Added diff adjustment tests

diffadjust/blkdiff.py

@@ -0,0 +1,47 @@
+import math
+import random
+
+hashpower = [float(x) for x in open('hashpower.csv').readlines()]
+
+target = 12
+seconds_in_day = 8640
+ema_factor = 0.005
+f = 30
+threshold = 1.5
+maxadjust = 0.1
+
+
+def expdiff(t):
+    return -math.log(random.random()) * t
+
+
+def adjust(timestamps, diffs):
+    if len(timestamps) < 7:
+        return diffs[-1]
+    blks_back = 5
+    delta = timestamps[-2] - timestamps[-2-blks_back]
+    expected = target * 0.693 * blks_back
+    if delta < expected / threshold:
+        fac = max(1 + 1. * delta / expected / f, 1 + maxadjust) ** 2
+    elif delta > expected * threshold:
+        fac = max(1 - 1. * delta / expected / f, 1 - maxadjust) ** 2
+    else:
+        fac = 1
+    return diffs[-1] * fac
+
+
+def test(source, adjust):
+    ema = target
+    chain = [0]
+    d = [source[0]]
+    emachain = [target] 
+    sourcechain = [0]
+    while chain[-1] < len(source) * seconds_in_day:
+        hashpower = source[int(chain[-1] // seconds_in_day)]
+        d.append(adjust(chain, d))
+        chain.append(chain[-1] + expdiff(d[-1] / hashpower))
+        ema = ema * (1 - ema_factor) + (chain[-1] - chain[-2]) * ema_factor
+        emachain.append(ema)
+        sourcechain.append(hashpower)
+    print 'min', min(emachain[500:]), 'max', max(emachain[500:]), 'avg', sum(emachain) / len(emachain)
+    return zip(emachain[500:], sourcechain[500:], chain[500:])