--- a/trafficintelligence/utils.py	Thu Feb 15 14:09:52 2024 -0500
+++ b/trafficintelligence/utils.py	Thu Feb 15 22:04:35 2024 -0500
@@ -11,7 +11,7 @@
 from scipy.stats import rv_continuous, kruskal, shapiro, lognorm, norm, t, chi2_contingency
 from scipy.spatial import distance
 from scipy.sparse import dok_matrix
-from numpy import zeros, array, exp, sum as npsum, int64 as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log, polyfit
+from numpy import zeros, array, exp, sum as npsum, int64 as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log, polyfit, float64
 from numpy.random import random_sample, permutation as nppermutation
 from pandas import DataFrame, concat, crosstab
 import matplotlib.pyplot as plt
@@ -431,13 +431,18 @@
         smoothed[point] = max(set(window_values), key=window_values.count)
     return smoothed
 
-def filterMovingWindow(inputSignal, halfWidth, mode = 'valid'):
+def filterMovingWindow(inputSignal, halfWidth):
     '''Returns an array obtained after the smoothing of the 1-D input by a moving average
     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')
-    return convolve(win/width, array(inputSignal), mode)
+    halfWidth = min(floor((len(inputSignal)-1)/2.), halfWidth)
+    win = ones(2*halfWidth+1)/(2*halfWidth+1)
+    filtered = array(inputSignal, dtype=float64)
+    filtered[halfWidth:-halfWidth] = convolve(inputSignal, win, 'valid') # .ravel()
+    for i in range(halfWidth-1):
+        filtered[i] = sum(inputSignal[:2*i+1])/(2*i+1)
+        filtered[-1-i] = sum(inputSignal[-1-2*i:])/(2*i+1)
+    return filtered
 
 def linearRegression(x, y, deg = 1, plotData = False):
     '''returns the least square estimation of the linear regression of y = ax+b