diff --git a/.gitignore b/.gitignore
index 9bea433..cfc9510 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,6 @@
-
+nimcache
+nimcache/*
+tests/bloom
+nim-bloom/bloom
 .DS_Store
+src/.DS_Store
\ No newline at end of file
diff --git a/nim-bloom/src/bloom.nim b/bloom_filter/bloom.nim
similarity index 65%
rename from nim-bloom/src/bloom.nim
rename to bloom_filter/bloom.nim
index ba3bdc8..096dd29 100644
--- a/nim-bloom/src/bloom.nim
+++ b/bloom_filter/bloom.nim
@@ -3,47 +3,18 @@ import hashes
 import strutils
 import private/probabilities
 
-# Import MurmurHash3 code with both 128-bit and 32-bit implementations
-{.compile: "murmur3.c".}
-
 type
-  HashType* = enum
-    htMurmur128,  # Default: MurmurHash3_x64_128
-    htMurmur32,   # MurmurHash3_x86_32
-    htNimHash  # Nim's Hash (currently Farm Hash)
-
   BloomFilterError* = object of CatchableError
   
-  MurmurHashes = array[0..1, int]
-  
   BloomFilter* = object
     capacity*: int
     errorRate*: float
     kHashes*: int
     mBits*: int
     intArray: seq[int]
-    hashType*: HashType
 
 {.push overflowChecks: off.}  # Turn off overflow checks for hashing operations
 
-proc rawMurmurHash128(key: cstring, len: int, seed: uint32,
-                     outHashes: var MurmurHashes): void {.
-  importc: "MurmurHash3_x64_128".}
-
-proc rawMurmurHash32(key: cstring, len: int, seed: uint32,
-                    outHashes: ptr uint32): void {.
-  importc: "MurmurHash3_x86_32".}
-
-proc murmurHash128(key: string, seed = 0'u32): MurmurHashes =
-  var hashResult: MurmurHashes
-  rawMurmurHash128(key, key.len, seed, hashResult)
-  hashResult
-
-proc murmurHash32(key: string, seed = 0'u32): uint32 =
-  var result: uint32
-  rawMurmurHash32(key, key.len, seed, addr result)
-  result
-
 proc hashN(item: string, n: int, maxValue: int): int =
   ## Get the nth hash using Nim's built-in hash function using
   ## the double hashing technique from Kirsch and Mitzenmacher, 2008:
@@ -76,8 +47,7 @@ proc getMOverNBitsForK(k: int, targetError: float,
     "Specified value of k and error rate not achievable using less than 4 bytes / element.")
 
 proc initializeBloomFilter*(capacity: int, errorRate: float, k = 0,
-                              forceNBitsPerElem = 0,
-                              hashType = htMurmur128): BloomFilter =
+                              forceNBitsPerElem = 0): BloomFilter =
   ## Initializes a Bloom filter with specified parameters.
   ##
   ## Parameters:
@@ -87,10 +57,6 @@ proc initializeBloomFilter*(capacity: int, errorRate: float, k = 0,
   ## See http://pages.cs.wisc.edu/~cao/papers/summary-cache/node8.html for
   ## useful tables on k and m/n (n bits per element) combinations.
   ## - forceNBitsPerElem: Optional override for bits per element
-  ## - hashType: Choose hash function:
-  ##   * htMurmur128: MurmurHash3_x64_128 (default) - recommended
-  ##   * htMurmur32: MurmurHash3_x86_32
-  ##   * htNimHash: Nim's Hash
   var
     kHashes: int
     nBitsPerElem: int
@@ -115,49 +81,23 @@ proc initializeBloomFilter*(capacity: int, errorRate: float, k = 0,
     errorRate: errorRate,
     kHashes: kHashes,
     mBits: mBits,
-    intArray: newSeq[int](mInts),
-    hashType: hashType
+    intArray: newSeq[int](mInts)
   )
 
 proc `$`*(bf: BloomFilter): string =
   ## Prints the configuration of the Bloom filter.
-  let hashType = case bf.hashType
-    of htMurmur128: "MurmurHash3_x64_128"
-    of htMurmur32: "MurmurHash3_x86_32"
-    of htNimHash: "NimHashHash"
-  
-  "Bloom filter with $1 capacity, $2 error rate, $3 hash functions, and requiring $4 bits of memory. Using $5." %
+  "Bloom filter with $1 capacity, $2 error rate, $3 hash functions, and requiring $4 bits of memory." %
     [$bf.capacity,
      formatFloat(bf.errorRate, format = ffScientific, precision = 1),
      $bf.kHashes,
-     $(bf.mBits div bf.capacity),
-     hashType]
-
-{.push overflowChecks: off.}  # Turn off overflow checks for hash computations
+     $(bf.mBits div bf.capacity)]
 
 proc computeHashes(bf: BloomFilter, item: string): seq[int] =
   var hashes = newSeq[int](bf.kHashes)
-  
-  case bf.hashType
-  of htMurmur128:
-    let murmurHashes = murmurHash128(item, 0'u32)
-    for i in 0..<bf.kHashes:
-      hashes[i] = abs((murmurHashes[0].int64 + i.int64 * murmurHashes[1].int64).int) mod bf.mBits
-  of htMurmur32:
-    let baseHash = murmurHash32(item, 0'u32)
-    # let rotated = ((baseHash shl 13) or (baseHash shr (32 - 13)))
-    let secondHash = murmurHash32(item & " b", 0'u32)
-    for i in 0..<bf.kHashes:
-      hashes[i] = abs((baseHash.int64 + i.int64 * secondHash.int64).int) mod bf.mBits
-  
-  of htNimHash:
-    for i in 0..<bf.kHashes:
-      hashes[i] = hashN(item, i, bf.mBits)
-  
+  for i in 0..<bf.kHashes:
+    hashes[i] = hashN(item, i, bf.mBits)
   hashes
 
-{.pop.}  # Restore overflow checks
-
 proc insert*(bf: var BloomFilter, item: string) =
   ## Insert an item (string) into the Bloom filter.
   let hashSet = bf.computeHashes(item)
diff --git a/nim-bloom/src/private/probabilities.nim b/bloom_filter/private/probabilities.nim
similarity index 100%
rename from nim-bloom/src/private/probabilities.nim
rename to bloom_filter/private/probabilities.nim
diff --git a/nim-bloom/.gitignore b/nim-bloom/.gitignore
deleted file mode 100644
index f595a4e..0000000
--- a/nim-bloom/.gitignore
+++ /dev/null
@@ -1,10 +0,0 @@
-nimcache
-nimcache/*
-tests/test
-benches/bench
-benches/bench_arch_end
-bloom
-*.html
-*.css
-.DS_Store
-src/.DS_Store
\ No newline at end of file
diff --git a/nim-bloom/LICENSE b/nim-bloom/LICENSE
deleted file mode 100644
index 10ea866..0000000
--- a/nim-bloom/LICENSE
+++ /dev/null
@@ -1,20 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2013 Nick Greenfield
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software is furnished to do so,
-subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
-FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/nim-bloom/README.md b/nim-bloom/README.md
deleted file mode 100644
index b042ea8..0000000
--- a/nim-bloom/README.md
+++ /dev/null
@@ -1,121 +0,0 @@
-# nim-bloom
-
-A high-performance Bloom filter implementation in Nim offering standard and custom hash function options with different performance characteristics and false positive rates.
-
-## Features
-
-- Fast string element insertion and lookup
-- Configurable error rates
-- Choice between standard Nim hash and custom MurmurHash3 (128-bit or 32-bit)
-- Optimized for supporting different use cases of speed and accuracy
-- Comprehensive test suite and benchmarks
-
-## Usage
-
-Basic usage (defaults to MurmurHash3_128):
-```nim
-import bloom2
-
-# Initialize with default hash (MurmurHash3_128)
-var bf = initializeBloomFilter(capacity = 10000, errorRate = 0.01)
-
-# Or explicitly specify hash type
-var bf32 = initializeBloomFilter(
-  capacity = 10000, 
-  errorRate = 0.01,
-  hashType = htMurmur32  # Use 32-bit implementation
-)
-
-# Basic operations
-bf.insert("test")
-assert bf.lookup("test")
-```
-
-## Hash Function Selection
-
-1. Use MurmurHash3_128 (default) when:
-    - You need the best balance of performance and accuracy
-    - Memory isn't severely constrained
-    - Working with large datasets
-    - False positive rates are important
-
-2. Use MurmurHash3_32 when:
-    - Running on 32-bit systems
-    - Memory is constrained
-    - Working with smaller datasets
-    - String concatenation overhead for second hash, causing higher insertion and lookup times, is acceptable.
