Just a little update of a previous script I posted here, it’s still very slow to run but it doesn’t rely on a hash function to create the final set of random data…

import signal
import sys

c = 0; f = 0
r = ""; t = 0; b = 0

def handler(signum, frame):
	global c, f
	global r, t, b
	f = 1
	s = str(c); m = len(s)
	if ((m % 2) == 1):
		s = s[1:]; m = len(s)
	#print(c)
	p = (m / 2)
	for x in range(0, p):
		d = ((int(s[x]) + int(s[x + p])) % 10)
		if (d > 7):
			if (int(s[x + p]) > 7):
				continue
			d = int(s[x + p])
		u = min(8 - b, 3)
		w = (3 - u)
		#print("\t(%s + %s) = (%d >> %d) <> w) <= 8):
			r += chr(t); t = 0; b = 0
		if (u < 3):
			u = (3 - u)
			w = ((2 ** u) - 1)
			#print("\t\t(%s + %s) = (%d & %d) << %d \t %d , %d" % (s[x], s[x + p], d, w, 8 - b - u, b, u))
			t += (((d & 7) & w) << (8 - b - u)); b += u

def srnd(size):
	global c, f
	global r, t, b
	r = ""; t = 0; b = 0
	signal.signal(signal.SIGALRM, handler)
	while (len(r) < size):
		signal.alarm(1)
		c = 0; f = 0
		while (f == 0):
			c += 1
		signal.alarm(0)
	o = r[0:size]
	c = 0; f = 0
	r = ""; t = 0; b = 0
	return o

def stoh(inpt):
	h = ""
	for d in inpt:
		a = hex(ord(d))
		a = a[2:]
		if (len(a) < 2):
			a = ("0" + a)
		h += a
	return h

e = stoh(srnd(32))
print(e)

