Analysis of Fake Numbers Station (Greensleeves Numbers Station) October 2024.

1 There was a new message from Greensleeves Numbers Station in mid October. I have not had much time to run any analysis, but for posterity I will leave the transcribed numbers below:

7 11 19 18 19 13 20 25 19 2 6 0 6 18 13 17 19 20 7 20 6 12 19 17 12 25 14 19 19 2 11 13 18 6 21 21 16 19 17 11 16 0 1 16 20 23 13 18 6 0 5 18 19 19 2 19 21 21 25 11 19 2 7 11 18 13 24 14 11 7 11 19 16 18 2 16 18 19 0 6 20 17 14 20 6 2 6 7 7 11 19 16 18 7 16 18 19 0 7 16 21 7 11 19 6 5 19 0 12 24 0 7 0 24 18 18 19 20 17 19 18 6 20 17 23 21 19 19 6 18 13 17 19 20 7 2 16 7 11 6 5 21 6 20 10 13 21 17 6 20 17 10 18 16 14 11 7 0 6 16 17 2 19 21 21 7 6 1 19 10 6 25 1 7 11 19 25 11 19 19 0 19 16 20 7 11 19 20 16 14 11 7 2 19 21 21 0 20 19 6 1 6 20 17 2 19 21 21 0 25 24 18 18 3 7 11 19 3 21 21 23 21 19 19 16 20 6 11 24 18 18 3 7 11 19 18 13 17 19 20 7 0 2 16 21 21 18 16 0 19 2 16 7 11 7 11 19 16 18 12 16 14 11 7 6 18 13 17 19 20 7 25 18 16 19 17 21 19 7 0 7 6 1 19 7 13 7 11 19 7 18 19 19 0 2 19 21 21 18 19 25 21 6 16 12 6 21 21 7 11 19 16 18 14 6 18 17 19 20 0 2 16 7 11 19 6 0 19 2 19 21 21 20 16 10 10 21 19 7 11 19 16 18 25 18 13 5 0 6 20 17 18 6 16 17 6 21 21 7 11 19 16 18 0 11 13 5 0 7 16 21 7 11 19 6 5 19 0 10 19 14 24 0 10 6 25 1 13 20 7 11 19 16 18 1 20 19 19 0

And some frequency analysis is below. I doubt I will make much progress on this in the near future.

import numpy as np
import matplotlib.pyplot as plt
import pprint

message = '''
7 11 19 18 19 13 20 25 19 2 6 0 6 18 13 17 19 20 7 20 6 12 19 17 12 25 14 19 19 2 11 13 18 6 21 21 16 19 17 11 16 0 1 16 20 23 13 18 6 0 5 18 19 19 2 19 21 21 25 11 19 2 7 11 18 13 24 14 11 7 11 19 16 18 2 16 18 19 0 6 20 17 14 20 6 2 6 7 7 11 19 16 18 7 16 18 19 0 7 16 21 7 11 19 6 5 19 0 12 24 0 7 0 24 18 18 19 20 17 19 18 6 20 17 23 21 19 19 6 18 13 17 19 20 7 2 16 7 11 6 5 21 6 20 10 13 21 17 6 20 17 10 18 16 14 11 7 0 6 16 17 2 19 21 21 7 6 1 19 10 6 25 1 7 11 19 25 11 19 19 0 19 16 20 7 11 19 20 16 14 11 7 2 19 21 21 0 20 19 6 1 6 20 17 2 19 21 21 0 25 24 18 18 3 7 11 19 3 21 21 23 21 19 19 16 20 6 11 24 18 18 3 7 11 19 18 13 17 19 20 7 0 2 16 21 21 18 16 0 19 2 16 7 11 7 11 19 16 18 12 16 14 11 7 6 18 13 17 19 20 7 25 18 16 19 17 21 19 7 0 7 6 1 19 7 13 7 11 19 7 18 19 19 0 2 19 21 21 18 19 25 21 6 16 12 6 21 21 7 11 19 16 18 14 6 18 17 19 20 0 2 16 7 11 19 6 0 19 2 19 21 21 20 16 10 10 21 19 7 11 19 16 18 25 18 13 5 0 6 20 17 18 6 16 17 6 21 21 7 11 19 16 18 0 11 13 5 0 7 16 21 7 11 19 6 5 19 0 10 19 14 24 0 10 6 25 1 13 20 7 11 19 16 18 1 20 19 19 0
'''

just_numbers = np.asarray(message.replace(' |', '').split(), dtype=int) # removes the pipes (|) and splits the numbers into a list and convert to an numpy array

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,6))

N, bins, patches = ax.hist(just_numbers, bins=np.arange(0, 28), color='r', edgecolor='k', align='left', label='Frequency of Number')
ax.set_xticks(np.arange(0, 28))
ax.legend()
plt.savefig('frequency_histogram.png')
plt.show()