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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2041,
+     "status": "ok",
+     "timestamp": 1613645397401,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "KBUdLfK8W14c"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from scipy import integrate, optimize\n",
+    "import time\n",
+    "from tqdm import tqdm\n",
+    "from matplotlib import animation\n",
+    "from IPython.display import HTML\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2029,
+     "status": "ok",
+     "timestamp": 1613645397405,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "2xE5VZejvJI6"
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C3edXWAuW14l"
+   },
+   "source": [
+    "# Cellular Automata S.I.R. Model\n",
+    "\n",
+    "### Description\n",
+    "\n",
+    "Based on the reference [paper](https://www.math.uh.edu/~zpkilpat/teaching/math4309/project/physa99_fuentes.pdf), the aim of the project is to simulate and then study the evolution of Susceptible-Infected-Removed populations over space and time.\n",
+    "\n",
+    "### Assignments\n",
+    "1. Consider a grid of a given form (squared or exagonal or whatever) and then let propagate the SIR model. \n",
+    "2. Repeat point 1 several times in order to obtain several reference datasets\n",
+    "3. Try to predict the evolution of the epidemic both in space and time on the basis of the previous steps, e.g. by means of \n",
+    "4. Define a metric on how the epidemic evolved e.g. assigning a probability for a given node not to get infected or predict the fraction of the nodes that got infected at the end of the epidemic\n",
+    "\n",
+    "### Contacts\n",
+    "* Leonardo Badia <badia@dei.unipd.it>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIR Model and Cellular Automata introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following project will be analized the $ SIR $ model for the evolution of infectivity nucleus inside a finite population exploiting Cellular Automata technique.\n",
+    "In the following project, the $ SIR $ model for the evolution of infectivity nucleus will be analyzed inside a finite population exploiting the Cellular Automata technique.\n",
+    "### SIR Model\n",
+    "The SIR model represents the evolution of an infection that is transmitted from person to person, where the healed persons can't take the infection anymore and the overall population sampled is divided into three groups: \n",
+    "* Susceptibles: containing the individuals that have not yet been infected and still can contract the infection; \n",
+    "* Infected: that represents the individuals that are currently infected and that can infect other people;\n",
+    "* Removed: that are the dead individuals or all the ones that recovered from the illness and are now immune.\n",
+    "\n",
+    "Partial differential equations theoretically describe SIR models:\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI + \\mu - \\mu S;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I -\\mu I;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I - \\mu R;\n",
+    "$$\n",
+    "\n",
+    "where $\\lambda$ is a parameter describing the rate of interactions between susceptibles and infectious, $\\gamma$ is the recovery rate and $\\mu$ is a parameter related to vital dynamics of the population (birth-death rate).\n",
+    "\n",
+    "### Cellular Automata\n",
+    "An alternative approach is by using Cellular Automata, which provides much better efficiency in terms of computational time. This is a discrete-time approach using matrices whose elements are configured in networks.\n",
+    "\n",
+    "A value describing its epidemiological state(S=0, I=1, R=-1) is assigned to every element of the matrix, which changes with respect to the states of neighbour elements, according to a fixed radius.\n",
+    "\n",
+    "The change of state from S to I depends, just as for the differential equation model, on the interaction between an infected element and its neighbours, which is based on an infection probability $p_i$.\n",
+    "\n",
+    "Once infected, the element recovers after a fixed number of iterations representing the total recovery time (which takes into account incubation, proper infection and latency times). \n",
+    "In this case, the vital dynamics are not considered ($\\mu=0$), so the equations governing the epidemic become:\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I;\n",
+    "$$\n",
+    "\n",
+    "Eventually, thanks to several simulations and Cellular Automata efficiency, an epidemiological spread can be performed, allowing to obtain accurate predictions in time and space.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 229842,
+     "status": "ok",
+     "timestamp": 1613645625241,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "mQl_wl7RW14m",
+    "outputId": "82b2c037-cd3d-4111-f242-ca0d0c231f47",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def draw_line(mat, point1, point2):\n",
+    "    \"\"\"\n",
+    "        function that draws the best possible line (made of 0s) between two points point1, point2\n",
+    "    \"\"\"\n",
+    "    x0, y0 = point1[0], point1[1];\n",
+    "    x1, y1 = point2[0], point2[1];\n",
+    "    if not (0 <= x0 < mat.shape[0] and 0 <= x1 < mat.shape[0] and\n",
+    "            0 <= y0 < mat.shape[1] and 0 <= y1 < mat.shape[1]):\n",
+    "        raise ValueError('Invalid coordinates.')\n",
+    "        \n",
+    "    if (x0, y0) == (x1, y1):\n",
+    "        mat[x0, y0] = 0\n",
+    "        return mat\n",
+    "    \n",
+    "    # Swap axes if Y slope is smaller than X slope\n",
+    "    transpose = abs(x1 - x0) < abs(y1 - y0)\n",
+    "    if transpose:\n",
+    "        mat = mat.T\n",
+    "        x0, y0, x1, y1 = y0, x0, y1, x1\n",
+    "        \n",
+    "    # Swap line direction to go left-to-right if necessary\n",
+    "    if x0 > x1:\n",
+    "        x0, y0, x1, y1 = x1, y1, x0, y0\n",
+    "        \n",
+    "    # Compute intermediate coordinates using line equation\n",
+    "    x = np.arange(x0 + 1, x1)\n",
+    "    y = np.round(((y1 - y0) / (x1 - x0)) * (x - x0) + y0).astype(x.dtype)\n",
+    "    \n",
+    "    # Write intermediate coordinates\n",
+    "    mat[x, y] = 0\n",
+    "\n",
+    "    return mat if not transpose else mat.T\n",
+    "\n",
+    "    \n",
+    "def new_population(N, n_vertices):\n",
+    "    \"\"\"\n",
+    "        function that generates a new population. A population is a matrix NxN containing 2 everywhere except\n",
+    "        inside a figure of n_vertices vertices where there are all 0s.\n",
+    "    \"\"\"\n",
+    "    from numpy import pi, floor, ceil, cos, sin;\n",
+    "    \n",
+    "    if n_vertices < 3: raise Exception('The number of vertices must be greater than 2.');\n",
+    "    \n",
+    "    if N%2 == 0: N+=1\n",
+    "        \n",
+    "    M = np.ones((N,N), dtype=int)+1 # matrix of 2s\n",
+    "\n",
+    "    center = [floor(N/2), floor(N/2)]\n",
+    "    radius = floor(N/2)\n",
+    "    angle = 2*pi/n_vertices\n",
+    "    \n",
+    "    vertices = np.zeros((n_vertices+1,2), dtype=int)\n",
+    "    for vertex in range(n_vertices): # for each vertex of the polygon\n",
+    "        x = int(floor(center[0] + radius*cos(vertex*angle))) # x coord of the vertex of the polygon\n",
+    "        y = int(floor(center[1] - radius*sin(vertex*angle))) # y coord of the vertex of the polygon\n",
+    "        M[x,y] = 0 # set to 0 the coordinate of the population matrix\n",
+    "        vertices[vertex] = [x,y] # list of coordinates of the vertices of the polygon\n",
+    "    vertices[n_vertices] = vertices[0] # add the first vertex at the end so that is a closed polygon\n",
+    "\n",
+    "    for i in range(1,len(vertices)):\n",
+    "        M = draw_line(M, vertices[i-1], vertices[i]) # trace the edges of the polygon\n",
+    "            \n",
+    "       \n",
+    "    for row in range(N): # the polygon is filled with 0s for each row\n",
+    "        row_i = M[row,:]   \n",
+    "        x1 = np.argwhere(M[row,:]==0)\n",
+    "        x2 = np.argwhere(M[row,:]==0)\n",
+    "        if len(x1)==0 or len(x2)==0: continue;\n",
+    "        x1, x2 = min(x1), max(x2)\n",
+    "        if x1==0 and x2==0: continue;\n",
+    "        M = draw_line(M, [row, x1], [row, x2])\n",
+    "          \n",
+    "    return M;\n",
+    "\n",
+    "def inizialization(population, infected, patients_0_nr=0, radius=0, random=False):\n",
+    "    \"\"\"\n",
+    "        Inizialization for patients zero, if random = True takes patients_0_nr random points inside\n",
+    "        a centered radius else takes coordinates\n",
+    "    \"\"\"\n",
+    "    centered = False\n",
+    "    center = int(np.floor(population.shape[0]/2))\n",
+    "    if not random:\n",
+    "        for coord in infected:\n",
+    "            if population[coord] == 2 : raise Exception('Bad inizialization, outside polygon');\n",
+    "            population[coord] = 1 # the people located at \"infected\" are turned into infected: 0 -> 1\n",
+    "    else:\n",
+    "        #we grab the indexes of the ones\n",
+    "        if centered:\n",
+    "            mask = np.ones((population.shape[0], population.shape[1]))\n",
+    "            mask[center-radius:center+radius, center-radius:center+radius]=0\n",
+    "            x,y = np.where(mask+population == 0)\n",
+    "            \n",
+    "        x,y = np.where(population == 0)\n",
+    "        #we chose one index randomly\n",
+    "        i = np.random.randint(len(x),size=patients_0_nr)\n",
+    "        for randnr in i:\n",
+    "            coord = (x[i],y[i])\n",
+    "            population[coord] = 1\n",
+    "    return population\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tunable Parameters\n",
+    "\n",
+    "* Susceptible $S \\rightarrow 0$\n",
+    "* Infected $I \\rightarrow 1$\n",
+    "* Recovered $R \\rightarrow -1$\n",
+    "$\\newline$\n",
+    "* cycles: lifetime of every experiment.\n",
+    "* experiments: number of experiments.\n",
+    "* resolution: $resolution^2$ is the number of nodes inside the outward matrix. The number of useful nodes for the experiment will be always $<resolution$. Increasing this value will make computational time increase rapidly.\n",
+    "* polygon_sides: the number of sides for the polygon ($3\\rightarrow$triangle, $4\\rightarrow$square, ...).\n",
+    "* p_i: probability for a node to get infected and change status from $S$ to $I$.\n",
+    "* t_r: time of recovery before an infected node changes status from $I$ to $R$.\n",
+    "* patients_0_nr: number of patients zero at the beginning of the simulation.\n",
+    "* radius: centered radius inside where the patients zero will be spawned randomly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cycles:86 , experiments:96 , resolution:142 , vertices:3, p_i:0.87 , t_r:3\n"
+     ]
+    }
+   ],
+   "source": [
+    "cycles = np.random.randint(30,100) #100\n",
+    "experiments = np.random.randint(10,100) #5\n",
+    "resolution, polygon_sides = np.random.randint(60,200), np.random.randint(3,15) #150, 7\n",
+    "p_i = round(np.random.random(),2) #0.5 \n",
+    "t_r = np.random.randint(1,6) #3\n",
+    "patients_0_nr, radius = 10, 25\n",
+    "\n",
+    "print('cycles:{} , experiments:{} , resolution:{} , vertices:{}, p_i:{} , t_r:{}'.format(cycles,experiments,resolution,polygon_sides,p_i,t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1-2. Algorithm\n",
+    "\n",
+    "The algorithm developed is based on the reference paper method. This replicates the propagation of the virus through the first-order neighbourhood since also in the paper they never go beyond. This propagation is slightly different from the original since some parameters are not so clear, in particular the random variable $h$ that discriminates the infection and the formula for obtaining $u_{i,j}$. The first parameter has a value from 0 to 1 but has a probability distribution p(h)=1, which has no clear meaning and hasn’t an extensive explanation. The formula instead has a strange normalization behaviour that makes it difficult to imagine how are the values in the experiment and so the meaning of $u_{i,j}$ and the conditions of infection. For this reason, we developed our version that is different but has the same behaviour. The algorithm cycles over all the elements in the matrix and, if it encounters an infected one, it explores the neighbours to infect them, with a probability $p_i$ for everyone. The elements infected in the actual cycle are skipped and considered in the next. In this way, the probability of being infected is proportional to the number of infected neighbours. The recovery instead is done as the original article, since an infected recovers in $t_r$ iterations. In this way,  the complexity of the algorithm results in the number of maximum cycles multiplied by the number of the matrix elements. Each of these iterations performs a constant number of operations. This makes the algorithm efficient for this type of problem.\n",
+    "\n",
+    "In order to build an efficient estimation from the simulations, an useful tool will be the list DF_List, which will be used later in order to have an average of all the experiments at time $t$ (Building process in the image).\n",
+    "\n",
+    "<img src=\"DFList.png\" width=\"800\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████| 96/96 [02:26<00:00,  1.52s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_experiments = pd.DataFrame(columns = ['EXP','S','I','R'])\n",
+    "df_S = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "df_I = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "df_R = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "\n",
+    "df_list_temp=[]\n",
+    "# initialize a list of length 'cycles' containing matrices of 50 (default initialisation value, random value different from -1,0,1,2)\n",
+    "for cycle in range(cycles):\n",
+    "    zero_mat = np.full((np.size(new_population(resolution, polygon_sides)),experiments), 50)\n",
+    "    # each matrix of the list refers to the i-th cycle, and the e-th column (e=0:9) contains the i-th population matrix vectorized for every experiment\n",
+    "    df_list_temp.append(zero_mat)\n",
+    "    \n",
+    "for experiment in tqdm(range(experiments)):\n",
+    "    population = new_population(resolution, polygon_sides);\n",
+    "    population_time = np.zeros(population.shape)-1\n",
+    "\n",
+    "    center = int(np.floor(resolution/2)) # just to keep track of where the center is\n",
+    "\n",
+    "    infected = [(center,center)] # initialization of where Infected are. Can be tuple or list of tuples\n",
+    "    population = inizialization(population, infected, patients_0_nr, radius, random=True)\n",
+    "    S=[]; I=[]; R=[]; \n",
+    "    for cycle in range(cycles):\n",
+    "        s,i,r=0,0,0\n",
+    "        for index,value in np.ndenumerate(population):\n",
+    "            if population_time[index]==cycle:pass #already processed\n",
+    "            elif value==2:pass #not in the region space\n",
+    "            elif value==1: #infect detected\n",
+    "                i+=1\n",
+    "                #cycle the neighbors for infection\n",
+    "                for j in range(-1,2):\n",
+    "                    for k in range(-1,2):\n",
+    "                        if 0<=index[0]+j<population.shape[0] and 0<=index[1]+k<population.shape[1]:\n",
+    "                            if population[index[0]+j,index[1]+k]==0 and np.random.choice([1, 0], p=[p_i, 1-p_i]): #element get infected\n",
+    "                                population[index[0]+j,index[1]+k]=1\n",
+    "                                population_time[index[0]+j,index[1]+k]=cycle #mark as infected in this cicle\n",
+    "                                if j==1 or (j==0 and k==1): s+=1 #the item will not be counted otherwise\n",
+    "                #infected recovers after t_r cycles\n",
+    "                if cycle-population_time[index]>=t_r:\n",
+    "                    population[index]=-1\n",
+    "                    population_time[index]=cycle\n",
+    "            elif value==0: s+=1\n",
+    "            elif value==-1: r+=1\n",
+    "        S.append(s) # number of S in cycle\n",
+    "        I.append(i) # number of I in cycle\n",
+    "        R.append(r) # number of R in cycle\n",
+    "        df_list_temp[cycle][:,experiment] = population.flatten() #write the current vectorized population matrix in the correspondant place of the matrix list\n",
+    "\n",
+    "        if i==0: break \n",
+    "\n",
+    "    s,i,r=0,0,0\n",
+    "    for index,value in np.ndenumerate(population):\n",
+    "        if value==1: i+=1\n",
+    "        elif value==0: s+=1\n",
+    "        elif value==-1: r+=1\n",
+    "    S.append(s) # number of S in last cycle\n",
+    "    I.append(i) # number of I in last cycle\n",
+    "    R.append(r) # number of R in last cycle\n",
+    "    S = np.concatenate((np.array(S),np.full(cycles-len(S)+1,S[len(S)-1])), axis=0)\n",
+    "    I = np.concatenate((np.array(I),np.full(cycles-len(I)+1,I[len(I)-1])), axis=0)\n",
+    "    R = np.concatenate((np.array(R),np.full(cycles-len(R)+1,R[len(R)-1])), axis=0)\n",
+    "    df_S = df_S.append(pd.DataFrame(S).T)\n",
+    "    df_I = df_I.append(pd.DataFrame(I).T)\n",
+    "    df_R = df_R.append(pd.DataFrame(R).T)\n",
+    "    \n",
+    "    DF_List=[] # final list with dataframe for each performed cycle, for every experiment\n",
+    "    for cycle in range(cycles):\n",
+    "        df_cycle=pd.DataFrame(df_list_temp[cycle])\n",
+    "        DF_List.append(df_cycle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dataset storing\n",
+    "\n",
+    "Data about the population is stored on three different .csv files for each $S,I,R$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_S.to_csv(r'Data/S_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r), index = False)\n",
+    "df_I.to_csv(r'Data/I_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r), index = False)\n",
+    "df_R.to_csv(r'Data/R_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r), index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Dataset loading\n",
+    " \n",
+    " Datasets can be loaded for the wanted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CurrentExperiment = True # do you want to analyze the results of the running experiment?\n",
+    "\n",
+    "if not CurrentExperiment: # no -> Please insert the experiment input values\n",
+    "    experiments = 100 # input desired number of experiments\n",
+    "    cycles = 100 # input desired number of cycles\n",
+    "    polygon_sides = 5 # input desired number of cycles\n",
+    "    p_i = 0.2 # input desired p_i (probability of infection)\n",
+    "    t_r = 3 # input desired t_r (time of recovery)\n",
+    "    \n",
+    "df_S = pd.read_csv(r'Data/S_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r))\n",
+    "df_I = pd.read_csv(r'Data/I_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r))\n",
+    "df_R = pd.read_csv(r'Data/R_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# graph of the number of cases for each cycle\n",
+    "fig2 = plt.figure(figsize = (15,7))\n",
+    "\n",
+    "plt.plot(df_S.mean(), label='S', zorder=10, linewidth=2.5, color='green');\n",
+    "plt.plot(df_I.mean(), label='I', zorder=20, linewidth=2.5, color='red');\n",
+    "plt.plot(df_R.mean(), label='R', zorder=10, linewidth=2.5, color='blue');\n",
+    "\n",
+    "plt.fill_between(np.arange(cycles+1),df_S.mean()-df_S.std(), df_S.mean()+df_S.std(), color='green', alpha=0.1)\n",
+    "plt.fill_between(np.arange(cycles+1),df_I.mean()-df_I.std(), df_I.mean()+df_I.std(), color='red', alpha=0.1)\n",
+    "plt.fill_between(np.arange(cycles+1),df_R.mean()-df_R.std(), df_R.mean()+df_R.std(), color='blue', alpha=0.1)\n",
+    "\n",
+    "plt.xlabel('time', fontsize=15);\n",
+    "plt.ylabel('number of cases', fontsize=15);\n",
+    "plt.legend(fontsize=15);\n",
+    "plt.grid(linestyle=\"--\", linewidth=0.6, zorder=-10);\n",
+    "plt.xticks(np.arange(0, cycles+1, 3));\n",
+    "plt.title('Number of cases with respect to time ({}x{}, {}v, p_i{}, t_r{})'.format(experiments, cycles, polygon_sides, p_i, t_r), fontsize=15)\n",
+    "\n",
+    "plt.savefig('images/{}x{}_{}v_{}pi_{}tr_NumberOfCases.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Space prediction\n",
+    "In order to obtain a prediction on space, at each time $t_i$ a sum between all the experiments is performed. Then, every \"average experiment\" (sum of all the experiments) is summed up to time $t_n$:\n",
+    "\n",
+    "$$\n",
+    "\\Phi = \\sum_{i=0}^{n} \\sum_{j=0}^{m} I(t_i, exp_j)\n",
+    "$$\n",
+    "\n",
+    "which, normalized, gives the percentage of times a given node got infected in a mean experiment:\n",
+    "\n",
+    "$$\n",
+    "p_\\% = \\frac{\\Phi}{max(\\Phi)}\n",
+    "$$\n",
+    "\n",
+    "If $m$ sufficiently large (i.e: number of experiments $m>>0$) the Law of Large Numbers (LLN) can be exploited to obtain a probability for a given node to get infected (A node can be seen as a Random Variable with probability of getting infected $p_i$):\n",
+    "\n",
+    "(LLN)\n",
+    "$X_1, X_2, ..., X_n$ infinite sequence of i.i.d. random variables with expected value $E(X_1) = E(X_2) = ...= \\mu$. \n",
+    "The sample average\n",
+    "$$\n",
+    "\\bar{X}_n=\\frac{1}{n}(X_1+...+X_n) \\longrightarrow \\mu \\text{ as } n\\longrightarrow\\infty\n",
+    "$$\n",
+    "\n",
+    "and in this case the higher $m$ the more $p_\\%$ resembles a probability distribution with respect to space, as it gets closer and closer to the mean value which corresponds also to the true value.\n",
+    "$$\n",
+    "p_\\% \\longrightarrow P(node=I)\\text{ , as number of experiments }\\longrightarrow \\infty\n",
+    "$$\n",
+    "\n",
+    "Note that $\\Phi$ can be calculated for $I$ (Infected) as for $S$ (Susceptibles) and $R$ (Recovered).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Infected $I$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_I = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_I = cycles*1//3\n",
+    "for i in range(finaltime_I):\n",
+    "    Space_Pred_Prob_I += (DF_List[i] == 1).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_I = Space_Pred_Prob_I/max(Space_Pred_Prob_I)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for I (t={}, {}x{}, {}v, p_i{}, t_r{})'.format(finaltime_I, experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_I.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_SpaceDistProb_I.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Susceptible $S$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_S = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_S = cycles\n",
+    "for i in range(finaltime_S):\n",
+    "    Space_Pred_Prob_S += (DF_List[i] == 0).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_S = Space_Pred_Prob_S/max(Space_Pred_Prob_S)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for S (t={}, {}x{}, {}v, p_i{}, t_r{})'.format(finaltime_S, experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_S.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_SpaceDistProb_S.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recovered $R$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_R = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_R = cycles\n",
+    "for i in range(finaltime_R):\n",
+    "    Space_Pred_Prob_R += (DF_List[i] == -1).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_R = Space_Pred_Prob_R/max(Space_Pred_Prob_R)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for R (t={}, {}x{}, {}v, p_i{}, t_r{})'.format(finaltime_R, experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_R.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_pi{}_tr{}_SpaceDistProb_R.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RuxErsZ0vJJG"
+   },
+   "source": [
+    "# 3. Time prediction: fit empirical data to SIR Model\n",
+    "Time prediction of the epidemic was performed by integrating the simplified model with $\\mu = 0$, then we find the best fit with the data from the training dataset that we have already computed.\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI \n",
