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Detection of Pulsars using Median Image Stacking (Binapprox)

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Time analysis of different library methods

While implementing the simple solutions, I wondered how to optimise the solutions using different library functions. Here I timed 3 approaches to calculating mean and medians of 1D arrays and how different library functions react to the different sizes of the array.
The three approaches are:
1. Naive calculation of mean and approach by implementing the algorithms for both.
2. using numpy mean and median functions.
3. using statistics mean and median functions.
def list_stats_1 (arr) :
size = len(arr)
arr.sort()
mid = math.floor(size/2)
mean = sum(arr)/size if size > 0 else 0
median = 0
if size > 0:
if size % 2 == 0:
median = (arr[mid-1]+arr[mid])/2
else:
median = arr[mid]
return np.round(median, 5), np.round(mean, 5)


def list_stats_2 (arr) :
mean = np.mean(arr)
median = np.median(arr)
return np.round(median, 5), np.round(mean, 5)


def list_stats_3 (arr) :
mean = statistics.mean(arr)
median = statistics.median(arr)
return np.round(median, 5), np.round(mean, 5)
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I ran the code on array of sizes from 10 to 10^8 having random numbers.
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