Test

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  "metadata": {
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      "name": "TensorFlow with GPU",
      "version": "0.3.2",
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      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/sharathsrini/Extended-Kalman-Filter-for-Sensor-Fusion/blob/master/Test)"
      ]
    },
    {
      "metadata": {
        "id": "bRqO9Qo-KgUd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "############PROGRAM STARTS HERE ######################\n",
        "import numpy as np\n",
        "import math as MT\n",
        "from math import floor\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "\n",
        "\n",
        "###CONSTANTS\n",
        "max_angle = 0.785398 #45Deg\n",
        "min_angle = -0.785398 #-45Deg\n",
        "free_space=0\n",
        "locked_space=1\n",
        "\n",
        "### HYPER PARAMETERS\n",
        "NUMBERS_OF_STEERS=4\n",
        "STEER_OFFSET=5.0*np.pi/180\n",
        "LENGTH=4.0\n",
        "NUM_THETA_CELLS =60\n",
        "\n",
        "### GRID MAKING \n",
        "grid_x_m = 50\n",
        "grid_y_m = 50\n",
        "\n",
        "### FOR CELL DIVISION\n",
        "coll_cell_side = 0.5\n",
        "grid_on_x = np.int( np.ceil(grid_x_m/coll_cell_side) )\n",
        "grid_on_y = np.int( np.ceil(grid_y_m/coll_cell_side) )\n",
        "\n",
        "### FIT ZEROS\n",
        "GRID_TEST = np.zeros((grid_on_x,grid_on_y),np.int)\n",
        "### INITIALIZE COST_MAPS AND ASTAR CLOSE MAPS\n",
        "closed_A_star=np.array([[free_space for x in range(grid_on_x)] for y in range(grid_on_y)])\n",
        "cost_map = np.array([[-1 for x in range(grid_on_x)] for y in range(grid_on_y)])\n",
        "policy_map = [[' ' for x in range(grid_on_x)] for y in range(grid_on_y)]\n",
        "\n",
        "### MOTION MATRIX FOR ASTAR\n",
        "motion_mat=np.array([[1,0],[-1,0],[0,-1],[0,1]])\n",
        "policy_mat=['>',]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IOZHrpeKyAa",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        "\n",
        "### STATE CLASS\n",
        "class state:\n",
        "  def __init__(self,x,y,theta,g,f,h,steer):\n",
        "    self.x=x\n",
        "    self.y=y\n",
        "    self.theta=theta\n",
        "    self.g=g\n",
        "    self.f=f\n",
        "    self.h=h\n",
        "    self.steer=steer\n",
        "    \n",
        " ## GOAL NODE        \n",
        "class goal:\n",
        "    def __init__(self, x, y):\n",
        "        self.x = x\n",
        "        self.y = y\n",
        "        \n",
        "        \n",
        "### INPUT VEHICLE CO-ORDINATES\n",
        "class vehicle_points():\n",
        "  def __init__(self,input_co_ordinates,center):\n",
        "    self.input_co_ordinates=input_co_ordinates\n",
        "    self.center=center\n",
        "    \n",
        "### PATH CLASS FOR TRACKING   \n",
        "class path():\n",
        "  def __init__(self,closed,came_from,final):\n",
        "    self.closed=closed\n",
        "    self.came_from=came_from\n",
        "    self.final=final\n",
        "    \n",
        "\n",
        "### AUGMENT DELTA +/- GIVEN OFFSET\n",
        "def delta_augmentation(delta, numbers, offset):\n",
        "    delta_list = []\n",
        "    delta_list.append(delta)\n",
        "    delta_calc_add=delta_calc_sub = delta\n",
        "    for i in range(0 ,numbers):\n",
        "        delta_calc_add += offset\n",
        "        delta_calc_sub -= offset\n",
        "        if delta_calc_add < max_angle:\n",
        "            delta_list.append(delta_calc_add)\n",
        "        if delta_calc_sub > min_angle:\n",
        "            delta_list.append(delta_calc_sub)\n",
        "    return delta_list\n",
        "  \n",
        "\n",
        "\n",
        "### NEW STATE TRANSITIONS\n",
        "def new_state_transition(current_state,goal,speed):\n",
        "    next_states = []\n",
        "    delta_angles = delta_augmentation( delta=current_state.steer, numbers=NUMBERS_OF_STEERS,offset=STEER_OFFSET)\n",
        "    DT=1.0/speed\n",
        "    for delta in delta_angles:\n",
        "        omega  = (speed / LENGTH) * np.tan(delta)\n",
        "        theta2 = normalize_theta(current_state.theta + (omega * DT))\n",
        "        dX = speed * np.cos(theta2) * DT\n",
        "        dY = speed * np.sin(theta2) * DT\n",
        "        #i=i+1\n",
        "        #print(i,[SPEED,np.cos(theta2),DT,omega,theta2,dX,dY])\n",
        "        x2     = current_state.x + dX\n",
        "        y2     = current_state.y + dY\n",
        "        g2     = current_state.g + np.sqrt(dX*dX + dY*dY)\n",
        "        arc_cost=arc_heuristic(goal.x-x2,goal.y-y2,theta2)         \n",
        "        #print(arc_cost)\n",
        "        h2     = euclidean_distance([x2,y2],[goal.x,goal.y])+arc_cost\n",
        "        if(cost_map[idx(x2)][idx(y2)]==-1):\n",
        "          h2+=100\n",
        "        else:\n",
        "          h2+=cost_map[idx(x2)][idx(y2)]\n",
        "        f2     = g2 + h2\n",
        "        new_state=state(x2,y2,theta2,g2,f2,h2,delta)\n",
        "        #jj=np.arctan2(goal.y-y2,goal.x-x2)\n",
        "        #print(['X: ',x2,'Y: ',y2,'ang_goal',normalize_theta(jj)*180/np.pi,'taken_angle',theta2*180/np.pi,'cost:',arc_cost])\n",
        "        next_states.append(new_state)\n",
        "    return next_states\n",
        "\n",
        "### TRANSFORM VEHICLE CO-ORDINATES \n",
        "def transform_vehicle_co_ordinates(vehicle_point_object, next_state, angle_of_rotation):\n",
        "    displaced_matrix = np.array([next_state[0]-vehicle_point_object.center[0],next_state[1]-vehicle_point_object.center[1]])\n",
        "    transformed_matrix=np.add(vehicle_point_object.input_co_ordinates,displaced_matrix)\n",
        "    return vehicle_points(rotate_vehicle_co_ordinates(vehicle_points(transformed_matrix,next_state),angle_of_rotation),next_state)\n",
        "  \n",
        "  \n",
        "### ROTATE VEHICLE CO-ORDINATES     \n",
        "def rotate_vehicle_co_ordinates(vehicle_point_object,angle_of_rotation):\n",
        "    rotation_matrix = np.array([[np.cos(angle_of_rotation), np.sin(angle_of_rotation)], \n",
        "                                [-np.sin(angle_of_rotation), np.cos(angle_of_rotation)]])\n",
        "    return np.add(vehicle_point_object.center,np.matmul(np.subtract(vehicle_point_object.input_co_ordinates,vehicle_point_object.center), rotation_matrix))\n",
        "  \n",
        "  \n",
        "### CHECK VEHICLE IN SAFE POSITION  \n",
        "def is_vehicle_in_safe_position(vehicle_point_object,grid):\n",
        "  for point in vehicle_point_object.input_co_ordinates:\n",
        "    if(is_within_grid( idx(point[0]),idx(point[1])) and  \n",
        "       (grid[idx(point[0])][idx(point[1])]==0)):\n",
        "      continue\n",
        "    else:\n",
        "      return False\n",
        "  return True\n",
        "\n",
        "### CHK A STAR VEHICLE:\n",
        "def A_vehicle_is_safe(vehicle_point_A,add_value,grid):\n",
        "  vp=vehicle_point_A.input_co_ordinates+add_value\n",
        "  for point in vp:\n",
        "    if(is_within_grid( idx(point[0]),idx(point[1])) and  \n",
        "       (grid[idx(point[0])][idx(point[1])]==0)):\n",
        "      continue\n",
        "    else:\n",
        "      #print('False',add_value)\n",
        "      return False\n",
        "  #('True',add_value)\n",
        "  return True\n",
        "    \n",
        "    \n",
        "\n",
        "### EUCLIDEAN DISTANCE\n",
        "def euclidean_distance(start_point,end_point):\n",
        "  return np.round(np.sqrt((end_point[0]-start_point[0])**2 +(end_point[1]-start_point[1])**2),4)\n",
        "\n",
        "### ARC HEURISTIC\n",
        "def arc_heuristic(x,y,theta_to_be_taken):\n",
        "  ang_rad=normalize_theta(np.arctan2(y,x))\n",
        "  diff=np.pi-abs(abs(theta_to_be_taken-ang_rad)-np.pi)\n",
        "  return diff\n",
        "  \n",
