diff --git a/cog.py b/cog.py
new file mode 100644
index 0000000..15ddb65
--- /dev/null
+++ b/cog.py
@@ -0,0 +1,49 @@
+from utils import Element, KComplexity
+import primitive_fucntions as pf
+
+a = Element("a", 1, "red")
+b = Element("b", 2, "blue")
+c = Element("c", 3, "green")
+d = Element("d", 4, "yellow")
+
+Elements = {a, b, c, d}
+
+def cog_1(E):
+    pf.sample(E)
+    among_us = []
+    pf.add(a, among_us)
+    pf.add(pf.sample(E), among_us)
+    pf.remove(b, among_us)
+
+kc = KComplexity()
+cog_1(Elements)
+print(kc.get_k_complexity())
+#print(kc.get_prim_counts())
+
+'''
+element_set = {a,b,c,d}
+S = ElementSet(element_set)
+kc = KComplexity()
+cf.iterate(S)
+print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())
+print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())
+print('Total K-complexity: ', kc.get_total_k_complexity())
+print('Total primitive counts: ', kc.get_total_prim_counts())
+
+kc.plot_prim_counts()
+kc.plot_total_prim_counts()
+kc.plot_cog_vs_prim()
+
+element_set = {a,b,c,d}
+S = ElementSet(element_set)
+cf.palindrome(S)
+print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())
+print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())
+print('Total K-complexity: ', kc.get_total_k_complexity())
+print('Total primitive counts: ', kc.get_total_prim_counts())
+
+
+kc.plot_prim_counts()
+kc.plot_total_prim_counts()
+kc.plot_cog_vs_prim()
+'''
\ No newline at end of file
diff --git a/cognitive_functions.py b/cognitive_functions.py
index 816d459..94f231b 100644
--- a/cognitive_functions.py
+++ b/cognitive_functions.py
@@ -1,225 +1,258 @@
 import primitive_fucntions as pf
 from collections import defaultdict
-from utils import Stopwatch, Element, ElementSet, Associations
- 
-# 1-D
+from utils import Stopwatch, ElementSet, Associations
 
-def iterate(S: ElementSet):     # 112233    
+# --- Cognitive Functions using Graph Node Structure --- #
 
-    # preprocessing (not part of measured cognitive process)
-    n = len(S.elements) # number of elements in the set
-    chunks = defaultdict(list) 
+def iterate(S: ElementSet):
+    n = len(S.elements)
+    chunks = defaultdict(list)
     stopwatch = Stopwatch()
-    # --- #
-    # select attribute which chunking is based on
-
-    ### ADD CODE HERE
-    """ graph algo here"""
-    bias = find_bias(S.elements, stopwatch)
-    for _ in range(n//2):
-        element = pf.sample(S)
-    result = None
-    time_elapsed = None
 
+    bias = find_bias(S, stopwatch)
+    for _ in range(n // 2):
+        element = pf.sample(S.elements)
 
-    # """ non graph algo here"""
     bias = find_bias(S, stopwatch)
     stopwatch.start()
     for _ in range(n):
-        element = pf.sample(S) # select an element in the set
+        element = pf.sample(S.elements)
         sorter = getattr(element, bias)
         chunks[sorter].append(element)
-        pf.setminus(S, element)
+        pf.setminus(S.elements, element)
     stopwatch.stop()
-    n = len(chunks) # reassign n
-    chunks = {tuple(v) for k, v in chunks.items()}
+
+    chunks = {tuple(v) for v in chunks.values()}
     stopwatch.start()
     result = []
-    for _ in range(n):
+    for _ in range(len(chunks)):
         chunk = pf.sample(chunks)
         stopwatch.stop()
         temp = list(chunk)
         stopwatch.start()
-        pf.append(result, temp)
+        result = pf.append(result, temp)
         pf.setminus(chunks, chunk)
     stopwatch.stop()
     time_elapsed = stopwatch.get_elapsed_time()
-
     return (result, time_elapsed)
 
-def palindrome(S):
-    # 123321
-    # preprocessing (not part of measured cognitive process)
-    n = len(S) // 2 # number of elements in basis
+def palindrome(S: ElementSet):
+    n = len(S.elements) // 2
     stopwatch = Stopwatch()
-    # select attribute which chunking is based on
     bias = find_bias(S, stopwatch)
     stopwatch.start()
     basis, rev = [], []
-    # write_random() based implementation
-    ### WRITE CODE HERE
-    while (len(S) > n):
-        element = pf.sample(S)
-        if len(basis)==0 or not (any(pf.check_if_same_type(element, chosen, bias) for chosen in basis)):
+
+    while len(S.elements) > n:
+        element = pf.sample(S.elements)
+        if len(basis) == 0 or not any(pf.check_if_same_type(element, chosen, bias) for chosen in basis):
             pf.pair(basis, element)
-            pf.setminus(S, element)
-    for _ in range(n):
-        element = pf.write_random(S, bias, getattr(basis[n-1-_], bias))
-        pf.pair(rev,element)
-        pf.setminus(S, element)
-    result = pf.append(basis,rev)
+            pf.setminus(S.elements, element)
+
+    for i in range(n):
+        target_type = getattr(basis[n - 1 - i], bias)
+        candidates = [u for u, v, d in S.graph.edges(data=True) if v == target_type and d['label'] == bias and u in S.elements]
+        if candidates:
+            element = pf.sample(candidates)
+            pf.pair(rev, element)
+            pf.setminus(S.elements, element)
+
+    result = pf.append(basis, rev)
     stopwatch.stop()
-    time_elapsed = stopwatch.get_elapsed_time()
-    
-    return (result, time_elapsed)      
+    return (result, stopwatch.get_elapsed_time())
 
-def alternate(S):
-    # 121212
-    # preprocessing (not part of measured cognitive process)
-    n = len(S) # number of elements in the set
+def alternate(S: ElementSet):
     stopwatch = Stopwatch()
-    # --- #
-    # select attribute which chunking is based on
-    ### ADD CODE HERE
-    ### subject knows what types of attributes are there
-    ### and what type of attribute to select
-
-    bias = find_bias(S,stopwatch,two_flag=True)
+    bias = find_bias(S, stopwatch, two_flag=True)
     result = []
-    while (len(S) > 0):
-        element = pf.sample(S)
+
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
         if len(result) == 0 or not pf.check_if_same_type(element, result[-1], bias):
-            pf.pair(result, element)    
-            pf.setminus(S, element)
-    time_elapsed = stopwatch.get_elapsed_time()
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
 
-    return (result, time_elapsed)
+    return (result, stopwatch.get_elapsed_time())
 
-def chaining(S, associations: dict):
-    # preprocessing (not part of measured cognitive process)
-    n = len(S) # number of elements in the set
+def chaining(S: ElementSet, associations: Associations):
     stopwatch = Stopwatch()
-    # --- #
     stopwatch.start()
     chunks = []
     result = []
-    while (len(S) > 0):
-        element = pf.sample(S)
-        if element in associations.keys():
+
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        if element in associations.associations:
             chunk = []
             pf.pair(chunk, element)
-            pf.setminus(S, element)
+            pf.setminus(S.elements, element)
+
             while True:
-                next_element = pf.sample(S)
-                if next_element == associations[element]:
+                next_element = pf.sample(S.elements)
+                if next_element == associations.associations[element]:
                     pf.pair(chunk, next_element)
-                    pf.setminus(S, next_element)
+                    pf.setminus(S.elements, next_element)
                     pf.pair(chunks, chunk)
                     break
-                else:
-                    continue
-        else:
-            continue
-    stopwatch.stop()
-    time_elapsed = stopwatch.get_elapsed_time()
 
-    return  (result, time_elapsed)
+    result = chunks
+    stopwatch.stop()
+    return (result, stopwatch.get_elapsed_time())
 
-def seriate(S):
+def seriate(S: ElementSet):
     #  123123
     pass
 
-# -------- 2-D -------- #
-
-def serial_crossed(S):
-    n = len(S) // 2 # number of elements in the basis
+def serial_crossed(S: ElementSet):
+    n = len(S.elements) // 2 # number of elements in the basis
     stopwatch = Stopwatch()
-    bias = find_bias(S, stopwatch, higher_dim=True)
+    bias = find_bias(S.elements, stopwatch, higher_dim=True)
     result = []
-    while len(S) > n:
-        element = pf.sample(S)
+    while len(S.elements) > n:
+        element = pf.sample(S.elements)
         if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
             pf.pair(result, element)
-            pf.setminus(S, element)
+            pf.setminus(S.elements, element)
     for _ in range(n):
-        element = pf.write_random(S, bias[1], getattr(result[_], bias[1]))
+        element = pf.write_random(S.elements, bias[1], getattr(result[_], bias[1]))
         pf.pair(result, element)
-        pf.setminus(S, element)
+        pf.setminus(S.elements, element)
     time_elapsed = stopwatch.get_elapsed_time()
     return (result, time_elapsed)
 
-def center_embedded(S):
-    n = len(S) // 2 # number of elements in the basis
+def center_embedded(S: ElementSet):
+    n = len(S.elements) // 2 # number of elements in the basis
     # 
     stopwatch = Stopwatch()
-    bias = find_bias(S, stopwatch, higher_dim=True)
+    bias = find_bias(S.elements, stopwatch, higher_dim=True)
     result = []
-    while len(S) > 0:
-        element = pf.sample(S)
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
         if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
             pf.pair(result, element)
-            pf.setminus(S, element)
+            pf.setminus(S.elements, element)
+        if len(result) == n:
+            break
     for _ in range(n):
-        element = pf.write_random(S, bias[1], getattr(result[n - 1 - _], bias[1]))
+        element = pf.write_random(S.elements, bias[1], getattr(result[n - 1 - _], bias[1]))
         pf.pair(result, element)
-        pf.setminus(S, element)
+        pf.setminus(S.elements, element)
     stopwatch.stop()
     time_elapsed = stopwatch.get_elapsed_time()
     return (result, time_elapsed)
 
-def tail_recursive(S):
+def tail_recursive(S: ElementSet):
     stopwatch = Stopwatch()
-    bias = find_bias(S, stopwatch, two_flag=True, higher_dim=True)
+    bias = find_bias(S.elements, stopwatch, two_flag=True, higher_dim=True)
     stopwatch.start()
     result = []
-    while len(S) > 0:
-        element = pf.sample(S)
-        pf.setminus(S, element)
-        paired_element = pf.write_random(S, bias[0], getattr(element, bias[0]))
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        pf.setminus(S.elements, element)
+        paired_element = pf.write_random(S.elements, bias[0], getattr(element, bias[0]))
         result = pf.append(result, pf.merge(element, paired_element))
-        pf.setminus(S, paired_element)
+        pf.setminus(S.elements, paired_element)
     stopwatch.stop()
     time_elapsed = stopwatch.get_elapsed_time()
     return (result, time_elapsed)
 
+'''
 
-# ---------------------------------------------------------------------#
+def serial_crossed(S: ElementSet):
+    n = len(S.elements) // 2
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, higher_dim=True)
+    result = []
+
+    while len(S.elements) > n:
+        element = pf.sample(S.elements)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
+
+    for i in range(n):
+        target_type = getattr(result[i], bias[1])
+        candidates = [u for u, v, d in S.graph.edges(data=True) if v == target_type and d['label'] == bias[1] and u in S.elements]
+        if candidates:
+            element = pf.sample(candidates)
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
+
+    return (result, stopwatch.get_elapsed_time())
+
+def center_embedded(S: ElementSet):
+    n = len(S.elements) // 2
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, higher_dim=True)
+    result = []
+
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
+        if len(result) == n:
+            break
 
-# utils
+    for i in range(n):
+        target_type = getattr(result[n - 1 - i], bias[1])
+        candidates = [u for u, v, d in S.graph.edges(data=True) if v == target_type and d['label'] == bias[1] and u in S.elements]
+        if candidates:
+            element = pf.sample(candidates)
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
 
-def find_bias(S,clock,two_flag=False,higher_dim=False):
-    """
-    Count unique values for both attributes, 
-    select the attribute with exactly 2 types while the other has != 2 types
+    stopwatch.stop()
+    return (result, stopwatch.get_elapsed_time())
 
-    Input Arguments:
-        S: set of elements to be experimented with
-        clock: stopwatch used to time primitive functions
-        two_flag: flag to indicate if the bias required needs only two attribute types
-        higher_dim: flag to indicate if the set of elements is 2-dimensional
+def tail_recursive(S: ElementSet):
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, two_flag=True, higher_dim=True)
+    stopwatch.start()
+    result = []
+
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        pf.setminus(S.elements, element)
+        target_type = getattr(element, bias[0])
+        candidates = [u for u, v, d in S.graph.edges(data=True) if v == target_type and d['label'] == bias[0] and u in S.elements]
+        if candidates:
+            paired_element = pf.sample(candidates)
+            result = pf.append(result, pf.merge(element, paired_element))
+            pf.setminus(S.elements, paired_element)
+
+    stopwatch.stop()
+    return (result, stopwatch.get_elapsed_time())
+
+'''
+
+def find_bias(S, clock, two_flag=False, higher_dim=False):
+    if hasattr(S, 'elements'):
+        elements = S.elements
+    else:
+        elements = S
 
