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* Config - remove unused keys * Remove aliases * Script - minor updates * Remove bitcoin papers * Delete all but a couple example events * Update all QE BIP links to reflect current reop location * One more BIP link update
105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
#!/usr/bin/env python
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from math import log
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from bitarray import bitarray # from pypi.python.org/pypi/bitarray
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import pyhash # from https://github.com/flier/pyfasthash
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# Based on BIP-37
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# https://github.com/dashevo/bips/blob/master/bip-0037.mediawiki
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# Defined in bloom.h
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# https://github.com/dashpay/dash/blob/master/src/bloom.h#L17-#L18
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MAX_BLOOM_FILTER_SIZE = 36000
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MAX_HASH_FUNCS = 50
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# Set of flags that control how matched items are added to the filter (per BIP-37)
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# https://github.com/dashpay/dash/blob/master/src/bloom.h#L24
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nFlags = 0
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nElements = 1 # Number of elements
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nFPRate = 0.0001 # False positive rate
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nFilterBytes = int(min((-1 / log(2)**2 * nElements * log(nFPRate)) / 8, MAX_BLOOM_FILTER_SIZE))
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# Calculate the number of hash functions to use in the filter
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# Limit the maximum number to 50 per BIP-37
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nHashFuncs = int(min(nFilterBytes * 8 / nElements * log(2), MAX_HASH_FUNCS))
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murmur3 = pyhash.murmur3_32()
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TEST_TXID = "019f5b01d4195ecbc9398fbf3c3b1fa9bb3183301d7a1fb3bd174fcfa40a2b65"
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def bloom_hash(nHashNum, data):
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seed = (nHashNum * 0xfba4c795 + nTweak) & 0xffffffff
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return( murmur3(data, seed=seed) % (nFilterBytes * 8) )
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# Bloom Filter creation
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def create_filter(nTweak):
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print('Creating bloom filter')
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vData = nFilterBytes * 8 * bitarray('0', endian="little")
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data_to_hash = TEST_TXID
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data_to_hash = data_to_hash.decode("hex")
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print('Filter bytes: {}; Hash functions: {}'.format(nFilterBytes, nHashFuncs))
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print(" Filter (As Bits)")
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print("nHashNum nIndex Filter 0123456789abcdef")
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print("~~~~~~~~ ~~~~~~ ~~~~~~ ~~~~~~~~~~~~~~~~")
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for nHashNum in range(nHashFuncs):
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nIndex = bloom_hash(nHashNum, data_to_hash)
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## Set the bit at nIndex to 1
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vData[nIndex] = True
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## Debug: print current state
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print(' {0:2} {1:2} {2} {3}'.format(
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nHashNum,
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hex(int(nIndex)),
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vData.tobytes().encode("hex"),
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vData.to01()
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))
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print('Bloom filter: {}\n'.format(vData.tobytes().encode("hex")))
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# Bloom Filter evaluation
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def evaluate_filter():
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print('Evaluating bloom filter')
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vData = bitarray(endian='little')
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vData.frombytes("b50f".decode("hex"))
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nHashFuncs = 11
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nTweak = 0
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nFlags = 0
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def contains(nHashFuncs, data_to_hash):
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for nHashNum in range(nHashFuncs):
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## bloom_hash as defined in previous section
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nIndex = bloom_hash(nHashNum, data_to_hash)
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if vData[nIndex] != True:
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print("MATCH FAILURE: Index {0} not set in {1}\n".format(
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hex(int(nIndex)),
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vData.to01()
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))
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return False
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print("MATCH SUCCESS\n")
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## Test 1: Same TXID as previously added to filter
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data_to_hash = TEST_TXID
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print('\nChecking: {}'.format(data_to_hash))
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data_to_hash = data_to_hash.decode("hex")
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contains(nHashFuncs, data_to_hash)
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## Test 2: Arbitrary string
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data_to_hash = "1/10,000 chance this ASCII string will match"
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print('Checking: {}'.format(data_to_hash))
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contains(nHashFuncs, data_to_hash)
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# Tweak is a random value added to the seed value in the hash function
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# used by the bloom filter
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nTweak = 0
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create_filter(nTweak)
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evaluate_filter()
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