-
-3. Use NimHash when:
-    - Consistency with Nim's hashing is important
-    - Working with smaller datasets where performance is less critical
-    - Future availability of better hash functions or performant implementations
-
-Nim's Hash Implementation:
-  - Default (no flags): Uses FarmHash implementation
-  - With `-d:nimStringHash2`: Uses Nim's MurmurHash3_32 implementation
-  - Our implementation allows explicit choice regardless of compilation flags and our MurmurHash3_32 performs better because of directly using a native C Implementation
-
-## Performance Characteristics
-### For 1M items - Random Strings
-```
-Insertion Speed:
-MurmurHash3_128: ~6.8M ops/sec
-MurmurHash3_32:  ~5.9M ops/sec
-FarmHash:        ~2.1M ops/sec
-
-False Positive Rates:
-MurmurHash3_128: ~0.84%
-MurmurHash3_32:  ~0.83%
-FarmHash:        ~0.82%
-```
-
-These measurements show MurmurHash3_128's balanced performance profile, offering best speed and competitive false positive rates.
-
-Performance will vary based on:
-- Choice of hash function
-- Hardware specifications
-- Data size and memory access patterns (inside vs outside cache)
-- Compiler optimizations
-
-For detailed benchmarks across different data patterns and sizes, see [benches](benches/).
-
-## Implementation Details
-
-### Double Hashing Technique
-This implmentation uses the Kirsch-Mitzenmacher method to generate k hash values from two initial hashes. The implementation varies by hash type:
-
-1. MurmurHash3_128:
-```nim
-h(i) = abs((hash1 + i * hash2) mod m)
-```
-- Uses both 64-bit hashes from 128-bit output
-- Natural double-hash implementation
-
-2. MurmurHash3_32:
-```nim
-let baseHash = murmurHash32(item, 0'u32)
-let secondHash = murmurHash32(item & " b", 0'u32)
-```
-- Uses string concatention by default for the second hash
-- Bit Rotation for second hash provides sufficient randomness in some use cases while being much faster than string concatenation (but results in higher FP rate)
-- Choose between bit rotation or string concatenation as per your use-case.
-
-3. Nim's Hash:
-```nim
-  let
-    hashA = abs(hash(item)) mod maxValue
-    hashB = abs(hash(item & " b")) mod maxValue
-  h(i) = abs((hashA + n * hashB)) mod maxValue
-```
-- Farm Hash or Nim's Murmur Hash based (if compliation flag is passed)
-- Uses string concatention by default.
-- Lower FP rate than bit rotation but comes at the cost of higher insertion and lookup times.
-
-*Tip:* Bit rotation values can be configurable as well. Use prime numbers for better mixing: 7, 11, 13, 17 for 32-bit; 21, 23, 27, 33 for 64-bit. Smaller rotations provides lesser mixing but as faster than higher rotations.
-
-## Testing
-
-Run the test suite:
-```bash
-nimble test
-```
\ No newline at end of file
diff --git a/nim-bloom/benches/bench.nim b/nim-bloom/benches/bench.nim
deleted file mode 100644
index 82d7ca9..0000000
--- a/nim-bloom/benches/bench.nim
+++ /dev/null
@@ -1,123 +0,0 @@
-import times, random, strutils
-include bloom
-
-type
-  DataPattern = enum
-    dpRandom,      # Random strings
-    dpSequential,  # Sequential numbers
-    dpFixed,       # Fixed length strings
-    dpLong,        # Long strings
-    dpSpecial      # Strings with special characters
-
-type 
-  BenchmarkResult = tuple[
-    insertTime: float,
-    lookupTime: float, 
-    falsePositives: int
-  ]
-
-proc generateBenchData(pattern: DataPattern, size: int, isLookupData: bool = false): seq[string] =
-  result = newSeq[string](size)
-  let offset = if isLookupData: size * 2 else: 0  # Ensure lookup data is well separated
-  
-  case pattern:
-  of dpRandom:
-    for i in 0..<size:
-      var s = ""
-      for j in 0..rand(5..15):
-        s.add(chr(rand(ord('a')..ord('z'))))
-      result[i] = s
-  of dpSequential:
-    for i in 0..<size:
-      result[i] = $(i + offset)  # Add offset for lookup data
-  of dpFixed:
-    for i in 0..<size:
-      result[i] = "fixed" & align($(i + offset), 10, '0')
-  of dpLong:
-    for i in 0..<size:
-      result[i] = repeat("x", 100) & $(i + offset)
-  of dpSpecial:
-    for i in 0..<size:
-      result[i] = "test@" & $(i + offset) & "#$%^&*" & $rand(1000)
-
-proc benchmarkHashType(hashType: HashType, size: int, errorRate: float, 
-                      data: seq[string], lookupData: seq[string]): BenchmarkResult =
-  # Initialize Bloom filter and run benchmark for given hash type
-  var bf = initializeBloomFilter(size, errorRate, hashType = hashType)
-  
-  # Measure insert time
-  let startInsert = cpuTime()
-  for item in data:
-    bf.insert(item)
-  let insertTime = cpuTime() - startInsert
-  
-  # Measure lookup time and count false positives  
-  var falsePositives = 0
-  let startLookup = cpuTime()
-  for item in lookupData:
-    if bf.lookup(item): falsePositives.inc
-  let lookupTime = cpuTime() - startLookup
-
-  result = (insertTime, lookupTime, falsePositives)
-
-proc printResults(hashName: string, result: BenchmarkResult, 
-                 dataSize: int, lookupDataSize: int) =
-  echo "\n", hashName, " Results:"
-  echo "  Insert time: ", result.insertTime, "s (", dataSize.float/result.insertTime, " ops/sec)"
-  echo "  Lookup time: ", result.lookupTime, "s (", lookupDataSize.float/result.lookupTime, " ops/sec)"
-  echo "  False positives: ", result.falsePositives, " (", 
-       result.falsePositives.float / lookupDataSize.float * 100, "%)"
-
-proc runBenchmark(size: int, errorRate: float, pattern: DataPattern, name: string) =
-  echo "\n=== Benchmark: ", name, " ==="
-  echo "Size: ", size, " items"
-  echo "Pattern: ", pattern
-  
-  # Generate test data
-  let data = generateBenchData(pattern, size, false)
-  let lookupData = generateBenchData(pattern, size div 2, true)
-  
-  # Run benchmarks for each hash type
-  let nimHashResult = benchmarkHashType(htNimHash, size, errorRate, data, lookupData)
-  let murmur128Result = benchmarkHashType(htMurmur128, size, errorRate, data, lookupData)
-  let murmur32Result = benchmarkHashType(htMurmur32, size, errorRate, data, lookupData)
-  
-  # Print individual results
-  printResults("Nim's Hash (Farm Hash)", nimHashResult, size, lookupData.len)
-  printResults("MurmurHash3_128", murmur128Result, size, lookupData.len)
-  printResults("MurmurHash3_32", murmur32Result, size, lookupData.len)
-  
-  # Print comparisons
-  echo "\nComparison (higher means better/faster):"
-  echo "  Insert Speed:"
-  echo "    Murmur128 vs NimHash: ", nimHashResult.insertTime/murmur128Result.insertTime, "x faster"
-  echo "    Murmur32 vs NimHash: ", nimHashResult.insertTime/murmur32Result.insertTime, "x faster"
-  echo "    Murmur128 vs Murmur32: ", murmur32Result.insertTime/murmur128Result.insertTime, "x faster"
-  
-  echo "  Lookup Speed:"
-  echo "    Murmur128 vs NimHash: ", nimHashResult.lookupTime/murmur128Result.lookupTime, "x faster"
-  echo "    Murmur32 vs NimHash: ", nimHashResult.lookupTime/murmur32Result.lookupTime, "x faster"
-  echo "    Murmur128 vs Murmur32: ", murmur32Result.lookupTime/murmur128Result.lookupTime, "x faster"
-  
-  echo "  False Positive Rates:"
-  let fpRateNimHash = nimHashResult.falsePositives.float / lookupData.len.float
-  let fpRateMurmur128 = murmur128Result.falsePositives.float / lookupData.len.float
-  let fpRateMurmur32 = murmur32Result.falsePositives.float / lookupData.len.float
-  
-  echo "    Murmur128 vs NimHash: ", fpRateNimHash/fpRateMurmur128, "x better"
-  echo "    Murmur32 vs NimHash: ", fpRateNimHash/fpRateMurmur32, "x better"
-  echo "    Murmur128 vs Murmur32: ", fpRateMurmur32/fpRateMurmur128, "x better"
-
-when isMainModule:
-  const errorRate = 0.01
-  
-  # Test each pattern
-  for pattern in [dpRandom, dpSequential, dpFixed, dpLong, dpSpecial]:
-    # Small dataset
-    runBenchmark(10_000, errorRate, pattern, "Small " & $pattern)
-    
-    # Medium dataset
-    runBenchmark(100_000, errorRate, pattern, "Medium " & $pattern)
-    
-    # Large dataset
-    runBenchmark(1_000_000, errorRate, pattern, "Large " & $pattern)
\ No newline at end of file
diff --git a/nim-bloom/benches/bench_results.txt b/nim-bloom/benches/bench_results.txt
deleted file mode 100644
index 57da231..0000000
--- a/nim-bloom/benches/bench_results.txt
+++ /dev/null
@@ -1,495 +0,0 @@
-
-=== Benchmark: Small dpRandom ===
-Size: 10000 items
-Pattern: dpRandom
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.006714000000000001s (1489425.081918379 ops/sec)
-  Lookup time: 0.003387999999999995s (1475796.930342387 ops/sec)
-  False positives: 51 (1.02%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001955999999999999s (5112474.437627814 ops/sec)
-  Lookup time: 0.0009989999999999999s (5005005.005005006 ops/sec)
-  False positives: 25 (0.5%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.002221999999999995s (4500450.045004511 ops/sec)
-  Lookup time: 0.001138s (4393673.110720562 ops/sec)
-  False positives: 43 (0.86%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.432515337423315x faster
-    Murmur32 vs NimHash: 3.021602160216029x faster
-    Murmur128 vs Murmur32: 1.135991820040897x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.391391391391387x faster