-    Output:
-        bias: the bias lol, the attribute the flip selected
-    """
-    if higher_dim == True:
-        chunk_bias, serial_bias = None, None
-        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
-                    for attr in ["attribute1", "attribute2"]}
-        chunk_bias = find_bias(S, clock, two_flag)
+    if higher_dim:
+        attribute_counts = {
+            attr: len(set(getattr(obj, attr) for obj in elements))
+            for attr in ["attribute1", "attribute2"]
+        }
+        chunk_bias = find_bias(elements, clock, two_flag)
         clock.start()
         serial_bias = 'attribute1' if chunk_bias == 'attribute2' else 'attribute2'
         clock.stop()
         return (chunk_bias, serial_bias)
     else:
-        bias = None
-        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
-                    for attr in ["attribute1", "attribute2"]}
+        attribute_counts = {
+            attr: len(set(getattr(obj, attr) for obj in elements))
+            for attr in ["attribute1", "attribute2"]
+        }
         if attribute_counts["attribute1"] == 2 and attribute_counts["attribute2"] != 2:
-            bias = "attribute1" if not two_flag else "attribute1"
+            return "attribute1"
         elif attribute_counts["attribute2"] == 2 and attribute_counts["attribute1"] != 2:
-            bias = "attribute2" if not two_flag else "attribute1"
+            return "attribute2"
         else:
             clock.start()
-            bias = "attribute1" if pf.flip(0.5) else "attribute2"  # Default random selection
+            bias = "attribute1" if pf.flip(0.5) else "attribute2"
             clock.stop()
-        return bias
\ No newline at end of file
+            return bias
diff --git a/experiments.ipynb b/experiments.ipynb
index 0562298..4be28c0 100644
--- a/experiments.ipynb
+++ b/experiments.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "08bf7d5a",
    "metadata": {},
    "outputs": [],
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 2,
    "id": "cd152c0d",
    "metadata": {},
    "outputs": [],
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 3,
    "id": "5bfc8429",
    "metadata": {},
    "outputs": [],
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "88c86b73",
    "metadata": {},
    "outputs": [],
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "321f3986-476b-44b9-affe-caa29093b36a",
    "metadata": {},
    "outputs": [],
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "b3cbc0bd",
    "metadata": {},
    "outputs": [],
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "84dc2684-1ed4-41cf-9f24-5b81b4f55864",
    "metadata": {
     "scrolled": true
@@ -112,7 +112,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1200x1000 with 4 Axes>"
       ]
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "164196ea-7f89-400f-bdaf-0b57b53bf1d6",
    "metadata": {},
    "outputs": [],
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "6bef8a1f-c98d-4e78-bffa-7d867276314e",
    "metadata": {},
    "outputs": [
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 10,
    "id": "42551054",
    "metadata": {},
    "outputs": [],
@@ -209,17 +209,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 11,
    "id": "3712fe91",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'set' object has no attribute 'elements'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m times \u001b[38;5;241m=\u001b[39m \u001b[43mrunReordering\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[10], line 6\u001b[0m, in \u001b[0;36mrunReordering\u001b[1;34m(element_set, n_runs)\u001b[0m\n\u001b[0;32m      4\u001b[0m time_list \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# 1-D\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m time_list\u001b[38;5;241m.\u001b[39mappend(\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterate\u001b[49m\u001b[43m(\u001b[49m\u001b[43melement_set\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m      7\u001b[0m time_list\u001b[38;5;241m.\u001b[39mappend(cf\u001b[38;5;241m.\u001b[39mpalindrome(element_set)[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m      8\u001b[0m time_list\u001b[38;5;241m.\u001b[39mappend(cf\u001b[38;5;241m.\u001b[39malternate(element_set)[\u001b[38;5;241m1\u001b[39m])\n",
+      "File \u001b[1;32mc:\\Users\\desig\\OneDrive\\Desktop\\VS Code\\LoT Modeling\\LoT Code\\LoT-modeling\\cognitive_functions.py:10\u001b[0m, in \u001b[0;36miterate\u001b[1;34m(S)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21miterate\u001b[39m(S: ElementSet):     \u001b[38;5;66;03m# 112233    \u001b[39;00m\n\u001b[0;32m      8\u001b[0m \n\u001b[0;32m      9\u001b[0m     \u001b[38;5;66;03m# preprocessing (not part of measured cognitive process)\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m     n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[43mS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43melements\u001b[49m) \u001b[38;5;66;03m# number of elements in the set\u001b[39;00m\n\u001b[0;32m     11\u001b[0m     chunks \u001b[38;5;241m=\u001b[39m defaultdict(\u001b[38;5;28mlist\u001b[39m) \n\u001b[0;32m     12\u001b[0m     stopwatch \u001b[38;5;241m=\u001b[39m Stopwatch()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'set' object has no attribute 'elements'"
+     ]
+    }
+   ],
    "source": [
     "times = runReordering(train_1, 20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "id": "3a971260",
    "metadata": {},
    "outputs": [
@@ -247,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "id": "7f77d49a",
    "metadata": {},
    "outputs": [
@@ -281,7 +295,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "lot_venv",
    "language": "python",
    "name": "python3"
   },
@@ -295,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/midtest/midcf.py b/midtest/midcf.py
new file mode 100644
index 0000000..d4cc343
--- /dev/null
+++ b/midtest/midcf.py
@@ -0,0 +1,273 @@
+import midpf as pf
+from collections import defaultdict
+from midutils import Stopwatch, Element, ElementSet, Associations
+ 
+# 1-D
+
+def iterate(S: ElementSet):     # 112233    
+
+    # preprocessing (not part of measured cognitive process)
+    n = len(S.elements) # number of elements in the set
+    chunks = defaultdict(list) 
+    stopwatch = Stopwatch()
+    # --- #
+    # select attribute which chunking is based on
+
+    ### ADD CODE HERE
+    """ graph algo here"""
+    bias = find_bias(S.elements, stopwatch)
+    for _ in range(n//2):
+        element = pf.sample(S.elements)
+    result = None
+    time_elapsed = None
+
+
+    # """ non graph algo here"""
+    bias = find_bias(S.elements, stopwatch)
+    stopwatch.start()
+    for _ in range(n):
+        element = pf.sample(S.elements) # select an element in the set
+        sorter = getattr(element, bias)
+        chunks[sorter].append(element)
+        pf.setminus(S.elements, element)
+    stopwatch.stop()
+    n = len(chunks) # reassign n
+    chunks = {tuple(v) for k, v in chunks.items()}
+    stopwatch.start()
+    result = []
+    for _ in range(n):
+        chunk = pf.sample(chunks)
+        stopwatch.stop()
+        temp = list(chunk)
+        stopwatch.start()
+        result = pf.append(result, temp)
+        pf.setminus(chunks, chunk)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+
+    return (result, time_elapsed)
+
+def palindrome(S: ElementSet):
+    n = len(S.elements) // 2
+    stopwatch = Stopwatch()
+    bias = find_bias(S.elements, stopwatch)  
+    stopwatch.start()
+    basis, rev = [], []
+
+    while len(S.elements) > n:
+        element = pf.sample(S.elements)
+        if len(basis) == 0 or not any(pf.check_if_same_type(element, chosen, bias) for chosen in basis):
+            pf.pair(basis, element)
+            pf.setminus(S.elements, element)
+
+    for _ in range(n):
+        element = pf.write_random(S.elements, bias, getattr(basis[n-1-_], bias))
+        pf.pair(rev, element)
+        pf.setminus(S.elements, element)
+
+    result = pf.append(basis, rev)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+def alternate(S: ElementSet):
+    # 121212
+    n = len(S.elements)  # Number of elements in the ElementSet
+    stopwatch = Stopwatch()
+
+    bias = find_bias(S.elements, stopwatch, two_flag=True)
+    result = []
+
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        if len(result) == 0 or not pf.check_if_same_type(element, result[-1], bias):
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
+
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+
+def chaining(S: ElementSet, associations: dict):
+    # preprocessing (not part of measured cognitive process)
+    n = len(S.elements) # number of elements in the set
+    stopwatch = Stopwatch()
+    # --- #
+    stopwatch.start()
+    chunks = []
+    result = []
+    while (len(S.elements) > 0):
+        element = pf.sample(S.elements)
+        if element in associations.associations:
+            chunk = []
+            pf.pair(chunk, element)
+            pf.setminus(S.elements, element)
+            while True:
+                next_element = pf.sample(S.elements)
+                if next_element == associations.associations[element]:
+                    pf.pair(chunk, next_element)
+                    pf.setminus(S.elements, next_element)
+                    pf.pair(chunks, chunk)
+                    break
+                else:
+                    continue
+        else:
+            continue
+    result = chunks
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+
+    return  (result, time_elapsed)
+
+def seriate(S: ElementSet):
+    #  123123
+    pass
+
+# -------- 2-D -------- #
+
+def serial_crossed(S: ElementSet):
+    n = len(S.elements) // 2 # number of elements in the basis
+    stopwatch = Stopwatch()
+    bias = find_bias(S.elements, stopwatch, higher_dim=True)
+    result = []
+    while len(S.elements) > n:
+        element = pf.sample(S.elements)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
+    for _ in range(n):
+        element = pf.write_random(S.elements, bias[1], getattr(result[_], bias[1]))
+        pf.pair(result, element)
+        pf.setminus(S.elements, element)
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+def center_embedded(S: ElementSet):
+    n = len(S.elements) // 2 # number of elements in the basis
+    # 
+    stopwatch = Stopwatch()
+    bias = find_bias(S.elements, stopwatch, higher_dim=True)
+    result = []
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            pf.setminus(S.elements, element)
+        if len(result) == n:
+            break
+    for _ in range(n):
+        element = pf.write_random(S.elements, bias[1], getattr(result[n - 1 - _], bias[1]))
+        pf.pair(result, element)
+        pf.setminus(S.elements, element)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+def tail_recursive(S: ElementSet):
+    stopwatch = Stopwatch()
+    bias = find_bias(S.elements, stopwatch, two_flag=True, higher_dim=True)
+    stopwatch.start()
+    result = []
+    while len(S.elements) > 0:
+        element = pf.sample(S.elements)
+        pf.setminus(S.elements, element)
+        paired_element = pf.write_random(S.elements, bias[0], getattr(element, bias[0]))
+        result = pf.append(result, pf.merge(element, paired_element))
+        pf.setminus(S.elements, paired_element)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+
+# ---------------------------------------------------------------------#
+
+# utils
+
+def find_bias(S,clock,two_flag=False,higher_dim=False):
+    
+    #Count unique values for both attributes, 
+    #select the attribute with exactly 2 types while the other has != 2 types
+
+    #Input Arguments:
+        #S: set of elements to be experimented with
+        #clock: stopwatch used to time primitive functions
+        #two_flag: flag to indicate if the bias required needs only two attribute types
+        #higher_dim: flag to indicate if the set of elements is 2-dimensional
+
+    #Output:
+        #bias: the bias lol, the attribute the flip selected
+    
+    if higher_dim == True:
+        chunk_bias, serial_bias = None, None
+        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
+                    for attr in ["attribute1", "attribute2"]}
+        chunk_bias = find_bias(S, clock, two_flag)
+        clock.start()
+        serial_bias = 'attribute1' if chunk_bias == 'attribute2' else 'attribute2'
+        clock.stop()
+        return (chunk_bias, serial_bias)
+    else:
+        bias = None
+        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
+                    for attr in ["attribute1", "attribute2"]}
+        if attribute_counts["attribute1"] == 2 and attribute_counts["attribute2"] != 2:
+            bias = "attribute1" if not two_flag else "attribute1"
+        elif attribute_counts["attribute2"] == 2 and attribute_counts["attribute1"] != 2:
+            bias = "attribute2" if not two_flag else "attribute1"
+        else:
+            clock.start()
+            bias = "attribute1" if pf.flip(0.5) else "attribute2"  # Default random selection
+            clock.stop()
+        return bias
+'''
+
+def find_bias(S, clock, two_flag=False, higher_dim=False):
+
+    Count unique values for both attributes, 
+    select the attribute with exactly 2 types while the other has != 2 types
+
+    Input Arguments:
+        S: set of elements or ElementSet
+        clock: stopwatch used to time primitive functions
+        two_flag: flag to indicate if the bias required needs only two attribute types
+        higher_dim: flag to indicate if the set of elements is 2-dimensional
+
+    Output:
+        bias: the attribute selected
+
+    # Handle both raw set/list and ElementSet
+    if isinstance(S, ElementSet):
+        elements = S.elements
+    else:
+        elements = S
+
+    if higher_dim:
+        chunk_bias, serial_bias = None, None
+        attribute_counts = {
+            attr: len(set(getattr(obj, attr) for obj in elements))
+            for attr in ["attribute1", "attribute2"]
+        }
+        chunk_bias = find_bias(elements, clock, two_flag)
+        clock.start()
+        serial_bias = 'attribute1' if chunk_bias == 'attribute2' else 'attribute2'
+        clock.stop()
+        return (chunk_bias, serial_bias)
+    else:
+        bias = None
+        attribute_counts = {
+            attr: len(set(getattr(obj, attr) for obj in elements))
+            for attr in ["attribute1", "attribute2"]
+        }
+
+        if attribute_counts["attribute1"] == 2 and attribute_counts["attribute2"] != 2:
+            bias = "attribute1"
+        elif attribute_counts["attribute2"] == 2 and attribute_counts["attribute1"] != 2:
+            bias = "attribute2"
+        else:
+            clock.start()
+            bias = "attribute1" if pf.flip(0.5) else "attribute2"
+            clock.stop()
+
+        return bias
+    
+'''
\ No newline at end of file
diff --git a/midtest/midpf.py b/midtest/midpf.py
new file mode 100644
index 0000000..d5338b6
--- /dev/null
+++ b/midtest/midpf.py
@@ -0,0 +1,160 @@
+import random
+
+SEED = 42
+random.seed(SEED)
+# Functions on lists (strings)
+
+def pair(L, C):
+    """Concatenates character C onto list L
+    Time complexity: O(1) amortized for lists, O(n) for strings"""
+    L.append(C)   # O(1) amortized
+
+def append(X, Y):
+    """Append lists X and Y
+    Time complexity: O(n+m) where n=len(X), m=len(Y)"""
+    return X + Y
+
+# Random functions
+
+def flip(p):
+    """Returns true with probability p
+    Time complexity: O(1)"""
+    return random.random() < p
+
+# Set functions
+
+# def union(set1, set2):
+#     """Union of twos sets
+#     Time complexity: O(len(set1) + len(set2))"""
+#     return set1 | set2
+
+
+def setminus(set1, s):
+    #Remove a string from a set
+    #Time complexity: O(1) for single item, O(len(s)) for set s
+    set1.remove(s)
+
+def sample(collection):
+    #Sample from a set or list of strings.
+    #Time complexity: O(1) for non-empty sets if using random.choice,
+    #O(n) for lists using random.sample.
+    if not collection:
+        return None
+    
+    if isinstance(collection, set):
+        collection = tuple(collection)  # Convert to tuple for sampling
+
+    return random.sample(collection, 1)[0]
+
+
+# # Function calls with memoization
+
+# memoization_cache = {}
+# def F(z):
+#     """Generic factor function
+#     Time complexity: Depends on implementation"""
+#     pass
+
+# def Fm(z):
+#     """Memoized version of factor function
+#     Time complexity: O(1) for repeated calls"""
+#     if z not in memoization_cache:
+#         memoization_cache[z] = F(z)
+#     return memoization_cache[z]
+
+# Token-related functions
+
+# def create_tokens():
+#     """Initiates a list of tokens [A1, A2, B3, B4]
+#     Time complexity: O(1)"""
+#     return ["A1", "A2", "B3", "B4"]
+
+def add(T, list):
+    """Adds token T to a list
+    Time complexity: O(n) due to copying"""
+    result = list.copy()
+    result.append(T)
+    return result
+
+def remove(T, list):
+    """Removes token T from a list
+    Time complexity: O(n) for search and removal"""
+    result = list.copy()
+    if T in result:
+        result.remove(T)
+    return result
+
+def check_if_same_type(e1, e2, bias):
+    """Returns True if tokens are same type
+    Time complexity: O(1)"""
+    return getattr(e1,bias) == getattr(e2,bias)
+
+
+
+def write_random(S, bias, type): 
+    #Returns one unused member of particular type
+    #Time complexity: O(n) to filter tokens
+    for element in S:
+        if getattr(element, bias) == type:
+            return element
+    #type_elements = [u for u, v, d in G.edges(data=True) if v == type and d["label"] == bias]
+       
+        ### WRITE CODE
+        ### maybe add a random list shuffling thing here
+        ### to make it less predictable
+    
+    pass
+
+
+    
+def implement(FUN, N):
+    """Keeps implementing a function N times
+    Time complexity: O(N * T) where T is time of FUN"""
+    results = []
+    for _ in range(N):
+        results.append(FUN())
+    return results
+
+def write_all(S, bias, type):
+    """Returns a sequential list of all members of a type
+    Time complexity: O(n) where n is number of tokens"""
+    result = []
+    for element in S:
+        if getattr(element, bias) == type:
+            result = pair(result, element)
+    return result
+
+# Additional functions
+
+def list_create(M):
+    """Create a blank list with slots for M items
+    Time complexity: O(M)"""
+    return [None] * M
+
+def merge(I, J):
+    """Merge two items I and J to create a list
+    Time complexity: O(1)"""
+    return [I, J]
+
+def remove_item(I, L):
+    """Remove item I from list L
+    Time complexity: O(n) for search and removal"""
+    result = L.copy()
+    if I in result:
+        result.remove(I)
+    return result
+
+def dim_set(D):
+    """Create a set containing items classified by dimension D
+    Time complexity: O(n*m) where n is number of items, m is time of D function"""
+    def classify(items, dimension_func=D):
+        result = set()
+        for item in items:
+            result.add(dimension_func(item))
+        return result
+    return classify
+
+def write_all_set(S):
+    """Write all items belonging to a particular set S
+    Time complexity: O(n log n) due to sorting"""
+    return " - ".join(sorted(S))
\ No newline at end of file
diff --git a/midtest/midtest.ipynb b/midtest/midtest.ipynb
new file mode 100644
index 0000000..5d3057a
--- /dev/null
+++ b/midtest/midtest.ipynb
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 433,
+   "id": "081f22f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from midutils import Element, KComplexity, ElementSet, Associations\n",
+    "import midcf as cf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 434,
+   "id": "831c5eaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = Element('A1','A','1')\n",
+    "b = Element('A2','A','2')\n",
+    "c = Element('B1','B','1')\n",
+    "d = Element('B2','B','2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 435,
+   "id": "cecfb41f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "33\n",
+      "{'add': 0, 'append': 3, 'check_if_same_type': 1, 'dim_set': 0, 'flip': 3, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 10, 'setminus': 10, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "cf.iterate(S)\n",
+    "\n",
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "result, elapsed_time = cf.palindrome(S)\n",
+    "\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 436,
+   "id": "030b28dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n",
+      "{'add': 0, 'append': 2, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 2, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 8, 'setminus': 6, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [Element(object=A2, attribute 1=A, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1)]\n",
+      "Elapsed time: 0.0016532998997718096\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.iterate(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 437,
+   "id": "034a7977",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n",
+      "{'add': 0, 'append': 1, 'check_if_same_type': 1, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]\n",
+      "Elapsed time: 0.0017292997799813747\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.palindrome(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 438,
+   "id": "df882383",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 3, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 4, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1)]\n",
+      "Elapsed time: 0.00031679985113441944\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.alternate(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 439,
