ColdAsIcev11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt \n",
    "from matplotlib import colors as clr\n",
    "from matplotlib.lines import Line2D\n",
    "import copy\n",
    "import random\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SET EPS AND KB VALUE\n",
    "eps = 1\n",
    "kb = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialise_state(N): #N is the grid dimension (in the above example, N=4)\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    grid = np.ones((N,N,2),dtype=int)\n",
    "    \n",
    "    return np.array(grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_vector(p1,p2,num):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    if num>4:\n",
    "        w = 0.12/num\n",
    "        h=0.9/num\n",
    "    else:\n",
    "        w = 0.03\n",
    "        h=0.2\n",
    "    p1 = np.array(p1)\n",
    "    p2 = np.array(p2)\n",
    "    dp = p2-p1\n",
    "    dx = dp[0]\n",
    "    dy = dp[1]\n",
    "\n",
    "    plt.arrow(p1[0], p1[1],\n",
    "                 dx / 2, dy / 2,\n",
    "                 width=w, head_width=h,\n",
    "                 facecolor=\"k\",\n",
    "                 zorder=0)\n",
    "\n",
    "    plt.arrow(p1[0] + dx / 2,\n",
    "                 p1[1] + dy / 2,\n",
    "                 dx / 2, dy / 2,\n",
    "                 width=w, head_width=0,\n",
    "                 facecolor=\"k\",\n",
    "                 zorder=0)\n",
    "\n",
    "def plot_vector_old(p1,p2):\n",
    "    '''\n",
    "    DEPRECIATED: As of 5th June 2020.\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    p1 = np.array(p1)\n",
    "    p2 = np.array(p2)\n",
    "    dp = p2-p1\n",
    "    plt.quiver(p1[0], p1[1], dp[0], dp[1],angles='xy', scale_units='xy', scale=1, headwidth = 5, headlength = 7)\n",
    "\n",
    "def get_coord_list(arr):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    coord_list=[]\n",
    "    num = len(arr)\n",
    "    for i in range(num):\n",
    "        temp_coord = []\n",
    "        for j in range(num):\n",
    "            current_elems = arr[i][j]\n",
    "            xpt = (num-1)-i\n",
    "            ypt = j\n",
    "            temp_coord.append((xpt,ypt))\n",
    "        coord_list.append(temp_coord)\n",
    "    return coord_list\n",
    "\n",
    "def visualise_2d_model(arr,savefig=False,savename=\".temp\",show=True):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    num = len(arr)\n",
    "    if num<4:\n",
    "        mksize = 35/num \n",
    "    else:\n",
    "        mksize=(35/num)+0.5\n",
    "    plt.axes().set_aspect('equal')\n",
    "    coord_list = get_coord_list(arr)\n",
    "    for i in range(num):\n",
    "        for j in range(num):\n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "            x_current = coord_list[i][j][1]\n",
    "            y_current = coord_list[i][j][0]\n",
    "\n",
    "            lower_neighbour_up_state = arr[(i+1)%num][j][0]\n",
    "            x_up = coord_list[(i+1)%num][j][1]\n",
    "            y_up = coord_list[(i+1)%num][j][0]\n",
    "\n",
    "            left_neighbour_right_state = arr[i][j-1][1]\n",
    "            x_left = coord_list[i][j-1][1]\n",
    "            y_left = coord_list[i][j-1][0]\n",
    "\n",
    "\n",
    "            current_down_state = -(lower_neighbour_up_state)\n",
    "            current_left_state = -(left_neighbour_right_state)\n",
    "\n",
    "#             plt.plot(x_current,y_current,'ob')\n",
    "            \n",
    "            plt.plot(x_current,y_current,\n",
    "                        marker=\"o\", markersize=mksize, markeredgecolor=\"k\",\n",
    "                        markerfacecolor=\"yellowgreen\",\n",
    "                        zorder=1)\n",
    "\n",
    "            if current_up_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current,y_current+1],num)\n",
    "            elif current_up_state == -1:\n",
    "                plot_vector([x_current,y_current+1],[x_current,y_current],num)\n",
    "\n",
    "            if current_right_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current+1,y_current],num)\n",
    "            elif current_right_state == -1:\n",
    "                plot_vector([x_current+1,y_current],[x_current,y_current],num)\n",
    "\n",
    "            if current_down_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current,y_current-1],num)\n",
    "            elif current_down_state == -1:\n",
    "                plot_vector([x_current,y_current-1],[x_current,y_current],num)\n",
    "\n",
    "            if current_left_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current-1,y_current],num)\n",
    "            elif current_left_state == -1:\n",
    "                plot_vector([x_current-1,y_current],[x_current,y_current],num)\n",
    "    plt.xlim(-1,num)\n",
    "    plt.ylim(-1,num)\n",
    "    plt.axis('off')\n",
    "    if savefig:\n",
    "        plt.savefig(f\"{savename}.png\",dpi=300)\n",
    "    if show:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def create_6_vertex_configs():\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    configlist = [(1,1,-1,-1),\n",
    "            (-1,-1,1,1),\n",
    "            (1,-1,-1,1),\n",
    "            (-1,1,1,-1),\n",
    "            (-1,1,-1,1),\n",
    "            (1,-1,1,-1)]\n",
    "    \n",
    "    for i in range(len(configlist)):\n",
    "        config=configlist[i]\n",
    "        plt.axes().set_aspect('equal')\n",
    "        if config[0]==1:\n",
    "            plot_vector([0,0],[0,1],5) #Up\n",
    "        else:\n",
    "            plot_vector([0,1],[0,0],5) #Up\n",
    "        if config[1]==1:\n",
    "            plot_vector([0,0],[1,0],5) #Right\n",
    "        else:\n",
    "            plot_vector([1,0],[0,0],5) #Right\n",
    "        if config[2]==-1:\n",
    "            plot_vector([0,-1],[0,0],5) #Down\n",
    "        else:\n",
    "            plot_vector([0,0],[0,-1],5)\n",
    "        if config[3]==-1:\n",
    "            plot_vector([-1,0],[0,0],5) #Left\n",
    "        else:\n",
    "            plot_vector([0,0],[-1,0],5)\n",
    "        plt.plot(0,0,\n",
    "                    marker=\"o\", markersize=35, markeredgecolor=\"k\",\n",
    "                    markerfacecolor=\"yellowgreen\",\n",
    "                    zorder=1)\n",
    "        plt.xlim(-1,1)\n",
    "        plt.ylim(-1,1)\n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_config(arr):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    flag=True\n",
    "    N=len(arr)\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):\n",
    "            \n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "\n",
    "            lower_neighbour_up_state = arr[(i+1)%N][j][0]\n",
    "            \n",
    "            left_neighbour_right_state = arr[i][j-1][1]\n",
    "\n",
    "\n",
    "            current_left_state = -(left_neighbour_right_state)\n",
    "            current_down_state = -(lower_neighbour_up_state)\n",
    "            \n",
    "            if (current_up_state + current_right_state + current_left_state + current_down_state) != 0:\n",
    "                flag=False\n",
    "                break\n",
    "    return flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LONG LOOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def long_loop(arr2, verbose=False):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    arr = copy.deepcopy(arr2)\n",
    "    N=len(arr)\n",
    "    iters=0\n",
    "    \n",
    "    n1 = np.random.randint(low=0, high=N)\n",
    "    n2 = np.random.randint(low=0, high=N)\n",
    "    inital_pt =(n1,n2)\n",
    "    prev_choice=None\n",
    "    \n",
    "    while True:\n",
    "        iters+=1\n",
    "        if n1==inital_pt[0] and n2==inital_pt[1] and iters!=1:\n",
    "            if verbose:\n",
    "                print(f\"Completed in {iters} iterations.\")\n",
    "#             assert(check_config(arr))\n",
    "            break\n",
    "        current_up_state = arr[n1][n2][0]\n",
    "        current_right_state = arr[n1][n2][1]\n",
    "\n",
    "        lower_neighbour_up_state = arr[(n1+1)%N][n2][0]\n",
    "        left_neighbour_right_state = arr[n1][n2-1][1]\n",
    "\n",
    "        current_down_state = -(lower_neighbour_up_state)    \n",
    "        current_left_state = -(left_neighbour_right_state)\n",
    "\n",
    "        current_states_dict = {\"up\":current_up_state,\"right\":current_right_state,\"down\":current_down_state,\"left\":current_left_state}\n",
    "        outgoing_state_dict={}\n",
    "        incoming_state_dict={}\n",
    "\n",
    "        for key in current_states_dict.keys():\n",
    "            if current_states_dict[key]==1:  #current state is outgoing\n",
    "                outgoing_state_dict[key]=current_states_dict[key]\n",
    "            else:\n",
    "                incoming_state_dict[key]=current_states_dict[key]\n",
    "\n",
    "        if prev_choice ==\"right\":\n",
    "            forbidden_choice=\"left\"\n",
    "        if prev_choice ==\"up\":\n",
    "            forbidden_choice=\"down\"\n",
    "        if prev_choice ==\"left\":\n",
    "            forbidden_choice=\"right\"\n",
    "        if prev_choice ==\"down\":\n",
    "            forbidden_choice=\"up\"\n",
    "        else:\n",
    "            forbidden_choice=None\n",
    "\n",
    "\n",
    "        while True:\n",
    "            out_choice = np.random.choice(list(outgoing_state_dict.keys()))\n",
    "            if out_choice !=forbidden_choice:\n",
    "                break\n",
    "\n",
    "        prev_choice=out_choice\n",
    "\n",
    "        if out_choice == \"up\":\n",
    "            arr[n1][n2][0]= - (arr[n1][n2][0])\n",
    "            n1=(n1-1)%N\n",
    "            n2=n2\n",
    "            continue\n",
    "\n",
    "        if out_choice == \"right\":\n",
    "            arr[n1][n2][1]= - (arr[n1][n2][1])\n",
    "            n1=n1\n",
    "            n2=(n2+1)%N\n",
    "            continue\n",
    "\n",
    "        if out_choice == \"down\":\n",
    "            arr[(n1+1)%N][n2][0]= - (arr[(n1+1)%N][n2][0])\n",
    "            n1=(n1+1)%N\n",
    "            n2=n2\n",
    "            continue\n",
    "\n",
    "        if out_choice == \"left\":\n",
    "            arr[n1][(n2-1)%N][1]= - (arr[n1][(n2-1)%N][1])\n",
    "            n1=n1\n",
    "            n2=(n2-1)%N\n",
    "            continue\n",
    "\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_states(num,error_threshold,return_dict = False,verbose=False):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    if not (error_threshold<=100 and error_threshold>0):\n",
    "        print(\"Error! Please input error_threshold as a value between 0 and 100\")\n",
    "        assert (error_threshold<=100 and error_threshold>0)\n",
    "    state_dict={}\n",
    "    oldarr = long_loop(initialise_state(num), verbose=False)\n",
    "    good_iterations = 0           #Iterations that gave us a new state, so good.\n",
    "    bad_iterations = 0               #Iterations that gave us an already found state,so a waste and hence bad.\n",
    "    while True:\n",
    "        newarr = long_loop(oldarr,verbose=False)\n",
    "        name =arr_to_string(newarr)\n",
    "        if name not in state_dict:\n",
    "            count_repetitions=0\n",
    "            state_dict[name]=1\n",
    "            good_iterations+=1\n",
    "        else:\n",
    "            bad_iterations+=1\n",
    "            count_repetitions+=1\n",
    "            state_dict[name]+=1\n",
    "        percent_approx_err=good_iterations*100/(good_iterations+bad_iterations)\n",
    "        if verbose:\n",
    "            print(f\"Good iterations = {good_iterations} and bad iterations = {bad_iterations} and Error % = {percent_approx_err}\", end=\"\\r\",flush=True)\n",
    "        if percent_approx_err < error_threshold:\n",
    "            break\n",
    "        oldarr=newarr\n",
    "    if return_dict:\n",
    "        return len(state_dict),state_dict\n",
    "    else:\n",
    "        return len(state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def state2to4(arr):\n",
    "    '''\n",
    "    Examine once.\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    fourstatearr=np.zeros((arr.shape[0],arr.shape[1],4))\n",
    "    N=len(arr)\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):\n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "            lower_neighbour_up_state = arr[(i+1)%N][j][0]\n",
