Road-Accident-Identifier-Tool.txt

# Using a sample of the accelerometer x, y, z data (50 lines).
# Purpose of code is to identify big rates of change in the data series, take an 
# average of the absolute value of the change accross the three given dimensions for 
# each time slice, and use a normal distribution to filter 3 sigma and 4 sigma events as possible accidents. 
# As with any series the tool will become more stable once its has filtered over a large enough (5000+) training set.
import statistics
import numpy
x = (1, 1, 1, 1, 1, -4, 3, 2, 1, 1, 1, 2, 3)
y = (1, 1, 1, 1, 1, 4, 3, -2, 1, 1, 1, 2, 3)
z = (1, 1, 1, 1, 1, 4, -3, 2, 1, 1, 1, 2, 3)
List = []
PossibleAccident = []
ProbableAccident = []
HighlyLikelyAccident = []
a = 0
a = int(a)
while a < len(x)-1:
    Value = (abs((x[a+1]/x[a]))+abs((y[a+1]/y[a]))+abs((z[a+1]/z[a])))/3
    List.insert(len(List), Value)
    print(List)
    print("Maximum acceleration delta", max(List))
    print("Minimum acceleration delta", min(List))
    print("Average acceleration delta", statistics.mean(List))
    a+=1
for i in List:
    if i > max(List)*0.7:
        print("High Value",i)
        print("List Standard Deviation is",numpy.std(List))
    if i > (statistics.mean(List)+(numpy.std(List)*3)):   
        print("Outlier and possible accident", i, List.index(i))
        PossibleAccident.insert(len(List), (i, List.index(i)))
        print("PossibleAccidents", PossibleAccident)
    if i > (statistics.mean(List)+(numpy.std(List)*3.5)):   
        print("Outlier and probable accident", i, List.index(i)) 
        ProbableAccident.insert(len(List), (i, List.index(i)))
        print("ProbableAccidents", ProbableAccidents)
    if i > (statistics.mean(List)+(numpy.std(List)*4)):   
        print("Outlier and highly likely to be an accident", i, List.index(i))
        HighlyLikelyAccident.insert(len(List), (i, List.index(i)))
        print("HighlyLikelyAccident", HighlyLikelyAccident)