+    "$$\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I\n",
+    "$$\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I\n",
+    "$$\n",
+    "The error of the fit was modeled as a normal distribution, 3 sigma rule was used so the training data should be inside the interval: \n",
+    "\n",
+    "$$\\mu(\\psi)-3\\sigma < \\psi < \\mu(\\psi)+3\\sigma$$\n",
+    "\n",
+    "where $\\psi$ is the function returned by \n",
+    "fitted_odeint\n",
+    " with popt containing the optimal \n",
+    "_lambda, gamma\n",
+    " by taking in account the error in pcov.\n",
+    "\n",
+    "As it can be seen the theoretical model sometimes differs from our implementation of the cellular automata algorithm. However, using a sparse random initialization on the entire surface the cellular automata algorithm converges to the theoretical model.\n",
+    "We can correct the prediction, taking into account the $t_r$ parameter, by performing the prediction with shifted data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Training_set_size = cycles*2//3\n",
+    "norm = max(df_S.mean()[0:Training_set_size]+df_I.mean()[0:Training_set_size]+df_R.mean()[0:Training_set_size])\n",
+    "ydataI = (np.array(df_I.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "ydataS = (np.array(df_S.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "ydataR = (np.array(df_R.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "xdata = (np.arange(Training_set_size))[t_r+1:]\n",
+    "\n",
+    "def sir_model(y, x, _lambda, gamma):\n",
+    "    S = -_lambda * y[0] * y[1]\n",
+    "    R = gamma * y[1]\n",
+    "    I = -(S + R)\n",
+    "    return S, I, R\n",
+    "\n",
+    "def fit_odeintI(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,1]\n",
+    "\n",
+    "def fit_odeintS(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,0]\n",
+    "\n",
+    "def fit_odeintR(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,2]\n",
+    "\n",
+    "I0 = ydataI[0]\n",
+    "S0 = 1 - I0\n",
+    "R0 = 0.0\n",
+    "\n",
+    "poptI, pcovI = optimize.curve_fit(fit_odeintI, xdata, ydataI)\n",
+    "poptS, pcovS = optimize.curve_fit(fit_odeintS, xdata, ydataS)\n",
+    "poptR, pcovR = optimize.curve_fit(fit_odeintR, xdata, ydataR)\n",
+    "\n",
+    "xdata = np.arange(cycles+1)+t_r+1\n",
+    "fittedI = fit_odeintI(xdata, *poptI)\n",
+    "fittedS = fit_odeintS(xdata, *poptS)\n",
+    "fittedR = fit_odeintR(xdata, *poptR)\n",
+    "\n",
+    "poptI_h = poptI + 3*np.diag(pcovI)**0.5 #error as normal distribution --> 3 sigma = 99.73% confidence\n",
+    "poptI_l = poptI - 3*np.diag(pcovI)**0.5\n",
+    "fittedI_h = fit_odeintI(xdata, *poptI_h)*norm\n",
+    "fittedI_l = fit_odeintI(xdata, *poptI_l)*norm\n",
+    "\n",
+    "poptS_h = poptS + 3*np.diag(pcovS)**0.5\n",
+    "poptS_l = poptS - 3*np.diag(pcovS)**0.5\n",
+    "fittedS_h = fit_odeintS(xdata, *poptS_h)*norm\n",
+    "fittedS_l = fit_odeintS(xdata, *poptS_l)*norm\n",
+    "\n",
+    "poptR_h = poptR + 3*np.diag(pcovR)**0.5\n",
+    "poptR_l = poptR - 3*np.diag(pcovR)**0.5\n",
+    "fittedR_h = fit_odeintR(xdata, *poptR_h)*norm\n",
+    "fittedR_l = fit_odeintR(xdata, *poptR_l)*norm\n",
+    "\n",
+    "\n",
+    "fitted_S = fittedS*norm\n",
+    "fitted_I = fittedI*norm\n",
+    "fitted_R = fittedR*norm\n",
+    "fig3 = plt.figure(figsize = (10,7))\n",
+    "\n",
+    "# plot of the fitted model\n",
+    "plt.plot(xdata,fitted_S, label='fitted S', zorder=20, linewidth=2.5, linestyle='--', color='orange')\n",
+    "plt.fill_between(xdata, fittedS_l,fittedS_h, color='orange', alpha=0.2)\n",
+    "plt.plot(xdata, fitted_I, label='fitted I', zorder=20, linewidth=2.5, linestyle='--', color='blue')\n",
+    "plt.fill_between(xdata, fittedI_l,fittedI_h, color='blue', alpha=0.2)\n",
+    "plt.plot(xdata, fitted_R, label='fitted R', zorder=20, linewidth=2.5, linestyle='--', color='green')\n",
+    "plt.fill_between(xdata, fittedR_l, fittedR_h, color='green', alpha=0.2)\n",
+    "\n",
+    "\n",
+    "xdata = np.arange(cycles+1)\n",
+    "#plot of the simulated model\n",
+    "plt.errorbar(xdata, df_S.mean(), df_S.std(), linestyle='-', marker='', color='orange', label='simulated S');\n",
+    "plt.errorbar(xdata, df_I.mean(), df_I.std(), linestyle='-', marker='', color='blue', label='simulated I');\n",
+    "plt.errorbar(xdata, df_R.mean(), df_R.std(), linestyle='-', marker='', color='green', label='simulated R');\n",
+    "plt.xlabel('time', fontsize=15);\n",
+    "plt.ylabel('number of cases', fontsize=15);\n",
+    "plt.title('Number Of Cases Prediction ({}x{}, {}v, p_i{}, t_r{})'.format(experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "plt.legend(fontsize=15);\n",
+    "plt.grid();\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_NumberOfCasesPrediction.png'.format(experiments,cycles,polygon_sides, p_i, t_r));\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction at t=20\n",
+      "S: 511+-36\n",
+      "I: 315+-14\n",
+      "R: 5783+-370\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict at t\n",
+    "t = 20\n",
+    "print(\"Prediction at t={}\".format(t))\n",
+    "print(\"S: {}+-{}\".format(round(fitted_S[t]), round(abs(fitted_S[t]-fittedS_l[t]))))\n",
+    "print(\"I: {}+-{}\".format(round(fitted_I[t]), round(abs(fitted_I[t]-fittedI_l[t]))))\n",
+    "print(\"R: {}+-{}\".format(round(fitted_R[t]), round(abs(fitted_R[t]-fittedR_l[t]))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The compartimental system goes:\n",
+    "$$\n",
+    "S \\longrightarrow I \\longrightarrow R\n",
+    "$$\n",
+    "\n",
+    "So the number of _new nodes_ infected can be computed by calculating the difference $S(t-1)-S(t)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xdata = np.arange(cycles+1+t_r)\n",
+    "fitted_S_EXT = np.append(df_S.mean()[0:t_r+1],fitted_S)\n",
+    "fittedS_l_EXT = np.append(df_S.mean()[0:t_r+1],fittedS_l)\n",
+    "fittedS_h_EXT = np.append(df_S.mean()[0:t_r+1],fittedS_h)\n",
+    "new_I = np.append(np.zeros(1),fitted_S_EXT[0:len(fitted_S_EXT)-1])-fitted_S_EXT\n",
+    "new_I_l = np.append(np.zeros(1),fittedS_l_EXT[0:len(fittedS_l_EXT)-1])-fittedS_l_EXT\n",
+    "new_I_h = np.append(np.zeros(1),fittedS_h_EXT[0:len(fittedS_h_EXT)-1])-fittedS_h_EXT\n",
+    "\n",
+    "fig3 = plt.figure(figsize = (10,7))\n",
+    "plt.plot(np.round(new_I)[1:])\n",
+    "plt.fill_between(xdata, np.round(new_I_l[1::]), np.round(new_I_h[1::]), color='blue', alpha=0.2)\n",
+    "plt.xlabel('time', fontsize=15)\n",
+    "plt.ylabel('new number of cases', fontsize=15)\n",
+    "plt.axvline(t_r+1, color='green', label='limit between empirical and predicted data: t={}'.format(t_r+1))\n",
+    "plt.legend(fontsize=15)\n",
+    "plt.grid()\n",
+    "plt.title('Number Of New Cases ({}x{}, {}v, p_i{}, t_r{})'.format(experiments,cycles,polygon_sides, p_i, t_r), fontsize=15)\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_NumberOfNewCases.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Prediction at the end of the epidemic\n",
+    "\n",
+    "The time prediction is helpful to estimate the number of cases during the pandemic. For example, since it has a bell shape, the maximum number of simultaneous infects can be estimated. Also the total number can be estimated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to predictions, during the epidemic there will be maximum 1190+-8 infected simultaneously.\n",
+      "Instead, the total number of infected at the end of the epidemic will be: 6656+-477.0 (~98.6%).\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"According to predictions, during the epidemic there will be maximum {}+-{} infected simultaneously.\".format(round(max(fitted_I)), round(abs(fitted_I[np.argmax(fitted_I)]-fittedI_l[np.argmax(fitted_I)]))))\n",
+    "print(\"Instead, the total number of infected at the end of the epidemic will be: {}+-{} (~{}%).\".format(round(sum(new_I[1:])), np.sum(np.round(abs(new_I_h-new_I_l))), round(round(sum(new_I[1:]))/(df_S.mean()[0]+df_I.mean()[0]), 3)*100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Animation of the Evolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 231675,
+     "status": "aborted",
+     "timestamp": 1613645627183,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "Qm8jUOKMvJJI"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import animation  \n",
+    "import matplotlib.image as mgimg\n",
+    "\n",
+    "#set up the figure\n",
+    "fig = plt.figure()\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "#initialization of animation, plot array of zeros \n",
+    "def init():\n",
+    "    imobj.set_data(DF_List[0][0].to_numpy().reshape(population.shape[0],population.shape[1]))\n",
+    "\n",
+    "    return  imobj,\n",
+    "\n",
+    "def animate(i): # animation of experiment 0 at time i\n",
+    "    imobj.set_data(DF_List[i][0].to_numpy().reshape(population.shape[0],population.shape[1]))\n",
+    "\n",
+    "    return  imobj,\n",
+    "\n",
+    "\n",
+    "## create an AxesImage object\n",
+    "plt.title('Evolution of the epidemic', fontsize=15)\n",
+    "imobj = ax.imshow(DF_List[0][0].to_numpy().reshape(population.shape[0],population.shape[1]), vmin=-1, vmax=2, cmap='rocket')#, origin='lower', alpha=1.0, zorder=1, aspect=1 )\n",
+    "\n",
+    "anim = animation.FuncAnimation(fig, animate, init_func=init, repeat = True,\n",
+    "                               frames=range(0,39), interval=200, blit=True, repeat_delay=1000)\n",
+    "\n",
+    "anim.save('images/{}x{}_edges{}_{}pi_{}tr_animation.gif'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 231672,
+     "status": "aborted",
+     "timestamp": 1613645627186,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "4ApfuK3IvJJJ",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(anim.to_html5_video())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results and Conclusions\n",
+    "\n",
+    "The results show that the algorithm developed can approximate the epidemic evolution given by the $SIR$ model. The numbers generated in the various simulations fit quite well the differential equations of the theoretical model, so the different characteristics of our Cellular Automata algorithm compared to the original one doesn't ruin the evolution behaviour and works fine. However, since a probability leads the expansion, it isn't possible to find such a parameter that discriminates the end of the epidemic at a certain time, as done in the original paper.  Instead, thanks to the spatial analysis, it is possible to see the probability distribution of a category at a certain time, to evaluate and compare the evolution state of various configurations. In this way, we can see how the change of the $p_i$ parameter impacts the simulation and how the different shapes also do. We can state that the increase in the probability increases the number of infected in general, as expected. The shape instead interestingly affects the distribution since at the corners the probability of being infected reduces. This happens proportionally to the reduction of the sides, meaning that a small angle in the corner protects the elements from infection more, probably because of the limited number of neighbours. Finally, the general evolution given by the number of the epidemic is predicted and described with good accuracy by the SIR model equations, removing the first $t_r$ samples since the R numbers can't increase.\n",
+    "\n",
+    "Following, some results using the Cellular Automata technique on the SIR model.\n",
+    "\n",
+    "### Experiments: $14x67$cycles, $14$ vertices, $p_i = 0.09$, $t_r=4$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/14x67_edges14_pi0.09_tr4_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/14x67_edges14_0.09pi_4tr_animation.gif\" width=\"500\" />\n",
+    "    \n",
+    "___   \n",
+    "    \n",
+    "### Experiments: $91x65$cycles, $8$ vertices, $p_i = 0.76$, $t_r=1$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/91x65_edges8_pi0.76_tr1_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/91x65_edges8_0.76pi_1tr_animation.gif\" width=\"500\" />\n",
+    "    \n",
+    "___\n",
+    "\n",
+    "### Experiments: $30x300$cycles, $3$ vertices, $p_i = 0.6$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/30x300_edges3_pi0.6_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/30x300_edges3_0.6pi_6tr_animation.gif\" width=\"500\" />\n",
+    "___\n",
+    "\n",
+    "### Experiments: $100x300$cycles, $3$ vertices, $p_i = 0.05$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/100x300_edges3_pi0.05_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/100x300_edges3_0.05pi_6tr_animation.gif\" width=\"500\" />\n",
+    "___\n",
+    "\n",
+    "### Experiments: $30x300$cycles, $6$ vertices, $p_i = 0.6$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/30x300_edges6_pi0.6_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/30x300_edges6_0.6pi_6tr_animation.gif\" width=\"500\" />\n",
+    "\n",
+    "___\n",
+    "\n",
+    "### Experiments: $100x300$cycles, $6$ vertices, $p_i = 0.05$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/100x300_edges6_pi0.05_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/100x300_edges6_0.05pi_6tr_animation.gif\" width=\"500\" />"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Copia di Project.ipynb",
+   "provenance": [
+    {
+     "file_id": "https://github.com/albertodetoni/LCP_projects_Y3/blob/master/.ipynb_checkpoints/Project_0.1-checkpoint.ipynb",
+     "timestamp": 1613645717249
+    }
+   ],
+   "toc_visible": true
+  },
+  "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/.ipynb_checkpoints/Project_0.1-checkpoint.ipynb b/.ipynb_checkpoints/Project_0.1-checkpoint.ipynb
new file mode 100644
index 0000000..4ba2b43
--- /dev/null
+++ b/.ipynb_checkpoints/Project_0.1-checkpoint.ipynb
@@ -0,0 +1,2248 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2041,
+     "status": "ok",
+     "timestamp": 1613645397401,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "KBUdLfK8W14c"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from scipy import integrate, optimize\n",
+    "import time\n",
+    "from tqdm import tqdm\n",
+    "from matplotlib import animation\n",
+    "from IPython.display import HTML\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2029,
+     "status": "ok",
+     "timestamp": 1613645397405,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "2xE5VZejvJI6"
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C3edXWAuW14l"
+   },
+   "source": [
+    "# Cellular Automata S.I.R. Model\n",
+    "\n",
+    "### Description\n",
+    "\n",
+    "Based on the reference [paper](https://www.math.uh.edu/~zpkilpat/teaching/math4309/project/physa99_fuentes.pdf), the aim of the project is to simulate and then study the evolution of Susceptible-Infected-Removed populations over space and time.\n",
+    "\n",
+    "### Assignments\n",
+    "1. Consider a grid of a given form (squared or exagonal or whatever) and then let propagate the SIR model. \n",
+    "2. Repeat point 1 several times in order to obtain several reference datasets\n",
+    "3. Try to predict the evolution of the epidemic both in space and time on the basis of the previous steps, e.g. by means of \n",
+    "4. Define a metric on how the epidemic evolved e.g. assigning a probability for a given node not to get infected or predict the fraction of the nodes that got infected at the end of the epidemic\n",
+    "\n",
+    "### Contacts\n",
+    "* Leonardo Badia <badia@dei.unipd.it>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIR Model and Cellular Automata introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following project will be analized the $ SIR $ model for the evolution of infectivity nucleus inside a finite population exploiting Cellular Automata technique.\n",
+    "### SIR Model\n",
+    "An infection which is transmitted from person to person and the overall population sampled is divided into three groups: \n",
+    "* Susceptibles: containing the individuals that have not yet been infected and still can contract the infection; \n",
+    "* Infected: that represents the individuals that are currently infected and that are able to infect other people;\n",
+    "* Removed, that are all the individuals that recovered from the illness and are now immune, and also people who died due to the infection.\n",
+    "\n",
+    "SIR models are theoretically described by partial differential equations:\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI + \\mu - \\mu S;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I -\\mu I;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I - \\mu R;\n",
+    "$$\n",
+    "\n",
+    "where $\\lambda$ is a parameter describing the rate of interactions between susceptibles and infectious, $\\gamma$ is the recovery rate and $\\mu$ is a parameter related to vital dynamics of the population (birth-death rate).\n",
+    "\n",
+    "### Cellular Automata\n",
+    "An alternative approach, which provides much better efficiency in terms of computational time is by using Cellular Automata, that is a discrete time approach using matrices whose elements are configured in networks.\n",
+    "\n",
+    "Every element of the matrix is assigned a value describing its epidemiological state (S=0, I=1, R=-1) which changes with respect to the states of neighbor elements, according to a fixed range.\n",
+    "\n",
+    "The change of state from S to I depends, just as for the differential equation model, on the interaction between an infected element and its neighbors, which is based on a infection probability $p_i$.\n",
+    "\n",
+    "Once infected, the element recovers after a fixed number of iterations representing the total recovery time (which takes into account incubation, proper infection and latency times). In this case the vital dynamics are not considered ($\\mu=0$), so the equations governing the epidemic become:\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I;\n",
+    "$$\n",
+    "\n",
+    "Eventually thanks to Cellular Automata several and efficient simulations of an epidemiological spread can be performed, allowing to obtain accurate predictions in time and space.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 229842,
+     "status": "ok",
+     "timestamp": 1613645625241,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "mQl_wl7RW14m",
+    "outputId": "82b2c037-cd3d-4111-f242-ca0d0c231f47",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def draw_line(mat, point1, point2):\n",
+    "    \"\"\"\n",
+    "        function that draws the best possible line (made of 0s) between two points point1, point2\n",
+    "    \"\"\"\n",
+    "    x0, y0 = point1[0], point1[1];\n",
+    "    x1, y1 = point2[0], point2[1];\n",
+    "    if not (0 <= x0 < mat.shape[0] and 0 <= x1 < mat.shape[0] and\n",
+    "            0 <= y0 < mat.shape[1] and 0 <= y1 < mat.shape[1]):\n",
+    "        raise ValueError('Invalid coordinates.')\n",
+    "        \n",
+    "    if (x0, y0) == (x1, y1):\n",
+    "        mat[x0, y0] = 0\n",
+    "        return mat\n",
+    "    \n",
+    "    # Swap axes if Y slope is smaller than X slope\n",
+    "    transpose = abs(x1 - x0) < abs(y1 - y0)\n",
+    "    if transpose:\n",
+    "        mat = mat.T\n",
+    "        x0, y0, x1, y1 = y0, x0, y1, x1\n",
+    "        \n",
+    "    # Swap line direction to go left-to-right if necessary\n",
+    "    if x0 > x1:\n",
+    "        x0, y0, x1, y1 = x1, y1, x0, y0\n",
+    "        \n",
+    "    # Compute intermediate coordinates using line equation\n",
+    "    x = np.arange(x0 + 1, x1)\n",
+    "    y = np.round(((y1 - y0) / (x1 - x0)) * (x - x0) + y0).astype(x.dtype)\n",
+    "    \n",
+    "    # Write intermediate coordinates\n",
+    "    mat[x, y] = 0\n",
+    "\n",
+    "    return mat if not transpose else mat.T\n",
+    "\n",
+    "    \n",
+    "def new_population(N, n_vertices):\n",
+    "    \"\"\"\n",
+    "        function that generates a new population. A population is a matrix NxN containing 2 everywhere except\n",
+    "        inside a figure of n_vertices vertices where there are all 0s.\n",
+    "    \"\"\"\n",
+    "    from numpy import pi, floor, ceil, cos, sin;\n",
+    "    \n",
+    "    if n_vertices < 3: raise Exception('The number of vertices must be greater than 2.');\n",
+    "    \n",
+    "    if N%2 == 0: N+=1\n",
+    "        \n",
+    "    M = np.ones((N,N), dtype=int)+1 # matrix of 2s\n",
+    "\n",
+    "    center = [floor(N/2), floor(N/2)]\n",
+    "    radius = floor(N/2)\n",
+    "    angle = 2*pi/n_vertices\n",
+    "    \n",
+    "    vertices = np.zeros((n_vertices+1,2), dtype=int)\n",
+    "    for vertex in range(n_vertices): # for each vertex of the polygon\n",
+    "        x = int(floor(center[0] + radius*cos(vertex*angle))) # x coord of the vertex of the polygon\n",
+    "        y = int(floor(center[1] - radius*sin(vertex*angle))) # y coord of the vertex of the polygon\n",
+    "        M[x,y] = 0 # set to 0 the coordinate of the population matrix\n",
+    "        vertices[vertex] = [x,y] # list of coordinates of the vertices of the polygon\n",
+    "    vertices[n_vertices] = vertices[0] # add the first vertex at the end so that is a closed polygon\n",
+    "\n",
+    "    for i in range(1,len(vertices)):\n",
+    "        M = draw_line(M, vertices[i-1], vertices[i]) # trace the edges of the polygon\n",
+    "            \n",
+    "       \n",
+    "    for row in range(N): # the polygon is filled with 0s for each row\n",
+    "        row_i = M[row,:]   \n",
+    "        x1 = np.argwhere(M[row,:]==0)\n",
+    "        x2 = np.argwhere(M[row,:]==0)\n",
+    "        if len(x1)==0 or len(x2)==0: continue;\n",
+    "        x1, x2 = min(x1), max(x2)\n",
+    "        if x1==0 and x2==0: continue;\n",
+    "        M = draw_line(M, [row, x1], [row, x2])\n",
+    "          \n",
+    "    return M;\n",
+    "\n",
+    "def inizialization(population, infected, patients_0_nr=0, radius=0, random=False):\n",
+    "    \"\"\"\n",
+    "        Inizialization for patients zero, if random = True takes patients_0_nr random points inside\n",
+    "        a centered radius else takes coordinates\n",
+    "    \"\"\"\n",
+    "    centered = False\n",
+    "    center = int(np.floor(population.shape[0]/2))\n",
+    "    if not random:\n",
+    "        for coord in infected:\n",
+    "            if population[coord] == 2 : raise Exception('Bad inizialization, outside polygon');\n",
+    "            population[coord] = 1 # the people located at \"infected\" are turned into infected: 0 -> 1\n",
+    "    else:\n",
+    "        #we grab the indexes of the ones\n",
+    "        if centered:\n",
+    "            mask = np.ones((population.shape[0], population.shape[1]))\n",
+    "            mask[center-radius:center+radius, center-radius:center+radius]=0\n",
+    "            x,y = np.where(mask+population == 0)\n",
+    "            \n",
+    "        x,y = np.where(population == 0)\n",
+    "        #we chose one index randomly\n",
+    "        i = np.random.randint(len(x),size=patients_0_nr)\n",
+    "        for randnr in i:\n",
+    "            coord = (x[i],y[i])\n",
+    "            population[coord] = 1\n",
+    "    return population\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tunable Parameters\n",
+    "\n",
+    "* Susceptible $S \\rightarrow 0$\n",
+    "* Infected $I \\rightarrow 1$\n",
+    "* Recovered $R \\rightarrow -1$\n",
+    "$\\newline$\n",
+    "* cycles: lifetime of every experiment.\n",
+    "* experiments: number of experiments.\n",
+    "* resolution: $resolution^2$ is the number of nodes inside the outward matrix. The number of useful nodes for the experiment will be always $<resolution$. Increasing this value will make computational time increase rapidly.\n",
+    "* polygon_sides: the number of sides for the polygon ($3\\rightarrow$triangle, $4\\rightarrow$square, ...).\n",
+    "* p_i: probability for a node to get infected and change status from $S$ to $I$.\n",
+    "* t_r: time of recovery before an infected node changes status from $I$ to $R$.\n",
+    "* patients_0_nr: number of patients zero at the beginning of the simulation.\n",
+    "* radius: centered radius inside where the patients zero will be spawned randomly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cycles:69 , experiments:53 , resolution:135 , edges:19\n"
