--- a/scripts/safety-analysis.py	Wed May 13 00:29:34 2020 -0400
+++ b/scripts/safety-analysis.py	Thu May 28 01:03:45 2020 -0400
@@ -18,6 +18,7 @@
 # TODO analyze only 
 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp'])
 parser.add_argument('-p', dest = 'prototypeDatabaseFilename', help = 'name of the database containing the prototypes')
+parser.add_argument('-c', dest = 'minPrototypeNMatchings', help = 'minimum number of matchings per prototype', type = int, default = 1)
 # parser.add_argument('--categorize', dest = 'categorize', help = 'computes interaction categories', action = 'store_true') TODO, add angle parameters in tracking.cfg - the safety analysis parameters should probably be spun off tracking.cfg
 parser.add_argument('--no-motion-prediction', dest = 'noMotionPrediction', help = 'does not compute indicators like TTC depending on motion prediction', action = 'store_true')
 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
@@ -61,6 +62,17 @@
         prototypes = storage.loadPrototypesFromSqlite(params.databaseFilename)
     else:
         prototypes = storage.loadPrototypesFromSqlite(args.prototypeDatabaseFilename)
+    if args.minPrototypeNMatchings > 0:
+        prototypes = [p for p in prototypes if p.getNMatchings() >= args.minPrototypeNMatchings]
+    else:
+        nProto0Matching = 0
+        for p in prototypes:
+            if p.getNMatchings() == 0:
+                nProto0Matching += 1
+                print("Prototype {} has 0 matchings".format(p))
+        if len(prototypes) == 0 or nProto0Matching > 0:
+            print('Database has {} prototypes without any matching. Exiting'.format(nProto0Matching))
+            sys.exit()
     for p in prototypes:
         p.getMovingObject().computeCumulativeDistances()
     predictionParameters = prediction.PrototypePredictionParameters(prototypes, params.nPredictedTrajectories, params.maxLcssDistance, params.minLcssSimilarity, params.lcssMetric, params.minFeatureTime, params.constantSpeedPrototypePrediction, params.useFeaturesForPrediction)