+   "id": "da9b45ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 0, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 6, 'remove': 0, 'remove_item': 0, 'sample': 4, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [[Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2)], [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]]\n",
+      "Elapsed time: 0.0013715000823140144\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "assoc = Associations({a: b, c: d})\n",
+    "S = ElementSet(element_set, assoc)\n",
+    "kc = KComplexity()\n",
+    "\n",
+    "result, elapsed_time = cf.chaining(S, assoc)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 440,
+   "id": "590c9d20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 0, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 0, 'setminus': 0, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [[Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2)], [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]]\n",
+      "Elapsed time: 0.0013715000823140144\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "cf.seriate(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 441,
+   "id": "7e2d4e07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 6, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 7, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2)]\n",
+      "Elapsed time: 0.0003237000200897455\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.serial_crossed(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 442,
+   "id": "092f34e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 3, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 4, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A1, attribute 1=A, attribute 2=1)]\n",
+      "Elapsed time: 0.0003361999988555908\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.center_embedded(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 443,
+   "id": "ca81ceaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13\n",
+      "{'add': 0, 'append': 2, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 2, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]\n",
+      "Elapsed time: 0.002274699741974473\n"
+     ]
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.tail_recursive(S)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 444,
+   "id": "5c80b83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "S.visualize()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lot_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/midtest/midutils.py b/midtest/midutils.py
new file mode 100644
index 0000000..74694e5
--- /dev/null
+++ b/midtest/midutils.py
@@ -0,0 +1,182 @@
+from functools import cached_property, cache, partial
+import time
+from operator import attrgetter
+from tabulate import tabulate
+import networkx as nx
+from typing import Literal
+import matplotlib.pyplot as plt
+from dataclasses import dataclass
+import midpf as pf
+import importlib
+
+# ---------------------------------------------------------------------#
+
+"""
+@dataclass
+class Element: # n-dimensional element
+    name : str
+    attribute1 : int | float | str
+    attribute2 : int | float | str | None = None
+    def __repr__(self): 
+        return f"Element(object={self.name}, attribute 1={self.attribute1}, attribute 2={self.attribute2})"
+    def __str__(self):
+        return f"{self.name}, {self.attribute1}, {self.attribute2})"
+"""
+
+class Element: # n-dimensional element
+    def __init__(self, name, attribute1, attribute2=None):
+        self.name = name
+        self.attribute1 = attribute1
+        self.attribute2 = attribute2
+    def __repr__(self): 
+        return f"Element(object={self.name}, attribute 1={self.attribute1}, attribute 2={self.attribute2})"
+    def __str__(self):
+        return f"{self.name}, {self.attribute1}, {self.attribute2})"
+
+@dataclass
+class  Associations: # n-dimensional association
+    associations: dict
+    positional: bool = False
+    def __repr__(self):
+        raise NotImplementedError
+    def build_updates(self, graph):
+        if self.positional:
+            for key, value in self.associations.items():
+                nx.set_node_attributes(graph, {key.name: {"position": value}})
+        else:
+            for key, value in self.associations.items():
+                graph.add_edge(key.name, value.name, label="precedes", directed=True)
+
+
+class ElementSet: # n-dimensional element set
+    def __init__(self, elements: set, associations: Associations = None):
+        self.elements = elements
+        self.associations = associations
+        self.graph = self.build_graph()
+    def __repr__(self):
+        raise NotImplementedError
+    def build_graph(self):
+        G = nx.MultiGraph()  # Create a directed graph
+        for obj in self.elements:
+            G.add_node(obj.name, type='element')  # Add object as a node
+            G.add_node(obj.attribute1, type='attribute1')  # Add color as a node
+            G.add_edge(obj.name, obj.attribute1, label="attribute")  # Add directed edge with label
+            if obj.attribute2:
+                # If the object has a second attribute, add it as a node and edge
+                G.add_node(obj.attribute2, type='attribute2')  # Add shape as a node
+                G.add_edge(obj.name, obj.attribute2, label="attribute")
+        if  self.associations:
+            self.associations.build_updates(G)  # Build associations
+            if hasattr(self, 'graph') and self.graph:
+                self.graph = G
+        return G
+    def visualize(self):
+        plt.figure(figsize=(10, 8))
+        pos = nx.spring_layout(self.graph)
+
+        # Draw nodes with different colors based on type
+        node_colors = []
+        for node, data in self.graph.nodes(data=True):
+            if data['type'] == 'element':
+                node_colors.append('skyblue')
+            elif data['type'] == 'attribute1':
+                node_colors.append('lightgreen')
+            elif data['type'] == 'attribute2':
+                node_colors.append('lightcoral')
+
+        nx.draw(self.graph, pos, with_labels=True, node_color=node_colors, node_size=500, font_size=10)
+        edge_labels = nx.get_edge_attributes(self.graph, 'label')
+        nx.draw_networkx_edge_labels(self.graph, pos, edge_labels=edge_labels)
+        plt.show()
+    @cached_property
+    def attribute1_types(self):
+        """Returns a set of unique attribute1 types."""
+        return set(obj.attribute1 for obj in self.elements)
+    @cached_property
+    def attribute2_types(self):
+        """Returns a set of unique attribute2 types."""
+        return set(obj.attribute2 for obj in self.elements if obj.attribute2 is not None)
+    def attribute_items(self, attribute):
+        def get_type(type_):
+            
+            return getattr(self, f"{attribute}_types", set())
+
+    
+    
+
+
+def pretty_view(sequence):
+    """Prints a sequence of elements in a pretty format."""
+    cute = list(zip(*map(attrgetter("name", "attribute1", "attribute2"), sequence)))
+    # Print as a table
+    print(tabulate(zip(*cute), headers=["object", "attribute 1", "attribute 2"], tablefmt="grid"))
+
+
+
+# ---------------------------------------------------------------------#
+
+class Stopwatch:
+    def __init__(self):
+        self._start_time = None
+        self._elapsed_time = 0
+        self._running = False
+        
+    def start(self):
+        """Start or resume the stopwatch."""
+        if not self._running:
+            self._start_time = time.perf_counter() - self._elapsed_time
+            self._running = True
+            # print("Stopwatch started.")
+
+    def stop(self):
+        """Stop the stopwatch and display the elapsed time."""
+        if self._running:
+            self._elapsed_time = time.perf_counter() - self._start_time
+            self._running = False
+            # print(f"Stopwatch stopped. Elapsed time: {self._elapsed_time:.2f} seconds.")
+
+    def reset(self):
+        """Reset the stopwatch to zero."""
+        self._elapsed_time = 0
+        if self._running:
+            self._start_time = time.perf_counter()
+        # print("Stopwatch reset.")
+
+    def get_elapsed_time(self):
+        """Get the current elapsed time without stopping the stopwatch."""
+        if self._running:
+            return time.perf_counter() - self._start_time
+        return self._elapsed_time
+
+class KComplexity:
+    def __init__(self):
+        self.prim = importlib.import_module('midpf')
+        self.call_counts = {}
+        self._wrap_prim_functions()
+
+    def _wrap_prim_functions(self):
+        for name in dir(self.prim):
+            func = getattr(self.prim, name)
+            if callable(func) and not name.startswith("__"):
+                self.call_counts[name] = 0
+                wrapper_func = self._make_wrapper(func, name)
+                setattr(self.prim, name, wrapper_func)
+
+    def _make_wrapper(self, func, name):
+        def wrapper(*args, **kwargs):
+            self.call_counts[name] += 1
+            return func(*args, **kwargs)
+        return wrapper
+    
+    """get dictionary of each prim function call count"""
+    def get_prim_counts(self):
+        return dict(self.call_counts)
+    
+    """get total number of prim function calls"""
+    def get_k_complexity(self):
+        return sum(self.call_counts.values())
+    
+    """reset all prim function call counts to zero"""
+    def reset(self):
+        for key in self.call_counts:
+            self.call_counts[key] = 0
\ No newline at end of file
diff --git a/newprim.py b/newprim.py
new file mode 100644
index 0000000..3ff9241
--- /dev/null
+++ b/newprim.py
@@ -0,0 +1,223 @@
+import random
+
+SEED = 42
+random.seed(SEED)
+# Functions on lists (strings)
+
+def pair(L, C):
+    """Concatenates character C onto list L
+    Time complexity: O(1) amortized for lists, O(n) for strings"""
+    L.append(C)   # O(1) amortized
+
+def append(X, Y):
+    """Append lists X and Y
+    Time complexity: O(n+m) where n=len(X), m=len(Y)"""
+    return X + Y
+
+# Random functions
+
+def flip(p):
+    """Returns true with probability p
+    Time complexity: O(1)"""
+    return random.random() < p
+
+# Set functions
+
+# def union(set1, set2):
+#     """Union of twos sets
+#     Time complexity: O(len(set1) + len(set2))"""
+#     return set1 | set2
+
+"""
+def setminus(set1, s):
+    #Remove a string from a set
+    #Time complexity: O(1) for single item, O(len(s)) for set s
+    set1.remove(s)
+"""
+
+def setminus(set1, s):
+    """
+    Removes element `s` from `set1`, which may be a set, list, or ElementSet.
+    Time complexity:
+        - O(1) for sets and ElementSet
+        - O(n) for lists
+    """
+    if hasattr(set1, "elements"):  # ElementSet
+        try:
+            set1.elements.remove(s)
+            set1.graph = set1.build_graph()  # Sync graph with element change
+        except KeyError:
+            pass  # Already removed or not found
+    elif isinstance(set1, set) or isinstance(set1, list):
+        set1.remove(s)
+    else:
+        raise TypeError(f"setminus does not support type: {type(set1)}")
+
+
+"""
+
+def sample(collection):
+    #Sample from a set or list of strings.
+    #Time complexity: O(1) for non-empty sets if using random.choice,
+    #O(n) for lists using random.sample.
+    if not collection:
+        return None
+    
+    if isinstance(collection, set):
+        collection = tuple(collection)  # Convert to tuple for sampling
+
+    return random.sample(collection, 1)[0]
+
+"""
+
+def sample(collection):
+    """
+    Samples a single Element from an ElementSet or a collection of Elements.
+    
+    Args:
+        collection: An ElementSet, set, list, or tuple of Element objects.
+
+    Returns:
+        A single randomly selected Element.
+    """
+    if not collection:
+        return None
+
+    # Handle ElementSet
+    if hasattr(collection, 'elements'):
+        collection = collection.elements
+
+    # Make sure it's a sequence
+    if isinstance(collection, set):
+        collection = tuple(collection)
+
+    return random.sample(collection, 1)[0]
+
+
+# # Function calls with memoization
+
+# memoization_cache = {}
+# def F(z):
+#     """Generic factor function
+#     Time complexity: Depends on implementation"""
+#     pass
+
+# def Fm(z):
+#     """Memoized version of factor function
+#     Time complexity: O(1) for repeated calls"""
+#     if z not in memoization_cache:
+#         memoization_cache[z] = F(z)
+#     return memoization_cache[z]
+
+# Token-related functions
+
+# def create_tokens():
+#     """Initiates a list of tokens [A1, A2, B3, B4]
+#     Time complexity: O(1)"""
+#     return ["A1", "A2", "B3", "B4"]
+
+def add(T, list):
+    """Adds token T to a list
+    Time complexity: O(n) due to copying"""
+    result = list.copy()
+    result.append(T)
+    return result
+
+def remove(T, list):
+    """Removes token T from a list
+    Time complexity: O(n) for search and removal"""
+    result = list.copy()
+    if T in result:
+        result.remove(T)
+    return result
+
+def check_if_same_type(e1, e2, bias):
+    """Returns True if tokens are same type
+    Time complexity: O(1)"""
+    return getattr(e1,bias) == getattr(e2,bias)
+
+"""
+
+def write_random(G, bias, type): 
+    #Returns one unused member of particular type
+    #Time complexity: O(n) to filter tokens
+    # for element in S:
+    #     if getattr(element, bias) == type:
+    #         return element
+    type_elements = [u for u, v, d in G.edges(data=True) if v == type and d["label"] == bias]
+       
+        ### WRITE CODE
+        ### maybe add a random list shuffling thing here
+        ### to make it less predictable
+    
+    pass
+"""
+
+def write_random(S, bias, type):
+    """
+    Returns a random element from S that matches a given bias and type.
+    Works whether S is an ElementSet or raw graph.
+    """
+    G = S.graph if hasattr(S, "graph") else S  # Unwrap ElementSet if needed
+
+    type_elements = [u for u, v, d in G.edges(data=True) if v == type and d["label"] == bias]
+    if not type_elements:
+        return None
+
+    element_name = random.choice(type_elements)
+    for obj in S.elements if hasattr(S, "elements") else S:
+        if getattr(obj, bias) == type and obj.name == element_name:
+            return obj
+
+    
+def implement(FUN, N):
+    """Keeps implementing a function N times
+    Time complexity: O(N * T) where T is time of FUN"""
+    results = []
+    for _ in range(N):
+        results.append(FUN())
+    return results
+
+def write_all(S, bias, type):
+    """Returns a sequential list of all members of a type
+    Time complexity: O(n) where n is number of tokens"""
+    result = []
+    for element in S:
+        if getattr(element, bias) == type:
+            result = pair(result, element)
+    return result
+
+# Additional functions
+
+def list_create(M):
+    """Create a blank list with slots for M items
+    Time complexity: O(M)"""
+    return [None] * M
+
+def merge(I, J):
+    """Merge two items I and J to create a list
+    Time complexity: O(1)"""
+    return [I, J]
+
+def remove_item(I, L):
+    """Remove item I from list L
+    Time complexity: O(n) for search and removal"""
+    result = L.copy()
+    if I in result:
+        result.remove(I)
+    return result
+
+def dim_set(D):
+    """Create a set containing items classified by dimension D
+    Time complexity: O(n*m) where n is number of items, m is time of D function"""
+    def classify(items, dimension_func=D):
+        result = set()
+        for item in items:
+            result.add(dimension_func(item))
+        return result
+    return classify
+
+def write_all_set(S):
+    """Write all items belonging to a particular set S
+    Time complexity: O(n log n) due to sorting"""
+    return " - ".join(sorted(S))
\ No newline at end of file
diff --git a/newtest.ipynb b/newtest.ipynb
new file mode 100644
index 0000000..3b19bbc
--- /dev/null
+++ b/newtest.ipynb
@@ -0,0 +1,957 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "081f22f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import Element, KComplexity, ElementSet, Associations\n",
+    "import cognitive_functions as cf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "831c5eaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = Element('A1','A','1')\n",
+    "b = Element('A2','A','2')\n",
+    "c = Element('B1','B','1')\n",
+    "d = Element('B2','B','2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e64a7c0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total K-complexity:  54\n",
+      "Total primitive counts:  {'add': 0, 'append': 6, 'check_if_same_type': 4, 'dim_set': 0, 'flip': 6, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 8, 'remove': 0, 'remove_item': 0, 'sample': 16, 'setminus': 14, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>add</th>\n",
+       "      <th>append</th>\n",
+       "      <th>check_if_same_type</th>\n",
+       "      <th>dim_set</th>\n",
+       "      <th>flip</th>\n",
+       "      <th>implement</th>\n",
+       "      <th>list_create</th>\n",
+       "      <th>merge</th>\n",
+       "      <th>pair</th>\n",
+       "      <th>remove</th>\n",
+       "      <th>remove_item</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>setminus</th>\n",
+       "      <th>write_all</th>\n",
+       "      <th>write_all_set</th>\n",
+       "      <th>write_random</th>\n",
+       "      <th>Total</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cognitive Function (calls)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>iterate (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>palindrome (4x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total Counts for each Primitive Function</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          add  append  check_if_same_type  \\\n",
+       "Cognitive Function (calls)                                                  \n",
+       "iterate (1x)                                0       2                   0   \n",
+       "palindrome (4x)                             0       4                   4   \n",
+       "Total Counts for each Primitive Function    0       6                   4   \n",
+       "\n",
+       "                                          dim_set  flip  implement  \\\n",
+       "Cognitive Function (calls)                                           \n",
+       "iterate (1x)                                    0     2          0   \n",
+       "palindrome (4x)                                 0     4          0   \n",
+       "Total Counts for each Primitive Function        0     6          0   \n",
+       "\n",
+       "                                          list_create  merge  pair  remove  \\\n",
+       "Cognitive Function (calls)                                                   \n",
+       "iterate (1x)                                        0      0     0       0   \n",
+       "palindrome (4x)                                     0      0     8       0   \n",
+       "Total Counts for each Primitive Function            0      0     8       0   \n",
+       "\n",
+       "                                          remove_item  sample  setminus  \\\n",
+       "Cognitive Function (calls)                                                \n",
+       "iterate (1x)                                        0       8         6   \n",
+       "palindrome (4x)                                     0       8         8   \n",
+       "Total Counts for each Primitive Function            0      16        14   \n",
+       "\n",
+       "                                          write_all  write_all_set  \\\n",
+       "Cognitive Function (calls)                                           \n",
+       "iterate (1x)                                      0              0   \n",
+       "palindrome (4x)                                   0              0   \n",
+       "Total Counts for each Primitive Function          0              0   \n",
+       "\n",
+       "                                          write_random  Total  \n",
+       "Cognitive Function (calls)                                     \n",
+       "iterate (1x)                                         0     18  \n",
+       "palindrome (4x)                                      0     36  \n",
+       "Total Counts for each Primitive Function             0     54  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "cf.iterate(S)\n",
+    "\n",
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "cf.palindrome(S)\n",
+    "\n",
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "cf.palindrome(S)\n",
+    "\n",
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "cf.palindrome(S)\n",
+    "\n",
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "cf.palindrome(S)\n",
+    "\n",
+    "print('Total K-complexity: ', kc.get_total_k_complexity())\n",
+    "print('Total primitive counts: ', kc.get_total_prim_counts())\n",
+    "kc.plot_total_prim_counts()\n",
+    "kc.plot_cog_vs_prim()\n",
+    "kc.show_cog_prim_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "030b28dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=A2, attribute 1=A, attribute 2=2)]\n",
+      "Elapsed time: 4.589976742863655e-05\n",
+      "iterate k-complexity:  18\n",
+      "iterate primitive count:  {'add': 0, 'append': 2, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 2, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 8, 'setminus': 6, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "kc = KComplexity()\n",
+    "result, elapsed_time = cf.iterate(S)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "034a7977",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]\n",
+      "Elapsed time: 8.070003241300583e-05\n",
+      "palindrome k-complexity:  9\n",