-    Murmur32 vs NimHash: 2.977152899824249x faster
-    Murmur128 vs Murmur32: 1.139139139139139x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 2.04x better
-    Murmur32 vs NimHash: 1.186046511627907x better
-    Murmur128 vs Murmur32: 1.72x better
-
-=== Benchmark: Medium dpRandom ===
-Size: 100000 items
-Pattern: dpRandom
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.04622000000000001s (2163565.556036348 ops/sec)
-  Lookup time: 0.02333199999999999s (2142979.59883422 ops/sec)
-  False positives: 402 (0.804%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.013294s (7522190.461862494 ops/sec)
-  Lookup time: 0.006861999999999979s (7286505.392014012 ops/sec)
-  False positives: 405 (0.8099999999999999%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01558700000000002s (6415602.745877968 ops/sec)
-  Lookup time: 0.008157999999999999s (6128953.174797745 ops/sec)
-  False positives: 406 (0.8120000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.476756431472846x faster
-    Murmur32 vs NimHash: 2.965291589144798x faster
-    Murmur128 vs Murmur32: 1.172483827290508x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.400174876129417x faster
-    Murmur32 vs NimHash: 2.860014709487619x faster
-    Murmur128 vs Murmur32: 1.188866219761006x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9925925925925927x better
-    Murmur32 vs NimHash: 0.9901477832512315x better
-    Murmur128 vs Murmur32: 1.002469135802469x better
-
-=== Benchmark: Large dpRandom ===
-Size: 1000000 items
-Pattern: dpRandom
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.4711130000000001s (2122632.998877127 ops/sec)
-  Lookup time: 0.2430289999999999s (2057367.639252929 ops/sec)
-  False positives: 4104 (0.8208%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1467989999999999s (6812035.504329053 ops/sec)
-  Lookup time: 0.07689400000000002s (6502457.929097197 ops/sec)
-  False positives: 4187 (0.8373999999999999%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1697340000000001s (5891571.517786653 ops/sec)
-  Lookup time: 0.08597399999999999s (5815711.726801126 ops/sec)
-  False positives: 4130 (0.826%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.209238482550974x faster
-    Murmur32 vs NimHash: 2.775595932459024x faster
-    Murmur128 vs Murmur32: 1.156234034291788x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.160571696101125x faster
-    Murmur32 vs NimHash: 2.826773210505501x faster
-    Murmur128 vs Murmur32: 1.118084635992405x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9801767375208981x better
-    Murmur32 vs NimHash: 0.9937046004842615x better
-    Murmur128 vs Murmur32: 0.9863864342010987x better
-
-=== Benchmark: Small dpSequential ===
-Size: 10000 items
-Pattern: dpSequential
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.004508000000000179s (2218278.615794056 ops/sec)
-  Lookup time: 0.002232000000000012s (2240143.369175615 ops/sec)
-  False positives: 42 (0.84%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001201000000000008s (8326394.671107357 ops/sec)
-  Lookup time: 0.0006349999999999412s (7874015.748032225 ops/sec)
-  False positives: 47 (0.9400000000000001%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.00151200000000018s (6613756.613755827 ops/sec)
-  Lookup time: 0.0007530000000000037s (6640106.241699835 ops/sec)
-  False positives: 51 (1.02%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.753538717735346x faster
-    Murmur32 vs NimHash: 2.981481481481245x faster
-    Murmur128 vs Murmur32: 1.258950874271582x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.514960629921604x faster
-    Murmur32 vs NimHash: 2.964143426294822x faster
-    Murmur128 vs Murmur32: 1.185826771653659x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.8936170212765957x better
-    Murmur32 vs NimHash: 0.8235294117647057x better
-    Murmur128 vs Murmur32: 1.085106382978724x better
-
-=== Benchmark: Medium dpSequential ===
-Size: 100000 items
-Pattern: dpSequential
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.04492400000000019s (2225981.65791113 ops/sec)
-  Lookup time: 0.02218900000000001s (2253368.78633557 ops/sec)
-  False positives: 436 (0.872%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.012737s (7851142.341210647 ops/sec)
-  Lookup time: 0.006547999999999998s (7635919.364691511 ops/sec)
-  False positives: 422 (0.844%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01476000000000011s (6775067.750677458 ops/sec)
-  Lookup time: 0.008092999999999684s (6178178.672927462 ops/sec)
-  False positives: 446 (0.8920000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.527047185365486x faster
-    Murmur32 vs NimHash: 3.043631436314354x faster
-    Murmur128 vs Murmur32: 1.1588286095627x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.388668295662801x faster
-    Murmur32 vs NimHash: 2.741752131471751x faster
-    Murmur128 vs Murmur32: 1.23594990836892x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.033175355450237x better
-    Murmur32 vs NimHash: 0.9775784753363228x better
-    Murmur128 vs Murmur32: 1.056872037914692x better
-
-=== Benchmark: Large dpSequential ===
-Size: 1000000 items
-Pattern: dpSequential
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.4540329999999999s (2202483.079423743 ops/sec)
-  Lookup time: 0.2223580000000003s (2248626.089459338 ops/sec)
-  False positives: 4037 (0.8074%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1384119999999998s (7224807.097650506 ops/sec)
-  Lookup time: 0.07243599999999972s (6902645.093599894 ops/sec)
-  False positives: 4205 (0.8410000000000001%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1643889999999999s (6083132.083046923 ops/sec)
-  Lookup time: 0.08048799999999989s (6212106.152469941 ops/sec)
-  False positives: 4024 (0.8048%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.280300840967551x faster
-    Murmur32 vs NimHash: 2.761942709062043x faster
-    Murmur128 vs Murmur32: 1.187678813975668x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.069716715445374x faster
-    Murmur32 vs NimHash: 2.762622999701826x faster
-    Murmur128 vs Murmur32: 1.111160196587335x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9600475624256836x better
-    Murmur32 vs NimHash: 1.003230616302187x better
-    Murmur128 vs Murmur32: 0.9569560047562424x better
-
-=== Benchmark: Small dpFixed ===
-Size: 10000 items
-Pattern: dpFixed
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.004486999999999686s (2228660.574994585 ops/sec)
-  Lookup time: 0.002098999999999851s (2382086.707956339 ops/sec)
-  False positives: 43 (0.86%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001263000000000236s (7917656.373711901 ops/sec)
-  Lookup time: 0.0006620000000001625s (7552870.090632588 ops/sec)
-  False positives: 47 (0.9400000000000001%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001565000000000261s (6389776.357826412 ops/sec)
-  Lookup time: 0.0007539999999996994s (6631299.734750655 ops/sec)
-  False positives: 35 (0.7000000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.552652414884281x faster
-    Murmur32 vs NimHash: 2.86709265175651x faster
-    Murmur128 vs Murmur32: 1.239113222486119x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.170694864047335x faster
-    Murmur32 vs NimHash: 2.783819628648127x faster
-    Murmur128 vs Murmur32: 1.13897280966694x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9148936170212766x better
-    Murmur32 vs NimHash: 1.228571428571429x better
-    Murmur128 vs Murmur32: 0.7446808510638298x better
-
-=== Benchmark: Medium dpFixed ===
-Size: 100000 items
-Pattern: dpFixed
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.04369800000000001s (2288434.253283903 ops/sec)
-  Lookup time: 0.02203400000000011s (2269220.295906316 ops/sec)
-  False positives: 411 (0.822%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.013401s (7462129.691814045 ops/sec)
-  Lookup time: 0.00719499999999984s (6949270.32661586 ops/sec)
-  False positives: 440 (0.88%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01487600000000011s (6722237.160526973 ops/sec)
-  Lookup time: 0.007677999999999852s (6512112.529304632 ops/sec)
-  False positives: 433 (0.8659999999999999%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.260801432728903x faster
-    Murmur32 vs NimHash: 2.937483194407078x faster
-    Murmur128 vs Murmur32: 1.110066412954266x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.062404447533092x faster
-    Murmur32 vs NimHash: 2.86975774941398x faster
-    Murmur128 vs Murmur32: 1.067129951355111x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.934090909090909x better
-    Murmur32 vs NimHash: 0.9491916859122402x better
-    Murmur128 vs Murmur32: 0.9840909090909089x better
-
-=== Benchmark: Large dpFixed ===
-Size: 1000000 items
-Pattern: dpFixed
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.4461910000000002s (2241192.673092912 ops/sec)
-  Lookup time: 0.2275340000000003s (2197473.784137752 ops/sec)
-  False positives: 4084 (0.8168%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1576560000000002s (6342923.834170589 ops/sec)
