103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
from math import fabs as fabs
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from math import sqrt as sqrt
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from scipy.special import erfc as erfc
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import numpy as np
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from scipy import stats
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class StartUPTest:
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@staticmethod
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def monobit_test(binary_data: bytes):
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length_of_bit_string = len(binary_data) * 8
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# Variable for S(n)
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count = 0
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# Iterate each byte in the string and compute for S(n)
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for byte in binary_data:
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# Iterate each bit in the byte
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for i in range(8):
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# Extract the i-th bit from the byte
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bit = (byte >> i) & 1
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if bit == 0:
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# If bit is 0, then -1 from the S(n)
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count -= 1
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else:
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# If bit is 1, then +1 to the S(n)
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count += 1
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# Compute the test statistic
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sObs = count / sqrt(length_of_bit_string)
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# Compute p-Value
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p_value = erfc(fabs(sObs) / sqrt(2))
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# return a p_value and randomness result
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return p_value, (p_value >= 0.01)
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@staticmethod
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def autocorrelation_test(binary_data: str):
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shift_feld = [0] * 5000
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max_korr_feld = [0] * 5000
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# Fill BitFeldB with data
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for tau in range(1, 5001):
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z_tau = 0
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for i in range(5000):
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z_tau += binary_data[i] ^ binary_data[i + tau]
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shift_feld[tau - 1] = z_tau
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# Debugging
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# for i in range(5000):
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# print(shift_feld[i], end=' ')
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# Find the index of the maximum deviation from 2500
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max_deviation = 0
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for tau in range(5000):
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deviation = abs(shift_feld[tau] - 2500)
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if deviation > max_deviation:
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max_deviation = deviation
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# Find all indices with the maximum deviation
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j = 0
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for tau in range(5000):
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deviation = abs(shift_feld[tau] - 2500)
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if deviation == max_deviation:
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max_korr_feld[j] = tau
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j += 1
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print("Maximale z_tau-Abweichung von 2500:", max_deviation)
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print("Aufgetreten für Shifts:")
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for k in range(j):
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print("Shift:", max_korr_feld[k] + 1)
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tau = max_korr_feld[0]
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z_tau = 0
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for i in range(10000, 15000):
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z_tau += StartUPTest.char_to_int(i, binary_data) ^ StartUPTest.char_to_int(i + tau + 1, binary_data)
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tau += 1
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ok = 2326 < z_tau < 2674
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return z_tau, ok
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@staticmethod
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def char_to_int(index, binary_data: str):
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if binary_data[index] == 49:
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value = 1
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else:
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value = 0
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return value
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@staticmethod
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def chi_square(byte_data: bytes):
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expected_probabilities = np.full(256, 1/256) # Assuming 256 possible byte values
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total_observations = len(byte_data)
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observed_data, _ = np.histogram(list(byte_data), bins=np.arange(257))
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expected_frequencies = expected_probabilities * total_observations
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chi2_statistic, p_value = stats.chisquare(observed_data, f_exp=expected_frequencies)
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return p_value, (p_value >= 0.01), chi2_statistic
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