorder_exclusions
-        else np.asarray([], np.int32),
+        "reorder_excl": (
+            np.asarray([x - 1 for x in args.reorder_exclusions], np.int32)
+            if args.reorder_exclusions
+            else np.asarray([], np.int32)
+        ),
         "exclusions": bool(args.reorder_exclusions),
         "reorder_alg_name": args.reorder_alg,
         "reorder_alg": None,
@@ -556,12 +558,12 @@ def parse_args(args: argparse.Namespace) -> ClustOptions:
         "out_clust_name": args.outputclusters,
         "summary_name": basenameout + ".out",
         "save_confs": bool(args.clusters_configurations),
-        "out_conf_name": basenameout + "_confs"
-        if args.clusters_configurations
-        else None,
-        "out_conf_fmt": args.clusters_configurations
-        if args.clusters_configurations
-        else None,
+        "out_conf_name": (
+            basenameout + "_confs" if args.clusters_configurations else None
+        ),
+        "out_conf_fmt": (
+            args.clusters_configurations if args.clusters_configurations else None
+        ),
         "plot": bool(args.plot),
         "evo_name": basenameout + "_evo.pdf" if args.plot else None,
         "dendrogram_name": basenameout + "_dendrogram.pdf" if args.plot else None,
@@ -777,7 +779,9 @@ def save_clusters_config(
                     # whereins = np.where(
                     #     np.isin(np.arange(natoms), reorderexcl[soluexcl]) is True
                     # )
-                    whereins = np.where(np.atleast_1d(np.isin(np.arange(natoms), reorderexcl)))
+                    whereins = np.where(
+                        np.atleast_1d(np.isin(np.arange(natoms), reorderexcl))
+                    )
                     Psolu = np.insert(
                         Pview,
                         [x - whereins[0].tolist().index(x) for x in whereins[0]],
@@ -850,7 +854,9 @@ def save_clusters_config(
 
                 # build the total molecule with the reordered atoms
                 # whereins = np.where(np.isin(np.arange(len(P)), reorderexcl) is True)
-                whereins = np.where(np.atleast_1d(np.isin(np.arange(len(P)), reorderexcl)))
+                whereins = np.where(
+                    np.atleast_1d(np.isin(np.arange(len(P)), reorderexcl))
+                )
                 Pr = np.insert(
                     Pview,
                     [x - whereins[0].tolist().index(x) for x in whereins[0]],
@@ -883,4 +889,6 @@ def save_clusters_config(
 
         # closes the file for the cnum cluster
         outfile.close()
- # type: ignore
+
+
+# type: ignore

clusttraj/metrics.py

@@ -9,19 +9,16 @@
 from scipy.cluster.hierarchy import cophenet
 from typing import Tuple
 import numpy as np
-# from .io import ClustOptions
 
 
 def compute_metrics(
-    # clust_opt: ClustOptions,
     distmat: np.ndarray,
     z_matrix: np.ndarray,
     clusters: np.ndarray,
 ) -> Tuple[np.float64, np.float64, np.float64, np.float64]:
     """Compute metrics to assess the performance of the clustering procedure.
 
         Args:
-        # clust_opt (ClustOptions): The clustering options.
         distmat: The distance matrix.
         z_matrix (np.ndarray): The Z-matrix from hierarchical clustering procedure.
         clusters (np.ndarray): The cluster classifications for each sample.

clusttraj/plot.py

@@ -12,10 +12,7 @@
 from .io import ClustOptions
 
 
-def plot_clust_evo(
-        clust_opt: ClustOptions, 
-        clusters: np.ndarray
-) -> None:
+def plot_clust_evo(clust_opt: ClustOptions, clusters: np.ndarray) -> None:
     """Plot the evolution of cluster classification over the given samples.
 
     Args:
@@ -25,30 +22,34 @@ def plot_clust_evo(
     Returns:
         None
     """
-    
+
     # Define a color for the lines
     line_color = (0, 0, 0, 0.5)
 
     # plot evolution with o cluster in trajectory
     plt.figure(figsize=(10, 6))
-    
+
     # Set the y-axis to only show integers
     plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))
 
