use random oracle for randomized satin columns and redo split stitches

1 files changed, 44 insertions, 0 deletions

diff --git a/lib/utils/prng.py b/lib/utils/prng.py
new file mode 100644
index 00000000..518049cb
--- /dev/null
+++ b/lib/utils/prng.py
@@ -0,0 +1,44 @@
+from hashlib import blake2s
+from math import ceil
+from itertools import count, chain
+import numpy as np
+
+
+def joinArgs(*args):
+    # Stringifies parameters into a slash-separated string for use in hash keys.
+    # Idempotent and associative.
+    return "/".join([str(x) for x in args])
+
+
+MAX_UNIFORM_INT = 2 ** 32 - 1
+
+
+def uniformInts(*args):
+    # Single pseudo-random drawing determined by the joined parameters.
+    # Returns 4 uniformly random uint64.
+    s = joinArgs(*args)
+    h = blake2s(s.encode()).hexdigest()
+    nums = []
+    for i in range(0, 64, 8):
+        nums.append(int(h[i:i+8], 16))
+    return np.array(nums)
+
+
+def uniformFloats(*args):
+    # returns an array of 8 floats in the range [0,1]
+    return uniformInts(*args) / MAX_UNIFORM_INT
+
+
+def nUniformFloats(n: int, *args):
+    # returns a fixed number (which may exceed 8) of floats in the range [0,1]
+    seed = joinArgs(*args)
+    nBlocks = ceil(n/8)
+    blocks = [uniformFloats(seed, x) for x in range(nBlocks)]
+    return np.concatenate(blocks)[0:n]
+
+
+def iterUniformFloats(*args):
+    # returns an infinite sequence of floats in the range [0,1]
+    seed = joinArgs(*args)
+    blocks = map(lambda x: list(uniformFloats(seed, x)), count(0))
+    return chain.from_iterable(blocks)