--- a/trafficintelligence/storage.py	Wed Mar 22 15:40:33 2023 -0400
+++ b/trafficintelligence/storage.py	Wed Mar 22 22:01:12 2023 -0400
@@ -756,7 +756,7 @@
                                     'weight': row[7],
                                     'precisions': array(literal_eval(row[8]))})
                 if len(gmm) > 0:
-                    tmp = mixture.GaussianMixture(len(gmm), covarianceType)
+                    tmp = mixture.GaussianMixture(n_components=len(gmm), covariance_type=covarianceType)
                     tmp.means_ = array([gaussian['mean'] for gaussian in gmm])
                     tmp.covariances_ = array([gaussian['covar'] for gaussian in gmm])
                     tmp.weights_ = array([gaussian['weight'] for gaussian in gmm])

--- a/trafficintelligence/tests/storage.txt	Wed Mar 22 15:40:33 2023 -0400
+++ b/trafficintelligence/tests/storage.txt	Wed Mar 22 22:01:12 2023 -0400
@@ -9,6 +9,7 @@
 
 >>> nonexistentFilename = "nonexistent"
 >>> loadTrajectoriesFromSqlite(nonexistentFilename, 'feature')
+Impossible to load from non-existing file nonexistent
 []
 
 >>> o1 = MovingObject.generate(2, Point(0.,0.), Point(1.,0.), TimeInterval(0,10))
@@ -116,7 +117,7 @@
 >>> from numpy.random import random_sample
 >>> nPoints = 50
 >>> points = random_sample(nPoints*2).reshape(nPoints,2)
->>> gmm = GaussianMixture(4, covariance_type = 'full')
+>>> gmm = GaussianMixture(n_components = 4, covariance_type = 'full')
 >>> tmp = gmm.fit(points)
 >>> gmmId = 0
 >>> savePOIsToSqlite('pois-tmp.sqlite', gmm, 'end', gmmId)

--- a/trafficintelligence/utils.py	Wed Mar 22 15:40:33 2023 -0400
+++ b/trafficintelligence/utils.py	Wed Mar 22 22:01:12 2023 -0400
@@ -431,15 +431,13 @@
         smoothed[point] = max(set(window_values), key=window_values.count)
     return smoothed
 
-def filterMovingWindow(inputSignal, halfWidth):
+def filterMovingWindow(inputSignal, halfWidth, mode = 'valid'):
     '''Returns an array obtained after the smoothing of the input by a moving average
-    The first and last points are copied from the original.'''
-    width = float(halfWidth*2+1)
+    The size of the output depends on the mode: 'full', 'same', 'valid'
+    See https://numpy.org/doc/stable/reference/generated/numpy.convolve.html.'''
+    width = min(len(inputSignal), int(halfWidth*2+1))
     win = ones(width,'d')
-    result = convolve(win/width,array(inputSignal),'same')
-    result[:halfWidth] = inputSignal[:halfWidth]
-    result[-halfWidth:] = inputSignal[-halfWidth:]
-    return result
+    return convolve(win/width, array(inputSignal), mode)
 
 def linearRegression(x, y, deg = 1, plotData = False):
     '''returns the least square estimation of the linear regression of y = ax+b