--- a/python/storage.py	Fri Oct 30 14:15:03 2015 -0400
+++ b/python/storage.py	Fri Oct 30 17:57:09 2015 -0400
@@ -7,6 +7,7 @@
 
 import sqlite3, logging
 from numpy import log, min as npmin, max as npmax, round as npround, array, sum as npsum, loadtxt
+from pandas import read_csv, merge
 
 
 commentChar = '#'
@@ -776,7 +777,6 @@
     objects = {} # dictionary of objects index by their id
 
     if usePandas:
-        from pandas import read_csv
         data = read_csv(filename, delimiter=';', comment='*', header=0, skiprows = 1, low_memory = lowMemory)
         generatePDLaneColumn(data)
         data['TIME'] = data['$VEHICLE:SIMSEC']*simulationStepsPerTimeUnit
@@ -863,22 +863,28 @@
     if more than proportionStationaryTime of their total time
     If lanes is not None, only the data for the selected lanes will be provided
     (format as string x_y where x is link index and y is lane index)'''
-    from pandas import read_csv
-    columns = ['NO', '$VEHICLE:SIMSEC', 'POS']
-    if lanes is not None:
-        columns += ['LANE\LINK\NO', 'LANE\INDEX']
-    data = read_csv(filename, delimiter=';', comment='*', header=0, skiprows = 1, usecols = columns, low_memory = lowMemory)
-    data = selectPDLanes(data, lanes)
-    data.sort(['$VEHICLE:SIMSEC'], inplace = True)
+    if filename.endswith(".fzp"):
+        columns = ['NO', '$VEHICLE:SIMSEC', 'POS']
+        if lanes is not None:
+            columns += ['LANE\LINK\NO', 'LANE\INDEX']
+        data = read_csv(filename, delimiter=';', comment='*', header=0, skiprows = 1, usecols = columns, low_memory = lowMemory)
+        data = selectPDLanes(data, lanes)
+        data.sort(['$VEHICLE:SIMSEC'], inplace = True)
 
-    nStationary = 0
-    nVehicles = 0
-    for name, group in data.groupby(['NO'], sort = False):
-        nVehicles += 1
-        positions = array(group['POS'])
-        diff = positions[1:]-positions[:-1]
-        if npsum(diff == 0.) >= proportionStationaryTime*len(positions):
-            nStationary += 1
+        nStationary = 0
+        nVehicles = 0
+        for name, group in data.groupby(['NO'], sort = False):
+            nVehicles += 1
+            positions = array(group['POS'])
+            diff = positions[1:]-positions[:-1]
+            if npsum(diff == 0.) >= proportionStationaryTime*(len(positions)-1):
+                nStationary += 1
+    elif filename.endswith(".sqlite"):
+        # select trajectory_id, t, s_coordinate, speed from curvilinear_positions where trajectory_id between 1860 and 1870 and speed < 0.1
+        # pb of the meaning of proportionStationaryTime in arterial network? Why proportion of existence time?
+        pass
+    else:
+        print("File type of "+filename+" not supported (only .sqlite and .fzp files)")
 
     return nStationary, nVehicles
 
@@ -890,7 +896,6 @@
     (if the time are closer than collisionTimeDifference)
     If lanes is not None, only the data for the selected lanes will be provided
     (format as string x_y where x is link index and y is lane index)'''
-    from pandas import read_csv, merge
     data = read_csv(filename, delimiter=';', comment='*', header=0, skiprows = 1, usecols = ['LANE\LINK\NO', 'LANE\INDEX', '$VEHICLE:SIMSEC', 'NO', 'POS'], low_memory = lowMemory)
     data = selectPDLanes(data, lanes)
     data = data.convert_objects(convert_numeric=True)
@@ -907,6 +912,10 @@
             yidx = diff[diff > 0].argmin()
             if abs(group.loc[xidx, '$VEHICLE:SIMSEC'] - group.loc[yidx, '$VEHICLE:SIMSEC']) <= collisionTimeDifference:
                 nCollisions += 1
+
+    # select TD1.link_id, TD1.lane_id from temp.diff_positions as TD1, temp.diff_positions as TD2 where TD1.link_id = TD2.link_id and TD1.lane_id = TD2.lane_id and TD1.id1 = TD2.id1 and TD1.id2 = TD2.id2 and TD1.t = TD2.t+0.1 and TD1.diff*TD2.diff < 0; # besoin de faire un group by??
+    # create temp table diff_positions as select CP1.t as t, CP1.link_id as link_id, CP1.lane_id as lane_id, CP1.trajectory_id as id1, CP2.trajectory_id as id2, CP1.s_coordinate - CP2.s_coordinate as diff from curvilinear_positions CP1, curvilinear_positions CP2 where CP1.link_id = CP2.link_id and CP1.lane_id = CP2.lane_id and CP1.t = CP2.t and CP1.trajectory_id > CP2.trajectory_id;
+    # SQL select link_id, lane_id, id1, id2, min(diff), max(diff) from (select CP1.t as t, CP1.link_id as link_id, CP1.lane_id as lane_id, CP1.trajectory_id as id1, CP2.trajectory_id as id2, CP1.s_coordinate - CP2.s_coordinate as diff from curvilinear_positions CP1, curvilinear_positions CP2 where CP1.link_id = CP2.link_id and CP1.lane_id = CP2.lane_id and CP1.t = CP2.t and CP1.trajectory_id > CP2.trajectory_id) group by link_id, lane_id, id1, id2 having min(diff)*max(diff) < 0
     return nCollisions
     
 def loadTrajectoriesFromNgsimFile(filename, nObjects = -1, sequenceNum = -1):