--- a/scripts/learn-poi.py	Mon Jun 20 10:56:41 2016 -0400
+++ b/scripts/learn-poi.py	Tue Jun 21 17:08:07 2016 -0400
@@ -16,6 +16,7 @@
 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.)
+parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance
 
 args = parser.parse_args()
 
@@ -44,18 +45,22 @@
     if not model.converged_:
         print('Warning: model for '+gmmType+' points did not converge')
     # plot
-    fig = plt.figure()
-    if args.worldImageFilename is not None and args.unitsPerPixel is not None:
-        img = plt.imread(args.worldImageFilename)
-        plt.imshow(img)
-    labels = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel)
-    plt.axis('image')
-    plt.title(gmmType)
-    print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
+    if args.display:
+        fig = plt.figure()
+        if args.worldImageFilename is not None and args.unitsPerPixel is not None:
+            img = plt.imread(args.worldImageFilename)
+            plt.imshow(img)
+        labels = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel)
+        plt.axis('image')
+        plt.title(gmmType)
+        print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components))))
     # save
     storage.savePOIs(args.databaseFilename, model, gmmType, gmmId)
     gmmId += 1
-                     
+
+if args.display:
+    plt.show()
+
 # fig = plt.figure()
 # if args.worldImageFilename is not None and args.pixelsPerUnit is not None:
 #     img = plt.imread(args.worldImageFilename)