--- a/scripts/process.py	Fri Jul 06 15:58:45 2018 -0400
+++ b/scripts/process.py	Fri Jul 06 18:12:15 2018 -0400
@@ -61,10 +61,10 @@
 parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event'])
 parser.add_argument('--min-user-duration', dest = 'minUserDuration', help = 'mininum duration we have to see the user to take into account in the analysis (s)', type = float, default = 0.1)
 parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = float, default = 15.)
-parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/event and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median'])
+parser.add_argument('--aggregation', dest = 'aggMethod', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median'])
 parser.add_argument('--aggregation-centile', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int)
 dpi = 150
-# unit of analysis: site or video sequence?
+# unit of analysis: site - camera-view
 
 # need way of selecting sites as similar as possible to sql alchemy syntax
 # override tracking.cfg from db
@@ -90,8 +90,7 @@
         tmpsites = getSite(session, siteId)
         sites.extend(tmpsites)
         for site in tmpsites:
-            for cv in site.cameraViews:
-                videoSequences.extend(cv.videoSequences)
+            videoSequences.extend(getSiteVideoSequences(site))
 else:
     print('No video/site to process')
 
@@ -147,7 +146,7 @@
         pool.join()
 
 elif args.process == 'prototype': # motion pattern learning
-    # learn by site by default -> group videos by site (or by camera view? TODO add cameraviews)
+    # learn by site by default -> group videos by camera view TODO
     # by default, load all objects, learn and then assign (BUT not save the assignments)
     for site in sites:
         print('Learning motion patterns for site {} ({})'.format(site.idx, site.name))
@@ -177,7 +176,6 @@
             outputPrototypeDatabaseFilename = args.databaseFilename
         else:
             outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
-        # TODO maintain mapping from object prototype to db filename + compute nmatchings before
         clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
         storage.savePrototypesToSqlite(str(parentPath/site.getPath()/outputPrototypeDatabaseFilename), [moving.Prototype(object2VideoSequences[trainingObjects[i]].getDatabaseFilename(False), trainingObjects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices])
 
@@ -232,7 +230,7 @@
         objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object', args.nObjects)
         for o in objects:
             if o.length() > minUserDuration:
-                row = [vs.cameraView.siteIdx, d, utils.framesToTime(o.getFirstInstant(), vs.cameraView.cameraType.frameRate, t1), o.getUserType()]
+                row = [vs.cameraView.site.name, d, utils.framesToTime(o.getFirstInstant(), vs.cameraView.cameraType.frameRate, t1), o.getUserType()]
                 tmp = o.getSpeeds()
                 for method,func in aggFunctions.items():
                     aggSpeeds = vs.cameraView.cameraType.frameRate*3.6*func(tmp)
@@ -252,7 +250,8 @@
             plt.close()
     elif args.output == 'event':
         data.to_csv('speeds.csv', index = False)
-if args.analyze == 'interaction':
+
+if args.analyze == 'interaction': # redo as for object, export in dataframe all interaction data
     indicatorIds = [2,5,7,10]
     conversionFactors = {2: 1., 5: 30.*3.6, 7:1./30, 10:1./30}
     maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.}