ai_search_algorithms.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "318473477_205589721.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOZnjxY-iaDx"
      },
      "source": [
        "from copy import deepcopy\r\n",
        "import math\r\n",
        "import functools \r\n",
        "import random\r\n",
        "import pandas as pd\r\n",
        "import time\r\n",
        "import resource\r\n",
        "import tracemalloc\r\n",
        "from time import sleep\r\n",
        "import networkx as nx\r\n",
        "import matplotlib.pyplot as plt\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vfK1uFy5_Jf"
      },
      "source": [
        "The following class contains the maintained information for identifying the state of the board, and the fields needed for the search algorithms below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7okw8ePaODAA"
      },
      "source": [
        "class TileState():\r\n",
        "  board = list() # [['b',1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]] - contains the board state as 2D list representation. Each inner array represents a row.\r\n",
        "  inverse_method = None # Pointer to the inverse method for the one which created the current instance.\r\n",
        "  parent_state = None # For backtracking when the solution was found.\r\n",
        "  h = 0 # The huristic function's value for the current state.\r\n",
        "  g = 0 # The cost of the best path from the initial state to this instance.\r\n",
        "  f = 0 # Stored value (maintained in the algorithms).\r\n",
        "  F = 0 # Dynamic value (maintained in the algorithms).\r\n",
        "  operators = list() # Available operators to apply when expanding this instance.\r\n",
        "  operators_table=[] # Contains the osf function state. This list is a list of tuples, where each tuple is a pair of list of operators and the delta f. \r\n",
        "  C = False # Collapsed flag.\r\n",
        "\r\n",
        "  # repr example for the complete state for the 15-puzzle.\r\n",
        "  # |'b',1,2,3| every element is row\r\n",
        "  # | 4 ,5,6,7|\r\n",
        "  # | 8 ,9,10,11|\r\n",
        "  # | 12 ,13,14,15|\r\n",
        "\r\n",
        "  def __init__(self,board):\r\n",
        "    self.board = board\r\n",
        "    self.h = self.culc_huristics()\r\n",
        "    self.f = self.g + self.h\r\n",
        "    self.operators = [self.move_blank_down,self.move_blank_up,self.move_blank_right,self.move_blank_left]\r\n",
        "    self.C = False\r\n",
        "\r\n",
        "  def culc_huristics(self): # Computes the huristic value of the current board by Manhatan Distance.\r\n",
        "    h = 0\r\n",
        "    for row in range(len(self.board)):\r\n",
        "      for col in range(len(self.board[0])):\r\n",
        "        element = self.board[row][col]\r\n",
        "        if isinstance(element,int):\r\n",
        "          expected_row,expected_col = math.floor(element/len(self.board)),element%len(self.board[0])\r\n",
        "        else:\r\n",
        "          expected_row,expected_col = 0,0\r\n",
        "        manhatan_dist = abs(expected_row - row) + abs(expected_col - col)\r\n",
        "        h += manhatan_dist\r\n",
        "\r\n",
        "    return h\r\n",
        "\r\n",
        "\r\n",
        "  def osf(self): # Returns the next operators to apply for the enhanced search algotihmns and the next delta F as tuple (see operators_table doc)\r\n",
        "    return (list(),None) if len(self.operators_table) == 0 else self.operators_table.pop(0)\r\n",
        "\r\n",
        "\r\n",
        "  # Called when a state is generated (on moves methods). It looks in the current state and the available operators and calculates delta_f for each available state from the current one.\r\n",
        "  def build_osf(self):\r\n",
        "    delta_f_by_operator_list = list()\r\n",
        "    current_h = self.culc_huristics()\r\n",
        "    blank_row, blank_col = self.get_blank_loc()\r\n",
        "\r\n",
        "    if blank_row == 0: # we can move down for sure.\r\n",
        "      #down (other number will move up)\r\n",
        "      other_card_number = self.board[blank_row + 1][blank_col]\r\n",
        "      expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "      huristics_change_of_selected_card = -1 if expected_other_card_row > blank_row + 1 else 1\r\n",
        "      delta_h = 1 + huristics_change_of_selected_card # since we move blank from the row it should be.\r\n",
        "      delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "      delta_f_by_operator = (self.move_blank_down , delta_f)\r\n",
        "      delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "    \r\n",
        "      if not blank_col == 0: # we can move left for sure (other number to right)\r\n",
        "        other_card_number = self.board[blank_row][blank_col - 1]\r\n",
        "        expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "        huristics_change_of_selected_card = -1 if expected_other_card_col > blank_col - 1 else 1\r\n",
        "        delta_h = -1 + huristics_change_of_selected_card\r\n",
        "        delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "        delta_f_by_operator = (self.move_blank_left , delta_f)\r\n",
        "        delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "      if not blank_col == len(self.board) - 1: # we can move the blank to the right (and the selected card to the left)\r\n",
        "        other_card_number = self.board[blank_row][blank_col + 1]\r\n",
        "        expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "        huristics_change_of_selected_card = -1 if expected_other_card_col < blank_col + 1 else 1\r\n",
        "        delta_h = 1 + huristics_change_of_selected_card\r\n",
        "        delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "        delta_f_by_operator = (self.move_blank_right , delta_f)\r\n",
        "        delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "    elif blank_row == len(self.board) - 1:\r\n",
        "      # up (and move the other card down)\r\n",
        "      other_card_number = self.board[blank_row - 1][blank_col]\r\n",
        "      expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "      huristics_change_of_selected_card = -1 if expected_other_card_row > blank_row - 1 else 1\r\n",
        "      delta_h = 1 + huristics_change_of_selected_card\r\n",
        "      delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "      delta_f_by_operator = (self.move_blank_up , delta_f)\r\n",
        "      delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "\r\n",
        "      if not blank_col == 0: # we can move left for sure (other number to right)\r\n",
        "        other_card_number = self.board[blank_row][blank_col - 1]\r\n",
        "        expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "        huristics_change_of_selected_card = -1 if expected_other_card_col > blank_col - 1 else 1\r\n",
        "        delta_h = -1 + huristics_change_of_selected_card\r\n",
        "        delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "        delta_f_by_operator = (self.move_blank_left , delta_f)\r\n",
        "        delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "      if not blank_col == len(self.board) - 1: # we can move the blank to the right (and the selected card to the left)\r\n",
        "        other_card_number = self.board[blank_row][blank_col + 1]\r\n",
        "        expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "        huristics_change_of_selected_card = -1 if expected_other_card_col < blank_col + 1 else 1\r\n",
        "        delta_h = 1 + huristics_change_of_selected_card\r\n",
        "        delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "        delta_f_by_operator = (self.move_blank_right , delta_f)\r\n",
        "        delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "    else:\r\n",
        "      #down (other number will move up)\r\n",
        "      other_card_number = self.board[blank_row + 1][blank_col]\r\n",
        "      expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "      huristics_change_of_selected_card = -1 if expected_other_card_row > blank_row + 1 else 1\r\n",
        "      delta_h = 1 + huristics_change_of_selected_card # since we move blank from the row it should be.\r\n",
        "      delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "      delta_f_by_operator = (self.move_blank_down , delta_f)\r\n",
        "      delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "      # up (and move the other card down)\r\n",
        "      other_card_number = self.board[blank_row - 1][blank_col]\r\n",
        "      expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "      huristics_change_of_selected_card = -1 if expected_other_card_row > blank_row - 1 else 1\r\n",
        "      delta_h = 1 + huristics_change_of_selected_card\r\n",
        "      delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "      delta_f_by_operator = (self.move_blank_up , delta_f)\r\n",
        "      delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "      if not blank_col == 0: # we can move left for sure (other number to right)\r\n",
        "        other_card_number = self.board[blank_row][blank_col - 1]\r\n",
        "        expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "        huristics_change_of_selected_card = -1 if expected_other_card_col > blank_col - 1 else 1\r\n",
        "        delta_h = -1 + huristics_change_of_selected_card\r\n",
        "        delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "        delta_f_by_operator = (self.move_blank_left , delta_f)\r\n",
        "        delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "      if not blank_col == len(self.board) - 1: # we can move the blank to the right (and the selected card to the left)\r\n",
        "        other_card_number = self.board[blank_row][blank_col + 1]\r\n",
        "        expected_other_card_row, expected_other_card_col = math.floor(other_card_number/len(self.board)),other_card_number%len(self.board[0])\r\n",
        "        huristics_change_of_selected_card = -1 if expected_other_card_col < blank_col + 1 else 1\r\n",
        "        delta_h = 1 + huristics_change_of_selected_card\r\n",
        "        delta_f = delta_h + 2 # since 2 is delta g\r\n",
        "        delta_f_by_operator = (self.move_blank_right , delta_f)\r\n",
        "        delta_f_by_operator_list.append(delta_f_by_operator)\r\n",
        "\r\n",
        "\r\n",
        "    delta_f_by_operator_list.sort(key=lambda delta_f_by_operator: delta_f_by_operator[1])\r\n",
        "    delta_fs_from_current_state = list(set(map(lambda x:x[1], delta_f_by_operator_list)))\r\n",
        "    delta_fs_from_current_state.sort()\r\n",
        "\r\n",
        "    # operators tables sorted from the best operators to the worst ones.\r\n",
        "\r\n",
        "    grouped_operators_by_delta_f = [[y[0] for y in delta_f_by_operator_list if y[1]==x] for x in delta_fs_from_current_state]\r\n",
        "    delta_f = list()\r\n",
        "    for i in range(len(delta_fs_from_current_state) - 1):\r\n",
        "      delta_f.append(delta_fs_from_current_state[i+1] - delta_fs_from_current_state[i])\r\n",
        "    delta_f.append(None)\r\n",
        "    operators_table = list()\r\n",
        "    for i in range(len(delta_f)):\r\n",
        "      operators_table.append((grouped_operators_by_delta_f[i],delta_f[i]))\r\n",
        "    self.operators_table = operators_table\r\n",
        "\r\n",
        "\r\n",
        "  def get_blank_loc(self):\r\n",
        "    for i in range(len(self.board)):\r\n",
        "      if 'b' in self.board[i]:\r\n",
        "        return i,self.board[i].index('b')\r\n",
        "\r\n",
        "  def move_blank_down(self):\r\n",
        "    blank_loc = self.get_blank_loc()\r\n",
        "    if blank_loc[0] == len(self.board) - 1:\r\n",
        "      return None\r\n",
        "\r\n",
        "    new_state = deepcopy(self)\r\n",
        "    new_state.inverse_method = new_state.move_blank_up\r\n",
        "    new_state.operators = [new_state.move_blank_down,new_state.move_blank_up,new_state.move_blank_right,new_state.move_blank_left]\r\n",
        "    new_state.operators.remove(new_state.inverse_method)\r\n",
        "    new_state.parent = self\r\n",
        "    new_state.build_osf()\r\n",
        "\r\n",
        "    new_state.board[blank_loc[0]][blank_loc[1]] = new_state.board[blank_loc[0] + 1][blank_loc[1]]\r\n",
        "    new_state.board[blank_loc[0] + 1][blank_loc[1]] = 'b'\r\n",
        "\r\n",
        "    new_state.h = new_state.culc_huristics()\r\n",
        "    new_state.f = new_state.g + new_state.h\r\n",
        "    new_state.C = False\r\n",
        "\r\n",
        "    return new_state\r\n",
        "\r\n",
        "  def move_blank_up(self):\r\n",
        "    blank_loc = self.get_blank_loc()\r\n",
        "    if blank_loc[0] == 0:\r\n",
        "      return None\r\n",
        "\r\n",
        "    new_state = deepcopy(self)\r\n",
        "    new_state.inverse_method = new_state.move_blank_down\r\n",
        "    new_state.operators = [new_state.move_blank_down,new_state.move_blank_up,new_state.move_blank_right,new_state.move_blank_left]\r\n",
        "    new_state.operators.remove(new_state.inverse_method)\r\n",
        "    new_state.parent = self\r\n",
        "    new_state.build_osf()\r\n",
        "    new_state.board[blank_loc[0]][blank_loc[1]] = new_state.board[blank_loc[0] - 1][blank_loc[1]]\r\n",
        "    new_state.board[blank_loc[0] - 1][blank_loc[1]] = 'b'\r\n",
        "\r\n",
        "    new_state.h = new_state.culc_huristics()\r\n",
        "    new_state.f = new_state.g + new_state.h\r\n",
        "    new_state.C = False\r\n",
        "\r\n",
        "    return new_state\r\n",
        "\r\n",
        "\r\n",
        "  def move_blank_right(self):\r\n",
        "    blank_loc = self.get_blank_loc()\r\n",
        "    if blank_loc[1] == len(self.board[0]) -1:\r\n",
        "      return None\r\n",
        "\r\n",
        "    new_state = deepcopy(self)\r\n",
        "    new_state.inverse_method = new_state.move_blank_left\r\n",
        "    new_state.operators = [new_state.move_blank_down,new_state.move_blank_up,new_state.move_blank_right,new_state.move_blank_left]\r\n",
        "    new_state.operators.remove(new_state.inverse_method)\r\n",
        "    new_state.parent = self\r\n",
        "    new_state.build_osf()\r\n",
        "    new_state.board[blank_loc[0]][blank_loc[1]] = new_state.board[blank_loc[0]][blank_loc[1] + 1]\r\n",
        "    new_state.board[blank_loc[0]][blank_loc[1] + 1] = 'b'\r\n",
        "\r\n",
        "    new_state.h = new_state.culc_huristics()\r\n",
        "    new_state.f = new_state.g + new_state.h\r\n",
        "    new_state.C = False\r\n",
        "\r\n",
        "    return new_state\r\n",
        "\r\n",
        "\r\n",
        "  def move_blank_left(self):\r\n",
        "    blank_loc = self.get_blank_loc()\r\n",
        "    if blank_loc[1] == 0:\r\n",
        "      return None\r\n",
        "\r\n",
        "    new_state = deepcopy(self)\r\n",
        "    new_state.inverse_method = new_state.move_blank_right\r\n",
        "    new_state.operators = [new_state.move_blank_down,new_state.move_blank_up,new_state.move_blank_right,new_state.move_blank_left]\r\n",
        "    new_state.operators.remove(new_state.inverse_method)\r\n",
        "    new_state.parent = self\r\n",
        "    new_state.build_osf()\r\n",
        "\r\n",
        "    new_state.board[blank_loc[0]][blank_loc[1]] = new_state.board[blank_loc[0]][blank_loc[1] - 1]\r\n",
        "    new_state.board[blank_loc[0]][blank_loc[1] - 1] = 'b'\r\n",
        "\r\n",
        "    new_state.h = new_state.culc_huristics()\r\n",
        "    new_state.f = new_state.g + new_state.h\r\n",
        "    new_state.C = False\r\n",
        "\r\n",
        "    return new_state\r\n",
        "\r\n",
        "\r\n",
        "  def board_state(self):\r\n",
        "    output = ''\r\n",
        "    for i in range(len(self.board)):\r\n",
        "      row = '| '\r\n",
        "      for j in range(len(self.board[i])):\r\n",
        "        row = row + str(self.board[i][j]) + ' '\r\n",
        "      \r\n",
        "      output = output + row + '|\\n' \r\n",
        "\r\n",
        "    return output + '\\n \\n'\r\n",
        "\r\n",
        "  def __repr__(self):\r\n",
        "    \r\n",
        "    output = ''\r\n",
        "    for i in range(len(self.board)):\r\n",
        "      row = '| '\r\n",
        "      for j in range(len(self.board[i])):\r\n",
        "        row = row + str(self.board[i][j]) + ' '\r\n",
        "      \r\n",
        "      output = output + row + '|\\n' \r\n",
        "\r\n",
        "    return output + '\\n \\n' \r\n",
        "\r\n",
        "  def __eq__(self,other):\r\n",
        "    if isinstance(other, TileState):\r\n",
        "        return self.board == other.board\r\n",
        "    return False\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4smpeaF2L-c"
      },
      "source": [
        "# This cell contains utils for execution of the search algorithms in the cells below.\r\n",
        "\r\n",
        "def main_backtracking(state):\r\n",
        "  res = backtracking(state)\r\n",
        "  return res[::-1]\r\n",
        "\r\n",
        "# build the path from the solution back to the initial state\r\n",
        "def backtracking(state):\r\n",
        "  if state.parent_state is None:\r\n",
        "    return [state]\r\n",
        "  returned_array = [state] + backtracking(state.parent)\r\n",
        "  return returned_array\r\n",
        "\r\n",
        "# returns the instance with the same board state.\r\n",
        "# Assumption: The instance exists exactly in one of the lists (open or closed)\r\n",
        "def get_existing_child(child,open,closed):\r\n",
        "  if child in open:\r\n",
        "    return open[open.index(child)]\r\n",
        "  else:\r\n",
        "    return closed[closed.index(child)]\r\n",
        "\r\n",
        "\r\n",
        "# This function is called from the benchmark function. This function creates an initial instance to run the search algorithms on.\r\n",
        "# When calling to this function, call it with the solution state (when the blank and the numbers are in their positions)\r\n",
        "# This function creates the initial state from defined number of 'steps' from the 'complete_state'\r\n",
        "def make_an_initial_state(complete_state, steps = 10):\r\n",
        "  if steps == 0:\r\n",
        "    # make all operators available when running the algorithm, especialliy the operator for the optimal path.\r\n",
        "    complete_state.operators = [complete_state.move_blank_down,complete_state.move_blank_up,complete_state.move_blank_right,complete_state.move_blank_left] \r\n",
        "    return complete_state\r\n",
        "  random.shuffle(complete_state.operators) # randomize the selection of the next step. Each state keeps the inverse operation for the one created them so the inverse function can not be chosen.\r\n",
        "  for operator in complete_state.operators:\r\n",
        "    new_state = operator()\r\n",
        "    if new_state is None: # illegal move\r\n",
        "      continue\r\n",
        "    return make_an_initial_state(new_state,steps-1) # the next step was selected.\r\n",
        "\r\n",
        "\r\n",
        "# Collapse shrinks the tree rooted in 'state_node' and contains only the children that are in 'children_to_collapse'.\r\n",
        "# The funcion also updates the F value of 'state_node' to be the minimum of the f-value of the frontier nodes.\r\n",
        "def collapse(state_node,children_to_collapse,open,closed):\r\n",
        "  frontier_f_values = [float('inf')]\r\n",
        "  childs_f = []\r\n",
        "  for child_state_node in children_to_collapse:\r\n",
        "    min_subtree_frontier_f_value = rec_collapse(child_state_node,open,closed)\r\n",
        "    frontier_f_values.append(min_subtree_frontier_f_value)\r\n",
        "    childs_f.append(child_state_node.f)\r\n",
        "  min_frontier_f_value = min(frontier_f_values)\r\n",
        "  if not min_frontier_f_value == float('inf'): # there was a child that was removed\r\n",
        "    state_node.F = min_frontier_f_value\r\n",
        "  else:\r\n",
        "    state_node.F = min(childs_f)\r\n",
        "\r\n",
        "\r\n",
        "  state_node.C = True\r\n",
        "\r\n",
        "\r\n",
        "def rec_collapse(state_node,open,closed):\r\n",
        "  if state_node in open: # this node is a frontier node.\r\n",
        "    open.remove(state_node)\r\n",
        "    return state_node.f\r\n",
        "  if state_node in closed: # the node is an internal node.\r\n",
        "    frontier_f_values = [float('inf')]\r\n",
        "    closed.remove(state_node)\r\n",
        "    for operator in state_node.operators:\r\n",
        "      child_state_node = state_node.operator()\r\n",
        "      if child_state_node is None:\r\n",
        "        continue\r\n",
        "      min_subtree_frontier_f_value = rec_collapse(child_state_node,open,closed)\r\n",
        "      frontier_f_values.append(min_subtree_frontier_f_value)\r\n",
        "    return min(frontier_f_values)\r\n",
        "  return float('inf') # this node wasn't in the subtree when calling to collapse.\r\n",
        "\r\n",
        "\r\n",
        "# restores the subtree rooted in node.\r\n",
        "# Assumption: the f function is non monotonically decreasing.\r\n",
        "def restore(node,open,closed,F_root):\r\n",
        "  for operator in node.operators:\r\n",
        "    child_state = operator() #generate with some h\r\n",
        "\r\n",
        "    if child_state is None: # child is illegal\r\n",
        "      continue\r\n",
        "\r\n",
        "    g_new = node.g + 2 # 2 - the cost of swaping between the blank and the number.\r\n",
        "\r\n",
        "    if child_state in open or child_state in closed: # we generated this child before and we are examing if we found a better path when restoring the subtree of root.\r\n",
        "      existing_child = get_existing_child(child_state,open,closed)\r\n",
        "      if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "      existing_child.g = g_new\r\n",
        "      existing_child.parent_state = node\r\n",
        "      if existing_child in closed:\r\n",
        "        for existing_child_operator in existing_child.operators:\r\n",
        "          child_of_existing = existing_child_operator() # generate the same grandchildren with the better path.\r\n",
        "          restore(child_of_existing,open,closed,F_root)\r\n",
        "      \r\n",
        "    else: # the simple case - we just generate a child which is not existing right now.\r\n",
        "      child_state.g = g_new\r\n",
        "      child_state.f = child_state.g + child_state.h\r\n",
        "      child_state.parent_state = node\r\n",
        "      if child_state.f >= F_root: # we arrived to the frontier of the root's subtree.\r\n",
        "        open.append(child_state)\r\n",
        "      else: # we need to expand the generated child.\r\n",
        "        closed.append(child_state)\r\n",
        "        restore(child_state,open,closed,F_root)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2JSOVOkaeaP"
      },
      "source": [
        "def a_star_late(state,goal_test= lambda state: state.h == 0):\r\n",
        "  state.g = 0\r\n",
        "  open = list()\r\n",
        "  open.append(state)\r\n",
        "  closed = list()\r\n",
        "  perfomance = dict()\r\n",
        "  \r\n",
        "  # for final results\r\n",
        "  perfomance['generated'] = 0\r\n",
        "  perfomance['expanded'] = 0\r\n",
        "  perfomance['max open length'] = 0\r\n",
        "  perfomance['max closed length'] = 0\r\n",
        "  perfomance['regenerations'] = 0\r\n",
        "\r\n",
        "  while(len(open) > 0):\r\n",
        "    best = open.pop(0)\r\n",
        "\r\n",
        "    if goal_test(best):\r\n",
        "      return perfomance,main_backtracking(best)\r\n",
        "\r\n",
        "    closed.append(best)\r\n",
        "    perfomance['expanded'] += 1\r\n",
        "    for operator in best.operators:\r\n",
        "      new_child_state = operator() #generate\r\n",
        "\r\n",
        "      if new_child_state is None: # move is illegal\r\n",