     # Increase tick size and font size
-    plt.tick_params(axis='both', which='major', direction='in', labelsize=12)
+    plt.tick_params(axis="both", which="major", direction="in", labelsize=12)
 
     plt.plot(range(1, len(clusters) + 1), clusters, markersize=4, color=line_color)
-    plt.scatter(range(1, len(clusters) + 1), clusters, marker="o", c=clusters, cmap=plt.cm.nipy_spectral)
+    plt.scatter(
+        range(1, len(clusters) + 1),
+        clusters,
+        marker="o",
+        c=clusters,
+        cmap=plt.cm.nipy_spectral,
+    )
     plt.xlabel("Sample Index", fontsize=14)
     plt.ylabel("Cluster classification", fontsize=14)
     plt.savefig(clust_opt.evo_name, bbox_inches="tight")
 
 
 def plot_dendrogram(
-        clust_opt: ClustOptions,
-        clusters: np.ndarray,
-        Z: np.ndarray
+    clust_opt: ClustOptions, clusters: np.ndarray, Z: np.ndarray
 ) -> None:
     """Plot a dendrogram based on hierarchical clustering.
 
@@ -65,18 +66,22 @@ def plot_dendrogram(
     plt.title("Hierarchical Clustering Dendrogram", fontsize=20)
     # plt.xlabel("Sample Index", fontsize=14)
     plt.ylabel(r"RMSD ($\AA$)", fontsize=18)
-    plt.tick_params(axis='y', labelsize=18)
+    plt.tick_params(axis="y", labelsize=18)
 
     # Define a color for the dashed and non-cluster lines
     line_color = (0, 0, 0, 0.5)
 
     # Add a horizontal line at the minimum RMSD value and set the threshold
     if clust_opt.silhouette_score:
         if isinstance(clust_opt.optimal_cut, (np.ndarray, list)):
-            plt.axhline(clust_opt.optimal_cut[0], linestyle="--", linewidth=2, color=line_color)
+            plt.axhline(
+                clust_opt.optimal_cut[0], linestyle="--", linewidth=2, color=line_color
+            )
             threshold = clust_opt.optimal_cut[0]
         elif isinstance(clust_opt.optimal_cut, (float, np.float32, np.float64)):
-            plt.axhline(clust_opt.optimal_cut, linestyle="--", linewidth=2, color=line_color)
+            plt.axhline(
+                clust_opt.optimal_cut, linestyle="--", linewidth=2, color=line_color
+            )
             threshold = clust_opt.optimal_cut
         else:
             raise ValueError("optimal_cut must be a float or np.ndarray")
@@ -86,9 +91,9 @@ def plot_dendrogram(
 
     # Use the 'nipy_spectral' cmap to color the dendrogram
     unique_clusters = np.unique(clusters)
-    cmap = cm.get_cmap('nipy_spectral', len(unique_clusters))
+    cmap = cm.get_cmap("nipy_spectral", len(unique_clusters))
     colors = [to_hex(cmap(i)) for i in range(cmap.N)]
-    
+
     hierarchy.set_link_color_palette(colors)
 
     # Plot the dendrogram
@@ -98,18 +103,14 @@ def plot_dendrogram(
         # leaf_font_size=8.0,  # Font size for the x axis labels
         no_labels=True,
         color_threshold=threshold,
-        above_threshold_color=line_color
+        above_threshold_color=line_color,
     )
 
     # Save the dendrogram to a file
     plt.savefig(clust_opt.dendrogram_name, bbox_inches="tight")
 
 
-def plot_mds(
-        clust_opt: ClustOptions, 
-        clusters: np.ndarray, 
-        distmat: np.ndarray
-) -> None:
+def plot_mds(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray) -> None:
     """Plot the multidimensional scaling (MDS) of the distance matrix.
 
     Args:
@@ -139,7 +140,7 @@ def plot_mds(
     coords = mds.fit_transform(squareform(distmat))
 
     # Set the figure size
-    plt.figure(figsize=(6,6))
+    plt.figure(figsize=(6, 6))
 
     # Configure tick parameters
     plt.tick_params(
@@ -165,9 +166,7 @@ def plot_mds(
 
 
 def plot_tsne(
-    clust_opt: ClustOptions, 
-    clusters: np.ndarray, 
-    distmat: np.ndarray
+    clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray
 ) -> None:
     """Plot the t-distributed Stochastic Neighbor Embedding 2D plot of the clustering.
 
@@ -194,11 +193,11 @@ def plot_tsne(
 
     # Define a list of unique colors for each cluster
     unique_clusters = np.unique(clusters)
-    cmap = cm.get_cmap('nipy_spectral', len(unique_clusters))
+    cmap = cm.get_cmap("nipy_spectral", len(unique_clusters))
     colors = [cmap(i) for i in range(len(unique_clusters))]
 
     # Set the figure size
-    plt.figure(figsize=(6,6))
+    plt.figure(figsize=(6, 6))
 
     # Configure tick parameters
     plt.tick_params(