    "            left_neighbour_right_state = arr[i][j-1][1]\n",
    "            current_left_state = -(left_neighbour_right_state)\n",
    "            current_down_state = -(lower_neighbour_up_state)\n",
    "            fourstatearr[i][j][0] = current_up_state\n",
    "            fourstatearr[i][j][1] = current_right_state\n",
    "            fourstatearr[i][j][2] = current_down_state\n",
    "            fourstatearr[i][j][3] = current_left_state\n",
    "\n",
    "    return fourstatearr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rot 90 anticlock\n",
    "#Up becomes left, left becomes down, down becomes right, right becomes up\n",
    "def rot90_anticlock(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    fourstatearr = state2to4(arr2)\n",
    "    fourstatearr = np.rot90(fourstatearr,1)\n",
    "    arr=np.zeros((fourstatearr.shape[0],fourstatearr.shape[1],2))\n",
    "    N=len(arr)\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):\n",
    "            current_up_state = fourstatearr[i][j][0]\n",
    "            current_right_state = fourstatearr[i][j][1]\n",
    "            current_down_state = fourstatearr[i][j][2]\n",
    "            current_left_state = fourstatearr[i][j][3]\n",
    "            \n",
    "            new_up_state = current_right_state\n",
    "            new_right_state = current_down_state\n",
    "            \n",
    "            arr[i][j][0]=new_up_state\n",
    "            arr[i][j][1]=new_right_state\n",
    "    \n",
    "    return arr.astype(int)\n",
    "\n",
    "#Rot 180 anticlock\n",
    "#Up becomes down, left becomes right, down becomes up, right becomes left\n",
    "def rot180_anticlock(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    fourstatearr = state2to4(arr2)\n",
    "    fourstatearr = np.rot90(fourstatearr,2)\n",
    "    arr=np.zeros((fourstatearr.shape[0],fourstatearr.shape[1],2))\n",
    "    N=len(arr)\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):\n",
    "            current_up_state = fourstatearr[i][j][0]\n",
    "            current_right_state = fourstatearr[i][j][1]\n",
    "            current_down_state = fourstatearr[i][j][2]\n",
    "            current_left_state = fourstatearr[i][j][3]\n",
    "            \n",
    "            new_up_state = current_down_state\n",
    "            new_right_state = current_left_state\n",
    "            \n",
    "            arr[i][j][0]=new_up_state\n",
    "            arr[i][j][1]=new_right_state\n",
    "    \n",
    "    return arr.astype(int)\n",
    "\n",
    "#Rot 270 anticlock\n",
    "#Up becomes right, left becomes up, down becomes left, right becomes down\n",
    "def rot270_anticlock(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    fourstatearr = state2to4(arr2)\n",
    "    fourstatearr = np.rot90(fourstatearr,3)\n",
    "    arr=np.zeros((fourstatearr.shape[0],fourstatearr.shape[1],2))\n",
    "    N=len(arr)\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):\n",
    "            current_up_state = fourstatearr[i][j][0]\n",
    "            current_right_state = fourstatearr[i][j][1]\n",
    "            current_down_state = fourstatearr[i][j][2]\n",
    "            current_left_state = fourstatearr[i][j][3]\n",
    "            \n",
    "            new_up_state = current_left_state\n",
    "            new_right_state = current_up_state\n",
    "            \n",
    "            arr[i][j][0]=new_up_state\n",
    "            arr[i][j][1]=new_right_state\n",
    "    \n",
    "    return arr.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Flip horizontally\n",
    "#Up becomes right, left becomes up, down becomes left, right becomes down\n",
    "def hor_flip(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    arr = np.flip(arr2,1)\n",
    "    proper_arr=np.zeros_like(arr2)\n",
    "    num = len(arr)\n",
    "    for i in range(num):\n",
    "        for j in range(num):\n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_left_state = arr[i][j][1]\n",
    "            \n",
    "            right_neighbour_left_state = arr[i][(j+1)%num][1]\n",
    "            current_right_state = - (right_neighbour_left_state)\n",
    "\n",
    "            proper_arr[i][j][0]=current_up_state\n",
    "            proper_arr[i][j][1]=current_right_state\n",
    "    return proper_arr.astype(int)\n",
    "\n",
    "#Flip vertically\n",
    "#Up becomes right, left becomes up, down becomes left, right becomes down\n",
    "def ver_flip(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    arr = np.flip(arr2,0)\n",
    "    proper_arr=np.zeros_like(arr2)\n",
    "    num = len(arr)\n",
    "    for i in range(num):\n",
    "        for j in range(num):\n",
    "            current_down_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "            \n",
    "            upper_neighbour_down_state = arr[i-1][j][0]\n",
    "            current_up_state = - (upper_neighbour_down_state)\n",
    "            \n",
    "            proper_arr[i][j][0]=current_up_state\n",
    "            proper_arr[i][j][1]=current_right_state\n",
    "    return proper_arr.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flip_secondary_diag(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    arr = copy.deepcopy(arr2)\n",
    "    N = len(arr)\n",
    "    for i in range(N):\n",
    "        for j in range(N):\n",
    "            if (i+j)<=N-1:\n",
    "                dist = N-(i+j+1)\n",
    "                arr[i][j][0], arr[i+dist][j+dist][0], arr[i][j][1], arr[i+dist][j+dist][1] = arr[i+dist][j+dist][1], arr[i][j][1], arr[i+dist][j+dist][0], arr[i][j][0]                \n",
    "    return arr.astype(int)\n",
    "\n",
    "def flip_primary_diag(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    arr = copy.deepcopy(arr2)\n",
    "    N = len(arr)\n",
    "    arr = rot90_anticlock(flip_secondary_diag(rot270_anticlock(arr)))\n",
    "    return arr.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_column_translations(arr):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    result_arr_list=[]\n",
    "    N=len(arr) \n",
    "    for i in range(1,N):\n",
    "        a1 = arr[:,0:i].reshape(N,-1,2)\n",
    "        a2 = arr[:,i:].reshape(N,-1,2)\n",
    "        res = np.hstack([a2,a1])\n",
    "        result_arr_list.append(res)\n",
    "    return result_arr_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_row_translations(arr):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    result_arr_list=[]\n",
    "    N=len(arr) \n",
    "    for i in range(1,N):\n",
    "        a1 = arr[0:i,:].reshape(-1,N,2)\n",
    "        a2 = arr[i:,:].reshape(-1,N,2)\n",
    "        res = np.vstack([a2,a1])\n",
    "        result_arr_list.append(res)\n",
    "    return result_arr_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arr_to_string(arr2):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    arr = copy.deepcopy(arr2)\n",
    "    name = ' '.join(map(str, arr.flatten())).replace(' ','')\n",
    "    return name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def string_to_arr(s):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    replaced_str = s.replace(\"-1\",\"0\")\n",
    "    arr=[]\n",
    "    for i in replaced_str:\n",
    "        if i=='1':\n",
    "            arr.append(1)\n",
    "        elif i==\"0\":\n",
    "            arr.append(-1)\n",
    "        else:\n",
    "            print(\"ERROR\")\n",
    "            assert(1==0)\n",
    "    arr = np.array(arr)\n",
    "    arr = arr.reshape(int(np.sqrt(len(arr)/2)),int(np.sqrt(len(arr)/2)),2)\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_symmetries(all_names, verbose=False):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    assert type(all_names)==list\n",
    "    for i,given_name in enumerate(all_names):\n",
    "        if verbose and i%100 ==0:\n",
    "            print(f\"Loading... {i}/{len(all_names)} done. Percent Completed = {100*i/len(all_names)}.\", end=\"\\r\",flush=True)\n",
    "#         print(\"*******************************\")\n",
    "#         print(f\"Original Name = {given_name}\")\n",
    "        arr = string_to_arr(given_name)\n",
    "        #Column Translation symmetries\n",
    "        templist=get_all_column_translations(arr)\n",
    "        for newarr in templist:\n",
    "            name = arr_to_string(newarr)\n",
    "#             print(f\"Col Trans Name = {name}\")\n",
    "            if name in all_names[i+1:]:\n",
    "                idx = all_names[i+1:].index(name) + i+1\n",
    "                del all_names[idx]\n",
    "                i = all_names.index(given_name)\n",
    "        \n",
    "        #Row Translation symmetries\n",
    "        templist=get_all_row_translations(arr)\n",
    "        for newarr in templist:\n",
    "            name = arr_to_string(newarr)\n",
    "#             print(f\"Row Trans Name = {name}\")\n",
    "            if name in all_names[i+1:]:\n",
    "                idx = all_names[i+1:].index(name) + i+1\n",
    "                del all_names[idx]\n",
    "                i = all_names.index(given_name)\n",
    "        \n",
    "        #Check 90 degree rotation symmetry\n",
    "        name = arr_to_string(rot90_anticlock(arr))\n",
    "#         print(f\"Rot 90 Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "\n",
    "        #Check 180 degree rotation symmetry\n",
    "        name = arr_to_string(rot180_anticlock(arr))\n",
    "#         print(f\"Rot 180 Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "\n",
    "        #Check 270 degree rotation symmetry\n",
    "        name = arr_to_string(rot270_anticlock(arr))\n",
    "#         print(f\"Rot 270 Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "\n",
    "        #Check horizontal flip symmetry\n",
    "        name = arr_to_string(hor_flip(arr))\n",
    "#         print(f\"Flip Hor Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "\n",
    "        #Check vertical flip symmetry\n",
    "        name = arr_to_string(ver_flip(arr))\n",
    "#         print(f\"Flip Ver Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "\n",
    "        #Check secondary diagonal flip symmetry\n",
    "        name = arr_to_string(flip_secondary_diag(arr))\n",
    "#         print(f\"Sec Diag Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "\n",
    "        #Check primary diagonal flip symmetry\n",
    "        name = arr_to_string(flip_primary_diag(arr))\n",
    "#         print(f\"Prim Diag Name = {name}\")\n",
    "        if name in all_names[i+1:]:\n",
    "            idx = all_names[i+1:].index(name) + i+1\n",
    "            del all_names[idx]\n",
    "            i = all_names.index(given_name)\n",
    "    return all_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENERGETIC: F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = [[[-1,1],[-1,1],[1,-1],[-1,1]],\n",
    "         [[-1,-1],[-1,-1],[-1,1],[1,-1]],\n",
    "         [[-1,1],[-1,1],[1,1],[-1,1]],\n",
    "         [[-1,1],[-1,1],[1,1],[-1,1]]]\n",
    "arr = np.array(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_2d_model(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_energy(arr,eps):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    N = len(arr)\n",
    "    s = 0\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):  \n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "            lower_neighbour_up_state = arr[(i+1)%N][j][0] \n",
    "            left_neighbour_right_state = arr[i][j-1][1]\n",
    "            if  current_up_state ==1 and lower_neighbour_up_state == -1 : \n",
    "                s+= 1\n",
    "            elif current_right_state == 1 and left_neighbour_right_state == -1:\n",
    "                s+= 1\n",
    "    return (-s*eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_atom_config(arr):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    tupdict = {\n",
    "        (1,1,-1,-1):1,\n",
    "        (-1,-1,1,1):2,\n",
    "        (1,-1,-1,1):3,\n",
    "        (-1,1,1,-1):4,\n",
    "        (-1,1,-1,1):5,\n",
    "        (1,-1,1,-1):6\n",
    "    }\n",
    "    num=len(arr)\n",
    "    typearr = np.zeros((arr.shape[0],arr.shape[1]))\n",
    "    for i in range(len(arr)):\n",
    "        for j in range(len(arr)):\n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "\n",
    "            lower_neighbour_up_state = arr[(i+1)%num][j][0]\n",
    "\n",
    "            left_neighbour_right_state = arr[i][j-1][1]\n",
    "\n",
