diff chemfp_clustering/nxn_clustering.py @ 0:a8ac5250d59c

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/chemfp_clustering/nxn_clustering.py	Tue Mar 26 13:05:41 2013 -0400
@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+"""
+    Modified version of code examples from the chemfp project.
+    http://code.google.com/p/chem-fingerprints/
+    Thanks to Andrew Dalke of Andrew Dalke Scientific!
+"""
+import matplotlib
+matplotlib.use('Agg')
+import sys
+import os
+import chemfp
+import scipy.cluster.hierarchy as hcluster
+import pylab
+import numpy
+
+
+def distance_matrix(arena,t):
+    n = len(arena)
+    # The Tanimoto search computes all of the scores when threshold=0.0.
+    # The SearchResult contains sparse data, so I set all values
+    # now to 1.0 so you can experiment with higher thresholds.
+    distances = numpy.ones((n, n), numpy.float64)
+
+    # Keep track of where the query subarena is in the query
+    query_row = 0
+
+    for query_arena in arena.iter_arenas():
+        results = arena.threshold_tanimoto_search_arena(query_arena, threshold=t)  
+    for q_i, hits in enumerate(results.iter_indices_and_scores()):
+            query_idx = query_row + q_i
+            for target_idx, score in hits:
+                distances[query_idx, target_idx] = 1.0 - score
+        query_row += len(query_arena)
+
+    return distances
+
+dataset = chemfp.load_fingerprints( sys.argv[1] )
+distances  = distance_matrix( dataset,float( sys.argv[2] ) )
+linkage = hcluster.linkage( distances, method="single", metric="euclidean" )
+
+# Plot using matplotlib, which you must have installed
+hcluster.dendrogram(linkage, labels=dataset.ids)
+
+pylab.savefig( sys.argv[3], format='svg' )
+
+
+
+
+
+
+