        "### NORMALIZE THETA\n",
        "def normalize_theta(theta):\n",
        "  if( theta<0 ):\n",
        "    theta +=( 2*np.pi )\n",
        "  elif( theta>2*np.pi ):\n",
        "    theta %=( 2*np.pi)\n",
        "  return theta\n",
        "\n",
        "### THETA TO STACK NUMBER\n",
        "def theta_to_stack_number(theta):\n",
        "  new = (theta+2*np.pi)%(2*np.pi)\n",
        "  stack_number = round(new*NUM_THETA_CELLS/2*np.pi)%NUM_THETA_CELLS\n",
        "  return int(stack_number)\n",
        "\n",
        "### FLOOR VALUE\n",
        "def idx(value):\n",
        "  return int(MT.floor(value))\n",
        "\n",
        "### CHECK WITHIN GRID  \n",
        "def is_within_grid(x,y):\n",
        "  return (x>=0 and x<grid_on_x and y>=0 and y<grid_on_y)\n",
        "\n",
        "### IS_GOAL_REACHED\n",
        "def is_goal_reached(start,goal):\n",
        "  result=False\n",
        "  if( idx(start[0]) == idx(goal[0]) and idx(start[1])==idx(goal[1])):\n",
        "    result=True\n",
        "  return result\n",
        "\n",
        "\n",
        "### A_STAR SEARCH\n",
        "def A_Star(current_state,goal,grid):\n",
        "  vehicle_point_A=vehicle_points(np.array([[0,2],[0,1],[0,-1],[0,-2],[1,0],[2,0],[-1,0],[-2,0]]),[0,0])\n",
        "  print(\"STARTED A*\")\n",
        "  open_list = []\n",
        "  open_list.append(current_state )\n",
        "  is_goal_attained=False\n",
        "  cost=0\n",
        "  heu=0\n",
        "  closed_A_star[current_state.x][current_state.y]=1\n",
        "  cost_map[current_state.x][current_state.y]=cost\n",
        "  \n",
        "  while(len(open_list)>0):\n",
        "    open_list.sort(key=lambda state_srt : float(state_srt.f))\n",
        "    old_state=open_list.pop(0)\n",
        "    if(goal.x==old_state.x and goal.y==old_state.y):\n",
        "      is_goal_attained=True\n",
        "      print(\"GOAL REACHED BY A*\")\n",
        "      return is_goal_attained\n",
        "    node=np.array([old_state.x,old_state.y])\n",
        "    for move in motion_mat:\n",
        "      nxt_node=node+move\n",
        "      if( is_within_grid(nxt_node[0],nxt_node[1])):\n",
        "        if(grid[nxt_node[0]][nxt_node[1]]==0 and closed_A_star[nxt_node[0]][nxt_node[1]]==0):\n",
        "          if(A_vehicle_is_safe(vehicle_point_A,np.array([nxt_node]),grid)):\n",
        "            g2=old_state.g+1\n",
        "            heu=euclidean_distance([nxt_node[0],nxt_node[1]],[goal.x,goal.y])\n",
        "            new_state=state(nxt_node[0],nxt_node[1],0,g2,g2+heu,heu,0)\n",
        "            open_list.append(new_state)\n",
        "            closed_A_star[nxt_node[0]][nxt_node[1]]=1\n",
        "            cost_map[nxt_node[0]][nxt_node[1]]=g2\n",
        "            #plt.plot([node[0],nxt_node[0]],[node[1],nxt_node[1]])\n",
        "  return is_goal_attained\n",
        "\n",
        "### SEARCH ALGORITHM\n",
        "def Hybrid_A_Star(grid,current_state,goal,vehicle_point_object,speed):\n",
        "  print(\"STARTED HYBRID A*\")\n",
        "  start_time = time.time()\n",
        "  closed = np.array([[[free_space for x in range(grid_on_x)] for y in range(grid_on_y)] for cell in range(NUM_THETA_CELLS)])\n",
        "  came_from = [[[free_space for x in range(grid_on_x)] for y in range(grid_on_y)] for cell in range(NUM_THETA_CELLS)]\n",
        "  is_goal_attained=False\n",
        "  stack_number=theta_to_stack_number(current_state.theta)\n",
        "  closed[stack_number][idx(current_state.x)][idx(current_state.y)]=1\n",
        "  came_from[stack_number][idx(current_state.x)][idx(current_state.y)]=current_state\n",
        "  total_closed=1\n",
        "  opened=[current_state]\n",
        "  \n",
        "  while (len(opened)>0):\n",
        "    opened.sort(key=lambda state_srt : float(state_srt.f))\n",
        "    state_now=opened.pop(0)\n",
        "    #print([state_now.x,state_now.y,state_now.theta*np.pi/180])\n",
        "    if(is_goal_reached([idx(state_now.x),idx(state_now.y)],[idx(goal.x),idx(goal.y)])):\n",
        "      is_goal_attained=True\n",
        "      print('GOAL REACHED BY HYBRID A*')\n",
        "      ret_path=path(closed,came_from,state_now)\n",
        "      end_time = time.time()\n",
        "      print(end_time - start_time)\n",
        "      return (is_goal_attained,ret_path)\n",
        "    \n",
        "    for evry_state in new_state_transition(state_now,goal,speed):\n",
        "      #print('Before',[evry_state.x,evry_state.y,evry_state.theta*np.pi/180])\n",
        "      if(not is_within_grid(idx(evry_state.x),idx(evry_state.y))):\n",
        "        continue\n",
        "      \n",
        "      stack_num=theta_to_stack_number(evry_state.theta)\n",
        "      #print([stack_num,idx(evry_state.x),idx(evry_state.y)])\n",
        "      if closed[stack_num][idx(evry_state.x)][idx(evry_state.y)]==0 and grid[idx(evry_state.x)][idx(evry_state.y)]==0:\n",
        "        new_vehicle_point_obj = transform_vehicle_co_ordinates(vehicle_point_object,[evry_state.x,evry_state.y],evry_state.theta)\n",
        "        #print(new_vehicle_point_obj.input_co_ordinates)\n",
        "        if(is_vehicle_in_safe_position(new_vehicle_point_obj,grid)):\n",
        "            opened.append(evry_state)\n",
        "            closed[stack_num][idx(evry_state.x)][idx(evry_state.y)]=1\n",
        "            came_from[stack_num][idx(evry_state.x)][idx(evry_state.y)]=state_now\n",
        "            total_closed+= 1\n",
        "            #print('After',[evry_state.x,evry_state.y,evry_state.theta*np.pi/180])\n",
        "            #plt.plot([state_now.x,evry_state.x],[state_now.y,evry_state.y])\n",
        "      #closed[stack_num][idx(evry_state.x)][idx(evry_state.y)]=1\n",
        "        #print('-------------')\n",
        "  print('No Valid path')\n",
        "  ret_path=path(closed,came_from,evry_state)\n",
        "  return (is_goal_attained,ret_path)\n",
        "\n",
        "\n",
        "\n",
        "### RECONSTRUCT PATH\n",
        "def reconstruct_path(came_from, start, final):\n",
        "    path                 = [(final)]\n",
        "    stack                = theta_to_stack_number(final.theta)\n",
        "    current              = came_from[stack][idx(final.x)][idx(final.y)]\n",
        "    stack                = theta_to_stack_number(current.theta)\n",
        "    while [idx(current.x), idx(current.y)] != [idx(start[0]), idx(start[1])] :\n",
        "        path.append(current)\n",
        "        current              = came_from[stack][idx(current.x)][idx(current.y)]\n",
        "        stack                = theta_to_stack_number(current.theta)\n",
        "    return path\n",
        "\n",
        "\n",
        "###DISPLAY PATH\n",
        "def show_path(path, start, goal,vehicle_pt_obj_act):\n",
        "  X=[start[0]]\n",
        "  Y=[start[1]]\n",
        "  Theta=[]\n",
        "  path.reverse()\n",
        "  X     += [p.x for p in path]\n",
        "  Y     += [p.y for p in path]\n",
        "  Theta+=[p.theta for p in path]\n",
        "  for i in range(len(X)-1):\n",
        "    Xj=[]\n",
        "    Yj=[]\n",
        "    vehicle_pt_obj_now=transform_vehicle_co_ordinates(vehicle_pt_obj_act,[X[i],Y[i]], Theta[i])\n",
        "    rev=vehicle_pt_obj_now.input_co_ordinates\n",
        "    revI=rev[:4]\n",
        "    revL=rev[4:]\n",
        "    revF=np.concatenate([revI,revL[::-1]])\n",
        "    l=np.append(revF,[revF[0]],axis=0)\n",
        "    #print(l)\n",
        "    for i in l:\n",
        "      Xj.append(i[0])\n",
        "      Yj.append(i[1])\n",
        "    plt.plot(Xj,Yj)\n",
        "  print([p.steer*180/np.pi for p in path])\n",
        "  plt.plot(X,Y, color='black')\n",
        "  plt.scatter([start[0]], [start[1]], color='blue')\n",
        "  plt.scatter([goal[0]], [goal[1]], color='red')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2exSViITSeqc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### PUT OBSTACLES:\n",
        "def put_obstacles(X_list,Y_list,grid):\n",