    "            current_down_state = -(lower_neighbour_up_state)\n",
    "            current_left_state = -(left_neighbour_right_state)\n",
    "\n",
    "            tup = (current_up_state,current_right_state,current_down_state,current_left_state)\n",
    "            atomtype = tupdict[tup]\n",
    "            typearr[i][j] = atomtype\n",
    "    return typearr.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_energy_using_atom_config(arr, eps):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    atom_config_arr = calculate_atom_config(arr).flatten()\n",
    "    energy = len(np.where(atom_config_arr==5)[0])+len(np.where(atom_config_arr==6)[0])\n",
    "    energy = -1*energy * eps\n",
    "    return energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#3b4cc0', '#dddcdc', '#b40426']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = plt.cm.coolwarm\n",
    "norm = clr.Normalize(vmin=-1, vmax=1)\n",
    "hexlist = [clr.to_hex(cmap(norm(-1))),clr.to_hex(cmap(norm(0))),clr.to_hex(cmap(norm(1)))]\n",
    "hexlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualise_atom_config_with_bonds(arr,savefig=False,savename=\".temp\",show=True):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    num = len(arr)\n",
    "    plt.axes().set_aspect('equal')\n",
    "    coord_list = get_coord_list(arr)\n",
    "    atom_config_arr = calculate_atom_config(arr)\n",
    "    for i in range(num):\n",
    "        for j in range(num):\n",
    "            current_up_state = arr[i][j][0]\n",
    "            current_right_state = arr[i][j][1]\n",
    "            x_current = coord_list[i][j][1]\n",
    "            y_current = coord_list[i][j][0]\n",
    "\n",
    "            lower_neighbour_up_state = arr[(i+1)%num][j][0]\n",
    "            x_up = coord_list[(i+1)%num][j][1]\n",
    "            y_up = coord_list[(i+1)%num][j][0]\n",
    "\n",
    "            left_neighbour_right_state = arr[i][j-1][1]\n",
    "            x_left = coord_list[i][j-1][1]\n",
    "            y_left = coord_list[i][j-1][0]\n",
    "\n",
    "\n",
    "            current_down_state = -(lower_neighbour_up_state)\n",
    "            current_left_state = -(left_neighbour_right_state)\n",
    "\n",
    "            if atom_config_arr[i][j]==5:\n",
    "                plt.plot(x_current,y_current,\n",
    "                            marker=\"o\", markersize=9, markeredgecolor=\"k\",\n",
    "                            markerfacecolor=\"cyan\",\n",
    "                            zorder=1)\n",
    "            elif atom_config_arr[i][j]==6:\n",
    "                plt.plot(x_current,y_current,\n",
    "                            marker=\"o\", markersize=9, markeredgecolor=\"k\",\n",
    "                            markerfacecolor=\"magenta\",\n",
    "                            zorder=1)\n",
    "            else:\n",
    "                plt.plot(x_current,y_current,\n",
    "                            marker=\"o\", markersize=9, markeredgecolor=\"k\",\n",
    "                            markerfacecolor=\"gray\",\n",
    "                            zorder=1)                \n",
    "            if current_up_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current,y_current+1],num)\n",
    "            elif current_up_state == -1:\n",
    "                plot_vector([x_current,y_current+1],[x_current,y_current],num)\n",
    "\n",
    "            if current_right_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current+1,y_current],num)\n",
    "            elif current_right_state == -1:\n",
    "                plot_vector([x_current+1,y_current],[x_current,y_current],num)\n",
    "\n",
    "            if current_down_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current,y_current-1],num)\n",
    "            elif current_down_state == -1:\n",
    "                plot_vector([x_current,y_current-1],[x_current,y_current],num)\n",
    "\n",
    "            if current_left_state == 1:\n",
    "                plot_vector([x_current,y_current],[x_current-1,y_current],num)\n",
    "            elif current_left_state == -1:\n",
    "                plot_vector([x_current-1,y_current],[x_current,y_current],num)\n",
    "    plt.xlim(-1,num)\n",
    "    plt.ylim(-1,num)\n",
    "    plt.axis('off')\n",
    "    if savefig:\n",
    "        plt.savefig(f\"{savename}.png\",dpi=300)\n",
    "    if show:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualise_final_state(arr,title=\"\",savefig=False,savename=\".temp\",show=True):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "\n",
    "    atom_config_arr = calculate_atom_config(arr)\n",
    "    colordict = {1:0,2:0,3:0,4:0,5:-1,6:1}\n",
    "    atom_config_arr_copy = np.copy(atom_config_arr)\n",
    "    for key, val in colordict.items(): atom_config_arr_copy[atom_config_arr==key] = val\n",
    "    flipped_arr = np.flip(atom_config_arr_copy,0)\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = plt.subplot(111)\n",
    "    \n",
    "    ax.set_aspect('equal')\n",
    "    cmap = plt.cm.coolwarm\n",
    "    norm = clr.Normalize(vmin=-1, vmax=1)\n",
    "    hexlist = [clr.to_hex(cmap(norm(-1))),clr.to_hex(cmap(norm(0))),clr.to_hex(cmap(norm(1)))]\n",
    "\n",
    "    legend_elements = [\n",
    "                    Line2D([0], [0], marker='o', color='w', label='Type 1/2/3/4',markerfacecolor=hexlist[1], markersize=10),\n",
    "                    Line2D([0], [0], marker='o', color='w', label='Type 5',markerfacecolor=hexlist[0], markersize=10),\n",
    "                    Line2D([0], [0], marker='o', color='w', label='Type 6',markerfacecolor=hexlist[2], markersize=10)\n",
    "                        ]\n",
    "    ax.pcolormesh(flipped_arr,cmap=cmap,norm=norm)\n",
    "\n",
    "    box = ax.get_position()\n",
    "    ax.set_position([box.x0, box.y0, box.width * 1, box.height])\n",
    "\n",
    "    ax.legend(handles=legend_elements,loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "    plt.title(title)\n",
    "    if savefig:\n",
    "        plt.savefig(f\"{savename}.png\",dpi=300)\n",
    "    if show:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metropolis_move(arr,temp,verbose = False):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    Dependencies: Uses energy(arr) function\n",
    "    and long_loop(arr) function.\n",
    "    \n",
    "    '''\n",
    "    new = long_loop(arr, verbose = verbose)\n",
    "    old_energy = calculate_energy(arr,eps)\n",
    "    new_energy = calculate_energy(new,eps)\n",
    "    delta_E = new_energy - old_energy\n",
    "\n",
    "    if temp >0:\n",
    "        beta = 1/(temp*kb)\n",
    "        if delta_E <0:\n",
    "            arr = new\n",
    "        elif np.random.uniform() < np.exp(-beta*delta_E):\n",
    "            arr = new\n",
    "    elif temp==0:\n",
    "        if delta_E <0:\n",
    "            arr = new\n",
    "    return arr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_polarization(arr):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    vert = 0\n",
    "    hor = 0\n",
    "    N = len(arr)**2\n",
    "    for i in arr:\n",
    "        for j in i:\n",
    "            vert+=j[0]\n",
    "            hor+=j[1]\n",
    "\n",
    "    polarization= ((1/((2**0.5)*N))*hor,(1/((2**0.5)*N))*vert)\n",
    "    \n",
    "    polval = np.sqrt(polarization[0]**2 + polarization[1]**2)\n",
    "    \n",
    "    return polval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def equilibrate_existing(arr2,temp,breaking_iters=3000,return_polarlist=False,verbose=False):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    assert temp>=0\n",
    "    arr = copy.deepcopy(arr2)\n",
    "    n=len(arr)\n",
    "    polar_list=[]\n",
    "    iter_list=[]\n",
    "    polval = calculate_polarization(arr)\n",
    "    for i in range(breaking_iters):\n",
    "        if verbose:\n",
    "            print(f\"Pol = {polval} and iter = {i}\",end=\"\\r\",flush=True)\n",
    "        arr=metropolis_move(arr,temp)\n",
    "        newpolval = calculate_polarization(arr)\n",
    "        polar_list.append(polval)\n",
    "        polval=newpolval\n",
    "    if return_polarlist:\n",
    "        return polar_list,arr\n",
    "    else:\n",
    "        return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def equilibrate(n,temp,breaking_iters=3000,return_polarlist=False,verbose=False):\n",
    "    '''\n",
    "    ~Function Description~\n",
    "    '''\n",
    "    assert temp>=0\n",
    "    arr = initialise_state(n)\n",
    "    polar_list=[]\n",
    "    iter_list=[]\n",
    "    polval = calculate_polarization(arr)\n",
    "    for i in range(breaking_iters):\n",
    "        if verbose:\n",
    "            print(f\"Pol = {polval} and iter = {i}\",end=\"\\r\",flush=True)\n",
    "        arr=metropolis_move(arr,temp)\n",
    "        newpolval = calculate_polarization(arr)\n",
    "        polar_list.append(polval)\n",
    "        polval=newpolval\n",
    "    if return_polarlist:\n",
    "        return polar_list,arr\n",
    "    else:\n",
    "        return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sp_heat_and_energy(n,temp,breaking_iters=3000):\n",
    "    arr = equilibrate(n,temp,breaking_iters=breaking_iters)\n",
    "    energy_list=[]\n",
    "    for i in range(100):\n",
    "        arr=metropolis_move(arr,temp)\n",
    "        en=calculate_energy(arr,eps)\n",
    "        energy_list.append(en)\n",
    "    energy_list = np.array(energy_list)\n",
    "    energy = energy_list.mean()\n",
    "    beta = 1/(temp*kb)\n",
    "    sp_heat = beta**2 *kb* ((energy_list**2).mean() - (energy_list.mean())**2)/n**2\n",
    "    return sp_heat, energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_2d_model(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_atom_config_with_bonds(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# brr = calculate_atom_config(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# brr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crr = np.zeros_like(brr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10 - 5 is Even, 6 is Odd\n",
    "# 20 - 5 is Odd, 6 is Even\n",
    "# 30 - ELSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n=len(brr)\n",
    "# for i in range(n):\n",
    "#     for j in range(n):\n",
    "#         if brr[i][j]==5 and j%2==0:         # Is 5 Even,\n",
    "#             if brr[i][(j+1)%n]==6:               #AND is right cell 6?\n",
    "#                                                  #If yes,\n",
    "#                 crr[i][j]=10                         #Current cell will be 10\n",
    "#                 crr[i][(j+1)%n]=10                   #Right Cell will be 10\n",
    "#                 if brr[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "#                     crr[i][(j-1)%n]=10                   #If yes, then left cell becomes 10\n",
    "#                 else:\n",
    "#                     crr[i][(j-1)%n]=30                   #If not, left cell becomes 30\n",
    "#             else:                                #If not, i.e if right cell is not 6,\n",
    "#                 if brr[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "#                     crr[i][j]=10                         #If yes, current\n",
    "#                     crr[i][(j-1)%n]=10                      #and left cell are 10\n",
    "#                     crr[i][(j+1)%n]=30                      #But\n",
    "#                 else:\n",
    "#                     crr[i][j]=30\n",
    "#                     crr[i][(j+1)%n]=30\n",
    "#                     crr[i][(j+1)%n]=30\n",
    "                    \n",
    "#         if brr[i][j]==5 and j%2==1:         # Is 5 Odd,\n",
    "#             if brr[i][(j+1)%n]==6:               #AND is right cell 6?\n",
    "#                                                  #If yes,\n",
    "#                 crr[i][j]=20                         #Current cell will be 10\n",
    "#                 crr[i][(j+1)%n]=20                   #Right Cell will be 10\n",
    "#                 if brr[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "#                     crr[i][(j-1)%n]=20                   #If yes, then left cell becomes 10\n",
    "#                 else:\n",
    "#                     crr[i][(j-1)%n]=30                   #If not, left cell becomes 30\n",
    "#             else:                                #If not, i.e if right cell is not 6,\n",
    "#                 if brr[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "#                     crr[i][j]=20                         #If yes, current\n",
