adding a python wrapper

chinese_whispers.py

@@ -0,0 +1,40 @@
+import sys
+import subprocess
+from pandas import read_csv
+
+
+def sort_input_sort(input_fpath):
+    sorted_fpath = input_fpath + ".sort"
+    df = read_csv(input_fpath, sep="\t", encoding='utf8', names=["node_i", "node_j", "sim_ij"])
+    df = df.sort_values(["node_i", "sim_ij"], ascending=False)
+    df.to_csv(sorted_fpath, sep="\t", header=False, index=False, encoding="utf-8")
+    return sorted_fpath
+
+
+def clustering(input_graph_fpath, output_clusters_fpath="", mode="global", clustering="cw", N=200, n=200, cw_option="TOP", java_xmx_gb=16):
+    """
+    input_graph_fpath in the 'node_i<TAB>node_j<TAB>sim_ij' format. Can be sorted in any way. 
+    mode: wsi or global; clustering: cw or mcl """
+
+    sorted_input_fpath = sort_input_sort(input_graph_fpath)
+
+    if output_clusters_fpath == "": output_clusters_fpath = input_graph_fpath + "-%s-%s-%d-%d-%s.clusters" % (mode, clustering, N, n, cw_option)
+
+    if mode == "wsi":
+        cmd = "java -Xms1G -Xmx%dG -cp target/chinese-whispers.jar de.tudarmstadt.lt.wsi.WSI -in %s -out %s -N %d -n %d  -clustering %s -cwOption %s" % (
+            java_xmx_gb, sorted_input_fpath, output_clusters_fpath, N, n, clustering, cw_option)
+    else:
+        cmd = "java -Xms1G -Xmx%dG -cp target/chinese-whispers.jar de.tudarmstadt.lt.cw.global.CWGlobal -in %s -out %s -N %d -cwOption %s" % (
+            java_xmx_gb, sorted_input_fpath, output_clusters_fpath, N, cw_option)
+
+    print("\nStart clustering of with the following parameters:")
+    print(cmd)
+
+    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
+    for line in iter(process.stdout.readline, ''):
+        sys.stdout.write(line)
+
+    print(("Output clusters:", output_clusters_fpath))
+    print(("Sorted input graph:", sorted_input_fpath))
+
+