def target(DB, length=0):
#returns the target difficulty at a paticular blocklength.
inflection=0.985#This constant is selected such that the 50 most recent blocks count for 1/2 the total weight.
history_length=400#How far back in history do we compute the target from.
if length==0: length=DB['length']
if length<4: return '0'*4+'f'*60#use same difficulty for first few blocks.
if length<=DB['length']: return targets[str(length)]#don't calculate same difficulty twice.
def targetTimesFloat(target, number): return buffer(str(hex(int(int(target, 16)*number)))[2:-1])
def weights(length): return [inflection**(length-i) for i in range(length)]
def estimate_target(DB):
def invertTarget(n): return buffer(str(hex(int('f'*128, 16)/int(n, 16)))[2:-1])#use double-size for division, to reduce information leakage.
def sumTargets(l):
if len(l)<1: return 0
while len(l)>1:
l=[hexSum(l[0], l[1])]+l[2:]
return l[0]
targets=recent_blockthings('target', DB, history_length)
w=weights(len(targets))
tw=sum(w)
targets=map(hexInvert, targets)#invert because target is proportional to 1/(# hashes required to mine a block on average)
weighted_targets=[targetTimesFloat(targets[i], w[i]/tw) for i in range(len(targets))]
return hexInvert(sumTargets(weighted_targets))#invert again to fix units
def estimate_time(DB):
timestamps=recent_blockthings('time', DB, history_length)
blocklengths=[timestamps[i]-timestamps[i-1] for i in range(1, len(timestamps))]
w=weights(len(blocklengths))#geometric weighting
tw=sum(w)#normalization constant
return sum([w[i]*blocklengths[i]/tw for i in range(len(blocklengths))])
return targetTimesFloat(estimate_target(DB), estimate_time(DB)/custom.blocktime(length))
def target(DB, length=0):
""" Returns the target difficulty at a paticular blocklength. """
if length == 0:
length = DB['length']
if length < 4:
return '0' * 4 + 'f' * 60 # Use same difficulty for first few blocks.
if length <= DB['length'] and str(length) in targets:
return targets[str(
length
)] # Memoized, This is a small memory leak. It takes up more space linearly over time. but every time you restart the program, it gets cleaned out.
def targetTimesFloat(target, number):
a = int(str(target), 16)
b = int(a * number)
return tools.buffer_(str(hex(b))[2:-1], 64)
def weights(length):
return [custom.inflection**(length - i) for i in range(length)]
def estimate_target(DB):
"""
We are actually interested in the average number of hashes required to
mine a block. number of hashes required is inversely proportional
to target. So we average over inverse-targets, and inverse the final
answer. """
def sumTargets(l):
if len(l) < 1:
return 0
while len(l) > 1:
l = [hexSum(l[0], l[1])] + l[2:]
return l[0]
targets = recent_blockthings('target', DB, custom.history_length)
w = weights(len(targets))
tw = sum(w)
targets = map(hexInvert, targets)
def weighted_multiply(i):
return targetTimesFloat(targets[i], w[i] / tw)
weighted_targets = [weighted_multiply(i) for i in range(len(targets))]
return hexInvert(sumTargets(weighted_targets))
def estimate_time(DB):
times = recent_blockthings('time', DB, custom.history_length)
blocklengths = [times[i] - times[i - 1] for i in range(1, len(times))]
w = weights(len(blocklengths)) # Geometric weighting
tw = sum(w) # Normalization constant
return sum(
[w[i] * blocklengths[i] / tw for i in range(len(blocklengths))])
retarget = estimate_time(DB) / custom.blocktime(length)
return targetTimesFloat(estimate_target(DB), retarget)
def target(DB, length=0):
""" Returns the target difficulty at a paticular blocklength. """
if length == 0:
length = DB['length']
if length < 4:
return '0' * 4 + 'f' * 60 # Use same difficulty for first few blocks.
if length <= DB['length']:
return targets[str(length)] # Memoized
def targetTimesFloat(target, number):
a = int(str(target), 16)
b = int(a * number)
return tools.buffer_(str(hex(b))[2: -1], 64)
def weights(length):
return [custom.inflection ** (length-i) for i in range(length)]
def estimate_target(DB):
"""
We are actually interested in the average number of hashes required to
mine a block. number of hashes required is inversely proportional
to target. So we average over inverse-targets, and inverse the final
answer. """
def sumTargets(l):
if len(l) < 1:
return 0
while len(l) > 1:
l = [hexSum(l[0], l[1])] + l[2:]
return l[0]
targets = recent_blockthings('target', DB, custom.history_length)
w = weights(len(targets))
tw = sum(w)
targets = map(hexInvert, targets)
def weighted_multiply(i):
return targetTimesFloat(targets[i], w[i]/tw)
weighted_targets = [weighted_multiply(i) for i in range(len(targets))]
return hexInvert(sumTargets(weighted_targets))
def estimate_time(DB):
times = recent_blockthings('time', DB, custom.history_length)
blocklengths = [times[i] - times[i - 1] for i in range(1, len(times))]
w = weights(len(blocklengths)) # Geometric weighting
tw = sum(w) # Normalization constant
return sum([w[i] * blocklengths[i] / tw for i in range(len(blocklengths))])
retarget = estimate_time(DB) / custom.blocktime(length)
return targetTimesFloat(estimate_target(DB), retarget)
def target(DB, length=0):
#returns the target difficulty at a paticular blocklength.
inflection=0.985#This constant is selected such that the 50 most recent
#blocks count for 1/2 the total weight.
history_length=400#How far back in history do we compute the target from.
if length==0: length=DB['length']
if length<4: return '0'*4+'f'*60#use same difficulty for first few blocks.
if length<=DB['length']: return targets[str(length)]#memoized
def targetTimesFloat(target, number):
return buffer(str(hex(int(int(target, 16)*number)))[2:-1])
def weights(length): return [inflection**(length-i) for i in range(length)]
def estimate_target(DB):
#We are actually interested in the average number of hashes requred
#to mine a block. number of hashes required is inversely proportional
#to target. So we average over inverse-targets, and inverse the final
#answer.
def sumTargets(l):
if len(l)<1: return 0
while len(l)>1:
l=[hexSum(l[0], l[1])]+l[2:]
return l[0]
targets=recent_blockthings('target', DB, history_length)
w=weights(len(targets))
tw=sum(w)
targets=map(hexInvert, targets)
def weighted_multiply(i): return targetTimesFloat(targets[i], w[i]/tw)
weighted_targets=[weighted_multiply(i) for i in range(len(targets))]
return hexInvert(sumTargets(weighted_targets))
def estimate_time(DB):
times=recent_blockthings('time', DB, history_length)
blocklengths=[times[i]-times[i-1] for i in range(1, len(times))]
w=weights(len(blocklengths))#geometric weighting
tw=sum(w)#normalization constant
return sum([w[i]*blocklengths[i]/tw for i in range(len(blocklengths))])
retarget=estimate_time(DB)/custom.blocktime(length)
return targetTimesFloat(estimate_target(DB), retarget)