+      "palindrome primitive count:  {'add': 0, 'append': 1, 'check_if_same_type': 1, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 2, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 2, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "result, elapsed_time = cf.palindrome(S)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "df882383",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B1, attribute 1=B, attribute 2=1)]\n",
+      "Elapsed time: 1.209997572004795e-05\n",
+      "alternate k-complexity:  18\n",
+      "alternate primitive count:  {'add': 0, 'append': 0, 'check_if_same_type': 4, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 5, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "result, elapsed_time = cf.alternate(S)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "da9b45ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [[Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)], [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2)]]\n",
+      "Elapsed time: 7.329997606575489e-05\n",
+      "chaining k-complexity:  21\n",
+      "chaining primitive count:  {'add': 0, 'append': 0, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 0, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 6, 'remove': 0, 'remove_item': 0, 'sample': 11, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "assoc = Associations({a: b, c: d})\n",
+    "result, elapsed_time = cf.chaining(S, assoc)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "590c9d20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seriate k-complexity:  0\n",
+      "seriate primitive count:  {'add': 0, 'append': 0, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 0, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 0, 'setminus': 0, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAeY5JREFUeJztnQeYnFUdr09CSKgJ0kKRDhI60kEUkBKkCwgiKALSpAkIJoBEQDoBpAuKGAGpioAQDaEqoRh6B0GqBEJJpCWQzH3ec+/ZezKZ3ezufLO78+V9n2cgOzs7Xzvld/7t9KpUKpUgIiIiIk1P7+4+AREREREpBoWdiIiISElQ2ImIiIiUBIWdiIiISElQ2ImIiIiUBIWdiIiISElQ2ImIiIiUBIWdiIiISElQ2ImIiIiUBIWdSB384he/CL169Qplg2vi2oriP//5T/zOK664oluOX2Y23njj+JLG8dBDD4W+ffuGV199NTRDG+hof0ust9564eijj27AGUpXorAT6SYuuuiiDg+87YEBnkE9veadd96w9tprh8svvzxMnTo19BRuu+22Hife8vuWvxZaaKFuPa9nnnkm3ism7J4CbZd7869//avm77fZZpuw5JJLhjJw7LHHht122y0sscQS07z/7LPPhi233DLMNddcsZ99//vfD++++25oVn72s5+FCy+8MLz99tvdfSpSB33q+WORmZ3jjjsuDBkypNPCbv755w8//OEPCz+vL3/5y+HUU0+N/2aiGTFiRNhnn33CCy+8EE477bQZ/v2nn34a+vQpbnhgQuQ7Z5111mmEHZNILXFX9PE7wuabbx5+8IMfTPPe7LPPHrpb2J1wwglRtFeLpb///e/ddl4zA4899li44447wv333z/N+2+88Ub4xje+EQYMGBBOOeWU8NFHH4WzzjorPPnkky0WvmZj++23D/37949j04knntjdpyOdRGEn0gk+/vjjMOecc0bx0V0CpC2YbPbYY4+Wn/fff/+w/PLLhwsuuCCcdNJJ0wisBNa8yZMnh9lmmy2+igTLTke+s+jjd4SvfOUr09y7nk4zCohm4ne/+11YfPHFo5syBzHHODB27Nj4e1hnnXXiwgBr5n777Reajd69e4edd945LgRZSJQxzGRmQFeslJL//e9/4Sc/+Um0bvTr1y8suOCCccB95JFHpvncgw8+GF0pCKE55pgjbLTRRuGf//xnzTg6rCbf+973wpe+9KWw4YYbTvO76ongm9/8Zjwmx15xxRXDxRdfPM1nOK+nn3463HPPPS3uvjxG5sMPP4znv9hii8XvWHbZZcPpp5/eaVcq18bExESUXEUc8+CDDw5XXXVVWGmlleJxRo4c2fK73JKWrhOLH6KH+7XAAguEn//856FSqYTXX3+9ZbWP23L48OHTHL865gcrJda6dKz0SuTHv+GGG+LP3Ktqfv3rX8ffPfXUUy3vPffcc3FywjWGQFxrrbXCzTffHIqA867lXqzVDtL9vemmm8LKK68c7y/3Od3jnDfffDNaVBdZZJH4uaWWWioceOCBUWhzz77zne/Ez22yySYt9+ruu+9uNcbunXfeid83cODAeA9WW2218Pvf/77mM8HKdOmll4ZlllkmHhu3/cMPPxwawTXXXBPWXHPNMPfcc8e2ssoqq4Rf/epXLb9///33w09/+tP4Pu5NPvOtb30rPP7449N9F/Fu2223XVxg0dcOP/zw8Le//W2ae9ORft4aPD/6c/XzvfHGG6O7OYk62GyzzeLC4Lrrrpvh99KXuXaulWdEf+Icc9d2e8aS9oJ7da+99orWfL5r4YUXjn222r3POMm9xVIpzUnPMzWIFMABBxwQBQETK4Phe++9F/7xj3/EmJg11lgjfubOO++MkwYTzbBhw+JqNQ2k9913X1x95zC5LrfccnGljphpDQZeJnAmHax5t9xyS/jxj38cB/KDDjoofubcc88NhxxySJy8iN8BJmH45JNP4sTDZI+ljYkDN9DQoUPDf//73/i3neHll18Os8wyS5hnnnla3uMeMAlxn3ALzygmatdddw0rrLBCdOf+9a9/Db/85S+jgEJgcd8QnwhFJmcEAq6qWnBdb731Vhg1alT4wx/+0OYxt95663ifOE/uS861114b7zXCCRDLX/va18Kiiy4aXeRM+vzdDjvsECfib3/72zO8T5999lkYP378NO8hRJgMOwpt7k9/+lN8/nzHeeedF3baaafw2muvhfnmmy9+hvtAW0PMY+UZNGhQfPa0X9oC9/DQQw+Nf3vMMcfE+w/p/7Xc2Ai9l156KT5XROL1118fRSnHOOyww6b5/NVXXx0XQjwTxMsZZ5wRdtxxx9heall2OwvPmji1TTfdNLYToD8isNI5cUyEFH2N8x43blxsWzx3FlYIX2CBQnujP/C3LCa4jrvuumu643a0n+fwHHhWaczI30c8s2iohu8jzGBGILwR7Zzbj370o/DFF1/E83nggQdavrc9Y0l7od3RPxh36OecP8+E68v7PfcJeC5f/epXO3QM6SFURErIgAEDKgcddFCrv586dWplueWWqwwePDj+O/HJJ59Ullpqqcrmm2/e8t6wYcNQcZXddtttuu9Jv8vhO6rhOEsvvfQ076200kqVjTbaaLrPnnTSSZU555yz8sILL0zz/pAhQyqzzDJL5bXXXqu0Bd85aNCgyrvvvhtfzz77bOXQQw+N57ntttu2fI6fe/fuXXn66aen+w5+x7VVX+d+++3X8t4XX3xR+fKXv1zp1atX5bTTTmt5/4MPPqjMPvvslT333LPlvVdeeSX+/e9+97uW93g+rQ1B1cfn3i+44ILxmIn//ve/8fxPPPHElvc23XTTyiqrrFL57LPPWt7j+W6wwQbxec8Ijlvrlc6ba1piiSXa1Q74uW/fvpWXXnqp5b3HH388vn/++ee3vPeDH/wgXsfDDz883femtnn99dfHv7vrrrtqPu+8HZ177rnxs1deeWXLe5MnT66sv/76lbnmmqsyceLEaZ7JfPPNV3n//fdbPvuXv/wlvn/LLbe0ea+4J3yu1nnD1ltvPc29Ouywwyr9+/ef5hlWw3ObMmXKNO9xnv369ZvmOQ8fPjwe+6abbmp579NPP43tPr9PHenntbjjjjtq3guumfdHjBgx3d8cddRR8Xd5G6zmzjvvjJ+hX1ZTfZ7tGUuq20B1f6NP8vOZZ55ZaQ+02wMPPLBdn5Weh65YKSVYpXC/YA2pBW6GF198MbpWseZhoeGFJQCLwr333jud2xMrYHvIA+0nTJgQvxeLA9YIfp4RWFe+/vWvR5dvOi9euHmmTJkSz21G4I7EtcMLy875558fLV9kxuZwXlg02wuWhQTWPywLaBisD/m9J56P6y0KLIVYGHIXGxYtnhG/S248rDO77LJLtECl+8bzHTx4cHzeWFpmBO4pLBn5i7/vDDwzXJyJVVddNboX073h/LFQbbvttjWtP52JccJahAUL61gCyxtWPwL8q13a3D/aWoK2B0U+v9Qu6F/cz9bAKopFDWjrPDustbSnPIwCdzZWWSxZCdyZ++67b939PIe/gfz+JKtoOt/W4kPTZ2qB9ZhniwWxrWde71iSfw+xmPSfDz74YIafT2OPNCe6YqWU4E7ac889Y4waroWtttoqZjouvfTS8fcM9sBnWoOBMx/QcQ21B1wYDNhjxoyJrrTq7yTOpy04tyeeeCKKslogcGYErpXLLrusJWkBFzJxOtW095oSeTwRcC18P27c6vfTpFgEKT4K1ysTMvDv1VdfPcY0Aa5HRCZxf7xau3cIgrYgBglBVgTV9wtoU2lyJd5x4sSJLa7kIiA+iuedBFIiuW6ra7FVn2Nq8+0RADMiFym4EHGL43rkGWyxxRZRhPNsq+POyMp85ZVXorhLJNd1ugYEc7XwJRY1pzP9vBbVoRdJcE2aNKmmKz//TC3+/e9/R7cyYQyNHEsSCFDc30ceeWQM+SDelvhAxsRapXy4XhMnmheFnZQSJgwsD3/+859jOYgzzzwzDmzEOzGxpFU67yMOaoGloKMlLxiwER7ESZ199tlRWLJSxopyzjnntCv5gc8QwNxaodAkZNqC2LL2iJOOlvHAStee96CtOMSOwsREnBzPk0mf2CsmPeIdE+neEt/XmoWteuLvKK1NdrkA6ep7Uy+dPccZWaYQInl2MwsLLGgkONx+++3xRawb4iIldvA8EeV77713zN5G+CBQSSTqTOJQZ/p5ThKT1SKXxAMgxq8a3uO8OxOTWfRYksM9xDKMhZhnwH2mJBJW7upYOmIxqxdr0jwo7KS0MPhiJeCFpYYA6JNPPjkKu+Qewy1WlHUGCG5mFU8WZm4JqRXU3ZpI4NxwmRV5Xj2RjloEcBkiAEaPHh2D7hEeyQ0LyRqL27FR9w7LDpNeNZ3dkQCrLG0wz+qt915RMxCLLxN/brXDPZ9+XwTpe55//vkW920OGdTVlkiECeKCF+dH3yQ5ApGB6Ma9Tubvb3/72zaFBscmmaLasoTVNqfefo6oAqyHOVgceXa1ijNTw641EZmfF+KK8IHWrHYdGUvaC8fFascLaybnSQb7lVde2fIZwhXIxm4tOUd6PsbYSenAelIdf4K1ANdHcp3gnmWQo9QDIqqazlaPT9aP3NrBuWCZqGVVqyUSsDbiemHgr4bPkz1XBrh+qHUPasHEzCSIC5YX2Ye5K5lnTDYoQqGWJaWIHQFoMzxPhFOCY2FJ7AwILyyRTOK1REJqRx25V4QdUNqCe5SgzRBniXWqOrO4s9CHuOe/+c1vpnNJYhVCILCISlS75rl2Yg4h/T39p9pSSMxpdWwkFlney8vY4AIl/KD6HOvp5wg4LGW1ng1Zprfeemss9ZNg0YGgTeVpWoO/5TqpFVdNuv6OjCUzAutpchEnuC9kalc/O+rywQYbbNDh40jPQIudlA4C54mTopYZ9buYzKgcT22uVF+NSYUJiYmHcgLUd2IQZ7JgRcwKn8m2oxA3lKwSlI9gMmGyYQKsFhtMOpQzoGQI1go+QwmGo446Kk5YxMBQooLPEexNRXssGtSdKoObJJVVIKifiZqJ7Lvf/W6rn8cSRxkOaqFxP5isq6E2HjUGqQ1GID1WPNy2CGV2CqhVD60jcH5su0TZFM6bCZNniHu8ukZie8H9SLgAgotyJ1hKaCsIGsqlkHSAZYX7QzgBkztuvlTfrBq+A3FL22GSJt6SdoPrmlI5TOZFQDvnGRC/RmkbrKe4Lh999NGYpINoy4v0kniDhYrzpn9i5URscm3JOkSbZ8cD+iPCgjZP+ZxkjU3Qtyi2TYII5U6wzvO55PpNVrwi+jnJNAj3ausgpWd4RlgYOQf6Oi5f2h7HaQv+hu3HKGGD5Yw4QyyYlDvhd5Sp6chYMiMQm7h1WTSSLEXpFK6JvlHd50huwUJoqZMmprvTckWKZtKkSbHkwGqrrVaZe+65Y+kQ/n3RRRdN99lHH320suOOO8aSD5RUoDzDLrvsUhk9evR0pSwoHdKeMhc333xzZdVVV63MNttslSWXXLJy+umnVy6//PL4OcoQJN5+++1YEoJz5Hd5uYL//e9/laFDh1aWXXbZWHpg/vnnjyU7zjrrrFi6oi34HkqpzAiO2VpJmNbKnVTfA8p/cH9ndA61yp1Q9uKQQw6pLLDAArFkSn4fq4+fGDVqVPwdn3/99ddrnvu///3vWEJkoYUWqsw666yVRRddtLLNNttUbrjhhrruSeLvf/97ZeWVV47PZfnll49lRVord1Lru2hjeSkYePXVV+M5cy9oh5Sz4G9py4nLLrssvk/Jm7ykR3WpCxg3blxlr732iu2G86QETH7v82dSqwRGa/e/Frfffntlk002iaVMuN+UETniiCNiiY0c7v8WW2wRy9ZwTosvvnhl//33j2VrEpQIOfLIIysLL7xwLJnzta99rTJmzJia1/jyyy/H/sPnuG/83Y033hjP/YEHHuhwP2+NRx55JH7nfffdN93vnnrqqXhNc8wxR2Weeeap7L777rFftwfaP/eeEi3cD67hW9/6VmXs2LEdHktmVO5k/PjxsT1xLPor5aDWXXfdynXXXTfNOVFqhnt/3HHHtesapGfSi/90t7gUERGpFyyS7ECBdXZG2c8dAWsXoRwzKqbd7OBCpzQMiRspQUSaD4WdiIg0HWTj5lndxJDhPiTGFtdjkVATkwQR3KZFJZ/0RNZff/14nZSLkuZFYSciIk0HcXPEghGjR9whmZ1smUWsHVYnkZkVkydERKTpIOGGxAiEHFY6kgJIrMlL4IjMjGixExERESkJ1rETERERKQkKOxEREZGSYIxdAVBY8q233oqFP904WURERIqEqDmK71N2J98qsBYKuwJA1LHtjIiIiEijYAs7dm5pC4VdAaQterjhbFEjIiIiUhQTJ06MBqT2bAmosCuA5H5F1CnsREREpBG0J9zL5AkRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRkqCwExERESkJCjsRERGRktB0wu7CCy8MSy65ZJhtttnCuuuuGx566KE2P3/99deHQYMGxc+vssoq4bbbbmv1swcccEDo1atXOPfccxtw5iIiIiKNpamE3bXXXhuOOOKIMGzYsPDII4+E1VZbLQwePDi88847NT9///33h9122y3ss88+4dFHHw077LBDfD311FPTffbPf/5zeOCBB8IiiyzSBVciIiIiMpMLu7PPPjvsu+++Ya+99gorrrhiuOSSS8Icc8wRLr/88pqf/9WvfhW23HLLcNRRR4UVVlghnHTSSWGNNdYIF1xwwTSfe/PNN8MhhxwSrrrqqjDrrLN20dWIiIiIzKTCbvLkyWHs2LFhs802a3mvd+/e8ecxY8bU/Bvezz8PWPjyz0+dOjV8//vfj+JvpZVWauAViIiIiDSWPqFJGD9+fJgyZUoYOHDgNO/z83PPPVfzb95+++2an+f9xOmnnx769OkTDj300Hafy6RJk+IrMXHixA5ciYiIiMhMbrFrBFgAcddeccUVMWmivZx66qlhwIABLa/FFlusoecpIiIiUiphN//884dZZpkljBs3bpr3+XmhhRaq+Te839bn77vvvph4sfjii0erHa9XX301HHnkkTHztjWGDh0aJkyY0PJ6/fXXC7lGERERkZlC2PXt2zesueaaYfTo0dPEx/Hz+uuvX/NveD//PIwaNarl88TWPfHEE+Gxxx5reZEVS7zd3/72t1bPpV+/fqF///7TvERERES6m6aJsQNKney5555hrbXWCuuss06sN/fxxx/HLFn4wQ9+EBZddNHoKoXDDjssbLTRRmH48OFh6623Dtdcc03417/+FS699NL4+/nmmy++csiKxaK3/PLLd8MVioiIiMwkwm7XXXcN7777bjj++ONjAsTqq68eRo4c2ZIg8dprr8VM2cQGG2wQrr766nDccceFY445Jiy33HLhpptuCiuvvHI3XoWIiIhIY+hVqVQqDfrumQayYkmiIN5Ot6yIiIh0l85omhg7EREREWkbhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiIhISVDYiYiIiJSEphN2F154YVhyySXDbLPNFtZdd93w0EMPtfn566+/PgwaNCh+fpVVVgm33XZby+8+//zz8LOf/Sy+P+ecc4ZFFlkk/OAHPwhvvfVWF1yJiIiIyEws7K699tpwxBFHhGHDhoVHHnkkrLbaamHw4MHhnXfeqfn5+++/P+y2225hn332CY8++mjYYYcd4uupp56Kv//kk0/i9/z85z+P///Tn/4Unn/++bDddtt18ZWJiIiI1E+vSqVSCU0CFrq11147XHDBBfHnqVOnhsUWWywccsghYciQIdN9ftdddw0ff/xxuPXWW1veW2+99cLqq68eLrnkkprHePjhh8M666wTXn311bD44ou367wmTpwYBgwYECZMmBD69+/f6esTERERqUdnNI3FbvLkyWHs2LFhs802a3mvd+/e8ecxY8bU/Bvezz8PWPha+zxw03r16hXmmWeeAs9eREREpPH0CU3C+PHjw5QpU8LAgQOneZ+fn3vuuZp/8/bbb9f8PO/X4rPPPosxd7hv21LEkyZNiq9cSYuIiIh0N01jsWs0JFLssssuAc/0xRdf3OZnTz311GgSTS/cwSIiIiLdTdMIu/nnnz/MMsssYdy4cdO8z88LLbRQzb/h/fZ8Pok64upGjRo1Q//10KFDo8s2vV5//fVOX5eIiIjITCfs+vbtG9Zcc80wevTolvdInuDn9ddfv+bf8H7+eUC45Z9Pou7FF18Md9xxR5hvvvlmeC79+vWL4i9/iYiIiHQ3TRNjB5Q62XPPPcNaa60VM1fPPffcmPW61157xd9Tg27RRReNrlI47LDDwkYbbRSGDx8ett5663DNNdeEf/3rX+HSSy9tEXU777xzLHVC5iwxfCn+bt55541iUkRERKRZaCphR/mSd999Nxx//PFRgFG2ZOTIkS0JEq+99lrMlE1ssMEG4eqrrw7HHXdcOOaYY8Jyyy0XbrrpprDyyivH37/55pvh5ptvjv/mu3LuuuuusPHGG3fp9YmIiIjMNHXseirWsRMREZFGUco6diIiIiLSNgo7ERERkZKgsBMREREpCQo7ERERkZKgsBMREREpCQo7ERERkZKgsBMREREpCQo7ERERkZKgsBMREREpCQo7ERERkZKgsBMREREpCQo7ERERkZKgsBMREREpCQo7ERERkZKgsBMRERGZWYXdiBEjwqRJk6Z7f/LkyfF3IiIiItI99KpUKpWO/MEss8wS/vvf/4YFF1xwmvffe++9+N6UKVPCzMbEiRPDgAEDwoQJE0L//v27+3RERERkJtUZHbbYoQN79eo13ftvvPFGPKiIiIiIdA992vvBr371q1HQ8dp0001Dnz7//0+x0r3yyithyy23bNR5ioiIiEhRwm6HHXaI/3/sscfC4MGDw1xzzdXyu759+4Yll1wy7LTTTu39OhERERHpLmE3bNiw+H8E3K677hpmm222os9FRERERLpC2CX23HPPlizYd955J0ydOnWa3y+++OL1nI+IiIiIdJWwe/HFF8Pee+8d7r///ppJFTNjVqyIiIhIUwq7H/7whzFx4tZbbw0LL7xwzQxZEREREWkCYUfyxNixY8OgQYMac0YiIiIi0ik6XMduxRVXDOPHj+/c0URERESk5wi7008/PRx99NHh7rvvjrtNUA05f4mIiIhIk2wp1rv3/9WC1bF1M3PyhFuKiYiISE/QGR2OsbvrrrvqOTcRERERaRAdFnYbbbRRY85ERERERLpW2N17771t/v4b3/hGPecjIiIiIl0l7DbeeOPp3svj7WbGGDsRERGRpsyK/eCDD6Z5sa3YyJEjw9prrx3+/ve/N+YsRURERKR4ix1ZGdVsvvnmoW/fvuGII46IxYtFREREpAksdq0xcODA8Pzzzxf1dSIiIiLSaIvdE088MV39uv/+97/htNNOC6uvvnpHv05EREREukvYId5Ilqiua7zeeuuFyy+/vKjzEhEREZFGC7tXXnllup0oFlhggTDbbLN19KtEREREpDuF3RJLLFHk8UVERESkO5Mn7rnnnrDtttuGZZddNr622267cN999xV1TiIiIiLSFcLuyiuvDJtttlmYY445wqGHHhpfs88+e9h0003D1Vdf3ZlzEBEREZEC6FWpzoKYASussELYb7/9wuGHHz7N+2effXa47LLLwrPPPhtmNiZOnBjr+02YMCH079+/u09HREREZlKd0WGL3csvvxzdsNXgjq1OrBARERGRrqPDwm6xxRYLo0ePnu79O+64I/5ORERERJokK/bII4+McXWPPfZY2GCDDeJ7//znP8MVV1wRfvWrXzXiHEVERESkEcLuwAMPDAsttFAYPnx4uO6661ri7q699tqw/fbbd/TrRERERKQ7y518+9vfDv/4xz/Ce++9F1/8u6tE3YUXXhiWXHLJWBB53XXXDQ899FCbn7/++uvDoEGD4udXWWWVcNttt03ze3JHjj/++LDwwgvH7F4yfl988cUGX4WIiIhINwq7Dz74IJx//vkxM6MasjRa+12RYBU84ogjwrBhw8IjjzwSVltttTB48ODwzjvv1Pz8/fffH3bbbbewzz77hEcffTTssMMO8fXUU0+1fOaMM84I5513XrjkkkvCgw8+GOacc874nZ999llDr0VERESk28qdnHTSSeGJJ56IFrBa7LLLLlFoHXvssaFRYKFbe+21wwUXXBB/njp1akzYOOSQQ8KQIUOm+/yuu+4aPv7443DrrbdOs6ct+90i5Lj0RRZZJMYN/vSnP20RqQMHDowxg9/97nfbdV6WOxEREZGmKndy4403hgMOOKDV3++///7hhhtuCI1i8uTJYezYsdFVmu9Ty89jxoyp+Te8n38esMalz1Oe5e23357mM9w4BGRr3wmTJk2KNzl/iYiIiHQ37RZ2//73v8Nyyy3X6u/5HZ9pFOPHjw9TpkyJ1rQcfkac1YL32/p8+n9HvhNOPfXUKADTyzIvIiIi0lTCbpZZZglvvfVWq7/nd1jQZgaGDh0azaHp9frrr3f3KYmIiIi0X9h99atfDTfddFOrv//zn/8cP9Mo5p9//igux40bN837/Ez5lVrwflufT//vyHdCv379oo87f4mIiIg0jbA7+OCDY+06EhdwiSb4Nxmx55xzTjjooIMadZ6hb9++Yc0115xm1wuSJ/h5/fXXr/k3vF+9S8aoUaNaPr/UUktFAZd/hng5smNb+04RERGRHkulAxxzzDGVXr16Vfr3719ZffXV44t/9+7du/Kzn/2s0miuueaaSr9+/SpXXHFF5Zlnnqnst99+lXnmmafy9ttvx99///vfrwwZMqTl8//85z8rffr0qZx11lmVZ599tjJs2LDKrLPOWnnyySdbPnPaaafF7/jLX/5SeeKJJyrbb799Zamllqp8+umn7T6vCRMmkFkc/y8iIiJSJB3RGR3aeeLkk0+OhYivuuqq8NJLL8VyIRtttFH43ve+F9ZZZ53QaChf8u6778aCwiQ3ULZk5MiRLckPr7322jRxfmx5dvXVV4fjjjsuHHPMMTHBA3fyyiuv3PKZo48+OpZE2W+//cKHH34YNtxww/idFDQWERERKWUdO2kd69iJiIhIU9WxExEREZGejcJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKQrvKnbCjRK9evdr1hY888ki95yQiIiIijRJ2O+ywQ2e+W0RERES6EOvYFYB17ERERKRRWMdOREREZCakXa7YL33pS+2OsXv//ffrPScRERERaZSwO/fcczvz3SIiIiLS04Tdnnvu2fgzEREREZHGC7vW+Oyzz8LkyZOnec/kAREREZHuocPJEx9//HE4+OCDw4ILLhjmnHPOGH+Xv0RERESkSYTd0UcfHe68885w8cUXh379+oXf/OY34YQTTgiLLLJIGDFiRGPOUkRERESKd8XecsstUcBtvPHGYa+99gpf//rXw7LLLhuWWGKJcNVVV4Xdd9+9o18pIiIiIt1hsaOcydJLL90ST5fKm2y44Ybh3nvvLeKcRERERKQrhB2i7pVXXon/HjRoULjuuutaLHnzzDNPZ85BRERERLpD2OF+ffzxx+O/hwwZEi688MIw22yzhcMPPzwcddRRRZyTiIiIiHTHXrGvvvpqGDt2bIyzW3XVVcPMiHvFioiISE/QGXXVsQOSJniJiIiISJO4YilxsuKKK0bVWA0KcqWVVgr33Xdf0ecnIiIiIkULO/aL3XfffWuaADEP7r///uHss89u79eJiIiISHcJOxImttxyy1Z/v8UWW8RYOxERERHp4cJu3LhxYdZZZ23193369AnvvvtuUeclIiIiIo0Sdosuumh46qmnWv39E088ERZeeOGOHl9EREREulrYbbXVVuHnP/95+Oyzz6b73aeffhqGDRsWttlmm6LOS0REREQaVccOV+waa6wRZplllnDwwQeH5ZdfPr7/3HPPxSLFU6ZMCY888kgYOHBgmNmwjp2IiIg0VR07BNv9998fDjzwwDB06NCQ9GCvXr3C4MGDo7ibGUWdiIiISE+hQwWKKUR82223hQ8++CC89NJLUdwtt9xy4Utf+lLjzlBERERE2kWndp5AyK299tqd+VMRERER6e7kCRERERHp2SjsREREREqCwk5ERESkJCjsREREREqCwk5ERESkJCjsREREREqCwk5ERESkJCjsREREREqCwk5ERESkJCjsREREREqCwk5ERESkJCjsREREREqCwk5ERESkJCjsREREREpC0wi7999/P+y+++6hf//+YZ555gn77LNP+Oijj9r8m88++ywcdNBBYb755gtzzTVX2GmnncK4ceNafv/444+H3XbbLSy22GJh9tlnDyussEL41a9+1QVXIyIiIjITCztE3dNPPx1GjRoVbr311nDvvfeG/fbbr82/Ofzww8Mtt9wSrr/++nDPPfeEt956K+y4444tvx87dmxYcMEFw5VXXhm/+9hjjw1Dhw4NF1xwQRdckYiIiEix9KpUKpXQw3n22WfDiiuuGB5++OGw1lprxfdGjhwZttpqq/DGG2+ERRZZZLq/mTBhQlhggQXC1VdfHXbeeef43nPPPRetcmPGjAnrrbdezWNh4eN4d955Z7vPb+LEiWHAgAHxmFgURURERIqiIzqjKSx2CDHcr0nUwWabbRZ69+4dHnzwwZp/gzXu888/j59LDBo0KCy++OLx+1qDmzbvvPO2eT6TJk2KNzl/iYiIiHQ3TSHs3n777egyzenTp08UYPyutb/p27dvFIQ5AwcObPVv7r///nDttdfO0MV76qmnRuWcXsToiYiIiMzUwm7IkCGhV69ebb5wn3YFTz31VNh+++3DsGHDwhZbbNHmZ4nDw7KXXq+//nqXnKOIiIhIW/QJ3ciRRx4ZfvjDH7b5maWXXjostNBC4Z133pnm/S+++CJmyvK7WvD+5MmTw4cffjiN1Y6s2Oq/eeaZZ8Kmm24aLXXHHXfcDM+7X79+8SUiIiLSk+hWYUdyA68Zsf7660eBRtzcmmuuGd8juWHq1Klh3XXXrfk3fG7WWWcNo0ePjmVO4Pnnnw+vvfZa/L4E2bDf/OY3w5577hlOPvnkwq5NREREpKtpiqxY+Na3vhWtbZdccklMithrr71iMgVZr/Dmm29Gq9uIESPCOuusE9878MADw2233RauuOKKmEVyyCGHtMTSJfcrom7w4MHhzDPPbDnWLLPM0i7BmTArVkRERBpFR3RGt1rsOsJVV10VDj744CjeyIbFCnfeeee1/B6xh0Xuk08+aXnvnHPOafksmawIuIsuuqjl9zfccEN49913Yx07Xoklllgi/Oc//+nCqxMRERGZiSx2PRktdiIiItIoSlfHTkRERERmjMJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKgsJOREREpCQo7ERERERKQtMIu/fffz/svvvuoX///mGeeeYJ++yzT/joo4/a/JvPPvssHHTQQWG++eYLc801V9hpp53CuHHjan72vffeC1/+8pdDr169wocfftigqxARERFpHE0j7BB1Tz/9dBg1alS49dZbw7333hv222+/Nv/m8MMPD7fccku4/vrrwz333BPeeuutsOOOO9b8LEJx1VVXbdDZi4iIiDSeXpVKpRJ6OM8++2xYccUVw8MPPxzWWmut+N7IkSPDVlttFd54442wyCKLTPc3EyZMCAsssEC4+uqrw8477xzfe+6558IKK6wQxowZE9Zbb72Wz1588cXh2muvDccff3zYdNNNwwcffBCtgu1l4sSJYcCAAfGYWBRFREREiqIjOqMpLHYIMYRWEnWw2Wabhd69e4cHH3yw5t+MHTs2fP755/FziUGDBoXFF188fl/imWeeCSeeeGIYMWJE/D4RERGRZqVPaALefvvtsOCCC07zXp8+fcK8884bf9fa3/Tt23c6y9vAgQNb/mbSpElht912C2eeeWYUfC+//HK7zoe/45UraREREZHupltNVEOGDInJCm29cJ82iqFDh0bX7B577NGhvzv11FOjSTS9FltssYado4iIiEhTWOyOPPLI8MMf/rDNzyy99NJhoYUWCu+8884073/xxRcxU5bf1YL3J0+eHDNcc6sdWbHpb+68887w5JNPhhtuuCH+nMIN559//nDssceGE044oVVBeMQRR0xjsVPciYiIyEwt7Ehu4DUj1l9//SjQiJtbc801W0TZ1KlTw7rrrlvzb/jcrLPOGkaPHh3LnMDzzz8fXnvttfh9cOONN4ZPP/205W9Izth7773DfffdF5ZZZplWz6dfv37xJSIiItKTaIoYO9ylW265Zdh3333DJZdcEpMiDj744PDd7363JSP2zTffjBmtJEGss8460UVKCRMsa8TikUVyyCGHRFGXMmKrxdv48eNbjteRrFgRERGRnkBTCDu46qqrophDvJG9ihXuvPPOa/k9Yg+L3CeffNLy3jnnnNPyWZIdBg8eHC666KJuugIRERGRxtIUdex6OtaxExERkUZRujp2IiIiIjJjFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlISFHYiIiIiJUFhJyIiIlIS+nT3CZSBSqUS/z9x4sTuPhUREREpGUlfJL3RFgq7Avjf//4X/7/YYot196mIiIhIifXGgAED2vxMr0p75J+0ydSpU8Nbb70V5p577tCrV68eoewRma+//nro37+/x/bYHttje2yP7bGb4NitgVRD1C2yyCKhd++2o+i02BUAN/nLX/5y6GnQILurUXpsj+2xPbbH9tgeuzhmZKlLmDwhIiIiUhIUdiIiIiIlQWFXQvr16xeGDRsW/++xPbbH9tge22N77OY4dhGYPCEiIiJSErTYiYiIiJQEhZ2IiIhISVDYiYiIiJQEhZ2IiMhMhKH15UZh10Tceeed3XLcnXfeORx33HEz7WAws163iJRzPGOHJMe18qKwaxKeeuqpsNlmm4Ujjjiiy4+93nrrhdNOOy2ceeaZM91gkAbBme2aZ7bn3BPwns+8dNWzP+CAA8Iqq6wSt8Gc2cRdZSa6VoVdk7D88suHK664Ivz6178OP/3pT7vsuFOmTInHu+CCC8LQoUPD8OHDw+eff95lx2cA6i7GjBkTHn/88fjv/fffP5x//vndes1dNTCxiEhi9qKLLgqjRo0KM9Pgm59DV51PvoB49913u+SYMzNtjStdMeakdvXCCy+ESZMmddni8fvf/3749NNPo5GAsb2rxd2LL77Y8u9LLrkkjB8/vkuOO/X/CdmeMsY0HOrYSfPwxz/+sdKvX7/KiSee2PBjffHFFy3/fvHFFysHHHBApXfv3pXzzjuvMmXKlIYfPz/GzTffXPn1r39dufzyyyvPPvtsQ487derUyttvv11ZaKGFKnvssUflBz/4QWWOOeaoPPbYY5WuvOZXX3218sILL7T6+0bAvaV9nXzyyZWf/exnlbnmmis++64gv7aPP/44tr/JkydP97tG895771X+97//VT7//POW9tBI8u/fb7/9Kl/72tcqEydObOgxO3JOjaK1Z9roZ51//xVXXFEZOnRo5Sc/+UnlT3/6U6Ur7+1NN91UWXLJJSvnnHNOSztvFP/4xz9a/v3QQw/F42600UYtY3xXPO8xY8ZU1lhjjXjPDzvssEqvXr0qL730UqUrOeeccyo77rhjHNvuuOOOSllR2DUBaSAaNWpUZdiwYbFT0imOPfbYLjn+UUcdVVlppZUq3//+9ysrrLBCFHenn356lwwGcPTRR1cWWWSRyrbbbltZZZVVKuuss04cHBrNo48+Wplvvvkqs846a+Xaa69teb8rrptr/spXvhJF1hZbbFE57bTTuuT477//fuWSSy6pzDbbbJX+/ftXXn/99fh+EjldMdmeeeaZcfBdf/31K4ceemjl+eefr3QV3OeNN964stZaa8X///vf/27YsaqfI4uJwYMHV+65555KV/P444/HRSPt/Kmnnqp5fkWSf/dvfvObOK5dcMEFlQ8//LDLhDzj2sCBA6Oo22mnnSrLLLNM5cgjj6x0BX/5y18qs88+e+Wiiy5q+MKJsZL54pprrulWccdihYUyY/ncc89dGTt27HQGhEZy+umnVxZYYIHKD3/4w8raa69dWXPNNbtkHukOFHZNwq233hoHAiY9BoODDz64Mssss1R++tOfNvS4t9xyS7TasNqiAzLxn3HGGVHc8f9Gd8orr7yysuiii8aBCBAdiB0GxkbBAMd1/etf/6ost9xy8fh77rln/DlR9MST30eskhzz+uuvr/z1r3+t7LPPPlFoMAF1BUwATAQ8dyx3tc6xUQwZMqQy//zzV37729/Gdo6QX3HFFaMFrdGwUGLg/8Mf/hAtHEsvvXRcyLz77ruFHwtrbM6vfvWr+IxZvEyYMKHSldxwww2xva277rqVTTfdNE66WJO6guOOOy4+7/XWWy8+Z/7/zjvvNKSP5d93++23R2Hz4IMPxp8RtCxmGG8aDc+X+/zLX/4y/vzZZ5/Fa0bgMsZ88MEHhR+TeYLr6y5xl77/wgsvjAvGVVddNY5zkyZNapiQn1L1nSyWR48eHf/95JNPVn784x9Xll9++crvfve7ls90lbGi0SjseiCIpxysJd/73vfiBJ/46KOP4kCAuDvmmGMadi5McnRCBp8cVtgc++KLL26oNYeBH1EF1113XRwUOGZy1xW52m1tcPnnP/8ZB8DddtutZZVZFNX3FffA8ccfH60XCQZ6Vptf/epXp7EcNuq6cUU+8cQTUVgNGDAgnk9X8PTTT1dWW221yn333Rd/RtQiMi699NJpBt1GDL5YJrEEjxw5smVBM88888R7kFPEsbEKYQ1M30cboE0vtdRScSGRaLSVFGjPWKVZMKXJHlHPJNgI8rbG9dG3H3nkkTjxM+luuOGGUeAVKe4IHUmW5/R9l112WcszYAFFO0vjCmNraoONAMssz5lzQOThFvz6178eF1JLLLFEZcSIEQ1p57S7vn37dqm4q35+WIYJaSG8BRGP0GtEO5+SHZcx9f77769svvnm07ikn3nmmSjuBg0aVDrLncKuh8EqZuGFF54mtor4CwY8zNg5WDH23nvvOBA3yoXABIeAS3FtqfNjwevTp088NuKvaNJxjjjiiLiypWMy8KXBl46LVef888+vfPrpp4UOBFgrmAyGDx9eefPNN+N7d999d5x4GZDSKp+JAXHdWXbfffdoiU3HZ/LhfvKqFuuI2A022KCy//77V4okv24GXCb6FO+DwOMeIO7ymM6f//zn8X4UeWxg0F1sscXixMIzyJ831//73/++xVVXNEw4WOtod7fddls8dhI79DMsasm6UC/jxo1rucfjx49vuddcHwIjLWQaaSVNkzeT/A477BD//Z///Cfefya7RGr/RZA/b0Q8oQ7f/OY3K88991zLObGIYqwj9CNZSusRGjxLQhpwv7311lst72OlIbQkPevUzoBYO8RWEpdFwTWnNkSMGZ6HeeedN97/1NZwxX/3u9+tNIrWxB1jG8+iyPZWPbawWOQepMUq8xniLl07sIikL9TD1Ky9YKmcc845o0Wa+3322WdP81nmtUMOOaTypS99KS4ky4LCrofBoL/66qtHy0VujWJiYbBDUOXgmsVdhBhkJVhEJ8wnMFaUDDbf+ta3pkla4NxwB+PGKWLF1drK/Oqrr24RO1jsEky2rMCIkykSvg8X3CabbBJj2xC1d911V/wdsU+Y7nFZ4SJk1V3PZI9oTRN8+p6HH344WooY8NKElw/KPIuiBEYOExnChpgjriuJV4QUgyGD43e+853KZpttFu9PvRPAJ5980vLvNKASS8czRawjcPIBn3aPEG5UAgtWM9o4Ey4TfbISppU9bSG1g6Igpo12nSY77jWCg+ew7777NlTcpcmPe8w95xoRdSRv5DG9hx9++HQehCLaGhMpngD+j8UuPy/EHRYkXLRFuCWxfmMRQzAnoYrISAvT3BVHu6SP4R0p0nL1yiuvxJguxEzq84STMKbR9tIYynEJuaj3mbdl6WTcqSXuCPU56KCDKkWQ3ztCHLCKMU/xvElW4ZqZW7gfjKc/+tGPYv9D6BbV3l9++eU4j+LeZiFKW8Ydnfft1BZYwHZVrF9XoLDrQaTOyGDGxI54SIHjTGyIDVaZubijk+Kmw31QRCdkUmV1S9B6CqLGakdMCOdEdiouky233DK+EvWIu2qz+Y033hgH93RNrLpYbXEeWLWYhBh8ybAq0oyP5RFhk2LpWLkz8CNe8wGQQeCkk05qOXZHz6E605XJlYzfFFv1wAMPxOtFSGHV4PkQeIyrEAttEeT3HPfjsssuW/nb3/4WrWZYEHDP/f3vf4+/5zkwAfG899prr7ozVZnQEErAJIZQTNaZb3zjG/Ge57F9TLYM+ttvv32hsTinnHJKi2sbiyDXxsCfW6w49lZbbRVfRccBYSGjHbMoo03n4o6M7KKtswnaF1Zw2tWdd94ZBQdiMg/1AMYAwg/qzdDNxxfaGG0NSzXXSQYw7sc33nhjms8jog888MC6Jtv8eTGucSws7q+99lrLopF+htBg4ueYiFwEZ9EZ0bQv7jkJQTzX6sUZ10/YCYu6JPSLGk/pb8kzkEDkVIs72mDR4oa5CYGeXNssnFgspzE2uaK//e1vxwSWorLgTz755DhX5kIVUY8nJA/vqKYs4k5h14NIjRlB9ec//zlOcKw0U0o4ggdzOa6F7bbbrrLNNttEN1k9A0E+cNEZsFZgLVhwwQVj5hDxJ8AEQJwfq1xWXwxQqRPWM/jlf8tgw3GZZLCMEUzOhM/EQqkVBiJM6lg0WdGn4xfVGRFryaXNdXMvEFxpwk3CKz/njh4b0UyAfB6rhxWMZ4qwTJMowhaRwaSPqEBscT/ShFDUhIMrGWtwnnULiEpWz1htalGPoGaFzATGCp6YSX5OcI9XXnnl+Ixpj5QnoM1jrS6y9AnWUO4rfQx3HPz3v/+N/Y12j3XnF7/4RRSaLLDqPXZrf4fI4Dxo87m4S5mMTIxFwnkgXGlLCax0HAtXMFZ/7gOTLRNyvSKjegFDwtWpp57a0oax/HPPifPKxV1OZ/p36h95O6WdJ3GX3LLcZ/oYmZoIXMbUIsaVWv2TRQIeFtoXY2w6DoKSLHAELwu5esiPi2BN4yVjCcI99wJhFKCME+E/OUWNp3zPzjvvHGMJgQUyFrsUt5pCaLgPjGu1nlln+Pzzz2PoCAl+uPVzEHdYEBl/GFvKisKuh4GViMkOlyACAPcIk1oqu8BKh46CsGNFS3ZPETCp4OpKKytM5QxymMmxaqROh7WJyShNVPV0wnwQQjiSHIA1kgEea1Ea8FPMBdYyhAb/r/f4tSZaXMtM6KxuWdXlgfME+TLZ1VNvihg5LDFMqkwuWE4Su+yyS3zOTK5J3HGdnAeiD0tpOueiXLE8YwZ9JvVa1iHEHUKb+5EP9kWIyl133TUeNxfo6fpw/WEp4n5hKaadd9Y62lrmLe0asYygYrGSFjAM/Ag6xCSTLZNfvcfO2xqudl65sMcKjUUyF3fcA1zUjbAgcI2I9rPOOqvlPSZgFlOMPdx3rKi5i7ReeMYsBnnmWJ3zNsTCFQFNuZHqbOEi+nZ+D7HccR65W5b/I/SxoBYlLoCxjCSzassd953FAuMN54ZFnDEW12FRsFBDsKYxBlHLvadP5+IOsUcfLILqcQHPE/MXYzvzSh7LyNjDHJdXGqj1HZ0dyz/88MPKueeeG6+Z/+cg6rn3LKjLkgVbjcKuB0GwLqu25Iai0+OKRfAw6efFHPldkVYbrCS4NvO6XXRMxB0uWMoAVIuaolxTTKqYzbFm5TDYMggzGNUSM0VYT1ghpyBprKSILla3JE/kVqStt966rkxBBnjiPZLrE9HKoJq71XFFVIs7BmYslYhuLClFWUfzZ8y1sbJP8Wv55xBWiI5GLGCYzLCUYLFK4j2fUHlOeSxeEZMtVlGsFNx3JlSEFBYr3EPJ5V6rXXVWYOX3Elcb4oUYRgRUHtfDYob7gPDPLZj1HLsW6buwWtC3cysZbQ335L333lt30kSttkZcLG2cxUIqX5Rg3MGCy++LgvuLi4/xIw+aZ5GWxF3Kli16XKOtEmZAf8YTUP07YstYtOWhDUXBs2PhmMIM8PRgKeOZ0+64H3n8bhHXm38H42n6mYURVjPG1NwyyGcILarOOq/nuLTl6rjkU045JYo7BH0OnqBGZtl3Nwq7HgQrCSxUxKHkDY6kBVZfTLJpRV8krFSJ32LCY2CvXvkQ25RisIog74wMakwwBOdjRakG1w0xL0VlQ+bHZqLFYoUrjnvNxENGGtYKYjBwSyF2EDZ5PF9nBgIGOI4FuNe431wvgja33DGxIbIRIOmaESGsdrHgtuau6sh1YxUhmDu5o7Ai4KJCdNQqTlvvwN/W3yOsiWtE1OSB8tWZ1kUNvkyyWOOqE5YQzhSiTuVOih7sOS7XSQIO95v4NSYc2mAu7mgXCO0iwdXHQiVfHGERZ7InbrVo8udNO2NBmtot/Z37jyWn2u2IICkqpu6EE06IIgbRjsjh2SLy0gKC+4GVEM9HykwuGtoV8cE8U6zAOQga+jnHz7N1i4DFEGKO/pTKmWCxS+Mp7Y5YwlzU1tPH878ljhALO1ZpIIaP8jVYx/IMcMZUBF9R7m76ESIaF+uqq64arzM9a8QdblkEfVvfUSYUdj0MVvN58HaKRWB1Q4fETVLPCq81NwUDL9YqBrvquCrcQlirinYLYSHBGsb3457BWkEMSl7bjYmHe1KEiyaH1SvHQ9Tl6fWcD25RBiMsZQzKlDXpbNxNGjhwRWCRYDBn8GHQxVLJPa8Wd6yoiW+iiGo6HlYUhEFnLCn54EWpEtoQVhMERLImYCHk2SPga8VVFWEdJZ6J4+P2zS3DlBph4cLgz+KBJA3ue5HJCsTTIFqJmcQNWV1KI8W04pYtWtyxMONepwB2SrnQBrAYYSnkniTRlVs76oXzxyrJpMe1cV8RGGmBQp/GG1BkAebqtoa1HxczE3naKYfxDGFB0kStLOd6xxnce8Tr5lnMaRcZBHyCWD9id4u43+m6cafStlOCBuKORR2LuNwtS/wbnpmiM44TabziGCycU3wwAo97QNJO0clAtCfGFYwDeBcSWEtZ1FJShXmMMY+FclEx0ridebYkeDGO7r///rHdYTFlccyzSYI2T4QrMwq7biINBJiOWd2kQYiVJIMtboQcsnuIVcC61lnyjkznY6ChMyIagBUcAcR0vtaC5osQd3nNtlQYkgGO5AwmdCYEJhvi+ZjsOZ8iV1ZMtMQTpTIbdH6eA6voFPuE0EVUInKKiCcEBtM0wSauuuqqmuKOSTDd6/T/euv1YcVA1GCtITYT6yTnk+I0GfyJt8E6WGS8DxCfiNsViyTPlAkAqwLWKyDkAEspq+188VLEcydon+Nh+UREMuiTnZeLZPogEwI1rRAc9Vx/9YSJWOMcEFn0NdzeqQA1rjieAeeT/13Rky4LI8QOEyzWMlxTiF0sRq319XogeJ3JlqxM+hauR64zCTmeO5nRLJ6qs8TrAfHMgo1XihFM/ZZ7j0sw37Umta8i7jftmcUXiyMWKml8YSxjnCUUgwUj8Yx4R4q87mrSdbF4YMHGwi3FTedlo4pqZ4RW0L/pX4k0hgPjKLGFjGu4ZNOY1tExNS+5xXekMas6ju60006LcYw8E8AbQzhRVxT97gko7LqB3EqAmRxLTqohhKsGNw2DAIM+ta5YVTJIplVgveAeYPJigCGuLa/lhOBCaDD5VqfIFw3ngTk+uQQQd0wA3Isvf/nL0XVCAHsSNEUNQggZhB0lDnjhOmAQYMLNB4Oceo+NVZBBlQmPwT0vQoq4I1OOZ5FEdqKopAUGWZ5pcr1hDSTGJ2WsJSspA2W9ZSaqwUrG80wTLfecNodVkgVGKmvDOTBwFyWkAbGMOy4PMWDQRzziokPQMelgUeP+U+6FyTlZ7TpK3k4IUmeBkFugCdqmjac2jeDFNcnkW+TiBQHFRMaEmyywWAWZ4LEgsbhA4PEcqmNb6wWXG6ItbUvWWlvDZYigLbKtUcic54rlNS0aua+8ENjVW0il33eG9L3Ac6aNI+DpY4zluP/SLhIsHhlP8UwwnqeQh0ZDe+ZesGAiCQuPQSPEDWMYccM8U/owLlnmNo5JklIeK5vo6HNn7GRuomJAgraENTQVUM+vbZNNNolhRNXMDOJOYddN4HLCHcOkx4CLSzBlJ2K9I8aL1TUiAAtevWnwCQZbVlYpeJlVJcfN90hEQDLoY70oguqBM3VorhlRy2CcYABkokFgEYuWLDfVW2/VA8fn3lK2hdgbJlsGY2JdOG69Ab1tHZd7wY4ZHJvMzwTCA7Gd7/BQJAhLLGJM+Eww1RlqWHCqA9qLmHARbTzHtEMHixlin4ih4zkj7gjyrt4ftQgRjzjDjY9Qq16kYBnHHUjb5zMspHg2iBImo7SnZGfbOYKNZ8z1YVFIFjomm+QOpG0z8eQV74sQd7j4OS4xR8RNslBKAiPB5Msz4LrrLfpc/axYoHFPcYlWtzXGOtpadaH1zrS11toIVncWTgit3PWGuOB+pBJGnaU6FAaLJwlPeXIV4oGaabjaq+NFu0pYpPtDv8b1y4ImHbvosBoMECyMscgzd+CBwO2LiGbcyb0R9bRrXPq8UvUGrpEFc27ln/L/rpv7j3GgrHF0baGw6waYxLAi4BoD3D4MOHQKJj0GpeQKYoVd5AboTDBpa7JatdpSKjyJA0V3fiaw6pUqK3smu+qJgXIYdFYEbpE7LeTuzVQIOYdj1gqyLRLEDu4IJn7czwlcpEW5uqvhnuIKpn4WQfP5Fkq4QXHJ5S6qzlJrEGViwaKBW4aFSnKbMNinXUVSMeSiwTpFrUesONW7GNCuWFSQhZruGXUMOcc8Rqij95xJDlcgiyisRpR1wGpCW0Zscr0kwmBFYSFRZDFcXGG427GOMXaQvECMHcfnvKqpNyMz9yIwkWOVTQWl8ThUtzXaANeOJbEe8nvFPebY+U4lWGIRFzz7JGpo44j2eoQV4g2LW4Lv4jg807T3bP47xAXuXxZzRdOe9lJrLGiUsOT+E9eGkSCFOdD+0u4PRRlEmCdpX8kgQJgBoRZ4oOjjn/2/nTxICMuf1cyEwq4bYEIhzoFsMSwpWORS1fe0hRaTcB5gXhQMgLidsJJU12qjQ7K1S55MUJS4Iz4Q0YT1AEGbLCi4nik9kPYeTRMNQoQBk0m2uoBmvVRfE0Ir1RLDktcVK2qOyWoWqwrPuq3z6wj5QM7gmi8KUi0rhH36HIsMMlKL2Ceyelu6FD+X4JkTw5kWD7iJiLkhiL3oe57HIyKsEFC4h2oVmQbaH5MAQqQe6zjWdvpQXl4DgYWFkLgqsgRZUGG1y2vkFdXPuMeMJ9UWUMQNIqc6drAeMYl4oj1h3cSlSrhIKslEn+V3LNDSNbJwpK2xkCvqehFOXBcuOqyUefxqstyxeOWY3Pt6A/YZr5KFM7V3FgFcP/GCydKb18Pjd5xb9TPpKEUXx+4IbbWT/PvTfeU92j1zDRbrorJu6V8sTmlrVIlI4i6FUKTi+eutt16cO2YGt2stFHbdRJp4MNPTEFOcGatqVn645erJBG2tIxHDxcDPKjIvUIrQoBOSkVuE5YABLp0/Qg5XBUIDMcMgi4WS7FMGW0z1+RZSaXDALcYkyaqvkWDFJEuLOKeid7NoC+45x8YlW3SwPAkouMMY4PIs62S9IXYRN0UjdlYg8YcJnEQYFiypbdMGEDcMwixacKHkhZGLGIS5n1hBGfTTtlmAJQFRSTurJe4Qc3ymnnJCTPC0axZMHDuHxRKxRim8IbdCFzn54OrG/ZfueWrHWMpxkXXGxVxNXp6DOFmEE9ec6u+l+4oLnnNJu+TQ1rBS1tPH8naGZRArDVZCRCOia/HFF5/Gcob4pA1iNUoxf9zvesc4LEdYgVPsGDGs/Ez7TnGy6RhcZ6qV2Vny6yasAbGIaMbK3lb7ya8TEdSZttbRBBPuCSVG0raPRe0YQ/IP/Ys2x+KUBBU8Pknc0Qbo38dWLRZnRnGnsOtmiKkioDWlvVMVnxiUokqa4PJATCEgU5A6JQ9wFfF/BkUmWjohg18RbiEmFQJpMYUjzFi55y5Y3LxMpAzAiDt+z6o7r3Sfr/waDfeFGKTOZmrVK/CLzMwDxDKTOEKKQZDnmtduoy0wSDLhIcKKHACxDNG2sFjhXk0Te7IcYhXlPRYuWEeLLM6aMm8Rr0x+tCtCHhIIN8Qmn6u1t3IRLn9coUw+TGjVOzdwv3OLUiNg8cRCEetjXvOQshskDqTt0zoLgpm2kwtp7jMLxVoJJwhN3Jc8ByzG9bS1vH+wMMC9h7BLIpZ+ROwebSsP73jwwQdjdigLmFS0t14QyFw3zzSJOxaieBnS4iWdU5FgfSZ2kAUR7ZhzQMTUypjPj41nhs9WF7+eERwHr0K69+0do5hzmMuKGlsI5yCGD/GewPLNcyZWtjpms2x7v3YUhV03w+DPRtSIIAZNYuzylPGOkndm4oVYSTOBcgwG/JQVyWBLNhGdHasOwq5IaxVlDlhRcdw0mVRPnHR23CWssnBVpADzol0ObX1fTwmsrec8qq8PYZfiJok3wT2HiyKf7KoFVRHPnGxj3B88++Q2YZKr3nAbywkWjyKFNPF6lJlIQdX8n8SY6tgmrCrVW1oVDf0XMc2xklsXtxShCLiRigRrBW7PPOuXMib0dUQHMW0IPFyWiP3OFrhO8H0pkYn+TNujBBNuZcQdQm5Gfa7etsbYhYBHcBBPmG/PxnNFyCGu+X2C50BsFmMdi4winj/hJRRWRzTm4i6VsGlNbHQWFkpYJFOSE/HBHKc6OQPy6yP2jRCDtG1eR0Cg4lHJ3ecdHU+LGFt4xlxDdUw07Z52h+WuCGt0WVDY9QAYnDEtkyJfVBo8Qc0IOlwRDDq4ArCWMLmkeDbeS4HtRZWYSB2b1TSTG7EvDAopXjB18uoB4Pjjj4+DVq20+I4cNwkHLJV5fa7WBvL8fWK/allyejL5+RMwT4Fn7jfB4gkmYMRdqgBfFNUDPIMvAjI9gzwbEjdJreD9olbUWIuYtIFMSI6dgukJqM4H/XTejRR3LNi431gvcUPi+iYEIi1uijg2izTEK6VymOCxjCbhRkwfpYR4H/cnVp5cANX7vHGz4lrNw0UYv4ihZV/j3MLUUStRW8cl4YkQA8prYBUiGQIRm9ck5N7iDkXI5QsYBHdndnlIz4r4QcRaHjvKIqZa3DGuYjHNa651huq+gYihLQGWx7x/0cZTzbj87+gDGAs6Wpg3WRwh7V5BrFx7xF1+/Dxeu73U6hvMi1ick4jNP0O/QnzinZD/i8Kuh0BHKWqiIb6BzDN2MGCwSd+LOwZh19ren0W6PVkVM5kz4aY6XdXJIGk/1DToIkTrTRjBPYGVkgGfAQ3BmKi+v/nPTIRMkLX2juyp5M+LuBIGelyNTOLcy1ykMsFhOcXCQZ3EouP5sBDiYue4aU/KPEuRyQGhUa+4aGtxhKsNFzDHzjMxsXTQBsn+TXSFix8LJtX2CUvIz6de93Oqn4ZFkPtOH2fiIzOQyTeJLfog146Vo969X6vhOWKNZ4zJEzIoHUQMJ244njfljIoKbWBhQOwiFskEx8ZSi8ejtWsswt2PoMSrgKUQVy9W8ZRlncRd7patt33l5Z3SziCpmDlJbrTxPPGNe0MMZ749Gtn99PeOijqyjFkQcI0dFXf5mEo4BOEP+Tg/I/LvZP7IF/qpPBKiPZ/TaIOcc1f06WZBYVcyaNzEsuACY2WbOlUa3HBRkb3FRNBVbkgCfHEzY0lKkwCTUl47j9gszrmjQcb5Rs6sxonbI2uN1TUuKq4Vl3StzycQIMT41bIoNQME7ROriLuJyRyRw7NHxOcTRCrvUWT2K5MGEx3tiokOywUCMxePqeI9E0+Rgy+Fb5l8aMscG6sYFiPaUoLYIxY5BJp3x8DPMyHkARdsygauFwQ0Exru1TxsAys94o4+0MhdDVL7QbjyrHmuubgjbpj+TlJQUeEdfA/XhuCo3nKR5CraO4vHooq4J2gz9C8WfQglrHB4VxDsCKsUG51i7qrPrTMgxlM9Syx/iFb6EM+UMRQrLXF1CcQP1lqETxrXUsZyvstER8D6hYuzI+IuH1NZcHCeKWGlo7DVYdoFiYVjgutErHJ+JP/xexZOHY0BLDsKuyanVkNGzBFbRMfKJznADUv8SSNKqVSTd3TEHUGupKSzkseqlK/ksWikjaM7c+2sVJlEWTUnSxWTAeIRy0Jupq/lqqi120QzwPmzFyfuqDS5ct+xqDDZMSnUSgoowgXKZMbEg/s3gXDk+SIqGZCxoGEtI0GoqOw4wCKElYRrpJ0j4nHNYbEkQYH2zyqe2Ju8Vlx3DPy4ZTkvSm/U655DSGONZoLNd1dIYHGmb5G8kVsoiya1H9ysSdzlY0peM7Bei116ZggYLFaMX4iM/Fki7rDUF1W3LM9oRVSRhZqXK+E4uP8Qd+laGVvrfb5cE+IQyzdCFRGTfyeeBWIHSdIgkQC3N+0dd3ue+Mar1p7PHYEFMQvj9oi76oVyR8fU/FkioClngruf+4ygz/f4ZUGD14k2TmZ/keNKWVDYNTF5Q6bzE9eWdjdIHYQMRMzhxExg2aBDEIvUVZ0g7/CcH5sx4y4tMhOTjp6KvRLjlbtTk7jDGkiGbk5n4096ErjdsAghXqt3jkgxb4ifIu5zbu2kLeECQ1zlK2qgrSG8mPgoe0JGZFHPm2MziSMeEZHE8NCmEDm0d+IMec7s6oLligmhK0vYtAbPhsmwMzFeCUqxIOrIZieLG4HD5J9vdp/aBJ+rZ1/pjoo7+hExbdVisjNegbbGJhZttGcmddp3/v1Y1op8xsQwYiHiWCycqr0JiA7ivsgsx21YDwhHkj4SLMiwuFEep/oeslgi2Y0FDeeFqE5tvOiM/tbEHRbLWrUvk/ejs2MqbRkXbiqWjpgn6QPRnhdzxzNBW8jrBcr/R2FXAhBKZKDSoRjshw0b1hJvwooS9xSDBGUK2MIouee6Q9zldLYz5oMJlhqSLhhcERgIONxeeWwGgx6ZmUz06VwYqHA1NJOlrrXnxbMmUJ8JqLr2IcHeuCGLdL+m58bgiyWO4+bbwiWqE2GKGHwRcrikEI75NTHZIe6Iv2LQx0WW/74nDPy1SlK0F9yelHHJS42kqv5YJavFXVeJ2NwtyxhT7SGop50xdpGQgRsf61SKNUviDlcdVvrWtiysB0QWwol9k3H3kZGJyMEFnoNbluD96l1NOgJCidjE1EYZrxCNLEpYtOGWrBaO3KfnnnsuWhGLEDdtzQVkPNcSd2l/8wRWcqy3nRV1LPzpw4zheVke5iu+k/kt7ZrUEysb9CQUdk1I3glZzeB6wSTPip5SAFjksJKkQQgXFZ0lTySop2ZXZ8uHFBlQnIKWGfTyfTAZEBhwGBhzYVE92GNxatQ2Vo0gv3eUlCDrlXqHaUJH3BGojquqtcLWnZ3w8mMT18IElyYyYmiYYBlw822Dqo9VxOCLZZZYJwZ4LLRMbDmIuhS4n++40ewDP1mYhDEwaVbvUoL7k3uBZZQSMkVfc3u+Jz1rzqUoAc0zJGwDtyQubNxxtL0UQ4e4wxrN2Fe0yxnvBwloeYgBbY++hbhOAjPR0e3n2rrPZH3mJT0QfPSvanFXXbamqJ0dGFNZsFXvrcyColrcMd/k7lgEYD3bEnJfGdNww+aCMc1XVDpg8dCo/bTLhMKuiSH9HUsVsQg5xGFgSUmCh0GQGDbcsvkOD/UOAgTmUnuOTDWSFdpbAT2tNDsCgdhpsOEccLemfUarrx9xh5BlcKje1orzaOZYDMpHUI+MhADiS7AkINzTYE/8Dav86q2jijo2WYEIqDxIncUFkx5xP0XtCVkNiS1YpWnbhBbwfImbrHY30r6J72t2MVerz2BtxwVWXbcMQUWbIHmkur13lNb6xozuZ555ynfU08eIGyQhJxV4xjpEP6ftMdYkUUMscRHW6Pz6aNe4+RGVjK05WCOxTuMRqbbcdZZ8zGRBRtvFs5CLdNyxxGgiKunXuEDp/0WQP1eujyLAxPfhzSBeOU/4QbjR76q3eEzPviN9rrX2gbubPo7rFU9U9cKeOMaeYH3v6SjsmhQGNQY6Brzq2DEg7oJsoXzFQ6YSn89XovVM8ky0DKzEtiEkcXfW6tzVZUWwMHR0uzTcrMmVlayNJFtgvcEVVS1kyJgs6lq7k3zSQkAx8KbCp8SiYKFih4kEljsCrtPew0Xx17/+NT7v3OWaP1dcJQhKtqWrtqTVC4M5lpv8Oomnw1rD4F8t7mplPjcjJAQlt3IScMTIknGaW06SW7behKhqqzB959xzz20R6+2pBYlLtp7jAnGSWG7SedDHEbOU0SGWFGt19bV2VNzVqtuZkg1YuODqReBV12FjHEJcs4AocoFIOyY2lXGLuD7qTebiLu0gQ5vHal3ELik53FP6N4W+832lKSWSizvGFWJF6yG/byzWU+JTuiYsd8wTjGNYbmuhuGsbhV2TUGsQYXXJCo+BpjoDikGZlV8e10PHoRPVsx9mshQiMNJm2LjjGATywqSJ6mwpgto7UlaEAS9ZpNKEjkBNFj+EBoM98SjVpQ4QQM06AOQiJl0Dk0kKICY2kFVt2mECoZ8mVQbGomOssPgSqE87bK3INOfMwF/khJf2X8UNicjIwVrMRIebrHqib3ZRR+A+AfRYiEhSSVY6LONkQSLuOlvKoj2LNjIfcfumLf9a24osv8+0EeqMcY6d3f4Qaxz9mCQTFn+IGeIngfcQeSTsJG9EPc+Z80S4APeSsSlloCIsEVAsmquTJnCL1msRz8+bWp/E86VFE4XVWSBVizssl4SPFL31IYtByk+l7dYYW/AEpHqU3KN8sVZP/86vm7GduYuFPvcaS2ES0sktS8Y/cY7SMRR2TUDekej0rGJTLAMuSSxmBPAS8MvqnlU+kwKumUZAFmJKP0fk5cUycQUlC0qtFPiOBNYSx4U4RVBgnQLcUUz0THSpRh8xKYg74rxq1bFqNnHHwEqcCRNsDq4hYl14/nnV+TQxEUOZB3EXKe5YKLCiT7FrucUDa16t4O6ioFYbWZ7EmVXvYkC7I8QgvxfNDveTOFGeN9dLnBXiKm2XRvIICQW4vxFDRcLkznNOGZpklHPsfKGRqFULsiP9u9oNiAcCS02y3LAwIykolUFK9wJBX0TbZjGcinpzjdWlYxCULJxZrHS0vmZ7r5uEA47DdeckcUebz3fQaUTfJhaZMYexI5UzwWKXxnruDeeRVxuot39zzbSzlMlP/+U4xE2mxD8s1tTrYyHT7Au1rkZh18PJGzRmaSxliDhiICgGS2dDzLCyZSXLCh/3KIN+kdsX5YMJxWdZRVFugIExr4BOB2UwyBMdOiPq8sribPSN+T/tdUghTgYAJptc3BH/QRB5UfEv3QXnj2sE93Zef4/K89xvgphzIYPYwrqSZ0wWDTXrKO3AgJy7p4jfRHjnu0w0AqzDtHsynqu33WNSKtNm38QqpuxSrFa4BdMes6lfcA/oFx0NaWiN9L0kDaRjcV9pb2mvX6zkuHyrJ/Z6ywYRDI+lj0k+36WAxCBi3ejniDvELLG2iSKeeRIuWItq1UOjvdO+sVzmuzoUAc8Q4cjxKWEDuYsVQYdblnbfqNjVRLp2vALc5+QRQeCxiGd8KSpBg0Ugi/A0nmOdpv2ccMIJcf4iTpgi3OmzZQmt6EoUdk0CCQL5ShozNQMCVjmEHS+sW3SQPA6qnu10WuvIWA5wA3J8xEa+8iMGiJpMCQb7zpQVyQdtrgdhR3ZYslQy+VWLOyYC7kEzJ0ekwQtXBKtVLBb5zhlkPXPfsaQQXI41ixhDBv+8QGkjYMLHbYILBVGPmGY1zXPpCqso18vAz3nUKr7a7OIOSyzWKBZluNwQ7FixyHDPa1MmN2ARW5NV3zvcjLgf0367+QKCNsezz8UXizrcdp0VdSwScDmmXWhwxVLaBCsZYwalRsiIpaQR7roitgfLQVwgKFgwI15SxmvelhB/uL7rqUFYDRb2vffeO4axEAuNFyJZw3Nxh+hhcdfoMS21BYwFCFmecdoxJnf5d+Y88naGh4fvxcWMUYJxBAthMg6k2D7aVJ59rKjrGAq7JgDTNB0uBUynGAgCeXF/IO6IMULcUfKCIPZ694as3jWCjkcMVxp8OHZynbCSpYMi6lj55pM8k1C+AXtHIRML9yvuEsQkRW+TSETccb24ZRvpCuxq0r3H/YMFBXGXW+OY6IktS24kJr9GFuHN7yWWpGRl4FkjpLuyADDijgkea1UjMn+7CywyBIvjWmXCx+rOMyZpJN1fwhyw4OAWL3JvaVyQtDMg/hULIdb/3D2IBYf+nVuQsShhPU6Wl85A6AjjCPFcCDqumzbNYoHrJ74WSx5u2SJiy9I9417mIhE370ILLRTFXW6RTolK9RYgrn5WZNYyThPvzDEYx7jmtGtOreSIrhjTSOAgIYsFHOEPeA2KuN/JOIEFNre4s1DB5ZwWC7Q/RD3jXbMv1LoThV0TQAIEAz4xELgk8hgI4nCYZJncWeGkmDs6ZXWto86A+5cJh2NiMWRAQnBgKif9HnHBipqBgJV3XgG93oEIaxwCFtGIeESsEuuBRSPtQYjg5TNkkzXzyq76XuWuaCx3DPyI3Nw1iSWTiaFWhl9njp22IqpFPsgi7mmHBJ/Xe+zOgNV6r732amrxnkPWIfGRWGNT3BUJEwicvBYjopr3i9wOkOeGSEagJ7DYYWVHVDEJ09YQPFhLc6sw/TLfLaGzED9LH8bbwD1IMWW4XXnOOfVM9qltE8PIdzOOIJxTGSUWrljusIAjJhGbjG311qnL+1QuGjk+oSNAX8bqzqIllazp6tjg1J+4dtoa4SDpHOoVWSxcSNAgUaR6fuEeA6KWRTzzSkJx1zkUdk1CEkxM8qxw0goSlyyWK1xiqROSvMBqMMXDdHaSR0gh1ujodDDcNMTyITBSMDExPhS1ZFAsepJnsiMJJLdOIFaZ8MimSuKO82rmASAf+LGMEsPIhJaKD2MtqSXucjorcvJYyGQtaM955nTHvU/n0uzijmfLBE/2H/X50v3E/U5fo2YZ1gvEF+KHnRaKIt07LJ98d+52RfiwWCMxhYLnuVW4EYKDcYSkkPzccH8irooE7wOilXp4XC8Wwt69e7dkw3IO1ARkYYzIKzK2jfg1hBzuVeCYHCct0rFYIuyo4VfPLiWt0Z5Fb63+VO/zxgrHooAY3XSf05hBcWnep/3hmeDVbMluPRGFXZOQOiUTPvWVmBDo/MRA5DWt6tmTM+/UWOQQUSQq5EVPWcUx4TDx1BKORUy06VoRM0x6yWqRJhaEJK4iBoTczduM4i6/X1grGOBYtWJBwSWCyxsRz4t7jzW2Vt3CzoDlIpVLwb2LK6g997CnWEV7ynkU4V7G/YlrGytY6r+4SOl/LNoQd/VuMN9ajUncfmSbUkon1cxLIofFBaUuusoyizWYGF7GNdp6kcfD48FClTp1wOIUN3D1LgeMq1jQithRIkG/omwI3hW8HJTo4bnzf+47VlvuMWVlcMUXMZZ1diwuerFE+8GNj6CurivKsbD8s6sI8aVFWQhndhR2TQbxGNQ8IvaByaDowQ8YbFhJYkXgGHmae4qVQHARC9HIDFTcQIiblDGWCxJijTjPZrfYJLCOkhSQ0v+BgQ6hl3bWQGxjwSBLrQhRg6UX6wCuJ7ISq7NNa5EfFzdcEe5++b8lXbCQIdr5dyPBQoSVm/CG9DyxZJFVfuedd7b6d10RwI+QRNTh+i06dhMhhysb8Ux/wyKXMoCBBJB6xXNbcG9xR2KVZ+G2//77x2xbwlyINYN8LK/nuvNnhaubhDaskzzn9u4QhLitd25J54FFFk8T8bkzqmOqqKsfhV0TwsDEBM+EX4+FrlZnRjQR80D2Ei5BBF6tQHXiILAmNNpqQpwPQpagbUQPqzvcGXlF8mYfCEj+YFLFAsnqNr+nrGSpaZbiqopI/8//jrgerAj5PsLt+TtEJ4ks7RGD0rGsX8Rdo+4rYg4rMIlIWK9w/yaLPJM/giO32nU1hAZwH4qwEKb2ivuaxDIsk2zFxc4WiClEXRo7WLxiKUP4FDmmUS4lbU3GNeFx4TiIVmKIedZpa8TqGo1FQC1MklAQkWQ0cxw8IbVcvdUhIUWdU3qWjN1Y7ggvyL1MzT5+90QUdiWgKIsdpUuwguV16ahhRfo7G3FXu167Ks6JlTRikwGKF2KkM/sT9lRYyTPgIeCStSa5n5mIsSxUl4vp7HXnz4rBnVU8rl9EJW7ZlJ2Wf646AzPtIJIq1UtxIGqIIaW/FWE9aq1vYrFKdcNIiiLmjMUSVpVUELi7KWKHA0rI4G5lIcr34XZFsLDPcv79XDvxXbUKnHcWxijuK3GKPE+SQhAx3HNKqKTP4GbHal60wKFkDmVikheA8kRce/Vew1Ddv/EU1JPt3Ja4Y2FOElz1nrNSHAo7iWApWn/99WPsWvXWTUnc4QLM9w3sSmHFRMQAhaum6C11unuy4j3KDJDwgms0r3KPqxMxW2u7tnqOjSjLA/GxJOB2z8UdpEKlRRWjlRlDO6duY7110/LnzcIACz8uvxTHRz+i/2Kt3WmnneJuJ0z8WPTKANmuLJaw0OXhJGTSE2ZCiRfiVokvxYKZtkgsGqyvCEkEO/2Mmn14QdJzyMfResRd9d/idcGtnfp7XpeQeMOUrJL/XSP7dy7uuBd4hKQxKOxkmsEfcccEX22dI06DrEzKnfQEmtF8n4tgMnqJNUnJH/yO+EkGPCx0lLzAgooFhWSVeq83PzZJGhREJZA5DxBnwqPoM/XLeP5kQrKyTjApKOq6hiKzIkl0wipH26ItYUHK918GhA8llQjwL7oIcHfdP64FDwTgbiYDkzaPuxUrNUkpWP+JNU37LDcKSlFxf4lNpvYfIpo4x6IWyXl2eyrsy/jB8RCS+baPwNizww47TLObBqKf0lad7d8dybplwZpXYJBiUdjNhLTl4kBwEGfDK+35msBq1IyCqqdBjSgsoyTAUGoBl1gCcUfWM5YTtt2hnE2KgSri3hNfQ6LEAw88UPP3BxxwQAwwJ34u35YOtw7u8CLdM9J46M9puy4s3EzkJCORlJRP4NWTa7OLO0IZaL+HHHJIrB1H1i9eB9yyWMWJeyMDl75VqxhwIyE+GktiXjuwHuibbMuW+i8lohB6WORYnBGjTL/P7w3JZ3mMNO53xpx8l4lGucpzT0tZkt96Ggq7mYy8I7GiIymBgZ40+wQDPgGuDDy19qJU3HWMPNkB9xr3lYEU9/KIESPiJJtX9KfcA9YErGcp6zgvVNteiO9JrnOOzQSHBS5tdo5VjmQZLBtYddKAS8wf7SF/zmTktiYGpedCkgtbVlWLN543Lv7qHWrKZD0hOQEBhbUKVyg/Ay5ABE9Xh3JUZ5Sn/lXPPWc8p8gypXIQrljc8thMrO/svYpVkjp5LM4ZW8jAzotN86q1TV9bx60365ZFbDOG0zQDCruZFNxxDOxsR8bETlHONOEntywiIN+QWTpOPgAi0nD5ECydCzUyxBB3ZLAlCHRmQmYA7kysFfFCBGRXW14QlbihEG7E37C65/kTY5SXfkgw+biqbl6IL0PYpLjN9CwR77hny57VjFjBopVfOwkUlB3J3ZddRbWI6+wiGSGV7/pBP8bihoWy+hhk5lI+BssdoTa4YIsqNt0Tsm5lehR2MyEkQ5DunywwxNvgEqSAJK6/BLEZrG610NUPWXfENxE/Rwxj9eqY4GbuP4Hc+YqWOoVU/u/I3qBYaXJLDKvoFKhNRhy1pCivQvzRvffeG9/HaktNLVfQzUlr4pt2lgLV8zbBVnRkQveUDNiuAEsWbZ79tRsdU9dIcKvjWk59FZGGC5bkNhKwiN2rtXc2CXIkQ6VxpN6+3pOybmVaFHYzGViKcMOkzFe2t2FFT3YYqz1WdbnlLqG46xj5/UIgE9dDYVjco1jnqF9VXdkegY07JXfRsCqvjnVsC+oNEkeEQGQ1jKUGEcegTzYasJqu3m8US55Zas1JPmmSdEPMJuI+r4WIpYYdDlgssE0W5XXSdn0zA1wz26SRANao7NfueOaIKARVAsHHDjLV4q66kHhnnntPz7qV/4/CruTkHTiZ3+nkxF4hAihAnLbYuf3222PGHKuuvICktB/cELlouvvuu6OQzsUy9xkLKe6L1rYtqmc1TS20tDMIK3Sscuyti/sp39WAsibE3eCmyWNupDkhY52kHJ4n/Zh6YanMB64vChLTt3nWJOik8WBmEHcsaOkHRdap6w7yPkr8M9Z3FmV/+9vfWt5ngY6Vltp9jPHES1OYuR56QtattB+FXYnJB2xWdnTAvC4ZxTtZ3RFUD6z8yMRkJaaFruNQToEA4nTvmESwljGZVpc2QNxhuSPwuRHbciHuCKimyj07CVAnjzhKSpqkuBbOgaBqSqoUvX2TdP2ijSK4PGdgcTFw4MBoAc4tvrjksaR01d6v0hiIZ+NZ04fJcEW05+KOZCxCP4h/W3vttevK/O2urFvpPAq7mcA9Q4ArgdK4+vI4G9yw1FSivhKTP2Z1XIRFxWDMjKR7x71ltcqARhwKiQx5QWBgIGawq94Yu9HijgE3bTpO8LyTfHOLOp4hcU4sEvJCvPwbccfkS3xVW98hzTOejxw5Moop9nIFdrRgcVYt7mgTiLJ6Crp3V9at1IfCruQQ14WoyzOo0qqKVT6TPJYjCtYSqF+mrbq6C9yriDmsY1hDGYCToKrOAiOBpZGCqpa4I3EG10xuyXGSb05S2RK2eGORVr3NG+KOmEsm2o7EakrPjaEkyxVBlZPEHZUM+Hc1HbXE95SsW+kcCrsSw2SNeybVSCN4HlM4nZ/4m2S1YZsuMpSaeauungYuL9zctaxltTLyukLcEXNHUDOlLqr3ypTmIJ9Usc4g2AipoHwNWa6bbrpp7M85CDqs8T7v5gbLLDF1CCyy2CF3sSLocIGymwbJIs2edSudpxf/CVJKeLT77rtvePPNN8NGG20URo0aFWafffYwYMCAMH78+PD++++Hu+66K8w111wtfzNlypQwyyyzdOt5l4VHH3007L333mGNNdYIZ511VnjmmWfCHnvsEVZbbbVw9tlnh6WXXrpLz4W2sMQSS4Tf//73Lc986tSpoXfv3l12HlIMt9xyS7j55pvDYostFo4//vj43r///e+w0047hYEDB4ahQ4eGjTfeeLq/s383J9dff30YOXJk2G677cLw4cPDW2+9FR577LHYjydPnhz69u0bP3frrbeGe+65J5x++ul19Wvmjl69eoUrr7wyjlMbbLBBfP+QQw4JY8aMCTvssEP8N3MJMMcsuuiiLX/vuNK9eOdLQGvanI65/fbbh7nnnjtcdNFFYfPNNw8nnnhiuOqqq8K2224bFl544Sj0chz0i+OrX/1quPzyy8MjjzwSjjrqqLDSSivFnxnwllxyyS4/F9oAbWGOOeZoed/Bt/lgQXbqqaeGP/zhD+G5555reX+ZZZYJf/rTn8I777wTzjjjjPC3v/1tur+1fzfnmP7000/H11e+8pX4bBFyCPePP/44/htxB9tss00488wzY79GXHWUL774omXueO2118LFF18cjj322PD3v/89vn/++edHkcfCgsXqK6+8EjbddNNw4IEHTvM9jivdixa7mYCPPvoofPbZZ2H++edveW+rrbYK8847b1yRycxjLUsrcVfUzUOtZ/Xiiy+Gn/70p+GFF14IP//5z8P3vve9lt+9/PLLYcMNNwzf/e53o2VYmovUR5OIZ5yGtddeOyy44ILhr3/9a7SaHXTQQaFPnz7h7rvvjos1RBk/F8GQIUPi4v9///tfFHeffPJJXJxuscUW8ff8Gw/Qe++9Fw0E//jHP1qshtIDqMONK90MOwVUB0vnVAe5UreMLCkCXU2U6FoIRCaZoifEOfm8m4e8vRAjSxmTVPuQf1NomLi66rqTZL9buqa5Ofnkk2MsNBn2QDYqdUdJiAOyUCllQvHzWlt4NUPWrTQGLXZNCqvxZ599Njz++OPt/puxY8eGc845J64IsRyxuitylSdto7VMOmu5+cUvfhHdrJ9//nmYMGFCOOGEE6IV+KWXXoqxTvTj/fbbL3znO9+Z5juMqWtOeG677bZbuOGGG8Kcc84ZDj300LDzzjvHn//zn//E50/sG+523rv00ks7/ZzzdkaoCO2LOYF2lbjjjjvCueeeG12+Rx99dNhss82mO1/bWQ+iQYJRGgiWN1Zyw4cPb6ny3d7NvMmQS6uz6g3ipfFoLZOOQnHYBRZYIFpLPvroo5jRTHmTVBeMQrFYVChIO3r06O4+XSkIstfZLYYdHdhdgp1q8NJQrohdHKqtZPVaaLsq61Yaj2aDJoSVERYfAlix3P3oRz8K8803X5t/8/9K28Q4r7Q6m3XWWbvojCWR7r1Ia+RB7/z7oYceipZ24puIayKm6pRTTgkrrrhitOAtt9xy4bTTTotB7LUyYaV54DmnuEgqGTDW/+tf/4rJCyQt9O/fP7z66qvh4IMPDk8++eQ03pZ6LGZk3XJcrHHEZ5KYQ2x2npiBlQ4rMe2MZCzpwXSBeJSCoO5cDvvzsfFyvg9paxah/P2bbrqpJW5DRHomxx9/fOW0006rLLroonG7OurT5RutU2T82GOPna7wsLF1zQkelF/+8pdxn1/qj2Ih41muscYalTPOOKPlM4cffnjcyaae51w9T7DPMDXqnnnmmcqYMWMqK6ywQqzDiYUYam1J1hPihaU2Crsm4dBDD41JD8n8TsFbTOYEzrJlEPu8ttZp858x6w8YMGC6IqYi0r3kEyXJEBS0xj3G/s30ffYdZueBPEHi61//emXEiBHxZ9385YBnjrt9nXXWqey1115xj++dd945Fj1PpGfdGXGXt5O0TzistdZaMcQHSJzA5UpyxscffxzfMzmieVDYNQlpCzAYN25cy0RAp6PiPPv4pSym1jrxJZdcEmNz2GVCRHomd999d6z0f+6558afL7jggpZt4PI4WzJiib3SQlc+3n333biHN2KLreLmm2++uONDTr1CviuzbqVrMcauSaCQMDFxqRI42bDEa1G/iJgbipJSe+iBBx6YJj4nxXSRNUX8xG9+85uYXSUiPY+333477LPPPrGI+KeffhrfO+CAA8K3v/3tWDCW2KZddtklFhtn9wFir4itIitRygM1R3nmDz/8cKwZRx250aNHFxavS3th54rbb789Zt9ShJj2xtzw4IMPxmxrYu3IviWmznjs5sJyJ00GBSMpHPnGG2/E8gerrrpqy4TwzW9+Myy00EJh2LBhMfA2cckll8RipiNGjAg77rhjN569iMyIJ554Im4NRjHa8847L6y55ppxIqYwLdtFkTCx1FJLxXIUliwqL3kZEhJoaAeI+Pz9emA7ySuuuCKst9564brrrgvLL798+OCDD+KxEJM//vGPp2lbljRpHhR2TQjbyLBVGKsq9ovMxR2ZcqzALrzwwvje888/H/crPfzww7XUiTSRuNtzzz3DWmutFQVc6uPVONmWm2oRV+/zTnVMjzjiiOjRoaIC38/i/49//GO47777wm9/+9v4WbxCq6yySiHXIV2Lwq5JIRWdjZirxR1b0LAxc975SY+nzImINNdWdEy8WGoOO+ywuNewSGfB0ss+s3h0KGSNy3+TTTYJ66yzTiybhZWOz/zsZz+L+9LedtttLhqaFIVdk4s74jDYGxJTOhNAvrJjJeYOByLNLe7233//uDBjUsYFK1IPiDb2F37zzTfjYoEYuptuuikMHTo0rLHGGtNYCrUINycKuxK4Zb/2ta+FZZZZJtx4443dfToiUjDEPOEqI/HJhZoUwfjx46PblULXuP3nnnvu8JOf/CQcd9xxLZ8pKpZPuh6FXQ8i70gd6VRkM/Xr189BX6SkuM+wNArEHLtOrLvuujGhQpofhV0PIRdybA9EevmRRx45w7+rHugd+EXKiRYUaaasW+k+VADdzPDhw+P/6UgErpJeThkTUs9nBB0wiThq2U2cOFFRJ1JSnGyl6PaU7DokUKR6iLaz5kcV0I2QTk4mEhlJkIpAUoxyRgGr+aqK+Jvvfe974cUXX+yCsxYRkTJQLeJMlCgHCrtuhBIlVI6/8847w6677hrfoxhkLtpwrfLKyXeU+PWvfx3T01MhUxEREZl5Udh1E0m8sVsE2wfdcccdsdo871OHLlX7xrWazOUTJkxoeS+JOrYJu/zyyy0+LCIiIiZPdAd5gsOkSZNiRuuoUaNi0cjVV189vP766zHTdckll4xbiBF3h5uWekNUDgd2liCbiRIICEIRERERhV03irqLLrooJjwQH7f44otHcXfooYfGrcEuu+yyaKlD2KXP77HHHtGSd//990fXLYkXbAguIiIiAgq7bgIXKhswn3XWWWHjjTeOwg7LHNmtu+++e9h2222jNa4W7DTBrhOt7R8pIiIiMycKu26Acibs/chOEaSZ55BuTjIF4m7llVeO/xYRERFpDyZPdAPPP/98WHbZZcNqq63W8l7S16Sbb7755uG3v/1tjL2rzogVERERaQ2FXReCNQ6IocPtinBD0KXyJfz+r3/9a3jttdeiK/b222+P8XWKOxEREWkPCrsGUi3IUvHHLbbYIvzzn/8MV199dRR0KTmCcia/+93vwsMPPzzN37mbhIiIiLSH/1ssTRqa/UpMHVY4dpTYbbfdwtZbbx1j7Pbaa68o5ihjQtkTCg1jzdthhx26+/RFRESkCTF5oguyX2+44Yaw3HLLRdfrrbfeGosRr7jiimHEiBHhxBNPDP3794+vgQMHxt9Rsw63rNu7iIiISEdQ2DWQa665Jhx++OFRzLHd11/+8pfw7W9/O1x33XUtO0WQSDF+/PhYn27ttdeOVj7i79LOEyIiIiLtRfXQQHC/IuAQdVjtcL1ecskl8T1csJ9//nlYfvnl4yt34SrqREREpDMYld9APvjgg2iNu/nmm8Pee+8dzjjjjLDffvvF31177bXhlFNOibF1OSZKiIiISGdRRRRYxqSar33ta+GFF16I23/98pe/DAceeGB8n23CEHtkxBJ3JyIiIlIE+vzqIC8qDMTQ4WIlCWLw4MHxddNNN0WrHSIO1yz/PvbYY8O4cePi79L38HsRERGRejB5opMQJ8fuEaeddlr8eciQIeHCCy8MSy21VHjqqafCT37ykzB8+PAwefLk8KMf/Sg8+eST8f211lorzDnnnGHkyJFmv4qIiEihaLHrJBtuuGE46qijYpkSRN4999wTX4svvni4//77w3e+853ocr3sssvCFVdcEevTPfPMM2HJJZcMyyyzjNmvIiIiUjiqik5A5ioWOSxvBxxwQHj55ZfDoEGDwqqrrhqF2nbbbRdj6Pg/Au7MM88Miy66aHzl36GoExERkSJRWXSQ3HW6xx57RIvd7rvvHoXdRx99FOaZZ54YM0d8HeKOunVY7n7961+Hueeeu+V7zH4VERGRolFddACsbEnUET935JFHhtVWWy384Q9/CM8++2w477zz4mdIhEji7o9//GN46623onVPREREpJFosesAycrGnq6XX355FHKUK2H/V6x1uGURfkOHDo2fRdxtv/328VW9f6yIiIhI0SjsOsjo0aPD9ddfH0uVUKcuse+++0Yh9+Mf/zha7MiSrRZxijoRERFpJAq7DkItujnmmCOstNJKLe+lOnTsKjHXXHPF2DsSJfbcc89uPVcRERGZuVDYtZMk3j799NNpdppIZQD5/4033hjWWGONWKPum9/8ZjeerYiIiMyM6BtsJ2lniE022SS8+OKL4dxzz215n9fHH38ckyjuuOOOsMUWW8RSJtSpExEREekq3HmiE1x66aXh4IMPjnu/brPNNqFv377hlFNOiUWIx44da306ERER6RYUdp2AW0aNukMPPTS6ZaldR0zdrbfe6jZhIiIi0m0o7Opg/PjxYcKECbGMiduEiYiISHejsCsQ69SJiIhId6KwExERESkJmpdERERESoLCTkRERKQkKOxERERESoLCTkRERKQkKOxERERESoLCTkRERKQkKOxERERESoLCTkRERKQkKOxERERESoLCTkRERKQkKOxEREREQjn4Px6CO8Tya+KnAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "cf.seriate(S)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "7e2d4e07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [Element(object=B2, attribute 1=B, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1)]\n",
+      "Elapsed time: 1.0700197890400887e-05\n",
+      "serial_crossed k-complexity:  14\n",
+      "serial_crossed primitive count:  {'add': 0, 'append': 0, 'check_if_same_type': 1, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "result, elapsed_time = cf.serial_crossed(S)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "092f34e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2)]\n",
+      "Elapsed time: 1.1899974197149277e-05\n",
+      "center_embedded k-complexity:  26\n",
+      "center_embedded primitive count:  {'add': 0, 'append': 0, 'check_if_same_type': 7, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 8, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "result, elapsed_time = cf.center_embedded(S)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ca81ceaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]\n",
+      "Elapsed time: 5.9200217947363853e-05\n",
+      "tail_recursive k-complexity:  13\n",
+      "tail_recursive primitive count:  {'add': 0, 'append': 2, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 2, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "element_set = {a,b,c,d}\n",
+    "S = ElementSet(element_set)\n",
+    "result, elapsed_time = cf.tail_recursive(S)\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", elapsed_time)\n",
+    "print(kc.get_cog_name() + ' k-complexity: ', kc.get_k_complexity())\n",
+    "print(kc.get_cog_name() + ' primitive count: ', kc.get_prim_counts())\n",
+    "kc.plot_prim_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5c80b83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total K-complexity:  119\n",