+     ]
+    }
+   ],
+   "source": [
+    "cycles = np.random.randint(20,100) #100\n",
+    "experiments = np.random.randint(10,100) #5 \n",
+    "resolution, polygon_sides = np.random.randint(100, 200), np.random.randint(3,20) #150, 7 \n",
+    "p_i = np.random.random_sample() #0.5 \n",
+    "t_r = 3 \n",
+    "patients_0_nr, radius = 10, 25\n",
+    "\n",
+    "print('cycles:{} , experiments:{} , resolution:{} , edges:{}'.format(cycles,experiments,resolution,polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1-2. Algorithm\n",
+    "\n",
+    "The algorithm developed is based on the reference paper method. This replicates the propagation of the virus through the first-order neighbourhood since also in the paper they never go beyond. This propagation is slightly different from the original since some parameters are not so clear, in particular the random variable $h$ that discriminates the infection and the formula for obtaining $u_{i,j}$. The first parameter has a value from 0 to 1 but has a probability distribution p(h)=1, which has no clear meaning and hasn’t an extensive explanation. The formula instead has a strange normalization behaviour that makes it difficult to imagine how are the values in the experiment and so the meaning of $u_{i,j}$ and the conditions of infection. For this reason, we developed our version that is different but has the same behaviour. The algorithm cycles over all the elements in the matrix and, if it encounters an infected one, it explores the neighbours to infect them, with a probability $p_i$ for everyone. The elements infected in the actual cycle are skipped and considered in the next. In this way, the probability of being infected is proportional to the number of infected neighbours. The recovery instead is done as the original article, since an infected recovers in $t_r$ iterations. In this way,  the complexity of the algorithm results in the number of maximum cycles multiplied by the number of the matrix elements. Each of these iterations performs a constant number of operations. This makes the algorithm efficient for this type of problem.\n",
+    "\n",
+    "In order to build an efficient estimation from the simulations, an useful tool will be the list DF_List, which will be used later in order to have an average of all the experiments at time $t$ (Building process in the image).\n",
+    "\n",
+    "<img src=\"DFList.png\" width=\"800\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 53/53 [02:12<00:00,  2.49s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_experiments = pd.DataFrame(columns = ['EXP','S','I','R'])\n",
+    "df_S = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "df_I = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "df_R = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "\n",
+    "df_list_temp=[]\n",
+    "# initialize a list of length 'cycles' containing matrices of 50 (default initialisation value, random value different from -1,0,1,2)\n",
+    "for cycle in range(cycles):\n",
+    "    zero_mat = np.full((np.size(new_population(resolution, polygon_sides)),experiments), 50)\n",
+    "    # each matrix of the list refers to the i-th cycle, and the e-th column (e=0:9) contains the i-th population matrix vectorized for every experiment\n",
+    "    df_list_temp.append(zero_mat)\n",
+    "    \n",
+    "for experiment in tqdm(range(experiments)):\n",
+    "    population = new_population(resolution, polygon_sides); # population is a matrix of 2 if outside the figure while 0 if inside the figure\n",
+    "    population_time = np.zeros(population.shape)-1\n",
+    "\n",
+    "    center = int(np.floor(resolution/2)) # just to keep track of where the center is\n",
+    "\n",
+    "    infected = [(center,center)] # initialization of where Infected are. Can be tuple or list of tuples\n",
+    "    population = inizialization(population, infected, patients_0_nr, radius, random=True)\n",
+    "    S=[]; I=[]; R=[]; \n",
+    "    for cycle in range(cycles):\n",
+    "        s,i,r=0,0,0\n",
+    "        for index,value in np.ndenumerate(population):\n",
+    "            if population_time[index]==cycle:pass #already processed\n",
+    "            elif value==2:pass #not in the region space\n",
+    "            elif value==1: #infect detected\n",
+    "                i+=1\n",
+    "                #cycle the neighbors for infection\n",
+    "                for j in range(-1,2):\n",
+    "                    for k in range(-1,2):\n",
+    "                        if 0<=index[0]+j<population.shape[0] and 0<=index[1]+k<population.shape[1]:\n",
+    "                            if population[index[0]+j,index[1]+k]==0 and np.random.choice([1, 0], p=[p_i, 1-p_i]): #element get infected\n",
+    "                                population[index[0]+j,index[1]+k]=1\n",
+    "                                population_time[index[0]+j,index[1]+k]=cycle #mark as infected in this cicle\n",
+    "                                if j==1 or (j==0 and k==1): s+=1 #the item will not be counted otherwise\n",
+    "                #infected recovers after t_r cycles\n",
+    "                if cycle-population_time[index]>=t_r:\n",
+    "                #if np.random.choice([1, 0], p=[p_r*(cycle-population_time[index]), 1-p_r*(cycle-population_time[index])]):\n",
+    "                #if np.random.choice([1, 0], p=[p_r, 1-p_r]):\n",
+    "                    population[index]=-1\n",
+    "                    population_time[index]=cycle\n",
+    "            elif value==0: s+=1\n",
+    "            elif value==-1: r+=1\n",
+    "        S.append(s) # number of S in cycle\n",
+    "        I.append(i) # number of I in cycle\n",
+    "        R.append(r) # number of R in cycle\n",
+    "        df_list_temp[cycle][:,experiment] = population.flatten() #write the current vectorized population matrix in the correspondant place of the matrix list\n",
+    "\n",
+    "        if i==0: break \n",
+    "\n",
+    "    s,i,r=0,0,0\n",
+    "    for index,value in np.ndenumerate(population):\n",
+    "        if value==1: i+=1\n",
+    "        elif value==0: s+=1\n",
+    "        elif value==-1: r+=1\n",
+    "    S.append(s) # number of S in last cycle\n",
+    "    I.append(i) # number of I in last cycle\n",
+    "    R.append(r) # number of R in last cycle\n",
+    "    S = np.concatenate((np.array(S),np.full(cycles-len(S)+1,S[len(S)-1])), axis=0)\n",
+    "    I = np.concatenate((np.array(I),np.full(cycles-len(I)+1,I[len(I)-1])), axis=0)\n",
+    "    R = np.concatenate((np.array(R),np.full(cycles-len(R)+1,R[len(R)-1])), axis=0)\n",
+    "    df_S = df_S.append(pd.DataFrame(S).T)\n",
+    "    df_I = df_I.append(pd.DataFrame(I).T)\n",
+    "    df_R = df_R.append(pd.DataFrame(R).T)\n",
+    "    \n",
+    "    DF_List=[] # final list with dataframe for each performed cycle, for every experiment\n",
+    "    for cycle in range(cycles):\n",
+    "        df_cycle=pd.DataFrame(df_list_temp[cycle])\n",
+    "        DF_List.append(df_cycle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dataset storing\n",
+    "\n",
+    "Data about the population is stored on three different .csv files for each $S,I,R$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_S.to_csv(r'Data/S_{}x{}_edges{}.csv'.format(experiments, cycles, polygon_sides), index = False)\n",
+    "df_I.to_csv(r'Data/I_{}x{}_edges{}.csv'.format(experiments, cycles, polygon_sides), index = False)\n",
+    "df_R.to_csv(r'Data/R_{}x{}_edges{}.csv'.format(experiments, cycles, polygon_sides), index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Dataset loading\n",
+    " \n",
+    " Datasets can be loaded for the wanted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CurrentExperiment = True # do you want to analyze the results of the running experiment?\n",
+    "\n",
+    "if not CurrentExperiment: # no -> Please insert the experiment input values\n",
+    "    experiments = 100 # input desired number of experiments\n",
+    "    cycles = 100 # input desired number of cycles\n",
+    "    polygon_sides = 5 # input desired number of cycles\n",
+    "    \n",
+    "df_S = pd.read_csv(r'Data/S_{}x{}_edges{}.csv'.format(experiments, cycles, polygon_sides))\n",
+    "df_I = pd.read_csv(r'Data/I_{}x{}_edges{}.csv'.format(experiments, cycles, polygon_sides))\n",
+    "df_R = pd.read_csv(r'Data/R_{}x{}_edges{}.csv'.format(experiments, cycles, polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# graph of the number of cases for each cycle\n",
+    "fig2 = plt.figure(figsize = (15,7))\n",
+    "\n",
+    "plt.plot(df_S.mean(), label='S', zorder=10, linewidth=2.5, color='green');\n",
+    "plt.plot(df_I.mean(), label='I', zorder=20, linewidth=2.5, color='red');\n",
+    "plt.plot(df_R.mean(), label='R', zorder=10, linewidth=2.5, color='blue');\n",
+    "\n",
+    "plt.fill_between(np.arange(cycles+1),df_S.mean()-df_S.std(), df_S.mean()+df_S.std(), color='green', alpha=0.1)\n",
+    "plt.fill_between(np.arange(cycles+1),df_I.mean()-df_I.std(), df_I.mean()+df_I.std(), color='red', alpha=0.1)\n",
+    "plt.fill_between(np.arange(cycles+1),df_R.mean()-df_R.std(), df_R.mean()+df_R.std(), color='blue', alpha=0.1)\n",
+    "\n",
+    "plt.xlabel('time', fontsize=15);\n",
+    "plt.ylabel('number of cases', fontsize=15);\n",
+    "plt.legend(fontsize=15);\n",
+    "plt.grid(linestyle=\"--\", linewidth=0.6, zorder=-10);\n",
+    "plt.xticks(np.arange(0, cycles+1, 3));\n",
+    "plt.title('Number Of Cases {}x{} {} vertices'.format(experiments,cycles,polygon_sides), fontsize=15)\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_NumberOfCases.png'.format(experiments,cycles,polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Space prediction\n",
+    "In order to obtain a prediction on space, at each time $t_i$ a sum between all the experiments is performed. Then, every \"average experiment\" (sum of all the experiments) is summed up to time $t_n$:\n",
+    "\n",
+    "$$\n",
+    "\\Phi = \\sum_{i=0}^{n} \\sum_{j=0}^{m} I(t_i, exp_j)\n",
+    "$$\n",
+    "\n",
+    "which, normalized, gives the percentage of times a given node got infected in a mean experiment:\n",
+    "\n",
+    "$$\n",
+    "p_\\% = \\frac{\\Phi}{max(\\Phi)}\n",
+    "$$\n",
+    "\n",
+    "If $m$ sufficiently large (i.e: number of experiments $m>>0$) the Law of Large Numbers (LLN) can be exploited to obtain a probability for a given node to get infected (A node can be seen as a Random Variable with probability of getting infected $p_i$):\n",
+    "\n",
+    "(LLN)\n",
+    "$X_1, X_2, ..., X_n$ infinite sequence of i.i.d. random variables with expected value $E(X_1) = E(X_2) = ...= \\mu$. \n",
+    "The sample average\n",
+    "$$\n",
+    "\\bar{X}_n=\\frac{1}{n}(X_1+...+X_n) \\longrightarrow \\mu \\text{ as } n\\longrightarrow\\infty\n",
+    "$$\n",
+    "\n",
+    "and in this case the higher $m$ the more $p_\\%$ resembles a probability distribution with respect to space, as it gets closer and closer to the mean value which corresponds also to the true value.\n",
+    "$$\n",
+    "p_\\% \\longrightarrow P(node=I)\\text{ , as number of experiments }\\longrightarrow \\infty\n",
+    "$$\n",
+    "\n",
+    "Note that $\\Phi$ can be calculated for $I$ (Infected) as for $S$ (Susceptibles) and $R$ (Recovered).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Infected $I$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_I = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_I = cycles-10\n",
+    "for i in range(finaltime_I):\n",
+    "    Space_Pred_Prob_I += (DF_List[i] == 1).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_I = Space_Pred_Prob_I/max(Space_Pred_Prob_I)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for I at t={} ({}x{}, {}v)'.format(finaltime_I, experiments, cycles, polygon_sides), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_I.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_SpaceDistProb_I.png'.format(experiments,cycles,polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Susceptible $S$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_S = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_S = cycles\n",
+    "for i in range(finaltime_S):\n",
+    "    Space_Pred_Prob_S += (DF_List[i] == 0).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_S = Space_Pred_Prob_S/max(Space_Pred_Prob_S)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for S at t={} ({}x{}, {}v)'.format(finaltime_S, experiments, cycles, polygon_sides), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_S.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_SpaceDistProb_S.png'.format(experiments,cycles,polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recovered $R$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_R = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_R = cycles\n",
+    "for i in range(finaltime_R):\n",
+    "    Space_Pred_Prob_R += (DF_List[i] == -1).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_R = Space_Pred_Prob_R/max(Space_Pred_Prob_R)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for R at t={} ({}x{}, {}v)'.format(finaltime_S, experiments, cycles, polygon_sides), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_R.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_SpaceDistProb_R.png'.format(experiments,cycles,polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RuxErsZ0vJJG"
+   },
+   "source": [
+    "# 3. Time prediction: fit empirical data to SIR Model\n",
+    "\n",
+    "Time prediction of the epidemic was performed by integrating the simplified model with $\\mu = 0$, then we find the best fit with the data from the training dataset that we have already computed.\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I;\n",
+    "$$\n",
+    "\n",
+    "The error of the fit was modeled as a normal distribution, 3 sigma rule was used so the training data should be inside the interval: \n",
+    "\n",
+    "$$\\mu(\\psi)-3\\sigma < \\psi < \\mu(\\psi)+3\\sigma$$\n",
+    "\n",
+    "\n",
+    "where $\\psi$ is the function returned by ```fitted_odeint``` with popt containing the optimal ```_lambda, gamma``` by taking in account the error in pcov.\n",
+    "\n",
+    "As we can see the theoretical model differs from our implementation of the cellular automata algorithm. However, using a sparse random initialization on the entire surface the cellular automata algorithm converges to the theoretical model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(42,) (42,)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Training_set_size = cycles*2//3\n",
+    "norm = max(df_S.mean()[0:Training_set_size]+df_I.mean()[0:Training_set_size]+df_R.mean()[0:Training_set_size])\n",
+    "ydataI = (np.array(df_I.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "ydataS = (np.array(df_S.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "ydataR = (np.array(df_R.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "xdata = (np.arange(Training_set_size))[t_r+1:]\n",
+    "\n",
+    "print(ydataI.shape, xdata.shape)\n",
+    "def sir_model(y, x, _lambda, gamma):\n",
+    "    S = -_lambda * y[0] * y[1]\n",
+    "    R = gamma * y[1]\n",
+    "    I = -(S + R)\n",
+    "    return S, I, R\n",
+    "\n",
+    "def fit_odeintI(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,1]\n",
+    "\n",
+    "def fit_odeintS(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,0]\n",
+    "\n",
+    "def fit_odeintR(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,2]\n",
+    "\n",
+    "I0 = ydataI[0]\n",
+    "S0 = 1 - I0\n",
+    "R0 = 0.0\n",
+    "\n",
+    "poptI, pcovI = optimize.curve_fit(fit_odeintI, xdata, ydataI)\n",
+    "poptS, pcovS = optimize.curve_fit(fit_odeintS, xdata, ydataS)\n",
+    "poptR, pcovR = optimize.curve_fit(fit_odeintR, xdata, ydataR)\n",
+    "\n",
+    "xdata = np.arange(cycles+1)+t_r+1\n",
+    "fittedI = fit_odeintI(xdata, *poptI)\n",
+    "fittedS = fit_odeintS(xdata, *poptS)\n",
+    "fittedR = fit_odeintR(xdata, *poptR)\n",
+    "\n",
+    "poptI_h = poptI + 3*np.diag(pcovI)**0.5 #error as normal distribution --> 3 sigma = 99.73% confidence\n",
+    "poptI_l = poptI - 3*np.diag(pcovI)**0.5\n",
+    "fittedI_h = fit_odeintI(xdata, *poptI_h)*norm\n",
+    "fittedI_l = fit_odeintI(xdata, *poptI_l)*norm\n",
+    "\n",
+    "poptS_h = poptS + 3*np.diag(pcovS)**0.5\n",
+    "poptS_l = poptS - 3*np.diag(pcovS)**0.5\n",
+    "fittedS_h = fit_odeintS(xdata, *poptS_h)*norm\n",
+    "fittedS_l = fit_odeintS(xdata, *poptS_l)*norm\n",
+    "\n",
+    "poptR_h = poptR + 3*np.diag(pcovR)**0.5\n",
+    "poptR_l = poptR - 3*np.diag(pcovR)**0.5\n",
+    "fittedR_h = fit_odeintR(xdata, *poptR_h)*norm\n",
+    "fittedR_l = fit_odeintR(xdata, *poptR_l)*norm\n",
+    "\n",
+    "\n",
+    "fitted_S = fittedS*norm\n",
+    "fitted_I = fittedI*norm\n",
+    "fitted_R = fittedR*norm\n",
+    "fig3 = plt.figure(figsize = (10,7))\n",
+    "\n",
+    "# plot of the fitted model\n",
+    "plt.plot(xdata,fitted_S, label='fitted S', zorder=20, linewidth=2.5, linestyle='--', color='orange')\n",
+    "plt.fill_between(xdata, fittedS_l,fittedS_h, color='orange', alpha=0.2)\n",
+    "plt.plot(xdata, fitted_I, label='fitted I', zorder=20, linewidth=2.5, linestyle='--', color='blue')\n",
+    "plt.fill_between(xdata, fittedI_l,fittedI_h, color='blue', alpha=0.2)\n",
+    "plt.plot(xdata, fitted_R, label='fitted R', zorder=20, linewidth=2.5, linestyle='--', color='green')\n",
+    "plt.fill_between(xdata, fittedR_l, fittedR_h, color='green', alpha=0.2)\n",
+    "\n",
+    "\n",
+    "xdata = np.arange(cycles+1)\n",
+    "#plot of the simulated model\n",
+    "plt.errorbar(xdata, df_S.mean(), df_S.std(), linestyle='-', marker='', color='orange', label='simulated S');\n",
+    "plt.errorbar(xdata, df_I.mean(), df_I.std(), linestyle='-', marker='', color='blue', label='simulated I');\n",
+    "plt.errorbar(xdata, df_R.mean(), df_R.std(), linestyle='-', marker='', color='green', label='simulated R');\n",
+    "plt.xlabel('time', fontsize=15);\n",
+    "plt.ylabel('number of cases', fontsize=15);\n",
+    "plt.title('Number Of Cases Prediction {}x{} {} vertices'.format(experiments,cycles,polygon_sides), fontsize=15);\n",
+    "plt.legend(fontsize=15);\n",
+    "plt.grid();\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_NumberOfCasesPrediction.png'.format(experiments,cycles,polygon_sides));\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The compartimental system goes:\n",
+    "$$\n",
+    "S \\longrightarrow I \\longrightarrow R\n",
+    "$$\n",
+    "\n",
+    "So the number of _new nodes_ infected can be computed by calculating the difference $S(t-1)-S(t)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Number Of New Cases for 53x69 19 vertices')"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xdata = np.arange(cycles+1+t_r)\n",
+    "fitted_S_EXT = np.append(df_S.mean()[0:t_r+1],fitted_S)\n",
+    "fittedS_l_EXT = np.append(df_S.mean()[0:t_r+1],fittedS_l)\n",
+    "fittedS_h_EXT = np.append(df_S.mean()[0:t_r+1],fittedS_h)\n",
+    "new_I = np.append(np.zeros(1),fitted_S_EXT[0:len(fitted_S_EXT)-1])-fitted_S_EXT\n",
+    "new_I_l = np.append(np.zeros(1),fittedS_l_EXT[0:len(fittedS_l_EXT)-1])-fittedS_l_EXT\n",
+    "new_I_h = np.append(np.zeros(1),fittedS_h_EXT[0:len(fittedS_h_EXT)-1])-fittedS_h_EXT\n",
+    "\n",
+    "fig3 = plt.figure(figsize = (10,7))\n",
+    "plt.plot(np.round(new_I)[1:])\n",
+    "plt.fill_between(xdata, np.round(new_I_l[1::]), np.round(new_I_h[1::]), color='green', alpha=0.2)\n",
+    "plt.xlabel('time', fontsize=15)\n",
+    "plt.ylabel('new number of cases', fontsize=15)\n",
+    "plt.axvline(t_r+1)\n",
+    "#plt.legend(fontsize=15)\n",
+    "plt.grid()\n",
+    "plt.title('Number Of New Cases for {}x{} {} vertices'.format(experiments,cycles,polygon_sides), fontsize=15)\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_NumberOfNewCases.png'.format(experiments,cycles,polygon_sides))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 231699,
+     "status": "ok",
+     "timestamp": 1613645627178,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "yZDLcVd_vJJH",
+    "outputId": "89655e59-3809-4341-d4af-691f6a7e1098",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction at t=20\n",
+      "S: 2889+-1\n",
+      "I: 1228+-64\n",
+      "R: 9738+-1643\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict at t\n",
+    "t = 20\n",
+    "print(\"Prediction at t={}\".format(t))\n",
+    "print(\"S: {}+-{}\".format(round(fitted_S[t]), round(abs(fitted_S[t]-fittedS_l[t]))))\n",
+    "print(\"I: {}+-{}\".format(round(fitted_I[t]), round(abs(fitted_I[t]-fittedI_l[t]))))\n",
+    "print(\"R: {}+-{}\".format(round(fitted_R[t]), round(abs(fitted_R[t]-fittedR_l[t]))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Prediction at the end of the epidemic\n",
+    "\n",
+    "The time prediction is helpful to estimate the number of cases during the pandemic. For example, since it has a bell shape, the maximum number of simultaneous infects can be estimated. Also the total number of nodes that have been infected can be estimated from the graph of the new cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to predictions, during the epidemic there will be maximum 1846+-17 infected simultaneously.\n",
+      "Instead, the total number of infected at the end of the epidemic will be: 12945+-17.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"According to predictions, during the epidemic there will be maximum {}+-{} infected simultaneously.\".format(round(max(fitted_I)), round(abs(fitted_I[np.argmax(fitted_I)]-fittedI_l[np.argmax(fitted_I)]))))\n",
+    "print(\"Instead, the total number of infected at the end of the epidemic will be: {}+-{}.\".format(round(sum(new_I[1:])), np.argmax(np.round(abs(new_I-new_I_l)))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Animation of the Evolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 231675,
+     "status": "aborted",
+     "timestamp": 1613645627183,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "Qm8jUOKMvJJI"
+   },
+   "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": [
+    "from matplotlib import animation  \n",
+    "import matplotlib.image as mgimg\n",
+    "\n",
+    "#set up the figure\n",
+    "fig = plt.figure()\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "#initialization of animation, plot array of zeros \n",
+    "def init():\n",
+    "    imobj.set_data(DF_List[0][0].to_numpy().reshape(population.shape[0],population.shape[1]))\n",
+    "\n",
+    "    return  imobj,\n",
+    "\n",
+    "def animate(i): # animation of experiment 0 at time i\n",
+    "    imobj.set_data(DF_List[i][0].to_numpy().reshape(population.shape[0],population.shape[1]))\n",
+    "\n",
+    "    return  imobj,\n",
+    "\n",
+    "\n",
+    "## create an AxesImage object\n",
+    "plt.title('Evolution of the epidemic', fontsize=15)\n",
+    "imobj = ax.imshow(DF_List[0][0].to_numpy().reshape(population.shape[0],population.shape[1]), vmin=-1, vmax=2, cmap='rocket')#, origin='lower', alpha=1.0, zorder=1, aspect=1 )\n",
+    "\n",
+    "anim = animation.FuncAnimation(fig, animate, init_func=init, repeat = True,\n",
+    "                               frames=range(0,49), interval=200, blit=True, repeat_delay=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 231672,
+     "status": "aborted",
+     "timestamp": 1613645627186,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "4ApfuK3IvJJJ",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(anim.to_html5_video())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Some Results"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Copia di Project.ipynb",
+   "provenance": [
+    {
+     "file_id": "https://github.com/albertodetoni/LCP_projects_Y3/blob/master/.ipynb_checkpoints/Project_0.1-checkpoint.ipynb",
+     "timestamp": 1613645717249
+    }
+   ],
+   "toc_visible": true
+  },
+  "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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diff --git a/Data/I_100x300_3v_0.05pi_6tr.csv b/Data/I_100x300_3v_0.05pi_6tr.csv
new file mode 100644
index 0000000..fb533e5
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diff --git a/Data/I_100x300_6v_0.05pi_6tr.csv b/Data/I_100x300_6v_0.05pi_6tr.csv