    "#                     crr[i][(j-1)%n]=20                      #and left cell are 10\n",
    "#                     crr[i][(j+1)%n]=30                      #But\n",
    "#                 else:\n",
    "#                     crr[i][j]=30\n",
    "#                     crr[i][(j+1)%n]=30\n",
    "#                     crr[i][(j+1)%n]=30\n",
    "#         if brr[i][j]==1 or brr[i][j]==2 or brr[i][j]==3 or brr[i][j]==4:\n",
    "#             crr[i][j]=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_toon_array(arr):\n",
    "    #COLOR CODES\n",
    "    # 10 - 5 is Even, 6 is Odd\n",
    "    # 20 - 5 is Odd, 6 is Even\n",
    "    # 30 - ELSE\n",
    "    n=len(arr)\n",
    "    config_array = calculate_atom_config(arr)\n",
    "    color_code_arr = np.zeros_like(config_array)\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            if config_array[i][j]==5 and j%2==0:         # Is 5 Even,\n",
    "                if config_array[i][(j+1)%n]==6:               #AND is right cell 6?\n",
    "                                                     #If yes,\n",
    "                    color_code_arr[i][j]=10                         #Current cell will be 10\n",
    "                    color_code_arr[i][(j+1)%n]=10                   #Right Cell will be 10\n",
    "                    if config_array[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "                        color_code_arr[i][(j-1)%n]=10                   #If yes, then left cell becomes 10\n",
    "                    else:\n",
    "                        color_code_arr[i][(j-1)%n]=30                   #If not, left cell becomes 30\n",
    "                else:                                #If not, i.e if right cell is not 6,\n",
    "                    if config_array[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "                        color_code_arr[i][j]=10                         #If yes, current\n",
    "                        color_code_arr[i][(j-1)%n]=10                      #and left cell are 10\n",
    "                        color_code_arr[i][(j+1)%n]=30                      #But\n",
    "                    else:\n",
    "                        color_code_arr[i][j]=30\n",
    "                        color_code_arr[i][(j+1)%n]=30\n",
    "                        color_code_arr[i][(j+1)%n]=30\n",
    "\n",
    "            if config_array[i][j]==5 and j%2==1:         # Is 5 Odd,\n",
    "                if config_array[i][(j+1)%n]==6:               #AND is right cell 6?\n",
    "                                                     #If yes,\n",
    "                    color_code_arr[i][j]=20                         #Current cell will be 10\n",
    "                    color_code_arr[i][(j+1)%n]=20                   #Right Cell will be 10\n",
    "                    if config_array[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "                        color_code_arr[i][(j-1)%n]=20                   #If yes, then left cell becomes 10\n",
    "                    else:\n",
    "                        color_code_arr[i][(j-1)%n]=30                   #If not, left cell becomes 30\n",
    "                else:                                #If not, i.e if right cell is not 6,\n",
    "                    if config_array[i][(j-1)%n]==6:               #Is left cell 6?\n",
    "                        color_code_arr[i][j]=20                         #If yes, current\n",
    "                        color_code_arr[i][(j-1)%n]=20                      #and left cell are 10\n",
    "                        color_code_arr[i][(j+1)%n]=30                      #But\n",
    "                    else:\n",
    "                        color_code_arr[i][j]=30\n",
    "                        color_code_arr[i][(j+1)%n]=30\n",
    "                        color_code_arr[i][(j+1)%n]=30\n",
    "            if config_array[i][j]==1 or config_array[i][j]==2 or config_array[i][j]==3 or config_array[i][j]==4:\n",
    "                color_code_arr[i][j]=30\n",
    "    return color_code_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualise_toon(arr,title=\"\",savefig=False,savename=\".temp\",show=True):\n",
    "    crr = get_toon_array(arr)\n",
    "    colordict = {10:-1,20:1,30:0}\n",
    "    crr_copy = np.copy(crr)\n",
    "    for key, val in colordict.items(): crr_copy[crr==key] = val\n",
    "    flipped_arr = np.flip(crr_copy,0)\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax = plt.subplot(111)\n",
    "    \n",
    "    ax.set_aspect('equal')\n",
    "    cmap = plt.get_cmap(\"PRGn\")\n",
    "    norm = clr.Normalize(vmin=-1.5, vmax=1.5)\n",
    "    hexlist = [clr.to_hex(cmap(norm(-1))),clr.to_hex(cmap(norm(0))),clr.to_hex(cmap(norm(1)))]\n",
    "\n",
    "    legend_elements = [\n",
    "                    Line2D([0], [0], marker='o', color='w', label='Other',markerfacecolor=hexlist[1], markersize=10),\n",
    "                    Line2D([0], [0], marker='o', color='w', label='5 is Even AND 6 is Odd',markerfacecolor=hexlist[0], markersize=10),\n",
    "                    Line2D([0], [0], marker='o', color='w', label='5 is Odd AND 6 is Even',markerfacecolor=hexlist[2], markersize=10)\n",
    "                        ]\n",
    "    ax.pcolormesh(flipped_arr,cmap=cmap,norm=norm)\n",
    "\n",
    "    box = ax.get_position()\n",
    "    ax.set_position([box.x0, box.y0, box.width * 1, box.height])\n",
    "\n",
    "    ax.legend(handles=legend_elements,loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "    plt.title(title)\n",
    "    if savefig:\n",
    "        plt.savefig(f\"{savename}.png\",dpi=300)\n",
    "    if show:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.001 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.22311111111111112 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.44522222222222224 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.6673333333333333 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.8894444444444445 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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NaxcSElI5ffp0t4Waa9euDf31r3/dJSoq6nqNy9y5c089+OCDRUbG56muXbsmJyQklHzxxRfHqpeNHDky5rvvvgs7ceLET8HBwSo3N9fWu3fvpNOnT/906NChwLS0tNTY2NhrpaWl0rJly8rJkyfn/cu//IvL97h9+/bgKVOmdC4uLrZaLBa1e/furJCQkFrFrY888kjn559//lyvXr2ueTLmBQsWtH377bc7AECrVq0qFyxYcMpV/7W1a9eGvvHGGx02bdpU53LxqKiobpmZmVmRkZEet5zmzIWIACCovNTeZeWcdZbcI/nXF14+V4TvPtoh2X8/Lg/PHt4lIMh2AAZnMJs3bz7c0JfT888/n1/fc6707t272NUXoVl27drVQimF7du3hxYWFlrCwsKuV5tarVa1cOHCti+88EKd99CpU6fSrKysAwBw4MCBwF/96ldxlZWVmDFjRq0AU15ejgkTJsQuWbLkWP/+/UvOnj1rDQwMrNM1ISMj44SnY16+fHnrP/3pT+22bt16KDIy0v7999+HjBo1qsv27duzbrvtNlPvTcCcCxHBXmbvsPurLKkZWGrKPZKPPV9lib28or1ZY6h5k7A5c+a079KlS0pCQkLy/ffff7un25g6dWrU3Llz29Xc5iuvvNIBAP793/+9Q2pqalJCQkLyM888cysAHDp0KPD2229PGTNmTOe4uLiUgQMHxhcXF7vsDr1kyZLw0aNHF/zjP/5j4fLly2+p+dyUKVPyHLcCaHB8ycnJZfPmzTv17rvv1rkZ2qpVq1onJSWV9O/fvwQAOnbsWOGqX1rfvn27fvvttyF2ux0jR46MiY+PT0lISEj+/e9/X+d3s2DBgo6vvfZaTnVQHzRo0NXRo0cXvPHGG+0BYOXKlWGxsbEpvXr16rpy5crr7+ns2bPWgQMHxiclJSWPHTu2s5GmpQwuRDcXly33FdBu74aDDbbc37PhoKhKZajlPgAMHjw4PiUlJWnBggVt3a27cOHCjvv27Ttw+PDhA3/+859d/qWemZnZKjExMbn63/79+4PGjx9/4dNPPw2vXmf16tVtxo8ff3HVqlVh2dnZLfbu3ZuVlZV1YPfu3SHr1q1rBQAnT55s8a//+q952dnZ+1u3bl3x4YcftnG1v9WrV4c/+uijF8eOHXshIyMjvOZznTt3LuvTp0/xokWLIty9twEDBlw9duxYC+flhw4dChIRDBo0KD45OTlp9uzZDd6Nc9u2bSG5ubkBR44c2X/48OEDTz31VJ1TbdnZ2cEDBw6sdauDPn36XM3Kygq+evWqTJ8+PWbNmjXZO3bsOJSXlxdQvc6LL754a//+/YuzsrIOpKenX8rNzW10DzcGFyKCLcCKwvyGb4NSdL4YtgCroe1v2bLl4IEDB7K++uqrI++991776i/2+nTt2rXkoYceil20aFF4QECAyz+be/fuXXzw4MED1f9SUlJKBw4cWFJQUGA7fvx4wLZt24Jbt25dER8fX7Z+/fqwb7/9Niw5OTk5JSUl+ejRoy0OHjzYAgCioqJKBwwYUAIAPXr0uHr8+PE6d9vcvHlzSHh4uD0hIaEsPT29cP/+/SH5+fm1DsYrr7ySu3Dhwo6VlXV7s9VU3yzAbrfLjh07Wq1YseLY9u3bD61du7bN6tWrQ+vbTmJiYumpU6eCHnvssU4rV64Ma9OmTUWDO66xfxHB7t27W0RHR5d269at1GKxYNy4cdeD0w8//BD6m9/8pgAAxowZczksLMyjbdfk1ZxLu9jwRhWlGSmi9CRBblait7maXZrFkwsUjCSdnekqPjVSsOnRRSRu9q2jKNeLnx2Xlff28ooeYe1aWS6fqz8fHtq2FezlFRUBQbZGt9yPiYkpB4CoqCj7fffdd2nbtm0thw8fXm8027Rp05F169aFfv7557fMmzfv1iNHjuwLCAiob/VaRowYcXHZsmVtzp49GzBy5MgLQNUX6tNPP5373HPP1boj56FDhwJr5jWsVqsqKSmp80f30qVLw3/++ecWUVFR3QDgypUr1qVLl7aZOXPm9e2lpqaWJicnX12yZInLmU+1bdu2hdx+++0lzsujo6PL+vXrV1R9CusXv/jF5czMzJAHHnjA5S+lXbt2Ffv27Tvw2WefhS1atKh9RkZG+IoVK47XXCcuLq5ky5YtIenp6de3sXPnzpDExMQSAC7vpVPNYmna3IMzFyKCAAV3DEls8MR62pBEJRZpdMv9wsJCy8WLFy3VP2/atCnsjjvuqPPlWq2iogJHjx4NHDFiRNGiRYtyioqKrJcvX/Z4yjRhwoQLn376afjatWvbjB8//iIADB8+vHDp0qVtL1++bAGAY8eOBVS37nenoqICa9euDf/xxx/3nz59+qfTp0//tHz58uwVK1aEO6/7yiuv5L799tsd69vWoUOHAl988cXoKVOm1Oku/dBDDxVmZWUFFxUVWcrLy7Fly5bQlJSUeq8Iy83NtVVUVGDixImX5syZc/qnn36qc6ngzJkzz7700kvRZ8+etQLA1q1bgzMyMiJmzpyZ371792s5OTmB+/fvDwKAjz/++Pr76devX9EHH3wQAQCffPJJWGFhYaOnrLxajIhgC7Sd6z40KSL778ddJvUj49shbWiSsgVYG91yPycnx/bQQw/FAVW3Sh45cmTBww8/XO8lzXa7XcaOHRtbVFRkVUrJlClTzrVt27bOaZnqnEv14xdeeCH38ccfv9i7d+9rV65csXTo0KGsc+fO5QDwq1/9qnD//v0t+vTpkwgAISEhlR999NExm83mNlO9bt260A4dOpTFxsZez9YPHz686De/+U3siRMnak2nevfufS0lJeXq/v37r3/Rnzp1KigpKSm5+lLkKVOm5DlfKQZUzUSmT59+rkePHkkigsGDB18eM2bM5frGdfz48YAnnngiprKyUgDg1VdfzXFeZ9y4cZdzcnIC+/XrlyQiqmXLlpUffPDBserj8uabb564//7748LDw+133nlncVZWVjAAzJ0798zIkSNvT05OTurfv39xZGRko29rIE25dWlj9ezVU23ZsqVJ2zDSW4yM0XGayRM32mkxT/ZjlpDgkJ1Kqd41l+3Zs+d4Wlra+fpeU0NYeam9y56vsmTPhoNSdL4YoW1bIW1IokobmqSaUudCN6Y9e/a0TUtLi3H1nN/NXNwV5RkpfvQmHQV4njAShOuMzU3BqsvtOh1/T754h0NP8WljmqJef82y2q8xcvw92a+O/XhBYUCQ7UD3e5Pbpw1NirAFWK328ooKsUiBY8bCCn3ymN8FFyIyVaktwHq95X5AEL8iyBgm9ImISDsGFyIi0s6rc97sgmMN5hw8SZQ6n5/XkZD1Jre1I5oaWRrJTRlpvOn2+BvI23jCOYcBGEu012Hg+Bs5bu7qgzxh5EIIIm/hCVUiqimozF7WQUFFBFgDLeUVZZUCKQi0BbLlPjUKT4sRUbWwa/bS5M8P/G/bSZ8+Yxn+wWhM+vQZy+cH/rftNXtpMoAwoxuOiorqlpCQkJyYmJicmpqa5GqdefPmtXvrrbfc9uaq9te//rVVt27dkmJjY1NiY2NTGupZFhIS0sPV8pEjR8b86U9/cllRX15ejjZt2qQ99dRTUTWX9+3bt2vN9/Dtt9+G9O3btytQ1bY+NDS0e1JSUnJMTExq7969uy5fvrx1feNau3ZtaGJiYnJcXFxKnz59urpa56677oo7f/68R0WMI0eOjImKiupW3W+tR48eiZ68zgycuRARAARds5d2ef5/f2fJyjt8fWFu0Tm89/el8v3x7TLvl7/r0sIW5BMt90+ePGmbOHFi7IoVK44OGjToam5urm3IkCHx0dHR5Q0VHjbGqlWrWsfGxpauWbOmzZtvvnm6ZjuUgoIC2yeffBI2evToOnU/NW8FsHXr1uBRo0bFhYSEHHdu43L+/HnrjBkzblu/fv2R+Pj4svo6BmzevLlRtxWYM2dOzuOPP36xMa8xA2cuRIQye1mHNQfWSc3AUlNW3mF8cWC9lFWU+0TL/TfeeKP9I488UjBo0KCrABAZGWn/wx/+kDN//vyOAHDw4MHA7t27J6ampibNmDHj1urXVVZW4tFHH72tS5cuKXfffXfc+fPn6/0De/ny5eHTpk07d+utt5Zt3LixZc3npk+ffm7u3Lm31vfaagMGDCh57rnnzrz11lt1jtv7778fft99912Mj48vA6r6rrnaRlRUVLfc3FxbYWGh5e67747r2rVrcnx8fMp7773XYA+zahUVFYiKiupWc/Zz2223pZ46dcp25swZ27Bhw7qkpqYmpaamJn311VctgarfxahRo2L69u3bNTo6utucOXMa/Xv3qTtROidTjSQ5fak4zZM7aepo7OhqG+6SzJ40V/QkYex8YYAn78dQot0DOpLkRjRXVwiDd6LsBKDOSgqq1dqsrxp84RdZf5X05Hvbu3j9VdTTELOmwYMHx4sIHn/88fxnn322wY4BCxcu7Fh9Z0dXp4SysrKCH3300VotVAYNGnQ1Ozs7GACmTZt225NPPpk/ffr0gtdee+36/V2WLl16S3Z2dtChQ4f25+TkBHTr1i1l4sSJdVqxFBcXy9atW0OXLl164tKlS9Zly5aFDxky5Er18//wD/9Q/MUXX9zyxRdfhLZu3brBjsF9+/a9unDhwjr9xg4fPtyivLxc+vbt2/XKlSuWqVOn5jV0F85Vq1aFdezYsfxvf/tbNgAUFBS4PFU2e/bs6Ndffz0SABISEkrWrFlzbOjQoZc++uijW2bMmFGwcePGltHR0WWdOnWyjxgxInbmzJnnhg0bVnzkyJHAYcOGxf/888/7ASA7O7vF1q1bD126dMmalJSU+txzz+UHBQV53NKFMxciQoA1EOeKGz4rlVd8HgFWzzoTO9Pdct/RNr7O8uouv7t27Wo1adKkCwAwZcqU61/YmzdvDh09evQFm82GmJiY8v79+7vsOPzJJ5/c0q9fv6LQ0NDK8ePHX1y/fn0bu732xOKll17K/cMf/hDp7r031GJ/7969IRs2bDiyYcOGI/Pnz4/cu3dvnXb/1Xr27Fny3XffhU2dOjVq/fr1rSIiIlwGtTlz5uRU34ZgzZo1xwBg7NixF1auXBkOAB999FF4dbfoLVu2hM2YMeO2xMTE5BEjRsQVFxdbq5uMDh069FJwcLCKjIy0h4eHl+fk5DRqMsLgQnRzOQXgkPO/8oqyyg6t2jX4wvat2qK8orzCxevdzlpctdxvaP1NmzYdeeqpp/J37tzZMi0tLdn5Do9JSUklO3bsqLWNLVu2hHTp0uV6t2WLxeLyW72hNvPVPv744/AtW7aERUVFdevVq1fy5cuXrWvXrq11b5X09PSi0tJSy/fff9/ge9mxY0dIXFxcne7G0dHRZYMHDy4MCwurjIyMtN95551FmZmZ9U4977jjjtJdu3Yd6NatW8m//du/RT377LNuA1u1wYMHXzlx4kTQmTNnbOvXr79l3LhxF4GqwJeZmZlVHYzy8vL2tmnTphIAas5SrFYr7Ha7+wNXA4MLEUEgBfcnDW3wlMeIpGFKxOITLfdnzZqVn5GREbF169ZgoOq2vC+99FL0rFmzzgJAz549i997771wAHjvvfeuX4F21113Fa1YsSLcbrfjxIkTAT/88EOdm3FduHDBkpmZ2SonJ2dvdYv9uXPnnvzLX/5Sp8X+Cy+8kPvmm2/W22J/+/btwfPnz7/1qaeeqtNN+uGHH760bdu2VuXl5SgqKrL8+OOPrbp161bvcTl+/HhAaGho5bRp0y48/fTT53bv3u32HGg1i8WC4cOHX5o2bVqnuLi4ko4dO1YAwKBBgwpff/316/mU6uOpQ7PeLMz5nL6fNPfzmJHxayscNVC86O74uxqbq9yNO0ZyO0b4ehPTxjIrVwUAgbbAc+nJwyO+P77dZVI/qX0CRiTfqwKtAT7Rcr9z587lH3zwwbHJkyfHXLlyxaKUkqlTp54bO3bsZQBYtGjRyTFjxty+aNGiDunp6devnJowYcKlb775Jqxr164psbGx1/r27VvntNiyZcvaDBgwoCg4OPh6sB0zZsyl3/3ud9ElJSUna677yCOPXH711VdrnS/LzMxslaPpyYIAAAv5SURBVJSUlFxSUmKJiIgonz9//klXN/zq2bPntSFDhlxOTExMsVgsmDBhQn6fPn3qvX/Lzp07g3/7299GWywW2Gw2tWjRIpe3f66ZcwGA3bt3Z7Vo0UKNGzfuwl133ZW0cOHC49XPLV68+NSTTz55W0JCQnJFRYXceeedRQMGDDjparuN5bblvoh0AvAhgI4AKgEsVkr9HxEJB5ABIAbAcQCjlVINXv7m3HLfWx2C/YmR9u1mfTlrqXh3obnG7+88Of6zMp5oUsv9a/bSLl8cWC9fZP1V8orPo32rthiRNEyNSL5XtbAFseU+