google/paranoid_crypto has a number of Randomness Tests in Python IIR >From grep '^#' https://github.com/google/paranoid_crypto/blob/main/docs/randomness_tests.md :
```md # Randomness tests ## Goal of the tests ## Non-goals ## Usage ## Tests ### NIST SP 800-22 #### Frequency (Monobits) Test #### Frequency Test within a Block #### Runs Test #### Test for the Longest Run of Ones in a Block #### Binary Matrix Rank Test #### Discrete Fourier Transform (Spectral) Test #### Non-Overlapping Template Matching Test #### Overlapping Template Matching Test. #### Maurer’s “Universal Statistical” Test #### Linear Complexity Test #### Serial Test. #### Approximate Entropy Test #### Cumulative Sums (Cusum) Test. #### Random Excursions Test. #### Random Excursions Variant Test ### Additional tests #### FindBias #### LargeBinaryMatrixRank #### LinearComplexityScatter ## Interface ### Repeating tests ## Testing ### Pseudorandom number generators for testing #### urandom #### mt19937 #### gmp_n #### mwc_n #### java #### lcgnist #### xorshift128+ #### xorshift* #### xorwow #### pcg64, philox, sfc64 #### jsf32, jsf64 ## Design decisions ``` $ grep '^\s*class' https://github.com/google/paranoid_crypto/blob/main/paranoid_crypto/lib/randomness_tests/rng.py : ```python class Rng: class Urandom(Rng): class Shake128(Rng): class Mt19937(Rng): class GmpRand(Rng): class XorShift128plus(Rng): class XorShiftStar(Rng): class Xorwow(Rng): class JavaRandom(Rng): class LcgNist(Rng): class Mwc(Rng): class NumpyRng(Rng): class Lehmer(Rng): class Pcg64(NumpyRng): class Philox(NumpyRng): class Sfc64(NumpyRng): class SubsetSum(Rng): ``` From https://github.com/google/paranoid_crypto/blob/16e5f47fcc11f51d3fb58b50adddd075f4373bbc/paranoid_crypto/lib/randomness_tests/random_test_suite.py#L42-L80 : ```python NIST_TESTS = [ (nist_suite.Frequency, []), (nist_suite.BlockFrequency, []), (nist_suite.Runs, []), (nist_suite.LongestRuns, []), (nist_suite.BinaryMatrixRank, []), (nist_suite.Spectral, []), (nist_suite.NonOverlappingTemplateMatching, []), (nist_suite.OverlappingTemplateMatching, []), (nist_suite.Universal, []), (nist_suite.LinearComplexity, [512]), (nist_suite.LinearComplexity, [1024]), (nist_suite.LinearComplexity, [2048]), (nist_suite.LinearComplexity, [4096]), (nist_suite.Serial, []), (nist_suite.ApproximateEntropy, []), (nist_suite.RandomWalk, []), ] EXTENDED_NIST_TESTS = [ (extended_nist_suite.LargeBinaryMatrixRank, []), # Computing the linear complexity has quadratic complexity. # A consequence of this is that LinearComplexityScatter only # uses a fraction of the input. A parameter [n, m] means # that n m-bit sequences are tested, where the i-th sequence # consists of the bits i, i + n, ..., i + (m-1) * m. (extended_nist_suite.LinearComplexityScatter, [32, 100000]), (extended_nist_suite.LinearComplexityScatter, [64, 50000]), (extended_nist_suite.LinearComplexityScatter, [128, 40000]), ] LATTICE_TESTS = [ (lattice_suite.FindBias, [256]), (lattice_suite.FindBias, [384]), (lattice_suite.FindBias, [512]), (lattice_suite.FindBias, [1024]), ] TESTS = NIST_TESTS + EXTENDED_NIST_TESTS + LATTICE_TESTS ``` - [ ] ENH: paranoid_crypto: add a __main__ so that python -m paranoid_crypto.randomness_tests calls eg: - [x] https://github.com/google/paranoid_crypto/blob/main/examples/randomness.py - [ ] REF: examples/randomness.py -> lib/randomness_tests/main.py - [ ] ENH: paranoid_crypto.randomness_tests: add a __main__ so that `python -m paranoid_crypto.randomness_tests -h` works - [ ] ENH: setup.py: console_scripts entrypoint for examples/randomness_tests/main.py - [ ] DOC: https://en.wikipedia.org/wiki/Randomness_test#Notable_software_implementations: link to google/paranoid_crypto On Tue, Nov 15, 2022 at 7:25 AM Chris Angelico <ros...@gmail.com> wrote: > On Tue, 15 Nov 2022 at 22:41, Chris Angelico <ros...@gmail.com> wrote: > > > > (I'm assuming that you sent this personally by mistake, and am > > redirecting back to the list. My apologies if you specifically didn't > > want this to be public.) > > > > On Tue, 15 Nov 2022 at 22:34, James Johnson <jj126...@gmail.com> wrote: > > > > > > It’s been a couple of years ago, but as I recall the duplicates seemed > to be of two or three responses, not randomly distributed. > > > > > > I looked at my code, and I DID salt the hash at every update. > > > > > > At this point, my curiosity is engaged to know if this s/w solution is > as good as others. I don’t have the training to test how often 9 follows 5, > for example, but I am competitive enough to ask how it holds up against > MTprng. I think it’s possibly very good, for s/w, and I’m emboldened to ask > you to modify the code (it requires you data enter the numbers back to the > machine, allowing time to pass;) to accumulate the results for 2 or 3 > million, and see how it holds up. I don’t think the numbers track the bell > curve on distribution . I speculate it’s more square. I suppose this is > desirable in a PRNG? > > > > > > > I'll get you to do the first step of the modification. Turn your code > > into a module that has a randbelow() function which will return a > > random integer from 0 up to the provided argument. (This is equivalent > > to the standard library's random.randrange() function when given just > > one argument.) If you like, provide several of them, as randbelow1, > > randbelow2, etc. > > > > Post that code, and then I'll post a test harness that can do some > > analysis for you. > > Here's a simple test harness. There are other tests you could use, but > this one is pretty straight-forward. > > https://github.com/Rosuav/shed/blob/master/howrandom.py > > For each test, it counts up how many times each possible sequence > shows up, then displays the most and least common, rating them > according to how close they came to a theoretical perfect > distribution. A good random number generator should produce results > that are close to 100% for all these tests, but the definition of > "close" depends on the pool size used (larger means closer, but also > means more CPU time) and the level of analysis done. In my testing, > all of the coin-flip data showed values +/- 1%, and the others never > got beyond 10%. > > This is the same kind of analysis that was used in the page that I > linked to earlier, so you can play with it interactively there if > you're curious. It also has better explanations than I would give. > > Again, there are plenty of other types of tests you could use, but > this is a pretty easy one. True randomness should show no biases in > any of these results, though there will always be some variance. > > ChrisA > _______________________________________________ > Python-ideas mailing list -- python-ideas@python.org > To unsubscribe send an email to python-ideas-le...@python.org > https://mail.python.org/mailman3/lists/python-ideas.python.org/ > Message archived at > https://mail.python.org/archives/list/python-ideas@python.org/message/5OWDW5XUE5JYAW3QKMNZYKXH3NNBPNNW/ > Code of Conduct: http://python.org/psf/codeofconduct/ >
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