        "        continue\r\n",
        "\r\n",
        "      if not new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['generated'] += 1\r\n",
        "\r\n",
        "      g_new = best.g + 2 #move blank and the other card.\r\n",
        "      if new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['regenerations'] += 1\r\n",
        "        existing_child = get_existing_child(new_child_state,open,closed)\r\n",
        "        if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "        if existing_child in closed:\r\n",
        "          closed.remove(existing_child)\r\n",
        "        if existing_child in open:\r\n",
        "          open.remove(existing_child)\r\n",
        "\r\n",
        "      new_child_state.g = g_new\r\n",
        "      new_child_state.f = new_child_state.g + new_child_state.h\r\n",
        "      new_child_state.parent_state = best\r\n",
        "\r\n",
        "      open.append(new_child_state)\r\n",
        "      \r\n",
        "\r\n",
        "    perfomance['max open length'] = perfomance['max open length'] if len(open) <= perfomance['max open length'] else len(open)\r\n",
        "    perfomance['max closed length'] = perfomance['max closed length'] if len(closed) <= perfomance['max closed length'] else len(closed)\r\n",
        "    \r\n",
        "    open.sort(key=lambda state: (state.f,state.h))\r\n",
        "  \r\n",
        "  return \"no path was found to the solution\"\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PrVuV0rndajT"
      },
      "source": [
        "def a_star_early(state,goal_test= lambda state: state.h == 0):\r\n",
        "  state.g = 0\r\n",
        "  U = float('inf')\r\n",
        "  open = list()\r\n",
        "  open.append(state)\r\n",
        "  closed = list()\r\n",
        "  perfomance = dict()\r\n",
        "  \r\n",
        "  # for final results\r\n",
        "  perfomance['generated'] = 0\r\n",
        "  perfomance['expanded'] = 0\r\n",
        "  perfomance['max open length'] = 0\r\n",
        "  perfomance['max closed length'] = 0\r\n",
        "  perfomance['regenerations'] = 0\r\n",
        "  solution_state = None\r\n",
        "\r\n",
        "  while(len(open) > 0 and open[0].f < U):\r\n",
        "    best = open.pop(0)\r\n",
        "    closed.append(best)\r\n",
        "    perfomance['expanded'] += 1\r\n",
        "\r\n",
        "    for operator in best.operators:\r\n",
        "      new_child_state = operator() #generate\r\n",
        "\r\n",
        "      if new_child_state is None: #move is illegal\r\n",
        "        continue\r\n",
        "\r\n",
        "      if not new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['generated'] += 1\r\n",
        "\r\n",
        "      g_new = best.g + 2 #move blank and the other card.\r\n",
        "      if new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['regenerations'] += 1\r\n",
        "        existing_child = get_existing_child(new_child_state,open,closed)\r\n",
        "        if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "        if existing_child in closed:\r\n",
        "          closed.remove(existing_child)\r\n",
        "        if existing_child in open:\r\n",
        "          open.remove(existing_child)\r\n",
        "\r\n",
        "      if goal_test(new_child_state):\r\n",
        "        if g_new < U:\r\n",
        "          solution_state = new_child_state\r\n",
        "          U = g_new\r\n",
        "          open = [state for state in open if state.f < U]\r\n",
        "\r\n",
        "      new_child_state.g = g_new\r\n",
        "      new_child_state.f = new_child_state.g + new_child_state.h\r\n",
        "      new_child_state.parent_state = best\r\n",
        "\r\n",
        "      if new_child_state.f < U:\r\n",
        "        open.append(new_child_state)\r\n",
        "      \r\n",
        "\r\n",
        "    perfomance['max open length'] = perfomance['max open length'] if len(open) <= perfomance['max open length'] else len(open)\r\n",
        "    perfomance['max closed length'] = perfomance['max closed length'] if len(closed) <= perfomance['max closed length'] else len(closed)\r\n",
        "    \r\n",
        "    open.sort(key=lambda state: (state.f,state.h))\r\n",
        "  \r\n",
        "  return \"no path was found to the solution\" if U == float('inf') else perfomance,main_backtracking(solution_state)\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgBTzwugaLxt"
      },
      "source": [
        "def partial_expansion_a_star(state,goal_test= lambda state: state.h == 0):\r\n",
        "  state.g = 0\r\n",
        "  state.F = state.h # State.F by default is state.f = state.g + state.h. state.g = 0 => state.F = state.f = state.h\r\n",
        "  open = list()\r\n",
        "  open.append(state)\r\n",
        "  closed = list()\r\n",
        "  perfomance = dict()\r\n",
        "\r\n",
        "  # for final results\r\n",
        "  perfomance['generated'] = 0\r\n",
        "  perfomance['expanded'] = 0\r\n",
        "  perfomance['max open length'] = 0\r\n",
        "  perfomance['max closed length'] = 0\r\n",
        "  perfomance['regenerations'] = 0\r\n",
        "\r\n",
        "  while(len(open) > 0):\r\n",
        "    best = open.pop(0)\r\n",
        "\r\n",
        "    if goal_test(best):\r\n",
        "      return perfomance,main_backtracking(best)\r\n",
        "\r\n",
        "    exceeding_children = list()\r\n",
        "    perfomance['expanded'] += 1\r\n",
        "\r\n",
        "    if best.C:\r\n",
        "      restore(best,open,closed,best.F)\r\n",
        "      best.C = False\r\n",
        "\r\n",
        "    for operator in best.operators:\r\n",
        "      new_child_state = operator() # generate\r\n",
        "      \r\n",
        "      if new_child_state is None: # move is illegal\r\n",
        "        continue\r\n",
        "\r\n",
        "      if not new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['generated'] += 1\r\n",
        "\r\n",
        "      g_new = best.g + 2 #move blank and the other card.\r\n",
        "      if new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['regenerations'] += 1\r\n",
        "        existing_child = get_existing_child(new_child_state,open,closed)\r\n",
        "        if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "        if existing_child in closed:\r\n",
        "          closed.remove(existing_child)\r\n",
        "        if existing_child in open:\r\n",
        "          open.remove(existing_child)\r\n",
        "\r\n",
        "        new_child_state.C = existing_child.C\r\n",
        "        new_child_state.F = existing_child.F\r\n",
        "\r\n",
        "      new_child_state.g = g_new\r\n",
        "      new_child_state.f = new_child_state.g + new_child_state.h\r\n",
        "\r\n",
        "      if new_child_state.C == False:\r\n",
        "        new_child_state.F = new_child_state.f\r\n",
        "\r\n",
        "      new_child_state.parent_state = best\r\n",
        "\r\n",
        "      if new_child_state.f == best.F:\r\n",
        "        open.append(new_child_state)\r\n",
        "      elif best.F < new_child_state.f:\r\n",
        "        exceeding_children.append(new_child_state)\r\n",
        "\r\n",
        "    if not len(exceeding_children) == 0 and best.f == best.F:\r\n",
        "      collapse(best,exceeding_children,open,closed) \r\n",
        "      open.append(best)\r\n",
        "    else:\r\n",
        "      closed.append(best)\r\n",
        "\r\n",
        "    perfomance['max open length'] = perfomance['max open length'] if len(open) <= perfomance['max open length'] else len(open)\r\n",
        "    perfomance['max closed length'] = perfomance['max closed length'] if len(closed) <= perfomance['max closed length'] else len(closed)\r\n",
        "    open.sort(key=lambda state: (state.F,state.h))\r\n",
        "  \r\n",
        "  return \"no path was found to the solution\"\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItI5xfglFXbb"
      },
      "source": [
        "def partial_expansion_a_star_early(state,goal_test= lambda state: state.h == 0):\r\n",
        "  state.g = 0\r\n",
        "  U = float('inf')\r\n",
        "  state.F = state.h # State.F by default is state.f = state.g + state.h. state.g = 0 => state.F = state.f = state.h\r\n",
        "  open = list()\r\n",
        "  open.append(state)\r\n",
        "  closed = list()\r\n",
        "  perfomance = dict()\r\n",
        "  # for final results\r\n",
        "  perfomance['generated'] = 0\r\n",
        "  perfomance['expanded'] = 0\r\n",
        "  perfomance['max open length'] = 0\r\n",
        "  perfomance['max closed length'] = 0\r\n",
        "  perfomance['regenerations'] = 0\r\n",
        "  \r\n",
        "  solution_state = None\r\n",
        "  while(len(open) > 0 and open[0].f < U):\r\n",
        "    best = open.pop(0)\r\n",
        "\r\n",
        "    \r\n",
        "    exceeding_children = list()\r\n",
        "    perfomance['expanded'] += 1\r\n",
        "    if best.C:\r\n",
        "      restore(best,open,closed,best.F)\r\n",
        "      best.C = False\r\n",
        "\r\n",
        "    for operator in best.operators:\r\n",
        "      new_child_state = operator() # generate\r\n",
        "      \r\n",
        "      if new_child_state is None: # move is illegal\r\n",
        "        continue\r\n",
        "      if not new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['generated'] += 1\r\n",
        "\r\n",
        "      g_new = best.g + 2 #move blank and the other card.\r\n",
        "      if new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['regenerations'] += 1\r\n",
        "        existing_child = get_existing_child(new_child_state,open,closed)\r\n",
        "        if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "        if existing_child in closed:\r\n",
        "          closed.remove(existing_child)\r\n",
        "        if existing_child in open:\r\n",
        "          open.remove(existing_child)\r\n",
        "\r\n",
        "        new_child_state.C = existing_child.C\r\n",
        "        new_child_state.F = existing_child.F\r\n",
        "\r\n",
        "      new_child_state.g = g_new      \r\n",
        "      new_child_state.f = new_child_state.g + new_child_state.h\r\n",
        "\r\n",
        "      if new_child_state.C == False:\r\n",
        "        new_child_state.F = new_child_state.f\r\n",
        "\r\n",
        "      new_child_state.parent_state = best\r\n",
        "\r\n",
        "      if new_child_state.f == best.F:\r\n",
        "        open.append(new_child_state)\r\n",
        "      elif best.F < new_child_state.f:\r\n",
        "        exceeding_children.append(new_child_state)\r\n",
        "\r\n",
        "      if goal_test(new_child_state):\r\n",
        "        if g_new < U:\r\n",
        "          solution_state = new_child_state\r\n",
        "          U = g_new\r\n",
        "          open = [state for state in open if state.f < U]\r\n",
        "\r\n",
        "    if not len(exceeding_children) == 0 and best.f == best.F:\r\n",
        "      collapse(best,exceeding_children,open,closed) \r\n",
        "      open.append(best)\r\n",
        "      \r\n",
        "    else:\r\n",
        "      closed.append(best)\r\n",
        "    \r\n",
        "    perfomance['max open length'] = perfomance['max open length'] if len(open) <= perfomance['max open length'] else len(open)\r\n",
        "    perfomance['max closed length'] = perfomance['max closed length'] if len(closed) <= perfomance['max closed length'] else len(closed)\r\n",
        "    open.sort(key=lambda state: (state.F,state.h))\r\n",
        "  \r\n",
        "  return \"no path was found to the solution\" if U == float('inf') else perfomance,main_backtracking(solution_state)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4j-TclIhM6wA"
      },
      "source": [
        "def enhanced_partial_expansion_a_star(state,goal_test= lambda state: state.h == 0):\r\n",
        "  state.g = 0\r\n",
        "  state.F = state.h # State.F by default is state.f = state.g + state.h. state.g = 0 => state.F = state.f = state.h\r\n",
        "  open = list()\r\n",
        "  open.append(state)\r\n",
        "  closed = list()\r\n",
        "  perfomance = dict()\r\n",
        "\r\n",
        "  # for final results\r\n",
        "  perfomance['generated'] = 0\r\n",
        "  perfomance['expanded'] = 0\r\n",
        "  perfomance['max open length'] = 0\r\n",
        "  perfomance['max closed length'] = 0\r\n",
        "  perfomance['regenerations'] = 0\r\n",
        "\r\n",
        "  while(len(open) > 0):\r\n",
        "    best = open.pop(0)\r\n",
        "    if goal_test(best):\r\n",
        "      return perfomance,main_backtracking(best)\r\n",
        "\r\n",
        "    perfomance['expanded'] += 1\r\n",
        "    (next_operators, delta_f) = best.osf()\r\n",
        "    children = []\r\n",
        "    \r\n",
        "    for operator in next_operators :\r\n",
        "      new_child_state = operator() #generate\r\n",
        "      if new_child_state is None: # move is illegal\r\n",
        "        continue\r\n",
        "\r\n",
        "      if not new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['generated'] += 1\r\n",
        "\r\n",
        "      g_new = best.g + 2 #move blank and the other card.\r\n",
        "      if new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['regenerations'] += 1\r\n",
        "        existing_child = get_existing_child(new_child_state,open,closed)\r\n",
        "        if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "        if existing_child in closed:\r\n",
        "          closed.remove(existing_child)\r\n",
        "        if existing_child in open:\r\n",
        "          open.remove(existing_child)\r\n",
        "\r\n",
        "        new_child_state.C = existing_child.C\r\n",
        "        new_child_state.F = existing_child.F\r\n",
        "        new_child_state.operators_table = existing_child.operators_table\r\n",
        "\r\n",
        "\r\n",
        "      new_child_state.g = g_new\r\n",
        "\r\n",
        "      new_child_state.f = new_child_state.g + new_child_state.h\r\n",
        "      new_child_state.F = new_child_state.F if new_child_state.C else new_child_state.f\r\n",
        "      new_child_state.parent_state = best\r\n",
        "\r\n",
        "      open.append(new_child_state)\r\n",
        "\r\n",
        "    if not delta_f is None:\r\n",
        "      best.C = True\r\n",
        "      best.F += delta_f\r\n",
        "      open.append(best)\r\n",
        "    else:\r\n",
        "      best.C = False\r\n",
        "      closed.append(best)\r\n",
        "\r\n",
        "    perfomance['max open length'] = perfomance['max open length'] if len(open) <= perfomance['max open length'] else len(open)\r\n",
        "    perfomance['max closed length'] = perfomance['max closed length'] if len(closed) <= perfomance['max closed length'] else len(closed)\r\n",
        "    open.sort(key=lambda state: (state.f,state.h))\r\n",
        "\r\n",
        "  return \"no path was found to the solution\"\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QGe4BAlBwn6"
      },
      "source": [
        "def enhanced_partial_expansion_a_star_early(state,goal_test= lambda state: state.h == 0):\r\n",
        "  state.g = 0\r\n",
        "  state.F = state.h\r\n",
        "  U = float('inf')\r\n",
        "  open = list()\r\n",
        "  open.append(state)\r\n",
        "  closed = list()\r\n",
        "  perfomance = dict()\r\n",
        "\r\n",
        "  # for final results\r\n",
        "  perfomance['generated'] = 0\r\n",
        "  perfomance['expanded'] = 0\r\n",
        "  perfomance['max open length'] = 0\r\n",
        "  perfomance['max closed length'] = 0\r\n",
        "  perfomance['regenerations'] = 0\r\n",
        "  \r\n",
        "  solution_state = None\r\n",
        "\r\n",
        "  while(len(open) > 0 and open[0].f < U):\r\n",
        "    best = open.pop(0)\r\n",
        "    if goal_test(best):\r\n",
        "      return perfomance,main_backtracking(best)\r\n",
        "\r\n",
        "    perfomance['expanded'] += 1\r\n",
        "    (next_operators, delta_f) = best.osf()\r\n",
        "    children = []\r\n",
        "    \r\n",
        "    for operator in next_operators :\r\n",
        "      new_child_state = operator()\r\n",
        "      if new_child_state is None:\r\n",
        "        continue\r\n",
        "\r\n",
        "      if not new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['generated'] += 1\r\n",
        "      g_new = best.g + 2 #move blank and the other card.\r\n",
        "      if new_child_state in closed or new_child_state in open:\r\n",
        "        perfomance['regenerations'] += 1\r\n",
        "        existing_child = get_existing_child(new_child_state,open,closed)\r\n",
        "        if existing_child.g <= g_new:\r\n",
        "          continue\r\n",
        "        if existing_child in closed:\r\n",
        "          closed.remove(existing_child)\r\n",
        "        if existing_child in open:\r\n",
        "          open.remove(existing_child)\r\n",
        "\r\n",
        "        \r\n",
        "        if goal_test(new_child_state):\r\n",
        "          if g_new < U:\r\n",
        "            solution_state = new_child_state\r\n",
        "            U = g_new\r\n",
        "            open = [state for state in open if state.f < U]\r\n",
        "        new_child_state.C = existing_child.C\r\n",
        "        new_child_state.F = existing_child.F\r\n",
        "        new_child_state.operators_table = existing_child.operators_table\r\n",
        "\r\n",
        "\r\n",
        "      new_child_state.g = g_new\r\n",
        "\r\n",
        "      new_child_state.f = new_child_state.g + new_child_state.h\r\n",
        "      new_child_state.F = new_child_state.F if new_child_state.C else new_child_state.f\r\n",
        "      new_child_state.parent_state = best\r\n",
        "\r\n",
        "      open.append(new_child_state)\r\n",
        "\r\n",
        "    if not delta_f is None:\r\n",
        "      best.C = True\r\n",
        "      best.F += delta_f\r\n",
        "      open.append(best)\r\n",
        "    else:\r\n",
        "      best.C = False\r\n",
        "      closed.append(best)\r\n",
        "\r\n",
        "    perfomance['max open length'] = perfomance['max open length'] if len(open) <= perfomance['max open length'] else len(open)\r\n",
        "    perfomance['max closed length'] = perfomance['max closed length'] if len(closed) <= perfomance['max closed length'] else len(closed)\r\n",
        "    open.sort(key=lambda state: (state.f,state.h))\r\n",
        "\r\n",
        "  return \"no path was found to the solution\" if U == float('inf') else perfomance,main_backtracking(solution_state)\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMOYqyfXsf3z"
      },
      "source": [
        "The next function generates instances for the 15-puzzle problem and runs each search algorithm on those instances."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhVLhqGHCNfa"
      },
      "source": [
        "# num_of_instances: number of generated instances to run the algorithmns on.\r\n",
        "# num_of_steps : Number of steps needed to arrive to the solution from the instance.\r\n",
        "# returns: Pandas Dataframe contains the perfomance of the algorithms for each instance.\r\n",
        "\r\n",
        "def benchmark(num_of_instances = 10,num_of_steps = 5):\r\n",
        "  temp_index_count = 0\r\n",
        "  summary_df = pd.DataFrame()\r\n",
        "  complete_state = TileState([['b',1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])\r\n",
        "  compared_algorithms = [a_star_late,a_star_early,partial_expansion_a_star,partial_expansion_a_star_early,enhanced_partial_expansion_a_star,enhanced_partial_expansion_a_star_early]\r\n",
        "  problem_instances = list()\r\n",
        "  for i in range(num_of_instances):\r\n",
        "    problem_instance = make_an_initial_state(complete_state,steps=num_of_steps)\r\n",
        "    while problem_instance in problem_instances: # for a case we random the same instance\r\n",
        "      problem_instance = make_an_initial_state(complete_state)\r\n",
        "    \r\n",
        "    problem_instances.append(problem_instance)\r\n",
        "    for algorithm in compared_algorithms:\r\n",
        "      problem_instance_to_exec = deepcopy(problem_instance)\r\n",
        "      print(algorithm.__name__.replace('_',' '))\r\n",
        "      problem_instance_to_exec.build_osf()\r\n",
        "      t0 = time.time()\r\n",
        "      tracemalloc.start()\r\n",
        "      algorithm_perfomance, path = algorithm(problem_instance_to_exec)\r\n",
        "      current, peak = tracemalloc.get_traced_memory()\r\n",
        "      tracemalloc.stop()\r\n",
        "      t1 = time.time()\r\n",
        "      algorithm_perfomance[\"Memory's peak usage [MB] \"] = peak / 10**6\r\n",
        "      algorithm_perfomance['runtime [Sec] '] = t1-t0\r\n",
        "      algorithm_perfomance['problem instance'] = str(problem_instance_to_exec)\r\n",
        "      algorithm_perfomance['algorithm name'] = algorithm.__name__.replace('_',' ')\r\n",
        "      algorithm_perfomance_df = pd.DataFrame(data=algorithm_perfomance,index=[temp_index_count])\r\n",
        "\r\n",
        "      summary_df = pd.concat([summary_df,algorithm_perfomance_df])\r\n",
        "      temp_index_count += 1\r\n",
        "\r\n",
        "  summary_df.set_index(['problem instance','algorithm name'], inplace=True)\r\n",
        "  return summary_df\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjlF34B5RQDR"
      },
      "source": [
        "Execution of all algorithms to all difficulty levels:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ocYE3bgAh9MH",
        "outputId": "6eab5f73-1353-4dd6-a9fc-449f9b613d67"
      },
      "source": [
        "benchmark_for_5_steps_df = benchmark(num_of_steps=5)\r\n",
        "benchmark_for_5_steps_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>generated</th>\n",
              "      <th>expanded</th>\n",
              "      <th>max open length</th>\n",
              "      <th>max closed length</th>\n",
              "      <th>regenerations</th>\n",
              "      <th>Memory's peak usage [MB]</th>\n",
              "      <th>runtime [Sec]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>problem instance</th>\n",
              "      <th>algorithm name</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 rowspan=\"6\" valign=\"top\">| 1 5 2 3 |\\n| 4 6 10 7 |\\n| 8 9 11 b |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.117864</td>\n",
              "      <td>0.015255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.128200</td>\n",
              "      <td>0.016295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.114784</td>\n",
              "      <td>0.011566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.124384</td>\n",
              "      <td>0.010688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.086608</td>\n",
              "      <td>0.008322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.111040</td>\n",
              "      <td>0.009570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| b 8 6 7 |\\n| 9 5 10 11 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108832</td>\n",
              "      <td>0.011859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0.110675</td>\n",
              "      <td>0.011778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0.152808</td>\n",
              "      <td>0.022083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0.156216</td>\n",
              "      <td>0.023550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.112126</td>\n",
              "      <td>0.011037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.113654</td>\n",
              "      <td>0.014679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 5 2 3 |\\n| 4 9 6 7 |\\n| 8 10 11 b |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.106648</td>\n",
              "      <td>0.010852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.110232</td>\n",
              "      <td>0.013327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.114800</td>\n",
              "      <td>0.011365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.116072</td>\n",