new file mode 100644
index 0000000..9606053
--- /dev/null
+++ b/Data/I_100x300_6v_0.05pi_6tr.csv
@@ -0,0 +1,101 @@
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diff --git a/Data/I_100x30_6v_0.6pi_6tr.csv b/Data/I_100x30_6v_0.6pi_6tr.csv
new file mode 100644
index 0000000..79cb2e0
--- /dev/null
+++ b/Data/I_100x30_6v_0.6pi_6tr.csv
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diff --git a/Data/I_13x76_5v_0.01pi_2tr.csv b/Data/I_13x76_5v_0.01pi_2tr.csv
new file mode 100644
index 0000000..9687368
--- /dev/null
+++ b/Data/I_13x76_5v_0.01pi_2tr.csv
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diff --git a/Data/I_14x67_14v_0.09pi_4tr.csv b/Data/I_14x67_14v_0.09pi_4tr.csv
new file mode 100644
index 0000000..744e726
--- /dev/null
+++ b/Data/I_14x67_14v_0.09pi_4tr.csv
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diff --git a/Data/I_28x34_8v_0.92pi_4tr.csv b/Data/I_28x34_8v_0.92pi_4tr.csv
new file mode 100644
index 0000000..7894db6
--- /dev/null
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diff --git a/Data/I_28x84_9v_0.97pi_2tr.csv b/Data/I_28x84_9v_0.97pi_2tr.csv
new file mode 100644
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diff --git a/Data/I_30x300_3v_0.05pi_6tr.csv b/Data/I_30x300_3v_0.05pi_6tr.csv
new file mode 100644
index 0000000..0ee6095
--- /dev/null
+++ b/Data/I_30x300_3v_0.05pi_6tr.csv
@@ -0,0 +1,31 @@
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diff --git a/Data/I_30x300_3v_0.6pi_6tr.csv b/Data/I_30x300_3v_0.6pi_6tr.csv
new file mode 100644
index 0000000..5d644d0
--- /dev/null
+++ b/Data/I_30x300_3v_0.6pi_6tr.csv
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diff --git a/Data/I_30x300_6v_0.05pi_6tr.csv b/Data/I_30x300_6v_0.05pi_6tr.csv
new file mode 100644
index 0000000..9df7db9
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diff --git a/Data/I_30x300_6v_0.6pi_6tr.csv b/Data/I_30x300_6v_0.6pi_6tr.csv
new file mode 100644
index 0000000..cd64789
--- /dev/null
+++ b/Data/I_30x300_6v_0.6pi_6tr.csv
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diff --git a/Data/I_31x68_11v_0.58pi_4tr.csv b/Data/I_31x68_11v_0.58pi_4tr.csv
new file mode 100644
index 0000000..3aa571b
--- /dev/null
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diff --git a/Data/I_38x57_4v_0.96pi_1tr.csv b/Data/I_38x57_4v_0.96pi_1tr.csv
new file mode 100644
index 0000000..3c7934d
--- /dev/null
+++ b/Data/I_38x57_4v_0.96pi_1tr.csv
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diff --git a/Data/I_45x51_4v_0.05pi_5tr.csv b/Data/I_45x51_4v_0.05pi_5tr.csv
new file mode 100644
index 0000000..67a8ef9
--- /dev/null
+++ b/Data/I_45x51_4v_0.05pi_5tr.csv
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diff --git a/Data/I_46x53_14v_0.7pi_2tr.csv b/Data/I_46x53_14v_0.7pi_2tr.csv
new file mode 100644
index 0000000..db541a1
--- /dev/null
+++ b/Data/I_46x53_14v_0.7pi_2tr.csv
@@ -0,0 +1,47 @@
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diff --git a/Data/I_48x65_12v_0.0pi_2tr.csv b/Data/I_48x65_12v_0.0pi_2tr.csv
new file mode 100644
index 0000000..a1e0358
--- /dev/null
+++ b/Data/I_48x65_12v_0.0pi_2tr.csv
@@ -0,0 +1,49 @@
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diff --git a/Data/I_62x79_20v_0.29pi_4tr.csv b/Data/I_62x79_20v_0.29pi_4tr.csv
new file mode 100644
index 0000000..847b605
--- /dev/null
+++ b/Data/I_62x79_20v_0.29pi_4tr.csv
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diff --git a/Data/I_64x35_9v_0.53pi_5tr.csv b/Data/I_64x35_9v_0.53pi_5tr.csv
new file mode 100644
index 0000000..279c305
--- /dev/null
+++ b/Data/I_64x35_9v_0.53pi_5tr.csv
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diff --git a/Data/I_67x87_14v_0.11pi_3tr.csv b/Data/I_67x87_14v_0.11pi_3tr.csv
new file mode 100644
index 0000000..7860d6d
--- /dev/null
+++ b/Data/I_67x87_14v_0.11pi_3tr.csv
@@ -0,0 +1,68 @@
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diff --git a/Data/I_69x127_18v_0.69pi_1tr.csv b/Data/I_69x127_18v_0.69pi_1tr.csv
new file mode 100644
index 0000000..478bb8b
--- /dev/null
+++ b/Data/I_69x127_18v_0.69pi_1tr.csv
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diff --git a/Data/I_75x55_11v_0.28pi_5tr.csv b/Data/I_75x55_11v_0.28pi_5tr.csv
new file mode 100644
index 0000000..8ea1b00
--- /dev/null
+++ b/Data/I_75x55_11v_0.28pi_5tr.csv
@@ -0,0 +1,76 @@
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diff --git a/Data/I_88x70_11v_0.25pi_2tr.csv b/Data/I_88x70_11v_0.25pi_2tr.csv
new file mode 100644
index 0000000..6b399f6
--- /dev/null
+++ b/Data/I_88x70_11v_0.25pi_2tr.csv
@@ -0,0 +1,89 @@
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diff --git a/Data/I_91x65_8v_0.76pi_1tr.csv b/Data/I_91x65_8v_0.76pi_1tr.csv
new file mode 100644
index 0000000..c77f2bf
--- /dev/null
+++ b/Data/I_91x65_8v_0.76pi_1tr.csv
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diff --git a/Data/I_92x66_7v_0.42pi_4tr.csv b/Data/I_92x66_7v_0.42pi_4tr.csv
new file mode 100644
index 0000000..63bc6c8
--- /dev/null
+++ b/Data/I_92x66_7v_0.42pi_4tr.csv
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diff --git a/Data/I_96x86_3v_0.87pi_3tr.csv b/Data/I_96x86_3v_0.87pi_3tr.csv
new file mode 100644
index 0000000..1ab81bc
--- /dev/null
+++ b/Data/I_96x86_3v_0.87pi_3tr.csv
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diff --git a/Data/R_100x300_3v_0.05pi_6tr.csv b/Data/R_100x300_3v_0.05pi_6tr.csv
new file mode 100644
index 0000000..6fb02ce
--- /dev/null
+++ b/Data/R_100x300_3v_0.05pi_6tr.csv
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diff --git a/Data/R_100x300_6v_0.05pi_6tr.csv b/Data/R_100x300_6v_0.05pi_6tr.csv
new file mode 100644
index 0000000..444eae4
--- /dev/null
+++ b/Data/R_100x300_6v_0.05pi_6tr.csv
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diff --git a/Data/R_100x30_6v_0.6pi_6tr.csv b/Data/R_100x30_6v_0.6pi_6tr.csv
new file mode 100644
index 0000000..7d9828c
--- /dev/null
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diff --git a/Data/R_13x76_5v_0.01pi_2tr.csv b/Data/R_13x76_5v_0.01pi_2tr.csv
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diff --git a/Data/R_14x67_14v_0.09pi_4tr.csv b/Data/R_14x67_14v_0.09pi_4tr.csv
new file mode 100644
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diff --git a/Data/R_28x34_8v_0.92pi_4tr.csv b/Data/R_28x34_8v_0.92pi_4tr.csv
new file mode 100644
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diff --git a/Data/R_28x84_9v_0.97pi_2tr.csv b/Data/R_28x84_9v_0.97pi_2tr.csv
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diff --git a/Data/R_30x300_3v_0.05pi_6tr.csv b/Data/R_30x300_3v_0.05pi_6tr.csv
new file mode 100644
index 0000000..15b8945
--- /dev/null
+++ b/Data/R_30x300_3v_0.05pi_6tr.csv
@@ -0,0 +1,31 @@
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diff --git a/Data/R_30x300_3v_0.6pi_6tr.csv b/Data/R_30x300_3v_0.6pi_6tr.csv
new file mode 100644
index 0000000..a93a68f
--- /dev/null
+++ b/Data/R_30x300_3v_0.6pi_6tr.csv
@@ -0,0 +1,31 @@
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diff --git a/Data/R_30x300_6v_0.05pi_6tr.csv b/Data/R_30x300_6v_0.05pi_6tr.csv
new file mode 100644
index 0000000..64c8992
--- /dev/null
+++ b/Data/R_30x300_6v_0.05pi_6tr.csv
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diff --git a/Data/R_30x300_6v_0.6pi_6tr.csv b/Data/R_30x300_6v_0.6pi_6tr.csv
new file mode 100644
index 0000000..6de6e7d
--- /dev/null
+++ b/Data/R_30x300_6v_0.6pi_6tr.csv
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diff --git a/Data/R_31x68_11v_0.58pi_4tr.csv b/Data/R_31x68_11v_0.58pi_4tr.csv
new file mode 100644
index 0000000..402ad1e
--- /dev/null
+++ b/Data/R_31x68_11v_0.58pi_4tr.csv
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diff --git a/Data/R_38x57_4v_0.96pi_1tr.csv b/Data/R_38x57_4v_0.96pi_1tr.csv
new file mode 100644
index 0000000..0339b78
--- /dev/null
+++ b/Data/R_38x57_4v_0.96pi_1tr.csv
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diff --git a/Data/R_45x51_4v_0.05pi_5tr.csv b/Data/R_45x51_4v_0.05pi_5tr.csv
new file mode 100644
index 0000000..e548f4a
--- /dev/null
+++ b/Data/R_45x51_4v_0.05pi_5tr.csv
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diff --git a/Data/R_46x53_14v_0.7pi_2tr.csv b/Data/R_46x53_14v_0.7pi_2tr.csv
new file mode 100644
index 0000000..7c7939e
--- /dev/null
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diff --git a/Data/R_48x65_12v_0.0pi_2tr.csv b/Data/R_48x65_12v_0.0pi_2tr.csv
new file mode 100644
index 0000000..b13f38b
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diff --git a/Data/R_62x79_20v_0.29pi_4tr.csv b/Data/R_62x79_20v_0.29pi_4tr.csv
new file mode 100644
index 0000000..be53788
--- /dev/null
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diff --git a/Data/R_64x35_9v_0.53pi_5tr.csv b/Data/R_64x35_9v_0.53pi_5tr.csv
new file mode 100644
index 0000000..bfc750b
--- /dev/null
+++ b/Data/R_64x35_9v_0.53pi_5tr.csv
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diff --git a/Data/R_67x87_14v_0.11pi_3tr.csv b/Data/R_67x87_14v_0.11pi_3tr.csv
new file mode 100644
index 0000000..b9dbe6b
--- /dev/null
+++ b/Data/R_67x87_14v_0.11pi_3tr.csv
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diff --git a/Data/R_69x127_18v_0.69pi_1tr.csv b/Data/R_69x127_18v_0.69pi_1tr.csv
new file mode 100644
index 0000000..cb65d8e
--- /dev/null
+++ b/Data/R_69x127_18v_0.69pi_1tr.csv
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diff --git a/Data/R_75x55_11v_0.28pi_5tr.csv b/Data/R_75x55_11v_0.28pi_5tr.csv
new file mode 100644
index 0000000..e1890ae
--- /dev/null
+++ b/Data/R_75x55_11v_0.28pi_5tr.csv
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diff --git a/Data/R_88x70_11v_0.25pi_2tr.csv b/Data/R_88x70_11v_0.25pi_2tr.csv
new file mode 100644
index 0000000..88db666
--- /dev/null
+++ b/Data/R_88x70_11v_0.25pi_2tr.csv
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diff --git a/Data/R_91x65_8v_0.76pi_1tr.csv b/Data/R_91x65_8v_0.76pi_1tr.csv
new file mode 100644
index 0000000..8c32e3d
--- /dev/null
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+0,10,69,219,436,703,1028,1375,1751,2168,2583,3020,3452,3848,4223,4578,4870,5085,5251,5372,5469,5559,5638,5693,5730,5752,5768,5782,5792,5796,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,72,210,410,660,954,1286,1649,2028,2447,2863,3224,3570,3864,4114,4278,4415,4542,4662,4778,4882,4976,5063,5134,5204,5264,5322,5377,5429,5475,5517,5559,5600,5635,5667,5696,5724,5748,5771,5783,5795,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,68,208,413,677,996,1365,1789,2198,2612,3045,3493,3944,4325,4631,4894,5103,5276,5402,5505,5589,5663,5719,5771,5790,5796,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,67,185,363,572,813,1055,1332,1621,1912,2218,2511,2804,3064,3305,3540,3775,4009,4233,4441,4649,4837,4999,5156,5290,5420,5545,5599,5643,5681,5712,5740,5763,5780,5791,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,74,218,443,727,1072,1464,1890,2309,2707,3080,3461,3823,4172,4500,4742,4947,5118,5269,5398,5508,5594,5659,5700,5733,5757,5776,5789,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,72,203,397,638,917,1236,1575,1951,2343,2768,3182,3540,3881,4191,4466,4734,5001,5153,5286,5387,5462,5520,5568,5608,5644,5675,5703,5726,5747,5763,5777,5786,5793,5796,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,73,212,421,673,939,1230,1546,1885,2258,2627,2962,3299,3639,3974,4309,4602,4850,5059,5232,5380,5494,5590,5668,5719,5748,5770,5786,5796,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,75,214,429,701,1017,1364,1754,2159,2594,2976,3321,3626,3915,4193,4447,4664,4841,4985,5116,5231,5337,5434,5519,5590,5651,5697,5731,5757,5774,5787,5795,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,71,212,416,688,1016,1396,1805,2237,2673,3129,3567,3980,4306,4589,4850,5082,5290,5454,5571,5644,5698,5734,5759,5778,5789,5796,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,67,201,405,652,925,1219,1526,1835,2165,2482,2790,3081,3362,3642,3920,4195,4469,4698,4880,5046,5179,5304,5407,5489,5562,5617,5662,5703,5734,5758,5774,5784,5792,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,69,205,411,663,935,1243,1578,1912,2261,2588,2913,3214,3498,3783,4043,4288,4502,4683,4857,5018,5162,5297,5420,5513,5580,5626,5667,5702,5731,5754,5772,5785,5793,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,62,180,374,626,928,1279,1630,2001,2374,2723,3026,3306,3561,3790,4007,4196,4377,4529,4655,4771,4873,4964,5045,5120,5192,5261,5327,5389,5448,5507,5561,5611,5657,5693,5723,5748,5767,5782,5792,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,69,189,377,626,922,1257,1614,1956,2278,2596,2872,3127,3361,3576,3786,3975,4156,4320,4487,4643,4794,4922,5037,5132,5204,5263,5316,5369,5419,5464,5507,5546,5583,5619,5650,5679,5704,5727,5746,5763,5777,5788,5795,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,76,215,422,680,992,1329,1667,2007,2351,2676,2986,3288,3575,3866,4153,4398,4629,4847,5061,5255,5434,5542,5625,5677,5717,5749,5769,5784,5794,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,74,215,440,731,1088,1498,1950,2436,2894,3276,3610,3908,4178,4422,4653,4866,5059,5231,5371,5486,5581,5659,5718,5757,5784,5795,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,69,199,386,569,759,952,1165,1393,1640,1895,2179,2468,2762,3075,3336,3589,3844,4100,4375,4648,4917,5145,5334,5439,5532,5607,5669,5721,5759,5783,5796,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,66,196,388,623,855,1068,1287,1500,1716,1931,2157,2388,2620,2860,3102,3333,3549,3769,3983,4188,4355,4514,4664,4810,4946,5074,5202,5329,5436,5519,5583,5623,5655,5684,5710,5731,5752,5768,5782,5792,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,66,189,369,600,886,1214,1586,1973,2370,2765,3177,3573,3886,4151,4388,4571,4725,4858,4980,5100,5209,5305,5400,5493,5574,5645,5698,5735,5763,5783,5795,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,74,217,426,674,974,1321,1685,2068,2441,2812,3186,3552,3921,4278,4615,4907,5111,5297,5464,5603,5698,5758,5790,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,64,200,398,647,925,1231,1562,1911,2255,2607,2967,3334,3708,4021,4272,4464,4617,4765,4900,5002,5075,5150,5217,5276,5336,5395,5451,5502,5550,5595,5638,5678,5710,5733,5753,5770,5784,5794,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,82,225,448,727,1058,1414,1827,2271,2685,3073,3416,3692,3943,4172,4388,4591,4763,4895,5019,5124,5225,5314,5402,5475,5538,5582,5620,5654,5685,5712,5736,5757,5774,5786,5794,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,69,202,415,683,994,1321,1685,2056,2420,2775,3095,3402,3679,3943,4204,4432,4634,4830,5021,5196,5342,5457,5557,5640,5701,5749,5778,5793,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,67,206,409,680,1015,1407,1807,2229,2631,3012,3389,3757,4103,4417,4671,4881,5051,5191,5308,5417,5511,5584,5644,5692,5731,5760,5784,5796,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,71,203,416,692,1029,1416,1870,2317,2773,3227,3648,4042,4393,4665,4892,5089,5271,5418,5531,5614,5678,5723,5753,5775,5790,5796,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,71,209,417,682,975,1302,1656,2008,2343,2653,2950,3192,3400,3605,3810,3995,4173,4339,4493,4648,4791,4917,5050,5163,5263,5355,5436,5505,5567,5622,5666,5704,5732,5754,5771,5786,5795,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,70,213,427,702,1059,1469,1893,2323,2714,3100,3478,3807,4091,4340,4572,4789,4982,5145,5276,5363,5437,5493,5536,5578,5615,5648,5677,5704,5726,5748,5765,5780,5791,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,70,204,398,660,987,1332,1677,2003,2311,2618,2945,3259,3540,3798,4039,4264,4471,4661,4836,4985,5117,5237,5344,5437,5524,5599,5661,5709,5744,5763,5780,5790,5795,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796
+0,10,65,205,399,666,975,1328,1677,2022,2385,2751,3091,3367,3601,3827,4043,4234,4411,4566,4702,4840,4963,5070,5144,5209,5272,5330,5391,5449,5503,5555,5605,5654,5700,5742,5767,5782,5792,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,67,211,411,679,1005,1361,1732,2098,2476,2833,3158,3432,3679,3916,4151,4374,4585,4784,4943,5084,5205,5306,5396,5471,5536,5592,5638,5675,5708,5732,5750,5766,5778,5787,5793,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,71,213,427,692,1013,1377,1797,2245,2683,3116,3539,3944,4300,4589,4824,5004,5162,5299,5418,5529,5624,5701,5749,5776,5789,5795,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,64,191,383,629,939,1269,1626,2023,2438,2841,3241,3623,3983,4318,4614,4880,5095,5250,5376,5481,5562,5629,5671,5708,5736,5757,5774,5786,5794,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,9,66,188,379,633,945,1300,1679,2077,2454,2801,3150,3497,3830,4136,4418,4671,4886,5060,5229,5380,5500,5596,5671,5724,5760,5777,5788,5795,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,66,202,401,660,966,1316,1686,2057,2393,2722,3045,3345,3648,3918,4183,4438,4681,4922,5132,5303,5403,5452,5496,5536,5575,5610,5643,5673,5699,5724,5745,5763,5778,5789,5796,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,71,202,388,623,896,1196,1492,1799,2111,2396,2654,2907,3136,3335,3521,3693,3860,3998,4140,4268,4395,4521,4621,4714,4807,4895,4976,5063,5145,5222,5296,5359,5417,5471,5524,5570,5619,5661,5697,5727,5751,5770,5785,5794,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,69,210,396,610,844,1116,1418,1752,2112,2482,2840,3191,3548,3914,4228,4521,4784,5014,5219,5361,5492,5611,5665,5706,5735,5757,5775,5787,5795,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797,5797
+0,10,75,208,405,643,912,1202,1500,1763,2018,2276,2510,2745,2947,3109,3270,3414,3555,3695,3828,3953,4069,4188,4306,4416,4527,4633,4736,4838,4939,5034,5123,5200,5273,5338,5401,5452,5500,5542,5583,5619,5654,5685,5713,5739,5759,5774,5786,5795,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796,5796
+0,10,75,213,405,630,885,1183,1489,1807,2140,2488,2849,3213,3560,3855,4121,4381,4615,4811,4977,5119,5241,5341,5428,5498,5560,5595,5626,5656,5684,5709,5731,5750,5763,5777,5787,5794,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
+0,10,68,220,442,737,1092,1504,1914,2274,2618,2931,3206,3462,3720,3957,4160,4344,4525,4684,4833,4966,5079,5173,5268,5359,5442,5514,5580,5640,5692,5734,5767,5786,5794,5797,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798,5798
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diff --git a/Data/R_92x66_7v_0.42pi_4tr.csv b/Data/R_92x66_7v_0.42pi_4tr.csv
new file mode 100644
index 0000000..fe059d6
--- /dev/null
+++ b/Data/R_92x66_7v_0.42pi_4tr.csv
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diff --git a/Data/R_96x86_3v_0.87pi_3tr.csv b/Data/R_96x86_3v_0.87pi_3tr.csv
new file mode 100644
index 0000000..a2e0bf6
--- /dev/null
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diff --git a/Data/S_100x300_3v_0.05pi_6tr.csv b/Data/S_100x300_3v_0.05pi_6tr.csv
new file mode 100644
index 0000000..531fdad
--- /dev/null
+++ b/Data/S_100x300_3v_0.05pi_6tr.csv
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diff --git a/Data/S_100x300_6v_0.05pi_6tr.csv b/Data/S_100x300_6v_0.05pi_6tr.csv
new file mode 100644
index 0000000..ccbca48
--- /dev/null
+++ b/Data/S_100x300_6v_0.05pi_6tr.csv
@@ -0,0 +1,101 @@
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diff --git a/Data/S_100x30_6v_0.6pi_6tr.csv b/Data/S_100x30_6v_0.6pi_6tr.csv
new file mode 100644
index 0000000..35b87e2
--- /dev/null
+++ b/Data/S_100x30_6v_0.6pi_6tr.csv
@@ -0,0 +1,101 @@
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diff --git a/Data/S_28x34_8v_0.92pi_4tr.csv b/Data/S_28x34_8v_0.92pi_4tr.csv
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diff --git a/Data/S_28x84_9v_0.97pi_2tr.csv b/Data/S_28x84_9v_0.97pi_2tr.csv
new file mode 100644
index 0000000..f8c0080
--- /dev/null
+++ b/Data/S_28x84_9v_0.97pi_2tr.csv
@@ -0,0 +1,29 @@
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diff --git a/Data/S_30x300_3v_0.05pi_6tr.csv b/Data/S_30x300_3v_0.05pi_6tr.csv
new file mode 100644
index 0000000..2ba7c8f
--- /dev/null
+++ b/Data/S_30x300_3v_0.05pi_6tr.csv
@@ -0,0 +1,31 @@
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diff --git a/Data/S_30x300_3v_0.6pi_6tr.csv b/Data/S_30x300_3v_0.6pi_6tr.csv
new file mode 100644
index 0000000..9f6a8a1
--- /dev/null
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diff --git a/Data/S_30x300_6v_0.05pi_6tr.csv b/Data/S_30x300_6v_0.05pi_6tr.csv
new file mode 100644
index 0000000..5013dde
--- /dev/null
+++ b/Data/S_30x300_6v_0.05pi_6tr.csv
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diff --git a/Data/S_30x300_6v_0.6pi_6tr.csv b/Data/S_30x300_6v_0.6pi_6tr.csv
new file mode 100644
index 0000000..7968650
--- /dev/null
+++ b/Data/S_30x300_6v_0.6pi_6tr.csv
@@ -0,0 +1,31 @@
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diff --git a/Data/S_31x68_11v_0.58pi_4tr.csv b/Data/S_31x68_11v_0.58pi_4tr.csv
new file mode 100644
index 0000000..ac0e519
--- /dev/null
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diff --git a/Data/S_38x57_4v_0.96pi_1tr.csv b/Data/S_38x57_4v_0.96pi_1tr.csv
new file mode 100644
index 0000000..ee02c75
--- /dev/null
+++ b/Data/S_38x57_4v_0.96pi_1tr.csv
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diff --git a/Data/S_45x51_4v_0.05pi_5tr.csv b/Data/S_45x51_4v_0.05pi_5tr.csv
new file mode 100644
index 0000000..b79cd80
--- /dev/null
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diff --git a/Data/S_46x53_14v_0.7pi_2tr.csv b/Data/S_46x53_14v_0.7pi_2tr.csv
new file mode 100644
index 0000000..22adc3c
--- /dev/null
+++ b/Data/S_46x53_14v_0.7pi_2tr.csv
@@ -0,0 +1,47 @@
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diff --git a/Data/S_48x65_12v_0.0pi_2tr.csv b/Data/S_48x65_12v_0.0pi_2tr.csv
new file mode 100644
index 0000000..9279a0d
--- /dev/null
+++ b/Data/S_48x65_12v_0.0pi_2tr.csv
@@ -0,0 +1,49 @@
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diff --git a/Data/S_62x79_20v_0.29pi_4tr.csv b/Data/S_62x79_20v_0.29pi_4tr.csv
new file mode 100644
index 0000000..4446657
--- /dev/null
+++ b/Data/S_62x79_20v_0.29pi_4tr.csv
@@ -0,0 +1,63 @@
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diff --git a/Data/S_64x35_9v_0.53pi_5tr.csv b/Data/S_64x35_9v_0.53pi_5tr.csv
new file mode 100644
index 0000000..bcd71bc
--- /dev/null
+++ b/Data/S_64x35_9v_0.53pi_5tr.csv
@@ -0,0 +1,65 @@
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diff --git a/Data/S_67x87_14v_0.11pi_3tr.csv b/Data/S_67x87_14v_0.11pi_3tr.csv
new file mode 100644
index 0000000..2c5571e
--- /dev/null
+++ b/Data/S_67x87_14v_0.11pi_3tr.csv
@@ -0,0 +1,68 @@
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diff --git a/Data/S_69x127_18v_0.69pi_1tr.csv b/Data/S_69x127_18v_0.69pi_1tr.csv
new file mode 100644
index 0000000..8c8fb6b
--- /dev/null
+++ b/Data/S_69x127_18v_0.69pi_1tr.csv
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diff --git a/Data/S_75x55_11v_0.28pi_5tr.csv b/Data/S_75x55_11v_0.28pi_5tr.csv
new file mode 100644
index 0000000..62c8396
--- /dev/null
+++ b/Data/S_75x55_11v_0.28pi_5tr.csv
@@ -0,0 +1,76 @@
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diff --git a/Data/S_88x70_11v_0.25pi_2tr.csv b/Data/S_88x70_11v_0.25pi_2tr.csv
new file mode 100644
index 0000000..c6befbd
--- /dev/null
+++ b/Data/S_88x70_11v_0.25pi_2tr.csv
@@ -0,0 +1,89 @@
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diff --git a/Data/S_91x65_8v_0.76pi_1tr.csv b/Data/S_91x65_8v_0.76pi_1tr.csv
new file mode 100644
index 0000000..c602979
--- /dev/null
+++ b/Data/S_91x65_8v_0.76pi_1tr.csv
@@ -0,0 +1,92 @@