        "    if(len(X_list)>0):\n",
        "      for i in  X_list:\n",
        "        x_XO=[]\n",
        "        x_YO=[]\n",
        "        for k in range(i[1],i[2]):\n",
        "          x_XO.append(i[0])\n",
        "          x_YO.append(k)\n",
        "          grid[i[0]][k]=1\n",
        "        plt.scatter(x_XO,x_YO)\n",
        "    if(len(Y_list)>0):\n",
        "      for i in Y_list:\n",
        "        y_XO=[]\n",
        "        y_YO=[]\n",
        "        for k in range(i[1],i[2]):\n",
        "          y_XO.append(i[0])\n",
        "          y_YO.append(k)\n",
        "          grid[k][i[0]]=1\n",
        "        plt.scatter(y_YO,y_XO)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZOneuDF-KiZN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "2b07fd44-791f-4fc3-e70a-76bb16e9ea4a"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "# Vehicle parameter\n",
        "W = 2.5 #[m] width of vehicle\n",
        "LF = 3.7 #[m] distance from rear to vehicle front end of vehicle\n",
        "LB = 1.0 #[m] distance from rear to vehicle back end of vehicle\n",
        "TR = 0.5 # Tyre radius [m] for plot\n",
        "TW = 1.2 # Tyre width [m] for plot\n",
        "MAX_STEER = 0.6 #[rad] maximum steering angle\n",
        "WB = 2.7  #[m] wheel base: rear to front steer\n",
        "\n",
        "def plot_car(x, y, yaw, steer, retrun_car = False):\n",
        "    car_color = \"-k\"\n",
        "\n",
        "    LENGTH = LB+LF\n",
        "\n",
        "    car_OutLine = np.array([[-LB, (LENGTH - LB), (LENGTH - LB), (-LB), (-LB)],\n",
        "                             [W / 2, W / 2, - W / 2, - W / 2, W / 2]])\n",
        "\n",
        "\n",
        "    rr_wheel = np.array([[TR, - TR, - TR, TR, TR],\n",
        "                         [-W / 12.0 + TW, - W / 12.0 + TW, W / 12.0 + TW, W / 12.0 + TW, - W / 12.0 + TW]])\n",
        "\n",
        "    rl_wheel = np.array([[TR, - TR, - TR, TR, TR],\n",
        "                         [-W / 12.0 - TW, - W / 12.0 - TW, W / 12.0 - TW, W / 12.0 - TW, - W / 12.0 - TW]])\n",
        "\n",
        "    fr_wheel = np.array([[TR, - TR, - TR, TR, TR],\n",
        "                         [- W / 12.0 + TW, - W / 12.0 + TW, W / 12.0 + TW, W / 12.0 + TW, - W / 12.0 + TW]])\n",
        "\n",
        "    fl_wheel = np.array([[TR, - TR, - TR, TR, TR],\n",
        "                         [-W / 12.0 - TW, - W / 12.0 - TW, W / 12.0 - TW, W / 12.0 - TW, - W / 12.0 - TW]])\n",
        "\n",
        "    Rot1 = np.array([[math.cos(yaw), math.sin(yaw)],\n",
        "                    [-math.sin(yaw), math.cos(yaw)]])\n",
        "    Rot2 = np.array([[math.cos(steer), -math.sin(steer)],\n",
        "                     [math.sin(steer), math.cos(steer)]])\n",
        "\n",
        "\n",
        "    fr_wheel = np.dot(fr_wheel.T, Rot2).T\n",
        "    fl_wheel = np.dot(fl_wheel.T, Rot2).T\n",
        "    fr_wheel[0,:] += WB\n",
        "    fl_wheel[0,:] += WB\n",
        "    fr_wheel = np.dot(fr_wheel.T, Rot1).T\n",
        "    fl_wheel = np.dot(fl_wheel.T, Rot1).T\n",
        "\n",
        "    car_OutLine = np.dot(car_OutLine.T, Rot1)\n",
        "\n",
        "    rr_wheel = np.dot(rr_wheel.T, Rot1).T\n",
        "    rl_wheel = np.dot(rl_wheel.T, Rot1).T\n",
        "\n",
        "    car_OutLine = car_OutLine.T\n",
        "    car_OutLine[0,:] += x\n",
        "    car_OutLine[1,:] += y\n",
        "    fr_wheel[0, :] += x\n",
        "    fr_wheel[1, :] += y\n",
        "    rr_wheel[0, :] += x\n",
        "    rr_wheel[1, :] += y\n",
        "    fl_wheel[0, :] += x\n",
        "    fl_wheel[1, :] += y\n",
        "    rl_wheel[0, :] += x\n",
        "    rl_wheel[1, :] += y\n",
        "\n",
        "    if retrun_car == False:\n",
        "        plt.plot(x, y, \"*\")\n",
        "        plt.plot(fr_wheel[0, :], fr_wheel[1, :], car_color)\n",
        "        plt.plot(rr_wheel[0, :], rr_wheel[1, :], car_color)\n",
        "        plt.plot(fl_wheel[0, :], fl_wheel[1, :], car_color)\n",
        "        plt.plot(rl_wheel[0, :], rl_wheel[1, :], car_color)\n",
        "        plt.plot(car_OutLine[0, :], car_OutLine[1, :], car_color)\n",
        "    else:\n",
        "        return car_OutLine[0, :], car_OutLine[1, :]\n",
        "\n",
        "plot_car(0.0, 0.0, np.pi, 0.0, retrun_car = False)\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFKCAYAAAAnj5dkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86deea590>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GnQoKc-BeRXn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def show_animation(path, start, goal,vehicle_pt_obj_act):\n",
        "  x =[]\n",
        "  y = []\n",
        "  yaw = []\n",
        "  steer = []\n",
        "  path.reverse()\n",
        "  x     += [p.x for p in path]\n",
        "  y     += [p.y for p in path]\n",
        "  yaw += [p.theta for p in path]\n",
        " \n",
        "  steer =[p.steer*180/np.pi for p in path]\n",
        "  print(type(steer[0]))\n",
        "  \n",
        "  for ii in range(0,len(x),5):\n",
        "      plt.cla()\n",
        "      plt.plot(x, y, \"-r\", label=\"Hybrid A* path\")\n",
        "      put_obstacles([[10,0,17],[27,0,13],[60,15,35]],[[26,0,30]],GRID_TEST)\n",
        "      plot_car(x[ii], y[ii], yaw[ii], steer[ii])\n",
        "      plt.grid(True)\n",
        "      plt.axis(\"equal\")\n",
        "      plt.pause(0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZbpDvcJDRw3I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "START=[40,45]\n",
        "SPEED=60\n",
        "goal_node = goal( 4,3)\n",
        "present_heading=(0)\n",
        "vehicle_pt_obj_actual = vehicle_points( np.array([[0.5,0.5],[0.5,1.5],[0.5,2.5],[0.5,3.5],[1.5,0.5],[1.5,1.5],[1.5,2.5],[1.5,3.5]]),[0,2] )\n",
        "vehicle_pt_obj=transform_vehicle_co_ordinates(vehicle_pt_obj_actual,START,present_heading)\n",
        "#print(vehicle_pt_obj.input_co_ordinates)\n",
        "current_state = state(vehicle_pt_obj.center[0], vehicle_pt_obj.center[1], present_heading, 0.0, 0.0, 0.0,0.0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lWsoNKl2CkDd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vJQLZcriX_il",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5399
        },
        "outputId": "1a81cd8c-9231-4fc5-c7d9-42ccc8d1fd11"
      },
      "cell_type": "code",
      "source": [
        "put_obstacles([[10,0,17],[27,0,13],[60,15,35]],[[26,0,30]],GRID_TEST)\n",
        "if(A_Star(state(goal_node.x,goal_node.y,0,0,0,0,0),goal(START[0],START[1]),GRID_TEST)):\n",
        "  process_further,ret_val=Hybrid_A_Star(GRID_TEST,current_state,goal_node,vehicle_pt_obj,SPEED)\n",
        "  if(process_further):\n",
        "    show_animation(reconstruct_path(ret_val.came_from,START,ret_val.final),START,[goal_node.x,goal_node.y],vehicle_pt_obj_actual)\n",
        "  else:\n",
        "    print(\"GOAL CANT BE REACHED!!\")\n",
        "else:\n",
        "  print(\"GOAL CANT BE REACHED!!\")\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "STARTED A*\n",
            "GOAL REACHED BY A*\n",
            "STARTED HYBRID A*\n",
            "GOAL REACHED BY HYBRID A*\n",
            "0.502863883972\n",