              "      <td>0.012423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.089080</td>\n",
              "      <td>0.006882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.105808</td>\n",
              "      <td>0.006898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 8 5 6 7 |\\n| 12 9 10 11 |\\n| 13 14 b 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.088312</td>\n",
              "      <td>0.007467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.089304</td>\n",
              "      <td>0.008092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.116068</td>\n",
              "      <td>0.008790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.089407</td>\n",
              "      <td>0.008481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.059840</td>\n",
              "      <td>0.004774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.070816</td>\n",
              "      <td>0.004728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 5 9 6 7 |\\n| 12 8 10 11 |\\n| b 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0.114380</td>\n",
              "      <td>0.013871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111387</td>\n",
              "      <td>0.013497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.130952</td>\n",
              "      <td>0.015137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0.153728</td>\n",
              "      <td>0.017441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0.131568</td>\n",
              "      <td>0.013417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0.120032</td>\n",
              "      <td>0.013833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 5 6 7 11 |\\n| 8 9 10 b |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.105104</td>\n",
              "      <td>0.010967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.098384</td>\n",
              "      <td>0.009976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111384</td>\n",
              "      <td>0.010494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111104</td>\n",
              "      <td>0.010258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.098216</td>\n",
              "      <td>0.007002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.088352</td>\n",
              "      <td>0.007554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 8 5 6 7 |\\n| 9 13 10 11 |\\n| b 12 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0.115913</td>\n",
              "      <td>0.014349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0.109676</td>\n",
              "      <td>0.012538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.111964</td>\n",
              "      <td>0.015218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.127416</td>\n",
              "      <td>0.021301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.082878</td>\n",
              "      <td>0.008094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.112252</td>\n",
              "      <td>0.012756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 5 9 6 7 |\\n| 8 13 10 11 |\\n| b 12 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.107368</td>\n",
              "      <td>0.011805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.124464</td>\n",
              "      <td>0.010494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.121664</td>\n",
              "      <td>0.013063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.114776</td>\n",
              "      <td>0.013197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.109497</td>\n",
              "      <td>0.009225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.102356</td>\n",
              "      <td>0.008830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 5 b 2 3 |\\n| 1 4 6 7 |\\n| 8 9 10 11 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.080144</td>\n",
              "      <td>0.009244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.106080</td>\n",
              "      <td>0.008661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0.113940</td>\n",
              "      <td>0.013963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0.133708</td>\n",
              "      <td>0.017344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.086348</td>\n",
              "      <td>0.008923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.109191</td>\n",
              "      <td>0.008889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 2 6 b |\\n| 4 5 7 3 |\\n| 8 9 10 11 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.102164</td>\n",
              "      <td>0.009347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.108760</td>\n",
              "      <td>0.010078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.108032</td>\n",
              "      <td>0.011126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.111104</td>\n",
              "      <td>0.011727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0.130136</td>\n",
              "      <td>0.011965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0.119764</td>\n",
              "      <td>0.011311</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                            generated  ...  runtime [Sec] \n",
              "problem instance                                   algorithm name                                      ...                \n",
              "| 1 5 2 3 |\\n| 4 6 10 7 |\\n| 8 9 11 b |\\n| 12 1... a star late                                     14  ...        0.015255\n",
              "                                                   a star early                                    14  ...        0.016295\n",
              "                                                   partial expansion a star                        14  ...        0.011566\n",
              "                                                   partial expansion a star early                  14  ...        0.010688\n",
              "                                                   enhanced partial expansion a star               11  ...        0.008322\n",
              "                                                   enhanced partial expansion a star early         11  ...        0.009570\n",
              "| 4 1 2 3 |\\n| b 8 6 7 |\\n| 9 5 10 11 |\\n| 12 1... a star late                                     16  ...        0.011859\n",
              "                                                   a star early                                    16  ...        0.011778\n",
              "                                                   partial expansion a star                        25  ...        0.022083\n",
              "                                                   partial expansion a star early                  25  ...        0.023550\n",
              "                                                   enhanced partial expansion a star               16  ...        0.011037\n",
              "                                                   enhanced partial expansion a star early         16  ...        0.014679\n",
              "| 1 5 2 3 |\\n| 4 9 6 7 |\\n| 8 10 11 b |\\n| 12 1... a star late                                     14  ...        0.010852\n",
              "                                                   a star early                                    14  ...        0.013327\n",
              "                                                   partial expansion a star                        14  ...        0.011365\n",
              "                                                   partial expansion a star early                  14  ...        0.012423\n",
              "                                                   enhanced partial expansion a star                9  ...        0.006882\n",
              "                                                   enhanced partial expansion a star early          9  ...        0.006898\n",
              "| 4 1 2 3 |\\n| 8 5 6 7 |\\n| 12 9 10 11 |\\n| 13 ... a star late                                     10  ...        0.007467\n",
              "                                                   a star early                                    10  ...        0.008092\n",
              "                                                   partial expansion a star                        10  ...        0.008790\n",
              "                                                   partial expansion a star early                  10  ...        0.008481\n",
              "                                                   enhanced partial expansion a star                6  ...        0.004774\n",
              "                                                   enhanced partial expansion a star early          6  ...        0.004728\n",
              "| 4 1 2 3 |\\n| 5 9 6 7 |\\n| 12 8 10 11 |\\n| b 1... a star late                                     15  ...        0.013871\n",
              "                                                   a star early                                    15  ...        0.013497\n",
              "                                                   partial expansion a star                        17  ...        0.015137\n",
              "                                                   partial expansion a star early                  18  ...        0.017441\n",
              "                                                   enhanced partial expansion a star               15  ...        0.013417\n",
              "                                                   enhanced partial expansion a star early         15  ...        0.013833\n",
              "| 4 1 2 3 |\\n| 5 6 7 11 |\\n| 8 9 10 b |\\n| 12 1... a star late                                     13  ...        0.010967\n",
              "                                                   a star early                                    13  ...        0.009976\n",
              "                                                   partial expansion a star                        13  ...        0.010494\n",
              "                                                   partial expansion a star early                  13  ...        0.010258\n",
              "                                                   enhanced partial expansion a star                9  ...        0.007002\n",
              "                                                   enhanced partial expansion a star early          9  ...        0.007554\n",
              "| 4 1 2 3 |\\n| 8 5 6 7 |\\n| 9 13 10 11 |\\n| b 1... a star late                                     16  ...        0.014349\n",
              "                                                   a star early                                    16  ...        0.012538\n",
              "                                                   partial expansion a star                        18  ...        0.015218\n",
              "                                                   partial expansion a star early                  18  ...        0.021301\n",
              "                                                   enhanced partial expansion a star               11  ...        0.008094\n",
              "                                                   enhanced partial expansion a star early         11  ...        0.012756\n",
              "| 4 1 2 3 |\\n| 5 9 6 7 |\\n| 8 13 10 11 |\\n| b 1... a star late                                     14  ...        0.011805\n",
              "                                                   a star early                                    14  ...        0.010494\n",
              "                                                   partial expansion a star                        16  ...        0.013063\n",
              "                                                   partial expansion a star early                  16  ...        0.013197\n",
              "                                                   enhanced partial expansion a star               12  ...        0.009225\n",
              "                                                   enhanced partial expansion a star early         12  ...        0.008830\n",
              "| 5 b 2 3 |\\n| 1 4 6 7 |\\n| 8 9 10 11 |\\n| 12 1... a star late                                     11  ...        0.009244\n",
              "                                                   a star early                                    11  ...        0.008661\n",
              "                                                   partial expansion a star                        14  ...        0.013963\n",
              "                                                   partial expansion a star early                  16  ...        0.017344\n",
              "                                                   enhanced partial expansion a star               11  ...        0.008923\n",
              "                                                   enhanced partial expansion a star early         11  ...        0.008889\n",
              "| 1 2 6 b |\\n| 4 5 7 3 |\\n| 8 9 10 11 |\\n| 12 1... a star late                                     11  ...        0.009347\n",
              "                                                   a star early                                    11  ...        0.010078\n",
              "                                                   partial expansion a star                        13  ...        0.011126\n",
              "                                                   partial expansion a star early                  13  ...        0.011727\n",
              "                                                   enhanced partial expansion a star               14  ...        0.011965\n",
              "                                                   enhanced partial expansion a star early         14  ...        0.011311\n",
              "\n",
              "[60 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WBIy1tADiMeu",
        "outputId": "b6be8f33-ca85-4b32-b04b-ae40db6d100f"
      },
      "source": [
        "benchmark_for_10_steps_df = benchmark(num_of_steps=10)\r\n",
        "benchmark_for_10_steps_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>generated</th>\n",
              "      <th>expanded</th>\n",
              "      <th>max open length</th>\n",
              "      <th>max closed length</th>\n",
              "      <th>regenerations</th>\n",
              "      <th>Memory's peak usage [MB]</th>\n",
              "      <th>runtime [Sec]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>problem instance</th>\n",
              "      <th>algorithm name</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 rowspan=\"6\" valign=\"top\">| 1 2 6 3 |\\n| 4 b 10 7 |\\n| 13 5 9 11 |\\n| 8 12 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>72</td>\n",
              "      <td>33</td>\n",
              "      <td>40</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>0.454476</td>\n",
              "      <td>0.137424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>72</td>\n",
              "      <td>33</td>\n",
              "      <td>39</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>0.480575</td>\n",
              "      <td>0.124191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>96</td>\n",
              "      <td>45</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>28</td>\n",
              "      <td>0.482436</td>\n",
              "      <td>0.198942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>99</td>\n",
              "      <td>46</td>\n",
              "      <td>16</td>\n",
              "      <td>19</td>\n",
              "      <td>31</td>\n",
              "      <td>0.558242</td>\n",
              "      <td>0.406917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>78</td>\n",
              "      <td>65</td>\n",
              "      <td>41</td>\n",
              "      <td>31</td>\n",
              "      <td>25</td>\n",
              "      <td>0.557966</td>\n",
              "      <td>0.141186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>78</td>\n",
              "      <td>65</td>\n",
              "      <td>41</td>\n",
              "      <td>31</td>\n",
              "      <td>25</td>\n",
              "      <td>0.538890</td>\n",
              "      <td>0.146733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 8 5 6 7 |\\n| 13 10 11 15 |\\n| 9 12 14 b |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>40</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.286272</td>\n",
              "      <td>0.059984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>40</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.292528</td>\n",
              "      <td>0.060667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>55</td>\n",
              "      <td>28</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "      <td>0.459282</td>\n",
              "      <td>0.123352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>58</td>\n",
              "      <td>29</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>19</td>\n",
              "      <td>0.447280</td>\n",
              "      <td>0.127898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>42</td>\n",
              "      <td>30</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>0.367840</td>\n",
              "      <td>0.075492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>42</td>\n",
              "      <td>30</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>0.390016</td>\n",
              "      <td>0.069718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 2 3 7 |\\n| 4 5 6 11 |\\n| 8 13 9 15 |\\n| 12 b 10 14 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>42</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0.300872</td>\n",
              "      <td>0.069984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>42</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0.279394</td>\n",
              "      <td>0.067138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>71</td>\n",
              "      <td>34</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>24</td>\n",
              "      <td>0.537192</td>\n",
              "      <td>0.153630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>73</td>\n",
              "      <td>35</td>\n",
              "      <td>15</td>\n",
              "      <td>17</td>\n",
              "      <td>26</td>\n",
              "      <td>0.528890</td>\n",
              "      <td>0.180150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>53</td>\n",
              "      <td>47</td>\n",
              "      <td>29</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0.342526</td>\n",
              "      <td>0.123546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>53</td>\n",
              "      <td>47</td>\n",
              "      <td>29</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0.439618</td>\n",
              "      <td>0.112998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 2 6 3 |\\n| 4 9 5 7 |\\n| 12 8 b 11 |\\n| 13 14 10 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>69</td>\n",
              "      <td>31</td>\n",
              "      <td>39</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.466500</td>\n",
              "      <td>0.103358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>69</td>\n",
              "      <td>31</td>\n",
              "      <td>38</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.432403</td>\n",
              "      <td>0.107114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>91</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>11</td>\n",
              "      <td>20</td>\n",
              "      <td>0.593884</td>\n",
              "      <td>0.176807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>94</td>\n",
              "      <td>42</td>\n",
              "      <td>23</td>\n",
              "      <td>13</td>\n",
              "      <td>23</td>\n",
              "      <td>0.564394</td>\n",
              "      <td>0.192832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>77</td>\n",
              "      <td>63</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>26</td>\n",
              "      <td>0.518460</td>\n",
              "      <td>0.130675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>77</td>\n",
              "      <td>63</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>26</td>\n",
              "      <td>0.506730</td>\n",
              "      <td>0.148096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 5 2 |\\n| 8 6 7 3 |\\n| b 9 10 11 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>59</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0.343684</td>\n",
              "      <td>0.089438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>59</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0.414628</td>\n",
              "      <td>0.089963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>84</td>\n",
              "      <td>39</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "      <td>0.534194</td>\n",
              "      <td>0.177460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>86</td>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>0.468784</td>\n",
              "      <td>0.163761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "      <td>18</td>\n",
              "      <td>0.422674</td>\n",
              "      <td>0.114638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "      <td>18</td>\n",
              "      <td>0.505308</td>\n",
              "      <td>0.111340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 5 10 9 7 |\\n| 12 8 6 11 |\\n| 13 b 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>57</td>\n",
              "      <td>26</td>\n",
              "      <td>32</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0.394012</td>\n",
              "      <td>0.089038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>57</td>\n",
              "      <td>26</td>\n",
              "      <td>31</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0.353808</td>\n",
              "      <td>0.085843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>81</td>\n",
              "      <td>38</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>24</td>\n",
              "      <td>0.739458</td>\n",
              "      <td>0.362233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>90</td>\n",
              "      <td>43</td>\n",
              "      <td>18</td>\n",
              "      <td>19</td>\n",
              "      <td>36</td>\n",
              "      <td>0.714204</td>\n",
              "      <td>0.205672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>56</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0.502492</td>\n",
              "      <td>0.109454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>56</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0.456508</td>\n",
              "      <td>0.116025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 5 2 3 |\\n| 8 4 9 6 |\\n| b 10 11 7 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>54</td>\n",
              "      <td>25</td>\n",
              "      <td>30</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0.485004</td>\n",
              "      <td>0.088279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>54</td>\n",
              "      <td>25</td>\n",
              "      <td>29</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431140</td>\n",
              "      <td>0.083716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>82</td>\n",
              "      <td>38</td>\n",
              "      <td>17</td>\n",
              "      <td>14</td>\n",
              "      <td>22</td>\n",
              "      <td>0.501946</td>\n",
              "      <td>0.187824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>86</td>\n",
              "      <td>40</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>0.469227</td>\n",
              "      <td>0.168835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>53</td>\n",
              "      <td>43</td>\n",
              "      <td>29</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>0.497840</td>\n",
              "      <td>0.093573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>53</td>\n",
              "      <td>43</td>\n",
              "      <td>29</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>0.473224</td>\n",