$ python sigrnd.py > test.rnd
e178c23644435d209a98623f67b39bd94124c4a03bb3f8e2d871954330ffda40
2b566db93a55190affef100972cf54f42440f225fc1f0f2ebb14062485c1bd87
dd9de46a255410fe9925ac634c1c5d90b48511486700f3c9f4666d97610d7f96
1fb232f334d4a15404d97e72fa39bc0bd70831d558ee46420944ee1475dc33cf
9f8ac9c0668f5f0bf816560413eb8d465fdf86e07a1c3cd039f5c35ca65688a1
111ee2fcb3729e6886534b261a4b3d7afb0e62dc0e6c18153fc0cef15d161ff4
7ce0411de8f635d08b1b2a5f7080893f94279637c614aca13d4136bff17f9ba6
a1e7f3592a4d5c07c56c84d4f1448928b910d1051a8e4f7113b536e941ddea72
3433453f211492348e4154bd3427d079ac8f5e4d1522cdff323fe30aaa6bcda1
00f4ff6b5337690def4ec26e832a6a032cbb024ac7d9d224712422b2e4dc41fc
361cb649e81cbe5c9ad970db125cd2309f8e196140e28d4ec5f16ea1bd3551c8
7283d4e8c0a42c400fa5f5db3573df1f446a09779ddc30ffa2b84153a3ec4c7b
86ec7f80766be8d27b24c7db7b609288c7c0ac5e6345dcf6207555c605f9222d
6595417501a0df5c0339705e3c471d5bec7a880c81e1295556dd2db8c8104323
bf166eb924ad195a028dee22a36e87b487e5c83f2d9b1fa2a6ada5b35c0192c0
b5bdc471b5d7b2f0f6b08db4274e3e1ec1e2eb5b20e450bfa25b79e8e8059d19
6e0d5ba936fc630d257c25fd27c3b99c3616ce17e470066a107cf9f56d74ced7
aaa12055cc5e6629c2ef17fa398b5aaffe24ebb01cd81edde2c5b79b75b1a2ae
06c83d2d79c64d5ee206c48efe15841224a4b4279efbbca2f47dc1d8a40bc5bd
daf5cc2d164e3bae9fcc4a8e698169556e96d19e47eaae842f5fa77d0342f00d
57095f3ed4445c0331bff1b86f883fc589437dcc8b45a0900bee2cf44609da99
95c522a66f0984cb9382ff737f340a8cec5434fe573d3fb6c48f13133eea6f9f
3ef172617d41b1ca588ba7ff28e242dd6488bf5a9ba41b2a8834b9a3cf35f752
1135a16689629f2560a8c4390a908ec345d5bcd82f78210597c99ee34eb15e94
dc9d4be19a93b602cf3bdeb0baf913c5273d020f75162e30636dfb0fcb80d121
a214d021f98d2f028e780747deb7bc9a368ec36e9d226f029f89d85325d6cf02
3dfe96a7bef6c8dcff22b163d8407fee1f8d52ff39144433d72da6918ee6c853
4097d09af9abfeaecd46bf8eb49933c0e8a903719cb21a0688e4d5e544fd1e11
e1616e89980c450022236a9b9bf05dd2e03f15b5e0af17674ffca56e6138c9d3
78193776e694cac8a8c3a219b06cb70918ef29151e54828bbc4a05ad1b8598ff
1279d3a8090e0a037feebe271445e27617c0cc41165bfd8a1cfac7830d7149dc
ea3321da8408630db9dbaf945eca0e05c8dde069cf6669ed112593e7dd66573b
e0c384c87016c273a81d7001aa8977eb0d6d2447fcffae13e43c5f46a2eae4ee
c1584801c2183baf511058a8cc329f8da81455824b4a8a3f4e5c9ff891ac3466
84164902fe12ff530e7c4345fc793a695986f1edc8432a94f993c687edc31a9e
c3a195f8800150eb8c0098422fd4c31d6fdbee7bca4715ea0cde8b1b83579d47
5dc80e604deb964dfab71c8c206ac4dcf5922fb53a5824b3406c9399bbd7fcba
25bbd158ed7623fb42656d29e9b56e9a2d1a763b7a00fcca1b02d262479f5a48
20bcc8976207c0a06aba765f0aa768db58c6c1ac36f9734f57a12969f55d7297
7ab12a2982aa2974092df7ef0813c8f1827d2fabf781b4de3f8c7d42720783c1
68dc9f744bd8794919c7abcbb0a7b3737b9f0caf974e0d6a2688241aa0559cd4
731e9f253a0c8827a10639066169ac33dfc9c9a5ddb85e8def8ec151ab352469
096ac68f4d62eaac2476977a498a362b5b45e18e5dbbb0f78c5649ec94817e76
0245dd2c4ef49382546aa06c3929ba4e70f210af3bedfc4c5eebc46bd9391683
bf7110c1e15f178ab1171c5f938ab1e2d0a1b9b29677b9f362b3132ef56dabbf
1643830ba06963161eaa04b9f8e088a4687d09e438e41d2a5466d6f78b274c9e
c9def9205f496b010ce2892135fef94fe2deb7f62640dff9f30994fd5f46e4de
938d483ed34deb033207ed1ec4cadbf8c3d9e8d86f93717575b4cd17eeb725c1
f96f1a0e38c9f7a2fe6a623fb3b62a151dc9b0f51b34861b7730b4058b1ded9b
6c7e48582ded238178c1036be88b7583803ece4127781b3cded024925e4c9e6b
6810854b6219e191fff2f7623c0827045fa4f097e043277d45260aeeb512c128
3512bb2fa47c1b3808f9b1109bd5cdc1506d9f472534c89f0433ab2d286f5cdd
ab8a7ad4fea0b0391fd162dfb975dfc190dfb200a6e2f9b70959c3dc4147416e
1824ac1a6806ac85ec8618426173bf1f77004d51446dc0cae18df7bd4d9da3df
fff792348efa02f21f40af926f1cda6bc3b486623aff46d9c13a6e14309fe2dd
28005590a0639de926f891c5e805d252375dc4602b0579c1cf7e64d06d83ce7c
951757ab8747578b690e490a306e94265912e43b2302c5d7476f98c29b667e7a
a91a3aa632cb30320b6097ae58051b58be361ab1682b2ecde9d74592592d2f1f
9c528fd8ec7c03467239cca5394038c61986ffe9bb60afd4d315781d5b2a09ec
759b5d68c9a6d0b0204f38b0d65de468cd5578f5d864ceb7f41ef10fa1b62802

Edit: A Linux entropy command line tester: http://www.fourmilab.ch/random/

As you can see below, we got up to 7.90 bits of entropy per byte while generating less than 2048 bytes of random data. We also almost hit the arithmetic mean value of 127.5 by scoring a 125. This process, however, took a really long time to run, but, all that was needed was a CPU, a counter, and a alarm signal interrupt.

$ ./ent test.rnd 
Entropy = 7.900037 bits per byte.

Optimum compression would reduce the size
of this 1920 byte file by 1 percent.

Chi square distribution for 1920 samples is 262.13, and randomly
would exceed this value 36.60 percent of the times.

Arithmetic mean value of data bytes is 125.3578 (127.5 = random).
Monte Carlo value for Pi is 3.187500000 (error 1.46 percent).
Serial correlation coefficient is 0.013566 (totally uncorrelated = 0.0).