# Open Data File and read the binary data of e
data_path = os.path.join(os.getcwd(), 'data', 'data.sqrt3')
handle = open(data_path)
data_list = []
for line in handle:
data_list.append(line.strip().rstrip())
binary_data = ''.join(data_list)
print('The statistical test of the Binary Expansion of SQRT(3)')
print('2.1. Frequency Test:\t\t\t\t\t\t\t\t\t', FrequencyTest.monobit_test(binary_data[:1000000]))
print('2.2. Block Frequency Test:\t\t\t\t\t\t\t\t', FrequencyTest.block_frequency(binary_data[:1000000]))
print('2.3. Run Test:\t\t\t\t\t\t\t\t\t\t\t', RunTest.run_test(binary_data[:1000000]))
print('2.4. Run Test (Longest Run of Ones): \t\t\t\t\t', RunTest.longest_one_block_test(binary_data[:1000000]))
print('2.5. Binary Matrix Rank Test:\t\t\t\t\t\t\t', Matrix.binary_matrix_rank_text(binary_data[:1000000]))
print('2.6. Discrete Fourier Transform (Spectral) Test: \t\t', SpectralTest.sepctral_test(binary_data[:1000000]))
print('2.7. Non-overlapping Template Matching Test:\t\t\t', TemplateMatching.non_overlapping_test(binary_data[:1000000], '000000001'))
print('2.8. Overlappong Template Matching Test: \t\t\t\t', TemplateMatching.overlapping_patterns(binary_data[:1000000]))
print('2.9. Universal Statistical Test:\t\t\t\t\t\t', Universal.statistical_test(binary_data[:1000000]))
print('2.10. Linear Complexity Test:\t\t\t\t\t\t\t', ComplexityTest.linear_complexity_test(binary_data[:1000000]))
print('2.11. Serial Test:\t\t\t\t\t\t\t\t\t\t', Serial.serial_test(binary_data[:1000000]))
print('2.12. Approximate Entropy Test:\t\t\t\t\t\t\t', ApproximateEntropy.approximate_entropy_test(binary_data[:1000000]))
print('2.13. Cumulative Sums (Forward):\t\t\t\t\t\t', CumulativeSums.cumulative_sums_test(binary_data[:1000000], 0))
print('2.13. Cumulative Sums (Backward):\t\t\t\t\t\t', CumulativeSums.cumulative_sums_test(binary_data[:1000000], 1))
result = RandomExcursions.random_excursions_test(binary_data[:1000000])
print('2.14. Random Excursion Test:')
print('\t\t STATE \t\t\t xObs \t\t\t\t P-Value \t\t\t Conclusion')
for item in result:
print('\t\t', repr(item[0]).rjust(4), '\t\t', item[1], '\t\t', repr(item[2]).ljust(14), '\t\t',
for line in handle:
data_list.append(line.strip().rstrip())
binary_data = ''.join(data_list)
print('The statistical test of the Binary Expansion of e')
print('2.01. Frequency Test:\t\t\t\t\t\t\t\t',
FrequencyTest.monobit_test(binary_data[:1000000]))
print('2.02. Block Frequency Test:\t\t\t\t\t\t\t',
FrequencyTest.block_frequency(binary_data[:1000000]))
print('2.03. Run Test:\t\t\t\t\t\t\t\t\t\t',
RunTest.run_test(binary_data[:1000000]))
print('2.04. Run Test (Longest Run of Ones): \t\t\t\t',
RunTest.longest_one_block_test(binary_data[:1000000]))
print('2.05. Binary Matrix Rank Test:\t\t\t\t\t\t',
Matrix.binary_matrix_rank_text(binary_data[:1000000]))
print('2.06. Discrete Fourier Transform (Spectral) Test:\t',
SpectralTest.sepctral_test(binary_data[:1000000]))
print(
'2.07. Non-overlapping Template Matching Test:\t\t',
TemplateMatching.non_overlapping_test(binary_data[:1000000], '000000001'))
print('2.08. Overlappong Template Matching Test: \t\t\t',
TemplateMatching.overlapping_patterns(binary_data[:1000000]))
print('2.09. Universal Statistical Test:\t\t\t\t\t',
Universal.statistical_test(binary_data[:1000000]))
print('2.10. Linear Complexity Test:\t\t\t\t\t\t',
ComplexityTest.linear_complexity_test(binary_data[:1000000]))
print('2.11. Serial Test:\t\t\t\t\t\t\t\t\t',
Serial.serial_test(binary_data[:1000000]))
print('2.12. Approximate Entropy Test:\t\t\t\t\t\t',
ApproximateEntropy.approximate_entropy_test(binary_data[:1000000]))
def execute(self):
print('Execute')
input = ''
self.__test_results = [(),(),(),(),(),(),(),(),(),(),(),(),(),(),(),()]
if (not len(self.__binary_data.get().strip()) == 0) and (not len(self.__file_name.get()) == 0):
messagebox.showwarning("Warning", 'You can only either input the binary data or read the data from from the file.')
elif not len(self.__binary_data.get().strip()) == 0:
print('User Input')
input = self.__binary_data.get()
elif not len(self.__file_name.get()) == 0:
print('File Input')
print(self.__file_name.get())
handle = open(self.__file_name.get())
data_list = []
for line in handle:
data_list.append(line.strip().rstrip())
input = ''.join(data_list)
else:
messagebox.showwarning("Warning", 'You must enter either input data or file name to read before you can execute the test.')