              "      <td>0.097019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 8 b 6 7 |\\n| 12 5 14 11 |\\n| 13 10 9 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>60</td>\n",
              "      <td>29</td>\n",
              "      <td>32</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>0.346476</td>\n",
              "      <td>0.091190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>60</td>\n",
              "      <td>29</td>\n",
              "      <td>31</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>0.418764</td>\n",
              "      <td>0.106043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>77</td>\n",
              "      <td>39</td>\n",
              "      <td>17</td>\n",
              "      <td>16</td>\n",
              "      <td>23</td>\n",
              "      <td>0.518322</td>\n",
              "      <td>0.163362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>80</td>\n",
              "      <td>41</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>28</td>\n",
              "      <td>0.616820</td>\n",
              "      <td>0.174686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>61</td>\n",
              "      <td>54</td>\n",
              "      <td>31</td>\n",
              "      <td>25</td>\n",
              "      <td>20</td>\n",
              "      <td>0.378630</td>\n",
              "      <td>0.107102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>61</td>\n",
              "      <td>54</td>\n",
              "      <td>31</td>\n",
              "      <td>25</td>\n",
              "      <td>20</td>\n",
              "      <td>0.526756</td>\n",
              "      <td>0.110303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 2 3 7 |\\n| 5 b 6 11 |\\n| 4 8 9 10 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>61</td>\n",
              "      <td>29</td>\n",
              "      <td>33</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>0.365796</td>\n",
              "      <td>0.090799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>61</td>\n",
              "      <td>29</td>\n",
              "      <td>32</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>0.369140</td>\n",
              "      <td>0.094566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>72</td>\n",
              "      <td>34</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>21</td>\n",
              "      <td>0.440324</td>\n",
              "      <td>0.167762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>74</td>\n",
              "      <td>35</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>23</td>\n",
              "      <td>0.496748</td>\n",
              "      <td>0.162850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>60</td>\n",
              "      <td>47</td>\n",
              "      <td>34</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0.432922</td>\n",
              "      <td>0.106865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>60</td>\n",
              "      <td>47</td>\n",
              "      <td>34</td>\n",
              "      <td>22</td>\n",
              "      <td>18</td>\n",
              "      <td>0.459341</td>\n",
              "      <td>0.101991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 2 3 7 |\\n| 4 5 6 11 |\\n| b 13 9 10 |\\n| 8 12 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>49</td>\n",
              "      <td>22</td>\n",
              "      <td>28</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>0.314540</td>\n",
              "      <td>0.074907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>49</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>0.280288</td>\n",
              "      <td>0.077660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>60</td>\n",
              "      <td>27</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>17</td>\n",
              "      <td>0.590340</td>\n",
              "      <td>0.110599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>62</td>\n",
              "      <td>29</td>\n",
              "      <td>14</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>0.441284</td>\n",
              "      <td>0.143742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>47</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>0.408876</td>\n",
              "      <td>0.081012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>47</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>0.526386</td>\n",
              "      <td>0.267176</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                            generated  ...  runtime [Sec] \n",
              "problem instance                                   algorithm name                                      ...                \n",
              "| 1 2 6 3 |\\n| 4 b 10 7 |\\n| 13 5 9 11 |\\n| 8 1... a star late                                     72  ...        0.137424\n",
              "                                                   a star early                                    72  ...        0.124191\n",
              "                                                   partial expansion a star                        96  ...        0.198942\n",
              "                                                   partial expansion a star early                  99  ...        0.406917\n",
              "                                                   enhanced partial expansion a star               78  ...        0.141186\n",
              "                                                   enhanced partial expansion a star early         78  ...        0.146733\n",
              "| 4 1 2 3 |\\n| 8 5 6 7 |\\n| 13 10 11 15 |\\n| 9 ... a star late                                     40  ...        0.059984\n",
              "                                                   a star early                                    40  ...        0.060667\n",
              "                                                   partial expansion a star                        55  ...        0.123352\n",
              "                                                   partial expansion a star early                  58  ...        0.127898\n",
              "                                                   enhanced partial expansion a star               42  ...        0.075492\n",
              "                                                   enhanced partial expansion a star early         42  ...        0.069718\n",
              "| 1 2 3 7 |\\n| 4 5 6 11 |\\n| 8 13 9 15 |\\n| 12 ... a star late                                     42  ...        0.069984\n",
              "                                                   a star early                                    42  ...        0.067138\n",
              "                                                   partial expansion a star                        71  ...        0.153630\n",
              "                                                   partial expansion a star early                  73  ...        0.180150\n",
              "                                                   enhanced partial expansion a star               53  ...        0.123546\n",
              "                                                   enhanced partial expansion a star early         53  ...        0.112998\n",
              "| 1 2 6 3 |\\n| 4 9 5 7 |\\n| 12 8 b 11 |\\n| 13 1... a star late                                     69  ...        0.103358\n",
              "                                                   a star early                                    69  ...        0.107114\n",
              "                                                   partial expansion a star                        91  ...        0.176807\n",
              "                                                   partial expansion a star early                  94  ...        0.192832\n",
              "                                                   enhanced partial expansion a star               77  ...        0.130675\n",
              "                                                   enhanced partial expansion a star early         77  ...        0.148096\n",
              "| 4 1 5 2 |\\n| 8 6 7 3 |\\n| b 9 10 11 |\\n| 12 1... a star late                                     59  ...        0.089438\n",
              "                                                   a star early                                    59  ...        0.089963\n",
              "                                                   partial expansion a star                        84  ...        0.177460\n",
              "                                                   partial expansion a star early                  86  ...        0.163761\n",
              "                                                   enhanced partial expansion a star               58  ...        0.114638\n",
              "                                                   enhanced partial expansion a star early         58  ...        0.111340\n",
              "| 4 1 2 3 |\\n| 5 10 9 7 |\\n| 12 8 6 11 |\\n| 13 ... a star late                                     57  ...        0.089038\n",
              "                                                   a star early                                    57  ...        0.085843\n",
              "                                                   partial expansion a star                        81  ...        0.362233\n",
              "                                                   partial expansion a star early                  90  ...        0.205672\n",
              "                                                   enhanced partial expansion a star               56  ...        0.109454\n",
              "                                                   enhanced partial expansion a star early         56  ...        0.116025\n",
              "| 1 5 2 3 |\\n| 8 4 9 6 |\\n| b 10 11 7 |\\n| 12 1... a star late                                     54  ...        0.088279\n",
              "                                                   a star early                                    54  ...        0.083716\n",
              "                                                   partial expansion a star                        82  ...        0.187824\n",
              "                                                   partial expansion a star early                  86  ...        0.168835\n",
              "                                                   enhanced partial expansion a star               53  ...        0.093573\n",
              "                                                   enhanced partial expansion a star early         53  ...        0.097019\n",
              "| 4 1 2 3 |\\n| 8 b 6 7 |\\n| 12 5 14 11 |\\n| 13 ... a star late                                     60  ...        0.091190\n",
              "                                                   a star early                                    60  ...        0.106043\n",
              "                                                   partial expansion a star                        77  ...        0.163362\n",
              "                                                   partial expansion a star early                  80  ...        0.174686\n",
              "                                                   enhanced partial expansion a star               61  ...        0.107102\n",
              "                                                   enhanced partial expansion a star early         61  ...        0.110303\n",
              "| 1 2 3 7 |\\n| 5 b 6 11 |\\n| 4 8 9 10 |\\n| 12 1... a star late                                     61  ...        0.090799\n",
              "                                                   a star early                                    61  ...        0.094566\n",
              "                                                   partial expansion a star                        72  ...        0.167762\n",
              "                                                   partial expansion a star early                  74  ...        0.162850\n",
              "                                                   enhanced partial expansion a star               60  ...        0.106865\n",
              "                                                   enhanced partial expansion a star early         60  ...        0.101991\n",
              "| 1 2 3 7 |\\n| 4 5 6 11 |\\n| b 13 9 10 |\\n| 8 1... a star late                                     49  ...        0.074907\n",
              "                                                   a star early                                    49  ...        0.077660\n",
              "                                                   partial expansion a star                        60  ...        0.110599\n",
              "                                                   partial expansion a star early                  62  ...        0.143742\n",
              "                                                   enhanced partial expansion a star               47  ...        0.081012\n",
              "                                                   enhanced partial expansion a star early         47  ...        0.267176\n",
              "\n",
              "[60 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Nsh23Lg_Fvc-",
        "outputId": "10733cb2-c46f-4537-8310-4a1b4e7c31bc"
      },
      "source": [
        "benchmark_for_15_steps_df = benchmark(num_of_steps=15)\r\n",
        "benchmark_for_15_steps_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>generated</th>\n",
              "      <th>expanded</th>\n",
              "      <th>max open length</th>\n",
              "      <th>max closed length</th>\n",
              "      <th>regenerations</th>\n",
              "      <th>Memory's peak usage [MB]</th>\n",
              "      <th>runtime [Sec]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>problem instance</th>\n",
              "      <th>algorithm name</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 rowspan=\"6\" valign=\"top\">| 4 1 3 7 |\\n| 8 5 11 6 |\\n| 12 9 2 b |\\n| 13 14 10 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>404</td>\n",
              "      <td>200</td>\n",
              "      <td>204</td>\n",
              "      <td>200</td>\n",
              "      <td>2</td>\n",
              "      <td>7.674584</td>\n",
              "      <td>1.235012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>404</td>\n",
              "      <td>200</td>\n",
              "      <td>203</td>\n",
              "      <td>200</td>\n",
              "      <td>2</td>\n",
              "      <td>1.700966</td>\n",
              "      <td>0.997248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>448</td>\n",
              "      <td>221</td>\n",
              "      <td>67</td>\n",
              "      <td>100</td>\n",
              "      <td>144</td>\n",
              "      <td>2.231894</td>\n",
              "      <td>1.547791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>459</td>\n",
              "      <td>227</td>\n",
              "      <td>66</td>\n",
              "      <td>106</td>\n",
              "      <td>155</td>\n",
              "      <td>1.920958</td>\n",
              "      <td>1.742564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>438</td>\n",
              "      <td>385</td>\n",
              "      <td>206</td>\n",
              "      <td>194</td>\n",
              "      <td>152</td>\n",
              "      <td>1.913619</td>\n",
              "      <td>1.320234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>438</td>\n",
              "      <td>385</td>\n",
              "      <td>206</td>\n",
              "      <td>194</td>\n",
              "      <td>152</td>\n",
              "      <td>1.821454</td>\n",
              "      <td>1.534414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 2 3 7 |\\n| 4 6 10 11 |\\n| 5 8 14 9 |\\n| 12 13 b 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>506</td>\n",
              "      <td>242</td>\n",
              "      <td>264</td>\n",
              "      <td>242</td>\n",
              "      <td>1</td>\n",
              "      <td>2.149398</td>\n",
              "      <td>1.352142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>506</td>\n",
              "      <td>242</td>\n",
              "      <td>263</td>\n",
              "      <td>242</td>\n",
              "      <td>1</td>\n",
              "      <td>2.071498</td>\n",
              "      <td>1.474506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>446</td>\n",
              "      <td>220</td>\n",
              "      <td>57</td>\n",
              "      <td>100</td>\n",
              "      <td>138</td>\n",
              "      <td>1.955870</td>\n",
              "      <td>1.837493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>451</td>\n",
              "      <td>223</td>\n",
              "      <td>56</td>\n",
              "      <td>103</td>\n",
              "      <td>144</td>\n",
              "      <td>2.065638</td>\n",
              "      <td>1.706326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>572</td>\n",
              "      <td>499</td>\n",
              "      <td>263</td>\n",
              "      <td>239</td>\n",
              "      <td>188</td>\n",
              "      <td>2.191914</td>\n",
              "      <td>2.118443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>572</td>\n",
              "      <td>499</td>\n",
              "      <td>263</td>\n",
              "      <td>239</td>\n",
              "      <td>188</td>\n",
              "      <td>2.542782</td>\n",
              "      <td>1.780700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 3 11 |\\n| 5 6 b 2 |\\n| 8 13 7 10 |\\n| 12 14 9 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>446</td>\n",
              "      <td>220</td>\n",
              "      <td>222</td>\n",
              "      <td>220</td>\n",
              "      <td>7</td>\n",
              "      <td>1.834570</td>\n",
              "      <td>1.414305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>446</td>\n",
              "      <td>220</td>\n",
              "      <td>221</td>\n",
              "      <td>220</td>\n",
              "      <td>7</td>\n",
              "      <td>1.919086</td>\n",
              "      <td>1.219167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>567</td>\n",
              "      <td>278</td>\n",
              "      <td>90</td>\n",
              "      <td>123</td>\n",
              "      <td>179</td>\n",
              "      <td>2.639138</td>\n",
              "      <td>2.210275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>578</td>\n",
              "      <td>286</td>\n",
              "      <td>89</td>\n",
              "      <td>131</td>\n",
              "      <td>191</td>\n",
              "      <td>2.758330</td>\n",
              "      <td>2.068062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>481</td>\n",
              "      <td>470</td>\n",
              "      <td>227</td>\n",
              "      <td>214</td>\n",
              "      <td>183</td>\n",
              "      <td>2.163506</td>\n",
              "      <td>1.841711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>481</td>\n",
              "      <td>470</td>\n",
              "      <td>227</td>\n",
              "      <td>214</td>\n",
              "      <td>183</td>\n",
              "      <td>2.237282</td>\n",
              "      <td>1.753305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 6 5 3 |\\n| 4 10 b 7 |\\n| 8 2 11 15 |\\n| 12 9 13 14 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>430</td>\n",
              "      <td>209</td>\n",
              "      <td>219</td>\n",
              "      <td>209</td>\n",
              "      <td>6</td>\n",
              "      <td>1.856570</td>\n",
              "      <td>1.213583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>430</td>\n",
              "      <td>209</td>\n",
              "      <td>218</td>\n",
              "      <td>209</td>\n",
              "      <td>6</td>\n",
              "      <td>1.836342</td>\n",
              "      <td>1.319340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>490</td>\n",
              "      <td>240</td>\n",
              "      <td>85</td>\n",
              "      <td>93</td>\n",
              "      <td>143</td>\n",
              "      <td>2.128222</td>\n",
              "      <td>1.645945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>495</td>\n",
              "      <td>244</td>\n",
              "      <td>84</td>\n",
              "      <td>97</td>\n",
              "      <td>149</td>\n",
              "      <td>1.770366</td>\n",
              "      <td>1.767089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>495</td>\n",
              "      <td>464</td>\n",
              "      <td>220</td>\n",
              "      <td>207</td>\n",
              "      <td>180</td>\n",
              "      <td>2.392970</td>\n",
              "      <td>1.452711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>495</td>\n",
              "      <td>464</td>\n",
              "      <td>220</td>\n",
              "      <td>207</td>\n",
              "      <td>180</td>\n",
              "      <td>1.883160</td>\n",
              "      <td>1.666265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 6 7 5 |\\n| 4 9 b 2 |\\n| 8 10 11 3 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>486</td>\n",
              "      <td>244</td>\n",
              "      <td>240</td>\n",
              "      <td>244</td>\n",
              "      <td>5</td>\n",
              "      <td>2.019546</td>\n",
              "      <td>1.278800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>486</td>\n",
              "      <td>244</td>\n",
              "      <td>239</td>\n",
              "      <td>244</td>\n",
              "      <td>5</td>\n",
              "      <td>1.937662</td>\n",
              "      <td>1.273982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>506</td>\n",
              "      <td>253</td>\n",
              "      <td>65</td>\n",
              "      <td>117</td>\n",
              "      <td>165</td>\n",
              "      <td>2.698382</td>\n",
              "      <td>1.906486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>518</td>\n",
              "      <td>259</td>\n",
              "      <td>65</td>\n",
              "      <td>123</td>\n",
              "      <td>178</td>\n",
              "      <td>2.377682</td>\n",
              "      <td>1.940967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>567</td>\n",
              "      <td>535</td>\n",
              "      <td>241</td>\n",
              "      <td>242</td>\n",
              "      <td>209</td>\n",
              "      <td>2.668714</td>\n",
              "      <td>1.976343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>567</td>\n",
              "      <td>535</td>\n",
              "      <td>241</td>\n",
              "      <td>242</td>\n",
              "      <td>209</td>\n",
              "      <td>2.598840</td>\n",
              "      <td>1.831522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 1 6 3 7 |\\n| 8 4 2 5 |\\n| 12 9 10 11 |\\n| 13 14 b 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>357</td>\n",
              "      <td>177</td>\n",
              "      <td>180</td>\n",
              "      <td>177</td>\n",
              "      <td>1</td>\n",
              "      <td>1.740906</td>\n",
              "      <td>1.078644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>357</td>\n",
              "      <td>177</td>\n",
              "      <td>179</td>\n",
              "      <td>177</td>\n",
              "      <td>1</td>\n",
              "      <td>1.402438</td>\n",
              "      <td>0.934344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>377</td>\n",
              "      <td>191</td>\n",
              "      <td>55</td>\n",
              "      <td>82</td>\n",
              "      <td>120</td>\n",
              "      <td>1.851726</td>\n",
              "      <td>1.301049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>382</td>\n",
              "      <td>195</td>\n",
              "      <td>54</td>\n",
              "      <td>86</td>\n",
              "      <td>126</td>\n",
              "      <td>1.775494</td>\n",