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+5788,5729,5592,5391,5126,4796,4422,3984,3538,3077,2598,2151,1740,1411,1153,938,749,578,435,335,255,197,151,113,83,57,36,21,9,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5729,5590,5387,5128,4816,4453,4080,3682,3272,2887,2526,2167,1839,1524,1229,963,746,555,435,353,302,259,218,182,151,123,98,75,55,38,24,13,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5726,5590,5394,5148,4843,4485,4092,3662,3205,2747,2313,1882,1528,1223,968,740,541,357,218,150,106,67,37,19,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5730,5586,5372,5086,4750,4366,3948,3501,3019,2544,2129,1751,1429,1142,909,712,550,407,288,190,126,78,43,19,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5734,5600,5404,5162,4880,4567,4227,3885,3539,3202,2878,2586,2295,2028,1790,1554,1334,1128,953,792,666,561,469,386,314,252,193,145,110,79,53,33,18,8,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5733,5613,5428,5190,4912,4589,4258,3932,3605,3289,2981,2685,2424,2171,1931,1708,1505,1316,1144,995,853,716,585,458,337,222,122,69,42,22,9,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5728,5585,5381,5114,4816,4485,4143,3791,3423,3038,2666,2281,1891,1514,1203,969,778,612,482,378,292,231,174,126,82,48,23,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5727,5590,5374,5093,4760,4396,4003,3602,3196,2804,2431,2094,1760,1427,1113,829,569,379,273,215,173,138,109,85,60,40,24,11,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5725,5580,5354,5064,4708,4297,3872,3448,3019,2571,2173,1823,1534,1293,1091,893,718,560,422,314,221,155,107,68,38,18,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5732,5601,5411,5176,4906,4605,4276,3951,3631,3298,2953,2596,2246,1907,1616,1367,1152,979,843,730,628,542,464,388,317,256,201,156,113,79,56,36,22,10,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+5788,5732,5600,5402,5147,4852,4530,4181,3815,3416,3003,2588,2162,1759,1399,1090,825,588,367,201,100,50,22,8,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5728,5583,5371,5110,4787,4431,4042,3661,3257,2855,2479,2112,1770,1533,1326,1154,993,845,707,588,491,417,343,283,227,183,138,101,69,39,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5737,5598,5399,5127,4775,4391,3984,3555,3107,2656,2204,1792,1406,1045,809,671,556,468,387,312,249,184,125,84,51,31,13,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5729,5579,5362,5095,4770,4423,4047,3630,3215,2778,2346,1950,1575,1220,928,713,547,426,329,239,160,105,68,46,30,16,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5726,5588,5388,5138,4844,4512,4149,3770,3351,2935,2574,2228,1934,1684,1520,1383,1256,1136,1020,916,822,735,664,594,534,476,421,369,323,281,239,198,163,131,102,74,50,27,15,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5730,5590,5385,5121,4802,4433,4009,3600,3186,2753,2305,1854,1473,1167,904,695,522,396,293,209,135,79,27,8,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5731,5613,5435,5226,4985,4743,4466,4177,3886,3580,3287,2994,2734,2493,2258,2023,1789,1565,1357,1149,961,799,642,508,378,253,199,155,117,86,58,35,18,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5724,5580,5355,5071,4726,4334,3908,3489,3091,2718,2337,1975,1626,1298,1056,851,680,529,400,290,204,139,98,65,41,22,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5726,5595,5401,5160,4881,4562,4223,3847,3455,3030,2616,2258,1917,1607,1332,1064,797,645,512,411,336,278,230,190,154,123,95,72,51,35,21,12,5,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5725,5586,5377,5125,4859,4568,4252,3913,3540,3171,2836,2499,2159,1824,1489,1196,948,739,566,418,304,208,130,79,50,28,12,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5723,5584,5369,5097,4781,4434,4044,3639,3204,2822,2477,2172,1883,1605,1351,1134,957,813,682,567,461,364,279,208,147,101,67,41,24,11,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5727,5586,5382,5110,4782,4402,3993,3561,3125,2669,2231,1818,1492,1209,948,716,508,344,227,154,100,64,39,20,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5731,5597,5393,5146,4873,4579,4272,3963,3633,3316,3008,2717,2436,2156,1878,1603,1329,1100,918,752,619,494,391,309,236,181,136,95,64,40,24,14,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5729,5593,5387,5135,4863,4555,4220,3886,3537,3210,2885,2584,2300,2015,1755,1510,1296,1115,941,780,636,501,378,285,218,172,131,96,67,44,26,13,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5736,5618,5424,5172,4870,4519,4168,3797,3424,3075,2772,2492,2237,2008,1791,1602,1421,1269,1143,1027,925,834,753,678,606,537,471,409,350,291,237,187,141,105,75,50,31,16,6,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5729,5609,5421,5172,4876,4541,4184,3842,3520,3202,2926,2671,2437,2222,2012,1823,1642,1478,1311,1155,1004,876,761,666,594,535,482,429,379,334,291,252,215,179,148,119,94,71,52,35,21,10,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5722,5583,5376,5118,4806,4469,4131,3791,3447,3122,2812,2510,2223,1932,1645,1400,1169,951,737,543,364,256,173,121,81,49,29,14,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5724,5583,5358,5067,4710,4300,3848,3362,2904,2522,2188,1890,1620,1376,1145,932,739,567,427,312,217,139,80,41,14,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5729,5599,5412,5229,5039,4846,4633,4405,4158,3903,3619,3330,3036,2723,2462,2209,1954,1698,1423,1150,881,653,464,359,266,191,129,77,39,15,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5732,5602,5410,5175,4943,4730,4511,4298,4082,3867,3641,3410,3178,2938,2696,2465,2249,2029,1815,1610,1443,1284,1134,988,852,724,596,469,362,279,215,175,143,114,88,67,46,30,16,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5732,5609,5429,5198,4912,4584,4212,3825,3428,3033,2621,2225,1912,1647,1410,1227,1073,940,818,698,589,493,398,305,224,153,100,63,35,15,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5724,5581,5372,5124,4824,4477,4113,3730,3357,2986,2612,2246,1877,1520,1183,891,687,501,334,195,100,40,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5734,5598,5400,5151,4873,4567,4236,3887,3543,3191,2831,2464,2090,1777,1526,1334,1181,1033,898,796,723,648,581,522,462,403,347,296,248,203,160,120,88,65,45,28,14,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5716,5573,5350,5071,4740,4384,3971,3527,3113,2725,2382,2106,1855,1626,1410,1207,1035,903,779,674,573,484,396,323,260,216,178,144,113,86,62,41,24,12,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5729,5596,5383,5115,4804,4477,4113,3742,3378,3023,2703,2396,2119,1855,1594,1366,1164,968,777,602,456,341,241,158,97,49,20,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5731,5592,5389,5118,4783,4391,3991,3569,3167,2786,2409,2041,1695,1381,1127,917,747,607,490,381,287,214,154,106,67,38,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5727,5595,5382,5106,4769,4382,3928,3481,3025,2571,2150,1756,1405,1133,906,709,527,380,267,184,120,75,45,23,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5727,5589,5381,5116,4823,4496,4142,3790,3455,3145,2848,2606,2398,2193,1988,1803,1625,1459,1305,1150,1007,881,748,635,535,443,362,293,231,176,132,94,66,44,27,12,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5728,5585,5371,5096,4739,4329,3905,3475,3084,2698,2320,1991,1707,1458,1226,1009,816,653,522,435,361,305,262,220,183,150,121,94,72,50,33,18,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5728,5594,5400,5138,4811,4466,4121,3795,3487,3180,2853,2539,2258,2000,1759,1534,1327,1137,962,813,681,561,454,361,274,199,137,89,54,35,18,8,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
+5788,5733,5593,5399,5132,4823,4470,4121,3776,3413,3047,2707,2431,2197,1971,1755,1564,1387,1232,1096,958,835,728,654,589,526,468,407,349,295,243,193,144,98,56,31,16,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5731,5587,5387,5119,4793,4437,4066,3700,3322,2965,2640,2366,2119,1882,1647,1424,1213,1014,855,714,593,492,402,327,262,206,160,123,90,66,48,32,20,11,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5727,5585,5371,5106,4785,4421,4001,3553,3115,2682,2259,1854,1498,1209,974,794,636,499,380,269,174,97,49,22,9,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5734,5607,5415,5169,4859,4529,4172,3775,3360,2957,2557,2175,1815,1480,1184,918,703,548,422,317,236,169,127,90,62,41,24,12,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5789,5732,5610,5419,5165,4853,4498,4119,3721,3344,2997,2648,2301,1968,1662,1380,1127,912,738,569,418,298,202,127,74,38,21,10,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5732,5596,5397,5138,4832,4482,4112,3741,3405,3076,2753,2453,2150,1880,1615,1360,1117,876,666,495,395,346,302,262,223,188,155,125,99,74,53,35,20,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5727,5596,5410,5175,4902,4602,4306,3999,3687,3402,3144,2891,2662,2463,2277,2105,1938,1800,1658,1530,1403,1277,1177,1084,991,903,822,735,653,576,502,439,381,327,274,228,179,137,101,71,47,28,13,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5788,5729,5588,5402,5188,4954,4682,4380,4046,3686,3316,2958,2607,2250,1884,1570,1277,1014,784,579,437,306,187,133,92,63,41,23,11,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5788,5723,5590,5393,5155,4886,4596,4298,4035,3780,3522,3288,3053,2851,2689,2528,2384,2243,2103,1970,1845,1729,1610,1492,1382,1271,1165,1062,960,859,764,675,598,525,460,397,346,298,256,215,179,144,113,85,59,39,24,12,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
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diff --git a/Data/S_92x66_7v_0.42pi_4tr.csv b/Data/S_92x66_7v_0.42pi_4tr.csv
new file mode 100644
index 0000000..58ed173
--- /dev/null
+++ b/Data/S_92x66_7v_0.42pi_4tr.csv
@@ -0,0 +1,93 @@
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diff --git a/Data/S_96x86_3v_0.87pi_3tr.csv b/Data/S_96x86_3v_0.87pi_3tr.csv
new file mode 100644
index 0000000..c17cea8
--- /dev/null
+++ b/Data/S_96x86_3v_0.87pi_3tr.csv
@@ -0,0 +1,97 @@
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+6740,6671,6515,6287,5991,5660,5293,4916,4526,4132,3725,3295,2900,2500,2153,1841,1559,1295,1085,902,763,679,604,533,463,397,334,275,217,181,154,132,111,92,75,60,46,34,24,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6527,6338,6092,5799,5472,5122,4753,4391,4040,3690,3340,3001,2692,2433,2193,1959,1743,1536,1334,1135,957,820,717,628,548,479,416,355,298,246,199,155,118,87,59,39,23,11,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6526,6317,6072,5807,5520,5222,4904,4573,4243,3908,3574,3254,2975,2730,2530,2366,2224,2106,2002,1900,1801,1701,1603,1506,1411,1318,1251,1185,1121,1063,1006,950,896,845,796,749,702,656,613,570,529,490,453,416,380,347,314,283,254,226,199,174,150,128,107,87,69,52,37,23,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6668,6535,6339,6088,5782,5436,5056,4661,4245,3839,3439,3043,2652,2291,2012,1755,1510,1281,1063,855,655,477,379,294,217,153,110,80,58,38,19,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6676,6534,6312,6027,5677,5292,4869,4413,3930,3453,2990,2520,2047,1585,1202,900,680,493,352,240,145,78,35,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6539,6349,6111,5827,5544,5252,4949,4630,4298,3975,3645,3312,2982,2646,2304,2062,1844,1646,1476,1323,1185,1069,969,883,808,736,670,613,566,531,496,463,431,400,371,342,317,289,263,239,216,195,174,155,136,119,103,88,75,62,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6535,6343,6098,5815,5489,5138,4759,4358,3937,3517,3114,2708,2318,1971,1676,1412,1198,1004,823,659,517,397,296,219,158,111,71,46,34,24,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6665,6511,6291,6036,5728,5392,5032,4672,4307,3931,3538,3138,2740,2334,1929,1549,1260,1032,844,673,532,403,332,268,209,159,112,71,37,12,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6670,6529,6329,6090,5815,5510,5167,4812,4430,4033,3612,3208,2807,2423,2114,1820,1549,1310,1108,910,735,586,452,350,287,231,182,139,101,75,57,40,25,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6517,6280,5976,5616,5208,4769,4313,3847,3360,2895,2469,2105,1874,1675,1484,1308,1154,1020,899,799,713,633,562,516,474,434,396,360,325,292,261,232,204,178,154,132,111,92,75,60,46,34,24,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6525,6308,6031,5717,5393,5077,4766,4496,4245,4002,3767,3535,3309,3090,2873,2672,2478,2293,2116,1943,1778,1625,1474,1326,1192,1108,1029,955,885,818,752,692,631,582,534,489,446,405,364,328,292,254,218,187,162,139,119,103,88,75,63,51,40,31,24,17,12,8,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6539,6355,6128,5858,5577,5294,5004,4714,4421,4128,3880,3633,3396,3186,2984,2792,2621,2454,2290,2133,1977,1827,1679,1544,1413,1285,1160,1041,928,824,722,627,537,458,389,327,273,226,185,148,115,85,58,34,24,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6529,6333,6072,5779,5455,5099,4714,4290,3832,3371,2892,2462,2078,1732,1429,1160,958,787,643,516,399,328,262,203,151,104,63,27,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6676,6531,6310,6031,5699,5321,4934,4542,4200,3871,3530,3190,2850,2496,2143,1794,1492,1265,1071,898,738,592,464,350,248,154,99,60,33,10,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6668,6514,6295,6005,5666,5288,4869,4444,4006,3534,3048,2571,2122,1740,1403,1115,849,619,446,347,256,169,89,50,14,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6535,6328,6078,5789,5483,5139,4771,4391,4025,3649,3262,2882,2498,2121,1770,1501,1267,1059,864,681,519,422,360,308,267,231,194,162,137,119,103,88,75,62,52,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6678,6535,6332,6066,5732,5347,4961,4581,4212,3840,3468,3097,2726,2378,2080,1845,1640,1471,1321,1187,1066,958,867,778,692,612,531,464,413,365,319,275,231,192,163,138,119,104,88,75,62,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6671,6514,6293,6007,5661,5269,4847,4388,3892,3451,3024,2590,2175,1781,1405,1077,800,580,410,279,176,95,37,16,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6528,6323,6077,5797,5498,5188,4855,4501,4144,3805,3460,3144,2851,2558,2270,1995,1724,1459,1201,945,711,549,422,329,254,191,140,95,60,33,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6517,6299,6035,5728,5393,5045,4709,4378,4066,3788,3527,3266,3012,2774,2553,2341,2133,1941,1757,1586,1440,1310,1199,1095,997,909,827,752,681,615,552,496,441,392,349,309,274,239,208,179,154,132,111,92,75,60,46,34,24,16,10,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6522,6291,5997,5665,5297,4902,4516,4146,3774,3392,3014,2668,2328,2008,1732,1490,1273,1074,891,753,638,539,458,393,342,296,252,210,172,137,107,90,75,62,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6535,6360,6127,5871,5582,5252,4882,4489,4078,3650,3215,2884,2565,2293,2066,1875,1683,1500,1331,1170,1036,916,801,693,618,553,491,432,378,324,275,229,185,143,104,83,65,52,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6533,6347,6112,5836,5501,5167,4804,4432,4034,3644,3272,2919,2601,2289,2007,1767,1556,1358,1163,977,800,658,524,401,288,179,123,77,38,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6532,6328,6073,5758,5405,5022,4679,4329,3974,3613,3274,2977,2707,2442,2180,1917,1659,1414,1172,948,745,560,454,385,323,269,217,172,133,100,72,48,28,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6669,6505,6272,5969,5633,5260,4870,4483,4118,3770,3453,3129,2810,2496,2239,2027,1834,1650,1473,1303,1135,974,820,679,561,456,373,311,257,210,170,138,112,91,71,55,41,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6669,6527,6331,6072,5754,5382,4970,4543,4104,3651,3193,2757,2314,1908,1527,1181,871,648,487,355,231,172,130,95,68,48,32,18,8,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6676,6530,6308,6018,5659,5228,4739,4221,3711,3199,2721,2298,1955,1654,1388,1150,938,765,613,497,404,328,266,220,185,159,136,119,103,88,75,62,51,41,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6531,6323,6054,5734,5374,4984,4550,4087,3629,3169,2691,2208,1756,1359,1021,729,519,335,173,62,41,27,15,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6528,6313,6033,5726,5372,4992,4587,4210,3828,3469,3135,2823,2518,2204,1887,1567,1316,1085,862,715,592,499,417,339,269,208,164,131,101,72,45,20,11,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6671,6518,6295,6006,5652,5273,4901,4520,4157,3794,3450,3113,2803,2490,2201,1916,1640,1407,1188,1048,941,846,760,684,609,537,470,407,345,287,234,184,148,117,89,66,45,29,17,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6518,6294,6038,5727,5393,5039,4683,4360,4087,3817,3552,3309,3085,2887,2697,2526,2367,2217,2073,1937,1810,1700,1617,1541,1468,1399,1333,1268,1208,1152,1097,1045,991,940,892,847,804,762,720,680,641,603,566,531,496,463,431,401,371,342,315,288,263,239,216,195,174,155,136,119,103,88,75,62,51,40,31,24,17,12,7,4,2,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6678,6530,6323,6063,5754,5402,5017,4631,4237,3853,3455,3078,2707,2365,2088,1823,1574,1424,1302,1196,1094,995,899,813,729,650,574,499,425,352,281,210,142,102,67,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6671,6519,6299,6005,5651,5247,4814,4370,3959,3571,3214,2901,2597,2327,2074,1832,1594,1383,1194,1022,899,800,709,628,552,480,411,354,301,247,200,156,114,76,39,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6680,6562,6396,6190,5941,5647,5328,5011,4677,4324,3960,3627,3297,2961,2632,2300,1973,1712,1498,1318,1148,1009,871,735,620,538,467,399,337,277,223,173,125,84,56,35,18,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6537,6337,6066,5747,5386,4986,4548,4125,3710,3308,2917,2528,2157,1831,1529,1247,1008,782,609,470,360,276,210,155,110,71,34,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6525,6300,6012,5664,5278,4871,4457,4066,3701,3387,3103,2821,2587,2368,2156,1953,1773,1612,1467,1331,1200,1069,943,820,705,597,493,398,313,246,185,134,88,52,27,11,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6530,6333,6085,5782,5427,5048,4660,4280,3882,3456,3052,2667,2281,1888,1532,1225,954,728,531,390,308,242,193,150,113,82,57,37,21,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6679,6533,6323,6044,5712,5321,4881,4402,3929,3478,3093,2726,2369,2011,1659,1328,1033,810,623,470,361,263,177,103,38,18,8,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6676,6542,6357,6109,5813,5476,5109,4766,4411,4037,3644,3257,2886,2550,2248,2006,1771,1541,1313,1090,925,788,657,538,425,322,251,218,189,161,137,111,88,67,49,31,15,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6669,6516,6298,6017,5680,5324,4953,4575,4206,3847,3540,3260,3037,2817,2594,2374,2162,1947,1774,1604,1441,1285,1180,1086,996,938,882,827,775,724,675,628,581,538,495,454,414,376,342,308,276,246,217,188,161,136,113,90,70,50,32,16,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6523,6312,6033,5710,5331,4948,4561,4221,3932,3690,3459,3232,3004,2800,2627,2461,2299,2145,1998,1855,1713,1583,1461,1345,1239,1144,1056,974,892,812,736,663,594,533,479,430,385,342,303,265,231,198,169,143,118,97,80,65,50,37,27,18,10,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6669,6529,6332,6091,5818,5519,5187,4824,4463,4080,3687,3313,2938,2592,2286,1996,1728,1489,1270,1061,861,717,613,514,419,326,279,240,205,172,142,116,93,72,53,38,25,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6531,6331,6087,5800,5479,5120,4726,4332,3960,3591,3224,2853,2500,2158,1848,1579,1339,1124,918,734,571,428,305,218,167,128,98,73,55,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6676,6538,6340,6088,5777,5422,5040,4647,4280,3921,3576,3253,2950,2649,2363,2113,1891,1688,1496,1310,1135,962,796,651,545,450,361,278,203,137,83,38,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6664,6512,6290,6000,5664,5305,4910,4477,4017,3529,3035,2567,2125,1721,1362,1030,749,504,329,191,81,32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6524,6297,6001,5644,5256,4854,4441,4064,3723,3430,3186,2954,2736,2524,2328,2167,2032,1901,1776,1665,1560,1461,1365,1274,1185,1099,1018,939,864,793,725,659,598,540,486,438,396,357,321,286,253,223,195,168,143,117,94,74,55,37,21,8,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6526,6312,6053,5751,5436,5126,4819,4502,4193,3873,3553,3234,2928,2657,2419,2200,1993,1795,1622,1457,1297,1149,1008,880,755,639,530,429,334,249,179,120,75,44,25,14,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6518,6293,6023,5715,5392,5061,4747,4437,4155,3884,3611,3345,3091,2841,2624,2421,2227,2039,1854,1677,1512,1346,1176,1016,853,693,534,387,283,210,151,112,79,50,27,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6533,6328,6061,5749,5417,5068,4691,4284,3866,3445,3029,2609,2184,1785,1476,1209,949,718,503,321,213,159,120,88,60,36,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6671,6514,6297,6024,5732,5429,5121,4787,4456,4124,3831,3557,3288,3030,2798,2571,2359,2166,1983,1809,1638,1472,1328,1191,1064,947,836,735,657,584,522,468,416,367,315,264,216,182,157,137,119,103,88,75,62,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6526,6298,6002,5642,5236,4809,4331,3831,3320,2790,2304,1863,1455,1088,784,537,334,176,90,49,24,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6674,6522,6292,5979,5615,5202,4764,4311,3835,3387,2956,2560,2187,1900,1642,1409,1198,1011,839,684,557,456,372,302,247,203,170,143,119,98,79,63,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6672,6522,6321,6077,5792,5466,5092,4703,4316,3955,3585,3233,2885,2539,2193,1906,1639,1397,1198,1058,940,834,734,642,559,486,427,375,330,286,247,208,174,144,116,92,70,52,37,24,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6544,6363,6131,5850,5545,5212,4891,4572,4248,3935,3611,3285,2957,2622,2282,1967,1651,1348,1053,795,661,552,458,371,305,266,239,216,195,174,155,136,119,103,88,75,62,51,40,32,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6668,6526,6331,6100,5831,5524,5169,4800,4406,4011,3649,3299,2969,2676,2407,2156,1912,1670,1483,1315,1165,1044,956,879,805,734,665,608,556,509,464,418,377,338,303,270,241,213,187,164,144,123,103,85,71,56,42,30,20,11,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6521,6291,5994,5660,5288,4870,4410,3919,3400,2896,2419,1959,1561,1232,952,736,565,436,343,280,232,191,159,130,105,83,63,46,33,20,10,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6676,6541,6349,6106,5817,5478,5088,4662,4244,3827,3447,3064,2739,2463,2196,1978,1804,1639,1482,1325,1169,1011,854,740,633,531,435,365,299,241,187,156,128,103,82,61,44,29,18,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6531,6321,6063,5776,5448,5088,4703,4310,3912,3525,3159,2789,2414,2067,1761,1475,1208,971,766,612,502,404,314,234,162,100,46,25,13,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6668,6517,6293,6026,5713,5372,4987,4581,4153,3713,3273,2871,2573,2297,2024,1762,1505,1309,1138,984,844,713,593,477,364,325,292,261,232,204,178,154,132,111,92,75,60,46,34,24,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6669,6521,6307,6039,5726,5364,4966,4528,4115,3742,3357,2988,2631,2288,1945,1656,1397,1180,996,841,731,639,560,489,428,376,333,297,267,240,216,195,174,155,136,119,103,88,75,62,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6673,6515,6280,5970,5630,5267,4880,4468,4043,3597,3129,2664,2208,1781,1374,1031,747,531,403,306,228,168,119,81,52,29,11,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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+6740,6670,6511,6294,5995,5648,5249,4847,4434,4045,3676,3335,3010,2690,2398,2108,1834,1569,1329,1159,997,852,711,576,470,390,320,257,202,158,118,86,63,43,28,17,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6551,6368,6133,5852,5536,5186,4835,4468,4076,3670,3261,2883,2541,2214,1900,1584,1289,1050,825,612,408,243,166,95,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6668,6515,6304,6034,5722,5383,5031,4664,4290,3917,3542,3151,2769,2450,2160,1891,1628,1406,1200,1030,877,743,623,525,441,379,333,292,253,217,183,154,127,103,81,63,47,34,24,16,9,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6675,6537,6348,6101,5805,5462,5082,4671,4243,3784,3300,2800,2305,1853,1458,1154,905,687,492,341,248,176,138,108,84,64,51,40,31,24,17,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6672,6524,6326,6081,5801,5487,5139,4763,4391,4003,3607,3221,2850,2529,2229,1962,1721,1495,1334,1207,1097,999,904,812,723,640,556,477,401,328,258,190,146,119,99,81,65,53,40,29,20,13,7,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6668,6512,6283,5989,5644,5230,4811,4392,3999,3635,3287,2962,2640,2322,2015,1708,1400,1100,863,658,489,345,260,193,147,109,79,56,40,28,18,12,7,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6670,6520,6300,6022,5682,5307,4928,4556,4195,3850,3507,3169,2828,2488,2158,1828,1544,1299,1057,872,751,649,554,464,379,300,229,174,128,90,60,36,17,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6670,6522,6296,6013,5700,5368,5009,4635,4249,3901,3569,3238,2907,2585,2259,1945,1653,1403,1182,977,789,622,513,413,323,235,201,174,150,130,111,94,78,64,51,40,30,21,14,8,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6677,6536,6339,6097,5808,5490,5178,4871,4556,4226,3891,3555,3219,2886,2559,2313,2082,1863,1676,1523,1422,1328,1237,1148,1063,981,903,824,754,687,619,556,497,440,387,337,292,246,205,170,135,104,76,51,30,16,8,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6672,6529,6317,6050,5740,5415,5073,4709,4352,4062,3782,3509,3239,2967,2697,2433,2171,1910,1655,1417,1206,1011,825,677,576,484,401,329,264,210,168,130,96,66,40,18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+6740,6678,6538,6325,6040,5695,5308,4889,4448,4002,3546,3147,2810,2543,2293,2063,1837,1629,1429,1232,1052,891,750,624,513,415,327,249,190,149,114,82,54,34,19,9,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
diff --git a/Project.ipynb b/Project.ipynb
index 6fc12a0..7e454c8 100644
--- a/Project.ipynb
+++ b/Project.ipynb
@@ -1,8 +1,62 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2041,
+     "status": "ok",