-  Lookup time: 0.07750000000000057s (6451612.903225759 ops/sec)
-  False positives: 4109 (0.8218%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1656709999999997s (6036059.418968932 ops/sec)
-  Lookup time: 0.08378900000000034s (5967370.418551337 ops/sec)
-  False positives: 4085 (0.8170000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 2.830155528492411x faster
-    Murmur32 vs NimHash: 2.693235388209168x faster
-    Murmur128 vs Murmur32: 1.050838534530874x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 2.935922580645144x faster
-    Murmur32 vs NimHash: 2.715559321629324x faster
-    Murmur128 vs Murmur32: 1.081148387096771x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9939157945972257x better
-    Murmur32 vs NimHash: 0.9997552019583843x better
-    Murmur128 vs Murmur32: 0.9941591628133366x better
-
-=== Benchmark: Small dpLong ===
-Size: 10000 items
-Pattern: dpLong
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.009589999999999321s (1042752.86757046 ops/sec)
-  Lookup time: 0.005217000000000027s (958405.2137243577 ops/sec)
-  False positives: 39 (0.7799999999999999%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001508000000000287s (6631299.734746749 ops/sec)
-  Lookup time: 0.0007299999999998974s (6849315.068494113 ops/sec)
-  False positives: 56 (1.12%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001934000000000324s (5170630.816958802 ops/sec)
-  Lookup time: 0.0009790000000000632s (5107252.298263205 ops/sec)
-  False positives: 40 (0.8%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 6.359416445621682x faster
-    Murmur32 vs NimHash: 4.95863495346314x faster
-    Murmur128 vs Murmur32: 1.282493368700236x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 7.146575342466795x faster
-    Murmur32 vs NimHash: 5.328907048007856x faster
-    Murmur128 vs Murmur32: 1.341095890411234x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.6964285714285714x better
-    Murmur32 vs NimHash: 0.975x better
-    Murmur128 vs Murmur32: 0.7142857142857143x better
-
-=== Benchmark: Medium dpLong ===
-Size: 100000 items
-Pattern: dpLong
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.09930899999999987s (1006958.080335117 ops/sec)
-  Lookup time: 0.04954899999999984s (1009102.100950577 ops/sec)
-  False positives: 393 (0.786%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.0148350000000006s (6740815.63869201 ops/sec)
-  Lookup time: 0.007865000000000677s (6357279.084551265 ops/sec)
-  False positives: 422 (0.844%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.0197699999999994s (5058168.942842845 ops/sec)
-  Lookup time: 0.01105200000000028s (4524068.041983236 ops/sec)
-  False positives: 404 (0.8080000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 6.69423660262864x faster
-    Murmur32 vs NimHash: 5.023216995447794x faster
-    Murmur128 vs Murmur32: 1.33265925176937x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 6.299936427208592x faster
-    Murmur32 vs NimHash: 4.483260948244532x faster
-    Murmur128 vs Murmur32: 1.405212968849248x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9312796208530807x better
-    Murmur32 vs NimHash: 0.9727722772277229x better
-    Murmur128 vs Murmur32: 0.9573459715639812x better
-
-=== Benchmark: Large dpLong ===
-Size: 1000000 items
-Pattern: dpLong
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 1.032455000000001s (968565.2159174002 ops/sec)
-  Lookup time: 0.5242619999999993s (953721.6124762058 ops/sec)
-  False positives: 4143 (0.8286%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1612590000000003s (6201204.273869975 ops/sec)
-  Lookup time: 0.08462199999999953s (5908628.961735751 ops/sec)
-  False positives: 4044 (0.8088%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.2145720000000004s (4660440.318401273 ops/sec)
-  Lookup time: 0.1070960000000003s (4668708.448494795 ops/sec)
-  False positives: 4039 (0.8078%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 6.402464358578429x faster
-    Murmur32 vs NimHash: 4.81169490893499x faster
-    Murmur128 vs Murmur32: 1.330604803452831x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 6.195339273475009x faster
-    Murmur32 vs NimHash: 4.895252857249551x faster
-    Murmur128 vs Murmur32: 1.265581054572108x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.024480712166172x better
-    Murmur32 vs NimHash: 1.025748947759346x better
-    Murmur128 vs Murmur32: 0.9987636003956479x better
-
-=== Benchmark: Small dpSpecial ===
-Size: 10000 items
-Pattern: dpSpecial
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.005548999999998472s (1802126.509281448 ops/sec)
-  Lookup time: 0.002686999999999884s (1860811.313732868 ops/sec)
-  False positives: 39 (0.7799999999999999%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001593999999998985s (6273525.721459452 ops/sec)
-  Lookup time: 0.0008440000000007331s (5924170.616108599 ops/sec)
-  False positives: 33 (0.66%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001865999999999701s (5359056.806003002 ops/sec)
-  Lookup time: 0.0009710000000016095s (5149330.587015152 ops/sec)
-  False positives: 51 (1.02%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.481179422836891x faster
-    Murmur32 vs NimHash: 2.973740621650247x faster
-    Murmur128 vs Murmur32: 1.170639899624146x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.183649289096623x faster
-    Murmur32 vs NimHash: 2.767250257461823x faster
-    Murmur128 vs Murmur32: 1.150473933650197x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.181818181818182x better
-    Murmur32 vs NimHash: 0.7647058823529411x better
-    Murmur128 vs Murmur32: 1.545454545454546x better
-
-=== Benchmark: Medium dpSpecial ===
-Size: 100000 items
-Pattern: dpSpecial
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.04318999999999917s (2315350.775642554 ops/sec)
-  Lookup time: 0.02338899999999988s (2137757.065287111 ops/sec)
-  False positives: 435 (0.8699999999999999%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.01633100000000098s (6123323.740125772 ops/sec)
-  Lookup time: 0.007922000000000651s (6311537.490532176 ops/sec)
-  False positives: 405 (0.8099999999999999%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01658399999999993s (6029908.345393174 ops/sec)
-  Lookup time: 0.00944400000000023s (5294366.793731341 ops/sec)
-  False positives: 368 (0.736%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 2.64466352336027x faster
-    Murmur32 vs NimHash: 2.604317414375262x faster
-    Murmur128 vs Murmur32: 1.015492009062454x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 2.952411007321126x faster
-    Murmur32 vs NimHash: 2.476598898771634x faster
-    Murmur128 vs Murmur32: 1.192123201211746x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.074074074074074x better
-    Murmur32 vs NimHash: 1.182065217391304x better
-    Murmur128 vs Murmur32: 0.9086419753086421x better
-
-=== Benchmark: Large dpSpecial ===
-Size: 1000000 items
-Pattern: dpSpecial
-
-Nim's Hash (Farm Hash) Results:
-  Insert time: 0.4443290000000015s (2250584.589347075 ops/sec)
-  Lookup time: 0.2233900000000002s (2238238.058999953 ops/sec)
-  False positives: 4096 (0.8191999999999999%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1440049999999999s (6944203.326273397 ops/sec)
-  Lookup time: 0.07467199999999963s (6695950.289265086 ops/sec)
-  False positives: 4137 (0.8274%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1650030000000005s (6060495.869772048 ops/sec)
-  Lookup time: 0.08332200000000078s (6000816.110991038 ops/sec)
-  False positives: 4222 (0.8444%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.085510919759742x faster
-    Murmur32 vs NimHash: 2.692854069319953x faster
-    Murmur128 vs Murmur32: 1.145814381445092x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 2.991616670237858x faster
-    Murmur32 vs NimHash: 2.681044622068578x faster
-    Murmur128 vs Murmur32: 1.115839940004302x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9900894367899443x better
-    Murmur32 vs NimHash: 0.9701563240170534x better
-    Murmur128 vs Murmur32: 1.020546289581822x better
diff --git a/nim-bloom/benches/bench_results_nimStringHash2.txt b/nim-bloom/benches/bench_results_nimStringHash2.txt
deleted file mode 100644
index e7857a9..0000000
--- a/nim-bloom/benches/bench_results_nimStringHash2.txt
+++ /dev/null
@@ -1,495 +0,0 @@
-
-=== Benchmark: Small dpRandom ===
-Size: 10000 items
-Pattern: dpRandom
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.004986000000000001s (2005615.724027276 ops/sec)
-  Lookup time: 0.00238s (2100840.336134454 ops/sec)
-  False positives: 43 (0.86%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001388s (7204610.951008644 ops/sec)
-  Lookup time: 0.000954s (5241090.146750525 ops/sec)
-  False positives: 25 (0.5%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001744000000000002s (5733944.954128432 ops/sec)
-  Lookup time: 0.000780999999999997s (6402048.655569807 ops/sec)
-  False positives: 43 (0.86%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.592219020172911x faster