              "      <td>1.492230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>396</td>\n",
              "      <td>359</td>\n",
              "      <td>181</td>\n",
              "      <td>176</td>\n",
              "      <td>135</td>\n",
              "      <td>2.009834</td>\n",
              "      <td>1.236870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>396</td>\n",
              "      <td>359</td>\n",
              "      <td>181</td>\n",
              "      <td>176</td>\n",
              "      <td>135</td>\n",
              "      <td>1.854474</td>\n",
              "      <td>1.231037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 5 6 4 3 |\\n| 8 1 2 7 |\\n| 9 13 10 11 |\\n| 12 14 b 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>451</td>\n",
              "      <td>227</td>\n",
              "      <td>220</td>\n",
              "      <td>227</td>\n",
              "      <td>10</td>\n",
              "      <td>1.967026</td>\n",
              "      <td>1.417594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>451</td>\n",
              "      <td>227</td>\n",
              "      <td>219</td>\n",
              "      <td>227</td>\n",
              "      <td>10</td>\n",
              "      <td>1.814350</td>\n",
              "      <td>1.265281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>465</td>\n",
              "      <td>240</td>\n",
              "      <td>80</td>\n",
              "      <td>94</td>\n",
              "      <td>142</td>\n",
              "      <td>1.549102</td>\n",
              "      <td>1.790079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>468</td>\n",
              "      <td>243</td>\n",
              "      <td>79</td>\n",
              "      <td>97</td>\n",
              "      <td>147</td>\n",
              "      <td>2.199718</td>\n",
              "      <td>1.658161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>519</td>\n",
              "      <td>487</td>\n",
              "      <td>220</td>\n",
              "      <td>226</td>\n",
              "      <td>188</td>\n",
              "      <td>2.153266</td>\n",
              "      <td>1.860606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>519</td>\n",
              "      <td>487</td>\n",
              "      <td>220</td>\n",
              "      <td>226</td>\n",
              "      <td>188</td>\n",
              "      <td>2.451112</td>\n",
              "      <td>1.613775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 1 2 3 |\\n| 8 6 7 11 |\\n| 12 5 15 14 |\\n| 13 9 b 10 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>422</td>\n",
              "      <td>215</td>\n",
              "      <td>207</td>\n",
              "      <td>215</td>\n",
              "      <td>2</td>\n",
              "      <td>1.811874</td>\n",
              "      <td>1.207378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>422</td>\n",
              "      <td>215</td>\n",
              "      <td>206</td>\n",
              "      <td>215</td>\n",
              "      <td>2</td>\n",
              "      <td>1.905246</td>\n",
              "      <td>1.307003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>609</td>\n",
              "      <td>311</td>\n",
              "      <td>111</td>\n",
              "      <td>130</td>\n",
              "      <td>183</td>\n",
              "      <td>2.632946</td>\n",
              "      <td>2.076971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>617</td>\n",
              "      <td>318</td>\n",
              "      <td>111</td>\n",
              "      <td>137</td>\n",
              "      <td>197</td>\n",
              "      <td>2.340762</td>\n",
              "      <td>2.397014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>459</td>\n",
              "      <td>417</td>\n",
              "      <td>205</td>\n",
              "      <td>210</td>\n",
              "      <td>175</td>\n",
              "      <td>2.152376</td>\n",
              "      <td>1.569735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>459</td>\n",
              "      <td>417</td>\n",
              "      <td>205</td>\n",
              "      <td>210</td>\n",
              "      <td>175</td>\n",
              "      <td>1.718018</td>\n",
              "      <td>1.669027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 4 b 1 2 |\\n| 8 5 6 3 |\\n| 12 9 15 7 |\\n| 13 14 11 10 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>215</td>\n",
              "      <td>103</td>\n",
              "      <td>112</td>\n",
              "      <td>103</td>\n",
              "      <td>1</td>\n",
              "      <td>1.172330</td>\n",
              "      <td>0.544916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>215</td>\n",
              "      <td>103</td>\n",
              "      <td>111</td>\n",
              "      <td>103</td>\n",
              "      <td>1</td>\n",
              "      <td>1.314074</td>\n",
              "      <td>0.544575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>192</td>\n",
              "      <td>93</td>\n",
              "      <td>33</td>\n",
              "      <td>38</td>\n",
              "      <td>54</td>\n",
              "      <td>1.103762</td>\n",
              "      <td>0.641366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>207</td>\n",
              "      <td>99</td>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>70</td>\n",
              "      <td>1.401826</td>\n",
              "      <td>0.692258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>229</td>\n",
              "      <td>202</td>\n",
              "      <td>112</td>\n",
              "      <td>97</td>\n",
              "      <td>73</td>\n",
              "      <td>1.415898</td>\n",
              "      <td>0.819344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>229</td>\n",
              "      <td>202</td>\n",
              "      <td>112</td>\n",
              "      <td>97</td>\n",
              "      <td>73</td>\n",
              "      <td>1.258176</td>\n",
              "      <td>0.636679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">| 5 4 7 6 |\\n| 2 9 1 3 |\\n| 8 b 10 11 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>398</td>\n",
              "      <td>201</td>\n",
              "      <td>192</td>\n",
              "      <td>201</td>\n",
              "      <td>9</td>\n",
              "      <td>1.678410</td>\n",
              "      <td>1.044744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>398</td>\n",
              "      <td>201</td>\n",
              "      <td>191</td>\n",
              "      <td>201</td>\n",
              "      <td>9</td>\n",
              "      <td>1.667998</td>\n",
              "      <td>1.238966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>430</td>\n",
              "      <td>221</td>\n",
              "      <td>82</td>\n",
              "      <td>82</td>\n",
              "      <td>124</td>\n",
              "      <td>2.000358</td>\n",
              "      <td>1.401198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>433</td>\n",
              "      <td>223</td>\n",
              "      <td>82</td>\n",
              "      <td>84</td>\n",
              "      <td>128</td>\n",
              "      <td>2.083806</td>\n",
              "      <td>1.463518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>450</td>\n",
              "      <td>436</td>\n",
              "      <td>191</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>1.812530</td>\n",
              "      <td>1.511532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>450</td>\n",
              "      <td>436</td>\n",
              "      <td>191</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>2.024626</td>\n",
              "      <td>1.403102</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                            generated  ...  runtime [Sec] \n",
              "problem instance                                   algorithm name                                      ...                \n",
              "| 4 1 3 7 |\\n| 8 5 11 6 |\\n| 12 9 2 b |\\n| 13 1... a star late                                    404  ...        1.235012\n",
              "                                                   a star early                                   404  ...        0.997248\n",
              "                                                   partial expansion a star                       448  ...        1.547791\n",
              "                                                   partial expansion a star early                 459  ...        1.742564\n",
              "                                                   enhanced partial expansion a star              438  ...        1.320234\n",
              "                                                   enhanced partial expansion a star early        438  ...        1.534414\n",
              "| 1 2 3 7 |\\n| 4 6 10 11 |\\n| 5 8 14 9 |\\n| 12 ... a star late                                    506  ...        1.352142\n",
              "                                                   a star early                                   506  ...        1.474506\n",
              "                                                   partial expansion a star                       446  ...        1.837493\n",
              "                                                   partial expansion a star early                 451  ...        1.706326\n",
              "                                                   enhanced partial expansion a star              572  ...        2.118443\n",
              "                                                   enhanced partial expansion a star early        572  ...        1.780700\n",
              "| 4 1 3 11 |\\n| 5 6 b 2 |\\n| 8 13 7 10 |\\n| 12 ... a star late                                    446  ...        1.414305\n",
              "                                                   a star early                                   446  ...        1.219167\n",
              "                                                   partial expansion a star                       567  ...        2.210275\n",
              "                                                   partial expansion a star early                 578  ...        2.068062\n",
              "                                                   enhanced partial expansion a star              481  ...        1.841711\n",
              "                                                   enhanced partial expansion a star early        481  ...        1.753305\n",
              "| 1 6 5 3 |\\n| 4 10 b 7 |\\n| 8 2 11 15 |\\n| 12 ... a star late                                    430  ...        1.213583\n",
              "                                                   a star early                                   430  ...        1.319340\n",
              "                                                   partial expansion a star                       490  ...        1.645945\n",
              "                                                   partial expansion a star early                 495  ...        1.767089\n",
              "                                                   enhanced partial expansion a star              495  ...        1.452711\n",
              "                                                   enhanced partial expansion a star early        495  ...        1.666265\n",
              "| 1 6 7 5 |\\n| 4 9 b 2 |\\n| 8 10 11 3 |\\n| 12 1... a star late                                    486  ...        1.278800\n",
              "                                                   a star early                                   486  ...        1.273982\n",
              "                                                   partial expansion a star                       506  ...        1.906486\n",
              "                                                   partial expansion a star early                 518  ...        1.940967\n",
              "                                                   enhanced partial expansion a star              567  ...        1.976343\n",
              "                                                   enhanced partial expansion a star early        567  ...        1.831522\n",
              "| 1 6 3 7 |\\n| 8 4 2 5 |\\n| 12 9 10 11 |\\n| 13 ... a star late                                    357  ...        1.078644\n",
              "                                                   a star early                                   357  ...        0.934344\n",
              "                                                   partial expansion a star                       377  ...        1.301049\n",
              "                                                   partial expansion a star early                 382  ...        1.492230\n",
              "                                                   enhanced partial expansion a star              396  ...        1.236870\n",
              "                                                   enhanced partial expansion a star early        396  ...        1.231037\n",
              "| 5 6 4 3 |\\n| 8 1 2 7 |\\n| 9 13 10 11 |\\n| 12 ... a star late                                    451  ...        1.417594\n",
              "                                                   a star early                                   451  ...        1.265281\n",
              "                                                   partial expansion a star                       465  ...        1.790079\n",
              "                                                   partial expansion a star early                 468  ...        1.658161\n",
              "                                                   enhanced partial expansion a star              519  ...        1.860606\n",
              "                                                   enhanced partial expansion a star early        519  ...        1.613775\n",
              "| 4 1 2 3 |\\n| 8 6 7 11 |\\n| 12 5 15 14 |\\n| 13... a star late                                    422  ...        1.207378\n",
              "                                                   a star early                                   422  ...        1.307003\n",
              "                                                   partial expansion a star                       609  ...        2.076971\n",
              "                                                   partial expansion a star early                 617  ...        2.397014\n",
              "                                                   enhanced partial expansion a star              459  ...        1.569735\n",
              "                                                   enhanced partial expansion a star early        459  ...        1.669027\n",
              "| 4 b 1 2 |\\n| 8 5 6 3 |\\n| 12 9 15 7 |\\n| 13 1... a star late                                    215  ...        0.544916\n",
              "                                                   a star early                                   215  ...        0.544575\n",
              "                                                   partial expansion a star                       192  ...        0.641366\n",
              "                                                   partial expansion a star early                 207  ...        0.692258\n",
              "                                                   enhanced partial expansion a star              229  ...        0.819344\n",
              "                                                   enhanced partial expansion a star early        229  ...        0.636679\n",
              "| 5 4 7 6 |\\n| 2 9 1 3 |\\n| 8 b 10 11 |\\n| 12 1... a star late                                    398  ...        1.044744\n",
              "                                                   a star early                                   398  ...        1.238966\n",
              "                                                   partial expansion a star                       430  ...        1.401198\n",
              "                                                   partial expansion a star early                 433  ...        1.463518\n",
              "                                                   enhanced partial expansion a star              450  ...        1.511532\n",
              "                                                   enhanced partial expansion a star early        450  ...        1.403102\n",
              "\n",
              "[60 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eh1kj0KrbQbm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "b166f47e-4c4d-4e28-81db-79f71fa275c0"
      },
      "source": [
        "full_benchmark_df = pd.concat([benchmark_for_5_steps_df,benchmark_for_10_steps_df,benchmark_for_15_steps_df])\r\n",
        "full_benchmark_df.to_csv('search_algo_comparison_15_puzzle.csv')\r\n",
        "full_benchmark_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>generated</th>\n",
              "      <th>expanded</th>\n",
              "      <th>max open length</th>\n",
              "      <th>max closed length</th>\n",
              "      <th>regenerations</th>\n",
              "      <th>Memory's peak usage [MB]</th>\n",
              "      <th>runtime [Sec]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>problem instance</th>\n",
              "      <th>algorithm name</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 rowspan=\"5\" valign=\"top\">| 1 5 2 3 |\\n| 4 6 10 7 |\\n| 8 9 11 b |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star late</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.117864</td>\n",
              "      <td>0.015255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.128200</td>\n",
              "      <td>0.016295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.114784</td>\n",
              "      <td>0.011566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.124384</td>\n",
              "      <td>0.010688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.086608</td>\n",
              "      <td>0.008322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">| 5 4 7 6 |\\n| 2 9 1 3 |\\n| 8 b 10 11 |\\n| 12 13 14 15 |\\n\\n \\n</th>\n",
              "      <th>a star early</th>\n",
              "      <td>398</td>\n",
              "      <td>201</td>\n",
              "      <td>191</td>\n",
              "      <td>201</td>\n",
              "      <td>9</td>\n",
              "      <td>1.667998</td>\n",
              "      <td>1.238966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>430</td>\n",
              "      <td>221</td>\n",
              "      <td>82</td>\n",
              "      <td>82</td>\n",
              "      <td>124</td>\n",
              "      <td>2.000358</td>\n",
              "      <td>1.401198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>433</td>\n",
              "      <td>223</td>\n",
              "      <td>82</td>\n",
              "      <td>84</td>\n",
              "      <td>128</td>\n",
              "      <td>2.083806</td>\n",
              "      <td>1.463518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>450</td>\n",
              "      <td>436</td>\n",
              "      <td>191</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>1.812530</td>\n",
              "      <td>1.511532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>450</td>\n",
              "      <td>436</td>\n",
              "      <td>191</td>\n",
              "      <td>200</td>\n",
              "      <td>159</td>\n",
              "      <td>2.024626</td>\n",
              "      <td>1.403102</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>180 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                            generated  ...  runtime [Sec] \n",
              "problem instance                                   algorithm name                                      ...                \n",
              "| 1 5 2 3 |\\n| 4 6 10 7 |\\n| 8 9 11 b |\\n| 12 1... a star late                                     14  ...        0.015255\n",
              "                                                   a star early                                    14  ...        0.016295\n",
              "                                                   partial expansion a star                        14  ...        0.011566\n",
              "                                                   partial expansion a star early                  14  ...        0.010688\n",
              "                                                   enhanced partial expansion a star               11  ...        0.008322\n",
              "...                                                                                               ...  ...             ...\n",
              "| 5 4 7 6 |\\n| 2 9 1 3 |\\n| 8 b 10 11 |\\n| 12 1... a star early                                   398  ...        1.238966\n",
              "                                                   partial expansion a star                       430  ...        1.401198\n",
              "                                                   partial expansion a star early                 433  ...        1.463518\n",
              "                                                   enhanced partial expansion a star              450  ...        1.511532\n",
              "                                                   enhanced partial expansion a star early        450  ...        1.403102\n",
              "\n",
              "[180 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnqucUIDNpwI"
      },
      "source": [
        "Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w30XF_HdHWbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "cbdee243-6568-4bf5-df4d-11cd409e1136"
      },
      "source": [
        "avg_perfomance_for_5_steps = benchmark_for_5_steps_df.reset_index().groupby(by='algorithm name').mean()\r\n",
        "avg_perfomance_for_5_steps['Difficulty Level'] = ['easy- 5 steps']*len(avg_perfomance_for_5_steps)\r\n",
        "avg_perfomance_for_5_steps.set_index(['Difficulty Level',avg_perfomance_for_5_steps.index], inplace=True)\r\n",
        "avg_perfomance_for_5_steps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>generated</th>\n",
              "      <th>expanded</th>\n",
              "      <th>max open length</th>\n",
              "      <th>max closed length</th>\n",
              "      <th>regenerations</th>\n",
              "      <th>Memory's peak usage [MB]</th>\n",
              "      <th>runtime [Sec]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>algorithm name</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 rowspan=\"6\" valign=\"top\">easy- 5 steps</th>\n",
              "      <th>a star early</th>\n",
              "      <td>13.4</td>\n",
              "      <td>5.8</td>\n",
              "      <td>7.6</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.109716</td>\n",
              "      <td>0.011474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star late</th>\n",
              "      <td>13.4</td>\n",
              "      <td>5.8</td>\n",
              "      <td>8.6</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.104673</td>\n",
              "      <td>0.011502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>11.4</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.098630</td>\n",
              "      <td>0.008964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>11.4</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.105327</td>\n",
              "      <td>0.009905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>15.4</td>\n",
              "      <td>6.8</td>\n",
              "      <td>6.2</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.119640</td>\n",
              "      <td>0.013280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>15.7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.123792</td>\n",
              "      <td>0.014641</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                          generated  ...  runtime [Sec] \n",
              "Difficulty Level algorithm name                                      ...                \n",
              "easy- 5 steps    a star early                                  13.4  ...        0.011474\n",
              "                 a star late                                   13.4  ...        0.011502\n",
