Added py directory

Author: hani-girgis

py/README.txt

@@ -0,0 +1,23 @@
+Author 
+
+Hani Z. Girgis, PhD
+
+Purpose 
+
+This program performs hierarchical clustering using all-versus-all identity scores produced by Identity.
+
+Requirements 
+
+Python 3 with SciPy and Matplotlib packages.
+
+Examples
+
+1. Display only sorted leaves to the terminal, i.e. no graphics: 
+	python3 makeTree.py all_vs_all_identity_scores 0
+
+2. Display tree:
+	python3 makeTree.py all_vs_all_identity_scores 1
+
+Note
+
+The hierarchical clustering algorithm may take long time.

py/makeTree.py

@@ -0,0 +1,86 @@
+#
+# Author: Hani Z. Girgis, PhD
+# Purpose: This program performs hierarchical clustering using the 
+#    all-versus-all identity scores produced by Identity.
+# Requirements: Python 3 with SciPy and Matplotlib packages.
+#
+
+import numpy as np
+import sys
+
+from scipy.cluster.hierarchy import linkage
+from scipy.cluster.hierarchy import dendrogram
+from scipy.cluster.hierarchy import leaves_list
+import matplotlib.pyplot as plt
+
+   
+def generate_tree(file, displayTree):    
+    file = open(file, 'r')
+    # List of labels
+    label_dict = {}
+    seq_index = 0
+    for line in file:
+        token_list = line.strip().split("\t")
+         
+        if not token_list[0] in label_dict:
+            label_dict[token_list[0]] = seq_index
+            seq_index += 1
+         
+        if not token_list[1] in label_dict:    
+            label_dict[token_list[1]] = seq_index
+            seq_index += 1
+ 
+    label_count = len(label_dict.keys())
+ 
+    # The matrix 
+    matrix = np.ones((label_count, label_count))
+    # Go to the begining of the file
+    file.seek(0)
+    for line in file:
+        token_list = line.strip().split("\t")
+        distance = 1.0 - float(token_list[2])
+ 
+        if(distance < 0.0):
+            distance = 0.0
+        elif(distance > 1.0):
+            distance = 1.0
+ 
+        id_1 = label_dict[token_list[0]]
+        id_2 = label_dict[token_list[1]]
+        if(id_1 < id_2):
+            matrix[id_1][id_2] = distance
+        else:
+            matrix[id_2][id_1] = distance
+      
+    file.close()
+ 
+    key_list = label_dict.keys()
+    name_list = [-1] * len(key_list)
+    for k in key_list:
+        name_list[label_dict[k]] = k; 
+     
+    l = list(matrix[np.triu_indices(label_count, 1)])
+    row_clusters = linkage(l, method='average', optimal_ordering=True)
+     
+    # Print ordered leaves 
+    if displayTree == 0:
+        leaf_list = leaves_list(row_clusters)
+        for index in leaf_list:
+            print(name_list[index])
+    else:
+        plt.figure()
+        dendrogram(row_clusters, orientation='left', labels=name_list, leaf_font_size=5, distance_sort='descending', show_leaf_counts=True)
+        plt.tight_layout()
+        plt.show()
+
+
+if len(sys.argv) != 3:
+    print("Use:", sys.argv[0], "allVsAllIdentityFile sortedLeavesOrTree")
+    print()
+    print("Please provide an all-versus-all file produced by Identity and 0 (display sorted leaves) or 1 (display a tree).")
+    print()
+    print("Display only sorted leaves: python3 makeTree.py all_vs_all_identity_scores 0")
+    print("Display tree: python3 makeTree.py all_vs_all_identity_scores 1")
+    print()
+else:
+    generate_tree(sys.argv[1], int(sys.argv[2]))