+      "Total primitive counts:  {'add': 0, 'append': 5, 'check_if_same_type': 13, 'dim_set': 0, 'flip': 7, 'implement': 0, 'list_create': 0, 'merge': 2, 'pair': 20, 'remove': 0, 'remove_item': 0, 'sample': 38, 'setminus': 28, 'write_all': 0, 'write_all_set': 0, 'write_random': 6}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>add</th>\n",
+       "      <th>append</th>\n",
+       "      <th>check_if_same_type</th>\n",
+       "      <th>dim_set</th>\n",
+       "      <th>flip</th>\n",
+       "      <th>implement</th>\n",
+       "      <th>list_create</th>\n",
+       "      <th>merge</th>\n",
+       "      <th>pair</th>\n",
+       "      <th>remove</th>\n",
+       "      <th>remove_item</th>\n",
+       "      <th>sample</th>\n",
+       "      <th>setminus</th>\n",
+       "      <th>write_all</th>\n",
+       "      <th>write_all_set</th>\n",
+       "      <th>write_random</th>\n",
+       "      <th>Total</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cognitive Function (calls)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>iterate (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>palindrome (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alternate (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chaining (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>seriate (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>serial_crossed (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>center_embedded (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tail_recursive (1x)</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total Counts for each Primitive Function</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          add  append  check_if_same_type  \\\n",
+       "Cognitive Function (calls)                                                  \n",
+       "iterate (1x)                                0       2                   0   \n",
+       "palindrome (1x)                             0       1                   1   \n",
+       "alternate (1x)                              0       0                   4   \n",
+       "chaining (1x)                               0       0                   0   \n",
+       "seriate (1x)                                0       0                   0   \n",
+       "serial_crossed (1x)                         0       0                   1   \n",
+       "center_embedded (1x)                        0       0                   7   \n",
+       "tail_recursive (1x)                         0       2                   0   \n",
+       "Total Counts for each Primitive Function    0       5                  13   \n",
+       "\n",
+       "                                          dim_set  flip  implement  \\\n",
+       "Cognitive Function (calls)                                           \n",
+       "iterate (1x)                                    0     2          0   \n",
+       "palindrome (1x)                                 0     1          0   \n",
+       "alternate (1x)                                  0     1          0   \n",
+       "chaining (1x)                                   0     0          0   \n",
+       "seriate (1x)                                    0     0          0   \n",
+       "serial_crossed (1x)                             0     1          0   \n",
+       "center_embedded (1x)                            0     1          0   \n",
+       "tail_recursive (1x)                             0     1          0   \n",
+       "Total Counts for each Primitive Function        0     7          0   \n",
+       "\n",
+       "                                          list_create  merge  pair  remove  \\\n",
+       "Cognitive Function (calls)                                                   \n",
+       "iterate (1x)                                        0      0     0       0   \n",
+       "palindrome (1x)                                     0      0     2       0   \n",
+       "alternate (1x)                                      0      0     4       0   \n",
+       "chaining (1x)                                       0      0     6       0   \n",
+       "seriate (1x)                                        0      0     0       0   \n",
+       "serial_crossed (1x)                                 0      0     4       0   \n",
+       "center_embedded (1x)                                0      0     4       0   \n",
+       "tail_recursive (1x)                                 0      2     0       0   \n",
+       "Total Counts for each Primitive Function            0      2    20       0   \n",
+       "\n",
+       "                                          remove_item  sample  setminus  \\\n",
+       "Cognitive Function (calls)                                                \n",
+       "iterate (1x)                                        0       8         6   \n",
+       "palindrome (1x)                                     0       2         2   \n",
+       "alternate (1x)                                      0       5         4   \n",
+       "chaining (1x)                                       0      11         4   \n",
+       "seriate (1x)                                        0       0         0   \n",
+       "serial_crossed (1x)                                 0       2         4   \n",
+       "center_embedded (1x)                                0       8         4   \n",
+       "tail_recursive (1x)                                 0       2         4   \n",
+       "Total Counts for each Primitive Function            0      38        28   \n",
+       "\n",
+       "                                          write_all  write_all_set  \\\n",
+       "Cognitive Function (calls)                                           \n",
+       "iterate (1x)                                      0              0   \n",
+       "palindrome (1x)                                   0              0   \n",
+       "alternate (1x)                                    0              0   \n",
+       "chaining (1x)                                     0              0   \n",
+       "seriate (1x)                                      0              0   \n",
+       "serial_crossed (1x)                               0              0   \n",
+       "center_embedded (1x)                              0              0   \n",
+       "tail_recursive (1x)                               0              0   \n",
+       "Total Counts for each Primitive Function          0              0   \n",
+       "\n",
+       "                                          write_random  Total  \n",
+       "Cognitive Function (calls)                                     \n",
+       "iterate (1x)                                         0     18  \n",
+       "palindrome (1x)                                      0      9  \n",
+       "alternate (1x)                                       0     18  \n",
+       "chaining (1x)                                        0     21  \n",
+       "seriate (1x)                                         0      0  \n",
+       "serial_crossed (1x)                                  2     14  \n",
+       "center_embedded (1x)                                 2     26  \n",
+       "tail_recursive (1x)                                  2     13  \n",
+       "Total Counts for each Primitive Function             6    119  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "S.visualize()\n",
+    "print('Total K-complexity: ', kc.get_total_k_complexity())\n",
+    "print('Total primitive counts: ', kc.get_total_prim_counts())\n",
+    "kc.plot_total_prim_counts()\n",
+    "kc.plot_cog_vs_prim()\n",
+    "kc.show_cog_prim_table()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lot_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/oldtest/oldcf.py b/oldtest/oldcf.py
new file mode 100644
index 0000000..566f981
--- /dev/null
+++ b/oldtest/oldcf.py
@@ -0,0 +1,271 @@
+import oldpf as pf
+from collections import defaultdict
+from oldutils import Stopwatch, Element, ElementSet, Associations
+ 
+# 1-D
+
+def iterate(S):     # 112233    
+
+    # preprocessing (not part of measured cognitive process)
+    n = len(S) # number of elements in the set
+    chunks = defaultdict(list) 
+    stopwatch = Stopwatch()
+    # --- #
+    # select attribute which chunking is based on
+
+    ### ADD CODE HERE
+    """ graph algo here"""
+    bias = find_bias(S, stopwatch)
+    for _ in range(n//2):
+        element = pf.sample(S)
+    result = None
+    time_elapsed = None
+
+
+    # """ non graph algo here"""
+    bias = find_bias(S, stopwatch)
+    stopwatch.start()
+    for _ in range(n):
+        element = pf.sample(S) # select an element in the set
+        sorter = getattr(element, bias)
+        chunks[sorter].append(element)
+        pf.setminus(S, element)
+    stopwatch.stop()
+    n = len(chunks) # reassign n
+    chunks = {tuple(v) for k, v in chunks.items()}
+    stopwatch.start()
+    result = []
+    for _ in range(n):
+        chunk = pf.sample(chunks)
+        stopwatch.stop()
+        temp = list(chunk)
+        stopwatch.start()
+        result = pf.append(result, temp)
+        pf.setminus(chunks, chunk)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+
+    return (result, time_elapsed)
+
+def palindrome(S):
+    # 123321
+    # preprocessing (not part of measured cognitive process)
+    n = len(S) // 2 # number of elements in basis
+    stopwatch = Stopwatch()
+    # select attribute which chunking is based on
+    bias = find_bias(S, stopwatch)
+    stopwatch.start()
+    basis, rev = [], []
+    # write_random() based implementation
+    ### WRITE CODE HERE
+    while (len(S) > n):
+        element = pf.sample(S)
+        if len(basis)==0 or not (any(pf.check_if_same_type(element, chosen, bias) for chosen in basis)):
+            pf.pair(basis, element)
+            pf.setminus(S, element)
+    for _ in range(n):
+        element = pf.write_random(S, bias, getattr(basis[n-1-_], bias))
+        pf.pair(rev,element)
+        pf.setminus(S, element)
+    result = pf.append(basis,rev)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    
+    return (result, time_elapsed)  
+
+def alternate(S):
+    # 121212
+    # preprocessing (not part of measured cognitive process)
+    n = len(S) # number of elements in the set
+    stopwatch = Stopwatch()
+    # --- #
+    # select attribute which chunking is based on
+    ### ADD CODE HERE
+    ### subject knows what types of attributes are there
+    ### and what type of attribute to select
+
+    bias = find_bias(S,stopwatch,two_flag=True)
+    result = []
+    while (len(S) > 0):
+        element = pf.sample(S)
+        if len(result) == 0 or not pf.check_if_same_type(element, result[-1], bias):
+            pf.pair(result, element)    
+            pf.setminus(S, element)
+    time_elapsed = stopwatch.get_elapsed_time()
+
+    return (result, time_elapsed)
+
+def chaining(S, associations: dict):
+    # preprocessing (not part of measured cognitive process)
+    n = len(S) # number of elements in the set
+    stopwatch = Stopwatch()
+    # --- #
+    stopwatch.start()
+    chunks = []
+    result = []
+    while (len(S) > 0):
+        element = pf.sample(S)
+        if element in associations.keys():
+            chunk = []
+            pf.pair(chunk, element)
+            pf.setminus(S, element)
+            while True:
+                next_element = pf.sample(S)
+                if next_element == associations[element]:
+                    pf.pair(chunk, next_element)
+                    pf.setminus(S, next_element)
+                    pf.pair(chunks, chunk)
+                    break
+                else:
+                    continue
+        else:
+            continue
+    result = chunks
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+
+    return  (result, time_elapsed)
+
+def seriate(S):
+    #  123123
+    pass
+
+# -------- 2-D -------- #
+
+def serial_crossed(S):
+    n = len(S) // 2 # number of elements in the basis
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, higher_dim=True)
+    result = []
+    while len(S) > n:
+        element = pf.sample(S)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            pf.setminus(S, element)
+    for _ in range(n):
+        element = pf.write_random(S, bias[1], getattr(result[_], bias[1]))
+        pf.pair(result, element)
+        pf.setminus(S, element)
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+
+
+def center_embedded(S):
+    n = len(S) // 2 # number of elements in the basis
+    # 
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, higher_dim=True)
+    result = []
+    while len(S) > 0:
+        element = pf.sample(S)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            pf.setminus(S, element)
+        if len(result) == n:
+            break
+    for _ in range(n):
+        element = pf.write_random(S, bias[1], getattr(result[n - 1 - _], bias[1]))
+        pf.pair(result, element)
+        pf.setminus(S, element)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+'''
+
+def center_embedded(S):
+    n = len(S) // 2 
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, higher_dim=True)
+    result = []
+
+    while len(S) > 0:
+        element = pf.sample(S)
+        if len(result) == 0 or pf.check_if_same_type(element, result[-1], bias[0]):
+            pf.pair(result, element)
+            if element in S:
+                pf.setminus(S, element)
+            else:
+                raise ValueError("[ERROR] Tried to remove an element not in S during first half.")
+        if len(result) == n:
+            break
+    if len(result) < n:
+        raise RuntimeError(f"[ERROR] Could not find enough matching elements for first half. Needed {n}, got {len(result)}")
+    # Second half of the result
+    if len(S) < n:
+        raise RuntimeError(f"[ERROR] Not enough elements left in S to complete second half. Needed {n}, have {len(S)}")
+    for _ in range(n):
+        ref_attr = getattr(result[n - 1 - _], bias[1], None)
+        if ref_attr is None:
+            raise AttributeError(f"[ERROR] Element at position {n - 1 - _} has no attribute '{bias[1]}'")
+        element = pf.write_random(S, bias[1], ref_attr)
+        if element is None:
+            raise ValueError(f"[ERROR] write_random() failed to find match for attribute '{ref_attr}'")
+        pf.pair(result, element)
+        if element in S:
+            pf.setminus(S, element)
+        else:
+            raise ValueError("[ERROR] Tried to remove an element not in S during second half.")
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+'''
+
+def tail_recursive(S):
+    stopwatch = Stopwatch()
+    bias = find_bias(S, stopwatch, two_flag=True, higher_dim=True)
+    stopwatch.start()
+    result = []
+    while len(S) > 0:
+        element = pf.sample(S)
+        pf.setminus(S, element)
+        paired_element = pf.write_random(S, bias[0], getattr(element, bias[0]))
+        result = pf.append(result, pf.merge(element, paired_element))
+        pf.setminus(S, paired_element)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+
+# ---------------------------------------------------------------------#
+
+# utils
+
+def find_bias(S,clock,two_flag=False,higher_dim=False):
+    '''
+    Count unique values for both attributes, 
+    select the attribute with exactly 2 types while the other has != 2 types
+
+    Input Arguments:
+        S: set of elements to be experimented with
+        clock: stopwatch used to time primitive functions
+        two_flag: flag to indicate if the bias required needs only two attribute types
+        higher_dim: flag to indicate if the set of elements is 2-dimensional
+
+    Output:
+        bias: the bias lol, the attribute the flip selected
+    '''
+    if higher_dim == True:
+        chunk_bias, serial_bias = None, None
+        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
+                    for attr in ["attribute1", "attribute2"]}
+        chunk_bias = find_bias(S, clock, two_flag)
+        clock.start()
+        serial_bias = 'attribute1' if chunk_bias == 'attribute2' else 'attribute2'
+        clock.stop()
+        return (chunk_bias, serial_bias)
+    else:
+        bias = None
+        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
+                    for attr in ["attribute1", "attribute2"]}
+        if attribute_counts["attribute1"] == 2 and attribute_counts["attribute2"] != 2:
+            bias = "attribute1" if not two_flag else "attribute1"
+        elif attribute_counts["attribute2"] == 2 and attribute_counts["attribute1"] != 2:
+            bias = "attribute2" if not two_flag else "attribute1"
+        else:
+            clock.start()
+            bias = "attribute1" if pf.flip(0.5) else "attribute2"  # Default random selection
+            clock.stop()
+        return bias
\ No newline at end of file
diff --git a/oldtest/oldpf.py b/oldtest/oldpf.py
new file mode 100644
index 0000000..d5338b6
--- /dev/null
+++ b/oldtest/oldpf.py
@@ -0,0 +1,160 @@
+import random
+
+SEED = 42
+random.seed(SEED)
+# Functions on lists (strings)
+
+def pair(L, C):
+    """Concatenates character C onto list L
+    Time complexity: O(1) amortized for lists, O(n) for strings"""
+    L.append(C)   # O(1) amortized
+
+def append(X, Y):
+    """Append lists X and Y
+    Time complexity: O(n+m) where n=len(X), m=len(Y)"""
+    return X + Y
+
+# Random functions
+
+def flip(p):
+    """Returns true with probability p
+    Time complexity: O(1)"""
+    return random.random() < p
+
+# Set functions
+
+# def union(set1, set2):
+#     """Union of twos sets
+#     Time complexity: O(len(set1) + len(set2))"""
+#     return set1 | set2
+
+
+def setminus(set1, s):
+    #Remove a string from a set
+    #Time complexity: O(1) for single item, O(len(s)) for set s
+    set1.remove(s)
+
+def sample(collection):
+    #Sample from a set or list of strings.
+    #Time complexity: O(1) for non-empty sets if using random.choice,
+    #O(n) for lists using random.sample.
+    if not collection:
+        return None
+    
+    if isinstance(collection, set):
+        collection = tuple(collection)  # Convert to tuple for sampling
+
+    return random.sample(collection, 1)[0]
+
+
+# # Function calls with memoization
+
+# memoization_cache = {}
+# def F(z):
+#     """Generic factor function
+#     Time complexity: Depends on implementation"""
+#     pass
+
+# def Fm(z):
+#     """Memoized version of factor function
+#     Time complexity: O(1) for repeated calls"""
+#     if z not in memoization_cache:
+#         memoization_cache[z] = F(z)
+#     return memoization_cache[z]
+
+# Token-related functions
+
+# def create_tokens():
+#     """Initiates a list of tokens [A1, A2, B3, B4]
+#     Time complexity: O(1)"""
+#     return ["A1", "A2", "B3", "B4"]
+
+def add(T, list):
+    """Adds token T to a list
+    Time complexity: O(n) due to copying"""
+    result = list.copy()
+    result.append(T)
+    return result
+
+def remove(T, list):
+    """Removes token T from a list
+    Time complexity: O(n) for search and removal"""
+    result = list.copy()
+    if T in result:
+        result.remove(T)
+    return result
+
+def check_if_same_type(e1, e2, bias):
+    """Returns True if tokens are same type
+    Time complexity: O(1)"""
+    return getattr(e1,bias) == getattr(e2,bias)
+
+
+
+def write_random(S, bias, type): 
+    #Returns one unused member of particular type
+    #Time complexity: O(n) to filter tokens
+    for element in S:
+        if getattr(element, bias) == type:
+            return element
+    #type_elements = [u for u, v, d in G.edges(data=True) if v == type and d["label"] == bias]
+       
+        ### WRITE CODE
+        ### maybe add a random list shuffling thing here
+        ### to make it less predictable
+    
+    pass
+
+
+    
+def implement(FUN, N):
+    """Keeps implementing a function N times
+    Time complexity: O(N * T) where T is time of FUN"""
+    results = []
+    for _ in range(N):
+        results.append(FUN())
+    return results
+
+def write_all(S, bias, type):
+    """Returns a sequential list of all members of a type
+    Time complexity: O(n) where n is number of tokens"""
+    result = []
+    for element in S:
+        if getattr(element, bias) == type:
+            result = pair(result, element)
+    return result
+
+# Additional functions
+
+def list_create(M):
+    """Create a blank list with slots for M items
+    Time complexity: O(M)"""
+    return [None] * M
+
+def merge(I, J):
+    """Merge two items I and J to create a list
+    Time complexity: O(1)"""
+    return [I, J]
+
+def remove_item(I, L):
+    """Remove item I from list L
+    Time complexity: O(n) for search and removal"""
+    result = L.copy()
+    if I in result:
+        result.remove(I)
+    return result
+
+def dim_set(D):
+    """Create a set containing items classified by dimension D
+    Time complexity: O(n*m) where n is number of items, m is time of D function"""
+    def classify(items, dimension_func=D):
+        result = set()
+        for item in items:
+            result.add(dimension_func(item))
+        return result
+    return classify
+
+def write_all_set(S):
+    """Write all items belonging to a particular set S
+    Time complexity: O(n log n) due to sorting"""
+    return " - ".join(sorted(S))
\ No newline at end of file
diff --git a/oldtest/oldtest.ipynb b/oldtest/oldtest.ipynb
new file mode 100644
index 0000000..95c2711
--- /dev/null
+++ b/oldtest/oldtest.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dd606f7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import oldpf as pf\n",
+    "import oldcf as cf\n",
+    "from oldutils import Element, KComplexity, Associations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "67d013a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = Element('A1','A','1')\n",
+    "b = Element('A2','A','2')\n",
+    "c = Element('B1','B','1')\n",
