I want to be good natured about it, not combative. I tried whatever random function I found in documentation online, and used it. Without analyzing the world, I was simply dissatisfied with the results.

Here’s what I settled on. I actually “put my thumb on the scales,” to rule out repetitions for seven questions, so randomness didn’t ultimately answer my question.

I’m not sure that the function I “got” was the Mersenne Twister. I do know Mersenne was a truly great mathematician.

I acknowledge that updating with time.asctime() EVERY time I update the hash would be random; I’m not qualified to know if it would be “more” random. After you ask about the bell curve v square distributions, I am quite out of my depth.

Here’s my “wizard” for anyone who wants to sell him cryptocurrency or overcome his objections to the environmentalist agenda, or query him about foreign policy. I hope you find it better than average.

https://drive.google.com/file/d/1EqQsMfBHDrNpOBQrI7CJxrpbowvKPD2G/

Regards,

James J

On Tue, Nov 15, 2022 at 4:31 AM Wes Turner <wes.turner@gmail.com> wrote:
While FWIU it is advisable to keep seeding an RNG after startup - that's what e.g. rngd does - the cryptography.io docs do advise to just use `os.urandom()` (which is not the same as random.SystemRandom()?).

There probably should be better default random in CPython; though I'm personally not at all qualified to assess, TIL about NIST 800-22 and FIPS 140-2 for evaluating sources of entropy.

Differential entropy > Differential entropies for various distributions


***

(TIL that quantum information is never destroyed; so which physical processes are actually nonreversible like hashing and RNG are supposed to be is up for consideration as classical information is a subset of quantum information. Do black holes shift gamma radiation may actually be relevant! Perhaps a permanent magnet under a (double-jointed?) bar and ball pendulum on a magnetic bearing would produce enough Brownian motion to get enough uniform random to call it sufficiently entropic for purposes of CSPRNG? Nondeterministic NDE fluid calculations diffract into many possible outcomes, but a brute force combinatorial search of all the possible return values is still deterministically ordered. And the quantum computing folks are working on increasing coherence / reducing error propagation; like ECC.)

- [ ] DOC: The docs could advise regarding which decent enough open source hw RNG would be supported by os.urandom or random.SystemRandom if rngd is not configured to keep nondeterministically seeding with entropy that should presumably be from a Uniform random entropic process

There should be better software random in python.


On Tue, Nov 15, 2022, 1:19 AM James Johnson <jj126979@gmail.com> wrote:
Thank you for replying with such specific assistance. I am made acutely aware that I am only a Python enthusiast, and not an academic.

Hashes are deterministic, not random, but byte by byte, they can be very random. Please accept the attached script as a "hack," that might be novel, or a curiosity. My first script was defective several ways. In this, I seed the hash by hashing the uu-8 ENCODED string value of the time - it is not random, but it is rarely the same twice. I used 0-9 because it is decimal, and 0-59, because hours and minutes come in 60's. 

I found that taking time.time_nc() gave me far more odds than evens. 

I do not know how the scheme I devised of adding hex digits together would scale to larger ranges - it is done by adding hex digits that vary from 0 - 15 repeatedly, but, since the number of hex digits varies with the range, it could actually work for larger ranges. 

The random function in Python is not really adequate for a magic eight ball program, so as a consumer I am dissatisfied without examining it at a quantum level, for repeated reference thousands of times each.

Thank you for your kind attention, and I hope the (much improved) hack can be helpful to other enthusiasts.

James J
(A.A. Faulkner University) 

On Mon, Nov 14, 2022 at 11:23 AM Wes Turner <wes.turner@gmail.com> wrote:
QRNG "Quantum Random Number Generation" -> Hardware random number generator > Physical phenomena with random properties > Quantum random properties
https://en.wikipedia.org/wiki/Hardware_random_number_generator#Quantum_random_properties


FWIW, SciPy and SymPy have various non-CSPRNG random distributions:



"RFC 8937: Randomness Improvements for Security Protocols" (2020)

***

FWIW,
UUIDs and W3C DIDs require entropy e.g. from a CSPRNG or better: 

> Universally Unique Identifier (UUID)
> A type of globally unique identifier defined by [RFC4122]. UUIDs are similar to DIDs in that they do not require a centralized registration authority. UUIDs differ from DIDs in that they are not resolvable or cryptographically-verifiable

On Mon, Nov 14, 2022, 11:54 AM Wes Turner <wes.turner@gmail.com> wrote:
https://docs.python.org/3/library/random.html :

> Warning: The pseudo-random generators of this module should not be used for security purposes. For security or cryptographic uses, see the secrets module


PEP 506 – Adding A Secrets Module To The Standard Library

PEP 12: new PEP template: 

Pseudorandom number generator > Cryptographic PRNGs

Random number generator attack > Defenses


/? CSPRNG

From "THE LINUX CSPRNG IS NOW GOOD!"

