This is a Python implementation of NIST's A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications
You need the following software and packages for this application:
- Python 3.6 and above (Tested with Python 3.8 already)
- Numpy and Scipy
pip3 install numpy, scipy
- You can start the program using your IDE feature (like run) to run Main.py or
python3 Main.py
-
Once you saw the interface, you can start using the test suite.
- Input Data - Input Data contains Binary Data, Binary Data File and String Data File
- Binary Data - You can only enter a BINARY STRING here (ex:1100100100001111110110101010001000100001011010001100001000110100110001001100011001100010100010111000)
- Binary Data File - This will open a file dialog where you can select a file to be read by program. The file you selected should contain only one set of data in BINARY FORM. (For example, please refer to data/data.e)
- String Data File - This will open a file dialog where you can select a file to be read by program. The file you selected can contain multiple set of data in STRING FORM. (For example, please refer to data/test_data_01.txt)
-
You can select the type of test you want to perform by clicking the corresponding checkbox or press "Select All Test" to select everything
-
You can cancel the selection by clicking the corresponding checkbox or press "De-Select All Test" to cancel everything
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Once you have your data ready and selected the test you want to perform, then you can press "Execute Test" button to execute the test
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The result will be displayed after the test done.
- There are multiple result for Random Excursion Test. Initially the program will displayed state '+1'. You can chechk the other resuld by changing the state (using drop down) and press "Update" button
- There are multiple result for Random Excursion Variant Test. Initially the program will displayed state '-1.0'. You can chechk the other resuld by changing the state (using drop down) and press "Update" button
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You can save the result to a text file by pressing "Save as Text File" button. This will display a file dialog where you can enter the file name for your result. You can check the text file after the result is saved.
-
"Reset" button will clear all input and variables. It is strongly suggested you use this feature if you want to execute test for another set of data
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"Exit" button will close this program
- You can also used this application by importing necessary library to your python code
import os
from FrequencyTest import FrequencyTest
from RunTest import RunTest
from Matrix import Matrix
from Spectral import SpectralTest
from TemplateMatching import TemplateMatching
from Universal import Universal
from Complexity import ComplexityTest
from Serial import Serial
from ApproximateEntropy import ApproximateEntropy
from CumulativeSum import CumulativeSums
from RandomExcursions import RandomExcursions
# Open Data File and read the binary data of e
data_path = os.path.join(os.getcwd(), 'data', 'data.e')
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 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.spectral_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]))
print('2.13. Cumulative Sums (Forward):\t\t\t\t\t', CumulativeSums.cumulative_sums_test(binary_data[:1000000], 0))
print('2.13. Cumulative Sums (Backward):\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[2], '\t\t', repr(item[3]).ljust(14), '\t\t',
(item[4] >= 0.01))
result = RandomExcursions.variant_test(binary_data[:1000000])
print('2.15. Random Excursion Variant Test:\t\t\t\t\t\t')
print('\t\t STATE \t\t COUNTS \t\t\t P-Value \t\t Conclusion')
for item in result:
print('\t\t', repr(item[0]).rjust(4), '\t\t', item[2], '\t\t', repr(item[3]).ljust(14), '\t\t',
(item[4] >= 0.01))
- Output of the code above:
The statistical test of the Binary Expansion of e
2.01. Frequency Test: (0.9537486285283232, True)
2.02. Block Frequency Test: (0.21107154370164066, True)
2.03. Run Test: (0.5619168850302545, True)
2.04. Run Test (Longest Run of Ones): (0.7189453298987654, True)
2.05. Binary Matrix Rank Test: (0.3061558375306767, True)
2.06. Discrete Fourier Transform (Spectral) Test: (0.8471867050687718, True)
Non-Overlapping Template Test DEBUG BEGIN:
Length of input: 1000000
Value of Mean (µ): 244.125
Value of Variance(σ): 236.03439331054688
Value of W: [239. 235. 254. 278. 207. 229. 225. 242.]
Value of xObs: 14.116057212121211
P-Value: 0.07879013267666338
DEBUG END.
2.07. Non-overlapping Template Matching Test: (0.07879013267666338, True)
2.08. Overlappong Template Matching Test: (0.11043368541387631, True)
2.09. Universal Statistical Test: (0.282567947825744, True)
2.10. Linear Complexity Test: (0.8263347704038304, True)
2.11. Serial Test: ((0.766181646833394, True), (0.46292132409575854, True))
2.12. Approximate Entropy Test: (0.7000733881151612, True)
2.13. Cumulative Sums (Forward): (0.6698864641681423, True)
2.13. Cumulative Sums (Backward): (0.7242653099698069, True)
2.14. Random Excursion Test:
STATE xObs P-Value Conclusion
'-4' 3.8356982129929085 0.5733056949947805 True
'-3' 7.318707114093956 0.19799602021827734 True
'-2' 7.861927251636425 0.16401104937943733 True
'-1' 15.69261744966443 0.007778723096466819 False
'+1' 2.4308724832214765 0.7868679051783156 True
'+2' 4.7989062888391745 0.44091173664620265 True
'+3' 2.3570405369127525 0.7978539716877826 True
'+4' 2.4887672641992014 0.7781857852321322 True
2.15. Random Excursion Variant Test:
STATE COUNTS P-Value Conclusion
'-9.0' 1450 0.8589457398254003 True
'-8.0' 1435 0.7947549562546549 True
'-7.0' 1380 0.5762486184682754 True
'-6.0' 1366 0.4934169340861271 True
'-5.0' 1412 0.6338726691411485 True
'-4.0' 1475 0.9172831477915963 True
'-3.0' 1480 0.9347077918349618 True
'-2.0' 1468 0.8160120366175745 True
'-1.0' 1502 0.8260090128330382 True
'+1.0' 1409 0.13786060890864768 True
'+2.0' 1369 0.20064191385523023 True
'+3.0' 1396 0.4412536221564536 True
'+4.0' 1479 0.939290606067626 True
'+5.0' 1599 0.5056826821687638 True
'+6.0' 1628 0.4459347106499899 True
'+7.0' 1619 0.5122068856164792 True
'+8.0' 1620 0.5386346977772863 True
'+9.0' 1610 0.5939303958223099 True
Process finished with exit code 0
- For more example, you can check test_pi.py, test_sqrt2.py, test_sqrt3.py
- Changed screen layout to fixedthe issue with the resolution lower than 1920 x 1080
- Fixed bug
- Initial Release
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