            "<type 'float'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pIh1ub2nnonN+ZalLz60OXC1YsIDS0lKmT5/Oaaed1rzdMPb+tIn8/AycTse+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\n7P8DEwGeFAP/qXoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dcc4b90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CuH74LwnLlpCwbDEJy5bi2rihUU8NZ2QSPPY4gp2OI3hcZ/ynn6n3f1pYwueL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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86e185850>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jDADS0tKoqqpi9erVPPjggwD06NGDWbNmafiGSyCAY8N/iVuzirg1K4lbsxrH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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dc1eb90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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58+ZRUFDAFVdcQadOncjOzqZXr1489dRTzJ8/n+zs7HDUK1QspmD/eg3WkmLw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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb8702b17d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sHvMUZtR2Nh+1nUVih8I3AkJJyYC5wldtZ/NR21kkdih8IyCcUn3m\na/d4DJ5kJ7WdzUdtZ5HYUa/wXbVqFX369GH69OkA5OXlMXToUHJzcxk1ahQ+ny6H7c3Oy87mCV+1\nnc1HbWeR2LHP8K2oqODBBx+ka9euNcfGjx9Pbm4uM2bMoG3btsyePbtJh4x24WTzXXYGtZ3NSG1n\nkdiwz/B1uVy88MIL5OTk1BxbunQpvXv3BqBXr14sXry46Sa0gri46v8HzVViUtvZfNR2FokN+wxf\np9NJfHz8bscqKytxuaovgTVr1oz8/Npbs7Kdfftvs4leVlBtZ/NR21kkdhzwSwqGw+F9vk9GRiJO\nZ/TdQ8zObtyCi8tha/TP2VB5BeUUltXddna44sjOSorwVJFjlq/DrjZ78utsOxdVFeNIDpGd3LC5\nzbjepqT1WpdV1tqg8E1MTKSqqor4+Hi2bNmy2yXp2hQVVTRoOCNlZ6eQn99492izbDYCXj/Fjfg5\nD0TQHyQzxc22Wn7cKCMlnqDP36jrN5PG/to2lmDQToY7nUJv0R6PZcSnE/TYya/c/7nNut6movVa\nV7StdW/fKDToR426devGvHnzAJg/fz49evRo2GSxxG6HelwliBS1nc1HbWeR2LHPM9/ly5czduxY\nNm3ahNPpZN68eTz22GOMHj2aWbNm0bJlSwYMGBCJWaOb3Q7BgNFT7GZQz/asXF/Mpvzq0pXdBq2y\n1XY20vntz2Z18VryPJsJEcKOnRbJB6ntLGIx+wzfTp068eqrr+5xfNq0aU0ykFWFExKxVVQaPcZu\ndrSdd9i17Zzbp6OBk8WuHW3nHXZtOw/u2N/AyUSkMWmHqwgJp6RgKzfPJhtqO5uP2s4isUPhGyHh\n5GRTbbKhvZ3NR3s7i8QOhW+EVIevec58tbez+WhvZ5HYofCNkHBSCjafD7zmOKNU29l81HYWiR0K\n3wjZ8cpGtjLzXDrU3s7mo72dRWKDwjdCQunVlxPtJXtuoGAU7e1sPtrbWSQ2KHwjJJyRCYCtsNDg\nSaqp7Ww+ajuLxA6Fb4SEtoevvcgc4au2s/mo7SwSOxS+ERLOyADAVmSOy85qO5uP2s4isUPhGyFm\nO/NV29l81HYWiR0K3wgJZ26/52uS8AW1nc1IbWeR2KDwjZCaM99Cc1x2BrWdzUhtZ5HYoPCNkJ33\nfM1x5qu2s/mo7SwSOxS+EbLznq85znzVdjYftZ1FYofCN1JcLkJJyaY581Xb2XzUdhaJHQrfCApn\nZqrtLHVS21kkdih8IyiUYZ7wBbWdzUhtZ5HYoPCNoHB6BraKCqiqMnoUQG1nM1LbWSQ2KHwjKJRZ\n3Xi2FxtfulLb2XzUdhaJHQrfCDLTiyuo7Ww+ajuLxA6FbwSZaYtJtZ3NR21nkdih8I2gmi0mTXDm\nq7az+ajtLBI7FL4RFErffs/XBGe+oLazGantLBIbFL4RVHPma4LCFajtbEZqO4vEBoVvBO18cQXj\nz3zVdjYftZ1FYofCN4J2hK8ZtphU29l81HYWiR0K3wja8cpGZnhxBbWdzUdtZ5HYofCNoHBaOmG7\n3RSFK7WdzUdtZ5HYofCNJLudcEYGtvytRk8CqO1sRmo7i8QGhW+EBdu2w7FhPQSNLzSp7Ww+ajuL\nxAaFb4QF27XH5vdj37TR0DnUdjYftZ1FYofCN8KCh1Rf0nX8YezZpdrO5qO2s0jsUPhGmFnCV21n\n81HbWSR2KHwjLNi+A2B8+KrtbD5qO4vEDoVvhAUP60g4Ph5bZYXRo6jtbEJqO4vEBoVvhIXT0ila\n9BWe+8YYPYraziaktrNIbFD4GiDY/lBITDR0BrWdzUdtZ5HYofCNUWo7m4/aziKxQ+Ebo9R2Nh+1\nnUVih8I3RqntbD5qO4vEDoVvDFPb2XzUdhaJDQrfGKa2s/mo7SwSGxS+MUptZ/NR21kkdih8Y5Ta\nzuajtrNI7FD4xii1nc1HbWeR2KHwjVFqO5uP2s4isUPhG8PUdjYftZ1FYoPCN4ap7Ww+ajuLxAaF\nb4xS29l81HYWiR0K3xiltrP5qO0sEjsUvjFKbWfzUdtZJHY0OHwfeughLr74YoYMGcJPP/3UmDNJ\nBKjtbD5qO4vEjgaF79dff826deuYNWsWY8aMYcwY418YXvaf2s7mo7azSGxoUPguXryYPn36ANCh\nQwdKSkrweDyNOpg0PbWdzUdtZ5HY0KDwLSgoICMjo+btzMxM8vNrb86KOantbD5qO4vEDmdjfJJw\nOLzXxzMyEnE6o+8eYna2dQsueQXlFJbV3XZ2uOLIzkqK8FSRY8av7WZPfp1t56KqYhzJIbKTGza3\nGdfblLRe67LKWhsUvjk5ORQUFNS8vXXrVrKzay/vABQVVTTkaQyVnZ1Cfr51f7Qj6A+SmeJmWy0/\nbpSREk/Q57fs+s36tQ0G7WS40yn0Fu3xWEZ8OkGPnfzK/Z/brOttKlqvdUXbWvf2jUKDLjufcsop\nzJs3D4BffvmFnJwckpOTGzadGEJtZ/NR21kkdjTozPf444/n6KOPZsiQIdhsNu69997Gnksi4OLT\nDwWq7/EWlVWRkRLPcR2zao5L5A089Byg+h5vYVUxmfHpdM46uua4iFiDLbyvG7aNIJouE+wQbZc3\nDoTXH8ThiiPo88fEGW80fG19QR8l3jLS3CkHfMYbDettTFqvdUXbWhv9srNYizvOQYuspJgI3mjh\ncrjITmymS80iFqXwFRERiTCFr4iISIQpfEVERCJM4SsiIhJhCl8REZEIU/iKiIhEmMJXREQkwhS+\nIiIiEabwFRERiTCFr4iISIQpfEVERCIsIi+sICIiIjvpzFdERCTCFL4iIiIRpvAVERGJMIWviIhI\nhCl8RUREIkzhKyIiEmFOowcwm4ceeogff/wRm83GHXfcQZcuXYweqUmsWrWKESNGcOWVV3LZZZeR\nl5fH7bffTjAYJDs7m0cffRSXy2X0mI1i3LhxfPvttwQCAa677jo6d+5s2bVWVlYyevRotm3bhtfr\nZcSIERxxxBGWXS9AVVUV5557LiNGjKBr166WXevSpUsZNWoUhx12GAAdO3bkmmuusex6Ad5++22m\nTJmC0+nkpptu4vDDD7fMenXmu4uvv/6adevWMWvWLMaMGcOYMWOMHqlJVFRU8OCDD9K1a9eaY+PH\njyc3N5cZM2bQtm1bZs+ebeCEjWfJkiWsXr2aWbNmMWXKFB566CHLrhVg4cKFdOrUienTp/PUU0/x\nyCOPWHq9ABMnTiQtLQ2w7p/jHU466SReffVVXn31Ve6++25Lr7eoqIhnn32WGTNmMGnSJD7++GNL\nrVfhu4vFixfTp08fADp06EBJSQkej8fgqRqfy+XihRdeICcnp+bY0qVL6d27NwC9evVi8eLFRo3X\nqE488USefvppAFJTU6msrLTsWgH69evHsGHDAMjLy6N58+aWXu/vv//OmjVr6NmzJ2DdP8d1sfJ6\nFy9eTNeuXUlOTiYnJ4cHH3zQUutV+O6ioKCAjIyMmrczMzPJz883cKKm4XQ6iY+P3+1YZWVlzeWb\nZs2aWWbdDoeDxMREAGbPns2pp55q2bXuasiQIdx6663ccccdll7v2LFjGT16dM3bVl4rwJo1axg+\nfDiXXHIJX375paXXu3HjRqqqqhg+fDi5ubksXrzYUuvVPd+9iNWdN6247gULFjB79mymTp3KmWee\nWXPcimsFmDlzJitWrOC2227bbY1WWu/cuXM59thjad26da2PW2mtAO3atePGG2+kb9++bNiwgcsv\nv5xgMFjzuNXWC1BcXMwzzzzDX3/9xeWXX26pP8sK313k5ORQUFBQ8/bWrVvJzs42cKLISUxMpKqq\nivj4eLZs2bLbJelo9/nnnzNp0iSmTJlCSkqKpde6fPlymjVrRosWLTjyyCMJBoMkJSVZcr2LFi1i\nw4YNLFq0iM2bN+NyuSz9tW3evDn9+vUDoE2bNmRlZfHzzz9bdr3NmjXjuOOOw+l00qZNG5KSknA4\nHJZZry477+KUU05h3rx5APzyyy/k5OSQnJxs8FSR0a1bt5q1z58/nx49ehg8UeMoKytj3LhxTJ48\nmfT0dMC6awVYtmwZU6dOBapvo1RUVFh2vU899RRvvvkmr7/+OoMHD2bEiBGWXStUN39ffPFFAPLz\n89m2bRsDBw607Hq7d+/OkiVLCIVCFBUVWe7Psl7V6H889thjLFu2DJvNxr333ssRRxxh9EiNbvny\n5YwdO5ZNmzbhdDpp3rw5jz32GKNHj8br9dKyZUsefvhh4uLijB71gM2aNYsJEyZwyCGH1Bx75JFH\nuOuuuyy3Vqj+sZs777yTvLw8qqqquPHGG+nUqRP//Oc/LbneHSZMmECrVq3o3r27Zdfq8Xi49dZb\nKS0txe/3c+ONN3LkkUdadr1QfftkR6P5+uuvp3PnzpZZr8JXREQkwnTZWUREJMIUviIiIhGm8BUR\nEYkwha+IiEiEKXxFREQiTOErIiISYQpfERGRCFP4ioiIRNj/B6oT+t7g7YJoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb87022df90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+p512Gu3atQMgPT2dyspKli9fzsMPPwxAt27dmDJlisI3UoJBHD/9l4QVy0hY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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86e1aa1d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bli0kPj+ZhJdewF5STDApGe/Ay/Gd3A3fSScTOPyI/V9KPrknXLnj7ZWV4vj5\nZ1yLviTh1ZdwPPMMPPMM3tN6UXndULx9zgSHw7D1GsFMa3X89COpg6/EuW4tvk7HUjb+P/iPPwF8\n7HEzhIce+g9//3t3br11JNdccw3vv/8h48dPqPMuRD6fjwEDLqG0tJRXXnmFtLSmpllzuJnp4xsJ\nVl5vMODcZ9u5JOCkzMRrd7tT6nysXuH71VdfMWXKFKZOnUpKSgqJiYlUVVWRkJDAli1byInS37My\nE9u2bSQ9NJaEma9j83gIZrspv/MeKq+6llB6RoPfbig5Bf/JXfGf3JWKkbfi+vgDmkx9FtcX83F9\nMR9/+yMpffxpOO+MRlyN7FcoRMIrL5I85g5sHg8VI26hfPSY6isZdejX7yKOP/4Ehg69lnfeeYul\nS5cwcuStpKenk5eXt+PORnlUVlZyyCHN+O67b7nooksYNGiQZb85i7VZeW/n/V52Li0tJTc3l5de\neomsrOqbbN99992ccMIJXHDBBTzwwAMceeSRDBhQd/MsGr/wI/nTpHPpElKvvwpH3l8EWreh4saR\nVF2aC02ahO19On7+icTnJhE/czoAthEjyL95dPWlaYsz+kzBVlpC8q03kTD7bYKZmZROfBZvn7Pq\n/fp+v59Ro/7Fa6+9VPf7sNlo1ao1n3++kLZtW0Tl12BDGf3xjTSrrzfo9bLhoQf2aju3+vcY7Cb/\nLZt9nfnu9/d8P/roIwoLC7n55psZNGgQgwYNYujQocyePZvc3FyKioro169fow4cM4JBmjw9gfR+\n52DfuoWyu+5l++LvqLrq2rAGL0CgYydKn5pM0bufVF/KfuopMnt2Je6L+WF9v7HO8dOPpJ9xGgmz\n38Z30skUfrbwgIIXwOl0cskllwEQt+NMuUOHo3nuuZe45ZZRNS/37LPTSFHBTqJcTds5GKw+sFvb\nOZodUOGqoaLxp7Jw/zRp276NlBFDif90DoFDmlH63Iv4Tu4Wtve3T1VVuCc9QWj8eGyBAJWDrqJs\n7MOQmGjMPGFmyJlCKETCy9NIvnt0vS8z78v69X9w4omdycrKpqqqivLysr1eZuvWEsD6Z0b/S+u1\njqDHwx/33Fn7c75Z2bT5v3GmvvR8UGe+0vjsGzeQccZpxH86B2/P0yn8bKFxwQvVG/I/9BBFc+bj\nP6YTTV59iYyze+FYtdK4mSzEVlpCypCrSRl1C6GkJIqnv0n53fc3OHgBmjVrTosWh7JtWwEuVxyH\nHNIcu736yzkxMYlZs95rrPFFDFOfvZ2jlcI3wmzFRaRdPgDHxg2U/2sUxTPeJmSSHYf8nbtQ+Mnn\nVF57Pc7ffiXjzJ7Ev1n3r5D2xgBtAAAXO0lEQVTJ/jl//J70Pqfuusz8+aIDvsxcG5fLxbJlP7Bh\nw1ZWrlzPjz/+xvz5XzNixC18882PnHpqz4MfXsRgO/d2rvUx7e0s9eb1knrNIJy//UrFkGFUjB4D\ndpN9COLjKXvoMYqnvkzIbif1xutJvmU4VETfr4sZyucj8bGHST/7dJy/r6Pipn9RNPsjgs1bNNq7\niIuLI2G32wgeddTR3H33/do+UizDyns7m+w7v4WFQqTcNhLXV1/gOec8yu8bZ/RE++Q9/0IK532J\nr9OxNHn9FTLO6onjl5+NHisqOFb+Rvq5fUh69EGCOU0pemM25WPuq94ERUQOSHb/Abhattp1omK3\n42rZKur3dlb4RkjiE4+SMON1fMcdT8nkqeAw/45fwbaHU/Thp1ReMxjnyt/IOLsXCVOn1L7bjFS3\n1ydPJKNPD+K+X0HVpbkUfrEYn/ZTFmkwq7adFb4R4Pz+O5IeGUegVWuKX30julrECQmUPfw4xa/O\nJJScTMqdo0i9fAC2/HyjJzOPUAjXp5+Q0bsHyffeSSglleKXplP69BRCaelGTycStfa3t3PQ44nw\nRI1H4RtuoRBJ940BoHTCM4SidDcw71nnULhgMd7TehE/by6ZPbvien92zJ8Fx329kPTzziTt8ktw\n/PdnqgYMZPuXS/H2Pc/o0USintrO0mCuOR/j+nohnjPPxtf9VKPHOSjBpodQPPMdyu4bh62okLRr\nryD9vDNxLl9m9GiR5fMRt/BL0i7pR3q/vsR9sxRP339Q+MUSSp95jlB2ttETiliC2s7SMD4fSf93\nNyGHg/J7xho9TeOw26kcNoLCr5biOfd84r5ZSkbfPqRcdyX239cZPV3Y2DesJ+HlaaRemUvWkW1I\n738ergWf4z2tF4WffE7JS68T6HCU0WOKWIqV286qX4ZRwmsv41yzmsorryXQ/kijx2lUgbZHUPLi\naziXLCb5vjtJeO8d4j/+AM9Fl+Dp+w+8p/Zs/Oe2QyFs27bh+GMdjvV/4Pjjdxzr/8Ce9xf4fNj8\nfgj4weev/nMoRCgpiVByMqHklJo/484kMWAjFB8PLhchVzwkJBCy2bAXF2ErLsZWUox9x/8da1bj\nXLO6Zgz/YW3xXDIQzwX98XU9pXHXKCJ7yO4/gIqVK/fa2zna287aXrIOB71lWyBAZpejsJWVsX3p\n96Z/rveg1hsKEf/u2yQ9cD+ODX9UH0pIwHtaL7xn9cVz5jkHtv6qKhy/r8OxZhXO1atwrF6FY81q\nHGvXYC+re8aQ3V69a5TDSWjH7lG28rLqID4IocQkvD1OxduzN95evQm2Pfyg3l6kWXn7wdpovday\ndcbrtd7VKL3PGeQMvNyAiervoG8pKAfOufwbHFs2UznoatMH70Gz2fD0uwjPP/rh/G458XM+xjXn\nI+LnfEz8nI9JAYLp6QSz3QSz3YTcOQSzswlmZGArKcFekI89Px97/tbq/woL93oXofh4Am0Px9em\nLYE2hxFo3YZAmzYEW7ch0PxQiI+vfcOSUAg8HmxlZdjKSrGVlZHpClG0pRCb1wMe747/e6rPlFPT\nCKWlEdzx/1BaGqGUVPNthiISA/bXds6+8OKovfSs8A2T+DkfAeA9p6/Bk0SQw4H/xL/jP/HvlI+5\nD/u6tdVBvOAz7Hl/YS/Ix7F2DbY6LrYEMzIIunPwH9OJwGGHE2jXjkC79viPaE/w0JYN+91om636\nknJCwq4ilDsFn4XPFESsoj5tZ1eUntwofMPENecjQomJeLufZvQohgm2PZzKG4ZTecPwXQcDAWzb\ntlWf7RZuJ5SaStCdQzAr+6BuNCAi1rOz7VzrXY2ivO2s8A0Dx9rVOFevwnPOedV3DDI5jy9AXkE5\nAV+A+Lgw77zlcBDKySGQk0MgvO9JRKLczrZzbc/5qu0se3HN+QQAz9nmvuQcCAaZ+fkaVqzKZ3up\nh8yUeI5r7+bS04/Aoec4RcQErNp21nfYMIhbvBAA7+lnGDzJvs38fA3zlv/JthIPoRBsK/Ewb/mf\nzPx8jdGjiYgA2ttZDoB96xZC8fGmbjl7fAFWrKp9f+YVqwrw+HRRWESMpb2d5YDYCwoIZrurm7Ym\nVVzmYXtJ7Z+4haVVFJdF7ye1iFiD9naW+guFsBfkV4eviaUlx5OZWntZISMlgbTk6C0yiIg1aG9n\nqb/ycmyVlQRNvrl+fJyD49rX/gPCce2zw996FhHZDyvv7azwbWT2gurnUUMmP/MFuLhnW1rmJGPf\ncXXcboOWOclc3LOtsYOJiOyQ3X8Arpatdu0yZ7fjatlKbWfZk628HIBQY99UIAxmLVjHxq1lBHds\nOBUMwcatZcxaYN27E4lIdFHbWeol2Lw5QPWddkxMbWcRMTu1naXeQukZBJNTcGzYYPQo+6S2s4iY\nndrOUn82G8GWrbBv3FB9Rx2TUttZRMxObWc5IIFWrbCXlmArLjJ6lDqp7SwiZqe2sxyQQMtWADg2\nmvvSs9rOImJ27gEDSe9zBs6sbLDbcWZlk97nDNwDBho92kHRjRXCIHhYdXg5fv0v/k7HGjxN3Xa2\nnXfave2c26e9gZOJiFSzORzkDLyc7AsvJtXhpyTgjOoz3p105hsGvpNOBiBuydcGT1I3tZ1FJJrY\n4+Np0uwQSwQvKHzDwt+xM8GUVOK+Xmj0KHVS21lExDgK33BwOPD9/WSc69Zi37LZ6GlqpbaziIhx\nFL5h4uvaHcC0Z79qO4uIGEfhGya+bqcAEPf1IoMnqZvaziIixlD4hom/cxeCySnEfbXA6FHqpL2d\nRUSMofANl7g4fKd0r37e14S/76u2s4iIcRS+YeQ7tScAri8XGDpHbdR2FhExjsI3jLynnQ5A3Bef\nGzzJ3tR2FhExjsI3jALt2hNo1hzXV1/suhelSajtbG7egJf8im14A16jRxGRMND2kuFks+E7rRcJ\nM17H+ctPpttq8uKebVm5oYhN+dWlK7sNWrjVdjZSIBjg7TUf8mP+LxR6isiIT6ez+xj6H3EuDrt+\nIBKxCp35hpnv5G4AOL/71uBJ9qa2s/m8veZDFvy5kO2eQkKE2O4pZMGfC3l7zYdGjyYijUjhG2a+\nzl0AcP74vcGT7EltZ/PxBrz8mP9LrY/9VPCLLkGLWIjCN8wCR3YgFB+P8wdzha/azuZT7Cml0FP7\nPaC3VxVR7CmN8EQiEi4K33CLi8N/9DE4f/0FPOYJNLWdzSctPoWM+PRaH8tMSCctPiXCE4lIuCh8\nI8Df+ThsPh/Olb8aPUoNtZ3Nx+Vw0dl9TK2Pdco+BpfDFeGJRCRcFL4R4O9c3XJ2/vSjwZPsSXs7\nm88Fbc+mRXJz7Du+NO3YaZHcnAvanm3wZCLSmBS+ERBs1gwA27ZtBk+yJ7WdzefddZ+wqewvglT/\nXniQIJvK/uLddZ8YPJmINCaFbwSEkqufq7OVmacwo7az+ajtLBI7FL4REExKBswVvmo7m4/aziKx\nQ+EbAaGU6jNfe1mZwZPsoraz+ajtLBI76hW+q1atok+fPrz22msA5OXlMWjQIHJzcxk5ciRery6H\n7cuuy87mCV+1nc1HbWeR2LHf8K2oqGDs2LF07dq15thTTz1Fbm4u06dPp3Xr1syaNSusQ0a7ULL5\nLjuD2s5mpLazSGzYb/i6XC6ef/55cnJyao4tXbqU3r17A9CrVy8WL14cvgmtIC6u+v8Bc5WY1HY2\nH7WdRWLDfsPX6XSSkJCwx7HKykpcrupLYFlZWeTn196alR3sO/6ZTXRbQbWdzUdtZ5HYcdC3FAyF\nQvt9mYyMRJzO6HsO0e1u3IKLy2Fr9LfZUHkF5Wwvrbvt7HDF4c5OivBUkWOWj8PuNpfl19l2Lqwq\nwpEcxJ3csLnNuN5w0nqtyyprbVD4JiYmUlVVRUJCAlu2bNnjknRtCgsrGjSckdzuFPLzG+852myb\nDb/HR1Ejvs2DEfAFyEyJZ1stv26UkZJAwOtr1PWbSWN/bBtLIGAnIz6d7Z7CvR7LSEgnUGYnv/LA\n5zbresNF67WuaFvrvn5QaNCvGnXr1o05c+YAMHfuXHr06NGwyWKJ3Q71uEoQKWo7m4/aziKxY79n\nvj///DOPPPIImzZtwul0MmfOHB577DFGjx7NzJkzad68Of369YvErNHNboeA3+gp9nBxz7as3FDE\npvzq0pXdBi3cajsb6YK2Z7O6aB15ZZsJEsSOnWbJh6jtLGIx+w3fjh078uqrr+51/MUXXwzLQFYV\napKIraLS6DH2sLPtvNPubefcPu0NnCx27Ww777R723lA+/MNnExEGpN2uIqQUEoKtnLzbLKhtrP5\nqO0sEjsUvhESSk421SYb2tvZfLS3s0jsUPhGSHX4mufMV3s7m4/2dhaJHQrfCAklpWDzesFjjjNK\ntZ3NR21nkdih8I2QnXc2spWa59Kh9nY2H+3tLBIbFL4REkyvvpxoL957AwWjaG9n89HeziKxQeEb\nIaGMTABs27cbPEk1tZ3NR21nkdih8I2Q4I7wtReaI3zVdjYftZ1FYofCN0JCGRkA2ArNcdlZbWfz\nUdtZJHYofCPEbGe+ajubj9rOIrFD4Rshocwdz/maJHxBbWczUttZJDYofCOk5sx3uzkuO4Pazmak\ntrNIbFD4Rsiu53zNceartrP5qO0sEjsUvhGy6zlfc5z5qu1sPmo7i8QOhW+kuFwEk5JNc+artrP5\nqO0sEjsUvhEUysxU21nqpLazSOxQ+EZQMMM84QtqO5uR2s4isUHhG0Gh9AxsFRVQVWX0KIDazmak\ntrNIbFD4RlAws7rxbC8yvnSltrP5qO0sEjsUvhFkppsrqO1sPmo7i8QOhW8EmWmLSbWdzUdtZ5HY\nofCNoJotJk1w5qu2s/mo7SwSOxS+ERRM3/GcrwnOfEFtZzNS21kkNih8I6jmzNcEhStQ29mM1HYW\niQ0K3wjadXMF48981XY2H7WdRWKHwjeCdoavGbaYVNvZfNR2FokdCt8I2nlnIzPcXEFtZ/NR21kk\ndih8IyiUlk7IbjdF4UptZ/NR21kkdih8I8luJ5SRgS1/q9GTAGo7m5HaziKxQeEbYYHWbXBs3AAB\n4wtNajubj9rOIrFB4RthgTZtsfl82Df9aegcajubj9rOIrFD4RthgcOqL+k6fjf27FJtZ/NR21kk\ndih8I8ws4au2s/mo7SwSOxS+ERZoezhgfPiq7Ww+ajuLxA6Fb4QF2rUnlJCArbLC6FHUdjYhtZ1F\nYoPCN8JCaekULviasvvGGT2K2s4mpLazSGxQ+Bog0PYISEw0dAa1nc1HbWeR2KHwjVFqO5uP2s4i\nsUPhG6PUdjYftZ1FYofCN0ap7Ww+ajuLxA6FbwxT29l81HYWiQ0K3ximtrP5qO0sEhsUvjFKbWfz\nUdtZJHYofGOU2s7mo7azSOxQ+MYotZ3NR21nkdih8I1Rajubj9rOIrFD4RvD1HY2H7WdRWKDwjeG\nqe1sPmo7i8QGhW+MUtvZfNR2FokdCt8Ypbaz+ajtLBI7FL4xSm1n81HbWSR2NDh8H3zwQS699FIG\nDhzIjz/+2JgzSQSo7Ww+ajuLxI4Ghe+yZctYv349M2fOZNy4cYwbZ/yN4eXAqe1sPmo7i8SGBoXv\n4sWL6dOnDwCHH344xcXFlJWVNepgEn5qO5uP2s4isaFB4VtQUEBGRkbN3zMzM8nPr705K+aktrP5\nqO0sEjucjfFGQqHQPh/PyEjE6Yy+5xDdbusWXPIKytleWnfb2eGKw52dFOGpIseMH9vNZfl1tp0L\nq4pwJAdxJzdsbjOuN5y0XuuyylobFL45OTkUFBTU/H3r1q243bWXdwAKCysa8m4M5XankJ9v3V/t\nCPgCZKbEs62WXzfKSEkg4PVZdv1m/dgGAnYy4tPZ7inc67GMhHQCZXbyKw98brOuN1y0XuuKtrXu\n6weFBl12PuWUU5gzZw4Av/zyCzk5OSQnJzdsOjGE2s7mo7azSOxo0Jnv8ccfzzHHHMPAgQOx2Wzc\ne++9jT2XRMClpx8BVD/HW1haRUZKAse1z645LpHX/4hzgerneLdXFZGZkE6n7GNqjouINdhC+3vC\nthFE02WCnaLt8sbB8PgCOFxxBLy+mDjjjYaPrTfgpdhTSlp8ykGf8UbDehuT1mtd0bbWRr/sLNYS\nH+egWXZSTARvtHA5XLgTs3SpWcSiFL4iIiIRpvAVERGJMIWviIhIhCl8RUREIkzhKyIiEmEKXxER\nkQhT+IqIiESYwldERCTCFL4iIiIRpvAVERGJMIWviIhIhEXkxgoiIiKyi858RUREIkzhKyIiEmEK\nXxERkQhT+IqIiESYwldERCTCFL4iIiIR5jR6ALN58MEH+eGHH7DZbNx555107tzZ6JHCYtWqVQwb\nNoyrrrqKf/7zn+Tl5TFq1CgCgQBut5vx48fjcrmMHrNRPProo3z77bf4/X6GDBlCp06dLLvWyspK\nRo8ezbZt2/B4PAwbNowOHTpYdr0AVVVVnHfeeQwbNoyuXbtadq1Lly5l5MiRtGvXDoD27dtz3XXX\nWXa9AO+99x5Tp07F6XRy0003ceSRR1pmvTrz3c2yZctYv349M2fOZNy4cYwbN87okcKioqKCsWPH\n0rVr15pjTz31FLm5uUyfPp3WrVsza9YsAydsPEuWLGH16tXMnDmTqVOn8uCDD1p2rQDz58+nY8eO\nvPbaa0yYMIGHH37Y0usFmDx5MmlpaYB1P493Oumkk3j11Vd59dVXufvuuy293sLCQp555hmmT5/O\nlClT+Oyzzyy1XoXvbhYvXkyfPn0AOPzwwykuLqasrMzgqRqfy+Xi+eefJycnp+bY0qVL6d27NwC9\nevVi8eLFRo3XqE488USefPJJAFJTU6msrLTsWgH69u3L4MGDAcjLy6Np06aWXu/atWtZs2YNPXv2\nBKz7eVwXK6938eLFdO3aleTkZHJychg7dqyl1qvw3U1BQQEZGRk1f8/MzCQ/P9/AicLD6XSSkJCw\nx7HKysqayzdZWVmWWbfD4SAxMRGAWbNmceqpp1p2rbsbOHAgt912G3feeael1/vII48wevTomr9b\nea0Aa9asYejQoVx22WUsWrTI0uv9888/qaqqYujQoeTm5rJ48WJLrVfP+e5DrO68acV1z5s3j1mz\nZjFt2jTOPPPMmuNWXCvAjBkz+PXXX7n99tv3WKOV1jt79my6dOlCy5Yta33cSmsFaNOmDcOHD+ec\nc85h48aNXHHFFQQCgZrHrbZegKKiIiZOnMhff/3FFVdcYanPZYXvbnJycigoKKj5+9atW3G73QZO\nFDmJiYlUVVWRkJDAli1b9rgkHe2++uorpkyZwtSpU0lJSbH0Wn/++WeysrJo1qwZRx11FIFAgKSk\nJEuud8GCBWzcuJEFCxawefNmXC6XpT+2TZs2pW/fvgC0atWK7OxsfvrpJ8uuNysri+OOOw6n00mr\nVq1ISkrC4XBYZr267LybU045hTlz5gDwyy+/kJOTQ3JyssFTRUa3bt1q1j537lx69Ohh8ESNo7S0\nlEcffZRnn32W9PR0wLprBVi+fDnTpk0Dqp9GqaiosOx6J0yYwFtvvcUbb7zBgAEDGDZsmGXXCtXN\n3xdeeAGA/Px8tm3bRv/+/S273u7du7NkyRKCwSCFhYWW+1zWXY3+x2OPPcby5cux2Wzce++9dOjQ\nweiRGt3PP//MI488wqZNm3A6nTRt2pTHHnuM0aNH4/F4aN68OQ899BBxcXFGj3rQZs6cydNPP81h\nhx1Wc+zhhx9mzJgxllsrVP/azV133UVeXh5VVVUMHz6cjh07cscdd1hyvTs9/fTTtGjRgu7du1t2\nrWVlZdx2222UlJTg8/kYPnw4Rx11lGXXC9VPn+xsNN9www106tTJMutV+IqIiESYLjuLiIhEmMJX\nREQkwhS+IiIiEabwFRERiTCFr4iISIQpfEVERCJM4SsiIhJhCl8REZEI+/9ArO9jdZRcnAAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86daa2f50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MrFkvcuqpAxk27PcR7EzEXKyadt7naee5c+fy6KOPcthhhzVumzhxImPHjsXr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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dcbfb50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wwb+Yz0fiE4+R+PhEbH4/Vbf9T+0ys1N/5UQaKqbTztJEQiFS7rwd17IP8fS7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\n4v97fAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb87022f050>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UEYlnjvVryRo6CNfHS/Cdcx6lb7xTG7wijWDpy0uuWrWKGTNmAFBUVERVVRV9+vRh/vz5\nACxYsIB+/fpFd0oRMb2kpR+RNXQQzg3rqbr+/yh/9gVITTV6LIlzlr285MiRI9m5cyf5+flcddVV\n3H777Vx33XXMmzeP/Px8SktLGTZsWCxmFRGTSn75BTL/MAxbZSXlDz1O5a13/vKPpchhSNTLSx70\nnG9ycjL333//PttnzpwZlYFEJI6Ew6ROvIOUxx4inJ1N+cyXCfTpa/RUkiAOdnnJ3AsujNtDzw0u\nXImI7KWykoyxo3G/81+CnY6i/OVXCHU8yuipJIE05PKSrjjtHOm4kIgcMvv2ArLOG4L7nf/i73c6\npe+8r+CVJpfIl5dU+IrIIbFv2UzW784i6ZuvqB51GWWzXyOSlW30WJKALN12FhHZzb5hPVnnD8Wx\naSOVf/0b3vse0qUiJaos23YWEQFwrF1TG7xbt+C99U6q/vYPXSpSoi5R284KXxE5KMf/vqsN3h3b\n8U68h+rr/8/okcQCDtZ2judrOyt8ReSAbCU7ycy/EHtRIRX3PkD1mGuNHkksoiFt53il8BWR/YtE\nSP/LdTh+2kblhFupufxKoycSC1HbWUQsKfn5Gbjf/g/+U/tRNf6vRo8jFqO2s4hYjuP7/5F2+98J\nZ2dT8fgz4Ii/24JK/FPbWUSso7qajDGXY6upoWLa44RbtjJ6IrEotZ1FxDKavfQczh++p/qyK/AP\nPcfoccSi1HYWEesIhWj29BNEkpOpvPkfRk8jFqa2s4hYhuvdt3Fs2kjNiJFEcnONHkcsTG1niwkE\nQuwsqiQQCO2zvaykOiG3H+56zbimaK01nrY3RrOnHgOg+qqxh/25RA5HIreddUvBPYTDYT75YD0/\nrinCW+EjLd1Nhy65nNK/I8sXb6jdXu4jLSMBtzdyve075wCwcW2x+dbUxGuNp+19BnbC3oib2Tu/\n/BzX8k/wDxxM6OiuTf0jJnLIcoePoGr1avzbttaWrux2XK2PjPu2sy0SiUSi/SSFhRXRfoomsXTh\nWr5ZtW2f7Tl5qRT/XKnt+9leH7PNaLXt3U9sTd/BnQHweNIb/DOYPnY0yXPnUPrKPAL9BzboY8zm\nUNabCBJ9vT/PfpnShe/tsz1r8BnkjbzYgIkazuNJ3+9jCt9dAoEQs59Zibd83/aczQb1/S1p+/6Z\nbUarbU/PcHPR6JNJSnI0/B/nqipyj+1E2ONh58qv4vamCYkeRr+WyOsN+3xsvP0WgsXF+zzmzMml\n/T8nmfrQ84HCV+d8d6ny+usNXth/4Gj7/pltRqtt91b4qPL6639wP1zvL8BWVYlv2O/jNnglsajt\nbAEpaS7SMur/DWp//w5p+/6ZbUarbU9Ld5OS5qr/wf1InvcaADXnDz+kjxOJFrWdLSApyUGHLvW/\nrKK5J1XbD7DdTLNoe632XXJJSjqEy0F6vbgWzifY6ShCx3Vr+MeJRFEit50dd955553RfpKqqkM7\n/GWUI9tn4/cFqfb6CfhDpGe4Obr7EZxx/rEE/CFtr2d7l24tyGuVTnWl8bNo+y/b+wzshG3XbnFq\nqvugP4Put94k+fV/U335lQT6nR6LH7eoach6E0mir7dZl654v/6KkLei9jyL3Y7ryDa0vOoabCa/\n3nhq6v5/OVDhqh6BQIhkVxI1/sBeew+BQIgqr5+UNFfCbT/c9ZpxTdFaazxth4YVcjIuuxj32/9h\n54fLCR1z7AHf1+wSuYBUn0Rfr9rOhyEevzES/Rv616y0XiutFQ6+XltFOTnHdiLUvgMlS1bGcLLo\n0Nc3cajtLCIJy/Xu29h8PnwqWonJqO0sIgnL/ebrAApfMR21nUUkIdnKSnF9sJDgsd0Ide5i9Dgi\ne0nktrPCV8TCXO+8hS0QwDdMe71iTrnDR+Bq0xZ2X6vcbsfVpm3cX9tZ4StiYc2en0HEZqNm2O+N\nHkWkXkWvvYp/y+bamyoAhMP4t2ym6LVXjR3sMCl8RSzK+ekKkj77FP+ZQwi372D0OCL7CPt8eL/4\nvN7HvF98QdhX/yWB44HCV8Simj31OADVV48zeBKR+qntLCIJxb55E+7/vkGgWw8CffoaPY5IvdR2\nFpGE0mz6U9jCYaqvvlZ3MBLTUttZRBKG/ccNNHthJqEWR9TePlDExNR2FpH45/eTcfWfsVVVUnn7\nP8F1aLcdFIk1tZ1FJO6l3juJpC8+p2bESHwjRho9jsgBqe0sInEv6cNFNHt0GqH2HfDee7/R44gc\nlNrOIhLXbIWFpF97FTgclD81g0ja/u+2ImIWajuLSPzasoWs4efg+HkHlX+/nWCv3xg9kUiDqO0s\nInHJsfoH6NMH5+ofqLrqGqqvvd7okUQOidrOIhJXnCtXkPW7M2HrVry3/ZPKiZN/+QdMJE6o7Swi\nccP1zltkXfg7bBUV8NxzVF93gy6mIXFHbWcRiQ/BIKmT7iLz0j+C3U75S3Pg0kuNnkqkURK57ew0\negARaRr2n7aRMebPJK1YRqh9B8qffYFg9+ONHkuk0Xa3nYPFxfs+praziBjN9f4CsgeeStKKZdSc\nP5yS95coeCXuqe0sIqZk81aQetsEMv94IbbKSiqmPEjF0zOJpGcYPZpIk1DbWUTMIxLB9Z83yD71\nJFKeepxgx06Uvr2QmsuuULFKEoraziJiCvZNG8m4eASZV4zCXlxE5Y0TKFm8TIeZJeEkcttZhSuR\neFFVRcoTj5Dy0P3Yamrw9+uPd8r9hDp1NnoykahoSNvZlZcX46mahsJXxOzCYdyv/D9S75mIo+An\nwp48Kh58FN/wETrELAlNbWcRMUTSkg/JGnwaGddfg71kJ5U33MjO5Z/j+/0fFLyS8BK57dygPd8p\nU6bw2WefEQwGGTNmDN27d+fmm28mFArh8XiYOnUqLt2UW6TJ2DdtJO22CbjffRuAmhEjqfz7bYSP\nbGPwZCKxlTt8BFWrV+PftrW2dGW342p9ZNy3nQ8avsuXL2ft2rXMmTOHkpISLrjgAnr37k1+fj5D\nhw7lgQceYO7cueTn58diXqH2YgrOz1ZhryiHYBCCQWyhIASCEAoRat+eYPfjCbdrr72jeOP30+yJ\nR0h9YAq26mr8ffpSedckgsf3MnoyEUPUtZ1326PtnDfyYuMGO0wHDd+TTjqJHj16AJCRkUF1dTUr\nVqzgrrvuAmDAgAHMmDFD4RstoRCO7/9H0srlJK1cRtLKFTi2bmnQh4YzMgn2OJ5g9+MJHt8T/xln\n6fWfJpb0yVLSbv4LzjWrCed6qHjgEZ3XFUs7WNs594IL4/bQ80HD1+FwkJKSAsDcuXM57bTTWLp0\nad1h5pycHAoLCw/4ObKzU3A6HU0wbmx5PAbecLy8HJ58EqZNg4KCX7bn5MB550GfPnDEEZCUBE7n\nL//ZbLB6NXz+OfbPP8f18RJcSz+q/di0tNrr/I4bB1277vOUhq43xky11vJyuOEGmDmz9us3diz2\nSZPIyMpqsqcw1XpjQOtNDNUFlQdsO2c4gjTz5MZ4qqbR4LbzwoULmTt3LjNmzODMM8+s2x6JRA76\nsSUlVY2bzkAeTzqFhRUxf17bjh2kPPMEyc89i728jHBqGv6RFxM4pQ+Bk08h1Omog+8JndIfdl1L\n3+atwPHtt7g+/ojkF5/D8dhj8Nhj+E8fQPWVV+MffCY4HIat1whmWqvjm6/JGH0pzg3rCXQ/Hu/U\nBwmecCIEgCaa0UzrjQWtN3GEQ84Dtp3LQ068Jl77gX4palD4LlmyhCeffJLp06eTnp5OSkoKNTU1\nJCcns2PHDvLi9HVWZmIrLib1nokkz3kZm89HONdD5S23U33ZFUSyshv9eSNp6QRP6U3wlN5Ujf8r\nrnf+S7PpT+H6cBGuDxcR7HI0Ffc/Auee0YSrkYOKREh+YSZpt/4Nm89H1XV/oXLCrbVHMkQE+KXt\nXLrwvX0ei/e280FfalRRUcGUKVN46qmnyNp1GKxPnz7Mnz8fgAULFtCvX7/oTpngnCuWkz3wVJq9\nMIPwES2pmPIgxZ99S9UNNx5W8O77RE78vxtG2RvvsPODj6kZeTGOtWvIOu8sGD8evN6mey7ZL1tF\nOeljLif9phuIpKZSNutVKm+7S8ErUg/LXtv57bffpqSkhBtuuIFRo0YxatQorr76aubNm0d+fj6l\npaUMGzYsFrMmnnCYZo9MI2vYUOw/78D7jzvYuezz2uvzNmsW1acOdetOxcNPUPrGu7WHsh9+mOb9\ne5P04aKoPq/VOb75mqwzTid53msETj6FkveX4h98ltFjiZhWol7b2RZpyEnbwxSP5yOifR7FtrOY\n9Ouuxv3efEJHtKTi6ZkETukTtec7oJoaPI8/QGTqVGyhENWjLsM7cTLsKtolGkPOkUUiJD8/g7Tb\nJsT8MHMinxOsj9abOMI+Hxtvv6X+c745ubT/5yRTH3o+0DlfXeHKAPYtm8k+43Tc783H338gJe8v\nNS54AZKT4Z57KJ2/iOBx3Wn24nNkDxmAY81q42ZKIHWHmW/+iw4zixyChlzbOV7p2s4xZisrJfPi\nETi2bKby/26m6uZbfjmXYbBgj56UvPsBaXf+g2bPPk32mf2pmPogvhEjDZupsrKSzZs3sWnTRjZt\n+pGNG3/E7U7mttvuIhAIEAj4STfxa5edX39J+ujLcP64gcDJp1D+9EzCrVobPZZIXEjkazsrfGPJ\n7yfjz6Nw/vA9VWPGUjXhVqMn2pfbjfee+/D3PpX0G8aRce1VVH+yFO+kKTE9DD116j1MnXrPfh9/\n5ZVZFBUV4XA4WLp0JZ3MdmefQICUh+4n5YEp2IJBqq7/v9rDzE79yIk0VCK3nfUvQaxEIqTfOB7X\nkg/xDT2XyjsnGT3RAfnPu4CSbj3IGH0ZzV5+gaRVKyl/cgah47od8ueqqqpi+fKPWbToAzweD9df\n/3+/PI/fz9atm9m4cSMbN/64aw93Ix99dODiVzgcoUOHjvz44wa++OJzU4WvY/UPpF83hqQvvyDU\nqjUV0x4j0H+g0WOJxCXLXttZmkbKA1NInv0ygV4nUP7EdHCY/4pf4Y6dKH3rvdrD0DOeIXvIALx3\nTKTmijEHvdDHqlUrWbbsExYtep8VKz4hEAjUPfbNN19TXFzEpk0b2bZtK+HdLcY9uHf9RpuTk0NW\nVjY1NTWUlpZQWVkJwF/+ciOdOx/NyJHD2bRpY9Mt+nCEwzR76nFS774Lm89HzUX5eP81mUhm012p\nSsRqLHttZzl8zi8/J/XeSYTatqPsxVfiq0WcnIx38v34Bwwm/YaxpN9yM64PFlLx0BNEPJ693jUY\nDPL663OZPPlfbNnzh+VX3njjNQCOOKIlJ598Cu3ataddu/a0b99h1587UFZWyqmnnkhxcTHFxcW4\n3W7atm1H+/Yd6NjxKIYN+z2VlbWvSzY8fCMRXAvnk3r3RJzffUM410P5Uw/hP/tcY+cSiXOWvraz\nHKZIhNQ7a8/tVkx7jEicXg3Mf9ZQShYvI33cGNwLF5DUvzcVk+/Df+75dXvBS5d+xLXXXlX3MTab\njSOPbENqaipVVVUUFBQQCPi54ILfM23a4zQ7wGuZ8/LyePPN+UQiYdq1a88RR7TE/qtimt/vx263\ns3Hjj9FZdAMkfbKU1El3kfTpCiI2GzUjRuK9624iufF5vVkRM2lI29kVp/+mKnyjzDX/HVyfLMV3\n5hACfU8zepzDEm5xBGVzXqfZk4+ROulOMq+4hMBJv8V71ySCJ55M5q7m4Vlnnc38+bX3od1zD7hz\n5y707z+QMWOuPWDw7nbKKb0P+LjL5aJ16yNjv+cbCJC0YhkpDz+Aa/EHAPjO/h2VE24l1PWY2M4i\nksDUdpbGCQRI/edtRBwOKm+faPQ0TcNup3rsdfiHDCV14p2433qT7LMHU3PeBXQcd8Oud4owZMg5\nrFy5jH79+tO//0BOP30ARx7GjeB/2FQCQNd2e19us1279ixd+hHV1dUNCvTGsm/ehGvR+7g+WEjS\nkg+xe2svauA/fQCVf7+t9mYIItKk1HaWRkl+6Xmc69ZSfekVhLocbfQ4TSrU8SjKZ76Ec/ky0u68\nheQ3X6fj2/8hMymJTd9+w0cfryLSrBm2JroX7RtLaw8tH5MawrFxA45NG3Fs/JHOP21jKVBy/lBa\nulwQCkIgiC0YhEiESGoqkbQ0ImnpdX/G05yUkI2I2w0uFxGXG5KTidhs2MtKsZWVYSsvw77r/451\na3GuW1s3S7BDR3x/GInv/OHz0MwnAAAWI0lEQVQEep/aJOsTkfqp7SyHJhQi5YEphFPTqLzp70ZP\nEzXBU3pT+s4HuN94jdR/3UXHzRv5fttWco5uR6D/QPxnnY3vzKGHdq67pgbHjxtwrFvD6tUF/Nub\nxerkFgDcf8dS8pfNpvvWbwHY/QKjrV9+Tg+7vfaqUQ4nkV1Xj7JVemuD+FdSD2GNkZRUfGcNxd9/\nEP4Bgwh37HQIHy0ih0NtZzkkzlWf4tixnepRl8dtyarBbDZ8w36P73fDaHPheXzx8RK2HdmGNvPf\nwT3/HdKBcFYW4VwP4VwPEU8e4dxcwtnZ2MrLsRcVYi8sxF74c+1/JSV1n/pkoEVOG8Zd+ggAV/h/\noNXvBlDR7nJC7duTt3YN3PZ3vvrXZE6+auy+s0Ui4PNh83qxeSuweb00d0Uo3VGCze8Dn3/X/321\ne8oZmUQyMwnv+n8kM5NIeoZprkImYiVqO8shc+8qHPmHnm3wJDHkcNC212/g4yV8+eBjpOTl4Z7/\nDq7F72Mv+Al7USGO9euw7edeHuHsbMKePILHdSfUoROhzp0Jde7Ce9W5nJeRCTYbi/vexPl9O9R9\nTNvmOQBs2ryp/plsttpDysnJvzSQPekEEvRC9CKJRG1nOWSu+W8TSUnB3/d0o0eJqXbt2gOwadOP\nnHJKb6qvGUf1NeN+eYdQCFtxce3ebslOIhkZhD15hHNy93ujgVY//MxJXWt/wD794ef9PN/Gpl6K\niBhMbWc5JI71a3GuXYNv6Lm1dwwyOV8gREFRJaFACHfS4V15q3372r3S/Yahw0EkL49QXh6hBn7O\n3cH76z8DZGc3JzMzy9DX+opIdKjtLIfENf9dAHxDzH3IORQOM+eDdXyxppCdFT6ap7vp1cXDRQOP\nwtHIc5y790SjEYbhcJgdO7bXXQM6JyeHM84YQrt27Vmz5gcikUiTtatFxBzUdpYGS1q2FAD/wDMM\nnuTA5nywjoWrtta9XVzuq3s7f3CXRn3O1q2PxOFwNNlh4Oefn8FNN91Ap05HsXXrFnw+316PL136\nKUVFhdTU1LBjx3aOOKJlkzyviJhDoradVeGMAvvPO4i43aZuOfsCIb5YU1jvY1+sKcIXaOhB4b0l\nJSXRunWb/YZvIBBg+fJPmDz5X5x33hAeffShA36+m2/+CwDr16/D4/HQqVNnOnbsRGpq7YuF+vY9\niZ9+2gbAV1992aiZRcScDtZ2Dv/ql/F4oj3fKLAXFRHO9Rz0zj9GKvP62Fle/zduSUUNZV4fedmN\nuwFE+/Yd+OijRVRWVpKamsqPP27gueee5ccf17N06RK83l+axj/88D8uuiifrVs3s2nT3rcV/PWh\n661bt/76qejZsxfHHtsNr9fLoEHmPtIgIodGbWdpuEgEe1EhwS5djZ7kgDLT3DTPcFNcTwBnpyeT\nmdb4IsPu876bN2/imGOO5be/7Vn3WG5uLh06dMDtdrN69Q+UlpZy3HH1X7SiZctWdOnSldWrv9/n\nMY8nj6OP7sqDDz5a93wikljUdpaGq6zEVl1N2OR3tXEnOejVxbPXOd/denXJPazW8+4wHDt2NBUV\nFdhsNiK7XttbVFREUVHRXu/fo0dPevfus8etBTvSpk1bkpOTCQQCPP74w7jdbtq370i7du1p27Zd\n3WFnEUlcajtLg9mLas+jRnI9B3lP413YvyOrN5eyrdBLOAJ2G7T2pHFh/46H9XlPPPEkAL777hvy\n8lrUu/faosURDBw4mEGDzuDcc8/f53aBuyUlJTF+/F8Pax4RiV9qO0uD2CorAYikNO58aSzNXbyB\nLT97694OR2DLz17mLt7Q6LYzQJ8+ffn223WkpqaSmpqK3+/niitGEQqFGDBgEP37D+KoozrrZUEi\nclCJ2nZW+DaxcKtWANgLfjJ4kgM7WNv596d3OqxDz3l7lCBcLhcvvjin0Z9LRKwpka/trJcaNbFI\nVjbhtHQcmzcf/J0N1JC2s4iIkRrSdo5XCt+mZrMRbtMW+5bNtXfUMandbef6HG7bWUSkKexuO9f7\nWJy3nRW+URBq2xZ7RTm2slKjR9mv3W3n+hxu21lEpCnsbjvXJ97bzgrfKAi1aQuAY4u5Dz1f2L8j\nbfLSsO/qPdlt0Cbv8NvOIiJNxTNiJFmDz8CZkwt2O86cXLIGn4FnxEijRzssKlxFQbhDbXg5vv8f\nwe7HGzzN/kWr7Swi0lRsDgd5Iy8m94ILyXAEKQ8543qPdzft+UZB4ORTAEha/onBk+xftK7tLCIS\nDXa3m2Ytj0iI4AWFb1QEu/UgnJ5B0idLjR5lv9R2FhExjsI3GhwOAr89BeeG9dh3bDd6mnqp7Swi\nYhyFb5QEevcFMO3er9rOIiLGUfhGSaDPqQAkffKxwZPsn9rOIiLGUPhGSbBHT8Jp6SQtWWz0KPu1\nu+0c3nUtkD3bziIiEj0K32hJSiJwat/a874mfL2v2s4iIsZR+EZR4LT+ALg+