Make Hashing Algorithm Faster (but dumber)

cubes.py

@@ -151,40 +151,27 @@ def generate_polycubes(n, use_cache=False):
 
 def rle(polycube):
     """
-    Computes a simple run-length encoding of a given polycube. This function allows cubes to be more quickly compared via hashing.
+    Computes a simple hash of a given polycube.
+    It does this by converting the polycube into a 1d array and then interpreting those bits as a unsigned integer
+    This function allows cubes to be more quickly compared via hashing.
 
-    Converts a {0,1} nd array into a tuple that encodes the same shape. The array is first flattened, and then the following algorithm is applied:
-
-    1) The first three values in tuple contain the x,y,z dimension sizes of the array
-    2) Each string of zeros of length n is replaced with a single value -n
-    3) Each string of ones of length m is replaced with a single value +m
+    Converts a {0,1} nd array into a single unique large integer
 
     Parameters:
     polycube (np.array): 3D Numpy byte array where 1 values indicate polycube positions
 
     Returns:
-    tuple(int): Run length encoded polycube in the form (X, Y, Z, a, b, c, ...)
+    int: a unique integer hash
 
     """
-    r = []
-    r.extend(polycube.shape)
-    current = None
-    val = 0
-    for x in polycube.flat:
-        if current is None:
-            current = x
-            val = 1
-            pass
-        elif current == x:
-            val += 1
-        elif current != x:
-            r.append(val if current == 1 else -val)
-            current = x
-            val = 1
-
-    r.append(val if current == 1 else -val)
-
-    return tuple(r)
+
+    pack_cube = np.packbits(polycube.flatten())
+    data = 0
+    for part in pack_cube:
+        data = (data << 8) + int(part)
+    out = (data * 100000) + (polycube.shape[0] * 1000) + (polycube.shape[1] * 10) + (polycube.shape[2]) # add volume dimensions to lower bits of hash
+    return out
+
 
 def cube_exists_rle(polycube, polycubes_rle):
     """