-    Murmur32 vs NimHash: 2.858944954128437x faster
-    Murmur128 vs Murmur32: 1.256484149855909x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 2.49475890985325x faster
-    Murmur32 vs NimHash: 3.047375160051228x faster
-    Murmur128 vs Murmur32: 0.8186582809224288x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.72x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 1.72x better
-
-=== Benchmark: Medium dpRandom ===
-Size: 100000 items
-Pattern: dpRandom
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.050316s (1987439.383098816 ops/sec)
-  Lookup time: 0.02490799999999999s (2007387.184840213 ops/sec)
-  False positives: 406 (0.8120000000000001%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.01539100000000002s (6497303.618998109 ops/sec)
-  Lookup time: 0.008371999999999991s (5972288.580984239 ops/sec)
-  False positives: 405 (0.8099999999999999%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01614399999999999s (6194251.734390489 ops/sec)
-  Lookup time: 0.008244000000000001s (6065016.982047549 ops/sec)
-  False positives: 406 (0.8120000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.269183288935089x faster
-    Murmur32 vs NimHash: 3.116699702675918x faster
-    Murmur128 vs Murmur32: 1.048924696251054x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 2.975155279503107x faster
-    Murmur32 vs NimHash: 3.021348859776805x faster
-    Murmur128 vs Murmur32: 0.9847109412326817x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.002469135802469x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 1.002469135802469x better
-
-=== Benchmark: Large dpRandom ===
-Size: 1000000 items
-Pattern: dpRandom
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.522163s (1915110.798735261 ops/sec)
-  Lookup time: 0.257452s (1942109.597128785 ops/sec)
-  False positives: 4130 (0.826%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1587529999999999s (6299093.560436657 ops/sec)
-  Lookup time: 0.07841999999999993s (6375924.509053818 ops/sec)
-  False positives: 4187 (0.8373999999999999%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.170582s (5862283.242077123 ops/sec)
-  Lookup time: 0.08690500000000001s (5753408.894770151 ops/sec)
-  False positives: 4130 (0.826%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.289153590798286x faster
-    Murmur32 vs NimHash: 3.061067404532718x faster
-    Murmur128 vs Murmur32: 1.074511977726406x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.282989033409847x faster
-    Murmur32 vs NimHash: 2.96245325355273x faster
-    Murmur128 vs Murmur32: 1.108199438918644x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9863864342010987x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9863864342010987x better
-
-=== Benchmark: Small dpSequential ===
-Size: 10000 items
-Pattern: dpSequential
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.004666000000000059s (2143163.309044122 ops/sec)
-  Lookup time: 0.002341000000000149s (2135839.384878122 ops/sec)
-  False positives: 51 (1.02%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001309999999999922s (7633587.786259995 ops/sec)
-  Lookup time: 0.0007189999999999142s (6954102.920724057 ops/sec)
-  False positives: 47 (0.9400000000000001%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001513000000000098s (6609385.327164147 ops/sec)
-  Lookup time: 0.0007410000000001027s (6747638.32658476 ops/sec)
-  False positives: 51 (1.02%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.561832061068959x faster
-    Murmur32 vs NimHash: 3.08393919365483x faster
-    Murmur128 vs Murmur32: 1.154961832061212x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.25591098748321x faster
-    Murmur32 vs NimHash: 3.159244264507185x faster
-    Murmur128 vs Murmur32: 1.030598052851448x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.085106382978724x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 1.085106382978724x better
-
-=== Benchmark: Medium dpSequential ===
-Size: 100000 items
-Pattern: dpSequential
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.0480590000000003s (2080775.713185863 ops/sec)
-  Lookup time: 0.02284599999999992s (2188566.926376616 ops/sec)
-  False positives: 446 (0.8920000000000001%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.01357099999999978s (7368653.746960551 ops/sec)
-  Lookup time: 0.008311999999999653s (6015399.422521907 ops/sec)
-  False positives: 422 (0.844%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01522699999999988s (6567281.802062178 ops/sec)
-  Lookup time: 0.007773999999999948s (6431695.394906141 ops/sec)
-  False positives: 446 (0.8920000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.541301304251793x faster
-    Murmur32 vs NimHash: 3.156169961253082x faster
-    Murmur128 vs Murmur32: 1.122024906049674x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 2.7485563041387x faster
-    Murmur32 vs NimHash: 2.938770259840504x faster
-    Murmur128 vs Murmur32: 0.9352743022136998x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.056872037914692x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 1.056872037914692x better
-
-=== Benchmark: Large dpSequential ===
-Size: 1000000 items
-Pattern: dpSequential
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.4705080000000001s (2125362.374284815 ops/sec)
-  Lookup time: 0.233136s (2144670.921693775 ops/sec)
-  False positives: 4024 (0.8048%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1423380000000001s (7025530.778850338 ops/sec)
-  Lookup time: 0.07601600000000008s (6577562.61839612 ops/sec)
-  False positives: 4205 (0.8410000000000001%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1583019999999999s (6317039.582570027 ops/sec)
-  Lookup time: 0.08089100000000027s (6181157.359904048 ops/sec)
-  False positives: 4024 (0.8048%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.305568435695316x faster
-    Murmur32 vs NimHash: 2.972217659915859x faster
-    Murmur128 vs Murmur32: 1.112155573353566x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.066933277204796x faster
-    Murmur32 vs NimHash: 2.88210060451718x faster
-    Murmur128 vs Murmur32: 1.064131235529365x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9569560047562424x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9569560047562424x better
-
-=== Benchmark: Small dpFixed ===
-Size: 10000 items
-Pattern: dpFixed
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.004584000000000366s (2181500.872600175 ops/sec)
-  Lookup time: 0.002313999999999705s (2160760.587727155 ops/sec)
-  False positives: 35 (0.7000000000000001%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001360000000000028s (7352941.176470438 ops/sec)
-  Lookup time: 0.0006840000000001289s (7309941.520466458 ops/sec)
-  False positives: 47 (0.9400000000000001%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001682999999999879s (5941770.647653428 ops/sec)
-  Lookup time: 0.0007459999999999134s (6702412.868633485 ops/sec)
-  False positives: 35 (0.7000000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.370588235294318x faster
-    Murmur32 vs NimHash: 2.723707664884548x faster
-    Murmur128 vs Murmur32: 1.237499999999886x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.383040935671446x faster
-    Murmur32 vs NimHash: 3.101876675603182x faster
-    Murmur128 vs Murmur32: 1.090643274853469x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.7446808510638298x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.7446808510638298x better
-
-=== Benchmark: Medium dpFixed ===
-Size: 100000 items
-Pattern: dpFixed
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.04620999999999986s (2164033.758926646 ops/sec)
-  Lookup time: 0.02306600000000003s (2167692.707881728 ops/sec)
-  False positives: 433 (0.8659999999999999%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.01405199999999995s (7116424.708226611 ops/sec)
-  Lookup time: 0.007185999999999915s (6957973.838018452 ops/sec)
-  False positives: 440 (0.88%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01542299999999974s (6483822.86195952 ops/sec)
-  Lookup time: 0.007922999999999902s (6310740.880979504 ops/sec)
-  False positives: 433 (0.8659999999999999%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.288499857671507x faster
-    Murmur32 vs NimHash: 2.996174544511485x faster
-    Murmur128 vs Murmur32: 1.097566182749772x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.209852490954677x faster
-    Murmur32 vs NimHash: 2.911270983213469x faster
-    Murmur128 vs Murmur32: 1.10256053437239x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9840909090909089x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9840909090909089x better
-
-=== Benchmark: Large dpFixed ===
-Size: 1000000 items
-Pattern: dpFixed
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.4785590000000002s (2089606.506198817 ops/sec)
-  Lookup time: 0.2445870000000001s (2044262.368809462 ops/sec)
-  False positives: 4085 (0.8170000000000001%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1526930000000002s (6549088.694308178 ops/sec)