+     "timestamp": 1613645397401,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "KBUdLfK8W14c"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from scipy import integrate, optimize\n",
+    "import time\n",
+    "from tqdm import tqdm\n",
+    "from matplotlib import animation\n",
+    "from IPython.display import HTML\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 2029,
+     "status": "ok",
+     "timestamp": 1613645397405,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "2xE5VZejvJI6"
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "C3edXWAuW14l"
+   },
    "source": [
     "# Cellular Automata S.I.R. Model\n",
     "\n",
@@ -16,19 +70,1667 @@
     "3. Try to predict the evolution of the epidemic both in space and time on the basis of the previous steps, e.g. by means of \n",
     "4. Define a metric on how the epidemic evolved e.g. assigning a probability for a given node not to get infected or predict the fraction of the nodes that got infected at the end of the epidemic\n",
     "\n",
-    "### Contancts\n",
+    "### Contacts\n",
     "* Leonardo Badia <badia@dei.unipd.it>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIR Model and Cellular Automata introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following project, the $ SIR $ model for the evolution of infectivity nucleus will be analyzed inside a finite population exploiting the Cellular Automata technique.\n",
+    "### SIR Model\n",
+    "The SIR model represents the evolution of an infection that is transmitted from person to person, where the healed persons can't take the infection anymore and the overall population sampled is divided into three groups: \n",
+    "* Susceptibles: containing the individuals that have not yet been infected and still can contract the infection; \n",
+    "* Infected: that represents the individuals that are currently infected and that can infect other people;\n",
+    "* Removed: that are the dead or all the ones that recovered from the illness and are now immune.\n",
+    "\n",
+    "Partial differential equations theoretically describe SIR models:\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI + \\mu - \\mu S;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I -\\mu I;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I - \\mu R;\n",
+    "$$\n",
+    "\n",
+    "where $\\lambda$ is a parameter describing the rate of interactions between susceptibles and infectious, $\\gamma$ is the recovery rate and $\\mu$ is a parameter related to vital dynamics of the population (birth-death rate).\n",
+    "\n",
+    "### Cellular Automata\n",
+    "An alternative approach is by using Cellular Automata, which provides much better efficiency in terms of computational time. This is a discrete-time approach using matrices whose elements are configured in networks.\n",
+    "\n",
+    "A value describing its epidemiological state(S=0, I=1, R=-1) is assigned to every element of the matrix, which changes with respect to the states of neighbour elements, according to a fixed radius.\n",
+    "\n",
+    "The change of state from S to I depends, just as for the differential equation model, on the interaction between an infected element and its neighbours, which is based on an infection probability $p_i$.\n",
+    "\n",
+    "Once infected, the element recovers after a fixed number of iterations representing the total recovery time (which takes into account incubation, proper infection and latency times). \n",
+    "In this case, the vital dynamics are not considered ($\\mu=0$), so the equations governing the epidemic become:\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I ;\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I;\n",
+    "$$\n",
+    "\n",
+    "Eventually, thanks to several simulations and Cellular Automata efficiency, an epidemiological spread can be performed, allowing to obtain accurate predictions in time and space.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 229842,
+     "status": "ok",
+     "timestamp": 1613645625241,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "mQl_wl7RW14m",
+    "outputId": "82b2c037-cd3d-4111-f242-ca0d0c231f47",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def draw_line(mat, point1, point2):\n",
+    "    \"\"\"\n",
+    "        function that draws the best possible line (made of 0s) between two points point1, point2\n",
+    "    \"\"\"\n",
+    "    x0, y0 = point1[0], point1[1];\n",
+    "    x1, y1 = point2[0], point2[1];\n",
+    "    if not (0 <= x0 < mat.shape[0] and 0 <= x1 < mat.shape[0] and\n",
+    "            0 <= y0 < mat.shape[1] and 0 <= y1 < mat.shape[1]):\n",
+    "        raise ValueError('Invalid coordinates.')\n",
+    "        \n",
+    "    if (x0, y0) == (x1, y1):\n",
+    "        mat[x0, y0] = 0\n",
+    "        return mat\n",
+    "    \n",
+    "    # Swap axes if Y slope is smaller than X slope\n",
+    "    transpose = abs(x1 - x0) < abs(y1 - y0)\n",
+    "    if transpose:\n",
+    "        mat = mat.T\n",
+    "        x0, y0, x1, y1 = y0, x0, y1, x1\n",
+    "        \n",
+    "    # Swap line direction to go left-to-right if necessary\n",
+    "    if x0 > x1:\n",
+    "        x0, y0, x1, y1 = x1, y1, x0, y0\n",
+    "        \n",
+    "    # Compute intermediate coordinates using line equation\n",
+    "    x = np.arange(x0 + 1, x1)\n",
+    "    y = np.round(((y1 - y0) / (x1 - x0)) * (x - x0) + y0).astype(x.dtype)\n",
+    "    \n",
+    "    # Write intermediate coordinates\n",
+    "    mat[x, y] = 0\n",
+    "\n",
+    "    return mat if not transpose else mat.T\n",
+    "\n",
+    "    \n",
+    "def new_population(N, n_vertices):\n",
+    "    \"\"\"\n",
+    "        function that generates a new population. A population is a matrix NxN containing 2 everywhere except\n",
+    "        inside a figure of n_vertices vertices where there are all 0s.\n",
+    "    \"\"\"\n",
+    "    from numpy import pi, floor, ceil, cos, sin;\n",
+    "    \n",
+    "    if n_vertices < 3: raise Exception('The number of vertices must be greater than 2.');\n",
+    "    \n",
+    "    if N%2 == 0: N+=1\n",
+    "        \n",
+    "    M = np.ones((N,N), dtype=int)+1 # matrix of 2s\n",
+    "\n",
+    "    center = [floor(N/2), floor(N/2)]\n",
+    "    radius = floor(N/2)\n",
+    "    angle = 2*pi/n_vertices\n",
+    "    \n",
+    "    vertices = np.zeros((n_vertices+1,2), dtype=int)\n",
+    "    for vertex in range(n_vertices): # for each vertex of the polygon\n",
+    "        x = int(floor(center[0] + radius*cos(vertex*angle))) # x coord of the vertex of the polygon\n",
+    "        y = int(floor(center[1] - radius*sin(vertex*angle))) # y coord of the vertex of the polygon\n",
+    "        M[x,y] = 0 # set to 0 the coordinate of the population matrix\n",
+    "        vertices[vertex] = [x,y] # list of coordinates of the vertices of the polygon\n",
+    "    vertices[n_vertices] = vertices[0] # add the first vertex at the end so that is a closed polygon\n",
+    "\n",
+    "    for i in range(1,len(vertices)):\n",
+    "        M = draw_line(M, vertices[i-1], vertices[i]) # trace the edges of the polygon\n",
+    "            \n",
+    "       \n",
+    "    for row in range(N): # the polygon is filled with 0s for each row\n",
+    "        row_i = M[row,:]   \n",
+    "        x1 = np.argwhere(M[row,:]==0)\n",
+    "        x2 = np.argwhere(M[row,:]==0)\n",
+    "        if len(x1)==0 or len(x2)==0: continue;\n",
+    "        x1, x2 = min(x1), max(x2)\n",
+    "        if x1==0 and x2==0: continue;\n",
+    "        M = draw_line(M, [row, x1], [row, x2])\n",
+    "          \n",
+    "    return M;\n",
+    "\n",
+    "def inizialization(population, infected, patients_0_nr=0, radius=0, random=False):\n",
+    "    \"\"\"\n",
+    "        Inizialization for patients zero, if random = True takes patients_0_nr random points inside\n",
+    "        a centered radius else takes coordinates\n",
+    "    \"\"\"\n",
+    "    centered = False\n",
+    "    center = int(np.floor(population.shape[0]/2))\n",
+    "    if not random:\n",
+    "        for coord in infected:\n",
+    "            if population[coord] == 2 : raise Exception('Bad inizialization, outside polygon');\n",
+    "            population[coord] = 1 # the people located at \"infected\" are turned into infected: 0 -> 1\n",
+    "    else:\n",
+    "        #we grab the indexes of the ones\n",
+    "        if centered:\n",
+    "            mask = np.ones((population.shape[0], population.shape[1]))\n",
+    "            mask[center-radius:center+radius, center-radius:center+radius]=0\n",
+    "            x,y = np.where(mask+population == 0)\n",
+    "            \n",
+    "        x,y = np.where(population == 0)\n",
+    "        #we chose one index randomly\n",
+    "        i = np.random.randint(len(x),size=patients_0_nr)\n",
+    "        for randnr in i:\n",
+    "            coord = (x[i],y[i])\n",
+    "            population[coord] = 1\n",
+    "    return population"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tunable Parameters\n",
+    "\n",
+    "* Susceptible $S \\rightarrow 0$\n",
+    "* Infected $I \\rightarrow 1$\n",
+    "* Recovered $R \\rightarrow -1$\n",
+    "$\\newline$\n",
+    "* cycles: lifetime of every experiment.\n",
+    "* experiments: number of experiments.\n",
+    "* resolution: $resolution^2$ is the number of nodes inside the outward matrix. The number of useful nodes for the experiment will be always $<resolution$. Increasing this value will make computational time increase rapidly.\n",
+    "* polygon_sides: the number of sides for the polygon ($3\\rightarrow$triangle, $4\\rightarrow$square, ...).\n",
+    "* p_i: probability for a node to get infected and change status from $S$ to $I$.\n",
+    "* t_r: time of recovery before an infected node changes status from $I$ to $R$.\n",
+    "* patients_0_nr: number of patients zero at the beginning of the simulation.\n",
+    "* radius: centered radius inside where the patients zero will be spawned randomly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cycles:86 , experiments:96 , resolution:142 , vertices:3, p_i:0.87 , t_r:3\n"
+     ]
+    }
+   ],
+   "source": [
+    "cycles = np.random.randint(30,100) #100\n",
+    "experiments = np.random.randint(10,100) #5\n",
+    "resolution, polygon_sides = np.random.randint(60,200), np.random.randint(3,15) #150, 7\n",
+    "p_i = round(np.random.random(),2) #0.5 \n",
+    "t_r = np.random.randint(1,6) #3\n",
+    "patients_0_nr, radius = 10, 25\n",
+    "\n",
+    "print('cycles:{} , experiments:{} , resolution:{} , vertices:{}, p_i:{} , t_r:{}'.format(cycles,experiments,resolution,polygon_sides,p_i,t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1-2. Algorithm\n",
+    "\n",
+    "The algorithm developed is based on the reference paper method. This replicates the propagation of the virus through the first-order neighbourhood since also in the paper they never go beyond. This propagation is slightly different from the original since some parameters are not so clear, in particular the random variable $h$ that discriminates the infection and the formula for obtaining $u_{i,j}$. The first parameter has a value from 0 to 1 but has a probability distribution p(h)=1, which has no clear meaning and hasn’t an extensive explanation. The formula instead has a strange normalization behaviour that makes it difficult to imagine how are the values in the experiment and so the meaning of $u_{i,j}$ and the conditions of infection. For this reason, we developed our version that is different but has the same behaviour. The algorithm cycles over all the elements in the matrix and, if it encounters an infected one, it explores the neighbours to infect them, with a probability $p_i$ for everyone. The elements infected in the actual cycle are skipped and considered in the next. In this way, the probability of being infected is proportional to the number of infected neighbours. The recovery instead is done as the original article, since an infected recovers in $t_r$ iterations. In this way,  the complexity of the algorithm results in the number of maximum cycles multiplied by the number of the matrix elements. Each of these iterations performs a constant number of operations. This makes the algorithm efficient for this type of problem.\n",
+    "\n",
+    "In order to build an efficient estimation from the simulations, an useful tool will be the list DF_List, which will be used later in order to have an average of all the experiments at time $t$ (Building process in the image).\n",
+    "\n",
+    "<img src=\"DFList.png\" width=\"800\">"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████| 96/96 [02:26<00:00,  1.52s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_experiments = pd.DataFrame(columns = ['EXP','S','I','R'])\n",
+    "df_S = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "df_I = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "df_R = pd.DataFrame(columns=np.arange(cycles+1))\n",
+    "\n",
+    "df_list_temp=[]\n",
+    "# initialize a list of length 'cycles' containing matrices of 50 (default initialisation value, random value different from -1,0,1,2)\n",
+    "for cycle in range(cycles):\n",
+    "    zero_mat = np.full((np.size(new_population(resolution, polygon_sides)),experiments), 50)\n",
+    "    # each matrix of the list refers to the i-th cycle, and the e-th column (e=0:9) contains the i-th population matrix vectorized for every experiment\n",
+    "    df_list_temp.append(zero_mat)\n",
+    "    \n",
+    "for experiment in tqdm(range(experiments)):\n",
+    "    population = new_population(resolution, polygon_sides);\n",
+    "    population_time = np.zeros(population.shape)-1\n",
+    "\n",
+    "    center = int(np.floor(resolution/2)) # just to keep track of where the center is\n",
+    "\n",
+    "    infected = [(center,center)] # initialization of where Infected are. Can be tuple or list of tuples\n",
+    "    population = inizialization(population, infected, patients_0_nr, radius, random=True)\n",
+    "    S=[]; I=[]; R=[]; \n",
+    "    for cycle in range(cycles):\n",
+    "        s,i,r=0,0,0\n",
+    "        for index,value in np.ndenumerate(population):\n",
+    "            if population_time[index]==cycle:pass #already processed\n",
+    "            elif value==2:pass #not in the region space\n",
+    "            elif value==1: #infect detected\n",
+    "                i+=1\n",
+    "                #cycle the neighbors for infection\n",
+    "                for j in range(-1,2):\n",
+    "                    for k in range(-1,2):\n",
+    "                        if 0<=index[0]+j<population.shape[0] and 0<=index[1]+k<population.shape[1]:\n",
+    "                            if population[index[0]+j,index[1]+k]==0 and np.random.choice([1, 0], p=[p_i, 1-p_i]): #element get infected\n",
+    "                                population[index[0]+j,index[1]+k]=1\n",
+    "                                population_time[index[0]+j,index[1]+k]=cycle #mark as infected in this cicle\n",
+    "                                if j==1 or (j==0 and k==1): s+=1 #the item will not be counted otherwise\n",
+    "                #infected recovers after t_r cycles\n",
+    "                if cycle-population_time[index]>=t_r:\n",
+    "                    population[index]=-1\n",
+    "                    population_time[index]=cycle\n",
+    "            elif value==0: s+=1\n",
+    "            elif value==-1: r+=1\n",
+    "        S.append(s) # number of S in cycle\n",
+    "        I.append(i) # number of I in cycle\n",
+    "        R.append(r) # number of R in cycle\n",
+    "        df_list_temp[cycle][:,experiment] = population.flatten() #write the current vectorized population matrix in the correspondant place of the matrix list\n",
+    "\n",
+    "        if i==0: break \n",
+    "\n",
+    "    s,i,r=0,0,0\n",
+    "    for index,value in np.ndenumerate(population):\n",
+    "        if value==1: i+=1\n",
+    "        elif value==0: s+=1\n",
+    "        elif value==-1: r+=1\n",
+    "    S.append(s) # number of S in last cycle\n",
+    "    I.append(i) # number of I in last cycle\n",
+    "    R.append(r) # number of R in last cycle\n",
+    "    S = np.concatenate((np.array(S),np.full(cycles-len(S)+1,S[len(S)-1])), axis=0)\n",
+    "    I = np.concatenate((np.array(I),np.full(cycles-len(I)+1,I[len(I)-1])), axis=0)\n",
+    "    R = np.concatenate((np.array(R),np.full(cycles-len(R)+1,R[len(R)-1])), axis=0)\n",
+    "    df_S = df_S.append(pd.DataFrame(S).T)\n",
+    "    df_I = df_I.append(pd.DataFrame(I).T)\n",
+    "    df_R = df_R.append(pd.DataFrame(R).T)\n",
+    "    \n",
+    "    DF_List=[] # final list with dataframe for each performed cycle, for every experiment\n",
+    "    for cycle in range(cycles):\n",
+    "        df_cycle=pd.DataFrame(df_list_temp[cycle])\n",
+    "        DF_List.append(df_cycle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dataset storing\n",
+    "\n",
+    "Data about the population is stored on three different .csv files for each $S,I,R$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "df_S.to_csv(r'Data/S_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r), index = False)\n",
+    "df_I.to_csv(r'Data/I_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r), index = False)\n",
+    "df_R.to_csv(r'Data/R_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r), index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Dataset loading\n",
+    " \n",
+    " Datasets can be loaded for the wanted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CurrentExperiment = True # do you want to analyze the results of the running experiment?\n",
+    "\n",
+    "if not CurrentExperiment: # no -> Please insert the experiment input values\n",
+    "    experiments = 100 # input desired number of experiments\n",
+    "    cycles = 100 # input desired number of cycles\n",
+    "    polygon_sides = 5 # input desired number of cycles\n",
+    "    p_i = 0.2 # input desired p_i (probability of infection)\n",
+    "    t_r = 3 # input desired t_r (time of recovery)\n",
+    "    \n",
+    "df_S = pd.read_csv(r'Data/S_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r))\n",
+    "df_I = pd.read_csv(r'Data/I_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r))\n",
+    "df_R = pd.read_csv(r'Data/R_{}x{}_{}v_{}pi_{}tr.csv'.format(experiments, cycles, polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# graph of the number of cases for each cycle\n",
+    "fig2 = plt.figure(figsize = (15,7))\n",
+    "\n",
+    "plt.plot(df_S.mean(), label='S', zorder=10, linewidth=2.5, color='green');\n",
+    "plt.plot(df_I.mean(), label='I', zorder=20, linewidth=2.5, color='red');\n",
+    "plt.plot(df_R.mean(), label='R', zorder=10, linewidth=2.5, color='blue');\n",
+    "\n",
+    "plt.fill_between(np.arange(cycles+1),df_S.mean()-df_S.std(), df_S.mean()+df_S.std(), color='green', alpha=0.1)\n",
+    "plt.fill_between(np.arange(cycles+1),df_I.mean()-df_I.std(), df_I.mean()+df_I.std(), color='red', alpha=0.1)\n",
+    "plt.fill_between(np.arange(cycles+1),df_R.mean()-df_R.std(), df_R.mean()+df_R.std(), color='blue', alpha=0.1)\n",
+    "\n",
+    "plt.xlabel('time', fontsize=15);\n",
+    "plt.ylabel('number of cases', fontsize=15);\n",
+    "plt.legend(fontsize=15);\n",
+    "plt.grid(linestyle=\"--\", linewidth=0.6, zorder=-10);\n",
+    "plt.xticks(np.arange(0, cycles+1, 3));\n",
+    "plt.title('Number of cases with respect to time ({}x{}, {}v, p_i{}, t_r{})'.format(experiments, cycles, polygon_sides, p_i, t_r), fontsize=15)\n",
+    "\n",
+    "plt.savefig('images/{}x{}_{}v_{}pi_{}tr_NumberOfCases.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Space prediction\n",
+    "In order to obtain a prediction on space, at each time $t_i$ a sum between all the experiments is performed. Then, every \"average experiment\" (sum of all the experiments) is summed up to time $t_n$:\n",
+    "\n",
+    "$$\n",
+    "\\Phi = \\sum_{i=0}^{n} \\sum_{j=0}^{m} I(t_i, exp_j)\n",
+    "$$\n",
+    "\n",
+    "which, normalized, gives the percentage of times a given node got infected in each experiment:\n",
+    "\n",
+    "$$\n",
+    "p_\\% = \\frac{\\Phi}{max(\\Phi)}\n",
+    "$$\n",
+    "\n",
+    "If $m$ sufficiently large (i.e: number of experiments $m>>0$) the Law of Large Numbers (LLN) can be exploited to obtain a probability for a given node to get infected (A node can be seen as a Random Variable with probability of getting infected $p_i$):\n",
+    "\n",
+    "(LLN)\n",
+    "$X_1, X_2, ..., X_n$ infinite sequence of i.i.d. random variables with expected value $E(X_1) = E(X_2) = ...= \\mu$. \n",
+    "The sample average\n",
+    "$$\n",
+    "\\bar{X}_n=\\frac{1}{n}(X_1+...+X_n) \\longrightarrow \\mu \\text{ as } n\\longrightarrow\\infty\n",
+    "$$\n",
+    "\n",
+    "and in this case the higher $m$ the more $p_\\%$ resembles a probability distribution with respect to space, as it gets closer and closer to the mean value which corresponds also to the true value.\n",
+    "$$\n",
+    "p_\\% \\longrightarrow P(node=I)\\text{ , as number of experiments }\\longrightarrow \\infty\n",
+    "$$\n",
+    "\n",
+    "Note that $\\Phi$ can be calculated for $I$ (Infected) as for $S$ (Susceptibles) and $R$ (Recovered).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Infected $I$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_I = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_I = cycles*1//3\n",
+    "for i in range(finaltime_I):\n",
+    "    Space_Pred_Prob_I += (DF_List[i] == 1).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_I = Space_Pred_Prob_I/max(Space_Pred_Prob_I)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for I (t={}, {}x{}, {}v, p_i{}, t_r{})'.format(finaltime_I, experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_I.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_SpaceDistProb_I.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Susceptible $S$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_S = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_S = cycles\n",
+    "for i in range(finaltime_S):\n",
