--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/rmsd_clustering.py	Mon Aug 24 06:08:17 2020 -0400
@@ -0,0 +1,114 @@
+import argparse
+import json
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+from scipy.cluster.hierarchy import cophenet, dendrogram, linkage
+from scipy.spatial.distance import pdist
+
+
+def json_to_np(fname, start=None, end=None):
+    """
+    Load json file and convert to numpy array
+    """
+    with open(fname) as f:
+        k = json.load(f)
+    print(np.array(k)[:, :, start:end].shape)
+    return np.array(k)[:, :, start:end]
+
+
+def flatten_tensor(tensor, normalize=True):
+    """
+    Flatten tensor to a 2D matrix along the time axis
+    """
+    av = np.mean(tensor, axis=(0, 1)) if normalize else 1
+    return np.mean(tensor/av, axis=2)
+
+
+def get_cluster_linkage_array(mat, clustering_method='average'):
+    Z = linkage(mat, clustering_method)
+    c, coph_dists = cophenet(Z, pdist(mat))
+    print('Cophenetic correlation coefficient: {}'.format(c))
+    return Z
+
+
+def plot_dist_mat(mat, output, cmap='plasma'):
+    """
+    Plot distance matrix as heatmap
+    """
+    fig, ax = plt.subplots(1)
+    p = ax.pcolormesh(mat, cmap=cmap)
+    plt.xlabel('Trajectory number')
+    plt.ylabel('Trajectory number')
+    plt.colorbar(p)
+    plt.draw()
+    plt.savefig(output, format='png')
+
+
+def plot_dendrogram(Z, output):
+    plt.figure(figsize=(25, 10))
+    plt.title('Hierarchical Clustering Dendrogram')
+    plt.xlabel('Trajectory index')
+    plt.ylabel('distance')
+    dendrogram(
+        Z,
+        leaf_rotation=90.,  # rotates the x axis labels
+        leaf_font_size=8.,  # font size for the x axis labels
+    )
+    plt.savefig(output, format='png')
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--json', help='JSON input file (for 3D tensor).')
+    parser.add_argument('--mat', help='Input tabular file (for 2D matrix).')
+    parser.add_argument('--outp-mat', help='Tabular output file.')
+    parser.add_argument('--Z', required=True,
+                        help='File for cluster linkage array.')
+    parser.add_argument('--dendrogram',
+                        help="Path to the output dendrogram file")
+    parser.add_argument('--heatmap',
+                        help="Path to the output distance matrix file")
+    parser.add_argument('--clustering-method', default='average',
+                        choices=['single', 'complete', 'average',
+                                 'centroid', 'median', 'ward', 'weighted'],
+                        help="Method to use for clustering.")
+    parser.add_argument('--cmap', type=str, default='plasma',
+                        help="Matplotlib colormap to use"
+                             "for plotting distance matrix.")
+    parser.add_argument('--start', type=int,
+                        help="First trajectory frame to"
+                             "calculate distance matrix")
+    parser.add_argument('--end', type=int,
+                        help="Last trajectory frame to"
+                             "calculate distance matrix")
+    parser.add_argument('--normalize', action="store_true",
+                        help="Normalize the RMSD variation over"
+                             "the trajectories before averaging.")
+    args = parser.parse_args()
+
+    print(args)
+    if args.json:
+        tensor = json_to_np(args.json, args.start, args.end)
+        mat = flatten_tensor(tensor, args.normalize)
+        np.savetxt(args.outp_mat, mat)
+    elif args.mat:
+        mat = np.loadtxt(args.mat)
+    else:
+        print("Either --json or --mat must be specified.")
+        exit(1)
+
+    Z = get_cluster_linkage_array(mat, args.clustering_method)
+    np.savetxt(args.Z, Z)
+
+    if args.heatmap:
+        plot_dist_mat(mat, args.heatmap, args.cmap)
+
+    if args.dendrogram:
+        plot_dendrogram(Z, args.dendrogram)
+
+
+if __name__ == "__main__":
+    main()