WmzoHPVR21lExDgK\n3yjynz4QgKQPPzB4kn2p7SwiYhyFbxSFOnch1LIVriUf1t4E2kTUdjY3f8hPYVUx/pDf6FFEJAp0\neclostkInD6A5Nkv4/zuG9NdavLC/h1ZvbmUbYW1pSu7DVp71HY2Uigc4rV1b/F14XeU+ErJdmfR\nw3Mcw486B4ddvxCJJArt+UZZ4JQ+ADg//8zgSfaltrP5vLbuLRZvXcpOXwkRIuz0lbB461JeW/eW\n0aOJSBNS+EZZoEdPAJxff2nwJHtT29l8/CE/Xxd+V+9j3xR9p0PQIglE4RtloaO7EnG7cX5lrvBV\n29l8ynwVlPjqvwf0zppSynwVMZ5IRKJF4RttSUkEjz0O5/ffgc88gaa2s/lkutPJdmfV+1jz5Cwy\n3ekxnkhEokXhGwPBHr2wBQI4V39v9Ch11HY2H5fDRQ/PcfU+1j33OFwOV4wnEpFoUfjGQLBHbcvZ\n+c3XBk+yN13b2XzO7ziE1mmtsO/60bRjp3VaK87vOMTgyUSkKSl8YyDcsiUAtuJigyfZm9rO5vPG\nhnfZ5v2JMLWvCw8TZpv3J97Y8K7Bk4lIU1L4xkAkrfZcnc1rnsKM2s7mo7aziHUofGMgnJoGmCt8\n1XY2H7WdRaxD4RsDkfTaPV+712vwJL9Q29l81HYWsY4Ghe+aNWsYPHgwL730EgAFBQWMGjWK/Px8\nxo8fj9+vw2EH8sthZ/OEr9rO5qO2s4h1HDR8q6qqmDhxIr17967b9vDDD5Ofn8+sWbNo164dc+fO\njeqQ8S6SZr7DzqC2sxmp7SxiDQcNX5fLxTPPPENeXl7dthUrVjBo0CAABgwYwLJly6I3YSJISqr9\nf8hcJSa1nc1HbWcRazho+DqdTpKTk/faVl1djctVewgsJyeHwsL6W7Oyi33XX7OJbiuotrP5qO0s\nYh2HfUvBSCRy0PfJzk7B6Yy/c4geT9MWXFwOW5N/zsYqKKpkZ8X+284OVxKe3NQYTxU7Zvk67Gm7\nt3C/beeSmlIcaWE8aY2b24zrjSatN3ElylobFb4pKSnU1NSQnJzMjh079jokXZ+SkqpGDWckjyed\nwsKmO0eba7MR9AUobcLPeThCgRDN090U1/Nyo+z0ZEL+QJOu30ya+mvbVEIhO9nuLHb6SvZ5LDs5\ni5DXTmH1oc9t1vVGi9abuOJtrQf6RaFRLzXq06cP8+fPB2DBggX069evcZNZid0ODThKECtqO5uP\n2s4i1nHQPd9vv/2We++9l23btuF0Opk/fz733XcfEyZMYM6cObRq1Yphw4bFYtb4ZrdDKGj0FHu5\nsH9HVm8uZVthbenKboPWHrWdjXR+xyGsLd1AgXc7YcLYsdMy7Qi1nUUSzEHDt1u3brz44ov7bJ85\nc2ZUBkpUkWYp2KqqjR5jL7vbzrvt2XbOH9zFwMmsa3fbebc9284jupxn4GQi0pR0hasYiaSnY6s0\nz0U21HY2H7WdRaxD4RsjkbQ0U11kQ9d2Nh9d21nEOhS+MVIbvubZ89W1nc1H13YWsQ6Fb4xEUtOx\n+f3gM8cepdrO5qO2s4h1KHxjZPedjWwV5jl0qGs7m4+u7SxiDQrfGAln1R5OtJftewEFo+jazuaj\nazuLWIPCN0Yi2c0BsO3cafAktdR2Nh+1nUWsQ+EbI+Fd4WsvMUf4qu1sPmo7i1iHwjdGItnZANhK\nzHHYWW1n81HbWcQ6FL4xYrY9X7WdzUdtZxHrUPjGSKT5rnO+JglfUNvZjNR2FrEGhW+M1O357jTH\nYWdQ29mM1HYWsQaFb4z8cs7XHHu+ajubj9rOItah8I2RX875mmPPV21n81HbWcQ6FL6x4nIRTk0z\nzZ6v2s7mo7aziHUofGMo0ry52s6yX2o7i1iHwjeGwtnmCV9Q29mM1HYWsQaFbwxFsrKxVVVBTY3R\nowBqO5uR2s4i1qDwjaFw89rGs73U+NKV2s7mo7aziHUofGPITDdXUNvZfNR2FrEOhW8MmekSk2o7\nm4/aziLWofCNobpLTJpgz1dtZ/NR21nEOhS+MRTO2nXO1wR7vqC2sxmp7SxiDQrfGKrb8zVB4QrU\ndjYjtZ1FrEHhG0O/3FzB+D1ftZ3NR21nEetQ+MbQ7vA1wyUm1XY2H7WdRaxD4RtDu+9sZIabK6jt\nbD5qO4tYh8I3hiKZWUTsdlMUrtR2Nh+1nUWsQ+EbS3Y7kexsbIU/Gz0JoLazGantLGINCt8YC7Vr\nj2PLZggZX2hS29l81HYWsQaFb4yF2nfEFghg37bV0DnUdjYftZ1FrEPhG2OhDrWHdB0/Grt3qbaz\n+ajtLGIdCt8YM0v4qu1sPmo7i1iHwjfGQh07AcaHr9rO5qO2s4h1KHxjLNS5C5HkZGzVVUaPoraz\nCantLGINCt8Yi2RmUbL4E7x3TjJ6FLWdTUhtZxFrUPgaINTxKEhJMXQGtZ3NR21nEetQ+FqU2s7m\no7aziHUofC1KbWfzUdtZxDoUvhaltrP5qO0sYh0KXwtT29l81HYWsQaFr4Wp7Ww+ajuLWIPC16LU\ndjYftZ1FrEPha1FqO5uP2s4i1qHwtSi1nc1HbWcR61D4WpTazuajtrOIdSh8LUxtZ/NR21nEGhS+\nFqa2s/mo7SxiDQpfi1Lb2XzUdhaxDoWvRantbD5qO4tYh8LXotR2Nh+1nUWso9Hhe/fdd3PRRRcx\ncuRIvv7666acSWJAbWfzUdtZxDoaFb4rV65k06ZNzJkzh0mTJjFpkvE3hpdDp7az+ajtLGINjQrf\nZcuWMXjwYAA6depEWVkZXq+3SQeT6FPb2XzUdhaxhkaFb1FREdnZ2XVvN2/enMLC+puzYk5qO5uP\n2s4i1uFsik8SiUQO+Hh2dgpOZ/ydQ/R4ErfgUlBUyc6K/bedHa4kPLmpMZ4qdsz4td3uLdxv27mk\nphRHWhhPWuPmNuN6o0nrTVyJstZGhW9eXh5FRUV1b//88894PPWXdwBKSqoa8zSG8njSKSxM3Jd2\nhAIhmqe7Ka7n5UbZ6cmE/IGEXb9Zv7ahkJ1sdxY7fSX7PJadnEXIa6ew+tDnNut6o0XrTVzxttYD\n/aLQqMPOp556KvPnzwfgu+++Iy8vj7S0tMZNJ4ZQ29l81HYWsY5G7fmecMIJHHfccYwcORKbzcYd\nd9zR1HNJDFw08Cig9hxvSUUN2enJ9OqSW7ddYm/4UecAted4d9aU0jw5i+65x9VtF5HEYIsc7IRt\nE4inwwS7xdvhjcPhC4RwuJII+QOW2OONh6+tP+SnzFdBpjv9sPd442G9TUnrTVzxttYmP+wsicWd\n5KBlbqolgjdeuBwuPCk5OtQskqAUviIiIjGm8BUREYkxha+IiEiMKXxFRERiTOErIiISYwpfERGR\nGFP4ioiIxJjCV0REJMYUviIiIjGm8BUREYkxha+IiEiMxeTGCiIiIvIL7fmKiIjEmMJXREQkxhS+\nIiIiMabwFRERiTGFr4iISIwpfEVERGLMafQAZnP33Xfz1VdfYbPZuOWWW+jRo4fRI0XFmjVrGDt2\nLJdddhl/+tOfKCgo4OabbyYUCuHxeJg6dSoul8voMZvElClT+OyzzwgGg4wZM4bu3bsn7Fqrq6uZ\nMGECxcXF+Hw+xo4dS9euXRN2vQA1NTWce+65jB07lt69eyfsWlesWMH48ePp3LkzAF26dOHKK69M\n2PUCvPnmm0yfPh2n08n111/P0UcfnTDr1Z7vHlauXMmmTZuYM2cOkyZNYtKkSUaPFBVVVVVMnDiR\n3r171217+OGHyc/PZ9asWbRr1465c+caOGHTWb58OWvXrmXOnDlMnz6du+++O2HXCrBo0SK6devG\nSy+9xLRp05g8eXJCrxfgiSeeIDMzE0jc7+PdTj75ZF588UVefPFFbrvttoReb0lJCY899hizZs3i\nySef5P3330+o9Sp897Bs2TIGDx4MQKdOnSgrK8Pr9Ro8VdNzuVw888wz5OXl1W1bsWIFgwYNAmDA\ngAEsW7bMqPGa1EknncRDDz0EQEZGBtXV1Qm7VoCzzz6b0aNHA1BQUECLFi0Ser3r169n3bp19O/f\nH0jc7+P9SeT1Llu2jN69e5OWlkZeXh4TJ05MqPUqfPdQVFREdnZ23dvNmzensLDQwImiw+l0kpyc\nvNe26urqusM3OTk5CbNuh8NBSkoKAHPnzuW0005L2LXuaeTIkdx4443ccsstCb3ee++9lwkTJtS9\nnchrBVi3bh1XX301f/zjH/n4448Ter1bt26lpqaGq6++mvz8fJYtW5ZQ69U53wOw6pU3E3HdCxcu\nZO7cucyYMYMzzzyzbnsirhVg9uzZfP/999x00017rTGR1jtv3jx69uxJmzZt6n08kdYK0L59e8aN\nG8fQoUPZsmULl1xyCaFQqO7xRFsvQGlpKY8++ig//fQTl1xySUJ9Lyt895CXl0dRUVHd2z///DMe\nj8fAiWInJSWFmpoakpOT2bFjx16HpOPdkiVLePLJJ5k+fTrp6ekJvdZvv/2WnJwcWrZsyTHHHEMo\nFCI1NTUh17t48WK2bNnC4sWL2b59Oy6XK6G/ti1atODss88GoG3btuTm5vLNN98k7HpzcnLo1asX\nTqeTtm3bkpqaisPhSJj16rDzHk499VTmz58PwHfffUdeXh5paWkGTxUbffr0qVv7ggUL6Nevn8ET\nNY2KigqmTJnCU089RVZWFpC4awVYtWoVM2bMAGpPo1RVVSXseqdNm8a///1vXnnlFUaMGMHYsWMT\ndq1Q2/x99tlnASgsLKS4uJjhw4cn7Hr79u3L8uXLCYfDlJSUJNz3su5q9Cv33Xcfq1atwmazcccd\nd9C1a1ejR2py3377Lffeey/btm3D6XTSokUL7rvvPiZMmIDP56NVq1bcc889JCUlGT3qYZszZw6P\nPPIIHTp0qNs2efJkbr311oRbK9S+7OYf//gHBQUF1NTUMG7cOLp168bf/va3hFzvbo888gitW7em\nb9++CbtWr9fLjTfeSHl5OYFAgHHjxnHMMcck7Hqh9vTJ7kbzNddcQ/fu3RNmvQpfERGRGNNhZxER\nkRhT+IqIiMSYwldERCTGFL4iIiIxpvAVERGJMYWviIhIjCl8RUREYkzhKyIiEmP/H7JZcuZpCJke\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86deb4810>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3dRTH3nxSRg4n/oPFBI9pTsmLswic03ChJsMwOOWUdhiGQf6+6yX/+Mdj3HLL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\n4SsiIhJhGr4iIiIRpuErIiISYf8f+4Qv5VhaKs8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb87ed17d90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4ePrpp8nPz284tmzZMgYPHgzAoEGDWLJkSewmtIOUlPp/R6xVYlLb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\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dab5550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pR9zn9CZ+9kyjx4pqzu++Jb3fGXsvM3/0uS4zi9TRnr2da3xMezvL4sV7m3jN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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dee2c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EdAYjHerXNhq2uov223YurSnDkRoiJ7Vxc5txvZGk9VqXVdbaqPBN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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dd91f90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SZ72Cv9cJVE2ZBrbE3/Er1LkLFW+/T90frsW+6kdyzxpI+rSpEPuOXnIKhWg1\n5QlyB/cn7esV1F9aRPlHS/APOM3oyUSSllnbzgrfOLB//RWZE8YT7NCRypdeTa4WcXo6nof+RuVL\nswm7XLjvvIOsy4Zj2c+WoiklHMbx/nvkDuqP6947CbuzqHx+JtWPTyWcHb0tIkVSzf72dg55vXGe\nKHoUvrEWDpN5310AVE9+knCS7gbmO/NsyhctwXfqQJwL5pM3oA+ON+el/Flw2meLyTn3DLIvuwTb\nv1dSP3wEZR8vwzfkXKNHE0l6ajtLizmK38Xx2WK8Z5yFv98pRo9zUEJtDqFy9r/w3DceS0U52Vdf\nTs65Z2Bf/rnRo8WX30/a4o/JvmQoOUOHkPbFMrxDzqP8o6VUP/kM4fzk3OhdJNGo7Swt4/eT+de7\nCdts1NwzzuhposNqpW70zZR/sgzvOeeT9sUycocMxn3NFVj/s87o6WLGumE96S9MJ+uKIlof1Ymc\nYefiWPQhvlMHUv7eh1Q9/wrBbkcbPaaIqZi57az6ZQylv/wC9rVrqLviaoJdjzJ6nKgKdj6Cqhkv\nY1+6BNd9d5L+xr9wvvsW3osuwTvkPHynDIj+ve1wGMv27dh+Xodt/c/Yfv4PtvU/Yy35Ffx+LIEA\nBAPgD0R+Hg4Tzswk7HIRdrkbf05BHhlBC2GnExwOwg4npKcTtliwVlZgqazEUlWJdcd/bWvXYF+7\npnGMwOGd8V4yAu8Fw/D3OTm6axSR3eQPG07tqlX4ftkUKV1ZrTjaHZb0bWdtL7kXB71lWzBIXs+j\nsXg8lC37OuHv9R7UesNhnK/PJfP/7se24efIofR0fKcOxHfmELxnnH1g66+vx/afddjWrsa+ZjW2\nNauxrV2D7ae1WD17nzFstUZ2jbLZCe/YPcpS44kE8UEIZ2Ti638KvgGD8A0cRKhzl4P69eLNzNsP\nNkXrNZets15p8l2NcgafTuGIywyYqPkO+i0F5cDZl3+Bbctm6kZelfDBe9AsFrxDL8J73lDsXy3H\nWfwujuJ3cBa/i7P4XdxAKCeHUH4BofwCwgWFhPLzCeXmYqmqwrqtFGtpKdbSrZEf5eV7PEXY6STY\nuQv+Tp0JdjqcYMdOBDt1ItSxE8FDDwOns+kNS8Jh8HqxeDxYPNVYPB7yHGEqtpRj8XnB69vxX2/k\nTDkrm3B2NqEd/w1nZxN2ZyXeZigiKWB/bef8Cy9O2kvPCt8YcRa/A4Dv7CEGTxJHNhuBE39L4MTf\nUnPXfVjX/RQJ4kUfYC35Feu2Umw/rcWyl4stodxcQgWFBI7tQfDwLgSPPJLgkV0JHNGV0GHtW/ba\naIslckk5PX1nEarAjd/EZwpd50FpAAAWoUlEQVQiZtGctrMjSU9uFL4x4ih+h3BGBr5+pxo9imFC\nnbtQd8NN1N1w086DwSCW7dsjZ7vlZYSzsggVFBJqna83GhCR3TS0nZt8V6MkbzsrfGPA9tMa7GtW\n4z373Mg7BiU4rz9IybYagv4gzrQY77xlsxEuLCRYWEgwts8kIkmuoe3c1D1ftZ1lD47i9wDwnpXY\nl5yDoRCzP1zLitWllFV7yXM76dW1gEtPOwKb7nGKSAIwa9tZf8PGQNqSxQD4Tjvd4En2bfaHa1mw\nfBPbq7yEw7C9ysuC5ZuY/eFao0cTEQG0t7McAOvWLYSdzoRuOXv9QVasbnp/5hWrt+H166KwiBhL\nezvLAbFu20YovyDStE1QlR4vZVVN/8Ytr66n0pO8v6lFxBy0t7M0XziMdVtpJHwTWLbLSV5W02WF\nXHc62a7kLTKIiDlob2dpvpoaLHV1hBJ8c31nmo1eXZv+B0Kvrvmxbz2LiOyHmfd2VvhGmXVb5D5q\nOMHPfAEuHtCZ9oUurDuujlst0L7QxcUDOhs7mIjIDvnDhuNo32HnLnNWK472HdR2lt1ZamoACEf7\nTQViYM6idWzc6iG0Y8OpUBg2bvUwZ5F5351IRJKL2s7SLKFDDwWIvNNOAlPbWUQSndrO0mzhnFxC\nLje2DRuMHmWf1HYWkUSntrM0n8VCqH0HrBs3RN5RJ0Gp7SwiiU5tZzkgwQ4dsFZXYamsMHqUvVLb\nWUQSndrOckCC7TsAYNuY2Jee1XYWkURXMHwEOYNPx946H6xW7K3zyRl8OgXDRxg92kHRGyvEQOjw\nSHjZfvg3gR7HGzzN3jW0nRvs2nYuGtzVwMlERCIsNhuFIy4j/8KLybIFqArak/qMt4HOfGPAf1Jv\nANKWfmbwJHuntrOIJBOr00mrtoeYInhB4RsTge7HEXJnkfbZYqNH2Su1nUVEjKPwjQWbDf9ve2Nf\n9xPWLZuNnqZJajuLiBhH4Rsj/j79ABL27FdtZxER4yh8Y8Tf92QA0j771OBJ9k5tZxERYyh8YyRw\nXE9CLjdpnywyepS90t7OIiLGUPjGSloa/pP7Re77JuDrfdV2FhExjsI3hvynDADA8fEiQ+doitrO\nIiLGUfjGkO/U0wBI++hDgyfZk9rOIiLGUfjGUPDIrgTbHorjk492vhdlglDbObH5gj5Ka7fjC/qM\nHkVEYkDbS8aSxYL/1IGkz3oF+/ffJdxWkxcP6MyqDRX8UhopXVkt0K5AbWcjBUNB5q59m29Lv6fc\nW0GuM4fjCo5l2BHnYLPqH0QiZqEz3xjz9+4LgP2rLw2eZE9qOyeeuWvfZtGmxZR5ywkTpsxbzqJN\ni5m79m2jRxORKFL4xpj/uJ4A2L/92uBJdqe2c+LxBX18W/p9k499t+17XYIWMRGFb4wFj+pG2OnE\n/k1iha/azomn0ltNubfp94Auq6+g0lsd54lEJFYUvrGWlkbgmGOx//A9eBMn0NR2TjzZTje5zpwm\nH8tLzyHb6Y7zRCISKwrfOAgc1wuL34991Q9Gj9JIbefE47A5OK7g2CYf65F/LA6bI84TiUisKHzj\nIHBcpOVs/+5bgyfZnfZ2TjwXdD6Ldq5Dse74o2nFSjvXoVzQ+SyDJxORaFL4xkGobVsALNu3GzzJ\n7tR2Tjyvr3uPXzy/EiLyuvAQIX7x/Mrr694zeDIRiSaFbxyEXZF7dRZP4hRm1HZOPGo7i6QOhW8c\nhDJdQGKFr9rOiUdtZ5HUofCNg7A7cuZr9XgMnmQntZ0Tj9rOIqmjWeG7evVqBg8ezMsvvwxASUkJ\nI0eOpKioiDFjxuDz6XLYvuy87Jw44au2c+JR21kkdew3fGtraxk3bhx9+vRpPPbYY49RVFTEzJkz\n6dixI3PmzInpkMku7Eq8y86gtnMiUttZJDXsN3wdDgfPPvsshYWFjceWLVvGoEGDABg4cCBLliyJ\n3YRmkJYW+W8wsUpMajsnHrWdRVLDfsPXbreTnp6+27G6ujocjsglsNatW1Na2nRrVnaw7vjfnEBv\nK6i2c+JR21kkdRz0WwqGw+H9fk5ubgZ2e/LdQywoiG7BxWGzRP3XbKmSbTWUVe+97WxzpFGQnxnn\nqeInUb4Pu9rsKd1r27m8vgKbK0SBq2VzJ+J6Y0nrNS+zrLVF4ZuRkUF9fT3p6els2bJlt0vSTSkv\nr23RcEYqKHBTWhq9e7T5FgsBr5+KKP6aByPoD5LndrK9iZcb5brTCfr8UV1/Ion29zZagkEruc4c\nyrzlezyWm55D0GOltO7A507U9caK1mteybbWff1DoUXh27dvX4qLi7nggguYP38+/fv3b/FwZubz\n+fB4qvF4PJRYLFRUVVHy4ft4PJ4dPyKPrVr1A8XF77Fq1c84nfF5iU9D23nB8k17PKa2szEa2s6L\nNi3e4zG1nUXMZb/hu3LlSiZMmMAvv/yC3W6nuLiYhx9+mLFjxzJ79mwOPfRQhg4dGo9ZE9qPP/7A\nbbfdSGnpNjyeKjwez54vwfrhexhx0V5/jTffnMfFF18a40l3unhAZ1ZtqOCX0kjpymqBdgVqOxvp\ngs5nsaZiHSWezYQIYcVKW9chajuLmMx+w7d79+689NJLexyfMWNGTAZKVkuWfMqXXy4nLy+P/PwC\nOnU6nMxMN263G5fLRf6//okrKwv79aNxuVy4XO4dP1y8+OIM3nrrdbKysuI6c0PbucGubeeiwV3j\nOotENLSdG+zadh7e9XwDJxORaDrowpVEZGZGykl/+ct9jBx55R6P5336CdhslN16+27HFy36kKqq\nKgCefXYqvXv3JSsrO+bz7q/tfNGpXXTpOc7213a+oMtZuvQsYhLaXjIKampqeO65pwEYN+4eRo68\ndI8WeNjlanKTjdtvv5WPP14IwEcfLeTDDxfEfmC0t3Mi0t7OIqlD4RsFa9asYsWKrwCorq6muPhd\n6urqdvuc79ody8rsDnt8bVqavfGsGWg8C4417e2ceLS3s0jqUPhGQeaOdy0C6NTpcCASwrua3WUg\n//jNxeDd/YzS5XITCAQaP/bEaf9n7e2ceLS3s0jqUPhGgcu1M3wtlshGyTU1kfD9cX05E175iu/d\nh7GyfXcm/ONrflxfvtvXencJ5Orq+Jz5gvZ2TkTa21kkNSh8o8Dt3nk5sOFeb8MZbLeOufz+jJ3N\n4SuOd9OtY27jx7sG965fFw/a2znxaG9nkdSg8I2CjIyd92xDO/Zv3jVEv/hxKxcHf+Z3S2bxxert\nu31taenW3T6uqYlP+Gpv58SjvZ1FUofCNwqs1p3/GzdsWA/sfs+3XYGLi7KqKFoyi8PYfavNb775\nerePO3bsFLtBd6G2c+JR21kkdSh8o8xms9G7d1+OP75n47ETuxUSzo1cau7N7me+69dv4dVX52Gx\nWDjhhN8wZswf4zKn2s6JR21nkdSh8I2SrVuraNPmENq1O4w33niPQw5pu9vjodw8AKzlZbsddzgc\nDBhwGi6Xe7fiVayp7Zx41HYWSR0K3yhyuVx7LUyF8yLha/mv8N39a+N7WVFt58SjtrNIalD4RpHL\n5d5rgDae+Zbt+XZxka91xa1s1UBt58SjtrNIalD4RpHL5aKurm63TTMaNNzz3feZb/zCV23nxKO2\ns0jqUPhGUcNrdp95Zgqnn34qb775euPrfnfe893bma+b+vp6/H5/XGZV2znxqO0skjoUvlHkckXa\nqA8++Fe++WYFV189kjZtstm6dSs4HIQyXXs9823YojJe933Vdk48ajuLpA6FbxQ1hK/X6+WOO+5s\nPP7NN5E3XQjn5e3Rdm7QsEuW9nZOXWo7i6QOhW8UVVVVAnDUUd3o0KEjAEOGnMfpp0eaqqHcvYdv\nwyXreN73Vds58ajtLJIaFL5REgwG+fTTxQBccsnv+L//uw+n08lf//pA4+eEc3Kx1NZCff0eX99w\n1hzPlxup7Zx41HYWSQ0K3yiZM2c2W7duBuDrr79i8+YSbrzxlsYzYIBQXqTxbK3Ys3QV7zNftZ0T\nj9rOIqlD4Rslu4bmO++8Rdu2h3Lzzf+72+eEdzSeLWV7XnqOd/iq7Zx41HYWSR0K3yi5+urruOWW\n24DIJeh77x1HZmbmbp+zty0mKysreOONecDO+8axprZz4lHbWSR1KHyjqG/ffgCMGnUjw4YN3+Px\nxi0m/+vMd9KkB1m2bAkAt912E2+++XqMJ1XbORGp7SySOhS+UZSZGTkzcTrTm3w8lLPjnu8uZ76r\nVv3Ic889Q5s2bRqPXX31yBhOuZPazolHbWeR1KDwjaKG+7bV1VVNPt545rujcBUOh7n77rEEg0Gu\nvnpU4+e98UZxjCeNUNs58ajtLJIaFL5RtL/S1M43V4ic+b7//nssWvQhp546sPFNFbp27Ubv3n1i\nPqvazolHbWeR1GE3egAz2fla3d3DNxgMUlPjodbvpwQoXbOakg/e549/vAWLxcJRRx3N1KlPANCt\nW7e4zNqctnNhbkZcZpGI5rSdCzJax3kqEYkFhW8UNZz5fvTRQnr37oXH48Hjqaa2tnb3T3z/vciP\nHZ555qnGn7dunR+XWRvaztubCGC1nY3R0HYu8+75OnC1nUXMReEbRU6nk759+/H11yvweDy4XC4O\nOaQtLpcLt9tNZqaL/Lmv4WrbFvvIqygvL+e9995m48YNHHJIW6ZMmcaJJ/42PrPuaDsvWL5pj8fU\ndjZGQ9t50abFezymtrOIuSh8o2zevHf2+Xjrjz4k1CqD8tvHAjBu3IN88MF8unbtRseOneIw4U4X\nD+jMqg0V/FIaKV1ZLdCuQG1nI13Q+SzWVKyjxLOZECGsWGnrOkRtZxGTUeEqzoIdO2HbuAGCkUKT\n1Wrl9NPPinvwgtrOiUhtZ5HUoPCNs2Cnzlj8fqy/7Hm5N57Udk48ajuLpA6Fb5wFD49c0rX9x9iz\nS+3tnHi0t7NI6lD4xlmihK/2dk482ttZJHUofOMs2LkLYHz4am/nxKO9nUVSh8I3zoJHdiWcno6l\nrnb/nxxj2ts58WhvZ5HUoPCNs3B2DuWLPsNz33ijR1HbOQGp7SySGhS+Bgh2PgIyjN26UW3nxKO2\ns0jqUPimKLWdE4/aziKpQ+GbotR2TjxqO4ukDoVvilLbOfGo7SySOhS+KUxt58SjtrNIalD4pjC1\nnROP2s4iqUHhm6LUdk48ajuLpA6Fb4pS2znxqO0skjoUvilKbefEo7azSOpQ+KYotZ0Tj9rOIqlD\n4ZvC1HZOPGo7i6QGhW8KU9s58ajtLJIaFL4pSm3nxKO2s0jqUPimKLWdE4/aziKpQ+GbotR2Tjxq\nO4ukjhaH7wMPPMCll17KiBEj+Pbbb6M5k8SB2s6JR21nkdTRovD9/PPPWb9+PbNnz2b8+PGMH2/8\nG8PLgVPbOfGo7SySGloUvkuWLGHw4MEAdOnShcrKSjweT1QHk9hT2znxqO0skhpaFL7btm0jNze3\n8eO8vDxKS5tuzkpiUts58ajtLJI67NH4RcLh8D4fz83NwG5PvnuIBQXmLbiUbKuhrHrvbWebI42C\n/Mw4TxU/ifi93ewp3Wvbuby+ApsrRIGrZXMn4npjSes1L7OstUXhW1hYyLZt2xo/3rp1KwUFTZd3\nAMrLa1vyNIYqKHBTWmrel3YE/UHy3E62N/Fyo1x3OkGf37TrT9TvbTBoJdeZQ5m3fI/HctNzCHqs\nlNYd+NyJut5Y0XrNK9nWuq9/KLTosvPJJ59McXExAN9//z2FhYW4XK6WTSeGUNs58ajtLJI6WnTm\ne8IJJ3DssccyYsQILBYL9957b7Tnkji49LQjgMg93vLqenLd6fTqmt94XOJv2BHnAJF7vGX1FeSl\n59Aj/9jG4yJiDpbw/m7YRkEyXSZokGyXNw6G1x/E5kgj6POnxBlvMnxvfUEfld5qsp3ugz7jTYb1\nRpPWa17JttaoX3YWc3Gm2Wibn5kSwZssHDYHBRmtdalZxKQUviIiInGm8BUREYkzha+IiEicKXxF\nRETiTOErIiISZwpfERGROFP4ioiIxJnCV0REJM4UviIiInGm8BUREYkzha+IiEicxeWNFURERGQn\nnfmKiIjEmcJXREQkzhS+IiIicabwFRERiTOFr4iISJwpfEVEROLMbvQAieaBBx7gm2++wWKxcOed\nd3LccccZPVJMrF69mtGjR3PllVfy+9//npKSEu644w6CwSAFBQVMmjQJh8Nh9JhRMXHiRL788ksC\ngQDXX389PXr0MO1a6+rqGDt2LNu3b8fr9TJ69Gi6detm2vUC1NfXc+655zJ69Gj69Olj2rUuW7aM\nMWPGcOSRRwLQtWtXrrnmGtOuF+CNN95g2rRp2O12brnlFo466ijTrFdnvrv4/PPPWb9+PbNnz2b8\n+PGMHz/e6JFiora2lnHjxtGnT5/GY4899hhFRUXMnDmTjh07MmfOHAMnjJ6lS5eyZs0aZs+ezbRp\n03jggQdMu1aAhQsX0r17d15++WUmT57MQw89ZOr1AkyZMoXs7GzAvL+PG5x00km89NJLvPTSS9x9\n992mXm95eTlPPvkkM2fOZOrUqXzwwQemWq/CdxdLlixh8ODBAHTp0oXKyko8Ho/BU0Wfw+Hg2Wef\npbCwsPHYsmXLGDRoEAADBw5kyZIlRo0XVSeeeCKPPvooAFlZWdTV1Zl2rQBDhgzh2muvBaCkpIQ2\nbdqYer0//fQTa9euZcCAAYB5fx/vjZnXu2TJEvr06YPL5aKwsJBx48aZar0K311s27aN3Nzcxo/z\n8vIoLS01cKLYsNvtpKen73asrq6u8fJN69atTbNum81GRkYGAHPmzOGUU04x7Vp3NWLECG6//Xbu\nvPNOU693woQJjB07tvFjM68VYO3atYwaNYrf/e53fPrpp6Ze76ZNm6ivr2fUqFEUFRWxZMkSU61X\n93z3IVV33jTjuhcsWMCcOXOYPn06Z5xxRuNxM64VYNasWfzwww/86U9/2m2NZlrvvHnz6NmzJ+3b\nt2/ycTOtFaBTp07cdNNNnH322WzcuJHLL7+cYDDY+LjZ1gtQUVHBE088wa+//srll19uqt/LCt9d\nFBYWsm3btsaPt27dSkFBgYETxU9GRgb19fWkp6ezZcuW3S5JJ7tPPvmEqVOnMm3aNNxut6nXunLl\nSlq3bk3btm05+uijCQaDZGZmmnK9ixYtYuPGjSxatIjNmzfjcDhM/b1t06YNQ4YMAaBDhw7k5+fz\n3XffmXa9rVu3plevXtjtdjp06EBmZiY2m80069Vl512cfPLJFBcXA/D9999TWFiIy+UyeKr46Nu3\nb+Pa58+fT//+/Q2eKDqqq6uZOHEiTz/9NDk5OYB51wqwfPlypk+fDkRuo9TW1pp2vZMnT+af//wn\nr776KsOHD2f06NGmXStEmr/PPfccAKWlpWzfvp1hw4aZdr39+vVj6dKlhEIhysvLTfd7We9q9F8e\nfvhhli9fjsVi4d5776Vbt25GjxR1K1euZMKECfzyyy/Y7XbatGnDww8/zNixY/F6vRx66KE8+OCD\npKWlGT3qQZs9ezaPP/44hx9+eOOxhx56iLvuust0a4XIy27+8pe/UFJSQn19PTfddBPdu3fnz3/+\nsynX2+Dxxx+nXbt29OvXz7Rr9Xg83H777VRVVeH3+7nppps4+uijTbteiNw+aWg033DDDfTo0cM0\n61X4ioiIxJkuO4uIiMSZwldERCTOFL4iIiJxpvAVERGJM4WviIhInCl8RURE4kzhKyIiEmcKXxER\nkTj7/99JiWfgLPqeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86dc18b10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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68XTsFPVpZw3fCEl48nESZkzH3/soyp99IToWyHc4qL5iLCVvLCLYpSuJj08m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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb86e157e50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "GCTXagtsFUgg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ngCLXNAp0EBq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    }
  ]
}