1dLUlvt2ALOUUrtEJBTAThH5GsBEAN8opeaKyIsAXgTwgqYxE5H3FbawBR14IOWX7dOT740IsAZYyyvKK0QsBY4ZCyv0yWNug4tSKhdAruPnIhHJAhAF4AEAdztWWwLgb2BwIfJ3pYHWgOst94Ns9V68RNSgRuVcRCQGQA8A2wF0cAQeKKVyRcRlkY2ITAYwGQBahAfXOgXirdMWuu506A1GaiY8aZyooxbDk5tiGTluRho/unKj34XUo9OJGS5fWllZWSn1XT1FZITj9sqV9T3v8dViItIKwKcAnlZKeXzeVSm1WCnVWynVOyCUfwURNYN9+fn5ravvtU7UVJWVlZKfn98awL761vFo5iIiAagKLB8ppVY5Fp8TkUjHrCUSQKOvIiEi89nt9ifPnj37/tmzZ1PB8gPSoxLAPrvd/mR9K7gNLlJVcfRHAFlKqf+q8dQaAI8BmOv4f3XTxkpEZujVq1cegPTmHgfdXDyZuQwEMAHATyKy27HsJVQFlU9E5AkAJwGMMmeIRETkb9zWuejkXOfiLe7qA270RLAuvlyDYxZd4/fWeEOCQ+rUuRA1B55/JSIi7RhciIhIOwYXIiLSzquNK/OPXWjwvL1ZuQ93xX+eNDj05Ny7L+UBzOgLZlaBpyHmpHLqHCfnpqGe8FYOz8xGlkRNxZkLERFpx+BCRETaMbgQEZF2rHPBzVnn4q36jeZsGqrjPfrSPWE8eT8bJq1inQv5BM5ciIhIOwYXIiLSjsGFiIi0Y3AhIiLtvFpE6cyMZK/LbQ5p3DZ0MStpbqSBpDNtxY1u3qKRY230uDnvy8h7NKvxpo7PnCfbCJkU0uT9EOnAmQsREWnH4EJERNoxuBARkXY+VUSpI5egYxveHEtjt3kjMqsBo5H8iY6Gn835O+TNwshXcOZCRETaMbgQEZF2DC5ERKSdT+Vc/P2GXDq268k2/b0hY3M2s2wsfxorwJwL+Q7OXIiISDsGFyIi0o7BhYiItGNwISIi7bzauDK74FitBKlz4rO5EqGuCuWGo3bTQ1eFcUaS826bKXqQP9bSdNKD/ZhVQOjNZpZN3a+hY62n12UduppoEnkDZy5ERKQdgwsREWnH4EJERNp5tYiyU3iMemboy9cf3+hNGs0qwPO3wj5n7nI5uhqLGtmuDt5snupsVsYTLKIkn8CZCxERacfgQkRE2jG4EBGRds3auFLHuXdnumoBdOQodN0Ey4xcgb/nbW4GRn5HGyatYs6FfILbmYuIfCAieSKyr8aycBH5WkSOOP5vY+4wiYjIn3hyWuzPAO51WvYigG+UUvEAvnE8JiIiAuBBcFFKfQvggtPiBwAscfy8BMCDmsdFRER+zGhCv4NSKhcAHP+3r29FEZksIpkiknk+/7zB3RERkT8xvXGlUmoxgMVAVUK/5nNmJKp9Kem8bsiXerazrPZ2dLxHX2rIeDMwpcmpCxuwqtGvITKD0ZnLORGJBADH/3n6hkRERP7OaHBZA+Axx8+PAVitZzhERHQj8ORS5OUAtgHoKiI5IvIEgLkAfiEiRwD8wvGYiIgIgAc5F6VUfYmRwZrH4lN0NB/0pfyPMyP5IOfcjydcHQMzimdd8aQI0Vu/IyM3iTOUs8to/EuIzMD2L0REpB2DCxERacfgQkRE2jG4EBGRds3aFbm5GEnWN2dy2EgC3EjRnq4uzu7c6Hcg9YRZn6eQ4BB2RSafwJkLERFpx+BCRETaMbgQEZF2pjeurCn/2IVa5/WNnHs3khdw3o8nxWlTUfs1zVkQ6W68zmMF3BftvbOh7nE0kmcywtV4G8us/JC38kEeNaV0c/i9lSMjMoIzFyIi0o7BhYiItGNwISIi7Zq1zsWM+g1XjORLfKnpoRmMvD9d5/jd5TV0NA11xaz37Et1O6xzIV/BmQsREWnH4EJERNoxuBARkXYMLkREpJ1PN670VgLZKB0FnWYxo3Glty6wMKt401vvWdeFHkb2w4Q++QrOXIiISDsGFyIi0o7BhYiItPNq48rG8qTBpDOzboLl6ty7kfF5q2njcLhpjOjidL4ZDTI93XeTt+kBI8061y2re0y8VTzrz0W6RJy5EBGRdgwuRESkHYMLERFp59N1Lq4YqUtwx2iTSl+uc3HHSENGbzXzNKvOxRUdzTl95XcKsM6FfAdnLkREpB2DCxERacfgQkRE2jG4EBGRds1aRGmkMZ8ZyVNPivY8KcDzJ0bG7lFxo9Ov1MhFAGYVUbrkNDxvFcb62x0uiRqLMxciItKOwYWIiLRjcCEiIu18qoiyuQr7jBbtGSkY1HGjKTNu0GWUt26c1dj9mrlvI7x18zYWUZKvaNLMRUTuFZFDIpItIi/qGhQREfk3w8FFRKwA3gYwHEAygF+LSLKugRERkf9qysylL4BspdTPSqkyAB8DeEDPsIiIyJ8ZzrmIyMMA7lVKPel4PAHAnUqp6U7rTQYw2fEwFcA+48P1qrYAzjf3IBrBn8brT2MF/Gu8XZVSoc09CKKmFFGKi2V1IpVSajGAxQAgIpn+kmz0p7EC/jVefxor4F/jFZHM5h4DEdC002I5ADrVeBwN4EzThkNERDeCpgSXHQDiRSRWRAIBjAGwRs+wiIjInxk+LaaUsovIdAB/BWAF8IFSar+bly02ur9m4E9jBfxrvP40VsC/xutPY6UbmFeLKImI6ObA9i9ERKQdgwsREWnnleDi621iROQDEckTkX01loWLyNcicsTxf5vmHGM1EekkIptEJEtE9ovIDMdyXx1vCxH5u4jscYz3947lsSKy3THeDMdFIT5BRKwi8qOIrHU89uWxHheRn0Rkd/VlyL76WaCbi+nBxU/axPwZwL1Oy14E8I1SKh7AN47HvsAOYJZSKglAPwBPOY6nr463FMA9Sqk0AN0B3Csi/QC8DuC/HeO9COCJZhyjsxkAsmo89uWxAsA/KaW616jF8dXPAt1EvDFz8fk2MUqpbwFccFr8AIAljp+XAHjQq4Oqh1IqVym1y/FzEaq+BKPgu+NVSqlix8MAxz8F4B4AKx3LfWa8IhIN4D4A7zseC3x0rA3wyc8C3Vy8EVyiAJyq8TjHsczXdVBK5QJVX+gA2jfzeOoQkRgAPQBshw+P13GaaTeAPABfAzgK4JJSyu5YxZc+E/8D4HkAlY7HEfDdsQJVgforEdnpaLUE+PBngW4eTWn/4imP2sRQ44hIKwCfAnhaKVVY9Qe2b1JKVQDoLiK3APgMQJKr1bw7qrpE5H4AeUqpnSJyd/ViF6s2+1hrGKiUOiMi7QF8LSIHm3tARIB3Zi7+2ibmnIhEAoDj/7xmHs91IhKAqsDykVJqlWOxz463mlLqEoC/oSpXdIuIVP9x4yufiYEA0kXkOKpO396DqpmML44VAKCUOuP4Pw9Vgbsv/OCzQDc+bwQXf20TswbAY46fHwOwuhnHcp0jB/BHAFlKqf+q8ZSvjredY8YCEQkGMARVeaJNAB52rOYT41VK/VYpFa2UikHV53SjUmocfHCsACAiLUUktPpnAENR1XXcJz8LdHPxSoW+iPwSVX8BVreJ+U/Td9oIIrIcwN2oaq1+DsArAD4H8AmA2wCcBDBKKeWc9Pc6ERkE4DsAP+H/5wVeQlXexRfHeweqkspWVP0x84lS6lURuR1Vs4NwAD8CGK+UKm2+kdbmOC32rFLqfl8dq2Ncnzke2gD8RSn1nyISAR/8LNDNhe1fiIhIO1boExGRdgwuRESkHYMLERFpx+BCRETaMbgQEZF2DC5ERKQdgwsREWn3/wAGG15fg9FcEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 1.1115555555555554 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 1.3336666666666666 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 1.5557777777777777 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 1.7778888888888889 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 2.0 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_arr=np.linspace(0.001,2,10)\n",
    "for temp in temp_arr:\n",
    "    print(f\"******* TEMP = {temp} *******\")\n",
    "    arr2 = equilibrate(50,temp=temp)\n",
    "    title=f\"T = {temp} K\"\n",
    "    visualise_toon(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.001 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 1.1119999999999999 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 2.223 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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ExMfHpyYkJKT87ne/a/S7WbhwYY8//OEPRY6kPmTIkEvjxo0rf/PNN7sDwJo1a0JiY2NT+/Xrl7hmzZor7+n06dPGwYMHxycnJ6dMmDChZ0ualjK5EN1cnLbcV0DYvi0Hmm25/+2WA6JsqkUt9wFg6NCh8ampqckLFy7s5mrdRYsW9cjNzc0/dOhQ/ocffuj0f+o5OTmdkpKSUhxfeXl5ARMnTjz72WefhTrWWbduXZeJEyeeW7t2bUhBQUGHffv2mc1mc/7evXuDNm7c2AkAjh8/3uHnP/95aUFBQV7nzp2tH330URdn+1u3bl3o448/fm7ChAlnV69eHdrwuZ49e9b279+/avHixV1dvbdBgwZdOnr0aIdrlx88eDBARDBkyJD4lJSU5Llz5zY7G+eOHTuCiouL/Q4fPpx36NCh/BkzZjQ61VZQUBA4ePDgq6Y66N+//yWz2Rx46dIlmTlzZsz69esLdu3adbC0tNTPsc5LL710y8CBA6vMZnP+6NGjzxcXF193DzcmFyKCyc+IirLmp0GpPFMFk5+xRdvPzs4+kJ+fb968efPhJUuWdHd8sDclMTGx+qGHHopdvHhxqJ+fn9P/NmdkZFQdOHAg3/GVmppaM3jw4Ory8nJTYWGh344dOwI7d+5sjY+Pr920aVPItm3bQlJSUlJSU1NTjhw50uHAgQMdACAyMrJm0KBB1QDQp0+fS4WFhY1m29y6dWtQaGioJSEhoXb06NEVeXl5QWVlZVcdjFdeeaV40aJFPWw25xOuOTQ1CrBYLLJr165On3766dGdO3cezMrK6rJu3brgpraTlJRUc+LEiYAnnngies2aNSFdunSxNrvjBvsXEezdu7dDVFRUTXp6eo3BYMBjjz12JTn95z//CX7qqafKAWD8+PEXQkJC3Np2Q0wuRDeXE6hvmX/Vl6XOagsJa/bzHsHdOsFSZ7U6eb3Lav6YmJg6AIiMjLTcd99953fs2NGxufW//PLLwzNmzCjbvXt3x969e6e4Ot3U0KhRo86tWLGiy8qVK0PHjBlzFqj/QH3uueeKHYno+PHjuc8///wZAGh4XcNoNCqLxdJoBLd8+fLQ7777rkNkZGR6z5490y9evGhcvnz5VSOctLS0mpSUlEvLli1zOvJx2LFjR9Btt91Wfe3yqKio2gEDBlRGRERYgoODbffcc8+FnJycoKa2ExYWZs3Nzc3/4Q9/WLl48eLu48ePj7l2nbi4uOrs7OyrtrF79+6gpKSkagBO59JxMBhalx6YXIgIApTfPiyp2RPrvYclKTHIdbfcr6ioMJw7d87gePzll1+G3H777Y0+XB2sViuOHDniP2rUqMrFixcXVVZWGi9cuOD2kGnSpElnP/vss9CsrKwuEydOPAcAI0eOrFi+fHm3CxcuGADg6NGjfo7W/a5YrVZkZWWFfvPNN3knT57cf/Lkyf2rVq0q+PTTT0OvXfeVV14pfuedd3o0ta2DBw/6v/TSS1FTp05t1F36oYceqjCbzYGVlZWGuro6ZGdnB6empjZ5R1hxcbHJarVi8uTJ5+fNm3dy//79jRLR7NmzT7/88stRp0+fNgLA9u3bA1evXt119uzZZXfcccfloqIi/7y8vAAA+Pjjj6+8nwEDBlQuXbq0KwB88sknIRUVFdc9ZOXdYkQEk7+p5I7hyV0L/lvo9KJ+RHwYeg9PViY/43W33C8qKjI99NBDcUD9VMljxowpf/jhh5u8pdlisciECRNiKysrjUopmTp1akm3bt0anZZxXHNx/Pziiy8WP/nkk+cyMjIuX7x40RAeHl7bs2fPOgD4yU9+UpGXl9ehf//+SQAQFBRkW7ly5VGTyeTySvXGjRuDw8PDa2NjY68Mn0aOHFn51FNPxR47dsyv4boZGRmXU1NTL+Xl5V35oD9x4kRAcnJyiuNW5KlTp5Zee6cYUD8SmTlzZkmfPn2SRQRDhw69MH78+AtNxVVYWOj39NNPx9hsNgGA1157rejadR577LELRUVF/gMGDEgWEdWxY0fb0qVLjzqOy1tvvXXs/vvvjwsNDbXceeedVWazORAA5s+ff2rMmDG3paSkJA8cOLAqIiLiuqc1kNZMXXq9okNj1PPDf9uqbbDOpTHWufg+XXUuW55du1spldFw2bffflvYu3fvM268PKSuxtLr281m+XbLAak8U4Xgbp3Qe1iS6j08WbWmzoVuTN9++2233r17xzh7zquSi6c+JHV8oAMeLIzzIt40AdfNxp2/2zmrn25NcgGAAEudtbuyqa4mP6PRUme1ikHK7SMWVujTVZpLLjwtRkQN1Zj8jFda7vsF8COCWoYX9ImISDsmFyIi0o7JhYiItOMJVSJqKKDWUhuuoLr6Gf0NddZam0DK/U3+bLlP14UjFyJyCLlsqUn5W/7fuz372fOGkUvH4dnPnjf8Lf/v3S5balIAhLR0w5GRkekJCQkpSUlJKWlpacnO1nnjjTfC3n77bZe9uRz+8Y9/dEpPT0+OjY1NjY2NTW2uZ1lQUFAfZ8vHjBkT8+c//9lpRX1dXR26dOnSe8aMGZENl2dmZiY2fA/btm0LyszMTATq29YHBwffkZycnBITE5OWkZGRuGrVqs5NxZWVlRWclJSUEhcXl9q/f/9EZ+vcddddcWfOnHGriHHMmDExkZGR6Y5+a3369Ely53VtgSMXIgKAgMuWml4v/P1Vg7n00JWFxZUlWPLf5fJ14U5548ev9upgCvCKlvvHjx83TZ48OfbTTz89MmTIkEvFxcWmYcOGxUdFRdU1V3h4PdauXds5Nja2Zv369V3eeuutkw3boZSXl5s++eSTkHHjxjWq+2k4FcD27dsDx44dGxcUFFT4wAMPXDXXzJkzZ4yzZs26ddOmTYfj4+Nrm+oYsHXr1uuaVmDevHlFTz755LnreU1b4MiFiFBrqQ1fn79RGiaWhsylh7Ahf5PUWuu8ouX+m2++2f2RRx4pHzJkyCUAiIiIsPz+978vWrBgQQ8AOHDggP8dd9yRlJaWljxr1qxbHK+z2Wx4/PHHb+3Vq1fq3XffHXfmzJkm/4O9atWq0OnTp5fccssttV988cVVvdBmzpxZMn/+/Fuaeq3DoEGDqn/5y1+eevvttxsdtw8++CD0vvvuOxcfH18L1Pddc7aNyMjI9OLiYlNFRYXh7rvvjktMTEyJj49PXbJkSbM9zBysVisiIyPTG45+br311rQTJ06YTp06ZRoxYkSvtLS05LS0tOTNmzd3BOp/F2PHjo3JzMxMjIqKSp83b951/949OnIJiw31imryjcM+17OdFc1vx5eq/N3e15bm93WjdVDwJu4c2zmrn3a1SjSARj2oFFSnLPPmZl+4wfwPGZ1yb3cnr78EN5pXDh06NF5E8OSTT5b94he/aLaoc9GiRT0cMzs6OyVkNpsDH3/88ataqAwZMuRSQUFBIABMnz791meeeaZs5syZ5X/4wx+uzO+yfPny7xUUFAQcPHgwr6ioyC89PT118uTJjVqxVFVVyfbt24OXL19+7Pz588YVK1aEDhs27KLj+e9///tVGzZs+N6GDRuCO3fu3GzH4MzMzEuLFi1q1G/s0KFDHerq6iQzMzPx4sWLhmnTppU2Nwvn2rVrQ3r06FH373//uwAAysvLnZ4qmzt3btTrr78eAQAJCQnV69evPzp8+PDzK1eu/N6sWbPKv/jii45RUVG10dHRllGjRsXOnj27ZMSIEVWHDx/2HzFiRPx3332XBwAFBQUdtm/ffvD8+fPG5OTktF/+8pdlAQEBblfdc+RCRPAz+qOkqvmzUqVVZ+Bn9Gt2nabobrlvbxvfaLmjy++ePXs6Pfvss2cBYOrUqVc+sLdu3Ro8bty4syaTCTExMXUDBw50Oi3yJ5988r0BAwZUBgcH2yZOnHhu06ZNXSyWqwcWL7/8cvHvf//7CFfvvbkW+/v27QvasmXL4S1bthxesGBBxL59+xq1+3fo27dv9VdffRUybdq0yE2bNnXq2rWr06Q2b968Ikf35/Xr1x8FgAkTJpxds2ZNKAA07BadnZ0dMmvWrFuTkpJSRo0aFVdVVWV0NBkdPnz4+cDAQBUREWEJDQ2tKyoquq7BCJML0c3Facv9OmutLbxTWLMv7N6pG+qsdV7Rcj85Obl6165dV20jOzs7qFevXle6LRsMBqef6s21mXf4+OOPQ7Ozs0MiIyPT+/Xrl3LhwgVjVlbWVXOrjB49urKmpsbw9ddfN/tedu3aFRQXF9eou3FUVFTt0KFDK0JCQmwRERGWO++8s7K5Fvu33357zZ49e/LT09Orf/3rX0f+4he/cJnYHIYOHXrx2LFjAadOnTJt2rTpe4899tg5oD7x5eTkmB3JqLS0dF+XLl1sANBwlGI0GuFsKoLmMLkQEQRSfn/y8GZPeYxKHqFEDF7Rcn/OnDllq1ev7rp9+/ZAoH5a3pdffjlqzpw5pwGgb9++VUuWLAkFgCVLlly5A+2uu+6q/PTTT0MtFguOHTvm95///KfRZFxnz5415OTkdCoqKtrnaLE/f/7843/5y18atdh/8cUXi996660mW+zv3LkzcMGCBbfMmDGjUTfphx9++PyOHTs61dXVobKy0vDNN990Sk9Pb/K4FBYW+gUHB9umT59+9rnnnivZu3dvk4noWgaDASNHjjw/ffr06Li4uOoePXpYAWDIkCEVr7/++pXrKY7jqQPvFiMi+Jv8S0anjOz6deFOpxf1k7snYFTKvcrf6OcVLfd79uxZt3Tp0qNTpkyJuXjxokEpJdOmTSuZMGHCBQBYvHjx8fHjx9+2ePHi8NGjR1+5c2rSpEnn//Wvf4UkJiamxsbGXs7MzGx0WmzFihVdBg0aVBkYGHgl2Y4fP/78q6++GlVdXX284bqPPPLIhddee+2q82U5OTmdkpOTU6qrqw1du3atW7BgwfFr7xQDgL59+14eNmzYhaSkpFSDwYBJkyaV9e/fv8n5W3bv3h34q1/9KspgMMBkMqnFixc7nf654TUXANi7d6+5Q4cO6rHHHjt71113JS9atKjQ8dz7779/4plnnrk1ISEhxWq1yp133lk5aNCg4862e71cdkUWkaUA7gdQqpRKsy8LBbAaQAyAQgDjlFIub33r26+vys7ObmXIreepbsU34gV9V3hBv30FBQa1quX+ZUtNrw35m2SD+R9SWnUG3Tt1w6jkEWpUyr2qgymALffpKq3tivwhgLcBfNRg2UsA/qWUmi8iL9l/frGVcRJR+6roYArIfyD1x91Hp9zb1c/oZ6yz1llFDOX2EQsr9MltLpOLUmqbiMRcs/gBAHfbHy8D8G8wuRDdCGr8jX5XWu4HmJq8eYmoWS295hKulCoGAKVUsYg0WWAjIlMATAGA6OjoFu7u+rg6TTQS92nZj46aHU/NROmpU4Guan8A905puTourupt3OUtE7618Wk+m81mk6buniJqCfv0yramnm/zu8WUUu8rpTKUUhndwpps/UNEbSe3rKyss2OudaLWstlsUlZW1hlAblPrtHTkUiIiEfZRSwSA676DhIg8w2KxPHP69OkPTp8+nQaWH5AeNgC5FovlmaZWaGlyWQ/gCQDz7d/XtXA7RNTG+vXrVwpgdHvHQTcXl/+LEZFVAHYASBSRIhF5GvVJ5R4ROQzgHvvPREREANy7W6ypK55DNcdCREQ3CJ5/JSIi7ZhciIhIOyYXIiLSzmVvMZ2iQ2PU88N/26pteMNkY9T2PNV/zFv6srnzd+1OrHNWP92otxhRe+DIhYiItGNyISIi7ZhciIhIOyYXIiLSjsmFiIi0Y3IhIiLtmFyIiEg7JhciItKupS3324SuAklPFeBR2xm5RcNsoW78GWwc5nrmTE+YBhYH042FIxciItKOyYWIiLRjciEiIu2YXIiISDsmFyIi0o7JhYiItGNyISIi7byqzkUXVzUq7ky69O6W1k8i5U7djo6aHHd408RZ7hwXT00Kx3omorbBkQsREWnH5EJERNoxuRARkXZMLkREpB2TCxERacfkQkRE2jG5EBGRdkwuRESknVcVUbpTUKij6M1TxY3uTADlTUV8nipc9CbeVDhKdCPhyIWIiLRjciEiIu2YXIiISDsmF7e8Db4AAAXZSURBVCIi0q5VF/RFpBBAJQArAItSKkNHUERE5Nt03C32Q6XUGQ3bISKiGwRPixERkXatTS4KwGYR2S0iU5ytICJTRCRHRHIu1lS1cndEROQLWntabLBS6pSIdAfwTxE5oJTa1nAFpdT7AN4HgOjQGNXcxkZuuc/lDt2ZIVJHwZo3FTdSY9pm8BymZzOuuFNQS3QjadXIRSl1yv69FMBfAWTqCIqIiHxbi5OLiHQUkWDHYwDDAeTqCoyIiHxXa06LhQP4q4g4tvMXpdQmLVEREZFPa3FyUUp9B6C3xliIiOgGwVuRiYhIOyYXIiLSjsmFiIi0Y3IhIiLtRKlm6xq16tuvr8rOzm7yeW2FcS54U4Gkp2ZCdIenZkvU8Xt253d4M84yGRQYtJsNZMkbcORCRETaMbkQEZF2TC5ERKQdkwsREWnH5EJERNoxuRARkXZMLkREpJ1X1bnooqO+4Uar+aD25amamzmrn2adC3kFjlyIiEg7JhciItKOyYWIiLRjciEiIu2YXIiISDsmFyIi0o7JhYiItGNyISIi7UztHUBb8FQBpI7CuJG4z+U6rt6PrknWPFWM6akiV28qUN047HMt23FptWd2Q+QKRy5ERKQdkwsREWnH5EJERNoxuRARkXZMLkREpB2TCxERacfkQkRE2jG5EBGRdjfkTJQ3Gk/NYuipwkQd3Clu9NRx00FX4W9QYBBnoiSvwJELERFpx+RCRETaMbkQEZF2TC5ERKRdq5KLiNwrIgdFpEBEXtIVFBER+bYWJxcRMQJ4B8BIACkAHhWRFF2BERGR72rNyCUTQIFS6julVC2AjwE8oCcsIiLyZS2ucxGRhwHcq5R6xv7zJAB3KqVmXrPeFABT7D+mAchtebge1Q3AmfYO4jr4Ury+FCvgW/EmKqWC2zsIotbMRClOljXKVEqp9wG8DwAikuMrBV6+FCvgW/H6UqyAb8UrIjntHQMR0LrTYkUAohv8HAXgVOvCISKiG0FrkssuAPEiEisi/gDGA1ivJywiIvJlLT4tppSyiMhMAP8AYASwVCmV5+Jl77d0f+3Al2IFfCteX4oV8K14fSlWuoF5tHElERHdHFihT0RE2jG5EBGRdh5JLr7WJkZECkVkv4js9cZbO0VkqYiUikhug2WhIvJPETls/96lPWN0aCLWV0XkpP347hWRH7dnjA4iEi0iX4qIWUTyRGSWfbm3Htum4vXK40s3lza/5mJvE3MIwD2ov315F4BHlVL5bbrjVhCRQgAZSimvLJwTkR8AqALwkVIqzb7sDQBnlVLz7Qm8i1LqxfaM0x6Xs1hfBVCllFrYnrFdS0QiAEQopfaISDCA3QAeBDAZ3nlsm4p3HLzw+NLNxRMjF7aJ0UwptQ3A2WsWPwBgmf3xMtR/yLS7JmL1SkqpYqXUHvvjSgBmAJHw3mPbVLxE7c4TySUSwIkGPxfB+/8BKACbRWS3vX2NLwhXShUD9R86ALq3czyuzBSRffbTZl5xmqkhEYkB0AfATvjAsb0mXsDLjy/d+DyRXNxqE+NlBiul+qK+4/MM+6kd0uddAL0A3AGgGMCb7RvO1USkE4DPADynlKpo73hccRKvVx9fujl4Irn4XJsYpdQp+/dSAH9F/ak9b1diPwfvOBdf2s7xNEkpVaKUsiqlbACWwIuOr4j4of6DeqVSaq19sdceW2fxevPxpZuHJ5KLT7WJEZGO9oujEJGOAIbDNzo5rwfwhP3xEwDWtWMszXJ8UNs9BC85viIiAP4EwKyU+mODp7zy2DYVr7ceX7q5eKRC334r5P/F/28T879tvtMWEpHbUD9aAerb4/zF2+IVkVUA7kZ9K/gSAK8A+BuATwDcCuA4gLFKqXa/kN5ErHej/pSNAlAIYKrjmkZ7EpEhAL4CsB+Azb74ZdRfx/DGY9tUvI/CC48v3VzY/oWIiLRjhT4REWnH5EJERNoxuRARkXZMLkREpB2TCxERacfkQkRE2jG5EBGRdv8P9psFc+AYUvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 