How to calculate the number of times the average value crosses (“zero” crossing)

Question

I have the following data array:

      import pandas as pd
      import numpy as np

      array = np.array([10, 20, 100, 6, -3, -4, 7, 100, -7, -99, 88])

I would like to calculate the number of times that the elements of the array cross the value of an average.

What I tried to do was:

      # Initially, I defined an average variable:
      Mean = 51.5

      # I tried to develop a function to do this calculation:
      def zero_crossing_avg(data):
         output = []
         running_total = data[0]
         count = 1

         for i in range(1, data.size):
             val = data[i]
             if val - data[i-1] < Mean:
                 running_total += val
                 count += 1
            else:
                 output.append(round(running_total/count))
                 running_total = val
                 count = 1
            return (len(output))

This function is not returning the correct value.

For example:

         zero_crossing_avg(array) 

Passing the array as an argument the output is: 3, but the desired output is: 5.

Explanation:

      #from 20 to 100 it passed the average (+1).

      #from 100 to 6 passed the average (+1).

      #from 7 to 100 passed the average (+1).

      #from 100 to -7 passed the average (+1).

      #from -99 to 88 passed the average (+1)

Total = 5

Answer 1

If you're using numpy, you won't need a loop for this:

import numpy as np

array = np.array([10, 20, 100, 6, -3, -4, 7, 100, -7, -99, 88])
mean  = 51.5

crossCount = np.sum((array[:-1]>mean) != (array[1:]>mean))

print(crossCount) # 5