-  Lookup time: 0.07818300000000011s (6395252.164792849 ops/sec)
-  False positives: 4109 (0.8218%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1651470000000002s (6055211.417706643 ops/sec)
-  Lookup time: 0.08524499999999957s (5865446.653762713 ops/sec)
-  False positives: 4085 (0.8170000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.134125336459429x faster
-    Murmur32 vs NimHash: 2.897775920846275x faster
-    Murmur128 vs Murmur32: 1.081562350598914x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.128391082460378x faster
-    Murmur32 vs NimHash: 2.869224001407723x faster
-    Murmur128 vs Murmur32: 1.090326541575527x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9941591628133366x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9941591628133366x better
-
-=== Benchmark: Small dpLong ===
-Size: 10000 items
-Pattern: dpLong
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.008112999999999815s (1232589.670898586 ops/sec)
-  Lookup time: 0.003777000000000363s (1323801.959226772 ops/sec)
-  False positives: 40 (0.8%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001597000000000293s (6261740.763931226 ops/sec)
-  Lookup time: 0.0007489999999998886s (6675567.423231967 ops/sec)
-  False positives: 56 (1.12%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001947999999999617s (5133470.2258737 ops/sec)
-  Lookup time: 0.001180999999999877s (4233700.254022458 ops/sec)
-  False positives: 40 (0.8%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 5.080150281777287x faster
-    Murmur32 vs NimHash: 4.164784394251238x faster
-    Murmur128 vs Murmur32: 1.219787100813563x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 5.042723631509913x faster
-    Murmur32 vs NimHash: 3.198137171888872x faster
-    Murmur128 vs Murmur32: 1.576769025367226x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.7142857142857143x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.7142857142857143x better
-
-=== Benchmark: Medium dpLong ===
-Size: 100000 items
-Pattern: dpLong
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.07904900000000037s (1265038.140899942 ops/sec)
-  Lookup time: 0.04046299999999992s (1235696.809430841 ops/sec)
-  False positives: 404 (0.8080000000000001%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.015733s (6356066.86582343 ops/sec)
-  Lookup time: 0.007823000000000135s (6391409.945033764 ops/sec)
-  False positives: 422 (0.844%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.02339799999999936s (4273869.561501099 ops/sec)
-  Lookup time: 0.01039399999999979s (4810467.577448624 ops/sec)
-  False positives: 404 (0.8080000000000001%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 5.024407296764786x faster
-    Murmur32 vs NimHash: 3.37845114967102x faster
-    Murmur128 vs Murmur32: 1.487192525265326x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 5.172312412118013x faster
-    Murmur32 vs NimHash: 3.892918991726066x faster
-    Murmur128 vs Murmur32: 1.328646299373592x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9573459715639812x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9573459715639812x better
-
-=== Benchmark: Large dpLong ===
-Size: 1000000 items
-Pattern: dpLong
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.814165s (1228252.25844884 ops/sec)
-  Lookup time: 0.4165090000000005s (1200454.251888913 ops/sec)
-  False positives: 4039 (0.8078%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1629300000000002s (6137605.10648744 ops/sec)
-  Lookup time: 0.08406800000000025s (5947566.255888073 ops/sec)
-  False positives: 4044 (0.8088%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.2405530000000002s (4157088.042967658 ops/sec)
-  Lookup time: 0.1411719999999992s (3541778.822996082 ops/sec)
-  False positives: 4039 (0.8078%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 4.997023261523347x faster
-    Murmur32 vs NimHash: 3.384555586502763x faster
-    Murmur128 vs Murmur32: 1.476419321180874x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 4.954429747347376x faster
-    Murmur32 vs NimHash: 2.950365511574554x faster
-    Murmur128 vs Murmur32: 1.679259646952452x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9987636003956479x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9987636003956479x better
-
-=== Benchmark: Small dpSpecial ===
-Size: 10000 items
-Pattern: dpSpecial
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.007111000000000089s (1406271.97299956 ops/sec)
-  Lookup time: 0.003309000000000673s (1511030.522816254 ops/sec)
-  False positives: 51 (1.02%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.001907000000000103s (5243838.489774232 ops/sec)
-  Lookup time: 0.0009350000000001302s (5347593.582886956 ops/sec)
-  False positives: 33 (0.66%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.001981000000000677s (5047955.577989189 ops/sec)
-  Lookup time: 0.0009660000000000224s (5175983.436852882 ops/sec)
-  False positives: 51 (1.02%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.728893550078503x faster
-    Murmur32 vs NimHash: 3.589601211508158x faster
-    Murmur128 vs Murmur32: 1.03880440482463x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.539037433155307x faster
-    Murmur32 vs NimHash: 3.425465838509933x faster
-    Murmur128 vs Murmur32: 1.033155080213784x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.545454545454546x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 1.545454545454546x better
-
-=== Benchmark: Medium dpSpecial ===
-Size: 100000 items
-Pattern: dpSpecial
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.05804300000000051s (1722860.637802993 ops/sec)
-  Lookup time: 0.03600399999999837s (1388734.585046169 ops/sec)
-  False positives: 368 (0.736%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.01871899999999904s (5342165.713980721 ops/sec)
-  Lookup time: 0.008850999999999942s (5649079.200090422 ops/sec)
-  False positives: 405 (0.8099999999999999%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.01748200000000111s (5720169.317011422 ops/sec)
-  Lookup time: 0.008943999999999619s (5590339.892665712 ops/sec)
-  False positives: 368 (0.736%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 3.100753245365857x faster
-    Murmur32 vs NimHash: 3.320157876672968x faster
-    Murmur128 vs Murmur32: 0.9339174101181689x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 4.067788950400927x faster
-    Murmur32 vs NimHash: 4.025491949910544x faster
-    Murmur128 vs Murmur32: 1.010507287312132x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 0.9086419753086421x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 0.9086419753086421x better
-
-=== Benchmark: Large dpSpecial ===
-Size: 1000000 items
-Pattern: dpSpecial
-
-Nim's Hash (MurmurHash3_32) Results:
-  Insert time: 0.5143779999999989s (1944095.58729184 ops/sec)
-  Lookup time: 0.2718509999999998s (1839242.820515651 ops/sec)
-  False positives: 4222 (0.8444%)
-
-MurmurHash3_128 Results:
-  Insert time: 0.1766860000000001s (5659757.988748398 ops/sec)
-  Lookup time: 0.08288000000000117s (6032818.532818447 ops/sec)
-  False positives: 4137 (0.8274%)
-
-MurmurHash3_32 Results:
-  Insert time: 0.1758810000000004s (5685662.464962092 ops/sec)
-  Lookup time: 0.09260200000000118s (5399451.415736092 ops/sec)
-  False positives: 4222 (0.8444%)
-
-Comparison (higher means better/faster):
-  Insert Speed:
-    Murmur128 vs NimHash: 2.911254994736417x faster
-    Murmur32 vs NimHash: 2.924579687402265x faster
-    Murmur128 vs Murmur32: 0.9954438948190591x faster
-  Lookup Speed:
-    Murmur128 vs NimHash: 3.280055501930454x faster
-    Murmur32 vs NimHash: 2.935692533638543x faster
-    Murmur128 vs Murmur32: 1.117302123552122x faster
-  False Positive Rates:
-    Murmur128 vs NimHash: 1.020546289581822x better
-    Murmur32 vs NimHash: 1.0x better
-    Murmur128 vs Murmur32: 1.020546289581822x better
diff --git a/nim-bloom/benches/config.nims b/nim-bloom/benches/config.nims
deleted file mode 100644
index 80091ff..0000000
--- a/nim-bloom/benches/config.nims
+++ /dev/null
@@ -1 +0,0 @@
-switch("path", "$projectDir/../src")
diff --git a/nim-bloom/bloom.nimble b/nim-bloom/bloom.nimble
deleted file mode 100644
index b6efff1..0000000
--- a/nim-bloom/bloom.nimble
+++ /dev/null
@@ -1,9 +0,0 @@
-# Package
-version       = "0.1.0"
-author        = "Waku Team"
-description   = "Efficient Bloom filter implementation for Nim"
-license       = "MIT"
-srcDir        = "src"
-
-# Dependencies
-requires "nim >= 1.0.0"
diff --git a/nim-bloom/src/.DS_Store b/nim-bloom/src/.DS_Store
deleted file mode 100644
index 961a3c0..0000000
Binary files a/nim-bloom/src/.DS_Store and /dev/null differ
diff --git a/nim-bloom/src/murmur3.c b/nim-bloom/src/murmur3.c
deleted file mode 100644
index 3d1b689..0000000
--- a/nim-bloom/src/murmur3.c
+++ /dev/null
@@ -1,314 +0,0 @@
-//-----------------------------------------------------------------------------
-// MurmurHash3 was written by Austin Appleby, and is placed in the public
-// domain. The author hereby disclaims copyright to this source code.