+    "    Space_Pred_Prob_S += (DF_List[i] == 0).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_S = Space_Pred_Prob_S/max(Space_Pred_Prob_S)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for S (t={}, {}x{}, {}v, p_i{}, t_r{})'.format(finaltime_S, experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_S.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_SpaceDistProb_S.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recovered $R$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7cq6Eu99KcFEfQIifjtHfk8tdwBMzT89PAR5NBgxQ/T4ktl3u7r9y988QwpVvsoweQgt/Iv/l3YnzCfdh2euUpP5zCR6ERt5HfHq/jjAYgOA6bs1BgOnBSeuPQCnufq+770pIAt2O8JT5CPCH+D5CGGQ57QMgy/T3wXgeNwKfb8nZKL5vr2xpcwXhuhY5ZzeQJ9dfP1we/39mUnZWPP6+hIHk0wiDqX7Ifb43Zfpz2ys3AltauyZGcZ07UfV6AxDfq/2A7/nMk2z7oepn+HLC9WhNqt6QCt/tXQcNMZmotezJhHhe68imNcnvDYQ3uggbPIEwmimOM04YmaUUH+huI54nAKs9BmMi/bressSR5gcICWidftQauPvt7l5M5ypG9FXPpxu7t4SD3kC4SYoRZHGNG8lO8Uu8VYSlqxcgekguI3wAUt5E+NL5VU+WD5aLgdfG0T7Q8DptRciGThnE/djpWK8nuPSreJM6UfZenEzwMLyf8CX0QB/Hv5Q4J7soiC7lN9J+rWbCvxIGT7kE0pSbgK0z5Z2uQ0/hCULMfrs0odGCONt2hPBVMYg4j5DLkLIbwQtyc5dzyOLuK+LT+ARBuyIdzPyA8IP6msSuxYQwz1VJ2XMI4bF/IHz2diTkW6TnB+1J2S8lDFSKAewPgKdaMuPAzJ5FyA24ipnzyvj/74oCd7+fEKZ8c3zdEEPb/bBpmiQaQ0svAS7p83hF+LTxGTazpwGvJuSfdKLq9S74S4Jneiahiape2pnwSkJiaGtS9VYEvYZyKiRZ3E1IYHoj4aK8g3DjPQRs3ZKIcQfBTbkHwUXXlCREyES9Lx7jbwhZ/EWSZZEw9oz4dxFLemEsP6Cl3l7x788RPvCfIHwpOXBI0uet9C7u9DpC9u5K2sWdWu34CiEeui8hae7oeN5/28v5JMcvrmVrIuQdBJfg3gQvxgMkyTYED8MKwo/9XsDbCa7OB2lOcDwlHuu1wBJg49x1iu+hE1zlryW4dx8DTuh0LXq85qV16KCPQEi6egD4n3jN30ZwCV9NFBdisPdjYcfthC/0IvPYSTQR6C8R8mcEV/gu8b1YlOxbhzA1ygnu0arJUHu3lJ9G+KweQkjA+y7hCf1VLfd/NhE400/TeSbl/4cQo9+2pG1xTz25pbzjdejlxXSuxQ/j+7k34UdhNc3JkDsRvpy/Gm0q7u2PtxzP6aIfEe+dg6Lt7yQ8hV5D+2fie4QB69Jo24UET+lGcf84IbT6S2KiLvAxQoLmc5M6lxLu63cRBj7/SBj4Hpv0NU74HriR8L39+mjT9TSLap1Kl6Q7gljVJwjfibsRQkD3Ej5/Yy113x6v673AkZljXQBcUOH+upfgEXprYvsdVEzipCURMpZ9henP+p7xWjeJO8VrOdlyHbte76T+CbGPiZJz63a93xnvuwMIn4WtZvDZfwbhs/XuaPs+hHveaUkqJeTArAHe0LW/CgZ9gDBSu5PwwbqVMC3kuRmj30YYZa2Mb/zRJEI9BLfoWYQvsbuBY5kWCklnBnyYMMqbLC4ymR8oQjb/7YRR30+Bbeh/0OBMZ5cWiT0fANZpqdtkR/z7lwS3zqOEH693tbSpdD6dbgCmZxocFa/bI/E6b9jSdkfCTf4o4cvrla3nT3Ap/pQwmHCiCljuOhGeGK4hfBGsIPxoTnS6Fj1e89I6lIgqEdQrfxbP8wHC/ZhmoRfXcMb3Y2LHawk/QI/Ga/H+zBdCr4OGlxK+vB6J+3ZpOeY3CLkXXdVHc8eP5esRpsveTfiyWw68NnP/z3TQsAEhdvyVkrYLCQOhd7SUl16HXl6EH7ZfED6PfyL8OLcdL76fl8drcjvhh3Es2b9etOX9XfpbSnhYeSxe468QB+It9Z4IfDme/58Jn8EXJvuPiOe/TVI2TvDqXUwU2yIkqp1E+D55lBCKOIIWNc54b3+T8DlfGe/zp7fUOQO4pMv5HUoYgDxIyFW4Jl6rtllshNyuR+N1e05m/yWEKZhd7y+CV/DX8f35JT2Iq5EfNKxDeBC8N17nc4gPvS3tvKWs6vWeiMc+ocSuKtd7XcIPe5Gjd2qF892K/Gd/MeE75NZ4f/6B8L25V+YYr4/3yfrd+rPYYEZY0PD+HfA6dy/TGhBCVCDO3vg9cIq7f2LY9gwKMzseeLa7/82wbSnDzF5D8A5s6clMnbUJM/s98I/u3lF0aoB9rUMYnO/h7heW1DuVMEBYMts2zTVzeb17xcy+CTzi7u/uVrfnaY5CiNkjxuNfRHDNbkx4cl2b+Axwk5lt6+7d46fD4xWEKY9r64DhaYTZL3M1NXAJcG3ZgGFtZgjXuzJmtiUh1Lt9lfoaNAgxv3gawY17D0GEZUWX+iOFu6+IU7Y3o0rS1ZBw908N24bZxN3vpDep8Jn290tCGGpGxBkKZQn8a3wQ7vMBM5PrnWjG/DshH+YegogZwJTHaZ8xyfl4Qk7bo4TQ8+V0ZwtCjkOlJOCBhCeEEEKI2cbC+gxHllR5jbtfMDfWzD5J6L8TX3P3A2LdvQg5KHsRku6Pd/du06B7t0mDhnLMbE/C6G2c4K48ZsgmCSFELYlu/rIn9pu8wvT4USGGK9OwwWaEmXjF1PD7POqBmNlXCLNTvhn/vomQBNyvvkUWhSdKiPP2v0iQ31wBXGpmZ7v79cO1TAgh6kd08985bDvmCg+6Ig0p7uh5eMzz8tybM73aLYTfrM3pXxQriwYN5ewE3OzutwCY2emEhJGOg4aJhZvLdSOEEPOEyVV3VF6au1dW33fLwL7vFz75We8lLEdQcKK7n9jDIXLnOfDfIw0aysmN3NpiRGa2jPhm2/hixsZa1UqFEEKIzsQBQi+DhFZWEOTMC7ZgFrwyfa09USMqjdzc/UR3X+LuSzRgEEKImjC1ZnCvmXM2cXVcM3s58OCg8xlAnoZuzMnITQghhCgjCjDtAmxiZisIs0gWALj7CQSVy70I66c8SljOe+Bo0FDOpcA2ZrY1QX98f4LojhBCiLrTtjL2LHbl/pYu+52w9MGsokFDCe4+aWaHAOcSplye4tPrygshhKgzU3M3aJgvaNDQBXc/h+D2EUIIIWqNBg1CCCFEH/gchifmCxo0VCCKPC0H7nD3vYdtjxBCiHlADcMTmnJZjQ8S1lEXQgghaosGDV0wsy0IK4udNGxbhBBCzCN8anCvEUHhie58DvgosGjIdgghhJhPDEaUaaSQp6EEM9sbuMfdL+tSb5mZLTez5VNTj8yRdUIIIcTcIk9DOa8E9onrlK8LbGBm33D3t6eVUs1wLVglhBA1YYTCCoNCnoYS3P0Id9/C3bciqEH+rHXAIIQQoqZMTQ3uNSJo0CCEEEKISig8URF3vwC4YMhmCCGEmCdI3EkIIYQQ1RihsMKg0KBBCCGE6IcaehqU09AFMzvMzK4zs2vN7Jtmtu6wbRJCCCGGgQYNJZjZ5sDfA0vcfTvC8tj7D9cqIYQQ84KpNYN7jQgKT3RnAniCma0G1gPuHLI9Qggh5gMKT4gUd78D+CxwG3AX8KC7/2S4VgkhhBDDQYOGEsxsI2BfYGvgacD6ZtYm7iQZaSGEqCESdxIt/BXwO3e/191XA2cCr2it5O4nuvsSd18yNrb+nBsphBBiCNRwlUsNGsq5DXi5ma1nZgbsBtwwZJuEEEKIoaBEyBLc/WIz+w5wOTAJXEFcmEoIIUTNGaGwwqDQoKEL7n4kcOSw7RBCCDG/cB+dqZKDQuEJIYQQQlRCg4aImZ1iZveY2bVJ2WfM7EYzu9rMzjKzDYdoohBCiPmEEiFrzanAni1l5wHbufv2wK+BI+baKCGEEPMUTbmsL+7+C+BPLWU/cffJ+OdFwBZzbpgQQggxT1AiZHUOAr41bCOEEELME0YorDAoNGiogJl9nDDl8rQO+5cBywBsfDESeBJCiBowQgtNDQoNGrpgZkuBvYHd3N1zddz9RKJ+w8TCzbN1hBBCiFFHg4YSzGxP4HBgZ3d/dNj2CCGEmEcoPFFfzOybwC7AJma2giDodASwDnBeUJHmInc/eGhGCiGEmD+M0KyHQaFBQ8Td35IpPnnODRFCCCHmKRo0CCGEEP2g8IQQQgghKlHD8ITEnSI5GelYfqiZ3WRm15nZscOyTwghhBg28jRMcyrwBeA/igIzew2wL7C9uz9uZpsOyTYhhBDzjRp6GjRoiLj7L8xsq5bi9wHHuPvjsc49c26YEEKIeYmWxhatbAu82swuNrMLzWzHYRskhBBCDAt5GsqZADYCXg7sCJxhZs9sVYaUjLQQQtQQhSdECyuAM+Mg4RIzmwI2Ae5NK0lGWgghakgNp1wqPFHO94BdAcxsW2AhcN8wDRJCCCGGhTwNkQ4y0qcAp8RpmKuApZ0WrRJCCFEzFJ6oLx1kpAHePqeGCCGEGA0UnhBCCCGEyCNPgxBCCNEPNQxPyNMAmNmWZvZzM7shykV/MJY/yczOM7PfxP83GratQggh5gk+NbjXiKBBQ2AS+LC7P4+gyfABM3s+8DHgfHffBjg//i2EEELUEoUnAHe/C7grbq80sxuAzQnrTuwSq30NuAA4fAgmCiGEmG/UMDyhQUMLcf2JFwMXA0+JAwrc/S4tWCWEEKJBDQcNCk8kmNkTge8CH3L3h3pot8zMlpvZ8qmpR2bPQCGEEGKIyNMQMbMFhAHDae5+Ziy+28w2i16GzYDsKpeSkRZCiBoyQgmMg0KeBsDMDDgZuMHd/y3ZdTawNG4vBb4/17YJIYSYp0xNDe41IsjTEHgl8A7gGjO7Mpb9H+AYwsqW7wJuA/YbjnlCCCHE8NGgAXD3/wasw+7d5tIWIYQQI0INwxMaNAghhBD9MEJhhUGhnAY6K0Im+z9iZm5mmwzLRiGEEGLYyNMQKBQhLzezRcBlZnaeu19vZlsCuxNyGoQQQohADcMT8jQQhJvc/fK4vRIoFCEBjgM+CmgqpRBCiGlqOHtCg4YWUkVIM9sHuMPdrxquVUIIIcTwUXgiIVWEJIQsPg7sUaHdMmAZgI0vZmxs/Vm0UgghxLxghDwEg0KehkhGEfJZwNbAVWZ2K7AFcLmZPbW1rbuf6O5L3H2JBgxCCFET3Af3GhHkaSCvCOnu1wCbJnVuBZa4+31DMVIIIYQYMho0BLKKkO5+zvBMEkIIMa+pYXhCgwa6KkIWdbaaG2uEEEKMBDUcNCinQQghhBCVkKdBCCGE6AeJO9WTTjLSZraDmV1kZlea2XIz22nYtgohhJgnzKG4k5ntaWY3mdnNZvaxzP7FZvZfZnZV/B07cDZOWZ6GQFZGGjgWONrdf2Rme8W/dxminUIIIeYLczRV0szGgS8SljRYAVxqZme7+/VJtQ8A17v768zsycBNZnaau68apC3yNFAqI+3ABrHaYuDO4VgohBCixuwE3Ozut8RBwOnAvi11HFgUJQSeCPyJ8EA8UORpaCGVkSYoQ55rZp8lDLBeMTzLhBBCzCvmbvbE5sDtyd8rgJe11PkCcDbh4XYR8Gb3wSddyNOQkMpIu/tDwPuAw9x9S+AwggBUrt2ymPOwfGrqkbkzWAghxPAYYE5D+jsSX8uSnnKSAK2xkdcCVwJPA3YAvmBmGzBgNGiIZGSkAZYCxfa3CS6iNiQjLYQQYiakvyPxdWKyewWwZfL3FrSHyw8EzvTAzcDvgOcO2k4NGsjLSEfuBHaO27sCv5lr24QQQsxTfGpwr3IuBbYxs63NbCGwPyEUkXIbsBuAmT0FeA5wy4DPWDkNkayMNPAe4HgzmwAeI65kKYQQQvjU3MyecPdJMzsEOBcYB05x9+vM7OC4/wTgn4FTzewaQjjj8NlYK0mDBrrKSL90Lm0RQgghWolrIZ3TUnZCsn0nsMds26FBgxBCCNEPNVx7QoMGIYQQoh8kI11PzGxdM7skkd88OpZ/xsxuNLOrzewsM9twyKYKIYQQQ0ODhsDjwK7u/iLC/NY9zezlwHnAdu6+PfBr4IjhmSiEEGJeMeWDe40ICk8A7u7Aw/HPBfHl7v6TpNpFwBvn2jYhhBDzlBrmNMjTEDGz8Tjd8h7gPHe/uKXKQcCP5twwIYQQYp6gQUPE3de4+w4Epa2dzGy7Yp+ZfZyw8MdpubaSkRZCiBoyh0tjzxc0aGjB3R8ALgD2BDCzpcDewNtiGCPXRjLSQghRN9wH9xoRNGgAzOzJxcwIM3sC8FfAjWa2J3A4sI+7PzpEE4UQQoiho0TIwGbA18xsnDCQOsPdf2BmNwPrAOeF5Sm4yN0PHqKdQggh5gsjFFYYFBo0AO5+NfDiTPmzh2COEEKIUWCEpkoOCoUnhBBCCFEJeRoIipDALwihiAngO+5+ZNx3KHAIYfbED939o8Ow8dKnLmlsr7vOagAWrrOmUbZg3UkAJhZOu8vG1wnb4+tOH2dsYfx/3enxoi0ca/ofwBaMh42FE0m9WDY+Pl020V7WaJOrZ8k4tVGWrBU2Ntb8f8pYpzXFik6GNAYupGTT/quWlR0v9xRT1R2a1stdyxyNa59c5zJbk+Na2XsznvmaqWpTr+TupWy9zL5UErjqda6awFZ2vDWTiQneVsZk/JyvWdNW5qtXJ8dZ01wfYHU4jq9K+ohlaT1fHY+3KilbNdX0f9gO9k09lnT7eLjma1ZNX/vJVeH6rn5s+r1fvSp83h97fLpsyV2XMdLUUEZag4ZAoQj5sJktAP7bzH4EPAHYF9je3R83s02HaqUQQoj5Qw3DExo00FkREngfcIy7Px7r3TMcC2HHPyxvbF/3zO0BGJ+YHuWOjYebd3zh9E08XngVFk4fp/AwNHkVCk/DgnZvQcO7ALAglk2kZQtiZ128D7my4kkwU2ZVn3a7Megn2mElPsUnGh/wl1TWQ5C73lnPT1JWPOHn6qXH63acgplc56wNXbxUrQzCg5Dbn3syTT87hbcgKfPxwtOQeB8seBia3r/Y1i2pV1RP/yjaZN57S67TFO3HgWC/JffhOO3Xqpid7lPTnos1a8KxR967UHOU0xDpoAi5LfBqM7vYzC40sx2HaqQQo8yoDBgGRQ0z6+uGT00N7DUqyNMQcfc1wA5Rr+GsqAg5AWwEvBzYETjDzJ7ZKvJkZsuAZQA2vhgJPAkhRA1QeEK4+wNmdgFBEXIFcGYcJFxiZlPAJsC9LW1OBE4EmFi4+azfReNjYVQ6loQnxheEbscWTHc/tm7xf5ekx3XibZCEHRphiQVJImSxvwhJJPubQhbjufBESXJkmihXPClWTWgbFGUhkH4S5Mqoej6Zviwdrxb7+7k+VZ++y8INY5mwQ9n71i10kaMs0Sz3XjSFTAbg2eiW6FbWttu9UryXab3xuJ2EIiyToOrFuaWJkEVoL+2iZV843urmfUyHOfyx6eONxZ+HXJhiLLF5Kh5pLA1ZxP1Tk9PWjI/X7wd2bUThCTorQgLfA3aN5dsCC4H7hmOlECPEXAz0hBg2PjW414ggT0OgkyLkQuAUM7sWWAUs7bT+hBBCiJqh8EQ9KVGEXAW8fe4tKue5N18LwK0vfk6jbGwi3LyWvKM2EV2DE9MuwuxMiRgmsHWSsMNEUW9BW73uIYuSGRVNOg3tIYvG/onMrTmshLbcODHneq6qjdDPU3i3/vo9dtX63cJFjZkwufetRHsj21dG2yKlzO0/6BBIt357DZ/kQl1Vr20aYogzJDxXL6E4sk9mZkqkZq2KZeumZWHmg3V5ThqjXVdkajKGLCamy57z62tLjyNGAw0ahBBCiH4YoVkPg0KDhhEmfYCy8c6ehspKjxNpvegtmKio05B6GqLnoNlLUdGrUDx1NZUNWekxx1TmiTGnQdGNXj0DVb+k+kqO7NHmnP5CrqxbcmJVyrwO/XgYqvbVeH8z9cq8CeOZeulxx1v2wXQCpLXrLzR/4ONnO+22ZLqpJdoNOb+BxWP746umywovwVTmxFO3fPE9k3gVxiaK467l7vsahieUrZQQtRquMLMfxL+fZGbnmdlv4v8bDdtGIUaC2fwRL5iLp7waPkkKUYYGDc18ELgh+ftjwPnuvg1wfvxbCCGE0OyJOmNmWwB/A3wK+F+xeF9gl7j9NeAC4PC5tq0Tz7jspsb2XTuHVbzT8ETDbZjKxY4VrsRkvFiEHQaV9NgIbSTGTMSy9Am0aJMLRTTVq3ibFm1n8gGsmuxW1VXdT/JfVaougNWrLd0o1WkoKRtU/70mQg6qj8ZCYpkQQzdPiJXdV6kOQrzXx3q/lxpJj01tOidAZsMUPv1ZK/Y3HaFwxyfy8j5ZhEaThOuxUPb05b8utXnkUXii1nwO+CiFuHrgKe5+F0D8XwtWCVEF6TQIsVYiTwNgZnsD97j7ZWa2Sx/tJSMthBA1Y5TWjBgUGjQEXgnsY2Z7AesCG5jZN4C7zWwzd7/LzDYjLGbVxlzLSOcowhJpKKKxnYYiMqtNNsIOVWdKpLMEipkSab0i3DDRZfZEEXbIykh3ycJvGJ+Z059z11eVA85lvHcjly1flbLQQj9P6722GdQKorkQREtZ0E6bHcLSMc195MqqHCM0qigtnZsVkTvGWCa0UWym93/RJl3RsrimWR2Q9q/wrIz0RPvXUrZeZn/TyqrFKrprpm0pwhKe3kp1cTQpPFFP3P0Id9/C3bcC9gd+5u5vB84GlsZqS4HvD8lEIUaLYU2TFULMKvI0lHMMYWXLdwG3AfsN2Z6OPPX8mwG4d99tpgsbawi1J0I2JUgVX/A570NGfyEtm663sL1emuA4nqvXcI9Ml01kynLehxwzWYwoN2e+LMmtWx8zienPxPtQtkjTbCZq5rwJg9ZpKMi8H5Z94u7y9daSOGtTafJt/D9zzbIeiW6ehoYXrIv3oZEUmbF9UMmRDZumSy3a11Qv2mULk9JCM2J8ul/PJFsW30drPTX0NGjQ0IK7X0CYJYG7/xHYbZj2CDGSDFqnYdDMJ1vE6DJCUyUHhT45QgghhKiEPA1rGU/+/m8a23982/PCRiY5silxMVc23p4wWYQlmpMjF2bqZcpySY+9JkKWud47Uaan0M2lXNDtaaKsj5nIOffztJ5btCiTCNjkYi+pV9pHVfuqhpca9bvYMdVu+4wok31OQwKFuz591or3S9P1bLRNj0fFsrH2siLDML0Ny5Ij04zEGO5rSnosCdk11VsTL8ia5NyKz/RYqi0RSL97akMNwxPyNCS0ykgn5R8xMzezTYZlmxBCiPmFT/nAXqOCBg3NtMpIY2ZbArsTEiGFEEKI2qLwRKSDjDTAcQSlyNGdbpmRkW7eX+JSnsjMqBjvNisiU6/Yn7qoi/05yeikbdZtXuYiT8m5YjMhiIZ7uWx+PlRf+XK8qiu+xMVfMdSQlXPOkVx7y4ZmBmBztv54teNXDeUU90Y/s0GqyoTbmvZ9JTMlsiGLNasTu4r+KS8rZg9NJu7/xmchIzedljVmQ2S+1pPzLj7HuZkSzfdwJkRZhCWS62y575S6MEIegkGhQcM0nyMMDhYVBWa2D3CHu1/VNG1RCCGEkCJkPcnJSJvZesDHgT0qtJ+XMtIbnxYiLfcve9F0YeMpNuN9SJ8oikFSzjPQpN1QovSY02TIJT1mkiNtfEF7vVxyZDdynoPMfPfGHP0yLwRgtqCtbFpNsqI6ZZMtPXoTuj3lV9a0mIE64yBULDPH6Fkxsp/gapErmL5/uYWoGsdOPxO9eh+m7+GG16FqcmSqcVJ4HZre5+LA3Twr7Ros0/ZN44X9SdKjxfey2wJYT/r69eU2iLUKDRoCbTLSwNeBrYHCy7AFcLmZ7eTuf0gbzwcZaSGEEHOMwhP1xN2PAI4AiJ6Gj7j736V1zOxWYIm73zfX9gkhhJiHaNAg1kpyN3bq6sy5m4v53U16DhnXd1nSY1VNhjTpsQhLZJMjk5BFGek8/lxCYpk+Qy50kX5Mirn6abV4DZq1Dzq7jftKZqyqf9Br0uGAlBF7Dy1US9gcOIVkdPr+xPfUrWLIolsyY+7ti6GKvpIjc4tTrSkJWVRc2KqpXnGcrOS8JtmJaTRoaCGVkW4p32qubRFCCDF/cZenQQghhBBVUHii3ljwsS4nTLPc28x2AE4gJEdOAu9390uGaGJfbHTS1Y3tBz64U3uF3MqXBU1hjLg/zeyeyIQdepSMzs6UaGq7oN2Wqu7trK5CdKVXDWPk9Bdy8sLdZl+V6WGk9CrFnAvbdFlZshFOqLoCZdWFeXLXvlf9h0GHJ1Lbc6eRmTjjLfua2vZVlpEVr9o2N0OiUZYJWaR95J6Ey2ZcVAxFbHTCFZXqibUPBauaaVWEPBY42t13AP4x/i2E6EYNV/8TNWTKB/caEeRpiHRQhHTC9EuAxcCdQzBtoGx4fHCUPPh//rJ9Z1MyXsbrUDz9p8lSVb0KZfoL2eTIpF5OIbHsaTSnl5CS01XI1i+SCnMJckm14jAz0Cio7nEYb9+fuS5dky17vaY9DwJ6eB5p2FIxmbKqN6NhcxethdzT/ZrMoliNRaLSMjqXrWlPSGzyZuTakjmfxvVJq2Ua5xasynk41tBe1lq/EyP04zYXjNKaEYNCg4ZpPkeLIiTwIeBcM/ss4SP7irk3S4i1mJmEIuTNEMOmhoMGhSdoVoRs2fU+4DB33xI4DDi5Q/tlZrbczJZPTT0yy9YKIYQQw0GehkCbIqSZfQN4HSHPAeDbwEm5xiOpCJmMkK3MNT6RCR2kZTk3d5n+Qq5tU3giozOQSZDLJvJlF6fKaCiUhiVyrt2kXs6VXfzRzbXba/gi567v57pkkyeLxcIy4Y4cTe78jOu+V6rqSTTq5+p1a5t5XypqIxTXMfthrpoImdNQSGgoUDfti+/LmmQhqmxYpKp2Q9xenehDNOzMLd6VnHHxHZHU2/C4i9vb1JkaOrvkaSAoQrr7FlGLYX/gZ+7+dkIOw86x2q7Ab4ZkohBCiHmGT/nAXqOCPA3lvAc43swmgMeIi1IJIYQQdUSDhhZSRUh3/2/gpcO0Z7ZYfMz/19h+6J/jQp5NErI5WdnMPPrc6otlIYvcqpn9zAioqnlQaCh0kznOhTEqurLb9nXso2x+fMVQRGZ/9rp0W/myLAyUo2lSSZGlPwPfbO59zlUbgGZDk/s/O1MitxJqZrZDcW9kpZtpL+s2QyOSnVGR1V8gV7G9LN2XiyRlwxKZ1V1jvcX/8ovMQQRQy0RIDRqEEEKIfqhhToMGDaI6VqIYmXlibXoCbngVKj7t5pL7utqXeQKumnyY8UiUqgLmtCC6KkKWnEcf2g2lSY/jmY92F+XI0qf65Hy9LGm0Kt36bST/Zd7LnPZAjpxNRZvUu5DVQaCtLKf4WXqPVPVIJGSTI4s/0rY5m8vyU6eaXCbhv1SLIpP0mNWqELVHg4ZIXPp6JeGjN+nuS8zsM4QZFKuA3wIHuvsDQzNSiLWM0gFDtzIhhswoJTAOCn0Sm3mNu+/g7kvi3+cB27n79sCvgSOGZ5oQQoh5xdQAXyOCPA0luPtPkj8vAt44LFtmkw0+EU7zoc+8rlqDbk+CZUluWXncLkmPZf3mKJUU7kDOHZ47XsPN3MW9XZUeF6fKJj2O53QzypNLKycYZq5L0db7CQOV9V/Vw1C1r2wyY0amuWrCa67bMrlpT96DqgmYOSxjTC7M0uirfTG15qTMzEJskzHZMjmfxf/082r2iVqhQcM0DvzEzBz4ShRsSjkI+NbcmyWEEGI+UsfwhAYN07zS3e80s02B88zsRnf/BYCZfZywNPZpuYZmtoyo4WDjixkbW3+ubBZCCDEsRiisMCg0aIi4+53x/3vM7CxgJ+AXZrYU2BvYzT23OP2Iykj3Q/70e6PqSoZNbbqEOarQrX4mfJELkeSz5TMu77JwSDfXfFlYIq3XWHW02oyUyiGBrOx0xqacy7tXBvHepnQJQzVCK920G8rKPA3VZOSmsytvZsIYJWGtppBK0XHTSpUVr3fuM5sLWRQzKiY1Y0KUo0RIwMzWN7NFxTawB3Ctme0JHA7s4+6PDtNGIYQQ8wufGtxrVJCnIfAU4CwLOgQTwH+6+4/N7GZgHUK4AuAidz94eGbOLhv87/9qbD/0hTd1rtjPwkzzfRpdWTLjVJcny7LkSM+UNbWtqEuRS44sSXq0nE5DP0/1Oc/QVIkyYj9kr0tLv1UXycp5e7otapZdEIreyrppGhTn00+yZeEWmUnSY2JfQ58h9SrE7cVH/jRnvejECP3YDwoNGgB3vwV4Uab82UMwR4j6UGXAIISYN2jQIIQQQvTBKIUVBoUGDSLLBoecAcDKf39H740Ll+h4pswSl2hG8riyZPRMKFnYKv/k216vq9x0rnFO/riqLkWZPHS3RaeqhiVySZQFTZoMufeocL9XDCP06k0oC5N0IhciKnZlzjGbHFmqzZweL3M/VPVdN4VPemyb0pAz76LJkCuTZHR/1HDQMI+CysPFzG41s2vM7EozW56UH2pmN5nZdWZ27DBtFEIIIYaJPA3NvMbd7yv+MLPXAPsC27v741HDQQghhJjT8ESczXc8wa13krsfk6mzC/A5YAFwn7vvPGg7NGgo533AMe7+OAQNhyHbM1zKZk3k9qVlvUYd5npmRdWQRWamROlc/ZRMaKHyTImyVStz8tC9hiQ67a+yr8mmktDGoPUXUtvLQhXdZMDLGJuBFHROVyF3vH5+eaqGHXIhi6IsCUkUUvKiN+Zq0GDhi+KLwO7ACuBSMzvb3a9P6mwIfAnY091vm62HXIUnpilkpC+LCo8A2wKvNrOLzexCM9txiPYJIYSoJzsBN7v7Le6+Cjid4AVPeStwprvfBrP3kCtPwzRtMtKE67MR8HJgR+AMM3tmqzLk2iwjveg9X29sr/zau8OGZ55aUqqW5faVJULO5Em16pN3t0WYskqB7cmRpefRzb4yb0EmIbDy4lP92FJCNpkw+z5XtC/39J/zUgzi8a6bdkOO3FN7bjGpXmm6jhW9eYUtuc9i+vUUvQm+pl2TYYOPndOHsSJlDsMTmwO3J3+vAF7WUmdbYIGZXQAsAo539/8YtCEaNEQ6yEivIIzcHLjEzKaATYB7W9rWQ0ZaCCHENG4DO1T68Bk5MVk4MddR62/NBPBSYDfgCcCvzOwid//1wIxEgwagIR095u4rExnpfwIeBnYFLjCzbYGFwH2djySEEEL0TvrwmWEFsGXy9xbAnZk697n7I8AjZvYLgmihBg2zQCcZ6YXAKWZ2LbAKWNpp0apasGYy/D+1YLqsIVM7OV0Wk/bcp12iVtxqXfx5RRsj467vh16lk7vKKsf/09Mokv9y5zbVJdxRUVq6bCGqrlTVZMgcr3Loo1gUKxdh8CJ8M9ZWljtGE4N2/5ZoN1Ru249RuX6L92DN6vK2ZfLQudDK5GR5vX4XFxNtzGF44lJgGzPbGrgD2J+Qw5DyfeALZjZBeMB9GXDcoA3RoIFSGelVwNvn3iIhhBDzHZ8aXHiitB/3STM7BDiXMBftFHe/zswOjvtPcPcbzOzHwNWEke1J7n7toG3RoEEIIYSY57j7OcA5LWUntPz9GeAzs2mHBg2ROMf1JGA7QoLJQcBNwLeArYBbgTe5+/3DsXD4LDroVABWfj3J1SnCEqmOQK6scJmm7u6GWzYJdxSu/ia3Pu1ty5iJxkM3ueLS1TCT7cJvmdNXyPZbMRyT02TI7a+qydAtJNHrrJPc8Wh/T6vPvJiH9DPzIkdOW6JMV6HbipbFZywXxkjK0tVsxcwYlVt2kEinYZrjgR+7+3MJoYobgI8B57v7NsD58W8hhBACdxvYa1SQpwEwsw2AvwQOgEYuwyoz2xfYJVb7GnABcPjcWzjPSBOtxotkrqRsYqKtzItEuey88vayNNu0kVyXDnHLtAxy9LMEc/EkmFMezOkGZBceSvurmICZs6ui9yG/fwBJjzn7ch6YHNnrM6RHtKqPhr3qj6QeoIZcQsW+Mh4ET5Mjc16Fhqpj8rkrcrST4zX0GSa1IJUYDBo0BJ5J0F74qpm9CLgM+CDwFHe/C8Dd79LaE0IIIQoUnqgvE8BLgC+7+4uBR+ghFGFmy8xsuZktn5p6ZLZsFEIIMY/wKRvYa1SQpyGwAljh7hfHv79DGDTcbWabRS/DZkBWy7tuipCLDjylsd1IikzdpJOZRMjCZZqGNgqXtnVJNCyTZO6mg1DQJFVdNaGyohR0NvTRa/0uC0f1GobpJ+kx11dpWKTi9ckt6lT00auEc0rZIlVd2yYu/FyiYVkf3RZnq0ouPJcLRRSfrW76C8X+VDI67t/gw9/v3T4hMsjTALj7H4Dbzew5sWg34HrgbGBpLFtKEM8QQgghcB/ca1SQp2GaQ4HTogrkLcCBhEHVGWb2LuA2YL8h2ieEEGIeMUphhUGhQUPE3a8ElmR27TbHpowUi94RpNJXnv6B6cLCnbqmPRThaZZ5ur+Nae0Gj+GLJoninHbDVHsme2v/M6aqS77MW93r7Ii0WjcNhZnMlKiq8VCV3EyTgkzIoLDJS++LLnQLQw161kZGWjqVT2+za6o9dJCGHRqzJppmRZSELNJQREPmPa2nWRNisGjQIIQYPFXzDYQYYeRpEKJfJtsXrMomPTYl2cWn17E+tBuqpuMM6qm5V3pNopyJiqVlEhf7SXocJGPj1QYOOe2GbnoOZUqK3cqKXd2SD8tI7SvziqT7CpszHoQmz0QuKXMy47nLJUfGwLgn3oUNDjurs31ixoxSLsKgUCJkxMw2NLPvmNmNZnaDmf1Fsu8jZuZmtskwbRRiZJCnQYi1EnkapilkpN8YkyHXAzCzLYHdCYmQQgghBKDwRG3pJCMddx8HfBRNtyxl0du/0thuJEXmkvES167H/dYUsohu2Zx2w3jOlU17vZTC3Vs1EbJqvVzCXVX39kySMnMaCl1CIZXDEoMM5eTCE3Mhn5fT7eimydCrBkQmZJINMTRVKFlgqilxMbPoVM7mIgSROd4Gh5zR2XYxUEZpzYhBofBEIJWRvsLMTjKz9c1sH+AOd79qyPYJMVooPCHEWok8DYFCRvpQd7/YzI4HjiJ4H/bo1tjMlgHLAGx8MWNj68+iqUIIIeYDdVx7QoOGQE5G+ihga+AqMwPYArjczHaKCpIN6iYj3ZUyGemcW7jJpTxb8+h73NfLccu+OarW69Zd5RUoxzrXn82QRJlnIRcimsm3bW6VzaxNxeyEzEyJbP0+vCNlktI5TYamUElmRkVObj2ne1LUS2ZKuDQZ5pwphSfqSQcZ6cvdfVN338rdtyIMLF7SOmAQQggh6oI8DdPkZKRFHxRJkSv/833ThcVT5kS79yFNIrPilswlgjUlH86B1sIg6JYwWeJByKo/NpX1eA2qLu41aAbhYejjGN5rX+n1nMokGpZ5Kbrdr9lkxpKkx1y/OfXHxCOhBMi5p46JkBo0REpkpIv9W82ZMUKsjdQxACzWauo45VLhCSGEEEJUQp6GiJltCJwEbEdQLD4I+DNwArAuMAm8390vGZaNo8ait365sb3yW4eGjZzc9FTGFZvzvKdJZMUiVjMZ91YMEwyMsiftqqGDmUhgz2ZIompyYis5DYUcg/ZSzOR4M1hQK7uYVVXJ6IxOg5Ifh0sdZaQ1aJgmpwh5BnC0u//IzPYCjgV2GaKNQggh5gl1DE9o0EBnRUgzc2CDWG0xcOdQDBRCCCHmARo0BFJFyBcBlwEfBD4EnGtmnyXkf7xiaBaOOmVZ4bmy1BVbSEovyNyuab1CZrrJ1V8co4trvqrcdFW3dpmbflCrbA5ihcxBhWiq6DQ01c9oKLTs69pX5feij/esotx0I9zQ1ebM8XL3f67fxuyJJGQhyeh5gXQa6kuhCPlld38x8AjwMeB9wGHuviVwGHByrrGZLTOz5Wa2fGrqkbmyWQghxBBxt4G9RgV5GgI5RciPAa8ieBwAvk1IlGxDipDdWbT/FwFY+Z3DpgvXtCtHFk9u1u0JdDwz3q3qLSieKHNP/IPwJMwGg0jUrJpsmZ5bmVdkBgqKpSqN/SQpVvUmZNt2SU7s93jdjlHVq7AmczxNX50X1DERUp4GOipCXk/IYdg5lu0K/GYI5gmxdlAmuSyEGAnkaZgmpwj5feB4M5sAHiMuSiWEEELUMadBg4ZIB0XI/wZeOvfWrMWsyeg05MpyrthMwljqHbRid+o/y5W17oPBu+F7JQkXWFmCY9XFqbpRXN+xjDxzeryZnHvO/d7af7Z+JjwyqPegVy2ITBilSWthJsmYVRdxy8hIL3r/t6r1K2aVUcpFGBQKTwghBo9i7kKslcjTIIQQQvRBHRMhNWgAYgJk6u97JvCPwObA64BVwG+BA939gTk3cC1i0Zs/39huzKRIV75szKhYMF1WuGctcQsXLvTcLIqu2g25NnMRghiAPkPTE3w4j3QmQi5UUezPhjFy1yXnJci1zZ3PmtXtZS129MQg3pf0/S5MmE1Z6lw4LVcvV9bQbkh+jeJnYtF7T5uhkWLQ1DGnQeEJwN1vcvcd3H0HQg7Do8BZwHnAdu6+PfBr4IjhWSmEEEIMF3ka2tkN+K27/x74fVJ+EfDG4Zi0llKSKOfJE6sVnoguT3PFs5nZeHu93JPyXCxYNSj1xxyZZMayp/ncvibvQy45stE4c61yXoD0eC0LO5mNtdswqCf+sXaPUnFuXT0cZeqPZQmbnfbnusgpR5Z5HZrq1dAHPiLUMRFSg4Z29ge+mSk/iOYQhhCiE5mVIPsKTwgxj1F4ouZEjYZ9COqPafnHCUtjZ4OKkpEWQghRB+RpaOavgcvd/e6iwMyWAnsDu7nn/YSSke6PRfsdD8DKMz88XVg8oaYu8rg4T3MIYXV7WWNxqi4hgTI3fFXSsEPOTV8Wlhh0KCTnIh/LhB0y+7NJlFXlplNavQhjY+2S0f2oP85AB6HUs9FHgmWTPkPZ8aouTlVWL/HULHrP13u2VcwNdfyy16ChmbeQhCbMbE/gcGBnd390aFYJMWpIHlrUAIUnaoyZrQfsDpyZFH8BWAScZ2ZXmtkJQzFOCCGEmAfI0xCJnoSNW8qePSRzasWiN/xrY3vl9w8PG+mMgOg2tyZXeSFBneoCLIj1p13FWWnpspkU3UIHubDDTEIRZVLQM1nVM01ELDt2ZuZF1xkVpW7/Aa82WdjSbdXT0uMOeHXIihLnXWdjFO9RtxUtxbxFsyeEEEIIUYk6Du00aBDzi1wiZDHfPl3UKademHvqK0uK7CfhL0fVxMZMH5UXm2o8qabHG2/e161tjqbjldhSWY+gpF6SHDl93D4UH6smPc7I61Fxcaqc/UXb1AuWSXDMlsWk30Xv/o+eTRZiLlBOA0FGOuYsFK+HzOxDcd+hZnaTmV1nZscO2VQhRhe53MVahmMDe40K8jQQZKSBHQAsyAneAZxlZq8B9gW2d/fHzWzT4VkphBBiPjFVwzmXGjS005CRNrPPAMe4++MA7n7PcE1b+ymSIlee9b+nCxuLUyW3a067oUiAtDSJMpRZkxuetnrTDTJyyblEx4oJjjkqhyS6yTmvqZi82TheNUnrysqNM/EclPXRrf9CYyKjOtld9rlzOKFrv7l6XhJ2aHqvJtvtayRCrmkvE2KeovBEO6mM9LbAq83sYjO70Mx2HKJdQggh5hFT2MBeo4I8DQmJjHSxmuUEsBHwcmBH4Awze2arMqSZLQOWAdj4YsbG1p87o4UQQgyFUcpFGBQaNDTTKiO9AjgzDhIuMbMpYBPg3rSRZKRngclV09uFmz5x3RYzKazb7Im4nb4pNhMHWy4UMFuhiMru/y6zQHKrUuZCFWVy1N3c/lmzKsyMGFQopMy+nB1V++02YyIjBd2YNZHTaUjDDxntBs2aEPMdhSeaaZKRBr4H7ApgZtsCC4H75t4sIUaMfqZSCjFiTA3wNSrI0xBJZKTfmxSfApxiZtcCq4ClnRatEoOlWMwKkgWt0qe04gk41WEonqgTRcjpZMbk4MWTXU5lMUeP3gXowcPQI7kFpprKct8+ucTP4ke9m1JmWZLgTJ7gy+jiVShdAKtb0mNFNcvs4lS9qj9m9Bea2haaDEtP6myTmNcoPFFjOshIrwLePhyLhBBCiPmFBg1CCCFEH4xSWGFQaNAQMbPDgHcTcuauAQ4E1gO+BWwF3Aq8yd3vH5KJ9WV1TIpM3efFXP2kzHIhgyLhL/UeF9oNntYvQhtpHxmZ5pKwQ18hibJEw4ScdkJZWdaWbudROSlxTef6A1Z9rCwP3WtYpNsiVrmww1RmMakYYmiSjM4uRFWyOJUYWeo4aFAiJGBmmwN/Dyxx9+2AcYJew8eA8919G+D8+LcQQghRSzRomGYCeIKZTRA8DHcSJKS/Fvd/Dfjb4ZgmhBBivqG1J2qKu99hZp8FbgP+DPzE3X9iZk9x97tinbu09sRwWPTmzwOw8juHTRdmQhYN7Ya0ceH+H18wXZZzPRfTDqrOqBgUVVdsLKnfRJneQ3ZfRruh23TJkpBBZQnqqpSd70y0FnLVymZMpNtJWaNN06yNkhkVk9PhCc2aGH2mRue3fmDI0wCY2UYEr8LWwNOA9c2s8qwJM1tmZsvNbPnU1COzZaYQQggxVORpCPwV8Dt3vxfAzM4EXgHcbWabRS/DZkB2wSopQs4Ni954XGO7snZD8bSX0W7wpKyRFNn0xBj/T70UGXpNgOya3FcxOTJL8cSfruNlmePlTF7TY39NugYl51QxkbQypZ6YLl6S3DXttWyqi1ehNBFSyY9rE6O0ZsSgkKchcBvwcjNbz8yMsNLlDcDZwNJYZynw/SHZJ8RokfvRnSXBKyGGhQ/wNSrI0wC4+8Vm9h3gcmASuILgOXgiYZGqdxEGFvsNz0ohhBBiuGjQEHH3I4EjW4ofJ3gdxHyjV+2GNAkwJwfcCGMkZeOZkEXmablUG6EbZW7wXL99uPpzoYOs3HSPVNZQaDToQyciV69q26py02VluXBHN8noqUzZ6qDjoOTHtYs66jRo0CCEEEL0wZQpp0EIIYQQIos8DZGcjLS7Pxb3fQT4DPBkd9fS2POAhnbDtz84XdiYPZHRbkjDE5apl5OWbrirk9kYGaqGJSqvzphtXLFNdqZEJqRSuM67rNZZmbLVMHMU9SzzvnQjN7uk0baixsQMVrRsqp8779zsiSKcJtYqRimBcVDI00CpjDRmtiVhyezbhmehEEKI+cbUAF+jgjwN0xQy0quZlpEGOA74KJpuOT9Jn+Ask/SY024Yj7d9Tu0vpxKZajxUVIwciDJiN82BsYwHJPcEX+bZ6KbNMNvTJKsmP87EI5JJeG3yFuSSGYsFqNKyQs2xi9JjTpNh0UGn9mG4EPMPDRoolZHeB7jD3a+yGia8CCGE6IxkpGtKBxnpdwIfB/6xQnvJSAshRM2Ywgb26oaZ7WlmN5nZzWbWccVlM9vRzNaY2RsHerIReRoCORnpAwmDiMLLsAVwuZnt5O5/SBtLRnp4LHrrlxvbK791aNhIXcXjYdvHp291K9zHmSS8Jmnpso9Hmkg36KF3t7BEWb0iZDGohaPKwgc53QXLJZKWMKjwR6+aEX3Ua4Q0svLQ7WWL3nFitf6F6IKZjQNfJOTXrQAuNbOz3f36TL1PA+fOli3yNARyMtJnuvum7r6Vu29FeKNe0jpgEEJkGPRql0LMQ+ZQRnon4GZ3v8XdVwGnE7zjrRwKfJcO6yQNAg0aCDLSQCEjfQ3huugxQQghREembHCvNMwdX8uSrjYHbk/+XhHLGsRZgK8HTpjNc1Z4ItJBRjrdv9XcWSP6oaHd8J3DpgsLt3HTLIvowm+SoC6ZidA0Bz9m1acrX/a6GuVsPoWXhSyqhj1ypNenzP6qsyEq91uy4mfTrIg17WUZWxohhm6S0WUrWqZtJ3PhCa1kKXonDXNnyCU9tDooPgcc7u5rZjNxX4MGIYQQog/mMAi3Atgy+XsLpmUBCpYAp8cBwybAXmY26e7fG6QhGjSItY/c3PokEdKjt8Amkts/96SaW8SqITy4OinLeC5yVF0Qqax+jm79Vu2j7DjpMaqeb78ehrGKGhPdFpMq+k/vhxwl6o9dF6cqtpO2SoCsD3OY9X4psI2ZbQ3cQRAffGuTLe5bF9tmdirwg0EPGEA5DQ3M7DAzu87MrjWzb5rZuma2g5ldZGZXxhjTTsO2U4iRYLZFoYSoEe4+CRxCmBVxA3CGu19nZgeb2cFzaYs8DTTJSD/f3f9sZmcwPZI72t1/ZGZ7AccCuwzPUiGEEPOFuRR3cvdzgHNayrJJj+5+wGzZoUHDNDkZaQc2iPsX0x5DEvOQIiESkqTI1KVchComM2VJvcL12Py9EOulD9KFZ3pQD9e9Jkp2CzGUHa/yIlEliaLpccqON6jFsXL0es2mMkmUmaRHT8NQRTJtTjJ6dVJP1IY6TizWoIFSGenbgXPjvjHgFcO0UwghhBgmGjTQJiP9APBtM3s7QVDjMHf/rpm9CTiZoB7Z2n4ZsAzAxhczNrb+XJkuujEZnw6TRMgZqUQ2NpPjNZIjKyYV5uimaJh7Si/2p/vms6jS1FT2PIqlxbsu8lWWAFkwNtY+NbJTWWsSY1LW8DCMjU17GIp6ExPw+GNhuzjG+HiTOqmoB/P40zZrKFsp0JCRdvfVwJkEr8LSuA3wbcIgog13P9Hdl7j7Eg0YhOhAyYChK1UHQ7nZFrmy3IyKXFmq71FQDBgSNGCoJ26De40KGjQEcjLSNxByGHaOdXYFfjMk+4QQQswzpgb4GhUUniDISJtZISM9CVxBUOa6AjjezCaAx4ghCDE6LNr/i0APKpEpRfJfmhxZSBSkHnLPJQFmQgczoVfVyZSZJDvmjlE89c9gWmVlD8MAaKhAdqJM/TGn+SH1R1FjNGiIdJCR/m/gpUMwRwghxDxnlDwEg0KDBiGEEKIP5lARct6gQUPEzD4IvIcwLf/f3f1zZvYZ4HXAKuC3wIHu/sDwrBR9M5mEIsbawwkNaek0nLD68fD/ePvHxBNPfkNlOvW4F8fuZwZE1VBEbjGnXsMhuZBEP/LQVc8tUjpTYiahmAyWhJ48NwPDM5oMGXnoRlkSnpBktKgbSoQEzGw7woBhJ+BFwN5mtg1wHrCdu28P/Bo4YnhWCjFCDPiHX4j5yCCXxh4V5GkIPA+4yN0fBTCzC4HXu/uxSZ2LgDcOwzgxc4qESICV3/5g2MgsPOTJU3HVz7GPTcX6yXLZxWEs40EoeyrvhbLjdfMWVF10qsRzkSYztnkO+kkALbsGNlZt2mWqyZCUNbwIxb7xBTD5eFOZjS/AC+9SqtuxqlWTYQIe+3N3W8RaTx2HxvI0BK4F/tLMNjaz9YC9aF6GFOAg4EdzbpkQYkY6DU1hh4JiwJDWW91e1hgwpGjAIGqMPA2Au99gZp8mhCMeBq4iTL0EwMw+Hv8+LddeipBCCFE/6uhp0KAh4u4nE2SiMbN/AVbE7aXA3sBu7p5NlnX3Ewm6Dkws3LyOCbUjxaL9jgdg5Zkfni6cbFf+yy5YVbjdm55eF8T602WNUEXOl7emj6+aqroG2STFismO8dyyGgpdQiplugtzmfSYPXbafyMRsosmQybpsdD1WLT0pAEbKkaVOn7Za9AQMbNN3f0eM3s68AbgL8xsT+BwYOci30EIIYSoKxo0TPNdM9sYWA18wN3vN7MvAOsA5wV1aS5y94OHaaQQQoj5wSjNehgUGjRE3P3VmbJnD8MWMUfkFiPKuNlTF2TjOyKj3UAye6IIVTTNqOiV3OqVM5GETtuWLR6V7ivrb7zL6pq9hh5yuhPZevHcyla77GRH0Saxt5Eo2SU8sejtX6nWn6gNdcxp0OwJIcTgkU6DEGsl8jSI2rLozZ9vbK/81qHtFRau21Y0k+TIBl28BQ0Fw5koPVbsz3Leh/E+vCOFqZ5oR1QZOOTOK9c2t2DW2Hi7t2FsbFpzo1ioanxiOtE11V8oPE3F8RYshMceba43PpFdClsIqGcipDwNETP7oJlda2bXmdmHkvJDzeymWH5sySGEEAUz8TRUbZsLT6QhhoLMzJhsaOqxTK6zBgyihCl8YK9RQZ4G2mSkVwE/NrMfAlsA+wLbu/vjZrbpEM0UQgghhooGDYGsjDSwBDjG3R8HcPd7hmeimFWKJ8+mBasyT6Nxf1NyZMOtn3ycilBF08pWRaJhpv8mSebwBJ0utDS9M3kKL/prCjFk2rTYHo5dMemxauIlmTBBIc8cj1Gq19DBzgbdvA9TGf2FssWpPCkrvBMZ7QYlP4oy6pi5o/BEoJOM9LbAq83sYjO70Mx2HKqVQowKSoQUNcAH+BoV5GmgVEZ6AtgIeDmwI3CGmT2zVRlSMtJCCCHqgAYNkQ4y0s8DzoyDhEvMbArYBLi3pa1kpEecwg298j/fV16xcJ2nbvvJTPJdsT91yTfKqoUsmkIgxWGy+hAZumgyZM+jYmgj60Uo2q7pPHuiTGoaOoQvyjwWGXnoprBDTka6qmR0LqFSiBbq6E/ToCGSk5Em3BO7AheY2bbAQuC+IZopxGig8ISoAVKErDc5GelTgFPM7FrCrIqlnRatEmsHi9765cb2ytM/EDasPDmyVLsh/YgVRenvabas5Im86ak5/j9eLTWp25P+QCj6GB/LLw5V1rQpGTTTpihL9TBaEiDNxvGp1c31xxfAqrCcdUP9cXxi+r0sjrFgYWMp7PQ+EEJMo0FDpIOM9Crg7UMwR4jRpupMiVzTsgFDSmYw0hgwpMQBQxO5mTGrpMkgemOU9BUGhQYNQgghRB/Ub8igQYMQnVkdn1pzSYWZEELzwlaFFHOaUBc/bmnTHkMMQyMXYijzJlgSnqi6EFVp/+UJjo0EyLSPIhSR0V/omggphMgyz7+pBouZnWJm98QchaLsSWZ2npn9Jv6/UbLvCDO7OcpIv3Y4VgsxgswgPCHEqDA1wNeoUKtBA3AqsGdL2ceA8919G+D8+Ddm9nxgf+AFsc2XLCvRJ4QQoo5o7Ym1HHf/hZlt1VK8L7BL3P4acAFweCw/PUpI/87MbiasTfGrOTFWDJ1F7zgRgJXfeG/PbYtkPptYZ7pwrMj0TyoWw3bPFWZIXe1FSGMGMyqybVO9hl49Bo22yQqUrWEKyCcx5mZblPTv6SyKRoihvSxfrz08sWj/L3bsSwgRqJunIcdT3P0ugPh/sSjV5sDtSb0VsUwI0Y3cCpRCrGVIRlqk5GQ7su+tZKTXclYnT6oliZBNFIs0JU+5jRtqIvnYFQ/SlvmRTdQfG1oQlvECpA/jVb0PU5mn/9zxClLvQzEgKFWQTDwNlumjzOswNpYsIpVZWCr+n9VkGBuH1Y83HdfGF+DFtMtimezE07Bov+M7n4cQJYxSLsKgkKcB7jazzQDi/8VKlisIi1YVbAHcmTuAu5/o7kvcfYkGDEKQ9zRUlYnOzWLIlGU1GYoBQ1ovp9MghOgLDRrgbGBp3F4KfD8p39/M1jGzrYFtgEuGYJ8QQoh5iBIh13LM7JuEpMdNzGwFcCRwDGH1yncBtwH7Abj7dWZ2BnA9YbL9B7xpNRxRFxYdeEpje+XXl4WNsZJFqtLtsVQaOdw+ljZdsDDuzIQT0uP1nNjYHjrw9Gk92mXZUESyXdiQ8xzMJG9hqlhgKqOhkB632F9VkyE5Ry+8Dul5F21yipBC9Mjo/NQPjloNGtz9LR127dah/qeAT82eRUIIIcToUKtBgxBCCDEo6pgIWatBQ1y1cm/gHnffLpY9CfgWsBVwK/Amd78/afN0QojiKHf/7FzbLOYZObe2ZWYsFC7xnNx0Ejmwhus8+SjmZkDE7Wap6jI9h8TFn6tWeP0z+5pCFmVRkdyqmanNuevSZkcmxJC7jlOZspz+QrZeEp6I79+iN3++s01CVMRrGKCoWyLkqVRUhEw4DvjR7JsmxFrEXCzDLYSYc2rlaehRERIz+1vgFuCROTFQzHsWHXQqACu/etB04Xh0HYxlEiHTp9yiLH2UL7bHujg6iyforNZCWo/2sjVTzf2HwvBfk9sjakukp1FmVlO/rRXLz6cpKTOT7JjzHDSmWKYehkJ3YTJJgJxsSYBMvUNalEoMkDqGJ/Q40EER0szWJwwejh6ibUKs3eRCF1U1GSbbQ0WNAYMQc0Adp1xq0NCZo4Hj3P3hbhXNbJmZLTez5VNTckoIIYRYO6lVeKIDd5vZZu5+V4si5MuAN5rZscCGwJSZPebuX2g9gLufCJwIMLFw89EZMoq+6ardkNFpyCVHNrQb0o9izoWeWdiqIS2dS1zM+k2TwsKGLm2LUIXlchTKFp1K2pQuRJWGGqba602HJNoXmCoNSaTbqWS0EiDFAKnjl70GDdOKkMeQKEK6+6uLCmZ2FPBwbsAghGgnO8gQYi1jlMIKg6JWn+yoCPkr4DlmtiKqQB4D7G5mvwF2j38LIYQQooVaeRp6VYRM2h01eGvEWkORnW/Jwqi58MRYyYyK1HWf02koZKHTekW1pJblZk/kyNUrKfNueeJjzeeRrd80O2J1U/1QNhmrZfQXMpoMntNpSGdKFNuSjBazRB1nT9Rq0CCEEEIMijqKO2nQIMQMyWo3jGU8DQ2dhqSsUHpMnpqt0SbVZFjTVpTzDDS+wtZMK0KatS9e1WizJnlWyuYhZBalGsscb03JM1duAapMUmRjISobq+ZhGBuDVY817Wvy4sQpmYve+uXOtgkheqJuOQ2nmNk9ZnZtUvYkMzvPzH4T/98oli8ws6+Z2TVmdoOZHTE8y4VYC8hpMlTUaciWFQMGIYbE1ABfo0KtBg30JiO9H7COu78QeCnw3oyapBBCiJriA/w3KtQqPNGjjLQD65vZBPAEYBXw0JwYKkaTJo2A+NFKtRvGY1lOLjkNDUxmynL6C1lNhkJaOtGCyJialYduhCwSmzMLbvX8WFSm01BxISpfnSg9FomNadtM0uOi/b/Yo6FCiG7UzdOQIysjDXyHsObEXcBtwGfd/U/DMVEIIcR8o47hiVp5GnpkJ0IW2NOAjYD/z8x+6u63tFY0s2XAMgAbX8zY2PpzaqgQQoi5Z8pHJ6wwKDRo6Cwj/Vbgx+6+GrjHzH4JLCGsetmEZKQFwKJ3/0djuzGTYjyZaZALRWRWw/QYErCmlSBjvfHkI5vTVcjpPoy17CORr87NrEjDJ2VhjBwZaen8ceOMiVSTIbfaZUZaelqCOiMZLU0GIWYVhSemZaQhkZEmhCR2tcD6wMuBG4dgnxBCiHmID/A1KtTK0xBlpHcBNjGzFcCRBNnoM6Kk9G2EWRMAXwS+ClwLGPBVd796zo0Wo8lkoiVQUGynnoaJzsmRTUqPLIgbGQ9CLjky7aN4Ss94JBraCOmhU5uzUyJ7jMDmFrbqVfVxbGzai5DzKsSyRW//Sm+2CTED6rj2RK0GDb3ISMclsffL1BVCDIqqmgwKO4h5yChNlRwUCk8IIYQQohK18jQIMVcses/XAVh5ygHThZMx6TBNZsxpMhRxgjQUUWyPZZIUuy06lZOHLsIOueTIAc//aoRAsgmOicR04WGYnGyvl7adzElGZ7wTQswyozRVclDUytPQQUZ6PzO7zsymzGxJUr67mV0WZaQvM7Ndh2O1EEKI+cgUPrDXqFCrQQN5GelrgTcAv2gpvw94XZSRXgp8fdatE0IIIeYxtQpP5GSk3f0GADNrrXtF8ud1wLpmto67P44QFSlWwARY+bV3h43UrV6EB3KzLBKXfCN0kHrhi5kX3aSls2XtfeTCGOnsivadFZ2zuRBDMStiqj1k4ZlZFLmZEul11KwJMQzqmAhZq0HDDPg74AoNGIQQQhTUMadBg4YumNkLgE8De5TUkYy06E7xVD1Z0dOQSY70pqKibdoHPZa1azI0PTvlvAlVVR8b9de071/TnuyY1WSYXNVev+FpKPGCCCFmhbrlNPSEmW0BnAW8091/26meu5/o7kvcfYkGDEJ0oOoARJoMYkRw94G9umFme5rZTWZ2s5l9LLP/bWZ2dXz9j5m9aDbOWZ6GDpjZhsAPgSPc/ZdDNkcIIcQ8Y65mPVhYJOaLwO7ACuBSMzvb3a9Pqv0O2Nnd7zezvyash/SyQdtSq0FDBxnpPwGfB54M/NDMrnT31wKHAM8GPmFmn4iH2MPd72k/shDdWXTgKUCSEAkwkVmQaSrjwp9uML2ZE1SonAiZKWvd12pXK026C5lQgbecW1lIIt2f019