3.334 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 4.445 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 5.556 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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bf/jw4fw//elPLv9Sz8nJ6ZKUlGR2/uTl5QVMmjTp3KeffhrqXGf9+vXdJk2adH7dunUhBQUFnfbv32+xWCz5e/fuDfrss8+6AMCJEyc6/fjHPy4tKCjI69q1q+3DDz/s5mp/69evD33yySfPT5w48dyaNWtCG77Wu3fv2gEDBlQtWbKke0ufbfDgwZePHTvW6frlhw4dChARDB06NN5sNifPmzev2dk4d+zYEVRcXOx35MiRvMOHD+fPmjWr0am2goKCwCFDhlwz1cGAAQMuWyyWwMuXL8vs2bNjNmzYULBr165DpaWlfs51XnnlldsHDRpUZbFY8seMGXOhuLj4hsdwY7gQEUx+RlSUNT8NSuXZKpj8jK3afnZ29sH8/HzL5s2bjyxdurSn84u9KYmJidWPPPJI7JIlS0L9/Pxc/tmckZFRdfDgwXznT0pKSs2QIUOqy8vLTYWFhX47duwI7Nq1qy0+Pr5206ZNIdu2bQsxm83mlJQU89GjRzsdPHiwEwBERkbWDB48uBoA+vbte7mwsLDRbJtbt24NCg0NtSYkJNSOGTOmIi8vL6isrOyag/Haa68VL168uJfd7nrCNaemWgFWq1V27drV5ZNPPjm2c+fOQ1lZWd3Wr18f3NR2kpKSak6ePBnw1FNPRa9duzakW7dutmZ33GD/IoK9e/d2ioqKqklLS6sxGAx44oknrobTf/7zn+BnnnmmHAAmTJhwMSQkxK1tN8RwIbq1nET9kPnX/FjrbPaQsGa/7xHcowusdTabi/e32Js/JiamDgAiIyOtDzzwwIUdO3Z0bm79L7744sisWbPKdu/e3Tk9Pd3c0ummhkaPHn1+5cqV3VatWhU6duzYc0D9F+rzzz9f7AyiEydO5L7wwgtnAaDhdQ2j0aisVmujFtyKFStCv/nmm06RkZFpvXv3Trt06ZJxxYoV17RwUlNTa8xm8+Xly5e7bPk47dixI+jOO++svn55VFRU7cCBAysjIiKswcHB9u9973sXc3JygpraTlhYmC03Nzf/O9/5TuWSJUt6TpgwIeb6deLi4qqzs7Ov2cbu3buDkpKSqgG4nEvHyWBoWzwwXIgIApTfNTyp2RPr6cOTlBjkhofcr6ioMJw/f97gfPzFF1+E3HXXXY2+XJ1sNhuOHj3qP3r06MolS5YUVVZWGi9evOh2k2ny5MnnPv3009CsrKxukyZNOg8Ao0aNqlixYkWPixcvGgDg2LFjfs6h+1tis9mQlZUV+vXXX+edOnXqwKlTpw6sXr264JNPPgm9ft3XXnut+N133+3V1LYOHTrk/8orr0RNnz690ejSjzzySIXFYgmsrKw01NXVITs7OzglJaXJO8KKi4tNNpsNU6ZMuTB//vxTBw4caBREc+fOPfPqq69GnTlzxggA27dvD1yzZk33uXPnlt19991XioqK/PPy8gIA4KOPPrr6eQYOHFi5bNmy7gDw8ccfh1RUVNxwk5V3ixERTP6mkrtHJHcv+G+hy4v6EfFhSB+RrEx+xhsecr+oqMj0yCOPxAH1UyWPHTu2/NFHH23ylmar1SoTJ06MraysNCqlZPr06SU9evRodFrGec3F+fzll18ufvrpp89nZGRcuXTpkiE8PLy2d+/edQDwgx/8oCIvL6/TgAEDkgAgKCjIvmrVqmMmk6nFK9WfffZZcHh4eG1sbOzV5tOoUaMqn3nmmdjjx4/7NVw3IyPjSkpKyuW8vLyrX/QnT54MSE5ONjtvRZ4+fXrp9XeKAfUtkdmzZ5f07ds3WUQwbNiwixMmTLjYVF2FhYV+zz77bIzdbhcAeOONN4quX+eJJ564WFRU5D9w4MBkEVGdO3e2L1u27JjzuLz99tvHH3zwwbjQ0FDrPffcU2WxWAIBYMGCBafHjh17p9lsTh40aFBVRETEDU9rIG2ZuvRGRYfGqBdG/LLJ1z01/4a39G0gctLVz2XL1HW7lVIZDZft27evMD09/awbbw+pq7H22bfZIvu2HJTKs1UI7tEF6cOTVPqIZNWWfi50c9q3b1+P9PT0GFev+VzLxZuCgUHXOjxujY3a8oCW7WzBura8vcIvwJR/9/3mnukjkrub/IxGa53NJgYpd7RY2EOf3OZz4UJE7arG5Ge8OuS+XwC/Iqh1eEGfiIi0Y7gQEZF2DBciItKOJ1SJqKGAWmttuILq7mf0N9TZau0CKfc3+XPIfbohbLkQkVPIFWuN+a/5f+8x9dMXDKOWjcfUT18w/DX/7z2uWGvMAEJau+HIyMi0hIQEc1JSkjk1NTXZ1Tpvvvlm2DvvvNPi2FxO//jHP7qkpaUlx8bGpsTGxqY0N2ZZUFBQX1fLx44dG/PHP/7RZY/6uro6dOvWLX3WrFmRDZdnZmYmNvwM27ZtC8rMzEwE6oetDw4Ovjs5OdkcExOTmpGRkbh69equTdWVlZUVnJSUZI6Li0sZMGBAoqt17r333rizZ8+61Ylx7NixMZGRkWnO8db69u2b5M772gNbLkQEAAFXrDV9Xvr76wZL6eGrC4srS7D0vyvkq8Kd8ub3X+/TyRTgFUPunzhxwjRlypTYTz755OjQoUMvFxcXm4YPHx4fFRVV11zHwxuxbt26rrGxsTUbNmzo9vbbb59qOBxKeXm56eOPPw4ZP358o34/DacC2L59e+C4cePigoKCCh966KFr5po5e/ascc6cOXds2rTpSHx8fG1TIwZs3br1hqYVmD9/ftHTTz99/kbe0x7YciEi1FprwzfkfyYNg6UhS+lhbMzfJLW2Oq8Ycv+tt97q+dhjj5UPHTr0MgBERERYf/3rXxctXLiwFwAcPHjQ/+67705KTU1NnjNnzu3O99ntdjz55JN39OnTJ+W+++6LO3v2bJN/YK9evTp05syZJbfffnvt559/fs1YaLNnzy5ZsGDB7U2912nw4MHVP/3pT0+/8847jY7bBx98EPrAAw+cj4+PrwXqx11ztY3IyMi04uJiU0VFheG+++6LS0xMNMfHx6csXbq02THMnGw2GyIjI9Matn7uuOOO1JMnT5pOnz5tGjlyZJ/U1NTk1NTU5M2bN3cG6n8X48aNi8nMzEyMiopKmz9//g3/3tlyaQNv6cjX3OyeurkzikJLvOW4eRMdM6wCANa0uEY0gEZjUCmoLlmWzc2+caPlHzLGfH9PF++/DDcGrxw2bFi8iODpp58u+8lPftLsiAGLFy/u5ZzZ0dUpIYvFEvjkk09eM4TK0KFDLxcUFAQCwMyZM+947rnnymbPnl3+m9/85ur8LitWrLitoKAg4NChQ3lFRUV+aWlpKVOmTGk0FEtVVZVs3749eMWKFccvXLhgXLlyZejw4cMvOV//1re+VbVx48bbNm7cGNy1a9dmRwzOzMy8vHjx4kbjjR0+fLhTXV2dZGZmJl66dMkwY8aM0uZm4Vy3bl1Ir1696v79738XAEB5ebnLU2Xz5s2L+u1vfxsBAAkJCdUbNmw4NmLEiAurVq26bc6cOeWff/5556ioqNro6Gjr6NGjY+fOnVsycuTIqiNHjviPHDky/ptvvskDgIKCgk7bt28/dOHCBWNycnLqT3/607KAgAC3h3Rhy4WI4Gf0R0lV82elSqvOws/o1+w6TdE95L5j2PhGy52j/O7Zs6fL1KlTzwHA9OnTr35hb926NXj8+PHnTCYTYmJi6gYNGuRyWuSPP/74toEDB1YGBwfbJ02adH7Tpk3drNZrGxavvvpq8a9//euIlj57c0Ps79+/P2jLli1HtmzZcmThwoUR+/fvbzTcv1O/fv2qv/zyy5AZM2ZEbtq0qUv37t1dhtr8+fOLnKM/b9iw4RgATJw48dzatWtDAaDhaNHZ2dkhc+bMuSMpKck8evTouKqqKqNzkNERI0ZcCAwMVBEREdbQ0NC6oqKiG2qMMFyIbi0uh9yvs9Xaw7uENfvGnl16oM5W5xVD7icnJ1fv2rXrmm1kZ2cH9enT5+poywaDweW3enPDzDt99NFHodnZ2SGRkZFp/fv3N1+8eNGYlZV1zdwqY8aMqaypqTF89dVXzX6WXbt2BcXFxTUa3TgqKqp22LBhFSEhIfaIiAjrPffcU9ncEPt33XVXzZ49e/LT0tKqf/7zn0f+5Cc/aTHYnIYNG3bp+PHjAadPnzZt2rTptieeeOI8UB98OTk5FmcYlZaW7u/WrZsdABq2UoxGI1xNRdAchgsRQSDlDyaPaPaUx+jkkUrE4BVD7r/44otla9as6b59+/ZAoH5a3ldffTXqxRdfPAMA/fr1q1q6dGkoACxduvTqHWj33ntv5SeffBJqtVpx/Phxv//85z+NJuM6d+6cIScnp0tRUdF+5xD7CxYsOPHnP/+50RD7L7/8cvHbb7/d5BD7O3fuDFy4cOHts2bNajSa9KOPPnphx44dXerq6lBZWWn4+uuvu6SlpTV5XAoLC/2Cg4PtM2fOPPf888+X7N27t8kgup7BYMCoUaMuzJw5MzouLq66V69eNgAYOnRoxW9/+9ur11Ocx1MHXnMhIvib/EvGmEd1/6pwp8uL+sk9EzDafL/yN/p5xZD7vXv3rlu2bNmxadOmxVy6dMmglJIZM2aUTJw48SIALFmy5MSECRPuXLJkSfiYMWOu3jk1efLkC//6179CEhMTU2JjY69kZmY2Oi22cuXKboMHD64MDAy8GrYTJky48Prrr0dVV1efaLjuY489dvGNN9645nxZTk5Ol+TkZHN1dbWhe/fudQsXLjxx/Z1iANCvX78rw4cPv5iUlJRiMBgwefLksgEDBjQ5f8vu3bsDf/azn0UZDAaYTCa1ZMkSl9M/N7zmAgB79+61dOrUST3xxBPn7r333uTFixcXOl97//33Tz733HN3JCQkmG02m9xzzz2VgwcPPuFquzeqxSH3RWQZgAcBlCqlUh3LQlF/6TAGQCGA8UqpFm990zHkPjXmaxf0qTFvGXL/irWmz8b8TbLR8g8prTqLnl16YHTySDXafL/qZArgkPt0jbYOuf8nAO8A+LDBslcA/EsptUBEXnE8f7mNdRJRx6roZArIfyjl+z3HmO/v7mf0M9bZ6mwihnJHi4U99MltLYaLUmqbiMRct/ghAPc5Hi8H8G8wXIhuBjX+Rr+rQ+4HmJq8eYmoWa295hKulCoGAKVUsYg02cFGRKYBmAYA0dHRN9VpFR2no7TMvjm8zWW4bQZunt+fO3Sdrmqpb087TxZmt9vt0tTdU0St4Zhe2d7U6+1+t5hS6n2lVIZSKqNHWJND/xBR+8ktKyvr6pxrnait7Ha7lJWVdQWQ29Q6rW25lIhIhKPVEgHghu8gISLPsFqtz505c+aDM2fOpILdD0gPO4Bcq9X6XFMrtDZcNgB4CsACx7/rW7kdImpn/fv3LwUwpqProFtLi3/FiMhqADsAJIpIkYg8i/pQ+Z6IHAHwPcdzIiIiAO7dLdbUFdxhmmshIqKbBM+/EhGRdgwXIiLSjuFCRETatTi2mE79+vdT2dnZ7b4fXR3fWuKpSa88OXZYS26mTrA3o6DAoEZjixF1BLZciIhIO4YLERFpx3AhIiLtGC5ERKQdw4WIiLRjuBARkXYMFyIi0o7hQkRE2t2UnSiJblXsREnegi0XIiLSjuFCRETaMVyIiEg7hgsREWnHcCEiIu0YLkREpB3DhYiItDN5cmdlx841O/EVJ6KiW5WnJrgj8hS2XIiISDuGCxERacdwISIi7RguRESkHcOFiIi0Y7gQEZF2DBciItKO4UJERNpxsjCiZniqc+OGSSu1bIeThZG3YMuFiIi0Y7gQEZF2DBciItKO4UJERNq1aVRkESkEUAnABsDKC4lERAToGXL/O0qpsxq2Q0RENwmeFiMiIu3aGi4KwGYR2S0i01ytICLTRCRHRHLOlrGBQ0R0K2hTJ0oRuV0pdVpEegL4J4AfKaW2NbU+O1G2D10d/dzpyNfcTKI6ecuspJ76vO5w55iwEyV5iza1XJRSpx3/lgL4C4BMHUUREZFva3W4iEhnEQl2PgYwAkCursKIiMh3teVusXAAfxER53b+rJTapKUqIiLyaa0OF6XUNwDSNdZCREQ3Cd6KTERE2jFciIhIO4YLERFpx3AhIiLtPDoTZUhMN3XPvJGDBPYAAAT0SURBVO96bH9N0TXrnw6+1klPR4dNbzr+OnjTbJXsREnegi0XIiLSjuFCRETaMVyIiEg7hgsREWnHcCEiIu0YLkREpB3DhYiItGvLqMg3rGtFV4za8kC770fHRFM326RY7vTF+Gzl31pcR0cflZutr4wn/psGAHimOw2RFmy5EBGRdgwXIiLSjuFCRETaMVyIiEg7hgsREWnHcCEiIu0YLkREpB3DhYiItPNoJ8qLIRfx2fCWO+q11Qy0vWOiJ+oE9NTqDk91OnSn8+koaOh06KEOhW51+Bze8ire1OmTyBPYciEiIu0YLkREpB3DhYiItGO4EBGRdgwXIiLSjuFCRETaMVyIiEg7hgsREWknSimP7Sw6NEa9MOKXTb7uqVkZibyNjtk5AWDL1HW7lVIZWjZG1AZsuRARkXYMFyIi0o7hQkRE2jFciIhIuzaFi4jcLyKHRKRARF7RVRQREfm2VoeLiBgBvAtgFAAzgMdFxKyrMCIi8l1tablkAihQSn2jlKoF8BGAh/SURUREvqzV/VxE5FEA9yulnnM8nwzgHqXU7OvWmwZgmuNpKoDc1pfrUT0AnO3oIm6AL9XrS7UCvlVvolIquKOLIGrLTJTiYlmjpFJKvQ/gfQAQkRxf6eDlS7UCvlWvL9UK+Fa9IpLT0TUQAW07LVYEILrB8ygAp9tWDhER3QzaEi67AMSLSKyI+AOYAGCDnrKIiMiXtfq0mFLKKiKzAfwDgBHAMqVUXgtve7+1++sAvlQr4Fv1+lKtgG/V60u10k3MowNXEhHRrYE99ImISDuGCxERaeeRcPG1YWJEpFBEDojIXm+8tVNElolIqYjkNlgWKiL/FJEjjn+7dWSNTk3U+rqInHIc370i8v2OrNFJRKJF5AsRsYhInojMcSz31mPbVL1eeXzp1tLu11wcw8QcBvA91N++vAvA40qp/HbdcRuISCGADKWUV3acE5FvA6gC8KFSKtWx7E0A55RSCxwB3k0p9XJH1umoy1WtrwOoUkot6sjariciEQAilFJ7RCQYwG4ADwOYAu88tk3VOx5eeHzp1uKJlguHidFMKbUNwLnrFj8EYLnj8XLUf8l0uCZq9UpKqWKl1B7H40oAFgCR8N5j21S9RB3OE+ESCeBkg+dF8P7/ARSAzSKy2zF8jS8IV0oVA/VfOgB6dnA9LZktIvsdp8284jRTQyISA6AvgJ3wgWN7Xb2Alx9fuvl5IlzcGibGywxRSvVD/YjPsxyndkif3wPoA+BuAMUA3urYcq4lIl0AfArgeaVURUfX0xIX9Xr18aVbgyfCxeeGiVFKnXb8WwrgL6g/teftShzn4J3n4ks7uJ4mKaVKlFI2pZQdwFJ40fEVET/Uf1GvUkqtcyz22mPrql5vPr506/BEuPjUMDEi0tlxcRQi0hnACPjGSM4bADzlePwUgPUdWEuznF/UDo/AS46viAiAPwCwKKV+1+Alrzy2TdXrrceXbi0e6aHvuBXy/+L/DxPzv+2+01YSkTtR31oB6ofH+bO31SsiqwHch/qh4EsAvAbgrwA+BnAHgBMAximlOvxCehO13of6UzYKQCGA6c5rGh1JRIYC+BLAAQB2x+JXUX8dwxuPbVP1Pg4vPL50a+HwL0REpB176BMRkXYMFyIi0o7hQkRE2jFciIhIO4YLERFpx3AhIiLtGC5ERKTd/wNi1qq5HSJ6BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 6.667000000000001 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 7.7780000000000005 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 8.889 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 10.0 *******\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_arr=np.linspace(0.001,10,10)\n",
    "for temp in temp_arr:\n",
    "    print(f\"******* TEMP = {temp} *******\")\n",
    "    arr2 = equilibrate(25,temp=temp)\n",
    "    title=f\"T = {temp} K\"\n",
    "    visualise_toon(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_final_state(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_equilibration_snapshots(n,temp_min=0.001,temp_max=15,n_points=50,verbose=False):\n",
    "    temp_arr=np.linspace(temp_min,temp_max,n_points)\n",
    "    if verbose:\n",
    "        print(f\" TEMP = {temp} *******\")\n",
    "    for temp in temp_arr:\n",
    "        arr2 = equilibrate(n,temp=temp)        \n",
    "        title=f\"T = {temp} K\"\n",
    "        visualise_final_state(arr2,title=title,savefig=True,savename=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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16xOLxdZynGfOXbp0qV2/fv1agG9961vd2rVrV/voo49+lN3yP3P++ecfnD179qZbbrmld2rb6tWr87t3714Ti8X4zne+Uz527Nj9Bw8etC984Quls2fPLpowYUIVwJw5c4pHjx5d1atXr8PLly9fX1hY6Hv37o0NGjRo0KRJkz7u3r17HOC1114r+va3v70LYO/evbEZM2Z0HjRo0IHjrVlnzCJyjEQiUVJVWWnJoZysurqaqqoqSyQSnbO1z6lTp3b/0Y9+1Klu/c477+z+z//8z53mzJlz+kUXXdR/9OjRffr06TNo8uTJPROJBAAvvfRS0dChQweUlZUNHDt27DlVVVXHZNrw4cMPDRkyJO0bmTt3bvHll19eWVxcnBg7dux+gMLCQh88ePCBrVu3tqnr96c//al43LhxlYWFhV5YWOgAhw4diiUSCaurpaKiIgegpKSkFuDee+8968EHH9yZn5+fON5joWAWad16AKVplk5VVVUNDitUVVYa0DnNa3s09Lr63HXXXRXPP/98RwjOzOfNm9f+tttu2wuwatWq05544okPN2zYsOa9994reP7558/Yvn177k9+8pOuS5Ys2bh27dp1gwcPPvDDH/7wuD4oFi5cWDRu3Lij/lTe7t27cxYvXlw8ZsyYTwAOHz7M1q1b8+vCfcOGDXn9+/cv69Onz7n333//zh49esQB5syZUzRq1KgqgL/85S9tKyoqcidOnJj6Z/gyoqEMETmGmRGPxxvsE4/HCf7AUXYMGjSoul27drVvvfVW4ebNm/OGDh36aadOnWoBzjvvvE9LS0trACZOnLh3yZIl7QA2bdpUcOGFFw4AOHz4sI0YMWJ/pvs7cOCAVVRUtOnXr19N3XPV1dU2fvz4c6ZOnVrev3//GoBFixa1u+CCC45st7S0tGbjxo1rP/jggzZXX31138mTJ+/r2rVr/NVXXy2eNm3artraWu6///4eL7zwwgcneiwUzCKt24fpnnT3Ybm5ubGGwjk3Nxd3rzWzDdkq5qabbqqYOXNmh61bt+bfcccdu+ueT/0AMDPcnUsvvbRqzpw5JxSACxYsOH3kyJGf1K0nEgkmTZrUa+DAgQcfeuihI/t+5ZVXiseMGVOZ+vqzzz77cN++fQ8tWrSo3eTJkz9etWpV2y9+8YsHKioqcjZv3lwwatSoUoCKioo248eP7zd79uz3LrnkkoOZ1KahDBFJZ09RUVGD1zoXFRc7sCebO73pppv2LVy48Iy1a9cWJg8xrFy58rT33nsvLx6PM2vWrPZf+tKX9v/DP/zD/jfffLPd2rVr8wCqqqpiq1evzs90X/Pnzy8eO3bskcCdNm1a9+rq6thTTz21Lbnf66+/fvpVV131CQRXZxw4cMAguLpkxYoVp5WVlR3629/+VjhgwICDOTk5lJSU1O7bt++d7du3r96+ffvqwYMHfzpnzpyMQxkUzCKSRiwW+6iouNjz89PnXH5+PkVFRR6LxdLO9XCi2rZt6xdddNEnEyZM2JuTk3Pk+WHDhu2/++67zyotLR10zjnnHLrhhhs+7tGjR/yXv/zlluuuu65PaWlp2YUXXjhgzZo1BanbfOaZZ84sKSkZsnr16tOuvvrq/qNGjeoLsHTp0naXX375fgjGjadPn95lw4YNhYMGDSobMGBA2c9//vMOW7duzW3Xrl1tUVFRAuDtt98uHDp06MDS0tKyL3/5y6X33Xdf+fDhww/Nmzev+PLLLz/mrPpEaShDRNKpjsVim7t269anqqrKqiorLR6Pk5ubS1FxsYehvJnPeZPJz372sx3J67W1tbz99tunzZkzZ1Py823btk0sWLDg/dTXT5gwoarukrb63HrrrftuvfXWfcnPbdy4Ma+kpORw3RUWpaWlNe6+PPW1jz32WIfLLrvsyPYnTpxYNXHixLWp/RYvXlw0a9asY+oDWL58+XEP9SiYRaQ+VbFYbG1RUVHnoqKiDmaW4+61BHf+7SLLd/699dZbhRMmTOh79dVX7ysrK6tp/BUnrn///jWLFy/e1Fi/u+++O6OhmqVLl278/FV9RsEsIg2pjsViHxJ+SZjNqzBSjRgx4uD27dtXpz4/fvz4T8aPH/9JutecqjTGLCISMQpmEZGIUTCLiESMxphFpCH5NTWJEnfv0KZNLHb4cCJhZnvy8jTtZ1NSMItIfYoOHartM2v+DpuzYId9tOsQJZ0LYuPHdOv41Su7dSgoyNkMHPdcEOXl5TnJd8XFYjFv3759HGDlypXrCgoKsjqJ/913393t97//fce6ffzgBz/Ydu21157QHBbNpdFgNrNngKuAXe5+zJ9CMbNRwH8AdbdFznL3R7NZpIg0u/xDh2r73PO/V8XWbPgsw3aUH+KXz75vf3mjwn7+/SF9CgpyIj/tJ8C0adPKH3744azeDNOUMhljfg64opE+S9x9aLgolEVOcjU1iZJZ83dYcignW7Ohitnzd1hNTfSn/TwZNfom3P2vwN5mqEVEml/aaT/dvdOcBTsavGh59oIdlnA/Kab9fOqpp0r69+9f9rWvfa3Xnj17ctL1iZJsfbqMNLN3zGyBmQ2qr5OZTTGzZWa2bO9eZb1IVLVpE+OjXYca7PPR7mry2mTvBDV52s8//OEPxemm/czNzT0y7efixYvb1U37OWDAgLKXX365w5YtW46Z3OOBBx7YtWXLltXr1q1b2759+/i0adPOylrRTSQbX/6tAHq5+34zuxKYA/RL19HdZwAzAM4991z9lV6Rlpd22s/DhxPDSjoXxHaU1x/OJZ3yqTmcqC3Iz4n0tJ91E9kDTJ06tWLixIl9slVvU/ncH3fuXuXu+8PH84E2ZtaxkZeJSISZ2Z7xY7o1ePI0YUw3j5lFftrPLVu2HPkTUS+++OIZpaWlGU+/2VI+9xmzmXUBPnJ3N7MRBGGf1f9ZItK88vJiH331ym4d/vJGRdovAAeVFjHhym6el9c0036WlJQcTjft58aNGwtHjhz5yQ033PBxLBajbtrPw4cPG8Ajjzyy/dxzzz3qKpFp06adtWHDhkKAnj17Vj/zzDNbsllzU8jkcrkXgFFARzPbBnwPaAPg7k8CE4E7zSwOHAQmubuGKURObtUFBTmbf/79IX1mz99hsxfssI92V1PSKZ8JY7r5hCu7eXgdc+Sn/Zw3b94J/4mnltJoMLv79Y20Pw48nrWKRCQqqgoKctZee1X3zhPGduuQ1yaWU3M4URsL7vw7qaf9jDrd+SciDanOy/ts2s+C/Ka70kzTfn7mlLgYW0TkVKJgFhGJGAWziEjEaIxZRBqSX3uougT3DrH8vFiiuiaB2Z6cgnxN+9mEFMwiUp+i+IGDfbZM/51tnfGCHdyyg8Je3WI9p1zfsdedN3TIbVt4Ukz7CfDII490fvbZZzvn5OT4mDFjPn788ce3Z3sf2aRgFpF08uMHDvZ58/KbYx8vXXnkyQPvf8j6B39s5XP+ZBe9+lyf3LaFkZ/2c/bs2UULFy4sXrdu3ZrCwkLfvn175HNPY8wicozaQ9UlW6b/zpJDOdnHS1ey5cnfWW11TeSn/Zw+fXqnBx98sLywsNABunfvHk/tEzUKZpHWLe20n7h32jrjhQan/dz61AtGbSLy035+8MEHBYsXLz59yJAhA0aMGFG6ZMmStidSX3OK/Cm9iDS/WH4eB7fsaLDPwa07iRXkZW2fydN+bt68OS/dtJ/AkWk/Aeqm/QQ4fPiwjRgxYn/qduPxOJWVlTmrVq1av2jRotNuvPHGc7Zu3fpu1gpvAgpmkdYt7bSfieqaYYW9usUOvJ+2GYDCnl1JHKqpzWlbEOlpP7t27Xr4uuuu2wcwevToT+PxuO3evTunLvSjSEMZInIssz09p1zf4NURPe+43smJRX7az6uuumrfwoULTwdYsWJFAUCUQxl0xiwiaeQU5H/U684bOpTP+VPaLwDPuHgovb5xg+fk50V+2s/77ruv4rrrruvdr1+/QXl5eYmZM2dGfrY5BbOIpFOd27Zw80WvPtdny5O/s61PvWAHt+6ksGdXet5xvff6xg0eXscc+Wk/CwsL/WSb+lPBLCL1qcptW7i299TJnXt/48YOsYK8nMShmlpyYnvCM2VN+9lEFMwi0pDqnPy8I9N+5rQtaLIdadrPz+jLP5HWJ5FIJBq8RlmaVnj8E/W1K5hFWp93d+/eXaxwbhmJRMJ2795dDNR7LbWGMkRamXg8fnt5efmvysvLB6OTs5aQAN6Nx+O319dBwSzSygwfPnwXcE1L1yH106eliEjEKJhFRCJGwSwiEjEKZhGRiFEwi4hEjIJZRCRiFMwiIhHTaDCb2TNmtsvM0t6lYoHHzGyTma0ys/OzX6aISOuRyRnzc8AVDbSPAfqFyxRg+ucvS0Sk9Wo0mN39r8DeBrqMA37jgaXAGWbWNVsFioi0NtkYY+7O0X83bFv43DHMbIqZLTOzZXv3NpT1IiKtVzbmykg3Q1XavxXm7jOAGQDtzij1r9+7LQu7PzV99z+ntHQJkTdw/fyWLkGkSWTjjHkb0CNp/Syg4b97LiIi9cpGMM8Fvh5enXExUOnuO7OwXRGRVqnRoQwzewEYBXQ0s23A94A2AO7+JDAfuBLYBBwAbmmqYkVEWoNGg9ndr2+k3YGpWatIRKSV051/IiIRo2AWEYkYBbOISMQomEVEIkbBLCISMQpmEZGIUTCLiESMgllEJGIUzCIiEaNgFhGJGAWziEjEKJhFRCJGwSwiEjEKZhGRiFEwi4hEjIJZRCRiFMwiIhGjYBYRiRgFs4hIxCiYRUQiRsEsIhIxCmYRkYhRMIuIRIyCWUQkYhTMIiIRo2AWEYkYBbOISMRkFMxmdoWZbTCzTWb2YJr2m81st5mtDJfbs1+qiEjrkNtYBzPLAZ4AvgJsA/5uZnPdfW1K1xfdfVoT1Cgi0qpkcsY8Atjk7u+7ew3we2Bc05YlItJ6ZRLM3YEPk9a3hc+lutbMVpnZy2bWIyvViYi0QpkEs6V5zlPW5wG93X0IsAj4ddoNmU0xs2VmtixeU3l8lYqItBKZBPM2IPkM+CxgR3IHd9/j7tXh6kxgeLoNufsMd7/A3S/IzSs+kXpFRE55mQTz34F+Zna2meUBk4C5yR3MrGvS6jXAuuyVKCLSujR6VYa7x81sGvAqkAM84+5rzOxRYJm7zwXuNrNrgDiwF7i5CWsWETmlNRrMAO4+H5if8tzDSY+/C3w3u6WJiLROuvNPRCRiFMwiIhGjYBYRiRgFs4hIxCiYRUQiRsEsIhIxCmYRkYhRMIuIRIyCWUQkYhTMIiIRo2AWEYkYBbOISMQomEVEIkbBLCISMQpmEZGIUTCLiESMgllEJGIUzCIiEaNgFhGJGAWziEjEKJhFRCJGwSwiEjEKZhGRiFEwi4hEjIJZRCRiFMwiIhGjYBYRiRgFs4hIxGQUzGZ2hZltMLNNZvZgmvZ8M3sxbH/TzHpnu1ARkdai0WA2sxzgCWAMUAZcb2ZlKd1uA/a5e1/gX4F/yXahIiKtRSZnzCOATe7+vrvXAL8HxqX0GQf8Onz8MnCZmVn2yhQRaT1yM+jTHfgwaX0bcFF9fdw9bmaVQAegIrmTmU0BpoSr1f/1yqh3T6ToJtKRlHpb0lXBfyJVE1Grp2/faNUTteMTKG3pAuT4ZRLM6c58/QT64O4zgBkAZrbM3S/IYP/NImr1QPRqUj0Ni1o9ENTU0jXI8ctkKGMb0CNp/SxgR319zCwXKAb2ZqNAEZHWJpNg/jvQz8zONrM8YBIwN6XPXOCm8PFEYLG7H3PGLCIijWt0KCMcM54GvArkAM+4+xozexRY5u5zgaeB35rZJoIz5UkZ7HvG56i7KUStHoheTaqnYVGrB6JZkzTCdGIrIhItuvNPRCRiFMwiIhHT5MEctdu5M6jnZjPbbWYrw+X2Jq7nGTPbZWZpr+m2wGNhvavM7PwWrmeUmVUmHZ+Hm7ieHmb2mpmtM7M1ZnZPmj7NdowyrKfZjpGZFZjZW2b2TljPI2n6aMqEk427N9lC8GXhZuAcIA94ByhL6XMX8GT4eBLwYgvXczPweFMel5T9fRk4H3i3nvYrgQUE14pfDLzZwvWMAl5pxuPTFTg/fHw6sKB4/+wAAAJSSURBVDHN/7NmO0YZ1tNsxyh8z+3Cx22AN4GLU/o0278xLdlZmvqMOWq3c2dST7Ny97/S8DXf44DfeGApcIaZdW3BepqVu+909xXh40+AdQR3miZrtmOUYT3NJnzP+8PVNuGS+o2+pkw4yTR1MKe7nTv1h/io27mButu5W6oegGvDX4lfNrMeadqbU6Y1N6eR4a/OC8xsUHPtNPwVfBjBWWGyFjlGDdQDzXiMzCzHzFYCu4CF7l7v8WmGf2OSBU0dzFm7nTtLMtnXPKC3uw8BFvHZmUZLac7jk4kVQC93Pw/4BTCnOXZqZu2APwL3untVanOalzTpMWqknmY9Ru5e6+5DCe7KHWFmg1PLTfeypqxJPp+mDuao3c7daD3uvsfdq8PVmcDwJqolU5kcw2bj7lV1vzq7+3ygjZl1bMp9mlkbghB83t1npenSrMeosXpa4hiF+/oY+DNwRUqTpkw4yTR1MEftdu5G60kZm7yGYAyxJc0Fvh5eeXAxUOnuO1uqGDPrUjc+aWYjCH6G9jTh/ozgztJ17v6zero12zHKpJ7mPEZm1snMzggfFwKjgfUp3TRlwkkmk9nlTpg33e3cTVnP3WZ2DRAP67m5qeoBMLMXCL7F72hm24DvEXyBg7s/CcwnuOpgE3AAuKWF65kI3GlmceAgMKmJ/5FfAkwGVofjqAAPAT2TamrOY5RJPc15jLoCv7bgD1rEgJfc/ZWW+jcm2aFbskVEIkZ3/omIRIyCWUQkYhTMIiIRo2AWEYkYBbOISMQomEVEIkbBLCISMf8fvs4JEFyE4XsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-208-fd8e87097bc7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msave_equilibration_snapshots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-205-e8c0d5df7be1>\u001b[0m in \u001b[0;36msave_equilibration_snapshots\u001b[1;34m(n, temp_min, temp_max, n_points, verbose)\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[0marr2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mequilibrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mf\"T = {temp} K\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m         \u001b[0mvisualise_final_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msavename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-204-c972169aabea>\u001b[0m in \u001b[0;36mvisualise_final_state\u001b[1;34m(arr, title, savefig, savename, show)\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{savename}.png\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     38\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    267\u001b[0m     \"\"\"\n\u001b[0;32m    268\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 269\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     41\u001b[0m             display(\n\u001b[0;32m     42\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m                 \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\display.py\u001b[0m in \u001b[0;36mdisplay\u001b[1;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[0;32m    311\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    312\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 313\u001b[1;33m             \u001b[0mformat_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    314\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m                 \u001b[1;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36mformat\u001b[1;34m(self, obj, include, exclude)\u001b[0m\n\u001b[0;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                 \u001b[1;31m# FIXME: log the exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[1;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    222\u001b[0m     \u001b[1;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[1;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m   2089\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2090\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2091\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2092\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2093\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 527\u001b[1;33m             \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    528\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    386\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1707\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1708\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1709\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1711\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2647\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2648\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2649\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\legend.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    668\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegendPatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    669\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legend_box\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    671\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'legend'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\offsetbox.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_offset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpy\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m             \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m         \u001b[0mbbox_artist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\offsetbox.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_offset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpy\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m             \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m         \u001b[0mbbox_artist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\offsetbox.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    304\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m             \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_offset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpy\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    307\u001b[0m             \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\offsetbox.py\u001b[0m in \u001b[0;36mset_offset\u001b[1;34m(self, xy)\u001b[0m\n\u001b[0;32m    680\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moffset_transform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 682\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moffset_transform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxy\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    683\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\transforms.py\u001b[0m in \u001b[0;36mtranslate\u001b[1;34m(self, tx, ty)\u001b[0m\n\u001b[0;32m   1970\u001b[0m         translate_mtx = np.array(\n\u001b[0;32m   1971\u001b[0m             [[1.0, 0.0, tx], [0.0, 1.0, ty], [0.0, 0.0, 1.0]], float)\n\u001b[1;32m-> 1972\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtranslate_mtx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1973\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1974\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "save_equilibration_snapshots(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('text', usetex=True)\n",
    "fig, axs = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 2.8))\n",
    "axs[0].scatter(temperature,ene,linestyle='-', marker='.', color='m',s = 5)\n",
    "axs[1].scatter(temperature,mag,linestyle='-', marker='.', color='c', s = 5)\n",
    "\n",
    "#axs[0].text(245, 0.95, \" $\\gamma = 0.15 $\", size=8,\n",
    "#         ha=\"right\", va=\"top\",\n",
    "#         bbox=dict(fc=\"none\"))\n",
    "#axs[1].text(3.1, 0.97, \" $\\gamma = 0.15 $\", size=8,\n",
    "#         ha=\"right\", va=\"top\",\n",
    "#         bbox=dict(fc=\"none\"))\n",
    "\n",
    "axs[0].set_ylabel( r'Energy', rotation=90, fontsize = 17)\n",
    "axs[1].set_ylabel( r'Magnetization', rotation=90, labelpad = 12, fontsize = 17)\n",
    "axs[0].set_xlabel( r'Temperature(K)', rotation=0, fontsize = 14)\n",
    "axs[1].set_xlabel( r'Temperature(K)', rotation=0, fontsize = 14)\n",
    "plt.savefig('2D_Ising_EandM.jpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_arr=np.linspace(0.001,15,200)\n",
    "for temp in temp_arr:\n",
    "    print(f\"******* TEMP = {temp} *******\")\n",
    "    arr2 = equilibrate(25,temp=temp)\n",
    "    percent_occ = -calculate_energy(arr2,eps=1)/(len(arr2)*len(arr2))*100\n",
    "    print(f\"Total RedBlue = {percent_occ} %\")\n",
    "    title=f\"T = {temp} K\"\n",
    "    visualise_final_state(arr2,title=title,savefig=True,savename=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr2 = equilibrate(25,temp=0.1)\n",
    "# visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr2 = equilibrate(25,temp=0.1)\n",
    "# visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr2 = equilibrate(25,temp=1)\n",
    "# visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_arr=np.linspace(0.1,4.5,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr2 = equilibrate(25,temp=1)\n",
    "# visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr2 = equilibrate(10,temp=0.15)\n",
    "visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr2 = equilibrate(10,temp=0.2)\n",
    "visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = equilibrate(10,temp=0.2)\n",
    "visualise_atom_config_with_bonds(arr2)\n",
    "visualise_final_state(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* TEMP = 0.1 *******\n",
      "******* TEMP = 0.3315789473684211 *******\n",
      "******* TEMP = 0.5631578947368422 *******\n",
      "******* TEMP = 0.7947368421052632 *******\n",
      "******* TEMP = 1.0263157894736843 *******\n",
      "******* TEMP = 1.2578947368421054 *******\n",
      "******* TEMP = 1.4894736842105265 *******\n",
      "******* TEMP = 1.7210526315789476 *******\n",
      "******* TEMP = 1.9526315789473687 *******\n",
      "******* TEMP = 2.18421052631579 *******\n",
      "******* TEMP = 2.4157894736842107 *******\n",
      "******* TEMP = 2.647368421052632 *******\n",
      "******* TEMP = 2.878947368421053 *******\n",
      "******* TEMP = 3.1105263157894742 *******\n",