-
-// Note - The x86 and x64 versions do _not_ produce the same results, as the
-// algorithms are optimized for their respective platforms. You can still
-// compile and run any of them on any platform, but your performance with the
-// non-native version will be less than optimal.
-
-#include "murmur3.h"
-
-//-----------------------------------------------------------------------------
-// Platform-specific functions and macros
-
-#ifdef __GNUC__
-#define FORCE_INLINE __attribute__((always_inline)) inline
-#else
-#define FORCE_INLINE
-#endif
-
-static inline FORCE_INLINE uint32_t rotl32 ( uint32_t x, int8_t r )
-{
-  return (x << r) | (x >> (32 - r));
-}
-
-static inline FORCE_INLINE uint64_t rotl64 ( uint64_t x, int8_t r )
-{
-  return (x << r) | (x >> (64 - r));
-}
-
-#define ROTL32(x,y) rotl32(x,y)
-#define ROTL64(x,y) rotl64(x,y)
-
-#define BIG_CONSTANT(x) (x##LLU)
-
-//-----------------------------------------------------------------------------
-// Block read - if your platform needs to do endian-swapping or can only
-// handle aligned reads, do the conversion here
-
-#define getblock(p, i) (p[i])
-
-//-----------------------------------------------------------------------------
-// Finalization mix - force all bits of a hash block to avalanche
-
-static inline FORCE_INLINE uint32_t fmix32 ( uint32_t h )
-{
-  h ^= h >> 16;
-  h *= 0x85ebca6b;
-  h ^= h >> 13;
-  h *= 0xc2b2ae35;
-  h ^= h >> 16;
-
-  return h;
-}
-
-//----------
-
-static inline FORCE_INLINE uint64_t fmix64 ( uint64_t k )
-{
-  k ^= k >> 33;
-  k *= BIG_CONSTANT(0xff51afd7ed558ccd);
-  k ^= k >> 33;
-  k *= BIG_CONSTANT(0xc4ceb9fe1a85ec53);
-  k ^= k >> 33;
-
-  return k;
-}
-
-//-----------------------------------------------------------------------------
-
-void MurmurHash3_x86_32 ( const void * key, int len,
-                          uint32_t seed, void * out )
-{
-  const uint8_t * data = (const uint8_t*)key;
-  const int nblocks = len / 4;
-  int i;
-
-  uint32_t h1 = seed;
-
-  uint32_t c1 = 0xcc9e2d51;
-  uint32_t c2 = 0x1b873593;
-
-  //----------
-  // body
-
-  const uint32_t * blocks = (const uint32_t *)(data + nblocks*4);
-
-  for(i = -nblocks; i; i++)
-  {
-    uint32_t k1 = getblock(blocks,i);
-
-    k1 *= c1;
-    k1 = ROTL32(k1,15);
-    k1 *= c2;
-
-    h1 ^= k1;
-    h1 = ROTL32(h1,13);
-    h1 = h1*5+0xe6546b64;
-  }
-
-  //----------
-  // tail
-
-  const uint8_t * tail = (const uint8_t*)(data + nblocks*4);
-
-  uint32_t k1 = 0;
-
-  switch(len & 3)
-  {
-  case 3: k1 ^= tail[2] << 16;
-  case 2: k1 ^= tail[1] << 8;
-  case 1: k1 ^= tail[0];
-          k1 *= c1; k1 = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
-  };
-
-  //----------
-  // finalization
-
-  h1 ^= len;
-
-  h1 = fmix32(h1);
-
-  *(uint32_t*)out = h1;
-}
-
-//-----------------------------------------------------------------------------
-
-void MurmurHash3_x86_128 ( const void * key, const int len,
-                           uint32_t seed, void * out )
-{
-  const uint8_t * data = (const uint8_t*)key;
-  const int nblocks = len / 16;
-  int i;
-
-  uint32_t h1 = seed;
-  uint32_t h2 = seed;
-  uint32_t h3 = seed;
-  uint32_t h4 = seed;
-
-  uint32_t c1 = 0x239b961b;
-  uint32_t c2 = 0xab0e9789;
-  uint32_t c3 = 0x38b34ae5;
-  uint32_t c4 = 0xa1e38b93;
-
-  //----------
-  // body
-
-  const uint32_t * blocks = (const uint32_t *)(data + nblocks*16);
-
-  for(i = -nblocks; i; i++)
-  {
-    uint32_t k1 = getblock(blocks,i*4+0);
-    uint32_t k2 = getblock(blocks,i*4+1);
-    uint32_t k3 = getblock(blocks,i*4+2);
-    uint32_t k4 = getblock(blocks,i*4+3);
-
-    k1 *= c1; k1  = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
-
-    h1 = ROTL32(h1,19); h1 += h2; h1 = h1*5+0x561ccd1b;
-
-    k2 *= c2; k2  = ROTL32(k2,16); k2 *= c3; h2 ^= k2;
-
-    h2 = ROTL32(h2,17); h2 += h3; h2 = h2*5+0x0bcaa747;
-
-    k3 *= c3; k3  = ROTL32(k3,17); k3 *= c4; h3 ^= k3;
-
-    h3 = ROTL32(h3,15); h3 += h4; h3 = h3*5+0x96cd1c35;
-
-    k4 *= c4; k4  = ROTL32(k4,18); k4 *= c1; h4 ^= k4;
-
-    h4 = ROTL32(h4,13); h4 += h1; h4 = h4*5+0x32ac3b17;
-  }
-
-  //----------
-  // tail
-
-  const uint8_t * tail = (const uint8_t*)(data + nblocks*16);
-
-  uint32_t k1 = 0;
-  uint32_t k2 = 0;
-  uint32_t k3 = 0;
-  uint32_t k4 = 0;
-
-  switch(len & 15)
-  {
-  case 15: k4 ^= tail[14] << 16;
-  case 14: k4 ^= tail[13] << 8;
-  case 13: k4 ^= tail[12] << 0;
-           k4 *= c4; k4  = ROTL32(k4,18); k4 *= c1; h4 ^= k4;
-
-  case 12: k3 ^= tail[11] << 24;
-  case 11: k3 ^= tail[10] << 16;
-  case 10: k3 ^= tail[ 9] << 8;
-  case  9: k3 ^= tail[ 8] << 0;
-           k3 *= c3; k3  = ROTL32(k3,17); k3 *= c4; h3 ^= k3;
-
-  case  8: k2 ^= tail[ 7] << 24;
-  case  7: k2 ^= tail[ 6] << 16;
-  case  6: k2 ^= tail[ 5] << 8;
-  case  5: k2 ^= tail[ 4] << 0;
-           k2 *= c2; k2  = ROTL32(k2,16); k2 *= c3; h2 ^= k2;
-
-  case  4: k1 ^= tail[ 3] << 24;
-  case  3: k1 ^= tail[ 2] << 16;
-  case  2: k1 ^= tail[ 1] << 8;
-  case  1: k1 ^= tail[ 0] << 0;
-           k1 *= c1; k1  = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
-  };
-
-  //----------
-  // finalization
-
-  h1 ^= len; h2 ^= len; h3 ^= len; h4 ^= len;
-
-  h1 += h2; h1 += h3; h1 += h4;
-  h2 += h1; h3 += h1; h4 += h1;
-
-  h1 = fmix32(h1);
-  h2 = fmix32(h2);
-  h3 = fmix32(h3);
-  h4 = fmix32(h4);
-
-  h1 += h2; h1 += h3; h1 += h4;
-  h2 += h1; h3 += h1; h4 += h1;
-
-  ((uint32_t*)out)[0] = h1;
-  ((uint32_t*)out)[1] = h2;
-  ((uint32_t*)out)[2] = h3;
-  ((uint32_t*)out)[3] = h4;
-}
-
-//-----------------------------------------------------------------------------
-
-void MurmurHash3_x64_128 ( const void * key, const int len,
-                           const uint32_t seed, void * out )
-{
-  const uint8_t * data = (const uint8_t*)key;
-  const int nblocks = len / 16;
-  int i;
-
-  uint64_t h1 = seed;
-  uint64_t h2 = seed;
-
-  uint64_t c1 = BIG_CONSTANT(0x87c37b91114253d5);
-  uint64_t c2 = BIG_CONSTANT(0x4cf5ad432745937f);
-
-  //----------
-  // body
-
-  const uint64_t * blocks = (const uint64_t *)(data);
-
-  for(i = 0; i < nblocks; i++)
-  {
-    uint64_t k1 = getblock(blocks,i*2+0);
-    uint64_t k2 = getblock(blocks,i*2+1);
-
-    k1 *= c1; k1  = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
-
-    h1 = ROTL64(h1,27); h1 += h2; h1 = h1*5+0x52dce729;
-
-    k2 *= c2; k2  = ROTL64(k2,33); k2 *= c1; h2 ^= k2;
-
-    h2 = ROTL64(h2,31); h2 += h1; h2 = h2*5+0x38495ab5;
-  }
-
-  //----------
-  // tail
-
-  const uint8_t * tail = (const uint8_t*)(data + nblocks*16);
-
-  uint64_t k1 = 0;
-  uint64_t k2 = 0;
-
-  switch(len & 15)
-  {
-  case 15: k2 ^= (uint64_t)(tail[14]) << 48;
-  case 14: k2 ^= (uint64_t)(tail[13]) << 40;
-  case 13: k2 ^= (uint64_t)(tail[12]) << 32;
-  case 12: k2 ^= (uint64_t)(tail[11]) << 24;
-  case 11: k2 ^= (uint64_t)(tail[10]) << 16;