+    "d = Element('B2','B','2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "162df936",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n",
+      "{'add': 0, 'append': 2, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 2, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 8, 'setminus': 6, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1)]\n",
+      "Elapsed time: 4.650000482797623e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.iterate(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "29d799b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n",
+      "{'add': 0, 'append': 1, 'check_if_same_type': 1, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=B2, attribute 1=B, attribute 2=2), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1)]\n",
+      "Elapsed time: 6.579997716471553e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.palindrome(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "56cb5c75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 10, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 11, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2)]\n",
+      "Elapsed time: 1.1900003300979733e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.alternate(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "18a226e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 0, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 6, 'remove': 0, 'remove_item': 0, 'sample': 7, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [[Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2)], [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]]\n",
+      "Elapsed time: 6.429999484680593e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "assoc = {a: b, c: d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.chaining(train_1, assoc)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f335aebe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 0, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 0, 'setminus': 0, 'write_all': 0, 'write_all_set': 0, 'write_random': 0}\n",
+      "Result: [[Element(object=B1, attribute 1=B, attribute 2=1), Element(object=B2, attribute 1=B, attribute 2=2)], [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2)]]\n",
+      "Elapsed time: 6.429999484680593e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "cf.seriate(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "571dd1f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 4, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 5, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=A1, attribute 1=A, attribute 2=1), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2)]\n",
+      "Elapsed time: 1.7200014553964138e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.serial_crossed(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "deae7813",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24\n",
+      "{'add': 0, 'append': 0, 'check_if_same_type': 6, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 0, 'pair': 4, 'remove': 0, 'remove_item': 0, 'sample': 7, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A1, attribute 1=A, attribute 2=1), Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2)]\n",
+      "Elapsed time: 1.4699995517730713e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.center_embedded(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ad5a4286",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13\n",
+      "{'add': 0, 'append': 2, 'check_if_same_type': 0, 'dim_set': 0, 'flip': 1, 'implement': 0, 'list_create': 0, 'merge': 2, 'pair': 0, 'remove': 0, 'remove_item': 0, 'sample': 2, 'setminus': 4, 'write_all': 0, 'write_all_set': 0, 'write_random': 2}\n",
+      "Result: [Element(object=A2, attribute 1=A, attribute 2=2), Element(object=B2, attribute 1=B, attribute 2=2), Element(object=B1, attribute 1=B, attribute 2=1), Element(object=A1, attribute 1=A, attribute 2=1)]\n",
+      "Elapsed time: 8.3099992480129e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_1 = {a,b,c,d}\n",
+    "kc = KComplexity()\n",
+    "result, time_elapsed = cf.tail_recursive(train_1)\n",
+    "print(kc.get_k_complexity())\n",
+    "print(kc.get_prim_counts())\n",
+    "print(\"Result:\", result)\n",
+    "print(\"Elapsed time:\", time_elapsed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ffe74253",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4.29997453e-06 4.39997530e-06 1.89998536e-06 2.29998841e-06\n",
+      "  2.00001523e-06 2.40001827e-06]\n",
+      " [4.20003198e-06 3.90000059e-06 1.79998460e-06 1.99998613e-06\n",
+      "  2.00001523e-06 2.30001751e-06]\n",
+      " [3.90002970e-06 4.20000288e-06 1.99998613e-06 2.19998765e-06\n",
+      "  1.89998536e-06 2.19998765e-06]\n",
+      " [4.29997453e-06 4.39997530e-06 1.90001447e-06 2.10001599e-06\n",
+      "  1.99998613e-06 2.09998689e-06]\n",
+      " [4.00000135e-06 3.90002970e-06 1.89998536e-06 1.79998460e-06\n",
+      "  1.90001447e-06 2.30001751e-06]\n",
+      " [4.00000135e-06 3.79999983e-06 1.90001447e-06 2.00001523e-06\n",
+      "  2.09998689e-06 2.29998841e-06]\n",
+      " [4.69997758e-06 3.90002970e-06 1.79998460e-06 1.99998613e-06\n",
+      "  2.09998689e-06 2.10001599e-06]\n",
+      " [3.90000059e-06 3.69999907e-06 1.80001371e-06 1.90001447e-06\n",
+      "  2.00001523e-06 2.20001675e-06]\n",
+      " [4.30003274e-06 4.30000364e-06 1.79998460e-06 2.09998689e-06\n",
+      "  2.09998689e-06 2.40001827e-06]\n",
+      " [4.30000364e-06 3.90000059e-06 1.80001371e-06 2.10001599e-06\n",
+      "  2.10001599e-06 2.20001675e-06]\n",
+      " [4.00003046e-06 3.69999907e-06 1.90001447e-06 2.00001523e-06\n",
+      "  1.62000069e-05 5.00003807e-06]\n",
+      " [1.22000056e-05 7.00002420e-06 2.19998765e-06 3.39999679e-06\n",
+      "  2.29998841e-06 2.20001675e-06]\n",
+      " [4.40003350e-06 4.40000440e-06 1.89998536e-06 2.20001675e-06\n",
+      "  2.20001675e-06 2.30001751e-06]\n",
+      " [4.50003427e-06 4.60000592e-06 2.09998689e-06 2.39998917e-06\n",
+      "  2.30001751e-06 2.20001675e-06]\n",
+      " [4.40003350e-06 4.20000288e-06 1.99998613e-06 2.19998765e-06\n",
+      "  2.09998689e-06 2.39998917e-06]\n",
+      " [4.39997530e-06 4.10000212e-06 2.00001523e-06 2.50001904e-06\n",
+      "  2.09998689e-06 2.29998841e-06]\n",
+      " [4.39997530e-06 4.10000212e-06 2.00001523e-06 2.10001599e-06\n",
+      "  2.40001827e-06 2.19998765e-06]\n",
+      " [4.30000364e-06 3.79999983e-06 1.99998613e-06 2.19998765e-06\n",
+      "  2.10001599e-06 2.40001827e-06]\n",
+      " [4.60003503e-06 4.10000212e-06 1.99998613e-06 2.09998689e-06\n",
+      "  2.19998765e-06 2.39998917e-06]\n",
+      " [4.19997377e-06 4.00000135e-06 2.20001675e-06 2.30001751e-06\n",
+      "  2.10001599e-06 2.39998917e-06]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def runReordering(element_set, n_runs):\n",
+    "    times = []\n",
+    "    for _ in range(n_runs + 1):\n",
+    "        time_list = []\n",
+    "        # 1-D\n",
+    "        time_list.append(cf.iterate(element_set)[1])\n",
+    "        time_list.append(cf.palindrome(element_set)[1])\n",
+    "        time_list.append(cf.alternate(element_set)[1])\n",
+    "        # time_list.append(cf.seriate(element_set)[1])\n",
+    "        # 2-D\n",
+    "        time_list.append(cf.serial_crossed(element_set)[1])\n",
+    "        time_list.append(cf.center_embedded(element_set)[1])\n",
+    "        time_list.append(cf.tail_recursive(element_set)[1]) \n",
+    "\n",
+    "        times.append(time_list)\n",
+    "        \n",
+    "    times = np.array(times)[1:]\n",
+    "        \n",
+    "    return (times)\n",
+    "\n",
+    "times = runReordering(train_1, 20)\n",
+    "print(times)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "33262df1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iterate            0.000005\n",
+      "Palindrome         0.000004\n",
+      "Alternate          0.000002\n",
+      "Serial Crossed     0.000002\n",
+      "Center Embedded    0.000003\n",
+      "Tail Recursive     0.000002\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.DataFrame(times, columns=['Iterate', 'Palindrome', 'Alternate', 'Serial Crossed', 'Center Embedded', 'Tail Recursive'])\n",
+    "sns.boxplot(data=df)\n",
+    "plt.title('Reordering Times for Cognitive Functions')\n",
+    "plt.xlabel('Cognitive Functions')\n",
+    "plt.ylabel('Time (s)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.grid(True)\n",
+    "plt.show() \n",
+    "print(df.mean())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lot_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/oldtest/oldutils.py b/oldtest/oldutils.py
new file mode 100644
index 0000000..b841c52
--- /dev/null
+++ b/oldtest/oldutils.py
@@ -0,0 +1,181 @@
+from functools import cached_property, cache, partial
+import time
+from operator import attrgetter
+from tabulate import tabulate
+import networkx as nx
+from typing import Literal
+import matplotlib.pyplot as plt
+from dataclasses import dataclass
+import oldpf as pf
+import importlib
+
+# ---------------------------------------------------------------------#
+
+"""
+@dataclass
+class Element: # n-dimensional element
+    name : str
+    attribute1 : int | float | str
+    attribute2 : int | float | str | None = None
+    def __repr__(self): 
+        return f"Element(object={self.name}, attribute 1={self.attribute1}, attribute 2={self.attribute2})"
+    def __str__(self):
+        return f"{self.name}, {self.attribute1}, {self.attribute2})"
+"""
+
+class Element: # n-dimensional element
+    def __init__(self, name, attribute1, attribute2=None):
+        self.name = name
+        self.attribute1 = attribute1
+        self.attribute2 = attribute2
+    def __repr__(self): 
+        return f"Element(object={self.name}, attribute 1={self.attribute1}, attribute 2={self.attribute2})"
+    def __str__(self):
+        return f"{self.name}, {self.attribute1}, {self.attribute2})"
+
+class  Associations: # n-dimensional association
+    def __init__(self, associations: dict, positional: bool = False):
+        self.associations = associations
+        self.positional = positional
+    def __repr__(self):
+        raise NotImplementedError
+    def build_updates(self, graph):
+        if self.positional:
+            for key, value in self.associations.items():
+                nx.set_node_attributes(graph, {key.name: {"position": value}})
+        else:
+            for key, value in self.associations.items():
+                graph.add_edge(key.name, value.name, label="precedes", directed=True)
+
+class ElementSet: # n-dimensional element set
+    def __init__(self, elements: set, associations: Associations = None):
+        self.elements = elements
+        self.associations = associations
+        self.graph = self.build_graph()
+    def __repr__(self):
+        raise NotImplementedError
+    def build_graph(self):
+        G = nx.MultiGraph()  # Create a directed graph
+        for obj in self.elements:
+            G.add_node(obj.name, type='element')  # Add object as a node
+            G.add_node(obj.attribute1, type='attribute1')  # Add color as a node
+            G.add_edge(obj.name, obj.attribute1, label="attribute")  # Add directed edge with label
+            if obj.attribute2:
+                # If the object has a second attribute, add it as a node and edge
+                G.add_node(obj.attribute2, type='attribute2')  # Add shape as a node
+                G.add_edge(obj.name, obj.attribute2, label="attribute")
+        if  self.associations:
+            self.associations.build_updates(G)  # Build associations
+            if self.graph:
+                self.graph = G
+        return G
+    def visualize(self):
+        plt.figure(figsize=(10, 8))
+        pos = nx.spring_layout(self.graph)
+
+        # Draw nodes with different colors based on type
+        node_colors = []
+        for node, data in self.graph.nodes(data=True):
+            if data['type'] == 'element':
+                node_colors.append('skyblue')
+            elif data['type'] == 'attribute1':
+                node_colors.append('lightgreen')
+            elif data['type'] == 'attribute2':
+                node_colors.append('lightcoral')
+
+        nx.draw(self.graph, pos, with_labels=True, node_color=node_colors, node_size=500, font_size=10)
+        edge_labels = nx.get_edge_attributes(self.graph, 'label')
+        nx.draw_networkx_edge_labels(self.graph, pos, edge_labels=edge_labels)
+        plt.show()
+    @cached_property
+    def attribute1_types(self):
+        """Returns a set of unique attribute1 types."""
+        return set(obj.attribute1 for obj in self.elements)
+    @cached_property
+    def attribute2_types(self):
+        """Returns a set of unique attribute2 types."""
+        return set(obj.attribute2 for obj in self.elements if obj.attribute2 is not None)
+    def attribute_items(self, attribute):
+        def get_type(type_):
+            
+            return getattr(self, f"{attribute}_types", set())
+
+    
+    
+
+
+def pretty_view(sequence):
+    """Prints a sequence of elements in a pretty format."""
+    cute = list(zip(*map(attrgetter("name", "attribute1", "attribute2"), sequence)))
+    # Print as a table
+    print(tabulate(zip(*cute), headers=["object", "attribute 1", "attribute 2"], tablefmt="grid"))
+
+
+
+# ---------------------------------------------------------------------#
+
+class Stopwatch:
+    def __init__(self):
+        self._start_time = None
+        self._elapsed_time = 0
+        self._running = False
+        
+    def start(self):
+        """Start or resume the stopwatch."""
+        if not self._running:
+            self._start_time = time.perf_counter() - self._elapsed_time
+            self._running = True
+            # print("Stopwatch started.")
+
+    def stop(self):
+        """Stop the stopwatch and display the elapsed time."""
+        if self._running:
+            self._elapsed_time = time.perf_counter() - self._start_time
+            self._running = False
+            # print(f"Stopwatch stopped. Elapsed time: {self._elapsed_time:.2f} seconds.")
+
+    def reset(self):
+        """Reset the stopwatch to zero."""
+        self._elapsed_time = 0
+        if self._running:
+            self._start_time = time.perf_counter()
+        # print("Stopwatch reset.")
+
+    def get_elapsed_time(self):
+        """Get the current elapsed time without stopping the stopwatch."""
+        if self._running:
+            return time.perf_counter() - self._start_time
+        return self._elapsed_time
+
+class KComplexity:
+    def __init__(self):
+        self.prim = importlib.import_module('oldpf')
+        self.call_counts = {}
+        self._wrap_prim_functions()
+
+    def _wrap_prim_functions(self):
+        for name in dir(self.prim):
+            func = getattr(self.prim, name)
+            if callable(func) and not name.startswith("__"):
+                self.call_counts[name] = 0
+                wrapper_func = self._make_wrapper(func, name)
+                setattr(self.prim, name, wrapper_func)
+
+    def _make_wrapper(self, func, name):
+        def wrapper(*args, **kwargs):
+            self.call_counts[name] += 1
+            return func(*args, **kwargs)
+        return wrapper
+    
+    """get dictionary of each prim function call count"""
+    def get_prim_counts(self):
+        return dict(self.call_counts)
+    
+    """get total number of prim function calls"""
+    def get_k_complexity(self):
+        return sum(self.call_counts.values())
+    
+    """reset all prim function call counts to zero"""
+    def reset(self):
+        for key in self.call_counts:
+            self.call_counts[key] = 0
\ No newline at end of file
diff --git a/oldtest/random.py b/oldtest/random.py
new file mode 100644
index 0000000..59fd7f8
--- /dev/null
+++ b/oldtest/random.py
@@ -0,0 +1,79 @@
+import oldpf as pf
+from oldutils import Stopwatch, Element, KComplexity
+
+a = Element('A1','A','1')
+b = Element('A2','A','2')
+c = Element('B1','B','1')
+d = Element('B2','B','2')
+
+train_1 = {a,b,c,d}
+
+def palindrome(S):
+    # 123321
+    # preprocessing (not part of measured cognitive process)
+    n = len(S) // 2 # number of elements in basis
+    stopwatch = Stopwatch()
+    # select attribute which chunking is based on
+    bias = find_bias(S, stopwatch)
+    stopwatch.start()
+    basis, rev = [], []
+    # write_random() based implementation
+    ### WRITE CODE HERE
+    while (len(S) > n):
+        element = pf.sample(S)
+        if len(basis)==0 or not (any(pf.check_if_same_type(element, chosen, bias) for chosen in basis)):
+            pf.pair(basis, element)
+            pf.setminus(S, element)
+    for _ in range(n):
+        element = pf.write_random(S, bias, getattr(basis[n-1-_], bias))
+        pf.pair(rev,element)
+        pf.setminus(S, element)
+    result = pf.append(basis,rev)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    
+    return (result, time_elapsed)
+
+def find_bias(S,clock,two_flag=False,higher_dim=False):
+    '''
+    Count unique values for both attributes, 
+    select the attribute with exactly 2 types while the other has != 2 types
+
+    Input Arguments:
+        S: set of elements to be experimented with
+        clock: stopwatch used to time primitive functions
+        two_flag: flag to indicate if the bias required needs only two attribute types
+        higher_dim: flag to indicate if the set of elements is 2-dimensional
+
+    Output:
+        bias: the bias lol, the attribute the flip selected
+    '''
+    if higher_dim == True:
+        chunk_bias, serial_bias = None, None
+        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
+                    for attr in ["attribute1", "attribute2"]}
+        chunk_bias = find_bias(S, clock, two_flag)
+        clock.start()
+        serial_bias = 'attribute1' if chunk_bias == 'attribute2' else 'attribute2'
+        clock.stop()
+        return (chunk_bias, serial_bias)
+    else:
+        bias = None
+        attribute_counts = {attr: len(set(getattr(obj, attr) for obj in S)) 
+                    for attr in ["attribute1", "attribute2"]}
+        if attribute_counts["attribute1"] == 2 and attribute_counts["attribute2"] != 2:
+            bias = "attribute1" if not two_flag else "attribute1"
+        elif attribute_counts["attribute2"] == 2 and attribute_counts["attribute1"] != 2:
+            bias = "attribute2" if not two_flag else "attribute1"
+        else:
+            clock.start()
+            bias = "attribute1" if pf.flip(0.5) else "attribute2"  # Default random selection
+            clock.stop()
+        return bias
+    
+kc = KComplexity()
+result, time_elapsed = palindrome(train_1)
+print(kc.get_k_complexity())
+print(kc.get_prim_counts())
+print("Result:", result)
+print("Elapsed time:", time_elapsed)
diff --git a/palindrome_test.py b/palindrome_test.py
new file mode 100644
index 0000000..58edb09
--- /dev/null
+++ b/palindrome_test.py
@@ -0,0 +1,42 @@
+from cognitive_functions import find_bias
+from utils import Element, ElementSet, Stopwatch, KComplexity
+import primitive_fucntions as pf
+
+def palindrome(S: ElementSet):
+    n = len(S.elements) // 2
+    stopwatch = Stopwatch()
+    bias = find_bias(S.elements, stopwatch)  
+    stopwatch.start()
+    basis, rev = [], []
+
+    while len(S.elements) > n:
+        element = pf.sample(S)
+        if len(basis) == 0 or not any(pf.check_if_same_type(element, chosen, bias) for chosen in basis):
+            pf.pair(basis, element)
+            pf.setminus(S, element)
+
+    for _ in range(n):
+        element = pf.write_random(S, bias, getattr(basis[n-1-_], bias))
+        pf.pair(rev, element)
+        pf.setminus(S, element)
+
+    result = pf.append(basis, rev)
+    stopwatch.stop()
+    time_elapsed = stopwatch.get_elapsed_time()
+    return (result, time_elapsed)
+
+e1 = Element("obj1", "red", "circle")
+e2 = Element("obj2", "blue", "square")
+e3 = Element("obj3", "red", "circle")
+e4 = Element("obj4", "blue", "square")
+
+element_set = ElementSet(elements=set([e1, e2, e3, e4]))
+
+kc = KComplexity()
+
+result, elapsed_time = palindrome(element_set)
+
+print(kc.get_k_complexity())
+print(kc.get_prim_counts())
+print("Result:", result)
+print("Elapsed time:", elapsed_time)
\ No newline at end of file
diff --git a/playground.ipynb b/playground.ipynb
index ffece1a..0897d34 100644
--- a/playground.ipynb
+++ b/playground.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,16 +34,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<networkx.classes.multigraph.MultiGraph at 0x124358980>"
+       "<networkx.classes.multigraph.MultiGraph at 0x1e9e6017ed0>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -57,12 +57,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -77,12 +77,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,21 +173,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2 --(color)--> blue\n",
-      "2 --(shape)--> square\n",
-      "4 --(color)--> blue\n",
-      "4 --(shape)--> circle\n",
-      "3 --(color)--> yellow\n",
-      "3 --(shape)--> circle\n",
-      "1 --(color)--> yellow\n",
-      "1 --(shape)--> square\n"
+      "4 --(attribute1)--> blue\n",
+      "4 --(attribute2)--> circle\n",
+      "3 --(attribute1)--> yellow\n",
+      "3 --(attribute2)--> circle\n",
+      "2 --(attribute1)--> blue\n",
+      "2 --(attribute2)--> square\n",
+      "1 --(attribute1)--> yellow\n",
+      "1 --(attribute2)--> square\n"
      ]
     }
    ],
@@ -210,7 +210,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "lot_venv",
    "language": "python",
    "name": "python3"
   },
@@ -224,7 +224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/primitive_fucntions.py b/primitive_fucntions.py
index 55fd25c..d5338b6 100644
--- a/primitive_fucntions.py
+++ b/primitive_fucntions.py
@@ -28,16 +28,16 @@ def flip(p):
 #     Time complexity: O(len(set1) + len(set2))"""
 #     return set1 | set2
 