if len(input) > 1000000:
input = input[:1000000]
checked = False
for item in self.__chb_var:
if item.get() == 1:
checked = True
break
if not checked:
messagebox.showwarning("Warning", "you must select a test to execute")
else:
if self.__chb_var[0].get() == 1:
print(self.__test_type[0], 'selected.')
result = ft.monobit_test(input)
self.__monobit_p_value.set(result[0])
self.__monobit_result.set(self.get_conclusion(result[1]))
self.__test_results[0] = result
if self.__chb_var[1].get() == 1:
print(self.__test_type[1], 'selected.')
result = ft.block_frequency(input)
self.__block_p_value.set(result[0])
self.__block_result.set(self.get_conclusion(result[1]))
self.__test_results[1] = result
if self.__chb_var[2].get() == 1:
print(self.__test_type[2], 'selected.')
result = rt.run_test(input)
self.__run_p_value.set(result[0])
self.__run_result.set(self.get_conclusion(result[1]))
self.__test_results[2] = result
if self.__chb_var[3].get() == 1:
print(self.__test_type[3], 'selected.')
result = rt.longest_one_block_test(input)
self.__long_run_p_value.set(result[0])
self.__long_run_result.set(self.get_conclusion(result[1]))
self.__test_results[3] = result
if self.__chb_var[4].get() == 1:
print(self.__test_type[4], 'selected.')
result = mt.binary_matrix_rank_text(input)
self.__matrix_p_value.set(result[0])
self.__matrix_result.set(self.get_conclusion(result[1]))
self.__test_results[4] = result
if self.__chb_var[5].get() == 1:
print(self.__test_type[5], 'selected.')
result = st.sepctral_test(input)
self.__spectral_p_value.set(result[0])
self.__spectral_result.set(self.get_conclusion(result[1]))
self.__test_results[5] = result
if self.__chb_var[6].get() == 1:
print(self.__test_type[6], 'selected.')
result = tm.non_overlapping_test(input)
self.__non_overlapping_p_value.set(result[0])
self.__non_overlapping_result.set(self.get_conclusion(result[1]))
self.__test_results[6] = result
if self.__chb_var[7].get() == 1:
print(self.__test_type[7], 'selected.')
result = tm.overlapping_patterns(input)
self.__overlapping_p_value.set(result[0])
self.__overlapping_result.set(self.get_conclusion(result[1]))
self.__test_results[7] = result
if self.__chb_var[8].get() == 1:
print(self.__test_type[8], 'selected.')
result = ut.statistical_test(input)
self.__statistical_p_value.set(result[0])
self.__statistical_result.set(self.get_conclusion(result[1]))
self.__test_results[8] = result
if self.__chb_var[9].get() == 1:
print(self.__test_type[9], 'selected.')
result = ct.linear_complexity_test(input)
self.__linear_p_value.set(result[0])
self.__linear_result.set(self.get_conclusion(result[1]))
self.__test_results[9] = result
if self.__chb_var[10].get() == 1:
print(self.__test_type[10], 'selected.')
result = serial.serial_test(input)
self.__serial_p_value_01.set(result[0][0])
self.__serial_p_result_01.set(self.get_conclusion(result[0][1]))
self.__serial_p_value_02.set(result[1][0])
self.__serial_p_result_02.set(self.get_conclusion(result[1][1]))
self.__test_results[10] = result
if self.__chb_var[11].get() == 1:
print(self.__test_type[11], 'selected.')
result = aet.approximate_entropy_test(input)
self.__entropy_p_value.set(result[0])
self.__entropy_result.set(self.get_conclusion(result[1]))
self.__test_results[11] = result
if self.__chb_var[12].get() == 1:
print(self.__test_type[12], 'selected.')
result = cst.cumulative_sums_test(input, 0)
self.__cusum_f_p_value.set(result[0])
self.__cusum_f_result.set(self.get_conclusion(result[1]))
self.__test_results[12] = result
if self.__chb_var[13].get() == 1:
print(self.__test_type[13], 'selected.')
result = cst.cumulative_sums_test(input, 1)
self.__cusum_r_p_value.set(result[0])
self.__cusum_r_result.set(self.get_conclusion(result[1]))
self.__test_results[13] = result
if self.__chb_var[14].get() == 1:
print(self.__test_type[14], 'selected.')
self.__excursion_result = ret.random_excursions_test(input)
for item in self.__excursion_result:
if self.__state_01.get() == item[0]:
self.__xObs_chi_01.set(item[2])
self.__p_value_01.set(item[3])
self.__conclusion_01.set(self.get_conclusion(item[4]))
self.__test_results[14] = self.__excursion_result
if self.__chb_var[15].get() == 1:
print(self.__test_type[15], 'selected.')
__variant_result = ret.variant_test(input)
self.__variant_result = ret.variant_test(input)
for item in self.__variant_result:
print(item)
if self.__state_02.get() == item[0]:
self.__count.set(item[2])
self.__p_value_02.set(item[3])
self.__conclusion_02.set(self.get_conclusion(item[4]))
self.__test_results[15] = self.__variant_result