-  case 10: k2 ^= (uint64_t)(tail[ 9]) << 8;
-  case  9: k2 ^= (uint64_t)(tail[ 8]) << 0;
-           k2 *= c2; k2  = ROTL64(k2,33); k2 *= c1; h2 ^= k2;
-
-  case  8: k1 ^= (uint64_t)(tail[ 7]) << 56;
-  case  7: k1 ^= (uint64_t)(tail[ 6]) << 48;
-  case  6: k1 ^= (uint64_t)(tail[ 5]) << 40;
-  case  5: k1 ^= (uint64_t)(tail[ 4]) << 32;
-  case  4: k1 ^= (uint64_t)(tail[ 3]) << 24;
-  case  3: k1 ^= (uint64_t)(tail[ 2]) << 16;
-  case  2: k1 ^= (uint64_t)(tail[ 1]) << 8;
-  case  1: k1 ^= (uint64_t)(tail[ 0]) << 0;
-           k1 *= c1; k1  = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
-  };
-
-  //----------
-  // finalization
-
-  h1 ^= len; h2 ^= len;
-
-  h1 += h2;
-  h2 += h1;
-
-  h1 = fmix64(h1);
-  h2 = fmix64(h2);
-
-  h1 += h2;
-  h2 += h1;
-
-  ((uint64_t*)out)[0] = h1;
-  ((uint64_t*)out)[1] = h2;
-}
-
-//-----------------------------------------------------------------------------
diff --git a/nim-bloom/src/murmur3.h b/nim-bloom/src/murmur3.h
deleted file mode 100644
index 6928384..0000000
--- a/nim-bloom/src/murmur3.h
+++ /dev/null
@@ -1,21 +0,0 @@
-//-----------------------------------------------------------------------------
-// MurmurHash3 was written by Austin Appleby, and is placed in the
-// public domain. The author hereby disclaims copyright to this source
-// code.
-
-#ifndef _MURMURHASH3_H_
-#define _MURMURHASH3_H_
-
-#include <stdint.h>
-
-//-----------------------------------------------------------------------------
-
-void MurmurHash3_x86_32 (const void *key, int len, uint32_t seed, void *out);
-
-void MurmurHash3_x86_128(const void *key, int len, uint32_t seed, void *out);
-
-void MurmurHash3_x64_128(const void *key, int len, uint32_t seed, void *out);
-
-//-----------------------------------------------------------------------------
-
-#endif // _MURMURHASH3_H_
\ No newline at end of file
diff --git a/nim-bloom/tests/config.nims b/nim-bloom/tests/config.nims
deleted file mode 100644
index 80091ff..0000000
--- a/nim-bloom/tests/config.nims
+++ /dev/null
@@ -1 +0,0 @@
-switch("path", "$projectDir/../src")
diff --git a/nim-bloom/tests/test.nim b/tests/bloom.nim
similarity index 53%
rename from nim-bloom/tests/test.nim
rename to tests/bloom.nim
index 88d70fe..eb5da32 100644
--- a/nim-bloom/tests/test.nim
+++ b/tests/bloom.nim
@@ -1,84 +1,7 @@
 import unittest
-import strutils
-include bloom
+include ../bloom_filter/bloom
 from random import rand, randomize
 
-suite "murmur":
-  # Test murmurhash3 implementations
-  setup:
-    var hashOutputs: MurmurHashes
-    hashOutputs = [0, 0]
-    rawMurmurHash128("hello", 5, 0'u32, hashOutputs)
-
-  test "murmur128 raw":
-    check int(hashOutputs[0]) == -3758069500696749310
-    check int(hashOutputs[1]) == 6565844092913065241
-
-  test "murmur128 wrapped":
-    let hashOutputs2 = murmurHash128("hello", 0'u32)
-    check hashOutputs2[0] == hashOutputs[0]
-    check hashOutputs2[1] == hashOutputs[1]
-
-  test "murmur32":
-    let hash1 = murmurHash32("hello", 0'u32)
-    let hash2 = murmurHash32("hello", 0'u32)
-    check hash1 == hash2  # Same input should give same output
-    
-    let hash3 = murmurHash32("hello", 10'u32)
-    check hash1 != hash3  # Different seeds should give different outputs
-
-suite "hash quality":
-  test "hash type selection":
-    let bfMurmur128 = initializeBloomFilter(100, 0.01, hashType = htMurmur128)
-    let bfMurmur32 = initializeBloomFilter(100, 0.01, hashType = htMurmur32)
-    let bfNimHash = initializeBloomFilter(100, 0.01, hashType = htNimHash)
-    
-    check bfMurmur128.hashType == htMurmur128
-    check bfMurmur32.hashType == htMurmur32
-    check bfNimHash.hashType == htNimHash
-
-  test "quality across hash types":
-    const testSize = 10_000
-    let patterns = @[
-      "shortstr",
-      repeat("a", 1000),  # Very long string
-      "special@#$%^&*()",  # Special characters
-      "unicode→★∑≈",  # Unicode characters
-      repeat("pattern", 10)  # Repeating pattern
-    ]
-    
-    for hashType in [htMurmur128, htMurmur32, htNimHash]:
-      var bf = initializeBloomFilter(testSize, 0.01, hashType = hashType)
-      var inserted = newSeq[string](testSize)
-      
-      # Test pattern handling
-      for pattern in patterns:
-        bf.insert(pattern)
-        check bf.lookup(pattern)
-      
-      # Test general insertion and lookup
-      for i in 0..<testSize:
-        inserted[i] = $i & "test" & $rand(1000)
-        bf.insert(inserted[i])
-      
-      # Verify all insertions
-      var lookupErrors = 0
-      for item in inserted:
-        if not bf.lookup(item):
-          lookupErrors.inc
-      check lookupErrors == 0
-      
-      # Check false positive rate
-      var falsePositives = 0
-      let fpTestSize = testSize div 2
-      for i in 0..<fpTestSize:
-        let testItem = "notpresent" & $i & $rand(1000)
-        if bf.lookup(testItem):
-          falsePositives.inc
-      
-      let fpRate = falsePositives.float / fpTestSize.float
-      check fpRate < bf.errorRate * 1.5  # Allow some margin but should be close to target
-
 suite "bloom filter":
   setup:
     let nElementsToTest = 10000
@@ -148,4 +71,46 @@ suite "bloom filter":
     let str = $bf3
     check str.contains("1000")  # Capacity
     check str.contains("4 hash")  # Hash functions
-    check str.contains("1.0e-02")  # Error rate in scientific notation
\ No newline at end of file
+    check str.contains("1.0e-02")  # Error rate in scientific notation
+
+suite "bloom filter special cases":
+  test "different patterns of strings":
+    const testSize = 10_000
+    let patterns = @[
+      "shortstr",
+      repeat("a", 1000),  # Very long string
+      "special@#$%^&*()",  # Special characters
+      "unicode→★∑≈",  # Unicode characters
+      repeat("pattern", 10)  # Repeating pattern
+    ]
+    
+    var bf = initializeBloomFilter(testSize, 0.01)
+    var inserted = newSeq[string](testSize)
+    
+    # Test pattern handling
+    for pattern in patterns:
+      bf.insert(pattern)
+      check bf.lookup(pattern)
+    
+    # Test general insertion and lookup
+    for i in 0..<testSize:
+      inserted[i] = $i & "test" & $rand(1000)
+      bf.insert(inserted[i])
+    
+    # Verify all insertions
+    var lookupErrors = 0
+    for item in inserted:
+      if not bf.lookup(item):
+        lookupErrors.inc
+    check lookupErrors == 0
+    
+    # Check false positive rate
+    var falsePositives = 0
+    let fpTestSize = testSize div 2
+    for i in 0..<fpTestSize:
+      let testItem = "notpresent" & $i & $rand(1000)
+      if bf.lookup(testItem):
+        falsePositives.inc
+    
+    let fpRate = falsePositives.float / fpTestSize.float
+    check fpRate < bf.errorRate * 1.5  # Allow some margin but should be close to target
\ No newline at end of file