+
 def setminus(set1, s):
-    """Remove a string from a set
-    Time complexity: O(1) for single item, O(len(s)) for set s"""
+    #Remove a string from a set
+    #Time complexity: O(1) for single item, O(len(s)) for set s
     set1.remove(s)
 
 def sample(collection):
-    """Sample from a set or list of strings.
-    Time complexity: O(1) for non-empty sets if using random.choice,
-    O(n) for lists using random.sample.
-    """
+    #Sample from a set or list of strings.
+    #Time complexity: O(1) for non-empty sets if using random.choice,
+    #O(n) for lists using random.sample.
     if not collection:
         return None
     
@@ -46,6 +46,7 @@ def sample(collection):
 
     return random.sample(collection, 1)[0]
 
+
 # # Function calls with memoization
 
 # memoization_cache = {}
@@ -88,13 +89,15 @@ def check_if_same_type(e1, e2, bias):
     Time complexity: O(1)"""
     return getattr(e1,bias) == getattr(e2,bias)
 
-def write_random(G, bias, type): 
-    """Returns one unused member of particular type
-    Time complexity: O(n) to filter tokens"""
-    # for element in S:
-    #     if getattr(element, bias) == type:
-    #         return element
-    type_elements = [u for u, v, d in G.edges(data=True) if v == type and d["label"] == bias]
+
+
+def write_random(S, bias, type): 
+    #Returns one unused member of particular type
+    #Time complexity: O(n) to filter tokens
+    for element in S:
+        if getattr(element, bias) == type:
+            return element
+    #type_elements = [u for u, v, d in G.edges(data=True) if v == type and d["label"] == bias]
        
         ### WRITE CODE
         ### maybe add a random list shuffling thing here
@@ -102,6 +105,8 @@ def write_random(G, bias, type):
     
     pass
 
+
+    
 def implement(FUN, N):
     """Keeps implementing a function N times
     Time complexity: O(N * T) where T is time of FUN"""
diff --git a/utils.py b/utils.py
index be59325..465c151 100644
--- a/utils.py
+++ b/utils.py
@@ -7,9 +7,14 @@
 import matplotlib.pyplot as plt
 from dataclasses import dataclass
 import primitive_fucntions as pf
+import importlib
+import copy
+from collections import defaultdict
+import numpy as np
 
 # ---------------------------------------------------------------------#
 
+"""
 @dataclass
 class Element: # n-dimensional element
     name : str
@@ -19,6 +24,17 @@ def __repr__(self):
         return f"Element(object={self.name}, attribute 1={self.attribute1}, attribute 2={self.attribute2})"
     def __str__(self):
         return f"{self.name}, {self.attribute1}, {self.attribute2})"
+"""
+
+class Element: # n-dimensional element
+    def __init__(self, name, attribute1, attribute2=None):
+        self.name = name
+        self.attribute1 = attribute1
+        self.attribute2 = attribute2
+    def __repr__(self): 
+        return f"Element(object={self.name}, attribute 1={self.attribute1}, attribute 2={self.attribute2})"
+    def __str__(self):
+        return f"{self.name}, {self.attribute1}, {self.attribute2})"
 
 @dataclass
 class  Associations: # n-dimensional association
@@ -34,6 +50,7 @@ def build_updates(self, graph):
             for key, value in self.associations.items():
                 graph.add_edge(key.name, value.name, label="precedes", directed=True)
 
+
 class ElementSet: # n-dimensional element set
     def __init__(self, elements: set, associations: Associations = None):
         self.elements = elements
@@ -53,8 +70,8 @@ def build_graph(self):
                 G.add_edge(obj.name, obj.attribute2, label="attribute")
         if  self.associations:
             self.associations.build_updates(G)  # Build associations
-        if self.graph:
-            self.graph = G
+            if hasattr(self, 'graph') and self.graph:
+                self.graph = G
         return G
     def visualize(self):
         plt.figure(figsize=(10, 8))
@@ -133,3 +150,160 @@ def get_elapsed_time(self):
         if self._running:
             return time.perf_counter() - self._start_time
         return self._elapsed_time
+
+class KComplexity:
+    def __init__(self):
+        self.prim = importlib.import_module('primitive_fucntions')
+        self.cog = importlib.import_module('cognitive_functions')
+        self.call_counts = {}
+        self.total_calls = {}
+        self.cog_func_calls = defaultdict(list)
+        self._wrap_prim_functions()
+        self._wrap_cog_functions()
+        self.current_cog_name = None
+
+    def _wrap_prim_functions(self):
+        for name in dir(self.prim):
+            func = getattr(self.prim, name)
+            if callable(func) and not name.startswith("__"):
+                self.call_counts[name] = 0
+                self.total_calls[name] = 0
+                wrapper_func = self._make_prim_wrapper(func, name)
+                setattr(self.prim, name, self._make_prim_wrapper(func, name))
+
+    def _wrap_cog_functions(self):
+        for name in dir(self.cog):
+            if name.startswith("__") or name == "find_bias":
+                continue
+            func = getattr(self.cog, name)
+            if callable(func):
+                setattr(self.cog, name, self._make_cog_wrapper(func, name))
+
+    def _make_prim_wrapper(self, func, name):
+        def wrapper(*args, **kwargs):
+            self.call_counts[name] += 1
+            self.total_calls[name] += 1
+            return func(*args, **kwargs)
+        return wrapper
+
+    def _make_cog_wrapper(self, func, name):
+        def wrapper(*args, **kwargs):
+            self.reset() 
+            result = func(*args, **kwargs)
+            self.cog_func_calls[name].append(copy.deepcopy(self.call_counts))
+            self.current_cog_name = name
+            return result
+        return wrapper
+    
+    def get_cog_name(self):
+        return self.current_cog_name
+
+    def get_prim_counts(self):
+        return dict(self.call_counts)
+    
+    def get_total_prim_counts(self):
+        return dict(self.total_calls)
+
+    def get_k_complexity(self):
+        return sum(self.call_counts.values())
+    
+    def get_total_k_complexity(self):
+        return sum(self.total_calls.values())
+
+    def reset(self):
+        for key in self.call_counts:
+            self.call_counts[key] = 0
+
+    def plot_prim_counts(self):
+        data = self.get_prim_counts()
+        plt.bar(data.keys(), data.values())
+        plt.xticks(rotation=45, ha='right')
+        plt.ylabel("Call Count")
+        plt.title(self.get_cog_name() + " Primitive Function Usage" + " (" + str(self.get_k_complexity()) + " calls)")
+        plt.tight_layout()
+        plt.show()
+
+    def plot_total_prim_counts(self):
+        data = self.get_total_prim_counts()
+        plt.bar(data.keys(), data.values())
+        plt.xticks(rotation=45, ha='right')
+        plt.ylabel("Call Count")
+        plt.title("Total Primitive Function Usage" + " (" + str(self.get_total_k_complexity()) + " total calls)")
+        plt.tight_layout()
+        plt.show()
+
+    def plot_cog_vs_prim(self):
+        import matplotlib.pyplot as plt
+        import matplotlib.cm as cm
+        import matplotlib.colors as mcolors
+
+        prim_names = list(self.call_counts.keys())
+
+        valid_cog_fns = {"iterate", "palindrome", "alternate", "chaining", "seriate", "serial_crossed", "center_embedded", "tail_recursive"}
+        cog_names = [cog for cog in self.cog_func_calls if cog in valid_cog_fns and len(self.cog_func_calls[cog]) > 0]
+
+        averaged_data = []
+        cog_labels = []
+        for cog in cog_names:
+            call_count = len(self.cog_func_calls[cog])
+            cog_labels.append(f"{cog} ({call_count}x)")
+
+            summed = defaultdict(int)
+            for d in self.cog_func_calls[cog]:
+                for k in d:
+                    summed[k] += d[k]
+            summed_data = {k: summed[k] for k in prim_names}
+            averaged_data.append(summed_data)
+
+
+        color_map = cm.get_cmap('tab20', len(prim_names))
+        colors = {prim: color_map(i) for i, prim in enumerate(prim_names)}
+
+        bottom = np.zeros(len(cog_labels))
+        for prim in prim_names:
+            heights = [d[prim] for d in averaged_data]
+            plt.bar(cog_labels, heights, bottom=bottom, label=prim, color=colors[prim])
+            bottom += heights
+
+        plt.xticks(rotation=45, ha='right')
+        plt.ylabel("Total Primitive Calls")
+        plt.title("All Cognitive Function Calls with Primitive Usage Breakdown" + " (" + str(self.get_total_k_complexity()) + " total primitive calls)")
+        plt.legend(title="Primitive Functions", bbox_to_anchor=(1.05, 1), loc='upper left')
+        plt.tight_layout()
+        plt.show()
+
+    def show_cog_prim_table(self):
+        from IPython.display import display
+        import pandas as pd
+
+        prim_names = list(self.call_counts.keys())
+        valid_cog_fns = {"iterate", "palindrome", "alternate", "chaining", "seriate", "serial_crossed", "center_embedded", "tail_recursive"}
+        
+        table_data = {}
+        row_labels = []
+
+        for cog in self.cog_func_calls:
+            if cog not in valid_cog_fns or len(self.cog_func_calls[cog]) == 0:
+                continue
+
+            call_count = len(self.cog_func_calls[cog])
+            label = f"{cog} ({call_count}x)"
+            row_labels.append(label)
+
+            summed = defaultdict(int)
+            for d in self.cog_func_calls[cog]:
+                for k in d:
+                    summed[k] += d[k]
+            
+            row = [summed.get(pf, 0) for pf in prim_names]
+            table_data[label] = row
+
+        df = pd.DataFrame.from_dict(table_data, orient='index', columns=prim_names)
+        total_row = df.sum(numeric_only=True)
+        total_row.name = "Total Counts for each Primitive Function"
+        df = pd.concat([df, pd.DataFrame([total_row])])
+        df["Total"] = df.sum(axis=1)
+        df.index.name = "Cognitive Function (calls)"
+
+        display(df)
+