> [ get random() is from OpenBSD and LibreSSL ]

> Performance and ChaCha20
> Some people would say they needed a userspace CSPRNG for PERFORMANCE. I never really believed most of them, but to be fair Linux was using a kinda slow SHA-1 extractor back then. However, since Linux 4.8 (2016) the default getrandom(2) source is a fast ChaCha20-based CSPRNG, with separate pools per NUMA node to avoid contention. (Just ignore the rude comments in the code about applications not running their own CSPRNG, this is still Linux after all.)
> 
> There's even a neat trick XOR'ing some of the CSPRGN output back into the ChaCha20 state to prevent an attacker from recovering any past output from before the time of compromise.
> 
> Some of these improvements came along thanks to the Wireguard work by Jason A. Donenfeld

"Problems emerge for a unified /dev/*random" (2022)


"""
How does the kernel initialize its CSPRNG?
The kernel has an “entropy pool,” a place where unpredictable input observed by the kernel is mixed and stored. That pool serves as a seed to the internal CSPRNG, and until some threshold of estimated entropy is reached initially, it is considered uninitialized.

Let’s now see how the kernel initializes its entropy pool.

1. After the kernel takes control on power-on, it starts filling its entropy pool by mixing interrupt timing and other unpredictable input.

2. The kernel gives control to systemd.

3. Next, systemd starts and initializes itself.

4. Systemd, optionally, loads kernel modules which will improve the kernel's entropy gathering process on a virtual machine (e.g., virtio-rng).

5. Systemd loads the rngd.service which will gather additional input entropy obtained via a random generator exposed by hardware (e.g., the x86 RDRAND instruction or similar) and jitter entropy1; this entropy is fed back into the kernel to initialize its entropy pool, typically in a matter of milliseconds.

After the last step, the kernel has its entropy pool initialized, and any systemd services started can take advantage of the kernel’s random generator.

Note that the virtio-rng kernel module loading in step (3), is an optional step which improves entropy gathering in a virtual machine by using the host's random generator to initialize the guest systems in KVM. The rngd.service loading at the final step (4) is what ensures that the kernel entropy pools are initialized on every scenario, and furthermore it continues mixing additional data in the kernel pool during system runtime.
"""


```c
/* fips.c -- Performs FIPS 140-1/140-2 RNG tests
```

/? FIPS 140-1/140-2 RNG tests

/? CMVP "cprng"



  > We test RNGs using the standard test suites: PractRand, TestU01 (BigCrush), DIEHARD(ER), NIST SP 800-22.

Randomness tests: 

/? nist 800-22 site:github.com





```rst
Random number generation
========================

When generating random data for use in cryptographic operations, such as an
initialization vector for encryption in
:class:`~cryptography.hazmat.primitives.ciphers.modes.CBC` mode, you do not
want to use the standard :mod:`random` module APIs. This is because they do not
provide a cryptographically secure random number generator, which can result in
major security issues depending on the algorithms in use.

Therefore, it is our recommendation to `always use your operating system's
provided random number generator`_, which is available as :func:`os.urandom`.
For example, if you need 16 bytes of random data for an initialization vector,
you can obtain them with:

.. doctest::

    >>> import os
    >>> iv = os.urandom(16)

This will use ``/dev/urandom`` on UNIX platforms, and ``CryptGenRandom`` on
Windows.

If you need your random number as an integer (for example, for
:meth:`~cryptography.x509.CertificateBuilder.serial_number`), you can use
``int.from_bytes`` to convert the result of ``os.urandom``:

.. code-block:: pycon

    >>> serial = int.from_bytes(os.urandom(20), byteorder="big")

In addition, the `Python standard library`_ includes the ``secrets`` module,
which can be used for generating cryptographically secure random numbers, with
specific helpers for text-based formats.

.. _`always use your operating system's provided random number generator`: https://sockpuppet.org/blog/2014/02/25/safely-generate-random-numbers/
.. _`Python standard library`: https://docs.python.org/3/library/secrets.html

```


On Mon, Nov 14, 2022, 10:57 AM Barry <barry@barrys-emacs.org> wrote:


> On 14 Nov 2022, at 14:31, James Johnson <jj126979@gmail.com> wrote:
>
> 
> I wrote the attached python (3) code to improve on existing prng functions. I used the time module for one method, which resulted in disproportionate odd values, but agreeable means.
>
> I used the hashlib module for the second. It is evident that the code is amateur, but the program might result in better PRN generation.
>
> The "app" lends itself to checking, using statistical tools (see comments.)

Have you used any cryptographic tools to prove the quality of your PRNG?
What results did you get?
How does your PRNG compare to what python already has?

Without that this analysis this will be unlikely to be considered as a candidate for python stdlib.

Barry

>
> I remain a fan,
>
> James Johnson
> <testrandom.py>
> _______________________________________________
> Python-ideas mailing list -- python-ideas@python.org
> To unsubscribe send an email to python-ideas-leave@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/VENAT2YTVYVHTBSEVHHMIURCU6MG2CEO/
> Code of Conduct: http://python.org/psf/codeofconduct/

_______________________________________________
Python-ideas mailing list -- python-ideas@python.org
To unsubscribe send an email to python-ideas-leave@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/HWQV4AKQAUM7CY4NWS2IRIVLRAYMKR3V/
Code of Conduct: http://python.org/psf/codeofconduct/
--
Truth causes consequences; consequences bring pain; pain exorcises guilt!