Add python script for running bloom filter example

scripts/bloom_filter.py

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