Linting and fix tests.

clusttraj/io.py

@@ -309,7 +309,7 @@ def configure_runtime(args_in: List[str]) -> ClustOptions:
     parser.add_argument(
         "--metrics",
         action="store_true",
-        help="compute metrics to evaluate the clustering procedure quality."
+        help="compute metrics to evaluate the clustering procedure quality.",
     )
 
     rmsd_criterion = parser.add_mutually_exclusive_group(required=True)

clusttraj/main.py

@@ -84,7 +84,7 @@ def main(args: List[str] = None) -> None:
         outclust_str += f"\nSilhouette score: {ss:.3f}\n"
         outclust_str += f"Calinski Harabsz score: {ch:.3f}\n"
         outclust_str += f"Davies-Bouldin score: {db:.3f}\n"
-        outclust_str += f"Cophenetic correlation coefficient: {cpcc:.3f}\n\n" 
+        outclust_str += f"Cophenetic correlation coefficient: {cpcc:.3f}\n\n"
 
     # save summary
     with open(clust_opt.summary_name, "w") as f:

clusttraj/metrics.py

@@ -1,6 +1,10 @@
 """Functions to compute evaluation metrics of the clustering procedure"""
 
-from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
+from sklearn.metrics import (
+    silhouette_score,
+    calinski_harabasz_score,
+    davies_bouldin_score,
+)
 from scipy.spatial.distance import squareform
 from scipy.cluster.hierarchy import cophenet
 from typing import Tuple
@@ -9,11 +13,14 @@
 
 
 def compute_metrics(
-	clust_opt: ClustOptions, distmat: np.ndarray, z_matrix: np.ndarray, clusters: np.ndarray
+    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.
+    """Compute metrics to assess the performance of the clustering procedure.
 
-	Args:
+        Args:
         clust_opt (ClustOptions): The clustering options.
         z_matrix (np.ndarray): The Z-matrix from hierarchical clustering procedure.
 
@@ -22,19 +29,18 @@ def compute_metrics(
         ch (np.float64): The Calinski Harabasz score.
         db (np.float64): The Davies-Bouldin score.
         cpcc (np.float64): The cophenetic correlation coefficient.
-	"""
+    """
 
-	# Compute the silhouette score
-	ss = silhouette_score(squareform(distmat), clusters, metric="precomputed")
+    # Compute the silhouette score
+    ss = silhouette_score(squareform(distmat), clusters, metric="precomputed")
 
-	# Compute the Calinski Harabasz score
-	ch = calinski_harabasz_score(squareform(distmat), clusters)
+    # Compute the Calinski Harabasz score
+    ch = calinski_harabasz_score(squareform(distmat), clusters)
 
-	# Compute the Davies-Bouldin score
-	db = davies_bouldin_score(squareform(distmat), clusters)
+    # Compute the Davies-Bouldin score
+    db = davies_bouldin_score(squareform(distmat), clusters)
 
-	# Compute the cophenetic correlation coefficient 
-	cpcc = cophenet(z_matrix)[0]
-
-	return ss, ch, db, cpcc
+    # Compute the cophenetic correlation coefficient
+    cpcc = cophenet(z_matrix)[0]
 
+    return ss, ch, db, cpcc

clusttraj/plot.py

@@ -102,20 +102,22 @@ def plot_mds(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray)
         right=False,
         labelbottom=False,
         labelleft=False,
-    ) 
+    )
 
     # Scatter plot the coordinates with cluster colors
     plt.scatter(
         coords[:, 0], coords[:, 1], marker="o", c=clusters, cmap=plt.cm.nipy_spectral
     )
 
-    plt.title('MDS Visualization')
+    plt.title("MDS Visualization")
 
     # Save the plot
     plt.savefig(clust_opt.mds_name, bbox_inches="tight")
 
 
-def plot_tsne(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray) -> None:
+def plot_tsne(
+    clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray
+) -> None:
     """Plot the t-distributed Stochastic Neighbor Embedding 2D plot of the clustering.
 
     Args:
@@ -129,14 +131,14 @@ def plot_tsne(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray
 
     # Initialize the tSNE model
     tsne = manifold.TSNE(
-         n_components=2, 
-         perplexity=30, 
-         learning_rate=200,
-         random_state=666,
-         n_jobs=clust_opt.n_workers,
+        n_components=2,
+        perplexity=30,
+        learning_rate=200,
+        random_state=666,
+        n_jobs=clust_opt.n_workers,
     )
 
-    # Perform the t-SNE and get the 2D representation 
+    # Perform the t-SNE and get the 2D representation
     coords = tsne.fit_transform(squareform(distmat))
 
     # Define a list of unique colors for each cluster
@@ -156,15 +158,14 @@ def plot_tsne(clust_opt: ClustOptions, clusters: np.ndarray, distmat: np.ndarray
         right=False,
         labelbottom=False,
         labelleft=False,
-    ) 
+    )
 
     # Create a scatter plot with different colors for each cluster
     for i, cluster in enumerate(unique_clusters):
         cluster_data = coords[clusters == cluster]
         plt.scatter(cluster_data[:, 0], cluster_data[:, 1], color=colors[i])
 
-    plt.title('t-SNE Visualization')
+    plt.title("t-SNE Visualization")
 
     # Save the plot
     plt.savefig(clust_opt.mds_name[:-7] + "tsne.pdf", bbox_inches="tight")
-

test/test_io.py

@@ -82,6 +82,7 @@ def test_parse_args():
         force=True,
         final_kabsch=True,
         silhouette_score=False,
+        metrics=False,
     )
     clust_opt = parse_args(args)
 
@@ -107,6 +108,7 @@ def test_parse_args():
         force=True,
         final_kabsch=True,
         silhouette_score=False,
+        metrics=False,
     )
     clust_opt = parse_args(args)