              "                 enhanced partial expansion a star             11.4  ...        0.008964\n",
              "                 enhanced partial expansion a star early       11.4  ...        0.009905\n",
              "                 partial expansion a star                      15.4  ...        0.013280\n",
              "                 partial expansion a star early                15.7  ...        0.014641\n",
              "\n",
              "[6 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "NAYRLSJwQ576",
        "outputId": "79101982-8f0c-4b44-b967-da12d7434d40"
      },
      "source": [
        "avg_perfomance_for_10_steps = benchmark_for_10_steps_df.reset_index().groupby(by='algorithm name').mean()\r\n",
        "avg_perfomance_for_10_steps['Difficulty Level'] = ['medium- 10 steps']*len(avg_perfomance_for_10_steps)\r\n",
        "avg_perfomance_for_10_steps.set_index(['Difficulty Level',avg_perfomance_for_10_steps.index], inplace=True)\r\n",
        "avg_perfomance_for_10_steps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>generated</th>\n",
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              "      <th>max open length</th>\n",
              "      <th>max closed length</th>\n",
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              "      <th>runtime [Sec]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>algorithm name</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 rowspan=\"6\" valign=\"top\">medium- 10 steps</th>\n",
              "      <th>a star early</th>\n",
              "      <td>56.3</td>\n",
              "      <td>26.1</td>\n",
              "      <td>30.2</td>\n",
              "      <td>26.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.375267</td>\n",
              "      <td>0.089690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star late</th>\n",
              "      <td>56.3</td>\n",
              "      <td>26.1</td>\n",
              "      <td>31.2</td>\n",
              "      <td>26.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.375763</td>\n",
              "      <td>0.089440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>58.5</td>\n",
              "      <td>49.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>22.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>0.443023</td>\n",
              "      <td>0.108354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>58.5</td>\n",
              "      <td>49.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>22.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>0.482278</td>\n",
              "      <td>0.128140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>76.9</td>\n",
              "      <td>36.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>14.1</td>\n",
              "      <td>21.8</td>\n",
              "      <td>0.539738</td>\n",
              "      <td>0.182197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>80.2</td>\n",
              "      <td>38.0</td>\n",
              "      <td>15.1</td>\n",
              "      <td>15.9</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.530587</td>\n",
              "      <td>0.192734</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                          generated  ...  runtime [Sec] \n",
              "Difficulty Level algorithm name                                      ...                \n",
              "medium- 10 steps a star early                                  56.3  ...        0.089690\n",
              "                 a star late                                   56.3  ...        0.089440\n",
              "                 enhanced partial expansion a star             58.5  ...        0.108354\n",
              "                 enhanced partial expansion a star early       58.5  ...        0.128140\n",
              "                 partial expansion a star                      76.9  ...        0.182197\n",
              "                 partial expansion a star early                80.2  ...        0.192734\n",
              "\n",
              "[6 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
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    {
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          "height": 257
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        "id": "y4Ygab4BRsdS",
        "outputId": "baaee5c6-42e7-4e8e-fdf4-d97caf7df038"
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      "source": [
        "avg_perfomance_for_15_steps = benchmark_for_15_steps_df.reset_index().groupby(by='algorithm name').mean()\r\n",
        "avg_perfomance_for_15_steps['Difficulty Level'] = ['hard- 15 steps']*len(avg_perfomance_for_15_steps)\r\n",
        "avg_perfomance_for_15_steps.set_index(['Difficulty Level',avg_perfomance_for_15_steps.index], inplace=True)\r\n",
        "avg_perfomance_for_15_steps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Difficulty Level</th>\n",
              "      <th>algorithm name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">hard- 15 steps</th>\n",
              "      <th>a star early</th>\n",
              "      <td>411.5</td>\n",
              "      <td>203.8</td>\n",
              "      <td>205.0</td>\n",
              "      <td>203.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.756966</td>\n",
              "      <td>1.157441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star late</th>\n",
              "      <td>411.5</td>\n",
              "      <td>203.8</td>\n",
              "      <td>206.0</td>\n",
              "      <td>203.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.390521</td>\n",
              "      <td>1.178712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>460.6</td>\n",
              "      <td>425.4</td>\n",
              "      <td>206.6</td>\n",
              "      <td>200.5</td>\n",
              "      <td>164.2</td>\n",
              "      <td>2.087463</td>\n",
              "      <td>1.570753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>460.6</td>\n",
              "      <td>425.4</td>\n",
              "      <td>206.6</td>\n",
              "      <td>200.5</td>\n",
              "      <td>164.2</td>\n",
              "      <td>2.038992</td>\n",
              "      <td>1.511983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>453.0</td>\n",
              "      <td>226.8</td>\n",
              "      <td>72.5</td>\n",
              "      <td>95.9</td>\n",
              "      <td>139.2</td>\n",
              "      <td>2.079140</td>\n",
              "      <td>1.635865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>460.8</td>\n",
              "      <td>231.7</td>\n",
              "      <td>71.8</td>\n",
              "      <td>100.8</td>\n",
              "      <td>148.5</td>\n",
              "      <td>2.069458</td>\n",
              "      <td>1.692819</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                          generated  ...  runtime [Sec] \n",
              "Difficulty Level algorithm name                                      ...                \n",
              "hard- 15 steps   a star early                                 411.5  ...        1.157441\n",
              "                 a star late                                  411.5  ...        1.178712\n",
              "                 enhanced partial expansion a star            460.6  ...        1.570753\n",
              "                 enhanced partial expansion a star early      460.6  ...        1.511983\n",
              "                 partial expansion a star                     453.0  ...        1.635865\n",
              "                 partial expansion a star early               460.8  ...        1.692819\n",
              "\n",
              "[6 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 615
        },
        "id": "bD5hjY63Rsg7",
        "outputId": "c59eacb2-a3c3-4da8-8297-f18447b349cd"
      },
      "source": [
        "avg_perfomance_by_difficulcy_df = pd.concat([avg_perfomance_for_5_steps,avg_perfomance_for_10_steps,avg_perfomance_for_15_steps])\r\n",
        "avg_perfomance_by_difficulcy_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">easy- 5 steps</th>\n",
              "      <th>a star early</th>\n",
              "      <td>13.4</td>\n",
              "      <td>5.8</td>\n",
              "      <td>7.6</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.109716</td>\n",
              "      <td>0.011474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star late</th>\n",
              "      <td>13.4</td>\n",
              "      <td>5.8</td>\n",
              "      <td>8.6</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.104673</td>\n",
              "      <td>0.011502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>11.4</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.098630</td>\n",
              "      <td>0.008964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>11.4</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.105327</td>\n",
              "      <td>0.009905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>15.4</td>\n",
              "      <td>6.8</td>\n",
              "      <td>6.2</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.119640</td>\n",
              "      <td>0.013280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>15.7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.123792</td>\n",
              "      <td>0.014641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">medium- 10 steps</th>\n",
              "      <th>a star early</th>\n",
              "      <td>56.3</td>\n",
              "      <td>26.1</td>\n",
              "      <td>30.2</td>\n",
              "      <td>26.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.375267</td>\n",
              "      <td>0.089690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star late</th>\n",
              "      <td>56.3</td>\n",
              "      <td>26.1</td>\n",
              "      <td>31.2</td>\n",
              "      <td>26.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.375763</td>\n",
              "      <td>0.089440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>58.5</td>\n",
              "      <td>49.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>22.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>0.443023</td>\n",
              "      <td>0.108354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>58.5</td>\n",
              "      <td>49.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>22.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>0.482278</td>\n",
              "      <td>0.128140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>76.9</td>\n",
              "      <td>36.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>14.1</td>\n",
              "      <td>21.8</td>\n",
              "      <td>0.539738</td>\n",
              "      <td>0.182197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>80.2</td>\n",
              "      <td>38.0</td>\n",
              "      <td>15.1</td>\n",
              "      <td>15.9</td>\n",
              "      <td>25.7</td>\n",
              "      <td>0.530587</td>\n",
              "      <td>0.192734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">hard- 15 steps</th>\n",
              "      <th>a star early</th>\n",
              "      <td>411.5</td>\n",
              "      <td>203.8</td>\n",
              "      <td>205.0</td>\n",
              "      <td>203.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.756966</td>\n",
              "      <td>1.157441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a star late</th>\n",
              "      <td>411.5</td>\n",
              "      <td>203.8</td>\n",
              "      <td>206.0</td>\n",
              "      <td>203.8</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.390521</td>\n",
              "      <td>1.178712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star</th>\n",
              "      <td>460.6</td>\n",
              "      <td>425.4</td>\n",
              "      <td>206.6</td>\n",
              "      <td>200.5</td>\n",
              "      <td>164.2</td>\n",
              "      <td>2.087463</td>\n",
              "      <td>1.570753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>enhanced partial expansion a star early</th>\n",
              "      <td>460.6</td>\n",
              "      <td>425.4</td>\n",
              "      <td>206.6</td>\n",
              "      <td>200.5</td>\n",
              "      <td>164.2</td>\n",
              "      <td>2.038992</td>\n",
              "      <td>1.511983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star</th>\n",
              "      <td>453.0</td>\n",
              "      <td>226.8</td>\n",
              "      <td>72.5</td>\n",
              "      <td>95.9</td>\n",
              "      <td>139.2</td>\n",
              "      <td>2.079140</td>\n",
              "      <td>1.635865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial expansion a star early</th>\n",
              "      <td>460.8</td>\n",
              "      <td>231.7</td>\n",
              "      <td>71.8</td>\n",
              "      <td>100.8</td>\n",
              "      <td>148.5</td>\n",
              "      <td>2.069458</td>\n",
              "      <td>1.692819</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                          generated  ...  runtime [Sec] \n",
              "Difficulty Level algorithm name                                      ...                \n",
              "easy- 5 steps    a star early                                  13.4  ...        0.011474\n",
              "                 a star late                                   13.4  ...        0.011502\n",
              "                 enhanced partial expansion a star             11.4  ...        0.008964\n",
              "                 enhanced partial expansion a star early       11.4  ...        0.009905\n",
              "                 partial expansion a star                      15.4  ...        0.013280\n",
              "                 partial expansion a star early                15.7  ...        0.014641\n",
              "medium- 10 steps a star early                                  56.3  ...        0.089690\n",
              "                 a star late                                   56.3  ...        0.089440\n",
              "                 enhanced partial expansion a star             58.5  ...        0.108354\n",
              "                 enhanced partial expansion a star early       58.5  ...        0.128140\n",
              "                 partial expansion a star                      76.9  ...        0.182197\n",
              "                 partial expansion a star early                80.2  ...        0.192734\n",
              "hard- 15 steps   a star early                                 411.5  ...        1.157441\n",
              "                 a star late                                  411.5  ...        1.178712\n",
              "                 enhanced partial expansion a star            460.6  ...        1.570753\n",
              "                 enhanced partial expansion a star early      460.6  ...        1.511983\n",
              "                 partial expansion a star                     453.0  ...        1.635865\n",
              "                 partial expansion a star early               460.8  ...        1.692819\n",
              "\n",
              "[18 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Yd8I0j9Tg4Z2",
        "outputId": "aa6b8378-6fc7-4499-9513-c73ac2f30443"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "algorithmns = set([algo for (difficulcy,algo) in avg_perfomance_by_difficulcy_df.index])\r\n",
        "difficulcies = ['easy- 5 steps','medium- 10 steps','hard- 15 steps']\r\n",
        "\r\n",
        "for parameter in avg_perfomance_by_difficulcy_df.columns:\r\n",
        "  plt.figure(figsize=(10,10))\r\n",
        "  for algorithmn in algorithmns:\r\n",
        "    print(algorithmn)\r\n",
        "    algo_perfomance_by_specific_param = list()\r\n",
        "    for difficulcy in difficulcies:\r\n",
        "      algo_perfomance_by_specific_param.append(avg_perfomance_by_difficulcy_df.loc[difficulcy].loc[algorithmn][parameter])\r\n",
        "    plt.plot([5,10,15],algo_perfomance_by_specific_param,marker='o')\r\n",
        "  \r\n",
        "  plt.legend(algorithmns)\r\n",
        "  plt.title(\"Comparing average #{} of algorithms by level\".format(parameter))\r\n",
        "  plt.ylabel('perfomance')\r\n",
        "  plt.show()\r\n",
        "  plt.close()\r\n",
        "\r\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OnjxJq1at2LFjB4GBgcD/7hGanp7OE088wS+//EJeXh6tW7dm9+7dhfoixK0q+F4VQojKQCm1V2vtV9K6++46ZDejpGSstOXlFRkZydq1awE4deoUCQkJhZKY2rVr4+DgwN/+9jdCQ0MJDQ0tsZ5Dhw4xc+ZMUlJSSE9Pp2fPnuZ1AwcOLJaMAfzwww8cOHCANWvWAJCamkpCQgJNmzZl+vTpxMbGYmNjw5kzZzh37hwAzZs3NydjBbVo0QJnZ2d+/fVXzp07h7e3tyRjQgghRAW4rxOyskaygt7awpmUzGLLmzhVY9WYDrfUZkxMDJs3b2bnzp04OjoSHBxc7HpLVapUYffu3fz444+sWbOGhQsXsmXLlmJ1hYeHEx0djcFgICoqipiYGPO66tWrl9i+1poFCxYUSt4AoqKiuHDhAnv37sXOzo4WLVqY47JUF8CoUaOIiorijz/+YOTIkeXdDUIIIYS4CZV6Dtnknm2pZld4lKmanS2Te7a95TpTU1OpU6cOjo6OxMfHs2vXrmJl0tPTSU1N5bHHHmP+/Pns378fgJo1a5KWlmYul5aWRqNGjcjOzmbFihXlar9nz5589NFHZGdnA3Ds2DGuXbtGamoqDRo0wM7Ojq1bt3Ly5Mly1ff444/z3XffsWfPnmJJnhBCCCHujPt6hKwsYd5NAHj3+6OcTcmksVM1Jvdsa15+K3r16sWiRYtwcXGhbdu2JZ4KTEtLo1+/fmRlZaG15h//+AcAQ4YMYfTo0URGRrJmzRpee+012rdvT/369Wnfvn2hZM2SUaNGkZSUhI+PD1pr6tevT3R0NEOHDqVPnz54eHjg5+dHu3btytWfqlWr0rVrV5ycnEo8RSqEEEKI23dfT+oXty8vLw8fHx9Wr15N69atrR2OuI/Ie1UIUdmUNqm/Up+yFKU7fPgwDz/8MCEhIZKMCSGEEBWoUp+yFKVzdXXlt99+s3YYQgghxH1PRsiEEEIIIaxMRsiEEEIIUWltiYpm/6YvyMu5ik2VWhj+Mohu4WF/ehySkAkhhBCiUtoSFc2v30YBxvtC5+VcNT3nT0/K5JSlEEIIISql/Zu+ID8Z+58c0/I/lyRkd6Ho6GgOHz5sfj5r1iw2b95c6jbh4eHm2yXdzUaNGlWob9bwxhtvWLV9IYQQd4e8nJLvd21peUWShOzAFzDfHSKcjL8P/PlZcUE5OTnFErI5c+bQvXt3K0Z15yxduhRXV1erxnArCVlubm4FRCKEEMKabKrUuqnlFalyJ2QHvoANz0HqKUAbf2947raSsqSkJNq1a8fQoUNxcXFhwIABZGRkAMbEyt/fH3d3d55++mnyL8obHBzMxIkT8fPz4+2332b9+vVMnjwZLy8vEhMTC41+WarDksTERHr16oWvry+dO3cmPj6enJwc/P39zffGnDZtGjNmzACMNxSfMmUKHh4eBAQEcPz4cQA2bNhA+/bt8fb2pnv37uYbk0dERDBy5EiCg4Np1aoVkZGRAFy7do3evXtjMBhwd3dn1apV5r7mX8x35cqVeHh44O7uzssvv2yOuUaNGsyYMQODwUBgYKC5rYJ2795Nhw4d8Pb2pmPHjhw9erRYmeTkZLp06YKXlxfu7u5s27aNqVOnkpmZiZeXF0OHDgUgLCwMX19f3NzcWLx4caE4XnzxRQwGAzt37ix1PwshhLj3OD3QpoSlVTD8ZdCfHsv9Pan/26nwx0HL60/vgdzrhZdlZ8K6Z2Hv8pK3ecADHn2r1GaPHj3Kxx9/TFBQECNHjuTDDz/kpZde4tlnn2XWrFkADB8+nK+//po+ffoAcOPGDXOikpCQQGhoKAMGDChWd2l1lOTpp59m0aJFtG7dmp9//pnx48ezZcsWoqKiGDBgAAsWLOC7777j559/Nm9Tu3ZtDh48yCeffMLEiRP5+uuv6dSpE7t27UIpxdKlS3nnnXf4+9//DkB8fDxbt24lLS2Ntm3bMm7cOL777jsaN27Mxo0bAeM9Pgs6e/YsL7/8Mnv37qVOnTr06NGD6OhowsLCuHbtGoGBgcydO5cpU6awZMkSZs6cWWj7du3asW3bNqpUqcLmzZuZPn06X375ZaEyn3/+OT179mTGjBnk5uaSkZFB586dWbhwIfv27TOXW7ZsGXXr1iUzMxN/f3/69++Ps7Mz165do3379uZ+CiGEuH9cvZjClbP/xcauLugc+ZalVRVNxspaXk7NmjUjKCgIgGHDhhEZGclLL73E1q1beeedd8jIyODy5cu4ubmZk6nBgweXq+7S6igqPT2dHTt2MHDgQPOy69eNfXNzc2P48OGEhoayc+dOqlatai7z5JNPmn+/8MILAJw+fZrBgweTnJzMjRs3aNmypbl87969sbe3x97engYNGnDu3Dk8PDx48cUXefnllwkNDaVz586FYtuzZw/BwcHUr18fgKFDhxIbG0tYWBhVq1YlNDQUAF9fXzZt2lSsb6mpqYwYMYKEhASUUuabqRfk7+/PyJEjyc7OJiwsDC8vrxL3U2RkJGvXrgXg1KlTJCQk4OzsjK2tLf379y9xGyGEEPe2De8vRedl8thzr9Cug6e1w7nPE7IyRrKY7246XVlE7Wbw14233KxSqtjzrKwsxo8fT1xcHM2aNSMiIoKsrCxzmerVq5dZb1l1FJWXl4eTk1Oh0aCCDh48iJOTE+fPn7cYf/7jCRMmMGnSJPr27UtMTAwRERHmMvb29ubHtra25OTk0KZNG3755Re++eYbZs6cSUhIiHlkryx2dnbmdvPrK+qVV16ha9eurF27lqSkJIKDg4uV6dKlC7GxsWzcuJHw8HAmTZrEU089VahMTEwMmzdvZufOnTg6OhIcHGzepw4ODnJDdSGEuA8l7U/gj2OxODcLuCuSMajsc8hCZoFdtcLL7KoZl9+G33//3Tzn6PPPP6dTp07mD/l69eqRnp5e6jcia9asSVpaWrHlN1MHQK1atWjZsiWrV68GQGvN/v37Afjqq6+4fPkysbGxTJgwgZSUFPN2+fO9Vq1aRYcOHQDjiFSTJk0AWL7cwuncAs6ePYujoyPDhg1j8uTJ/PLLL4XWBwQE8NNPP3Hx4kVyc3NZuXIljzzySJn15isYT1RUVIllTp48ScOGDRk9ejSjRo0yx2BnZ2ceUUtNTaVOnTo4OjoSHx/Prl27yh2DEEKIe9N3ixaBqkLoxLHWDsWscidknoOgT6RxRAxl/N0n0rj8NrRt25YPPvgAFxcXrly5wrhx43BycmL06NG4u7vTs2dP/P39LW4/ZMgQ3n33Xby9vUlMTDQvv5k68q1YsYKPP/4Yg8GAm5sb69at4+LFi0ydOpWlS5fSpk0bnn32WZ5//nnzNleuXMHT05P333+f+fPnA8bJ+wMHDsTX15d69eqV2e7BgwcJCAjAy8uLV199tdgcsEaNGvHWW2/RtWtXDAYDvr6+9OvXr8x6802ZMoVp06bh7e1d4ggaGEe/DAYD3t7erFq1ytzHp59+Gk9PT4YOHUqvXr3IycnBxcWFqVOnEhgYWO4YhBBC3Ht2r4vh2uWjPOzfm3pNG1g7HDNV1rf07mZ+fn46fyJ8viNHjuDi4mKliIzfsgwNDeXQoUNWi+F2tGjRgri4uHIlXULcDmu/V4UQlc+N6zf4aNTTaJ3L+KVLqepgX/ZGd5BSaq/W2q+kdZV7hEwIIYQQlcb3i1aSc+Mi/v2G/+nJWFnu70n9VtCiRYt7dnQMjCN8QgghxP3mcvJFju1cj2Pth+jQ/+672LqMkAkhhBDivvf1+4tB3+AvY8ZiY3P3pT93X0RCCCGEEHfQ8bgjXDixkwYtO/Kw7905d1USMiGEEELct/Ly8vhh8UcoZU+fiU9bOxyLJCETQgghxH1rx5pNZKb+RpuOfXF6wNna4VgkCdldJDo6msOHD99WHTExMebbDlmyb98+vvnmm9tqRwghhLjbXc+4Ttz6z6hStR49xgyxdjilqvQJ2cbfNtJjTQ88l3vSY00PNv5267dMul23kpBZuihqaSQhE0IIURl8++Gn5GZfocOAcKraVy17Ayuq1AnZxt82ErEjguRryWg0ydeSidgRcdtJWVhYGL6+vri5ubF48eISy0ydOhVXV1c8PT156aWX2LFjB+vXr2fy5Ml4eXmRmJjIkiVL8Pf3x2Aw0L9/fzIyMgAIDw9n7NixtG/fnilTpliMY/fu3XTo0AFvb286duzI0aNHuXHjBrNmzWLVqlV4eXmxatUqrl27xsiRIwkICMDb25t169bdVv+FEEIIa7t4+jyJcd9QvW5bAvoFWzucMt3X1yF7e/fbxF+Ot7j+wIUD3Mi7UWhZVm4Ws/4zizXHSr5PZLu67Xg54OVS2122bBl169YlMzMTf39/+vfvj7Pz/85bX7p0ibVr1xIfH49SipSUFJycnOjbty+hoaEMGDAA+N+tkgBmzpzJxx9/zIQJEwA4ffo0O3bsKPXm1+3atWPbtm1UqVKFzZs3M336dL788kvmzJlDXFwcCxcuBGD69Ol069aNZcuWkZKSQkBAAN27dy/XDc+FEEKIu9HX7y0CncOj48dZO5Ryua8TsrIUTcbKWl5ekZGRrF27FoBTp06RkJBQKCGrXbs2Dg4O/O1vfyM0NNTinK9Dhw4xc+ZMUlJSSE9Pp2fPnuZ1AwcOLDUZA+ONs0eMGEFCQgJKKfMNtYv64YcfWL9+PfPmzQOMNzH//fff5bY2Qggh7knxOw5w6dRuGrUJprnHw9YOp1zu64SsrJGsHmt6kHwtudjyRtUb8a9e/7qlNmNiYti8eTM7d+7E0dGR4OBgsrKyCpWpUqUKu3fv5scff2TNmjUsXLiQLVu2FKsrPDyc6OhoDAYDUVFRxMTEmNeVZ/TqlVdeoWvXrqxdu5akpCSCg4NLLKe15ssvv6Rt27Y31VchhBDibpOXl8fmjxehbBwJnTja2uGUW6WeQ/a8z/M42DoUWuZg68DzPs/fcp2pqanUqVMHR0dH4uPj2bVrV7Ey6enppKam8thjjzF//nz2798PQM2aNUlLSzOXS0tLo1GjRmRnZ7NixYpbiqVJkyYAREVFmZcXbadnz54sWLCA/BvN//rrrzfdlhBCCHE3iP38G66n/47rI09Qy7m2tcMpt0qdkPVu1ZuIjhE0qt4IhaJR9UZEdIygd6vet1xnr169yMnJwcXFhalTpxIYGFisTFpaGqGhoXh6etKpUyf+8Y9/ADBkyBDeffddvL29SUxM5LXXXqN9+/YEBQXRrl27m45lypQpTJs2DW9v70LfxuzatSuHDx82T+p/5ZVXyM7OxtPTEzc3N1555ZVb7r8QQghhLVnpGfz67efYOTSk+6gB1g7npqj8UZF7kZ+fn46Liyu07MiRIzL3SYh7gLxXhRB32po3P+Lkvo10Gzkd754drR1OMUqpvVprv5LWVeoRMiGEEELcH/747Qwn931PrQbud2UyVhZJyIQQQghxz9sYuQjQPPbsvXGZi6IkIRNCCCHEPe1QTBwpyb/S1LUbTdo2t3Y4t0QSMiGEEELcs/Jyctm6fAnKtga9nx9p7XBumSRkQgghhLhnbfkkmhsZZ/DsPpAaTjWtHc4tk4RMCCGEEPek9JQ0DmxeTVXHJnR7Ksza4dwWScjuQtHR0Rw+fNj8fNasWWzevLnUbcLDw1mzpuT7b95NRo0aVahv1vDGG29YtX0w3tHB0i2zhBBClM/GyH+hc9PpOmI0NlVKv53g3a7SJ2SpGzaQ0C2EIy6uJHQLIXXDBqvGk5OTUywhmzNnDt27d7diVHfO0qVLcXV1tWoMt5KQ5ebm3rH2C16kVwghxK05HZ/E6f/+iFMjb9yDS7y01z2lUidkqRs2kPzKLHLOngWtyTl7luRXZt1WUpaUlES7du0YOnQoLi4uDBgwgIyMDMCYWPn7++Pu7s7TTz9tvlVRcHAwEydOxM/Pj7fffpv169czefJkvLy8SExMLDT6ZakOSxITE+nVqxe+vr507tyZ+Ph4cnJy8Pf3N98bc9q0acyYMQOAFi1aMGXKFDw8PAgICOD48eMAbNiwgfbt2+Pt7U337t05d+4cABEREYwcOZLg4GBatWpFZGQkANeuXaN3794YDAbc3d1ZtVJw0QEAACAASURBVGqVua/5F/NduXIlHh4euLu78/LL/7vvaI0aNZgxYwYGg4HAwEBzWwXt3r2bDh064O3tTceOHTl69GixMsnJyXTp0gUvLy/c3d3Ztm0bU6dOJTMzEy8vL4YOHQpAWFgYvr6+uLm5sXjx4kJxvPjiixgMBnbu3Fnmfi1rPw0fPpygoCCGDx9uricvL4/WrVtz4cIF8/OHH37Y/FwIIUTJvv1gEaDo/dxYa4dyZ2it79kfX19fXdThw4fNj5PnztVJw4Zb/Dni4akPt21X7OeIh6fFbZLnzi3WZkEnTpzQgN6+fbvWWuu//vWv+t1339Vaa33p0iVzuWHDhun169drrbV+5JFH9Lhx48zrRowYoVevXl3ic0t1FN0mX7du3fSxY8e01lrv2rVLd+3aVWut9aFDh3S7du30pk2btJeXl75+/brWWuvmzZvr119/XWut9fLly3Xv3r211lpfvnxZ5+Xlaa21XrJkiZ40aZLWWuvZs2frDh066KysLH3hwgVdt25dfePGDb1mzRo9atQocxwpKSnmvu7Zs0efOXNGN2vWTJ8/f15nZ2frrl276rVr12qttQbM/Zo8ebJ+7bXXivUrNTVVZ2dna6213rRpk37iiSeKlZk3b565Lzk5Ofrq1ataa62rV69eqFz+Ps3IyNBubm764sWL5jhWrVpVrN7S9mtp+8nHx0dnZGRorbXeunWred9GRETo+fPna621/v7770vsy/2o4HtVCCFuxi/f/UfPG9Rbr37jQ2uHclOAOG0hp6li3XTQuvSNGze1vLyaNWtGUFAQAMOGDSMyMpKXXnqJrVu38s4775CRkcHly5dxc3OjT58+AAwePLhcdZdWR1Hp6ens2LGDgQMHmpddv34dADc3N4YPH05oaCg7d+6katWq5jJPPvmk+fcLL7wAwOnTpxk8eDDJycncuHGDli1bmsv37t0be3t77O3tadCgAefOncPDw4MXX3yRl19+mdDQUDp37lwotj179hAcHEz9+vUBGDp0KLGxsYSFhVG1alXz/CpfX182bdpUrG+pqamMGDGChIQElFJkZ2cXK+Pv78/IkSPJzs4mLCwMLy+vEvdTZGQka9euBeDUqVMkJCTg7OyMra0t/fv3v6n9Wtp+6tu3L9WqVStW38iRI+nXrx8TJ05k2bJl/PWvfy0xTiGEEMZpH9s+/xibKrUInTDC2uHcMfd1QvbA9Omlrk/oFmI8XVlElcaNaf7pJ7fcrlKq2POsrCzGjx9PXFwczZo1IyIigqysLHOZ6tWrl1lvWXUUlZeXh5OTE/v27Stx/cGDB3FycuL8+fMW489/PGHCBCZNmkTfvn2JiYkhIiLCXMbe3t782NbWlpycHNq0acMvv/zCN998w8yZMwkJCWHWrFll9hHAzs7O3G5+fUW98sordO3albVr15KUlERwcHCxMl26dCE2NpaNGzcSHh7OpEmTeOqppwqViYmJYfPmzezcuRNHR0eCg4PN+9TBwQFb2+KTREvbr6XtJ0vHuFmzZjRs2JAtW7awe/duVqxYYXHfCCFEZbd56Rqys87h22csDjUcrR3OHVOp55A1eGEiysGh0DLl4ECDFybeVr2///67ec7R559/TqdOncwf8vXq1SM9Pb3Ub0TWrFmTtLS0Ystvpg6AWrVq0bJlS1avXg0YT0/v378fgK