--- a/trafficintelligence/utils.py	Fri Nov 18 15:55:09 2022 -0500
+++ b/trafficintelligence/utils.py	Thu Dec 08 10:24:22 2022 -0500
@@ -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