      "******* TEMP = 3.342105263157895 *******\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\numerictypes.py\u001b[0m in \u001b[0;36missubclass_\u001b[1;34m(arg1, arg2)\u001b[0m\n\u001b[0;32m    327\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0missubclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    329\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: issubclass() arg 1 must be a class",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-92-db545a0b8cc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtemp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp_arr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"******* TEMP = {temp} *******\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0msp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0men\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_sp_heat_and_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbreaking_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0msp_heat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0menergy_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0men\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-59-71ae98824ae0>\u001b[0m in \u001b[0;36mget_sp_heat_and_energy\u001b[1;34m(n, temp, breaking_iters)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_sp_heat_and_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbreaking_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0marr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mequilibrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbreaking_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbreaking_iters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0menergy_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0marr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetropolis_move\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-52-0a897f909cef>\u001b[0m in \u001b[0;36mequilibrate\u001b[1;34m(n, temp, breaking_iters, return_polarlist, verbose)\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Pol = {polval} and iter = {i}\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\\r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0marr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetropolis_move\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m         \u001b[0mnewpolval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculate_polarization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mpolar_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpolval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-49-aa44bfe1609a>\u001b[0m in \u001b[0;36mmetropolis_move\u001b[1;34m(arr, temp, verbose)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     '''\n\u001b[1;32m----> 9\u001b[1;33m     \u001b[0mnew\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlong_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m     \u001b[0mold_energy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculate_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0meps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mnew_energy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculate_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0meps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-7-8826b45a2a1d>\u001b[0m in \u001b[0;36mlong_loop\u001b[1;34m(arr2, verbose)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m             \u001b[0mout_choice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutgoing_state_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     55\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mout_choice\u001b[0m \u001b[1;33m!=\u001b[0m\u001b[0mforbidden_choice\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.randint\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_dtype.py\u001b[0m in \u001b[0;36m_name_get\u001b[1;34m(dtype)\u001b[0m\n\u001b[0;32m    345\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    346\u001b[0m     \u001b[1;31m# append bit counts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 347\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0m_name_includes_bit_suffix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    348\u001b[0m         \u001b[0mname\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m\"{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitemsize\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_dtype.py\u001b[0m in \u001b[0;36m_name_includes_bit_suffix\u001b[1;34m(dtype)\u001b[0m\n\u001b[0;32m    324\u001b[0m         \u001b[1;31m# implied\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m     \u001b[1;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflexible\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0m_isunsized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    327\u001b[0m         \u001b[1;31m# unspecified\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\numerictypes.py\u001b[0m in \u001b[0;36missubdtype\u001b[1;34m(arg1, arg2)\u001b[0m\n\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m     \"\"\"\n\u001b[1;32m--> 392\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0missubclass_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgeneric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m         \u001b[0marg1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0missubclass_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgeneric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\numerictypes.py\u001b[0m in \u001b[0;36missubclass_\u001b[1;34m(arg1, arg2)\u001b[0m\n\u001b[0;32m    327\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0missubclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 329\u001b[1;33m     \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    330\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "temp_arr=np.linspace(0.1,4.5,20)\n",
    "sp_heat=[]\n",
    "energy_list=[]\n",
    "for temp in temp_arr:\n",
    "    print(f\"******* TEMP = {temp} *******\")\n",
    "    sp,en = get_sp_heat_and_energy(n=3,temp=temp,breaking_iters=4000)\n",
    "    sp_heat.append(sp)\n",
    "    energy_list.append(en)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.0,\n",
       " 0.3503459787851437,\n",
       " 0.12075025949154385,\n",
       " 0.03797501643655483,\n",
       " 0.06951909105232765,\n",
       " 0.21353460941369376,\n",
       " 0.03209519088990509,\n",
       " 0.10029453433206671,\n",
       " 0.062407741810616035,\n",
       " 0.023600482878538376,\n",
       " 0.0774671783744188,\n",
       " 0.008579658737240854,\n",
       " 0.04444272662984814,\n",
       " 0.03571577489821648,\n",
       " 0.020755001543608192,\n",
       " 0.026209280738415327,\n",
       " 0.028023074826601664,\n",
       " 0.030392481513184674,\n",
       " 0.0303670781893004]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_heat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(temp_arr,sp_heat,c=\"red\")\n",
    "plt.plot(temp_arr,sp_heat)\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.ylabel(\"Specific Heat\")\n",
    "plt.title(\"T vs C\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(temp_arr,energy_list,c=\"green\")\n",
    "plt.plot(temp_arr,energy_list)\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.ylabel(\"Energy\")\n",
    "plt.title(\"E vs C\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_spec_heat(arr,temperature,energy,eps=1):#eps is energy for config 5\n",
    "#     '''\n",
    "#     Author: Vinay Patil\n",
    "#     ~Function Description~\n",
    "#     '''\n",
    "# #     kb=1.38064 *10**(-23)\n",
    "#     kb=1\n",
    "#     diversion=np.zeros(n)\n",
    "#     energy_div=np.zeros(n)\n",
    "#     spec_heat=np.zeros(n)\n",
    "#     n=len(temperature)\n",
    "#     N=len(arr)\n",
    "\n",
    "#     for i in range(n):\n",
    "#         if i==0:\n",
    "#             spec_heat[i]=abs(energy[i+1]-energy[i])/h #O(h) differentiation at the end points\n",
    "#         elif i==n-1:\n",
    "#             spec_heat[i]=abs(energy[i]-energy[i-1])/h #O(h) differentiation at the end points\n",
    "#         else:\n",
    "#             spec_heat[i]=abs((energy[i+1]-energy[i-1])/2*h) #O(h^2) differentiation\n",
    "#             diversion[i]=abs((spec_heat[i]/N**2)-kb*((ln2)**2)*(28/45))\n",
    "#             energy_div[i]=abs((energy[i]/N**2)-(eps/3))\n",
    "\n",
    "#     minpos = diversion.index(min(diversion))\n",
    "#     print(\"The critical temp found is at\",temp_plot[minpos]) #ye minpos +1 kar\n",
    "#     print(\"The error in Specific Heat per vertex is\",min(diversion))                                \n",
    "#     plt.scatter(temperature, spec_heat)\n",
    "#     plt.plot(temperature, spec_heat)\n",
    "#     plt.xlabel(\"Temperature\")\n",
    "#     plt.ylabel(\"Specific Heat\")\n",
    "#     plt.title(\"T vs C\")\n",
    "#     plt.show()\n",
    "#     return spec_heat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ERROR MODELLING: TRUE AND APPROX ERROR COMPARISONS FOR N=1,2,3,4,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_val_dict = {1:4,2:18,3:148,4:2940,5:142815}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_states_err(num,error_threshold,return_dict = False,verbose=False):\n",
    "    true_err_list = []\n",
    "    approx_err_list = []\n",
    "    if not (error_threshold<=100 and error_threshold>0):\n",
    "        print(\"Error! Please input error_threshold as a value between 0 and 100\")\n",
    "        assert (error_threshold<=100 and error_threshold>0)\n",
    "    state_dict={}\n",
    "    oldarr = long_loop(initialise_state(num), verbose=False)\n",
    "    good_iterations = 0           #Iterations that gave us a new state, so good.\n",
    "    bad_iterations = 0               #Iterations that gave us an already found state,so a waste and hence bad.\n",
    "    while True:\n",
    "        newarr = long_loop(oldarr,verbose=False)\n",
    "        name =arr_to_string(newarr)\n",
    "        if name not in state_dict:\n",
    "            count_repetitions=0\n",
    "            state_dict[name]=1\n",
    "            good_iterations+=1\n",
    "        else:\n",
    "            bad_iterations+=1\n",
    "            count_repetitions+=1\n",
    "            state_dict[name]+=1\n",
    "        percent_approx_err=good_iterations*100/(good_iterations+bad_iterations)\n",
    "        if verbose:\n",
    "            print(f\"Good iterations = {good_iterations} and bad iterations = {bad_iterations} and Error % = {percent_approx_err}\", end=\"\\r\",flush=True)\n",
    "        true_err_list.append((true_val_dict[num] - good_iterations)*100/true_val_dict[num])\n",
    "        approx_err_list.append(percent_approx_err)\n",
    "        if percent_approx_err < error_threshold:\n",
    "            break\n",
    "        oldarr=newarr\n",
    "    if return_dict:\n",
    "        return len(state_dict),state_dict,true_err_list,approx_err_list\n",
    "    else:\n",
    "        return len(state_dict),true_err_list,approx_err_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_states_err(4,5,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************ N = 1 ************\n",
      "Completed in 0.0 seconds.\n",
      "************ N = 2 ************\n",
      "Completed in 0.03125 seconds.\n",
      "************ N = 3 ************\n",
      "Completed in 0.453125 seconds.\n",
      "************ N = 4 ************\n",
      "Completed in 13.65625 seconds.\n",
      "************ N = 5 ************\n",
      "Completed in 885.5 seconds.\n"
     ]
    }
   ],
   "source": [
    "errorlistdict={}\n",
    "for n in true_val_dict.keys():\n",
    "    t1=time.process_time()\n",
    "    print(f\"************ N = {n} ************\")\n",
    "    cnt, true_err_list,approx_err_list = count_states_err(n,5)\n",
    "    errorlistdict[n]=[true_err_list,approx_err_list]\n",
    "    t2=time.process_time()\n",
    "    print(f\"Completed in {t2-t1} seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sid\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:132: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n in errorlistdict.keys():\n",
    "    true=errorlistdict[n][0]\n",
    "    approx=errorlistdict[n][1]\n",
    "    plt.plot(true, label='True Error')\n",
    "    plt.plot(approx, label='Approx Error')\n",
    "    plt.legend()\n",
    "    plt.title(f\"For {n}x{n} grid\")\n",
    "    plt.xlabel(\"No. of iterations\")\n",
    "    plt.ylabel(\"% Error\")\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}