+++orLly8TGxvLhAkTSElJMW+XP99r1apVdOjQATCOSDVp0gSA5cuXl7kPzp49i6OjI8OGDWPy5Mn88ssvhdYHBATw008/cfHiRXJzc1m5ciWPPPJImfXmKxhPVFRUiWVOnjxJw4YNGT16NKNGjTLHYGdnZx5RS01NpU6dOjg6OhIfH8+uXbvKbLu0/Xqz+ynfqFGjGDZsGAMHDiwxCRRCCAFXL6Vy+KevsK/RnC7/95i1w7mjKnVCVrtPHxq9NocqjRuDUlRp3JhGr82htoVTgOXVtm1bPvjgA1xcXLhy5Qrjxo3DycmJ0aNH4+7uTs+ePfH397e4/ZAhQ3j33Xfx9vYmMTHRvPxm6si3YsUKPv74YwwGA25ubqxbt46LFy8ydepUli5dSps2bXj22Wd5/vnnzdtcuXIFT09P3n//febPnw8YJ6UPHDgQX19f6tWrV2a7Bw8eJCAgAC8vL1599VVmzpxZaH2jRo1466236Nq1KwaDAV9fX/r161dmvfmmTJnCtGnT8Pb2tvitxZiYGAwGA97e3qxatcrcx6effhpPT0+GDh1Kr169yMnJwcXFhalTpxIYGFiu9kvar3Dz+ylf3759SU9Pl9OVQghRiq/fW4LOy6D738ZgY3N/pTBKl/EtvbuZn5+fzv/GXr4jR47g4uJipYiM37IMDQ3l0KFDVovhdrRo0YK4uLibSibE7YuLi+OFF15g27Zt1g7lT2Pt96oQ4t5y8uBx1rw+CedmfoTPK98UmLuNUmqv1rrEa3Tc13PIhLgXvPXWW3z00Ucyd0wIIUrx7YcfgapC6MT75DIXRVTYeJ9SaplS6rxS6lCBZRFKqTNKqX2mn8cKrJumlDqulDqqlOpZUXFVtBYtWtyzo2NgHOGT0bE/19SpUzl58iSdOnWydihCCHFX2r0uhmuXj/KQ32PUa9rA2uFUiIo8ARsF9Cph+XyttZfp5xsApZQrMARwM23zoVJKZjYLIYQQldyN6zfYuSYKW7s6PDp+eNkb3KMqLCHTWscCl8tZvB/wb631da31CeA4EFBRsQkhhBDi3vDDP/9Nzo2L+PUdhr2jfdkb3KOs8RWFZ5VSB0ynNOuYljUBThUoc9q0rBil1NNKqTilVJzcXkYIIYS4f6X8cYljO9ZTrfZDdBzwF2uHU6H+7ITsI+AhwAtIBv5+sxVorRdrrf201n75V3kXQgghxP1nw3uL0fo6PZ4eW2GXuUjdsIGEbiEccXEloVvIbd3P+nb8qQmZ1vqc1jpXa50HLOF/pyXPAM0KFG1qWlYpFLx5uDVFREQwb968O15vSkoKH374ofn52bNnGTBgQKnbJCUl4e7ufsdjudPi4uJ47rnnrBpDTEwMO3bssGoMUPjG8UIIcbuO7z3C+RM7qN+yAw/7VcwlclI3bCD5lVnGu/ZoTc7ZsyS/MssqSdmfmpAppRoVePo4kP91xPXAEKWUvVKqJdAa2P1nxHTs5z9YPv0/fDB2C8un/4djP//xZzRbaeTk5BRLyBo3bnxXJKB3gp+fH5GRkVaN4VYSMksX071Vubm5d7Q+IUTllpeXx6Z/LkIpe/pOHFNh7Zyf/x66yC0IdVYW5+e/V2FtWlKRl71YCewE2iqlTiul/ga8o5Q6qJQ6AHQFXgDQWv8X+AI4DHwHPKO1rvC/8Md+/oOtK+JJv2y8MXT65etsXRF/20nZZ599Zr5K/ZgxY8wfVjVq1GDGjBkYDAYCAwM5d+6ceZvY2Fg6duxIq1atzMlKeno6ISEh+Pj44OHhYb4afFJSEi4uLowePRo3Nzd69OhBZmYmAMePH6d79+4YDAZ8fHzMV/p/99138ff3x9PTk9mzZ5vbnTt3Lm3atKFTp04cPXq0xP6Eh4czduxY/Pz8aNOmDV9//bU5js6dO+Pj44OPj485KYiJiaFz58707dsXV1dXpk6dSmJiIl5eXkyePLnQ6JelOkpTUl/Wrl1LSEgIWmuSk5Np06YNf/zxB1FRUfTr14/g4GBat27Nq6++aq4nLCwMX19f3NzcWLx4sXm5peO0evVq3N3dMRgMdOnSxdzX/BuhX758mbCwMDw9PQkMDOTAgQOAceRx5MiRBAcH06pVK4sJ3Lhx4/Dz88PNza3QMSooMjISV1dXPD09GTJkCElJSSxatIj58+fj5eXFtm3b2LBhA+3bt8fb25vu3bub44+IiGD48OEEBQUxfHjxbypZeo2Utp9efPFFDAaD+VZhAMuWLWPixP/dfmzJkiXmm9QLIUR57PxyMxmpibTu0AenB5wrrJ2c5OSbWl6htNb37I+vr68u6vDhw+bHsauO6q/m7bX489EzW/TCMT8W+/nomS0Wt4lddbRYm0XbDw0N1Tdu3NBaaz1u3Di9fPlyrY23RNDr16/XWms9efJk/dprr2mttR4xYoQeMGCAzs3N1f/973/1Qw89pLXWOjs7W6empmqttb5w4YJ+6KGHdF5enj5x4oS2tbXVv/76q9Za64EDB+pPP/1Ua611QECA/uqrr7TWWmdmZupr167p77//Xo8ePVrn5eXp3Nxc3bt3b/3TTz/puLg47e7urq9du6ZTU1P1Qw89pN99991ifRoxYoTu2bOnzs3N1ceOHdNNmjQx152Zmam11vrYsWM6/3hs3bpVOzo66t9++01rrfWJEye0m5ubub6Czy3VUXSbfJb6orXWQ4cO1QsWLNC9e/fWn3/+udZa63/961/6gQce0BcvXtQZGRnazc1N79mzR2ut9aVLl7TW2rz84sWLpR4nd3d3ffr0aa211leuXDH3tXfv3lprrZ999lkdERGhtdb6xx9/1AaDQWut9ezZs3WHDh10VlaWvnDhgq5bt6759VFQfjw5OTn6kUce0fv37y9WplGjRjorK6tQDLNnzy503C5fvqzz8vK01lovWbJET5o0yVzOx8dHZ2Rk3NR+LW0/rVq1ylzHI488ovfs2aPT0tJ0q1atzH3s0KGDPnDgQLE2C75XhRAiX9a1LD1/6DD93rAR+nrW9Qpt61hwV324bbtiP8e6dquQ9oA4bSGnqdRX6s/NKfm2UZaWl8ePP/7I3r17zfeZzMzMpEED40Xsqlatah5N8fX1ZdOmTebtwsLCsLGxwdXV1TyiobVm+vTpxMbGYmNjw5kzZ8zrWrZsiZeXl7mupKQk0tLSOHPmDI8//jgADqYbp//www/88MMPeHt7A8aRt4SEBNLS0nj88cdxdHQEjPdTtGTQoEHY2NjQunVrWrVqRXx8PC1btuTZZ59l37592NracuzYMXP5gIAAWrZsWeb+ys7OtlhHSSz1pUuXLixYsAB3d3cCAwN58sknzdv85S9/wdnZ+B/WE088wfbt282nGteuXQvAqVOnSEhIwNnZ2eJxCgoKIjw8nEGDBvHEE08Ui2379u18+eWXAHTr1o1Lly5x9epVAHr37o29vT329vY0aNCAc+fO0bRp00Lbf/HFFyxevJicnBySk5M5fPgwnp6ehcrk34MzLCyMsLCwEvfR6dOnGTx4MMnJydy4caPQcejbty/VqlW7qf1qaT/Z2trSv3//YnXVqFGDbt268fXXX+Pi4kJ2djYeHh4lxiqEEEV999Fn5GZfofP/vURV+6oV2pb9ww8VGw1TDg40eGGihS0qzn2dkHUe1KbU9cun/8d8urKgGnXtefxFn1tqU2vNiBEjePPNN4uts7OzQykFgK2tbaF5PPb29oXqAOMNrC9cuMDevXuxs7OjRYsWZJnOdRcsb2traz5laSmmadOmMWZM4fPw771X/nPk+XEXfD5//nwaNmzI/v37ycvLMyeAANWrVy9XvaXVURJLfQFjImJjY8O5c+fIy8szfyOnpNhjYmLYvHkzO3fuxNHRkeDgYPO+tXScFi1axM8//8zGjRvx9fVl79695eojFD9eRedwnThxgnnz5rFnzx7q1KlDeHi4OZ6CNm7cSGxsLBs2bGDu3LkcPHiwWJkJEyYwadIk+vbtS0xMDBEREeZ1lo6Lpf1a2n5ycHDA1rbk6zePGjWKN954g3bt2skN04UQ5Xbx9HmO79lI9bptCegXXKFtXdv1M9e2bcexQwdunDxJTnIyVRo1osELE6ndp0+Ftl2S++tW6TepQ7+HqFK18C6oUtWGDv0euuU6Q0JCWLNmDefPnweM84pOnjx5S3WlpqbSoEED7Ozs2Lp1a5n11KxZk6ZNmxIdHQ3A9evXycjIoGfPnixbtoz09HQAzpw5w/nz5+nSpQvR0dFkZmaSlpbGhlK+VbJ69Wry8vJITEzkt99+o23btqSmptKoUSNsbGz49NNPLU7srlmzJmlpaRb7WJ468lnqS05ODiNHjmTlypW4uLjwj3/8w7zNpk2buHz5MpmZmURHRxMUFERqaip16tTB0dGR+Ph4du3aVWq7AImJibRv3545c+ZQv359Tp06VWh9586dzfejjImJoV69etSqVavMegGuXr1K9erVqV27NufOnePbb78tViYvL49Tp07RtWtX3n77bVJTU0lPTy+2f1NTU2nSxHgZv+XLl5erfUv79Vb2E0D79u05deoUn3/+eaHRSiGEKM3X7y0CncOj48dVaDu56emcnT6Nqs2b0+yDhbTe8iMuRw7TesuPVknG4D4fIStLm/YPALBzXSLpl69To649Hfo9ZF5+K1xdXXn99dfp0aMHeXl52NnZ8cEHH9C8efObrmvo0KH06dMHDw8P/Pz8aNeuXZnbfPrpp4wZM4ZZs2ZhZ2fH6tWr6dGjB0eOHKFDhw6A8ZTSZ599ho+PD4MHD8ZgMNCgQQPzadaSPPjggwQEBHD16lUWLVqEg4MD48ePp3///nzyySf06tXL4uiLs7MzQUFBuLu78+ijj/LMM8+Y15W3jnyW+rJo0SI6d+5Mp06dMBgM+Pv707t3b8B4+rR///6cPn2aYcOG4efnh4eHB4sWLcLFxYW2bdsSGBhY5r6dPHkyCQkJaK0JCQnBYDDw008/mdfnT9739PTE0dGx3MkQgMFgwNvbm3bt2tGsWTOCgoKKlcnNzWXYsGGkpqaitea5557DycmJPn36MGDAANatW8eCBQuIiIhg4MCB1KlTh27dunHixIky27e0X3v16nXT+ynfoEGD2LdvH3Xq1Cm7sBCi0ovfeYBLp3bzQJtgmns8XKFtnXvjTXL+OEeLz1dgY5q2Y20q//TYvcjPz08Xve7RkSNHcHGpmOuVVFbh4eGEhoaWee2wu1FUVBRxcXEsXLjQ2qFUOqGhobzwwguEhISUuF7eq0KIfHl5eXw4egI3Ms4zasESatVzqrC20rZs4fT4Z3AeM+ZPnyumlNqrtfYraV2lPmUphLjzUlJSaNOmDdWqVbOYjAkhREHbVn7L9fSTuHZ5okKTsZzLl0l+ZRb27dpR/5nxFdbOrajUpyxF+URFRVk7hFsWHh5OeHi4tcOoVJycnMr8tqwQQuTLSs/gl29WYOfQkO6jB1ZYO1pr/pg9m7yrV2m8bBmqasV+g/NmyQiZEEIIIaxm44Ll5OVcpdOTf6NKlYobJ7q6fj1pmzZTf+LzOLQt/SoM1iAJmRBCCCGs4txvZ0na9z216rvj06tjhbWTnZzMH6/PpZqvL3Xv0rMmkpAJIYQQwiq+XvARoHn02bEV1obOy+Ps9Ono3Fwav/kGysL1E61NEjIhhBBC/OkOxcSRcvZXmrh2pWm7FhXWzpXPV5KxcxcNX36Zqg8+WGHt3C5JyO4C4eHh5huKW1NERATz5s274/WmpKTw4Ycfmp+fPXu2zEtoFLwB+d0sLi6O5557zqoxxMTElOum7EIIcbfIy8ll6/IlKNsahD73twpr5/qJE5yfN4/qXTrjNKjivjBwJ1T6b1ke2baVbf/+hLRLF6npXI/OQ57CpXNXa4d138jJyTEnZOPHG79i3Lhx47siAb0T/Pz88PMr8ZIyf5qYmBhq1KhBx47ln3+Rk5NToZNnhRCiNFs/WceNjDMYevyVGnVqVkgbOieHs1OnouztafTa68Vuo3e3qdQjZEe2beWHxQtJu3gBtCbt4gV+WLyQI9u23la9n332GQEBAXh5eTFmzBjz7YBq1KjBjBkzMBgMBAYGmm8UDhAbG0vHjh1p1aqVOVlJT08nJCQEHx8fPDw8WLduHWAcPXJxcWH06NG4ubnRo0cP870sjx8/Tvfu3TEYDPj4+JCYmAjAu+++i7+/P56ensyePdvc7ty5c2nTpg2dOnXi6NGjJfYnPDycsWPH4ufnR5s2bfj666/NcXTu3BkfHx98fHzMozQxMTF07tyZvn374urqytSpU0lMTMTLy4vJkycXGv2yVEdpSurL2rVrCQkJQWtNcnIybdq04Y8//iAqKop+/foRHBxM69atefXVV831hIWF4evri5ubG4sXLzYvt3ScVq9ejbu7OwaDgS5dupj7mn8j8suXLxMWFoanpyeBgYEcOHAA+N8V/IODg2nVqhWRkZEl9mvcuHH4+fnh5uZW6BgVFBkZiaurK56engwZMoSkpCQWLVrE/Pnz8fLyYtu2bWzYsIH27dvj7e1N9+7dzfFHREQwfPhwgoKCGD58eJn7WQghKkJ6Shr7N39BVcfGdBsRVmHtXFq6lKz9B2g0exZ2DRtUWDt3yn39L/LWqMWcP/mbxfXJx46Sm5NdaFnOjet8vyiSA1u+L3GbBs1b0TX8aYt1HjlyhFWrVvGf//wHOzs7xo8fz4oVK3jqqae4du0agYGBzJ07lylTprBkyRJmzpxpjCU5me3btxMfH0/fvn0ZMGAADg4OrF27llq1anHx4kUCAwPp27cvAAkJCaxcuZIlS5YwaNAgvvzyS4YNG8bQoUOZOnUqjz/+OFlZWeTl5fHDDz+QkJDA7t270VrTt29fYmNjqV69Ov/+97/Zt28fOTk5+Pj44OvrW2K/kpKS2L17N4mJiXTt2pXjx4/ToEEDNm3ahIODAwkJCTz55JPk3znhl19+4dChQ7Rs2ZKkpCQOHTrEvn37zHWZ92cpdZTEUl8ef/xxvvzySz744AO+++47Xn31VR54wHgLrN27d3Po0CEcHR3Nt1Ty8/Nj2bJl1K1bl8zMTPz9/enfvz/Ozs4Wj9OcOXP4/vvvadKkCSkpKcVimz17Nt7e3kRHR7Nlyxaeeuopc5/j4+PZunUraWlptG3blnHjxmFnZ1do+7lz51K3bl1yc3MJCQnhwIEDeHp6Firz1ltvceLECezt7UlJScHJyYmxY8dSo0YNXnrpJQCuXLnCrl27UEqxdOlS3nnnHf7+978DcPjwYbZv3061atUs7mMhhKhIGyP/hc5NJ/ipl7CpUjET7LMOH+bCwg+o9dij1HrssQpp4067rxOyshRNxspaXh4//vgje/fuNd8XMjMzkwYNjJl51apVzaMpvr6+bNq0ybxdWFgYNjY2uLq6mkc0tNZMnz6d2NhYbGxsOHPmjHldy5Yt8fLyMteVlJREWloaZ86c4fHHHwfAwcEBMCYxP/zwA97e3oBx5C0hIYG0tDQef/xxHE338cpP9koyaNAgbGxsaN26Na1atSI+Pp6WLVvy7LPPsm/fPmxtbQtdDDQgIICWLVuWub+ys7Mt1lESS33p0qULCxYswN3dncDAwEI3tP7LX/6Cs7MzAE888QTbt2/Hz8+PyMhI1q5dC8CpU6dISEjA2dnZ4nEKCgoiPDycQYMG8cQTTxSLbfv27Xz55ZcAdOvWjUuXLnH16lUAevfujb29Pfb29jRo0IBz587RtGnTQtt/8cUXLF68mJycHJKTkzl8+HCxhMzT05OhQ4cSFhZGWFjJ/1mePn2awYMHk5yczI0bNwodh759+0oyJoSwmjNHT3L6vz9S+wEvPLpWzHSPvOvXOfvyVKrUqUPDV16pkDYqwn2dkJU2kgWw+Jm/Gk9XFlGzXn0Gz37rltrUWjNixAjefPPNYuvs7OzM57BtbW3Jyckxr7O3ty9UB8CKFSu4cOECe/fuxc7OjhYtWpCVlVWsvK2trfmUpaWYpk2bxpgxYwotf++998rdr6Ln3pVSzJ8/n4YNG7J//37y8vLMCSBQ5k3C85VWR0ks9QWMiYiNjQ3nzp0jLy8PGxsbi7HHxMSwefNmdu7ciaOjI8HBweZ9a+k4LVq0iJ9//pmNGzfi6+vL3r17y9VHKH68Ch57gBMnTjBv3jz27NlDnTp1CA8PN8dT0MaNG4mNjWXDhg3MnTuXgwcPFiszYcIEJk2aRN++fYmJiSEiIsK8rrzHRQghKsI3CxcBitDnx1VYGxciI7mekECzxf+kSp06FdbOnVap55B1HvIUVaraF1pWpao9nYc8dct1hoSEsGbNGs6fPw8Y5xWdPHnylupKTU2lQYMG2NnZsXXr1jLrqVmzJk2bNiU6OhqA69evk5GRQc+ePVm2bBnp6ekAnDlzhvPnz9OlSxeio6PJzMwkLS2NDRs2WKx79erV5OXlkZiYyG+//Ubbtm1JTU2lUaNG2NjY8Omnn5rnypUUV1pamsU+lqeOfJb6kpOTw8iRI1m5ciUuLi784x//MG+zadMmLl++TGZmJtHR0QQFBZGamkqdOnVwdHQkPj6eXbt2ldouQGJiIu3bt2fOnDnUr1+fU6dOFVrfuXNnVqxYARjnltWrV49atWqVWS/A1atXqV69OrVr1+bcuXN8++23xcrk5eVx6tQpunbtyttvv01qairp6enF9m9qaipNmjQBYPny5eVqXwghKtqv3+/g6vmDNDf04IFWTSqkjYy4OC4v+xdOgwZRwzTX915xX4+QlSX/25R38luWrq6uvP766/To0YO8vDzs7Oz44IMPaN68+U3XNXToUPr06YOHhwd+fn60a9euzG0+/fRTxowZw6xZs7Czs2P16tX06NGDI0eO0KFDB8A4af2zzz7Dx8eHwYMHYzAYaNCggfk0a0kefPBBAgICuHr1KosWLcLBwYHx48fTv39/PvnkE3r16mVx9MXZ2ZmgoCDc3d159NFHeeaZZ8zryltHPkt9WbRoEZ07d6ZTp04YDAbzXDEwnj7t378/p0+fZtiwYfj5+eHh4cGiRYtwcXGhbdu2BAYGlrlvJ0+eTEJCAlprQkJCMBgM/PTTT+b1+ZP3PT09cXR0vKlkyGAw4O3tTbt27WjWrBlBQUHFyuTm5jJs2DBSU1PRWvPcc8/h5OREnz59GDBgAOvWrWPBggVEREQwcOBA6tSpQ7du3Thx4kS54xBCiIqQk5PDts+XYWNbi8cmhFdIG7np1zg7L09xHgAAIABJREFUdRp2TZvS8OUpFdJGRVL5p8fuRX5+frroBPAjR47g4uJipYjuT+Hh4YSGhpZ57bC7UVRUFHFxcSxcuNDaoYgi5L0qROXx/T9XcWjLp/j2GUPwsD4V0kbyrNmkrF5N888+xdHCF9SsTSm1V2td4uS5Sn3KUgghhBAV6+qlVP4b8yX2NR6ky//1rpA20n/6iZQvvqDuyL/etclYWSr1KUtRPlFRUdYO4ZaFh4cTfpfeSFYIISqDr99fis7LIGTk0+YvW91JOVeukDzzFexbt6a+le+ccjvuy4RMa33XX5FXiMrsXp4qIYQov5MHE0k++hN1m/rjEuRVIW2ce+01clJSaLb4n9jY25e9wV3qvjtl6eDgwKVLl+QPvhB3Ka01ly5dKvMSJ0KIe993H30Eqgqhzxe/VNGdkLpxI1e/+Zb6z4zH4VbnpB74Aua7Q4ST8feBL+5skOV0342QNW3alNOnT3PhQvHriwkh7g4ODg7FLowrhLi/7N4QQ/qleFr5hVH/wQfueP3Z587zx5zXcDB44jxq1K1VcuAL2PAcZJuu5Zl6yvgcwHPQnQm0nO67hMzOzq5cV4gXQgghRMXIuZHNztVR2NrV4bFn7vy9c7XWJM+cib5+ncZvvYWqcovpzI9z/peM5cvONC7/kxOy++6UpRBCCCGs67t//puc6xfx6zsMe8c7P68rZdUXXNu2jQaTX8L+dgZhUk/f3PIKJAmZEEIIIe6YlD8ucew/66hWuxUdB/zljtd/4/ffOffOO1Tv2IE6Be5bfEtqNS55ee0/f0qFJGRCCCGEuGM2vL8Era/TY/TYO36ZC52by9mp01C2tjSaOxd1O/VrDTUbFV9uVw1CZt16vbdIEjIhhBBC3BHH9/4/e/ceZmO9/3/8+ZkzM4YZM+bgTIpEckglFZGzina7VErOKWrvfjmktJPIV9smapyjnY4o57MkUUSIhISYI8OMMeawZt2/P2Zqk9Naa9aa4+txXV1m7nV/3vdrf797X7197nu9759JPLyZ8Bq3c12zG91eP3nOHM7v2EHkKyPxjbpMM+WMXR/Bie1w4wNQvipgcv/sMrnAnx+DEvhQv4iIiBQ8u93OmmkxYPzo/Hw/t9fP+OUASZMmU65tW4K75PP1S6d+hWUvQvU74aHZ4OXtnpD5oB0yERERybetC9eRnvIr19/WldCoMLfWtrKyiB06FK/gYCL/9Vr+hr/nZMOCPuDtC92mFYlmDLRDJiIiIvmUlZHJ9198gLdfRdoNzOeD9peRNPVdMvfvp8q7U/EJDc1fsQ1vQuwO+NvcQnl4/0q0QyYiIiL5smLqh+RkJ3P7Q0/h5+/n1trpO3dyasYMynfrRrnWrfNX7Lev4ZuJ0Lgn1H/APQHdRA2ZiIiIuOzk8UQObVtKYMj1NL+/lVtr29PTiRs2HN/ISCJGDM9fsfRkWNgfKtaG9uPcE9CNdMtSREREXLZ00jSwbLQfONDttRMnvE3W0aNUmzsX76Ag1wtZFix+Ds4lwaNrwS/QfSHdRDtkIiIi4pL9W3Zz6th3RNZpSY2b67i1dtrmzZyeP5/QJ3sS2PzW/BXbMRf2L82dLxbdyD0B3UwNmYiIiDjNbrezbtY0jFcZujzf1621c1JSiBvxMn61ahH+wgv5K5Z0AFYMg1r3wO3PuiOeR+iWpYiIiDjtm49XknH2KDfe/RjBYRXcWjt+zBhsJ09SY8oUvAICXC9ky4QFT4NfWXhwGrj5zQHupIZMREREnJKRls4Pyz7EJyCCtv3+5tbaqatWk7p4CWGDBlGmwU35K7budYjfA49+DOUi3RPQQ4puqygiIiJF0rIp87DbUrjzkafx8XHf3o4tKYn4UaMIqF+fsAH981fs0DrYMgWa9YEbOrgnoAepIRMRERGHJRyO5ciPqygXXp8mHVq4ra5lWcS9Ogp7ejrR49/C+Pq6XiwtCRYNgPC6cN8bbsvoSWrIRERExGHL3okBy06HQe4dc5GycCFpGzYQ/o8X8K9d2/VClgVfDoKMFOg+C3zLuC+kB+kZMhEREXHITxt/4HTsDirXa0PVejXcVjfr+AkSxrxJ2WbNCO3ZM3/Fvp8BB1dBh/EQmc9n0AqQdshERETkmuy2HDa8Px3jHUjnIb3dVtey24kbPhyMIWrsWEx+vgmZsA9Wj4Q698Gt/dyWsSCoIRMREZFr2vDBYrLST9Cg9d8ICinntrrJ8+aRvm0bESNG4FelsuuFss/Dgt4QUB7ufxeMcVvGgqBbliIiInJVaWfOsmvNJ/iViebepx50W93MQ4dI+vdEglq1ony3fNZd8yok7oPHFkBQuHsCFiDtkImIiMhVLX/nfaycNO7u2RcvH2+31LSys4kdOgyvwECiRr+Oyc+O1i8r4fvpcNsgqNPGLfkKmnbIRERE5IpOHDjG7z+to3xkIxq2bua2uidjppGxdy+VJ03CJyzM9UJn4+HLZyCiAbQZ5bZ8BU07ZCIiInJFy6fEANDpuQFuq3l+zx5OxsQQ3LULwe3uc72Q3Q5fDISsdOg+E3z83ZaxoGmHTERERC7rx9VbSE3YTbWGHYi6ropbatozMogdOgyfsDAiR47MX7Gt78Kv66HzRKhU1y35CosaMhEREbmEzWbj6w9n4eUdTKfBvdxWN2niRLIOH6bqrJl4Bwe7XihuF6x9Dep2hibuy1dYdMtSRERELrFu9kKyM+K5pcMjlC1X1i01z239juS58wjp0YOgFvl47VLWOfi8NwSGQdd3it2Ii8vRDpmIiIhcJC05lb0bPsc/qBp3PdbZLTVz0tKIHTEcv+rVqfTiP/NXbNUIOHUIen4JZUPdkq+wqSETERGRiyz5z0wsezr39uqHV34m518g4c2x2OITqDH/Q7zK5mPHbd9i+OF9aPE81LrbLdmKAt2yFBERkT8d3fMrsb98RWiVZtS7s5Fbap5dv56UhQup2LcvZRrlo2bKCVj8HETfAq1edku2okINmYiIiPxp5XsxYLzpPKS/W+rZkpOJe+VV/OvWJXzQM64XsufAov6Qkw3dZ4GPn1vyFRW6ZSkiIiIAbFvyNWmnfqZW0/sJrxaZ73qWZRE/ahT21FSiZ8/G+OWjido8CY5sgvunQsXa+c5W1GiHTERERLBlZfPtZ3Pw8q1Ax0E93VIzdfFizq5ZS/iQwQTccL3rhY7/ABvGQP0HodFjbslW1KghExEREVZN/wRbZhLNOj+Of9n8T7zPjosj/o0xlGncmNBe+ZgTlnkWFvSGclG5A2BLwIiLy9EtSxERkVLuTEIyv2z+gjLBtbjj4Xy8yiiPZbcTO2IEVk4O0ePGYrzz8ULy5S/BmaPw1DIoE5LvbEWVdshERERKuSWTpmPZM7mv3wC3jLk4Pf8j0rdsJeKll/CrVs31Qns+h13zoeWLUP2OfOcqytSQiYiIlGKHd/xC4q+bCatxG9c1uzHf9TJ/+43ECRMIbNmSCn9/2PVCp4/C0n9AlVvh7qH5zlXUqSETEREppex2O6umvQfGjy5D+uW7nmWzETtsGMbfn6g33sC4+rxXjg0W9gPLDt1ngHfJf8Kq5P8nFBERkcv6btF60s8c4vrbHyI0Ojzf9U7NnEnGrt1U/vfb+EZUcr3Qpgnw+1boNhNCauQ7V3GgHTIREZFSKCsjk+8WzcPbryLtBvbId72MfftImjKV4I4dCO7Y0fVCx7bCxreg4SPQ8G/5zlVceKwhM8bMNsYkGmN+uuDY/xlj9htjdhtjFhljKuQdr2GMOW+M+THvnxhP5RIRERFY+e58crKTua37k/j552/qvT0zk9ihw/AJCSHilVdcL5SRAgv6QoVq0PH/8pWpuPHkDtn7QPu/HFsD3GRZVkPgADD8gs9+tSyrUd4/AzyYS0REpFQ7eTyRg98voWzI9dz2QOt810uaPJnMgweJGvMGPiEujqawLFj6AqSeyL1VGRCc71zFiccaMsuyvgaS/3JstWVZtrxftwJVPHV9ERERubxlk6eDZaP9wPzvf6Rv307y7DlUePhhgu66y/VCuz6GnxZAq+FQtVm+cxU3hfkM2dPAigt+r2mM2WmM2WiMaXmlRcaYfsaY7caY7UlJSZ5PKSIiUoL8snUPJ49uJfK6O6l5cz5eZwTkpJ0jdthwfKtUIWLoS64XOvUrLH8RqreAO/+Rr0zFVaF8y9IY8zJgAz7MOxQHVLMs65QxpgnwhTGmvmVZqX9da1nWdGA6QNOmTa2CyiwiIlLc2e121s6chvEqQ6chffNdL3H8eLJPnKD6fz/AKzDQtSI52bCwL3h5Q7fpuX+WQgW+Q2aMeQroDDxmWZYFYFlWpmVZp/J+/gH4Fchf2y4iIiIX+eaTlWScPUK9lg9SoVL+XkOUtnEjZz79lNCne1G2SRPXC301Fk78AF0mQ/nS+yRTgTZkxpj2wEtAV8uy0i84Hm6M8c77uRZQBzhckNlERERKsoxz5/lh6Yf4+FeibZ/8jZOwnT5N3MhX8K9Th/DBg10v9Nsm2PRvuOUJqP9AvjIVdx67ZWmM+Qi4BwgzxhwHRpH7rUp/YE3e9N6ted+ovAt43RiTDdiBAZZlJV+2sIiIiDht2ZR52G0p3PXYMHz8fPNVK2H0aGxnzlB1+jS8/P1dK5KenDuNv2JtaD8uX3lKAo81ZJZlPXqZw7OucO4CYIGnsoiIiJRmiUfiOLJzJeXC6tOk4535qpWybBmpy1cQ/vwQAurVc62IZcGSwXAuCR5dA/5B+cpUEmhSv4iISAm3dHIMWHY6PDswX3WyExKJf300ATc3pGKfPq4X2jEPfl4C974C0bfkK1NJoYZMRESkBNv79Q5On/iByvXuoWq9Gi7XsSyLuJEjsTIziR43DuPj4k22pAOwchjUvBtuf87lPCWNGjIREZESym63s/796RjvQDoPyceOFnDmk085t2kTlV58Ef+aNV0rYsuEBb3BJwAenAZeakP+oP9LiIiIlFAb5i0m69xxbmr1EEEh5Vyuk3XsGAnjxxN4x+2E9LjcI+IOWj8a4nfD/VMhOMr1OiWQGjIREZESKD31HLtXf4JvmSja9Ormch0rJ4fYYcMx3t5EjRmDcXVX69f18O070LQ31O3ocp6SSg2ZiIhICbR00mzsOWe554m+ePm4Pv0+ec4czu/YQeQrI/GNcnFX69xJWDQAwuvCfW+4nKUkK5RXJ4mIiIjnnDhwjN9/Wkf5iJtpeO+tLtfJ+OUASZMmU65tW4K7dHGtiGXBl4Pg/Bl4fCH4lXU5T0mmhkxERKSEWTElBoBOgwe4XMPKyiJ26FC8goOJ/Ndr5A10d962mXBgJbR/CyJvcjlPSaeGTEREpAT5cc1WUhJ2U61Be6Kuq+pynaSp75K5fz9V3p2KT2ioa0US9sHqkXBdW2je3+UspYGeIRMRESkh7LYcvv5wFl7e5eg05GmX66Tv3MmpGTMo360b5Vq3dq1I9vncERf+5eCBd8HVHbZSQg2ZiIhICbFm9gKyz8fRqP0jlC3n2rNa9vR04oYNxzcykogRw/MRZhQk7oMHYiCokut1SgndshQRESkB0pJT2bvhc/wDq3L34y4+gA8kTnibrKNHqTZ3Lt5BLr5j8sAq+H4a3PYM1GnjcpbSRDtkIiIiJcCSSTOx7Om06tUPLxdnhaVt3szp+fMJfbIngc1d/Hbm2QT44hmIuAnuHeVajVJIDZmIiEgxd2zvYWL3byS0clPqt3TtZd05KSnEjXgZv1q1CH/hBdeC2O3wxQDISoPus8A3wLU6pZBuWYqIiBRzK6a+B8aLTkNcH3MRP2YMtpMnqTFlCl4BLjZS372XO5G/07+hUl2Xs5RG2iETEREpxrYv/Zq0Uz9Ts3EHKlWPdKlG6qrVpC5eQtiAAZRp4OKssLjdsPY1uKETNHX9G56llRoyERGRYsqWlc3mT+fg5VOBjoOecK1GUhLxo0YRUL8+YQNcnBWWlZ474qJMKHR9RyMuXKCGTEREpJhaNf0TbJlJNO3yGAGBZZxeb1kWca+Owp6eTvT4tzC+vi4GGQEnD0K3aRBY0bUapZwaMhERkWLoTEIyv2z+koDgmrR4uJ1LNVIWLiRtwwbC//EC/rVruxbk5yXwwxxoMRhq3eNaDVFDJiIiUhwtnTQDy55B274DXBpzkXX8BAlj3qRss2aE9uzpWoiUE7D4OYhqBK1GulZDADVkIiIixc7hnb+Q8Os3hFVvzvW31nd6vWW3Ezd8OBhD1NixGFfmltlzYFF/sGXljrjw8XO+hvxJDZmIiEgxYrfbWRUTA8aPLs+79hB+8rx5pG/bRsSI4fhVqexakG8nw5FN0OEtCLvOtRryJzVkIiIixch3X6wn/cxB6jTvTGh0uNPrMw8dIunfEwlq1Yry3bq5FuLED7D+DbjxAbjlcddqyEXUkImIiBQTWRmZfLdoHt5+obQf2MPp9VZ2NrFDh+EVGEjU6NcxroynyDwLC/pAUCR0+Y9GXLiJJvWLiIgUEyvfnU9OVjJ3PPICfgH+Tq8/GTONjL17qTxpEj5hYa6FWDEUTh+Bp5ZBmRDXasgltEMmIiJSDCTHJnHw+6WUDanD7Q/e6/T683v2cDImhuCuXQhud59rIX5aAD9+CC3/CdXvcK2GXJYaMhERkWJgyX+mgZVNu/7Ov6/SnpFB7NBh+ISFETnSxfEUZ47BkhegSjO4e6hrNeSKdMtSRESkiDvw/U+cPPodEdfdSa1bbnB6fdLEiWQdPkzVWTPxDg52PkCODRb0BcsO3WaAt4sT/eWK1JCJiIgUYXa7nTXTYzBeAXQe0s/p9ee2fkfy3HmE9OhBUIsWroXY9Db8vjW3GQut6VoNuSrdshQRESnCNn+6ioyzR6h75wNUqOTcQ/Q5aWnEjhiOX/XqVHrxn64FOPYdbHwLGjwMDR92rYZck3bIREREiqiMc+fZvuS/+PiHc19f55uhhDfHYotPoMb8D/EqW9aFACmwsA+UrwKdJji/XhymHTIREZEiavnUedhtKbR45Gl8/Jx7buvs+vWkLFxIxb59KdOokfMXtyxY+o/c91V2nwkB5Z2vIQ5TQyYiIlIEJR6J47cdKwkKu5GmHVs6tdaWnEzcK6/iX7cu4YOecS3A7k/gp8/hnuFQ9VbXaojDdMtSRESkCFo6OQYsOx0HOTfmwrIs4keNwp6aSvTs2Rg/F176nXwYlr0I1e6Alv9wfr04TTtkIiIiRczeTTs5feIHouveTdUbazm1NnXxYs6uWUv4kMEE3HC98xfPyc4dceHlBd2mg5e38zXEadohExERKULsdjsb5kzDeAXSZUgfp9Zmx8URP/oNyjRuTGivXq4F+GocnNgOD82BClVdqyFO0w6ZiIhIEfLVB0vIPHec+q26ExTq+BBXy24ndsQILLud6HFjMd4u7Gwd+SZ35tgtj8NN3ZxfLy5TQyYiIlJEpKeeY9eqj/EtE0Xbp7s7tfb0/I9I37KViJdewq9aNRcungwL+0FoLWj/lvPrJV/UkImIiBQRSyfPwZ5zlrsf74OXj+M7XJm//UbihAkEtmxJhb+7MLzVsmDp85CWkDviwj/I+RqSL2rIREREioC4Q7/z+561lI9oyM1tmju8zrLZiB02DOPvT9Qbb2CMcf7iOz+AfV9C61egcmPn10u+6aF+ERGRImDZ5BgAOg0e6NS6UzNnkrFrN9FvT8A3opLzFz55EFYMhZp3wR2DnV8vbqEdMhERkUK2a+13pCTsomqDNkRd5/g3GzP27SNpylSCO3agfKdOzl/YlgULeoNPADw4LXfUhRQK7ZCJiIgUIrsth43/nYmXdzk6D3Z8VIU9M5PYocPwCQkh4pVXXLv4+tEQtwsemQ/B0a7VELdQQyYiIlKI1s5ZSPb5OG7p2JeywYEOr0uaPJnMgwepOn0aPiEhzl/41w3w7WRo+jTUdWF3TdxKe5MiIiKFJC05lZ/Wf45/YBXueaKLw+vSt28nefYcKjz8MEF33eX8hc+dhEUDIOwGuG+M8+vF7dSQiYiIFJKlk2dh2c/Rqld/vBx8fisn7Ryxw4bjW6UKEUNfcv6ilgVfPgvnk+GhWeBX1vka4na6ZSkiIlIIft93mBM/f0VI5SbUb3mLw+sSx48n+8QJqv/3A7wCHb/F+afts+DACmg/DiIbOL9ePEI7ZCIiIoVg+dQYMF50HjzA4TVpGzdy5tNPCX26F2WbNHH+ook/w6qX4bo20Nzx64rnqSETEREpYNuXbyLt5D5qNm5PpRpRDq2xnT5N3MhX8K9Th/DBLswLy86Az3uDfzl44D1wZYCseIxuWYqIiBQgW1Y2mz+ejZdPBToO6unwuoTRo7GdOUPV6dPw8vd3/sJrR0HiXujxGQS5MEBWPEo7ZCIiIgVo9YxPsWUm0aRzDwICyzi0JmXZMlKXryB80DME1Kvn/EUPrIbvYqD5QLj+PufXi8epIRMRESkgZxJPs/+bLwgoV4M7/97eoTXZCYnEvz6agJsbUrFPH+cvmpYIXz4DETdBm9ecXy8FQrcsRURECsjSSdOx7Bm07TfAoTEXlmURN3IkVmYm0ePGYXyc/Ne23Q5fDITMs/DkEvANcDG5eJp2yERERArAb7sOkHBoM2HVm3P9rTc5tObMJ59ybtMmKr34Iv41azp/0e9i4NBaaDcGKrlwq1MKjBoyERGRArDyvRgwvnR5vr9D52cdO0bC+PEE3nE7IT0edf6CcbtzH+S/oSM07e38eilQashEREQ8bMuidaSfPkCdWzsTGh1+zfOtnBxihw3HeHsTNWYMxsEp/n/KSocFfaBMKHSdohEXxYCeIRMREfGgrIxMvls4D2+/UNo/08OhNclz5nB+xw6i3xqHb5Rjc8ousvplOPkLPPEFBFZ0fr0UOO2QiYiIeNDK9+aTk3WK5g/2xC/g2vPDMn45QNKkyZRr25bgrl2dv+DPS2H7bLhjMNRu5UJiKQxqyERERDwkOTaJg98tpWyFOjR/oPU1z7eysogdOhSv4GAi//UaxtlbjamxsPhZiLoZWr/iYmopDLplKSIi4iFLJk0HK4t2Axwbc5E09V0y9++nyrtT8QkNde5idjss6g+2TOg+C3z8XEwthUENmYiIiAcc+H4vJ49sJaL2ndS65YZrnp++cyenZsygfLdulGt97d20S3w7GX77Grq+A2F1XEgshUm3LEVERNzMbrezZkYMxsufzkP6Xvv89HTihg3HNzKSiBHDnb/giR2wfjTceD/c8oQLiaWwaYdMRETEzb79dDUZqb9Rt+UjVIi49q3HxAlvk3X0KNXmzsU7KMi5i2Wm5Y64CIqELpM04qKYUkMmIiLiRhnnzrNtyX/x8Q+nXb+/X/P8tM2bOT1/PqFP9iSw+a3OX3DlUEg+DE8tgzIhLiSWokC3LEVERNxo+dQPsNvO0OLhXvj4+V713JyUFOJGvIxfrVqEv/CC8xf7aSHs/C+0/CfUaOFiYikKtEMmIiLiJolH4/ltxwqCKtajaee7rnl+/Jgx2E6epMaUKXgFOPni7zPHYMnzULkp3DPMxcRSVHh0h8wYM9sYk2iM+emCY6HGmDXGmIN5f4bkHTfGmMnGmEPGmN3GmMaezCYiIuJuyybFgJVDh0EDr3lu6qrVpC5eQtiAAZRp4NjLxv9kz4GF/cCyQ/eZ4H31nTgp+jx9y/J9oP1fjg0D1lmWVQdYl/c7QAegTt4//YD3PJxNRETEbfZu2knyie1E33AP1erXuuq5tqQk4keNIqB+fcIGOPay8YtsehuObYFOb0NoTRcTS1Hi0YbMsqyvgeS/HL4fmJv381zggQuOz7NybQUqGGNceIGXiIhIwbLb7WyYMx3jVZYuz/e56rmWZRH36ijs6elEj38L4+vk7tbv38NX46DB3+Dma39pQIqHwnioP8KyrLi8n+OBiLyfKwO/X3De8bxjFzHG9DPGbDfGbE9KSvJsUhEREQds/O8SMs/9Tv1WDxEUGnzVc1MWLiRtwwbC//EC/rVrO3ehjBRY0BvKV87dHZMSo1C/ZWlZlgVYTq6ZbllWU8uymoaHh3somYiIiGPSz6bz48qP8S0Txb1Pd7vquVnHT5Aw5k3KNmtGaM+ezl9s2YuQciL31UgB5V1MLEVRYTRkCX/cisz7MzHv+Amg6gXnVck7JiIiUmQtmzQbe85Z7nqsNz4+Vx5eYNntxA0fDsYQNXYsxoF3W15k1yew59Pcb1RWdWFemRRphdGQLQaezPv5SeDLC473zPu25W1AygW3NkVERIqcuEPHObZnLcERDWnU9rarnps8bx7p27YRMWI4flUueSLn6pJ/g2X/hGq3584ckxLHo3PIjDEfAfcAYcaY48AoYBzwqTGmN3AUeDjv9OVAR+AQkA708mQ2ERGR/Fr2TgwAHZ8dcNXzMg8dIunfEwlq1Yry3a5+W/MSOdm5r0YyXtBtOnh5uxpXijCPNmSWZT16hY/uvcy5FjDIk3lERETcZfe670mJ/5GqN7Wj8vXVrnielZ1N7NBheAUGEjX6dYyz75rc+Bac2A4PzYEKV76OFG+a1C8iIuIkuy2Hrz6YgZd3OToPefqq556MmUbG3r1UnjQJn7Aw5y50ZDN8PQEaPQ43ObmzJsWK3mUpIiLipHXvLyL7fBwN7/s7ZYMDr3je+T17OBkTQ3DXLgS3u8+5i5w/nTuNP7QWdHgrn4mlqNMOmYiIiBPSTp9lz/rP8AusQqueXa94nj0jg9ihw/AJCyNy5EjnLmJZsGQIpMVD79XgH5TP1FLUqSETERFxwtJJs7ByztH6qaF4XWV0RdLEiWQdPkzVWTPxDr76sNhL7Pwv7PsS2rwGlZvkK68UD7plKSIi4qDf9x3mxM8bCKnchPp3Nb7ieee2fkfy3HmE9OhBUIsWzl3k5EFY8RLUvAvuGJLPxFJcqCETERFx0Iqp08B40Xnwlcdc5KSlETtiOH7Vq1PpRSdnhtnPWZvDAAAgAElEQVSyckdc+PjDg9PA2eGxUmzplqWIiIgDflj+DWdP7qVG4y5UqhF1xfMS3hyLLT6BGvM/xKtsWecusuENiPsR/v4hBEfnM7EUJ2q9RURErsGWlc03H8/Gy6c8nZ698jsoz65fT8rChVTs25cyjRo5d5FfN8DmSdCkF9TrnM/EUtyoIRMREbmGNTM/w5aZSJNOjxEQWOay59iSk4l75VX869YlfNAzzl3g3ClYNADCrod2b7ohsRQ3umUpIiJyFWcST/PzpkUElKvBnY+0v+w5lmURP2oU9tRUomfPxvj5OX4By4LFz8L5ZHjsM/Bz8janlAjaIRMREbmKZZNmYNnP06ZP/yuOuUhdvJiza9YSPmQwATdc79wFts+GX5bnjriIapjvvFI8qSETERG5gt92HSD+0DdUrNacG25rcNlzsuPiiB/9BmUaNya0Vy/nLpC4H1aNgNr3QvOBbkgsxZUaMhERkStY9V4MGB86D+l/2c8tu53YESOw7Haix43FeHs7Xjw7Axb0Br8geOA9jbgo5fT/fRERkcvY+sV6zp0+wHXNOhNWpdJlzzk9/yPSt2wl4qWX8KtWzbkLrH0NEn7KbcbKReQ/sBRrashERET+Iiszi60L5uLtG0qHQY9d9pzM334jccIEAlu2pMLfH3buAgfXwHfvQfMBcL2TLx2XEkkNmYiIyF+sem8+OVmnaP5gT/wC/C/53LLZiB02DOPvT9Qbb2CMcbx4WiJ8MRAq1Yc2/3JjainONPZCRETkAslxJzmwdQllK1xH8wdbX/acUzNnkrFrN9FvT8A34vK3My/Lbs9txjLPQs/F4BvgptRS3KkhExERucDS/0wHK4t2/QdedsxFxr59JE2ZSnDHDpTv1Mm54t9Pg0NroeMEiLjRTYmlJNAtSxERkTwHvt9L0pEtVKrdglqNb7jkc3tmJrFDh+ETEkLEK684Vzz+J1jzKlzfAZr1cVNiKSm0QyYiIgLY7XbWzpiG8fKny5B+lz0nafJkMg8epOq0GHxCQhwvnpWeO+KiTAjcPwWceeZMSgXtkImIiADffr6G86mHuaHFA1SICL3k8/Tt20mePYcKDz9M0N13O1d89UhI2g8PxkBgmJsSS0nicENmjKlujGmT93MZY0w5z8USEREpOJnpmWxb/AE+/uG06/f3Sz7PSTtH7LDh+FapQsTQl5wrvn8ZbJ8FdzwHtS//JQERhxoyY0xf4HNgWt6hKsAXngolIiJSkJZPnYc9+wx3/K0XPn6+l3yeOH482SdOED32TbwCAx0vnBoHXz4LUTdD61fdmFhKGkd3yAYBLYBUAMuyDgJOfM9XRESkaEo6Fs/hH5YTVLEezbrcdcnnaRs3cubTTwl9uhdlmzZ1vLDdDov6gy0Dus8CHz83ppaSxtGH+jMty8r6Y/CdMcYHsDyWSkREpIAsnTQNrBzaDxxwyWe206eJG/kK/nXqED54sHOFt7wDv22ELpMhrI6b0kpJ5WhDttEYMwIoY4xpCzwDLPFcLBEREc/7+ZsfST6+jagbWlG9Qe1LPk8YPRrbmTNUnT4NL/9LJ/Zf0YkdsO51qNcVGvd0Y2IpqRy9ZTkMSAL2AP2B5cBIT4USERHxNLvdzro50zFeZek85NK5YCnLlpG6fAXhg54hoF49xwtnpsGCPhAUAV0macSFOMTRHbIywGzLsmYAGGO8846leyqYiIiIJ3394VIy045xU+snCK5Y/qLPshMSiX99NAE3N6RiHyeHuK4cBsmH4amlUPbS8Rkil+PoDtk6chuwP5QB1ro/joiIiOeln01n54qP8Q2I5N7e3S/6zLIs4kaOxMrMJHrcOIyPEzPU9y6CnR9Ay39AjTvdnFpKMkcbsgDLstL++CXv57KeiSQiIuJZyybPwZ6Tyl2P98HnLw3XmU8+5dymTVR68UX8a9Z0vOiZ32HJEKjcBO4Z7ubEUtI52pCdM8Y0/uMXY0wT4LxnIomIiHhO3KHjHNu9huBKDWjU9raLPss6doyE8eMJvON2Qno86nhRew4s7Jf7Z/eZ4H3pLDORq3F0H/Z54DNjTCxggEjg0lHGIiIiRdyyd2IA6PjsxWMurJwcYocNx3h7EzVmDMbLibcLfvNvOPYtPDgNQmu5M66UEg41ZJZlbTPG1AVuyDv0i2VZ2Z6LJSIi4n67128jJf5Hqt7Ujso3VL/os+Q5czi/YwfRb43DNyrK8aK/b4MNY+Gmh6Ch9irENU48qUgzoEbemsbGGCzLmueRVCIiIm5mt+Wwcd4MjHcQHZ976qLPMn45QNKkyZRr25bgrl0dL5qRCgt6Q/nK0PnfGnEhLnOoITPGfADUBn4EcvIOW4AaMhERKRbWz/2CrPOxNGrfm6AK5f48bmVlETt0KF7BwUT+6zWMM03V8hch5XfotRICyl/7fJErcHSHrClwo2VZel2SiIgUO2mnz7J73af4la1Mqycu3gFLmvoumfv3U+XdqfiEOjE3bPensPsTuGcEVGvu5sRS2jj6xOJP5D7ILyIiUuwsnTwLK+ccrZ7qh5eP95/H03fu5NSMGZTv1o1yrVs7XjD5N1j6D6h6G7T8pwcSS2nj6A5ZGLDPGPM9kPnHQcuynLjRLiIiUvB+//kIJ/ZtICS6MTfd3eTP4/b0dOKGDcc3MpKIEU7MDcuxwcK+YLyg+wzwduZxbJHLc/S/Ra95MoSIiIinrJj6HmDo9NzFYy4SJ7xN1tGjVHv/fbyDghwvuPEtOL4Nus+CCtXcG1ZKLUfHXmz0dBARERF3+2HFZs4m7aXGLZ2JqBX95/G0zZs5PX8+oU/2JPA2J57/OvotbJoAjR6DBg95ILGUVg49Q2aMuc0Ys80Yk2aMyTLG5BhjUj0dTkRExFU2m41vPp6Nl095Oj3b88/jOSkpxI14Gb9atQh/4QXHC54/DQv6QoXq0OEtDySW0szRh/qnAI8CB8l9sXgfYKqnQomIiOTXmumfYctIoHHHHgQE/e/1y/FjxmA7eZLot97CKyDAsWKWBUtfgLT43FuV/uWuvUbECQ6/F8KyrEOAt2VZOZZlzQHaey6WiIiI61JPnuHnTQsJKFedlo92+N/xlatIXbyEsAEDKNPgJscL/vgh7F0ErV6GKk2ufb6Ikxx9qD/dGOMH/GiMGQ/E4UQzJyIiUpCW/GcGlv089/buj1feOyltSUnEv/YaAfXrEzagv+PFTv0Ky1+CGi2hxRAPJZbSztGm6gnAG3gWOAdUBbp7KpSIiIirjuw6SPzBTVSseit1b28IgGVZxL06Cnt6OtHj38L4+jpWzJaV+2okH7/cF4d7eV97jYgLHP2W5dG8H88D//JcHBERkfxZ+d57YHzo/Pz/xlykLFxI2oYNVBo2FP/atR0vtmEMxO6Ev/83932VIh7i6LcsOxtjdhpjko0xqcaYs/qWpYiIFDXffbmBc6cPcF2zToRVqQRA1vETJIx5k7LNmhHas+c1Klzg8FeweRI0eQrqdfFIXpE/OPoM2X+AbsAevc9SRESKoqzMLLZ8/j7eviF0GPQ4AJbdTtzw4WAMUWPHYrwcfFInPRkWDYCwOtDuTQ+mFsnl6DNkvwM/qRkTEZGiatV7H5GTdYpbH+iJX4A/AMnz5pG+bRsRI4bjV8XBW46WBYufg/RT0H0m+AV6MLVILkd3yF4ClhtjNnLxuyz/7ZFUIiIiTkiOO8mBrYspW742t3W7F4DMQ4dI+vdEglq1ony3bo4X+2EO7F8K942BqJs9lFjkYo42ZGOANCAA8PNcHBEREectnTQdrCza9h+Al5cXVnY2sUOH4RUYSNTo1zHGOFYocT+sHAG1W8Ntz3g2tMgFHG3Ioi3LcmKCnoiISME4tG0fSb9toVKtFlzXpB4AJ2OmkbF3L5UnTcInLMyxQtkZsKBP7i3KB2LA0efNRNzA0f+2LTfG3OfRJCIiIk6y2+2snhGDMf50GdIXgPN79nAyJobgrl0IbufEv7rW/QsS9sAD70K5CA8lFrk8RxuygcBKY0xG3sgLjb0QEZFC9+3nazifcpjrW9xPhciK2DMyiB06DJ+wMCJHjnS80MG1sPVduLUfXN/Oc4FFrsDRwbB6i6qIiBQpmemZbF/8X3z8w2jf/xEAkiZOJOvwYarOmol3cLBjhdIS4YsBUOlGaPu6BxOLXJmjz5BhjOkK3JX361eWZS31TCQREZFrWz71A3KyT9Py8Rfx8fPl3NbvSJ47j5AePQhq0cKxIpYFXzwDGanQczH4lvFsaJErcHRS/zhgCLAv758hxpixngwmIiJyJUnH4jn8wzKCKtbl1i73kJOWRuyI4fhVr06lF//peKHvpsGhNdBuDETc6LnAItfg6A5ZR6CRZVl2AGPMXGAnMNxTwURERK5k6aRpYOXQfuBAABLeHIstPoEa8z/Eq2xZx4rE/wRrXoXr20OzPh5MK3Jtznynt8IFP5d3dxARERFH/Lz5R5KPbyPq+ruo3qA2Z9etI2XhQir27UuZRo0cK5J9Hhb0hjIV4P6p4OicMhEPcXSHbCyw0xizATDkPks2zGOpRERELsNut7Nu9nSMV1k6P98XW3Iyca+Owr9uXcIHOTHIdfVISNoPjy+EQAfnlIl40FUbMmNMC8uyNgMLga+AZnkfDbUsK97D2URERC7y9fxlZKYdo36rxykXGsyJwYOxp6YSPXs2xs/BF8nsXw7bZsLtz8J193o2sIiDrrVDNhloAmyxLKsxsNjzkURERC6VfjadnSs+wjcgkjZ9HiJ18WLOrllLpRf/ScAN1ztWJDUOvhwEkQ3h3lc9G1jECddqyLKNMdOBKsaYyX/90LKswZ6JJSIicrHl77yP3ZZKqyefw0pKIn70G5Rp3JjQXr0cK2C3584byz4P3WeBj79nA4s44VoNWWegDdAO+MHzcURERC4Vf/gER3etJrjSTdzcpjnHevfGstuJHjcW4+3tWJEtU+DwV9BlEoQ7uKMmUkCu2pBZlnXSGPMZuS8Xn1tAmURERC6ybHIMYNHx2YGcnv8R6Vu2Evnaa/hVq+ZYgdidsO51qNcFGj/p0awirrjm2AvLsnKARwogi4iIyCX2bNjOmbidVKl/L2F+dhInTCCwZUsq/P1hxwpknYMFfSAwHLpM1ogLKZIcHXux2RgzBfgEOPfHQcuydjh7QWPMDXl1/lALeJXcOWd9gaS84yMsy1rubH0RESk57LYcvpo3HeMdRKdBPYl9pj/G35+oN97AONpYrRwGp36FJ5dA2VDPBhZxkaMN2R+T9i5866oFtHb2gpZl/fJHPWOMN3ACWAT0AiZaljXB2ZoiIlIyrZ/7BVnpsTRq9zQZn31Mxq7dRL89Ad+ISo4V2PsF7JgHd/4Darb0bFiRfHCoIbMsq5WHrn8v8KtlWUcd/puOiIiUCmlnzrJ73Wf4la3M7bfW4egjowju2IHynTo5ViDlOCwZDNGNodUIz4YVySdHXy4eYYyZZYxZkff7jcaY3m64/iPARxf8/qwxZrcxZrYxJuQKWfoZY7YbY7YnJSVd7hQRESkBlk2ajZWTxt2P9SJ++Mv4hIQQ8corji2258DCfrl/dp8J3r6eDSuST46+y/J9YBUQnff7AeD5/FzYGOMHdAU+yzv0HlCb3NuZccDbl1tnWdZ0y7KaWpbVNDw8PD8RRESkiDq+/wjH962nQvQtROzcRObBg0S9MRqfkMv+Xf1S30yEo5uh4wSoWNuzYUXcwNGGLMyyrE8BO4BlWTYgJ5/X7gDssCwrIa9mgmVZOZZl2YEZwK35rC8iIsXUiikxgKFNm7tJnj2HCg8/TNDddzu2+Ph22PAm3NQdbtaQACkeHG3IzhljKpL7ID/GmNuAlHxe+1EuuF1pjIm64LMHgZ/yWV9ERIqhHSu/JTXpJ6o3aEv2xLfwrVKFiKEvObY4IxUW9IbgytDp3xpxIcWGo9+y/Ae577GsZYzZDIQDD7l6UWNMINAW6H/B4fHGmEbkNn1H/vKZiIiUAjabjW8+moWXT3mank/k/IkTVP9gHl6BgY4VWP7/4Mwx6LUCylTwbFgRN3K0IdtH7miKdOAs8AW5z5G5xLKsc0DFvxx7wtV6IiJSMqyd8RnZGQnc3Lgz5+dMIrT305Rt2tSxxbs/g90fwz3Dodptng0q4maO3rKcB9QF3gTeAa4HPvBUKBERKX1ST55h39eL8A+sRpWlH+Jfpw7hgwc7tvj0EVj2D6h6G7R80aM5RTzB0R2ymyzLuvGC3zcYY/Z5IpCIiJROSybNxLKn06RMENbpM0RPm4aXv/+1F+bYYEHf3J+7TQdvR//VJlJ0OLpDtiPvQX4AjDHNge2eiSQiIqXNkV0HiT/wNRVCb6TCuiWEPzuIgBtvvPZCgK/Hw/HvofNECKnu2aAiHuLoXyOaAN8aY47l/V4N+MUYswewLMtq6JF0IiJSKqyMiQHjQ8OfdhBwc0Mq9unj2MKjW+Dr/4Obe0ADl79rJlLoHG3I2ns0hYiIlFrff/kV55J/ITqgNuXOfk30uBkYHwf+9XT+DCzsCxWqQ8fxng8q4kGOvsvyqKeDiIhI6ZOVmcWWz9/Hy7s8N21bT6URw/CvWfPaCy0Llj4PZ+Pg6dXgX87zYUU8yNFnyERERNxuVcxH2LJOUjvZRvnbmhHS41HHFv44H/Yuyn1peJUmng0pUgD0VRQRESkUZ+JPcXDLEvy9I6hzag9Rc77EeDmwT3Dq19wBsDVaQot8vVZZpMjQDpmIiBSKJf+ZjmVlctNvR4ge+TK+UVHXXmTLyn01krcvPDgNvLw9H1SkAGiHTERECtyh7T+T+Nu3lLeFc13TIIK7dnVs4VdvQuxOeHgelK/s2ZAiBUgNmYiIFCi73c7q6e+B8aPxyd+JnPEJxpGXgB/eCN/8Bxo/CTfe7/mgIgVIDZmIiBSoLQvWcj7lMFFpFagzahA+oaHXXpSeDIv6Q8XroP1Yz4cUKWBqyEREpMBkpmfy/Rfz8CKYOxtUp1zr1tdeZFmw+Dk4dxJ6fAJ+gZ4PKlLA9FC/iIgUmOXvzMVuO0PdDB8qjxzu2KIf3of9S6HNKIi62aP5RAqLdshERKRAnDyeyOEdKwiwwrhr+EC8g4KuvSjpF1g5HGq1gtsGeT6kSCHRDpmIiBSIxWMnAjm0qFOHwNuaX3uBLRM+7w1+ZeHBGHBkRplIMaUdMhER8bh967Zy+uQeQkxlGr7yT8cWrXsdEvbAo59AuUjPBhQpZPrrhoiIeJTdbmfd7OkYAuj07NN4BQRce9GhtbBlCjTrCze093xIkUKmhkxERDxq7dh3ybIlUivqJiLudOBWZVoSLBoI4fXgvtGeDyhSBOiWpYiIeEzakd/5ac8mvL1C6DR26LUXWBZ8+QxkpEDPL8C3jOdDihQB2iETERGPsCyLL0e9jWWdo0Xnv+FbxoHm6vvpcHA13PcGRNT3fEiRIkINmYiIeMSvs/5LfMYRAgNr0ewxB95VmbAXVr8CddrBrX09H1CkCFFDJiIibpd1/ARrV20ALDq/+MK1F2Sfzx1xEVAe7p8KjrzbUqQE0TNkIiLiVpbdzpbhYzjnlUj0dXdT5caa1160+hVI+hkeXwBB4Z4PKVLEaIdMRETc6uT7c/kx6yzGK5Au/2/AtRf8sgK2zYDbn4Xr2ng+oEgRpIZMRETcJvPQITZ+vgYbp2nQ5mGCKpS7+oKz8fDlIIhsAPe+WjAhRYog3bIUERG3sLKzOTz0ZY4FW/gFRHPvkw9cfYHdDosGQFY6dJ8NPv4FE1SkCNIOmYiIuMXJmGlszQnAstK558l+ePl4X33B1qlweAO0Hwvh1xdMSJEiSg2ZiIjk2/k9ezjwwWck+5+hQtQtNGjV9OoLYn+Etf+Cup2hyVMFklGkKFNDJiIi+WLPyCB26DB217wOMHQafI0H+bPOwYI+EBgOXd/RiAsR1JCJiEg+JU2cyIE0QzoJVG/Ujshala++YOVwOHUIuk2DsqEFE1KkiFNDJiIiLju39TtOzv2AA1Uq4uUTTOfnnrz6gn1fwo65cOfzUPOuggkpUgyoIRMREZfkpKURO2I4+xq0wJZzils6PEpAUNkrL0g5DosHQ3RjaPVywQUVKQbUkImIiEsS3hzL2VOp/O5zBv+gatzVo9OVT7bn5I64yMmG7jPB27fggooUA2rIRETEaWfXrSNl4UL2NG+NZT9Pm94D8PK6yr9SNv8HjmyCjv8HFWsXXFCRYkINmYiIOMWWnEzcq6NIqd+Ukyn7Ca3SjLp3NLzyguPbYf0YqN8NGvUouKAixYgaMhERcZhlWcSPGoU9NZUfQyuC8aHzkP5XXpB5Fhb0huBo6DxRIy5ErkANmYiIOCx18WLOrllL/IM9OZd6iFpNOhJeLfLKC5b/PzhzDLrNgDIVCi6oSDGjhkxERBySHRdH/Og38L2lMbt++wlv3xA6Dnriygv2fA67PoK7XoLqtxdcUJFiSA2ZiIhck2W3EztiBJbdzr6bW2LLOknTro/jX/YKLwQ/fRSWvgBVm8Nd/69gw4oUQ2rIRETkmk7P/4j0LVspM2gIh35cS5nytbjjobaXPznHBgv75v7cbTp4+xRcUJFiSv8rERGRq8r87TcSJ0wgsGVLNh46gWVlcl+/gVcec/H1/8Hv30H3WRBSo0CzihRX2iETEZErsmw2YocNw/j7c/7Rp0k8/C3hNW/nuqb1Lr/g2Fb4ejzc/Cg0eKhgw4oUY2rIRETkik7NnEnGrt1UGjmS9R9/BMaPzkP6Xf7k82dgQV+oUC13AKyIOEy3LEVE5LIy9u0jacpUgjt24OdMf9JTfuX6O/5GaFTYpSdbVu5D/KknoPdq8C9X8IFFijHtkImIyCXsmZnEDh2GT0gIIS++xPdfzMPHL4x2Ax69/IJdH8HehdBqBFRpWrBhRUoA7ZCJiMglkiZPJvPgQapOi2HNR8vIyT7NnT3+iZ+/36Unn/o1dwBs9TvhzhcKPqxICaAdMhERuUj69u0kz55DhYcfJqN2PQ5tW0Zg6A00v7/VpSfnZMOCPuDlA92mgZd3wQcWKQHUkImIyJ9y0s4RO2w4vlWqEDH0JZb+JwYsG+0HDLj8gg1vQuwO6DIJylcp2LAiJYhuWYqIyJ8Sx48n+8QJqn8wjwO7f+XU798TWeduatxc59KTf/savpkIjXtC/QcKPqxICaIdMhERASBt40bOfPopoU/3IqBxY9bOnIbxKkOX5/teenJ6MizsDxVrQ/txBR9WpITRDpmIiGA7fZrYkSPxr1OH8MGD2fTRCjLTjnLj3Y8RHFbh4pMtC5YMhnNJ8Oha8AssnNAiJYgaMhERIWH0aHJOn6HatGlkZeewY/l8fAIiaNvvb5eevGMu/LwE2o6G6EYFH1akBNItSxGRUi5l2TJSl68g/NlBBNx4I8vemYvdlkLLR3vj4/OXv7cnHYAVw6DWPXD7s4URV6REUkMmIlKKZSckEv/6aAJubkjFPn1IOBzLkR9XUS68Po3b33HxybZMWPA0+JWFB6fBlV4uLiJO0y1LEZFSyrIs4kaOxMrMJHrcOIyPD8veiQEsOgwaeOmCda9D/B549GMoF1ngeUVKMv31RkSklDrzyaec27SJSi++iH/Nmvz01XZOx+6g8o2tqFqvxsUnH1oHW6ZAsz5wQ4dCyStSkqkhExEphbKOHSNh/HgC77idkB6PYrflsGHuDIx3IJ0H97745LQkWDQAwuvCfW8UTmCREk4NmYhIKWPl5BA7bDjG25uoMWMwXl5smPclWeknaHjvwwSFlLvgZAsWPwsZKdB9FviWKbzgIiWYniETESllkufM4fyOHUS/NQ7fqCjSzpxl19pP8SsbTesn/zJxf9tMOLASOoyHyJsKJ7BIKaAdMhGRUiTjlwMkTZpMubZtCe7aFYDl77yPlZPG3U/0xcvngpeDJ+yDVS9Dnfvg1n6FlFikdFBDJiJSSlhZWcQOHYpXcDCR/3oNYwwnfjnK7z+tpXxkIxq2bva/k7PPw4LeEFAe7n8XjCm84CKlgG5ZioiUEklT3yVz/36qvDsVn9BQAJZPiQEMnZ4bcPHJa16FxH3w2AIICi/4sCKljHbIRERKgfSdOzk1Ywblu3WjXOvWAPy4egupiXuofvN9RF1X5X8nH1gF30+H2wZBnTaFlFikdFFDJiJSwtnT04kbNhzfyEgiRgwHwGaz8fWHs/DyDqbjc0/97+Sz8fDFQIhoAG1GFU5gkVJItyxFREq4xAlvk3X0KNXefx/voCAA1s1aQHZGPE0696NsubK5J9rtuc1YVjp0nwk+/oWYWqR0UUMmIlKCpX2zmdPz5xP6ZE8Cb2sOQOqpFPZ+tQD/oGrc9Vjn/5289V34dT10ngiV6hZSYpHSqdAaMmPMEeAskAPYLMtqaowJBT4BagBHgIctyzpdWBlFRIqznJQU4l5+Gb9atQh/4YU/jy+dNBPLns69T/fD648XhMftgrWvQd3O0KRX4QQWKcUK+xmyVpZlNbIsq2ne78OAdZZl1QHW5f0uIiIuiB8zBtvJk0S/9RZeAQEAHN3zK3G/bCS0SjPqtWiUe2LWOVjQBwLDoOs7GnEhUggKuyH7q/uBuXk/zwUeuMq5IiJyBakrV5G6eAlhAwZQpsH/JuyvfO89MN50HtL/fyevGgEnD8KD06BsaCGkFZHCbMgsYLUx5gdjzB8joCMsy4rL+zkeiPjrImNMP2PMdmPM9qSkpILKKiJSbNiSkoh/7TUC6tcnbMD/Gq9tS74m7dR+ajXpSHi1yNyD+xbDD+9DiyFQ6+7CCSwihfpQ/52WZZ0wxlQC1hhj9l/4oWVZljHG+uui/9/efYfHVZ55H/8+ozaaGfVmySq23LuNBW4CAoRAIBACCWmbsHRIdklI8gZIgTQ2JG+yhGzyBgiQtmR3SSAJnQBhE1yxDbj3om4Vq480Gs2c8/5xRpKFZVyQZiTr97kuX7LmHAmugg8AACAASURBVGtueSz51vP8zn1s234YeBigrKzsqOMiIuOZbdvU3X0PVlcXBT+4D5OQAEAo2MvqPzyGKyGdSz7/Wefkthp4+l+hYBGc9/UYVi0iMVshs227JvK2AfgTcBZQb4zJB4i8bYhVfSIiY1HbU0/R+dpr5HzpdpKmTu1//MWH/ptQTxNnXv4ZkjxJYIXhTzdDuBeuehTiE2NYtYjEpCEzxniNMSl9vwc+AGwFngauiZx2DfCXWNQnIjIWBatrqL/33/CceSaZn/1s/+Ot9c3sXv0XklNLWf7RC50HVz0AB1+HS34IWVNiVLGI9InVlmUe8CfjXMkTD/zetu0XjTHrgSeMMdcDFcDVMapPRGRMsS2LurvuAmPI//73Ma6Bn7efeeBhbKuHD9x0izPmonojvHYvzPkILPx0DKsWkT4xachs294PLBji8cPABdGvSERkbGv+7W/pWr+e/Hu/R2LhxP7H927cQcO+VeRMWsbUM2dDTwc8eT2k5DsDYDXiQmRU0KR+EZExrmfvXhr//X58551H2pVX9j9uWRYvP/QgmEQ+9MXIxewv3AGtFfDPz0FyRowqFpF3Gm1zyERE5CTYvb3U3nEnLo+H/O98G3PEitfap16lq20f05deTmZ+Nmz5I7z9OJz9FShZHsOqReSdtEImIjKGNT34EIFt25j4wAPE5+T0Px4M9PDGn39HXGIWF936SWipgGe/BIVnwbl3xLBiERmKVshERMao7i1baHrwQVIvv4zUiz4w6NgLP3+ccG8zS6+6hsR4Fzx1E9gWXPVLiNPP4iKjjRoyEZExyAoEqL3jTuKzs5nwjW8MOtZU3cDe9c/izZjO0ivOh9d/BFVrnRB/xqTYFCwi70o/JomIjEGN999PcP9+ih59hLjU1EHHnn3gIbBDXHzrrVC5Fv7+A5j/CZj/sRhVKyLHoxUyEZExxr92Hc2/+S0Zn/oUvhUrBh3btXYLhyvXMWFqOZNm5MKTN0J6MVzyf2NUrYicCK2QiYiMIeGODmq/dheJJSXkfuXLg45ZlsUrjzyIcSVz6W03wLO3Q3sNXPcSuFOP8RFFZDRQQyYiMobUf/8+QofqmfT7x3F5PIOOrfzvFwl0VDD73E+RXvcSbH0Szv8GFJ0Zo2pF5ESpIRMRGSM6Xn2VtqeeIuvmm0leuHDQsUBnFxufe5x4dx4XfvRMeOR9ULICyr8Um2JF5KQoQyYiMgaEmpupu/sekmbOJOfznzvq+HM/+y1WqI3yj19D/F9uBlccXPmw81ZERj2tkImIjHK2bXPonnuw2tspeOwxTGLioOP1+2s5+PZLpGTPYXHS/0LNRvjYbyCtMDYFi8hJU0MmIjLKtT/9NB0vv0LuV76Me8b0o44/9x8Pgm3xwY+ugL9/FhZ9BuZcEYNKReRUactSRGQU662r49B3v0fyGWeQee21Rx3f+veNtNS+ycQZZ1P01p2QNQUuvi8GlYrIe6GGTERklLIti9qvfQ3bsii47/uYuMF5MCsU5rVfP4yJ8/KhKZuhswGuegSSfDGqWEROlRoyEZFRquX3/0XXmrXkffWrJBYXH3X8td89TbCrhnnzF+I78Be44JtQsCgGlYrIe6WGTERkFOo5cICGH/0I79lnk/7xq4863tXuZ/PLT5CYPIELev8DJp8Ly/41BpWKyHBQqF9EZJSxQyFq77wTk5RE/ve+hzHmqHOefeAxrHAH55aEcSUmwUceApd+xhYZq9SQiYiMMocfeYTAps0U/PhHJOTlHnW8ZnclVVtfJS2thPnmP+HD/wWp+TGoVESGi36cEhEZRQLbt9P4s5+TeskHSbv00iHPef5nDwJwaebLUHY9zLwkmiWKyAhQQyYiMkpYPT3U3nEncRnp5H3zm0Oe8/bLa2mv30xxRir5RRnwge9FuUoRGQnashQRGSUaf/pTevbsoeihB4nPyDjqeCgU4h//+Qgul5dL816Dq16ARM8QH0lExho1ZCIio0DXhg00P/Yr0q++Gt+55w55zquPPUVv4BBnZNt4Pvh1mDA3ylWKyEjRlqWISIyFO/3U3nkXCYWF5N3x1SHP6WxuZ9trfyQpIYNzl7hhyc1RrlJERpJWyEREYqzhhz+kt6aGkt/9FpfXO+Q5z9z/MLbVxQWTm3Fd+XsYYhSGiIxdashERGKo8+9/p/WJJ8i8/jo8ZWVDnlOxZR+1u/9BZnIGs669BnxHj8IQkbFNW5YiIjESammh9hvfIGnaNHJuu+2Y5734sweAOC69qAimvT96BYpI1GiFTEQkRuq/+13CLa0UP/QQrqSkIc/Z8OTzdLbuZ3JOHrkfvTvKFYpItGiFTEQkBtqee472518g518+j3v27CHPCQV6WPXU73G5fFzylS9AgjvKVYpItKghExGJst76Bg5957u4F8wn64YbjnneS/d9n1ColTOXnIF70vwoVigi0aaGTEQkimzbpu4b38Du6aHgvvsw8UMnR1q3rmbXzi243bks/9cvR7lKEYk2NWQiIlHU+j9P4H/9dXK/8hWSJk8e+qRgF8/+9FFsO8iFN9yEKy4uukWKSNSpIRMRiZJgZSX1P/wh3uXLyPjUJ4953v7H7qa+rYHsgnlMP3tpFCsUkVhRQyYiEgV2OEztnXdh4uLIv/dejGvob7/Wtqd5aVU9mAQu+z+3R7lKEYkVjb0QEYmC5l/9iu4336TgB/eRkJ8/9Enttax77BG6ghbTl15JZkFOdIsUkZhRQyYiMsICu3bT+MBPSbnwQlIvv3zok6wwwSduZl2dj7iEBC763D9Ft0gRiSltWYqIjCA7GKT2jjtwpaYy4dvfwhzrHpSrf8qL692Ewx0sueoaEpMSo1uoiMSUGjIRkRHU+PP/R8/OneR/59vEZ2YOfVLNRppe+Dl72sJ4Mqaz7CMXRLdIEYk5NWQiIiOk6623OPzLX5J25ZWkXHCMJqunE568gecOnQ12iItvvSW6RYrIqKCGTERkBFhdXdTdeRcJEyaQ97W7jn3iC3ewqyqBps5G8qauYPKC6dErUkRGDYX6RURGQMOPfkywooLiX/+aOJ9v6JO2Pon15n/ySuPHMK42PvSFm6JbpIiMGlohExEZZp0rV9Hy+9+Tec1n8S5dMvRJrZXwzO2sDH2EQPchZpZfQXpuRnQLFZFRQw2ZiMgwCre1Uff1r5NYWkrO7ccY7BoOwVM3EQjFs7Gil/ikXD5w49XRLVRERhU1ZCIiw+jQvfcSamqi4Ac/wOV2D33Syn+HyjU813MNVqiN8k9cR3xiQnQLFZFRRQ2ZiMgwaX/xJdqffobsW24hed7coU+qXAf/ex8NRZ/m4N638WXPZvEl5dEtVERGHTVkIiLDINTYyKFvfQv3nDlk33Lz0CcF2uCpGyCtkGe3ZIJtccnnNeZCRNSQiYi8Z7ZtU3f3PVhdXRT84D5MwjG2H5/7MrTVsG3S3bTUvc3EWe+jaHZpdIsVkVFJDZmIyHvU9tRTdL72Gjlfup2kqVOHPmnT/8CWP2Cdcyd/e/oVjMvLh75wQ3QLFZFRSw2ZiMh7EKyuof7ef8Nz5plkfvazQ5/UvN9ZHStezmsHpxH0VzP3/I/iy0iJbrEiMmqpIRMROUW2ZVF3111gDPnf/z7GNcS31HAvPHkjuFx0XfgAm19+goTkfN5/7ZXRL1hERi01ZCIip6j5t7+la/168r52F4mFE4c+6X/vg5oN8KGf8Oxv/ooV7uDcf7oBV3xcdIsVkVFNDZmIyCno2buXxn+/H99555F25TFWuw6uhNd/DIv+iZrEMqq2vEJa3gIWvP8Y0/tFZNxSQyYicpLs3l5q77gTl8dD/ne+jTHm6JO6W+CpmyCzFC7+AS/87EEALr1NYy5E5Gi6ubiIyElqevAhAtu2MfGBB4jPyTn6BNuGZ74AnfVw/cu8/Y+ttNVvpnjexeRPLYp+wSIy6mmFTETkJHRv2ULTgw+SevllpF70gaFPeut3sP0vcP43sfIW8I/HH8UVl8KlX7guusWKyJihhkxE5ARZgQC1d9xJfHY2E77xjaFPatoDL9wBk8+B5bfx8mNP0ttdx4KLPoEnxRPdgkVkzNCWpYjICWq8/36C+/dT9OgjxKWmHn1CKAhPXg/xbvjIQ3S2drLttT+S5C3ifZ+5LPoFi8iYoRUyEZET4F+7jubf/JaMT30K34oVQ5/0t+9C3Sb48M8gtYBnHngE2+rivGtvwjXUjDIRkQh9hxAROY5wRwe1X7uLxJIScr/y5aFP2vcarP4plF0HMy+lctt+anf+nYyJi5lz9qLoFiwiY462LEVEjqP++/cROlTPpN8/jsszRA7Mfxj+dAtkz4AP3AvACz//BRgXH/rCrVGuVkTGIq2QiYi8i45XX6XtqafIuvFGkhcuPPoE24an/wW6m+Gjj0Kihw3Pv07n4R1MPuNicksmRL9oERlztEImInIMoeZm6u6+h6SZM8n5/OeGPmnDo7Drebj4Ppgwj1Cwl1X//Riu+HQu+fwxbjYuIvIOashERIZg2zaH7rkHq72dgscewyQmHn1Sww546esw9f2wxJnA/9LD/0Oop5GzPvJ53N7kKFctImOVtixFRIbQ/vTTdLz8CjlfuA33jOlHn9AbgD9eD0kpcMUvwBhaG1rYteovuFMmseLqi6JftIiMWVohExGJaHvmGRru/wmhujoAEkpKyLz22qFPfuUeaNgGn/oD+HIBePaBh7GtABfedIvGXIjISdF3DBERnGas7pt3E6qtdYL6tk3o0CHan3/+6JN3/xXWPQhLboXpzu2T9r+1i/q9K8kuWcL0s+ZGuXoRGeui3pAZY4qMMa8ZY7YbY7YZY74QefxbxpgaY8zbkV+XRLs2ERmf7GCQ+vt+gB0IDH68p4eG+38y+OTOBvjL5yBvLrz/WwBYlsVLDz4IJpHLvnhzdIoWkdNKLLYsQ8CXbdt+0xiTAmw0xrwcOXa/bds/ikFNIjKO2LZNb0UFnStX4V+5Ev8bb2B3dQ15bt/2JQCWBX++FXo64JpnIMENwLo//42u1j1MW3oVmQU50fgUROQ0E/WGzLbtOqAu8vsOY8wOYGK06xCR8SXc2UnX2rV0rlyJf+UqequrAUgoLib9ig/T/uKLhJtbjvpz8fn5A++sexD2vgKX/hhyZwEQDPSw7k+/JS4xk4tv/VRUPhcROf3ENNRvjJkELALWASuAfzHGfBbYgLOKdtR3R2PMTcBNAMXFxVGrVUTGFtuyCGzfgX/l63SuXEn325sgFMLl8eBZupTM667FV15OYuT7SPKiRdR98+5B25bG7Sb39i867xza4gT5Z1wCZdf3n/PiL35PONjM8o9/kUR3UlQ/RxE5fcSsITPG+IAngS/att1ujPkF8F3Ajrz9MXDdO/+cbdsPAw8DlJWV2dGrWERGu1BjI52rVuFfuQr/6tWEm5sBSJo9i6xrr8VbXo5n0cIhZ4qlXXYZQP9VlvH5+eTe/kXn8WCXM+IiORMu/xkYA0BzbSN71j2LJ2MaS644P3qfqIicdmLSkBljEnCascdt234KwLbt+iOO/xJ4Nha1icjYYQWDdL/5Jv6VK+lcuYqenTsBiMvKwlu+Al95Od7ly4nPzj6hj5d22WX9jdkgf/06NO2Cz/wZvFn9Dz/zk4fADnLRzRpzISLvTdQbMmOMAR4Fdti2/e9HPJ4fyZcBfATYGu3aRGR0s22b4MGDzgrYypX41693wvgJCXgWLSLnS1/CV76CpJkzMcPVIO14FjY8Bstvgynn9T+8+42tNFWsI29KOaWLZgzPc4nIuBWLFbIVwGeALcaYtyOPfQ34pDFmIc6W5UFA146LyOAw/usr6a2pAQbC+N7ys/GcdRZxPu/wP3l7rXPj8PwFcP43+x+2LIuXH34Q43LzoS/eNPzPKyLjTiyuslwJmCEODTF9UUTGG9uyCGzbjn/VyqHD+NdfNyiMP2IsC/50M4R64KrHIH4gd7bqiZcIdBxk5tmfID03Y2TrEJFxQbdOEpGY621owL9qtbMNuXo14RbnAmv37NlkXXcd3vIVeBYOHcYfVpufgFe/A23V4E6FQBtc/h+QPbX/lIC/mw3PPE58Ug4X3fTxka1HRMYNNWQiEnV9YfzO11/Hv3IVPbt2AZEw/tnlJx3GHxabn4BnboPebuf9QBuYOIh3Dzrt+Z//FivUytmfvoP4xITo1ScipzU1ZCIy4o4K47/xBnZ398iG8U/Wq98ZaMb6Cw87j8+/GoCGg3UcePNFfFmzKLvk7BgUKSKnKzVkIjIiwp2d+Nes6W/C+sP4JcWkf+QjzkywkQrjnyjbhsP7oHI1tFUNfU5bdf9vn/3pg2BbfPDzt0apQBEZL9SQiciwOCqM/9bbEA73h/Gzbrgeb3k5iUVFsSsyHIL6LVCxBirXQOVa8Dc4x4wLbOvoP5NWCMC219+ipWYjBTPPp3hOaRSLFpHxQA2ZiJyydw3jX3999ML4xyywG6o3RJqvNVD1BgQ7nWPpxTDlfCheCiXLoW7T4AwZQEIyXHA3lmXx2q8ewri8XPaFG2LzuYjIaU0NmYicMCsYpHvjxv4bdPeH8bOz8Z1zNt6+MH5W1nE+0gjpaoaqdVCx2ln9qn0LrF7AQO5sWPAJKF7m/EqbOPjP5kSGu/ZdZZlWCBfcDfOv5n9/8xd6/NXMveCz+DJTo/5picjpTw2ZiBxTfxj/9ZV0rlpJ1xvrB8L4Z5xBzpe/hK+8nKQZM2ITxm+rjmw/Rhqwhu3O464EmHgGLPt8pAFbAsknMC9s/tX9Af4+Xe1+Nr303yQk53PhdVeNwCchIqKGTETeIdzRgX/t2ncN43uXnIXLG+Uwvm1D4y6n+aqI5L/aKp1jiSlQdBbMvdJpwCYudrYbh8FzP/0VVriD8z79BVzxccPyMUVE3kkNmcg454Txt/XfoLv77UgY3+uNbRg/3OvkuipWDwTwu5udY95cKFkWWQFbCnlzIW74v53V7a2icssrpOXNZ+GFS4f944uI9FFDJjIO9TY0DMwEW72acGsrAO45c8i64QZ85StIXrgQkxDFwac9nVC93mm+KlY7YfxQJGCfWQozLnGasOJlzvtmqDuwDa/nfvogAJfepjEXIjKy1JCJjAPvGsY/9xy85WfjXbGc+MzM6BXlb4o0X5EMWN1mZxCrcTkrXouvieS/lkLKhOjVFbHplXW01W+iaN7F5E+N4agOERkX1JCJnIZs2yZ44KCzDTkawvi2Da0VA81XxRo4vMc5FpcEhWVQfruzAlZ4lnMfyRiyQmH+/p+P4IpL4UO3XRvTWkRkfFBDJnKaCHd0DJ6MX1sLQGJJCelXXom3fAXes6IUxrcs54rHvu3HyjXQUeccc6dB0VJY9GkoXg4FCyE+aeRrOgF/+/Wf2fTyE1ihdgCyS5bhSY3hnQREZNxQQyYyRtnhMIHt2/tv0N29adNAGH/ZUrJuutEJ4xcWjnwxoR5n5ld/AH8d9LQ5x1IKnMGrxcuctzmzIFb3q3wXf/v1n3nrhV8Dof7HmirW87df/5nz//mKmNUlIuODGjKRMaS3vgH/qlEQxg+0QdX6ge3Hmo0Q7nGOZU+HOVcMNGHpxVEJ4J8sy7Ko31/Lvje3Ubt7D1VbXuHIZswRYtPLT6ghE5ERp4ZMZBSzenoiYXynCevZvRuAuJxsfOee68wEi0YYv+PQ4AB+/Tbnvo8mDvIXwFk3DgTwvdkjW8spsEJhqndVsP+t7Rzau4eW2oN0d9RiW323STKAfYw/2x61OkVk/FJDJjKKOGH8A5GZYJEwfiCASUggefFicr/yZbx9YfyRWnWybWjeP7D9WLEaWg44xxI8TgD/nK86AfyJZZDkG5k6TlEo2MvBLXup2LSDQ/v20lpfQaCzDuxg5AwXCe5cMifOI2dSKSVzZ1K6eDYP3XLDkM2XK163ShKRkaeGTCTG3jWMf9VVIx/GD4egfusRAfy14G9wjiVnOitfZ17vBPDz50NcFGeTHUdPVw8H3t5FxZYd1O/fR1tjJUH/IQa2HuNJ9E4gZ1IZeaVTKZk3k9JFM0h0H30RwYILrz4qQwbxLLjw6qPOFREZbmrIRKLMDocJbNvWPxMs6mH83m5n6GrlWmf7sWo9BDucY2nFMOW8gQB+1rRRE8Dvavezb+MOKrftpOHAPjqaqugNNACWc4JJxO0rYMK0FUyYMpXJC2ZTPLeU+MQTayD7cmJ9V1m64lNZcOHVyo+JSFQY2x46NzEWlJWV2Rs2bIh1GSLH1Vvf4ATxV63Ev2o14bY2MAb3nDl4y1fgKy8necGCkQnjdzVD1bqBDFjtW2D1OsdyZw80X8VLIS0KV2SegPamVvZu2E719l00Vu2n83AVoZ7D9OW8jCuZ5NSJZORPIn/aNEoXzWLi9BLda1JERjVjzEbbtsuGOqYVMpER0B/Gf32lE8bf4wxBjcvJxve+941sGL+t2ln96suANWx3HnclQMEiWPY5Z/uxeAkkZwz/85+k5tpG9q7fRvWuXRyuOoi/pZpwb0v/cVdcCp70iWROL6NgxnSmLJ5D7qR8XKNk5U5EZDioIRMZBscN43/48pEJ49s2NO6KzP6KrIC1VTrHEn1QdBbMuTISwF8MCcnD99wnybIsGg7WsW+jM2aiueYgXa3VWOGO/nNcCen4MgrJKiqncMYMShfPJrswN2Y1i4hEixoykVMUbm/Hv2Zt/+2JQrXOJPrESZNI/+hHB8L4Hs8wPmkv1G06YgTFGuhudo55c5ztx2Wfc97mzYW42HyJW5ZFzc6DHHh7J7V7dtNSW0F3ew221dV/TnxiNqm5U8kpLqVw9gymls0mNTs9JvWKiMSaGjKRE3S8ML7vppuGP4zf0wnV6wdWwKo3QG+kqckshRkfHMiAZZbGZABrKBSicvM+DmyOjJmoOxgZMxEZFBsZM5FRMIfcyVMoijRfuiWRiMgANWQi7+LdwvhZN94w/GF8f1Ok+YpkwOo2gR0GDEyYC4s+42w/Fi+DlAnD85wnIRjo4cDbuzm4eQcN+/fR1lBBzzvHTHjyyClZTO7k0siYiVkkeUbHvSpFREYrNWQiR7B6eujasKF/JtigMP555w2E8TOGIQxv29BaMbD1WLkGmpxJ/MQlOQNYy7/oBPCLznRuyh1FXR1d7H9zB5VbnTET7U1V9HY3AGHnBJOI25fPhKnLyZsyhUnzZzNp/tQTHjMhIiID1JDJuDYojP/6SrrWHxHGL1tM7hUfdsL406e/9zC+ZTlXPB4ZwO9whsDiToOipbDgk872Y8EiiI/eqlJnczt7N2yjavsuGiv203G4ilBPE4PGTKQUkFNyLvnTpjF5wWyKZk3SmAkRkWGihkzGnaiF8UM9zsyvvun3VWudm3IDpOQfMf9rmTMPLEpjHJrrmti3YTvVO3fRVHUAf3PVoDETJs6HJ20i+dPOcMZMnDGbvNKJGjMhIjKC1JDJac8Ohwls3ToQxt+82Qnj+3x4ly3Fe9PNkTD+xPf2RIF2qHrDmX5fuRZqNkIo4BzLng6zP+xsP5Ysg/SSEQ/gW5ZFU2U9+zZuo2bXbg5XH6CrtQYrPHC/Rld8Ot6MiWQVrmDiTKf5yimOfjZNRGS8U0Mmp6Xe+vqBmWCr1wyE8efOJeumG50w/vz57y2M31HvNF99GbD6rWBbYOIgfwGUXT8QwPdmD98nNwTLsqjdXcn+t7ZTt3sPzXUH6W6rxbb8/efEJWaRklNKTvFkCmfNYMri2aTnjcBgWhEROWlqyOS0cKwwfnxOzvCE8W0bmvcPTL+vXOO8D5DgcQL453zVuf1Q4ZmQ5Bumz+xooVCIqm37OfD2Dg7t3UPLoQoCHbVHjJkwxLtzycifRc6kUornzKR08Sx86SkjVpOIiLw3ashkTLJtm+D+/ZFVsFXDH8a3wnBoSyR8H9mC9Dc4x5IznVWvxdc6GbD8BRA3MlcWBnuCHNy0m4ObdlC/fx9tDZX0+OvAjtyLkjgSPXlkFy8ib/IUSubNYvKiGbi9sZvILyIiJ08NmYwZ4fZ2/KvX4F/lNGGhukgYf/Jk0j/2MXzlK/CceeaphfF7u53MV8UaZxuyaj0EI7f0SSuGKec5q1/Fy5082AgE3AOdXex7cyeVW3dSf2AfHY1VBLvrGRgzkUCSN5+80qVMmDKVSfNnUTx/KolJicNei4iIRJcaMhm1BoXxX1/phPEtKxLGX4b3llvwrlhxamH87haoXDeQAat9C6zIqlPubJh/deQqyGWQNoyT9yM6WzqcMRPbdtFYuZ+OpipCPY30j5kwbtypBRQUn0v+tEjzNadUYyZERE5TashkVHnXMP7NN516GL+t5ojtxzXOPDAAV4Iz86vv/o9FS8AzvEH31kOHneZrx24OVx2gs6WKcLC5/7hxeSNjJhZRMG0akxfNIn9akcZMiIiMI2rIJKasnh661m/ovz1Rz569QCSMf/75zkyw5ScZxrdtZ+L9kQH81krnWKIPis6COVc6W5ATF0Pi8N38u6HiEPs2bqV21x4OVx/E31qNFWrrP+6KT8ObXkhW4TIKZk5nyhlzyC3RmAkRkfFODZlEVV8Yv/P11/H3hfF7eo4I41+Bt/xskqZPO/EwfrgX6jYPHkHRHVmB8uY4K19LP+c0YHnzIO69/7O3LIu6PVXOmIk9e2muOUBXew12+MgxE5mkZE0iu3gyhTOnM7VsDukTst7zc4uIyOlHDZmMuHBbmzMZf6gw/tVXn3wYP+iH6vUDAfzqDdDb5RzLmAwzPug0YcXLIGvKex7AaoXCVG7bz8HNO6jbs5eWQwcJtNdi25Ghrxjik3JInzCTnOJSiubMYOri2fgyU9/T84qIyPihhkyGnR0OE9iyhc7ITLChwvi+8hUkTDzBML7/8BH3f1wNdZvADgMGJsyFRZ+JXAG5DFLz31PtzpiJPVRu2cmhfXtpra84esxEch5ZxQvJnVRK8dyZTDljnMX7HwAAFJlJREFUJm7f8G17iojI+KOGTIZF76FD/TPB/GvWYB0Rxs++xbk1UfL8+Zj44/yTs20n73VkAL9pt3MsLsnJfJV/0Rk/UXSmc1PuUxTwd3PgrV1UbNlB/YF9tDdWEuw6esxE7uQl5JVOYdKCWUxaMF1jJkREZNipIZNTYgUCdG3YGGnCXie4dx/ghPFTTiaMb1nQuGOg+apYAx21zrGkNCheAgs+6QxgLVgE8UmnVG9nawf7N+6gcttOGg/up/1wFaFAI2A5J5gkklMmkj/9bCZMncbkhbMomlNK/PEaSBERkWGg/23khNi2TXDfvv4bdB8ZxvecWUb6R66MTMY/Thg/1OPM/OprvqrWQiByFWJKfmT213Lnbe4scJ383K3Whhb2bdhG9Y5dNFYeoLO5inDwcP9x4/KQnDaRCaXzyZ8+jdJFsymYXqwxEyIiEjNqyOSYnDD+mv4mLHToEACJpaWkf/xqfOXlThg/+V1u0xNoh+o3Bq5+rNkIoUgYPmsazP6ws/1YvBQyJp10AL+puoG9G7ZSs3M3h6sP4G9555iJVDxpE8kqXMLEGdOZsngO2cV5ar5ERGRUUUMm/Y4Zxk9Jwbt0Kd5bbz1+GL+jfnAAv34r2BaYOMifD2XXO9Pvi5aCL+eEa7Msi/r9Nex7czu1u3bTXHuQrrYa7HBn/zlxCRn4skrILppM4cwZTCmbTWZ+9nv5KxEREYkKNWTj3DHD+PPmHT+Mb9vQvH9g+7FytfM+QHwyFJbBOf/H2X4sPBOSfCdUkxUKU7XjIAc2baduzx5a6ioIdNRiW92RMwzxSdmk500np6SUotkzKF08m9SsUw/4i4iIxJIasnHGCgT6J+N3rlo5EMbPzSXlggucmWDLlg0dxrfCcGgLVK51mq/KtdBZ7xxLznAar8XXOhmwCfMh/vhXI4aCvRzcvJeDm7c7YyYOVUbGTAQjZ7hISM4jc+I8cidPoXjuTErPmIUnRWMmRETk9KGG7DR3zDB+YiKessWkX3kV3vIVJE0bIozf2+1kvvoD+G9AsMM5llYEk891th+Ll0P2dDhOLqunq4f9b+2gYstOZ8xEQ9+YiVDkjHiSvBPInXQmuaVTmDR/FpMXTifRfWpXVoqIiIwVashOQ6ccxu9ugcp1Axmw2rcgHFmpypkF8z82EMBPL3rXGrra/ezdsL1/zERHUxW9gQYGxkwk4vYVMGF6OROmTGXy/FkUz5+iMRMiIjIu6X+/04AdDtO9eTP+vjD+li0DYfxly/B+7lZ85eUkFBQM/oNtNUcE8NdAw3bABle8M/NryS3O9mPREvBkHvP525ta2bN+G9Xbd9FUdYDOw1WEgk39x40rmeTUQnJL51IwbTqTF85k4vQSXPEnP9JCRETkdKSGbIzqravrXwHzr1mD1d7uhPHnzyP7llsiYfx5A2F824bGXZEBrJEMWGulcyzR54Tu51zh5MAmLobEoTNaTdUN7Nu4jZpduzlcdYDOlmqs3tb+4664FDzphWROPJOC6dOYsngOuZPyNWZCRETkXaghGyPeNYz//vcfHcYP9zr3fOwL31euga7IcFRPtpP9WnKr8zZvHsQN/qfgjJmoZd+b26jbvccZM9FagxXu6D8nLiEDX0YRWUXnUDhjOqWLZ5NdmBuVvw8REZHTiRqyUcq2bYJ79/bPBOvasOGIMH7Z0WH8oB+q18PbkS3I6vXQ2+V8sIxJMO2igQB+1pRBA1itUJjqbfs58PaOyJiJg3S317xjzEQWqXlTySkqpXD2DKaWzSY1Oz3afy0iIiKnJTVko0i4tXUgjL9q9UAYf8oUMj7xcbzl5XjKypwwvv+w03j99TfO27pNYIUAAxPmwqJ/crYfi5dBan7/c4SCvRx8cycVm3dGxkxUEOisHTxmwp1LRsFcZ8zEnJlMWTwLT6o3+n8hIiIiI+zbf/sdTx74JVZcC65wBldNvpF7zv9M1OtQQxZDdihE95Ytxw7jf/5z+FasICE/38l7Va6Bl//gbEE27XI+SFySk/laflskgH8WuJ0BqcFAD/vf2kXFltep37+XtoZKgv5DHDlmItGTR05JGXmlUyiZN4vJC2eQ5NGYCREROf19+2+/4w8V92PiezGAHd/CHyruh78R9abM2LYd1SccTmVlZfaGDRtiXcZJebcwvm9FuRPGnzsH07LniAD+GmivcT5AUqpz1WPf9mPBIkhw09XRxf6N26nY6oyZaG+sHGLMRD7peSVMmDKVkgWzmDRvKvGJCTH7uxAREXmvLMvicFcnDf42Dne109zdQXNXO62BTtqDnbT3dNAR7MLf20FXqJvukJ+ecBdBq5t2dmFc4aM+pgllsPn6fwx7rcaYjbZtlw11TCtkI8wJ46/vvz1RcF8kjJ+XR8qF78dXXo73rDLi/PudAP6O78Nf10IgcoNs34SB5qtkGeTOpr2lk30btlH11E4aK593xkz0HAac5tq4kklOKSB38nlMmDqN0kWzKZyhMRMiIjI6hMJhmrraI01UR6SJ6qA10EFbTwcdQT+dQT/+3k66Q110h7sIhLvotbrptbsJ2d1YJoBlAmCCGHP8xSXbNhgrCWO7icNNnEmGIZoxACuuZbg/5eNSQzbM+sP4r68cCOMHgwNh/KuuwnvWQpKSmjCVa6HyP+AXGyEUcD5A1jSYdbmz/Vi8jOZuL3vf3E7133dzuOr3+FuqCfcO/EMxcT48aRPJn17mjJk4Yw55pQUaMyEiIsMqGArR4G+n8YiVqJbudtoCnbRGmih/r5/OSBMVCHUTsPz9TVTYDhA2AWwTwLiCx39CwLZdkSYqmTjcxBs3iS4fKa5s3HFe3HEePAkefAk+fIleUhN9pLlTSE/ykZGcQrY3lSxPCnnedNLcnqP+b5z/6DnY8Uc3X67wELcPHGFqyIbBoDD+ylWE6p37OyZOmULGJz+Bd/FcPNnduOo3QuWv4MktYFtg4iB/PpRdh1W4lEYzlX07aqnZupvml96iq/WZQWMmXPHp+DIKySwqp3DmdKYsnqMxEyIickxdvT00+dtp6GzjcGQrryXQQVvAT1tPe38T5e/1O01UuMvZzrMHmiirv4nqPaHntK04jJ2Ey07GhZsEk4zblUaSawLuOA/J8V488R58iU4TlZLoJcOdQro7hUxPKlnJKWR5Usn1pZGS6B7RBYarJt/oZMiO+NxsK4GPTr5xxJ7zWNSQvYu2Z56h4f6fEKqrIz4/n9zbv0jaZZc5YfzNW/pnggW2bHXC+KmpeJctxbtwBr5CmwT/dqj8A6y+z/mA8clQWIa14svUmNkcqDXU7q+gZWMF3e2PY1td/c8dl5hFSs4UcoqdMRNTFs8mPTf6HbuIiERXR083jf52GjvbOdzdTnO3k4dqC3TQ1tNJZ9BPZ6+znRcIdxEId9MT9hO0AoToJmx3Y5meSBMVOv4TArYVj7HduCLbeQmuZDyuDJLiPJEmyoMn3osvwUtKoo80t4+0JB/pySlkJaeS5XFWonK8qaQkJR//CUeJe87/DPyNQVdZfjRGV1kq1H8Mbc88Q90378YOBAYeTEggacYMeisrnTC+y4V77lx8C6fhLY4jOW4/pmYddDorZCRnECpcRqU5gwPNaRw61EprfRWBjlqweyIf1EWCO4eU7GJyJpVSPHsGpYtn4UtPGZHPS0REhpdlWbT3NVH9maiBrbz2nk6ngYqsRjl5KD9Bq3tgOw9nFcp29WDM0Lmmd7KthP4mKt64iTfJJLo8JLmSna28eC/eBOdXaqKPVLeznZfh9pGVnEpmpIHK9qbiSdDV9dGgUP8paLj/J4ObMYDeXnq2byft/cvxTYrH66slrmkVBF+EvRD0TeJA4kUcTJpAw+Fe2irr6XmzDvh75APEkeiZQHbJGeRNnkLJ3JlMXjQDt3fs/DQhInI6sCyLloCfhs42mvyRK/MiK1HtPZ3OSlRvZ/92XiCynRc8IlQ+uImyTuh5bSsJl+3GZScRZ5JJMMmkxKXidnlwxzsrUb4EH94EZysv3Z1CmttHujuFbE8qmZ5Ucr2pZHtSSYzXf+GnE72axxCqq2Vb8TwqMw227ccYL8XNNnMqN1OQ+QSBliR2tJVT2fsJGjpdtLc10xtoBA44v0wCSd4C8qYuY0LpVCbNn0Xx/KkkJiXG+lMTERmTQuEwh7s7aexso+mIUHl/ExXspDPYSVdfExXuHliJsgP9V+bZ9DVRJ3hlnp10xEpUMgnGjScuPRIqT3ZWohK9+BJ9pCQ4TVS620dGciqZyU4jleNLIyvZR3ycrnaXoakhO4YdpfOo8AXBdvbfbdtPRYaLmowz+es+F6FgMxAEdmJcbtwpE8mZNJv8qVOZvHA2RbMma8yEiIx7wVAoMt6gneaudpq7OmgOONt5bT1OIzX4yrwuApazEhXqH2/Qc5LjDVyDxhvEm2QSjQdfXBbuOGclyhPvxZfoxZfgIzXJS1qS00Rl9jVRXidUnuH26qp1iQo1ZMdQkWL6m7EBFiFa8fqmk19QRsG0aUw+Yzb5Uwv1BSsip41Ab5BGfwcN/langeruoDnQQVvAyUP1NVH+Xj9dIT894e5IE9VF6MiVKNNzEuMN4gY1UQkmmSRXCimuXNxxXpL7xhsk+vAleElN8pGe5GznZSY7gfIsTyp5vjRSk5L1PVnGHDVkx2DZ/mMcsbnlFz+Oai0iIsfj7+mh0d9GY1c7h7vaaYlkovpWoZwmqgt/qHPQeINe29nOC/c3UadyZZ6Th4onmWRXGomufJLjnavzvAlOA+VL9JKWFJkR1ddEJaeS7U1xxhuMoSvzREbCqGvIjDEXAw8AccAjtm3fF4s6XHEpg2aAHfm4iJyeonmTYcuy6AgGaOq7Mq+7g5a+QZs9ftp6OpzxBkFnFao75GSieixnWnmIdzZRJ39lXpxxVqK8rsxB4w36rsxLSfSSlpRKmtuZE9U3IyrXl6Yr80SG2ahqyIwxccDPgQuBamC9MeZp27a3R7uW4JyFxG9ew8CNuAHiCc5ZGO1SRCQKTuQmw5Zl0RboosHfTlNXW/92XktkK6+vieq/Mi9yu5eeyHiDwVfmBU7iyrxEZyvPdkeuzHPjdWXjdnlxxycffWVeZCuvL1Se5Uklx5tGtseHO0EXFomMRqNqDpkxZhnwLdu2L4q8fxeAbdvfH+r8kZxDNv/Rc1iyp4AZFTbG8mO7vOwqMayZsZvcuEUj8pwiEjsN4beGnERu2y5c4VRs03PC4w1s24CdGBlv0HdlXjIJkflQyf0rUQOTyp1QeQqZnpRIE5VCrjeNzOQUjTcQOU2MpTlkE4GqI96vBpYceYIx5ibgJoDi4uIRK8SKa2HtrBbWznrHARuaeytG7HlFJEbijnVbGIv8pHmR270k401IISXRQ2pSCqmJPicP5RlYicr1ppGR7NV4AxE5KaOtITsu27YfBh4GZ4VspJ7HFc445g1H377+lZF6WhGJkXe7yfBL//z/YlCRiIwno+264Bqg6Ij3CyOPRd1Vk2/EthIGPWZbCVwVgxuOisjI09e8iMTSaGvI1gPTjDGTjTGJwCeAp2NRyD3nf4aPldyOCWVg22BCGXys5PaY3HBUREaevuZFJJZGVagfwBhzCfATnLEXj9m2fe+xzh3JUL+IiIjIcBpLoX5s234eeD7WdYiIiIhEy2jbshQREREZd9SQiYiIiMSYGjIRERGRGFNDJiIiIhJjashEREREYkwNmYiIiEiMqSETERERiTE1ZCIiIiIxpoZMREREJMbUkImIiIjEmBoyERERkRhTQyYiIiISY2rIRERERGJMDZmIiIhIjKkhExEREYkxNWQiIiIiMaaGTERERCTG1JCJiIiIxJgaMhEREZEYM7Ztx7qGU2aMaQQqYl3HGJINNMW6CDmKXpfRR6/J6KTXZfTRa3JySmzbzhnqwJhuyOTkGGM22LZdFus6ZDC9LqOPXpPRSa/L6KPXZPhoy1JEREQkxtSQiYiIiMSYGrLx5eFYFyBD0usy+ug1GZ30uow+ek2GiTJkIiIiIjGmFTIRERGRGFNDJiIiIhJjasjGCWNMujHmj8aYncaYHcaYZbGuabwzxtxujNlmjNlqjPkvY4w71jWNR8aYx4wxDcaYrUc8lmmMedkYsyfyNiOWNY5Hx3hd/m/ke9hmY8yfjDHpsaxxvBnqNTni2JeNMbYxJjsWtZ0O1JCNHw8AL9q2PRNYAOyIcT3jmjFmInAbUGbb9lwgDvhEbKsat34NXPyOx+4EXrVtexrwauR9ia5fc/Tr8jIw17bt+cBu4K5oFzXO/ZqjXxOMMUXAB4DKaBd0OlFDNg4YY9KAc4BHAWzbDtq23RrbqgSIB5KNMfGAB6iNcT3jkm3b/wCa3/Hwh4HfRH7/G+CKqBYlQ74utm3/1bbtUOTdtUBh1Asbx47xtQJwP/BVQFcJvgdqyMaHyUAj8CtjzFvGmEeMMd5YFzWe2bZdA/wI5yfKOqDNtu2/xrYqOUKebdt1kd8fAvJiWYwM6TrghVgXMd4ZYz4M1Ni2vSnWtYx1asjGh3jgDOAXtm0vAvxoCyamIpmkD+M0ywWA1xjzT7GtSoZiO7OB9JP/KGKM+ToQAh6PdS3jmTHGA3wNuDvWtZwO1JCND9VAtW3b6yLv/xGnQZPYeT9wwLbtRtu2e4GngOUxrkkG1Btj8gEibxtiXI9EGGP+GfgQ8GlbgzRjbQrOD5WbjDEHcbaQ3zTGTIhpVWOUGrJxwLbtQ0CVMWZG5KELgO0xLEmcrcqlxhiPMcbgvCa60GL0eBq4JvL7a4C/xLAWiTDGXIyTVbrctu2uWNcz3tm2vcW27VzbtifZtj0J54f/MyL/58hJUkM2fvwr8LgxZjOwEPi3GNczrkVWK/8IvAlswfla1C1IYsAY81/AGmCGMabaGHM9cB9woTFmD85q5n2xrHE8Osbr8jMgBXjZGPO2MebBmBY5zhzjNZFholsniYiIiMSYVshEREREYkwNmYiIiEiMqSETERERiTE1ZCIiIiIxpoZMREREJMbUkImIiIjEmBoyERERkRj7/8wuofczRGcPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "a star early\n",
            "partial expansion a star\n",
            "a star late\n",
            "partial expansion a star early\n",
            "enhanced partial expansion a star early\n",
            "enhanced partial expansion a star\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "od-x_2gylVo2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}