Iy6InYtE7TuxsmxBrFzsBNxerLJvZ6cC+QGPQ4O7/k9S/CNhiNgyp1aChREb6rEzdTwKfnF2LhFhLqTqzQogRZg7v8s2B25O/V1DuRXgX8KPZMKRWgwYhhBBiUAxyymWaUB850d0Ld5plmmQ7N7PXEAYNrxqYcQm1GjSY2SnA3sA97r5dLNsPOAp4HrCTuy9P6m8PfAXYgDCo3NHdH5tru8VaRpP7PUpLW2b2REqZdkPXEEPFsoZ9XZ6fqoQiMmVNoYhcyCKnyVDMnkjKpMkg1kbiAKFTzG0FsGXy9xbAna2V4m/WScBfu/sfB24k9Zs9cSoVFSHNbAL4BnCwu7+AkAuxGiFEdxSeEDVgDmWkLwW2MbOtzWwhsD9wdlrBzJ4OnAm8w91/PSsnTM08Db0oQhJ0Ga5296tivVkZtYn6kVWJHEueuHM6DcWTeWYRK2+Xc2imLBEy9SrkFrbKUTZ1Mpfs6JlFqXIehlS/otXDsFrjdTH/qDJVckD9TJrZIcC5wDhwirtfZ2YHx/0nAP8IbAx8Kf6eTbr7kk7H7JdaDRp6ZFvAzexcwsyK09392CHbJMRokJt1kZsNkiublE6DEK24+znAOS1lJyTb7wbe3dpu0GjQ0JkJQiLJjsCjwPlmdpm7n99aUYqQQghRP+oYhNOgoTMrgAvd/T4AMzsHeAnQNmhIE1gmFm5evxVMRN8sWnoSACu/niRNF6GKXHJkmiTZeEqf/hgXoYrKyZGd9peR8yJkEiEbYYnCzpwWxWQmTNEUugjHKK6TEPOJOi5YVbdEyF44F9jezNaLSZE7kwhpCCE6U7o6phBiZKmVp6EXRcgoxflvhKxVB85x9x8OyXQhhBDzjLmSkZ5P1GrQ0IsiZKz/DcK0SyFml6aVL+NMnm7hifH48fXctIgMaXigqJabodGN3OyJaH+Th2GqJWSRXakyN2MimSmhxavEPGauZk/MJxSeEEIMnqoDECHESFErT4MQ85ViMStItBsmkh/eNZnkSMtkPZZpN4xlvAr9PDa0ehBIPAxTGZu7qT626jSYwarHgWZNCyHmG3UMT9TK02Bmp5jZPWZ2bVL2GTO7Ma5BflZcErvYd0Rcu/wmM3vtUIwWYhSpqsmQK4sDBiHmOz7Af6NCrQYN5GWkzwO2c/ftgV8DRwCY2fMJUp0viG2+FNc0F0IIIWpJrcITHWSkf5L8eRHwxri9L0EF8nHgd2Z2M2FN81/Nha2ixjRc+JkxfbeFrRp00W4o2lbNPcjUyyY9liU7dgtTFAmQkowWI8KUEiFrz0FMr0GeW7988zm3SIhRJBd2EGItwwf4GhU0aIiY2ceBSeC0oihTrdP65cvMbLmZLZ+aemS2TBRCCCGGSq3CE50ws6XA3sBuPj3xttL65SAZaTFYihkDK7960HRhVe2GQkMh1VIoirqthpkhq+w4lesjE57wlrJseCIJRUyG7UXvPQ0hRgHNnqghZrYncDiwj7s/muw6G9jfzNYxs62BbYBLhmGjECNHTgBKiLWMKXxgr1GhVp6GDjLSRwDrAOfFNcgvcveD41rlZxDWm5gEPuAS1BdzyWTyFF54E7ppN+QolCOTpl7WpGxBqnR/N09DzsOwukX1cWysUbboPV8vMUoIMR+o1aChg4z0ySX1PwV8avYsEmItJZcImZOElky0GGHqKCNdq0GDEEIIMShGKawwKGo1aDCzUwgJj/e4+3ax7DPA64BVwG+BA939gaTN0wkhiqPc/bNzbrSoLam7fuUpB4SN1RkZ6dxTfVPIIu4fTz7ua6Yy9TJ4u2R0W4Jjur9Mi2HNmsw+aTIIMUrULRHyVCoqQiYcx7R2gxCiCtJpEDWgjjLStfI09KgIiZn9LXALIPEFIYQQTSinQRwEfAvAzNYnTMXcHfjIMI0SolS7IUeTxHTxMU+e/hthiYpTI3P6C91WtGydKdG0L2oyvO+b1foXQswLNGiIZBQhjwaOc/eHrezLObRdBiwDsPHFjI2tP5umCjH/0awIUQOUCFlTOihCvgx4o5kdC2wITJnZY+7+hdb2UoQUc8ZkxcTB3GJWnkmirEqqyTBZovCY8Sbkyjz+/9AX3sQGh5zRmy1CzBMUnqghiSLkzqkipLu/OqlzFPBwbsAghMiQmxWRKdOAQYjRolaDhl4UIYdmpBBCiJGgjuEJq6N7ZTZReELMFStPemfYWLBwunDBgvB/qslQbKdlufBFGd2SHnO6Cy1lnuyTh0HMFZOr7ihPSpsB2z/1Lwb2fX/1H341a3YOkrrpNAgh5gLpNAixVlKr8IQQQggxKKZq6Kmv1aChFxlpM1sAnAS8hHCd/sPd/+9wLBciQ5kEc7eVKovZE93CFLkVLQsvQtpHYUs61TLKRjfCEpKMFmsZo6TkOCjqFp44leoy0vsB67j7C4GXAu9tVZMUQnRgjVaRF2JtpFaehh5lpB1Y38wmgCcQPBEPzYWdQlShUFNc+eXMiu9TyUe7cKGOj0+XFR6GfhasaixOlQwMMotS5TwMGxx2Vnl/QowQdQxP1M3T0I2DmF6c6juENSfuAm4DPuvufxqWYUKMEp7TZNCAQaxl1HHBKg0aIhkZ6Z2ANcDTgK2BD5vZMzu0XWZmy81s+dSU1rYSQgixdlKr8EQnOshIvxX4sbuvBu4xs18CSwirXjYhGWkxTHzV441tK8IJ40nooCibTJ4RJuJHv8u6Ko3QRlanIekjSkt7U8gieBvkYRBrKwpP1JBERnqfVEaaEJLY1QLrAy8HbhyGjUKMHJopIWpAHcMTtfI09Cgj/UXgq8C1gAFfdferh2G3EEKI+UcdPQ21GjS4eybNnJM71H2YMO1SiHnNBh88s7H90HGvDxsLpr/MrAgtTCQf95xiY06zoaHT4O1lyTF8MoYlUg+DvA1CrHXUatAghJgjNGAQNWCUwgqDQoMGIdYiiqTDh/5130aZT0R9hvSHPHoVbCxJhMxpNsQkSp/KeRraEyHTsg0+dk6P1gsxWrhnlFfXcmqVCGlmp5jZPWZ2bVL2z2Z2tZldaWY/MbOnxfLdzewyM7sm/r/r8CwXYrTQgEGItZNaDRrIy0h/xt23d/cdgB8A/xjL7wNeF2WklwJfnysjhRBCzH+m8IG9RoVahSc6yEin0tDrE+SjcfcrkvLrgHXNbB13fxwh5juPPTa9XchHp4mOsazpq6osETLVachqN8TFqVaFMMWDR+7G4qPP78NwIUYH1+yJemJmnwLeCTwIvCZT5e+AKzRgEKIaGjAIsXZSt/BEFnf/uLtvSZCQPiTdZ2YvAD4NvLdTe8lICyFE/ahjeMLq5l6J4YkfuPt2mX3PAH5Y7DOzLYCfAQe6+y+rHF8y0mK+8eCRu4WNZKaEFaGIirMnmogzKRraDNAITyz+5AUzMVWIgTO56o4uWun9s/lGLxjY9/0d9183a3YOktp7Gsxsm+TPfYhS0Wa2IfBD4IiqAwYhhBBibaZWOQ0dZKT3MrPnAFPA74GDY/VDgGcDnzCzT8SyPdz9nrm1WogZUiRFJp4ELzwMueTHHGnSY6HZkHghmrwOQtQEyUiv5fQoI/1J4JOza5EQQohRpY6KkLUPTwghhBCiGrXyNJjZKcDewD1JsuM/A/sSwhP3AAe4+51x3/bAV4AN4v4d3f2x3LGFmK8s/nRIyXng75dMF1omEbIqRXgikYze8AuX9WueECNL3SYSQP08DadSURHSzCaAbwAHu/sLCLkQWoVHCCEEUM8pl7UaNLj7L4A/tZRlFSGBPYCr3f2qWO+P7q5sLyGEELWlVuGJTnRQhNwWcDM7F3gycLq7HzskE4WYMRv+v+WN7fvfvX3YqBqemGp/EtropKsHYZYQI4vCEzWlgyLkBPAq4G3x/9eb2W659lKEFEKI+jHlPrDXqCBPQzP/SRB0OhJYAVzo7vcBmNk5wEuANlF9dz8ROBGkCClGg6lHVrUXlnkdUk9DRiRSCFEPau9p6KQICZwLbG9m68WkyJ2B6+faPiGEEPMTdx/Ya1SolaehF0VId7/fzP4NuJSQHHmOu/9wKIYLIYSYd4zSrIdBUbsFq2YbhSfEKHHv67bpXgnwJDyx6Q9vni1zhBg4s7lg1eInPmtg3/cPPvzbkViwqlaeBiGEEGJQ1PGhW4MGIYQQog9GadbDoKhVIqSZnWJm95jZtZl9HzEzN7NNkrIjzOxmM7vJzF47t9YKMftMPuQVXzReQoj6UqtBA3kZacxsS2B34Lak7PnA/sALYpsvmdn43JgphBBivuMD/Dcq1Co84e6/MLOtMruOAz4KfD8p25egAvk48DszuxnYCfjVrBsqxByx2YXTSY2/f+lzOtZ7xmU3zYU5QowUCk/UEDPbB7ijWGMiYXPg9uTvFbFMCCGEqCW18jS0YmbrAR8nLE7VtjtTlh1WmtkyYBmAjS9mbGz9gdkohBBifqLZE/XjWcDWwFVmBrAFcLmZ7UTwLGyZ1N0CuDN3EMlIi7WBx1YuAMBs+hZ2H4mp40IMhVHKRRgUtQ5PuPs17r6pu2/l7lsRBgovcfc/AGcD+5vZOma2NbANcMkQzRVCCCGGSq0GDVFG+lfAc8xshZm9q1Ndd78OOIOw3sSPgQ+4+5q5sVQIIcR8Zy7XnjCzPeP0/5vN7GOZ/WZm/y/uv9rMXjIb5ywZ6QGj8IQYda7c4sWN7R1WXDFES4SYObMpI71ggN/3q0vsjNP9f02QBlhBWBPpLe5+fVJnL+BQYC/gZcDx7v6yQdlXUCtPgxBCCDGC7ATc7O63uPsq4HSCLEDKvsB/eOAiYEMz22zQhmjQIIQQQvSBD/DVhSoSAHMjEzDImIxejdjUstluoz7Uh/pQH+pjZn3Mpxdh2v7y5LUs2bcfcFLy9zuAz7e0/yHwquTv84GXDtpOeRpmh2Vz0EZ9qA/1oT7Ux8z6mDe4+4nuviR5nZjsriIBUFkmYCZo0CCEEELMby4FtjGzrc1sIWFdpLNb6pwNvDPOong58KC73zVoQ+ou7iSEEELMa9x90swOAc4FxoFT3P06Mzs47j8BOIcwc+Jm4FHgwNmwRYOG2eHE7lVm3EZ9qA/1oT7Ux8z6GBnc/RzCwCAtOyHZduADs22HdBqEEEIIUQnlNAghhBCiEho0CCGEEKISymkYAGb2XIIa1+YEnY47gbPd/YaK7V9FUPy61t1/MmuGCiGEEDNAnoYZYmaHEyQ9jbAK5qVx+5u5RUVim0uS7fcAXwAWAUd2ajPbmNliMzvGzG40sz/G1w2xbMMObfZsaX9yXCjlP83sKaPQR6/Hn6/nsbb0MRfvRz99CCECGjTMnHcBO7r7Me7+jfg6huA56LSK5oJkexmwu7sfDewBvC3XYA6+GM8A7gd2cfeN3X1j4DWx7NsdzuNfku1/Be4CXkcYOH1lRPro9fjz9TzWlj7m4v3op49iFcGXmdkbzOz1cbvyYkhm9kQze0mnAZn6mNs+RJ8MWzpz1F/AjcAzMuXPAG7q0OYqYCNgY2B5y74rOrQ5FzgceGpS9tRYdl6m/uXJ9knAJ6NNhwHfy9TP2lq2r6WPK1v2XTkKffR6/Pl6HmtLH3PxfvTZxx6E+e8/ip+nk4Afx7I9OrT5UrL9KuA24OeE9QH2Uh9z24deg3kpp2HmfAg438x+w/RiIU8Hng0c0qHNYuAyQhjDzeyp7v4HM3tiLMuxlbt/Oi1w9z8Anzazg7rYuMTdd4jbx5nZ0kyd35vZR4GvufvdANEjcQDNi6CkbGpm/yvavIGZmcdPNHkv1nzso9fjz9fzWFv6mIv3o58+jgf+yt1vTQvNbGvC3PnnZdq8PNn+Z+Bv3f1yM3smwTtyTkt99TG7fYgBoPDEDHH3HwPbAkcTvAE/AY4CnhP35dps5e7PdPet4/9/iLumgNd36Or3ZvZRS0ILZvYUCzkVHb8YzezDxC/GZF/ufX8zwfNxoZndb2Z/Ai4AngS8qYNN/07IxXgi8DVgk2jXU4Er50kf98c+Nu7QR6/HH9Z51KWPmbwfF5jZn3rs49SKfUwQtP1buYPmcGMnNnD3ywHc/RaCqp/6GF4fok8k7jQimNlGwMcIszQ2jcV3E/TGj3H3+1vqH9lyiC+5+73xi/FYd39npo/nEhY5ucjdH07K9+w0AIptNgcurtLGzHYiiJddamYvAPYEbvCgdtbp3NM2z49tbixr09L+6+7+jip1Y/3/yF2fkvqvJuSwXOMVZ79YlxkzZvYywjk+aGbrEd77lwDXAf/i7g9WbPNi4PpcGzP7e+Asd+/kVcjZ3VMbCzr5bwHucPefmtnbgFdEm05099Ut9dch6OoX9d8a69+Qq5+0ezZhwL0lMAn8Gvhm7jq11N8i1v9Nl/pHEAYgpzM9SN8y2nqGu//fTJtHCe5yA7YCnu7u95vZGHC1u2+nPgbax9MJA8hsH2IwaNCwFmBmB7r7V2dSP/4YfIDw5bwD8EF3/37cd7m7vyRznEMJIZhKbeJA5q8JTwnnEX40LwT+CjjX3T+V6aO1zcsIT5HZNmbWuogLwK7AzwDcfZ+Z1I9tLnH3neL2uwnX7XuEOOt/eUiE7dbmEOCsTm3M7DrgRR40508EHgG+C+wWy9+Q6aO1zaPAdzq1MbMH43F/C/wn8G13vy9zPTq1+WZsc29J/dMI790TgAeB9eN570b4/lnaof56wAMEb8CZsT7ufkCmj78H9gZ+QdDev5KQBPl64P3ufsFM6iftng/sQxgkG+FJ92x3v75D/We0FN3l7qvMbBPgL939zEyb5zE9fbufPu5099Vd+pjpecxFHwO/VmJA+DxIrNBrZi/gtpnWB64Bnhi3tyKs5/7B+PcVHY7TU5tYf5zwg/AQwQ0J4Qfl6pI+KrcBLge+AewC7Bz/vytu75ypf0Uv9VvPjZBx/+S4vT7B2zDjNgTvS+OcWvZd2aGPntrEcx8jDFxOBu4lJJMtBRZ1Oo9e2hTvEWEgcDcwHv+2Du9fT/XTeyRurwdcELefXnYfVq2v12i8gE2HbUMdXsppGBEsTJfMva4BcvPje6pP+BJ9GMBDctEuwF+b2b/ROTmz1zaT7r7G3R8FfuvuD8W2fybkc+Totc0SQpLpxwlLw14A/NndL3T3CzP1X9pjfYAxM9vIzDYmPC3fG216hODqHkSba82sWKXuKjNbAmBm2wJZF30fbdzdp9z9J+7+LuBpwJcI4Z9bOvTRa5uxGKJYRPiBXhzL1yEfe+61fsFEUm9RNPS2kjY91bf+NC16btMJM/tRL/U7tTGzDczs/5rZ183sLS37vtThOGmbt3ZrY2ZPNbMvm9kXzWxjMzsqfvecYWabdegj1+aaTm3M7EmtL+CS+Bl7UpdLI2aAZk+MDk8BXktwo6YY8D8DqP8HM9vB3a8EcPeHzWxv4BTghR1s6rXNKjNbLw4AXtowyGwxnQcNPbVx9ynCDJFvx//vpuQ+77V+pJ/ZL722eTdwvJn9A3Af8Cszu50Qv313hz56bdPUr4d8gbOBs83sCR366LXNyYRpyeOEgdm3zewWQrb86QOoD2G63aVmdhHwl8CnAczsycCfBlAfQgb/zwhaEH+I9Z9KmKHxbWD3HtoszbUxs7YQYLGLEP5r39F7m68S8je+CxxkZm8E3uruj9M8g6Gszd91aXMq8EOCF+3nwGmEcNC+wAnx/ypt/qakzX3A71vKNid4Gh14ZodzETNl2K4Ovaq9CF+mr+qw7z8HUH8LEg2Iln2v7FDeUxtgnQ51NwFe2GFfz21a6v0NIQmw6nXuqX5L2/WArQfZhvAU/CLCgOkpFY9ZqQ2wbR/n2E+bpwFPi9sbAm8EdhpU/VjvBbHecyva1Gv9fjQtetWPWEMYZPw88/pzh+P01IZ2XYqPA78kzD65vEMfPbWhORR3W9mx+m0DfIQQFnthUva7Xu9NvXp/KRFSCCG6YGY/AX5KXgtid3f/q5m2MbNrgde7+28yx7rd3bfMlPfUxsxuAF7gwcNWlC0FPkrIT2pNSOy5jZld5e4vitufdPd/SPZd4+5tXsg+22wBHEfwpB0JXOXu8jDMMsppEEKI7qR6E61aEPsNqM1RdP5OPrRDea9t/oswO6iBu38N+DCwqsNxem3z/Rh2o+XH/9nATR366LmNu69w9/0IXpXzCF47McvI0yCEEDPAepzy3E8b9VHeJubTPMvdr+2nD1EdDRqEEGIGmNlt7v702WyjPma3D1EdzZ4QQogumNnVnXaRn8Lccxv1Mbt9iMGgQYMQQnSn1ynM/bRRH7PbhxgAGjQIIUR3fkCYKXBl6w4zu2BAbdTH7PYhBoByGoQQQghRCU25FEIIIUQlNGgQQgghRCU0aBBCCCFEJTRoEEIIIUQlNGgQQgghRCX+fxqgynyagjaNAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Space_Pred_Prob_R = pd.Series(np.zeros(np.size(population)))\n",
+    "finaltime_R = cycles\n",
+    "for i in range(finaltime_R):\n",
+    "    Space_Pred_Prob_R += (DF_List[i] == -1).sum(axis=1)\n",
+    "\n",
+    "Space_Pred_Prob_R = Space_Pred_Prob_R/max(Space_Pred_Prob_R)\n",
+    "\n",
+    "plt.figure(figsize=(8, 7));\n",
+    "plt.title('Spatial Distribution Probability for R (t={}, {}x{}, {}v, p_i{}, t_r{})'.format(finaltime_R, experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "sns.heatmap(pd.DataFrame(Space_Pred_Prob_R.to_numpy().reshape((population.shape[0],population.shape[1]))));\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_pi{}_tr{}_SpaceDistProb_R.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RuxErsZ0vJJG"
+   },
+   "source": [
+    "# 3. Time prediction: fit empirical data to SIR Model\n",
+    "Time prediction of the epidemic was performed by integrating the simplified model with $\\mu = 0$, then we find the best fit with the data from the training dataset that we have already computed.\n",
+    "$$\n",
+    "    \\frac{dS}{dt} = -\\lambda SI \n",
+    "$$\n",
+    "$$\n",
+    "    \\frac{dI}{dt} = \\lambda SI -\\gamma I\n",
+    "$$\n",
+    "$$\n",
+    "    \\frac{dR}{dt} = \\gamma I\n",
+    "$$\n",
+    "The error of the fit was modeled as a normal distribution, 3 sigma rule was used so the training data should be inside the interval: \n",
+    "\n",
+    "$$\\mu(\\psi)-3\\sigma < \\psi < \\mu(\\psi)+3\\sigma$$\n",
+    "\n",
+    "where $\\psi$ is the function returned by \n",
+    "fitted_odeint\n",
+    " with popt containing the optimal \n",
+    "_lambda, gamma\n",
+    " by taking in account the error in pcov.\n",
+    "\n",
+    "As it can be seen the theoretical model sometimes differs from our implementation of the cellular automata algorithm. However, using a sparse random initialization on the entire surface the cellular automata algorithm converges to the theoretical model.\n",
+    "We can correct the prediction, taking into account the $t_r$ parameter, by performing the prediction with shifted data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Training_set_size = cycles*2//3\n",
+    "norm = max(df_S.mean()[0:Training_set_size]+df_I.mean()[0:Training_set_size]+df_R.mean()[0:Training_set_size])\n",
+    "ydataI = (np.array(df_I.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "ydataS = (np.array(df_S.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "ydataR = (np.array(df_R.mean()[0:Training_set_size])/norm)[t_r+1:]\n",
+    "xdata = (np.arange(Training_set_size))[t_r+1:]\n",
+    "\n",
+    "def sir_model(y, x, _lambda, gamma):\n",
+    "    S = -_lambda * y[0] * y[1]\n",
+    "    R = gamma * y[1]\n",
+    "    I = -(S + R)\n",
+    "    return S, I, R\n",
+    "\n",
+    "def fit_odeintI(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,1]\n",
+    "\n",
+    "def fit_odeintS(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,0]\n",
+    "\n",
+    "def fit_odeintR(x, _lambda, gamma):\n",
+    "    return integrate.odeint(sir_model, (S0, I0, R0), x, args=(_lambda, gamma))[:,2]\n",
+    "\n",
+    "I0 = ydataI[0]\n",
+    "S0 = 1 - I0\n",
+    "R0 = 0.0\n",
+    "\n",
+    "poptI, pcovI = optimize.curve_fit(fit_odeintI, xdata, ydataI)\n",
+    "poptS, pcovS = optimize.curve_fit(fit_odeintS, xdata, ydataS)\n",
+    "poptR, pcovR = optimize.curve_fit(fit_odeintR, xdata, ydataR)\n",
+    "\n",
+    "xdata = np.arange(cycles+1)+t_r+1\n",
+    "fittedI = fit_odeintI(xdata, *poptI)\n",
+    "fittedS = fit_odeintS(xdata, *poptS)\n",
+    "fittedR = fit_odeintR(xdata, *poptR)\n",
+    "\n",
+    "poptI_h = poptI + 3*np.diag(pcovI)**0.5 #error as normal distribution --> 3 sigma = 99.73% confidence\n",
+    "poptI_l = poptI - 3*np.diag(pcovI)**0.5\n",
+    "fittedI_h = fit_odeintI(xdata, *poptI_h)*norm\n",
+    "fittedI_l = fit_odeintI(xdata, *poptI_l)*norm\n",
+    "\n",
+    "poptS_h = poptS + 3*np.diag(pcovS)**0.5\n",
+    "poptS_l = poptS - 3*np.diag(pcovS)**0.5\n",
+    "fittedS_h = fit_odeintS(xdata, *poptS_h)*norm\n",
+    "fittedS_l = fit_odeintS(xdata, *poptS_l)*norm\n",
+    "\n",
+    "poptR_h = poptR + 3*np.diag(pcovR)**0.5\n",
+    "poptR_l = poptR - 3*np.diag(pcovR)**0.5\n",
+    "fittedR_h = fit_odeintR(xdata, *poptR_h)*norm\n",
+    "fittedR_l = fit_odeintR(xdata, *poptR_l)*norm\n",
+    "\n",
+    "\n",
+    "fitted_S = fittedS*norm\n",
+    "fitted_I = fittedI*norm\n",
+    "fitted_R = fittedR*norm\n",
+    "fig3 = plt.figure(figsize = (10,7))\n",
+    "\n",
+    "# plot of the fitted model\n",
+    "plt.plot(xdata,fitted_S, label='fitted S', zorder=20, linewidth=2.5, linestyle='--', color='orange')\n",
+    "plt.fill_between(xdata, fittedS_l,fittedS_h, color='orange', alpha=0.2)\n",
+    "plt.plot(xdata, fitted_I, label='fitted I', zorder=20, linewidth=2.5, linestyle='--', color='blue')\n",
+    "plt.fill_between(xdata, fittedI_l,fittedI_h, color='blue', alpha=0.2)\n",
+    "plt.plot(xdata, fitted_R, label='fitted R', zorder=20, linewidth=2.5, linestyle='--', color='green')\n",
+    "plt.fill_between(xdata, fittedR_l, fittedR_h, color='green', alpha=0.2)\n",
+    "\n",
+    "\n",
+    "xdata = np.arange(cycles+1)\n",
+    "#plot of the simulated model\n",
+    "plt.errorbar(xdata, df_S.mean(), df_S.std(), linestyle='-', marker='', color='orange', label='simulated S');\n",
+    "plt.errorbar(xdata, df_I.mean(), df_I.std(), linestyle='-', marker='', color='blue', label='simulated I');\n",
+    "plt.errorbar(xdata, df_R.mean(), df_R.std(), linestyle='-', marker='', color='green', label='simulated R');\n",
+    "plt.xlabel('time', fontsize=15);\n",
+    "plt.ylabel('number of cases', fontsize=15);\n",
+    "plt.title('Number Of Cases Prediction ({}x{}, {}v, p_i{}, t_r{})'.format(experiments,cycles,polygon_sides, p_i, t_r), fontsize=15);\n",
+    "plt.legend(fontsize=15);\n",
+    "plt.grid();\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_NumberOfCasesPrediction.png'.format(experiments,cycles,polygon_sides, p_i, t_r));\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction at t=20\n",
+      "S: 511+-36\n",
+      "I: 315+-14\n",
+      "R: 5783+-370\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict at t\n",
+    "t = 20\n",
+    "print(\"Prediction at t={}\".format(t))\n",
+    "print(\"S: {}+-{}\".format(round(fitted_S[t]), round(abs(fitted_S[t]-fittedS_l[t]))))\n",
+    "print(\"I: {}+-{}\".format(round(fitted_I[t]), round(abs(fitted_I[t]-fittedI_l[t]))))\n",
+    "print(\"R: {}+-{}\".format(round(fitted_R[t]), round(abs(fitted_R[t]-fittedR_l[t]))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The compartimental system goes:\n",
+    "$$\n",
+    "S \\longrightarrow I \\longrightarrow R\n",
+    "$$\n",
+    "\n",
+    "So the number of _new nodes_ infected can be computed by calculating the difference $S(t-1)-S(t)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xdata = np.arange(cycles+1+t_r)\n",
+    "fitted_S_EXT = np.append(df_S.mean()[0:t_r+1],fitted_S)\n",
+    "fittedS_l_EXT = np.append(df_S.mean()[0:t_r+1],fittedS_l)\n",
+    "fittedS_h_EXT = np.append(df_S.mean()[0:t_r+1],fittedS_h)\n",
+    "new_I = np.append(np.zeros(1),fitted_S_EXT[0:len(fitted_S_EXT)-1])-fitted_S_EXT\n",
+    "new_I_l = np.append(np.zeros(1),fittedS_l_EXT[0:len(fittedS_l_EXT)-1])-fittedS_l_EXT\n",
+    "new_I_h = np.append(np.zeros(1),fittedS_h_EXT[0:len(fittedS_h_EXT)-1])-fittedS_h_EXT\n",
+    "\n",
+    "fig3 = plt.figure(figsize = (10,7))\n",
+    "plt.plot(np.round(new_I)[1:])\n",
+    "plt.fill_between(xdata, np.round(new_I_l[1::]), np.round(new_I_h[1::]), color='blue', alpha=0.2)\n",
+    "plt.xlabel('time', fontsize=15)\n",
+    "plt.ylabel('new number of cases', fontsize=15)\n",
+    "plt.axvline(t_r+1, color='green', label='limit between empirical and predicted data: t={}'.format(t_r+1))\n",
+    "plt.legend(fontsize=15)\n",
+    "plt.grid()\n",
+    "plt.title('Number Of New Cases ({}x{}, {}v, p_i{}, t_r{})'.format(experiments,cycles,polygon_sides, p_i, t_r), fontsize=15)\n",
+    "\n",
+    "plt.savefig('images/{}x{}_edges{}_{}pi_{}tr_NumberOfNewCases.png'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Prediction at the end of the epidemic\n",
+    "\n",
+    "The time prediction is helpful to estimate the number of cases during the pandemic. For example, since it has a bell shape, the maximum number of simultaneous infects can be estimated. Also the total number can be estimated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to predictions, during the epidemic there will be maximum 1190+-8 infected simultaneously.\n",
+      "Instead, the total number of infected at the end of the epidemic will be: 6656+-477.0 (~98.6%).\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"According to predictions, during the epidemic there will be maximum {}+-{} infected simultaneously.\".format(round(max(fitted_I)), round(abs(fitted_I[np.argmax(fitted_I)]-fittedI_l[np.argmax(fitted_I)]))))\n",
+    "print(\"Instead, the total number of infected at the end of the epidemic will be: {}+-{} (~{}%).\".format(round(sum(new_I[1:])), np.sum(np.round(abs(new_I_h-new_I_l))), round(round(sum(new_I[1:]))/(df_S.mean()[0]+df_I.mean()[0]), 3)*100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Animation of the Evolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 231675,
+     "status": "aborted",
+     "timestamp": 1613645627183,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "Qm8jUOKMvJJI"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import animation  \n",
+    "import matplotlib.image as mgimg\n",
+    "\n",
+    "#set up the figure\n",
+    "fig = plt.figure()\n",
+    "ax = plt.gca()\n",
+    "\n",
+    "#initialization of animation, plot array of zeros \n",
+    "def init():\n",
+    "    imobj.set_data(DF_List[0][0].to_numpy().reshape(population.shape[0],population.shape[1]))\n",
+    "\n",
+    "    return  imobj,\n",
+    "\n",
+    "def animate(i): # animation of experiment 0 at time i\n",
+    "    imobj.set_data(DF_List[i][0].to_numpy().reshape(population.shape[0],population.shape[1]))\n",
+    "\n",
+    "    return  imobj,\n",
+    "\n",
+    "\n",
+    "## create an AxesImage object\n",
+    "plt.title('Evolution of the epidemic', fontsize=15)\n",
+    "imobj = ax.imshow(DF_List[0][0].to_numpy().reshape(population.shape[0],population.shape[1]), vmin=-1, vmax=2, cmap='rocket')#, origin='lower', alpha=1.0, zorder=1, aspect=1 )\n",
+    "\n",
+    "anim = animation.FuncAnimation(fig, animate, init_func=init, repeat = True,\n",
+    "                               frames=range(0,39), interval=200, blit=True, repeat_delay=1000)\n",
+    "\n",
+    "anim.save('images/{}x{}_edges{}_{}pi_{}tr_animation.gif'.format(experiments,cycles,polygon_sides, p_i, t_r))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 231672,
+     "status": "aborted",
+     "timestamp": 1613645627186,
+     "user": {
+      "displayName": "",
+      "photoUrl": "",
+      "userId": ""
+     },
+     "user_tz": -60
+    },
+    "id": "4ApfuK3IvJJJ",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(anim.to_html5_video())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results and Conclusions\n",
+    "\n",
+    "The results show that the algorithm developed can approximate the epidemic evolution given by the $SIR$ model. The numbers generated in the various simulations fit quite well the differential equations of the theoretical model, so the different characteristics of our Cellular Automata algorithm compared to the original one doesn't ruin the evolution behaviour and works fine. However, since a probability leads the expansion, it isn't possible to find such a parameter that discriminates the end of the epidemic at a certain time, as done in the original paper.  Instead, thanks to the spatial analysis, it is possible to see the probability distribution of a category at a certain time, to evaluate and compare the evolution state of various configurations. In this way, we can see how the change of the $p_i$ parameter impacts the simulation and how the different shapes also do. We can state that the increase in the probability increases the number of infected in general, as expected. The shape instead interestingly affects the distribution since at the corners the probability of being infected reduces. This happens proportionally to the reduction of the sides, meaning that a small angle in the corner protects the elements from infection more, probably because of the limited number of neighbours. Finally, the general evolution given by the number of the epidemic is predicted and described with good accuracy by the SIR model equations, removing the first $t_r$ samples since the R numbers can't increase.\n",
+    "\n",
+    "Following, some results using the Cellular Automata technique on the SIR model.\n",
+    "\n",
+    "### Experiments: $14x67$cycles, $14$ vertices, $p_i = 0.09$, $t_r=4$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/14x67_edges14_pi0.09_tr4_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/14x67_edges14_0.09pi_4tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/14x67_edges14_0.09pi_4tr_animation.gif\" width=\"500\" />\n",
+    "    \n",
+    "___   \n",
+    "    \n",
+    "### Experiments: $91x65$cycles, $8$ vertices, $p_i = 0.76$, $t_r=1$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/91x65_edges8_pi0.76_tr1_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/91x65_edges8_0.76pi_1tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/91x65_edges8_0.76pi_1tr_animation.gif\" width=\"500\" />\n",
+    "    \n",
+    "___\n",
+    "\n",
+    "### Experiments: $30x300$cycles, $3$ vertices, $p_i = 0.6$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/30x300_edges3_pi0.6_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/30x300_edges3_0.6pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/30x300_edges3_0.6pi_6tr_animation.gif\" width=\"500\" />\n",
+    "___\n",
+    "\n",
+    "### Experiments: $100x300$cycles, $3$ vertices, $p_i = 0.05$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/100x300_edges3_pi0.05_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/100x300_edges3_0.05pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/100x300_edges3_0.05pi_6tr_animation.gif\" width=\"500\" />\n",
+    "___\n",
+    "\n",
+    "### Experiments: $30x300$cycles, $6$ vertices, $p_i = 0.6$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/30x300_edges6_pi0.6_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/30x300_edges6_0.6pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/30x300_edges6_0.6pi_6tr_animation.gif\" width=\"500\" />\n",
+    "\n",
+    "___\n",
+    "\n",
+    "### Experiments: $100x300$cycles, $6$ vertices, $p_i = 0.05$, $t_r=6$\n",
+    "<br/>\n",
+    "  <img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_SpaceDistProb_S.png\" width=\"310\" />\n",
+    "  <img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_SpaceDistProb_I.png\" width=\"310\" /> \n",
+    "  <img align=\"left\" src=\"images/100x300_edges6_pi0.05_tr6_SpaceDistProb_R.png\" width=\"310\" />\n",
+    "</p>\n",
+    "<br/>\n",
+    "\n",
+    "<img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_NumberOfCasesPrediction.png\" width=\"450\" />\n",
+    "<img align=\"left\" src=\"images/100x300_edges6_0.05pi_6tr_NumberOfNewCases.png\" width=\"450\" />\n",
+    "\n",
+    "<br/>\n",
+    "<img align=\"middle\" src=\"images/100x300_edges6_0.05pi_6tr_animation.gif\" width=\"500\" />"
+   ]
   }
  ],
  "metadata": {
+  "colab": {
+   "name": "Copia di Project.ipynb",
+   "provenance": [
+    {
+     "file_id": "https://github.com/albertodetoni/LCP_projects_Y3/blob/master/.ipynb_checkpoints/Project_0.1-checkpoint.ipynb",
+     "timestamp": 1613645717249
+    }
+   ],
+   "toc_visible": true
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -44,9 +1746,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
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