diff --git a/Project.ipynb b/Project.ipynb
index 6fc12a0..7031ecc 100644
--- a/Project.ipynb
+++ b/Project.ipynb
@@ -1,52 +1,19063 @@
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-    "# 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",
-    "### Contancts\n",
-    "* Leonardo Badia <badia@dei.unipd.it>"
-   ]
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+      "source": [
+        "#Cellular Automata S.I.R. Model\n",
+        "\n",
+        "##Description\n",
+        "Based on the reference paper, 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",
+        "\n",
+        "\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",
+        "## Group Members\n",
+        "\n",
+        "\n",
+        "1.   Alberto Muraro - alberto.muraro.2@studenti.unipd.it\n",
+        "2.   Matteo Moro - matteo.moro.3@studenti.unipd.it\n",
+        "3.   Alberto Zancanaro - albi25.zanca@gmail.com (alberto.zancanaro.1@studenti.unipd.it)\n",
+        "4.   Andrea Michielon - andrea.michielon97@gmail.com\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NeXadJ2LHRXo",
+        "colab_type": "text"
+      },
+      "source": [
+        "##Libraries Used"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "U1TP_ScmpN85",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "84a0b49d-a98b-4ea2-c490-5c7a006bcbeb"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "import scipy as sc\n",
+        "from scipy.integrate import odeint\n",
+        "\n",
+        "import networkx as nx\n",
+        "import math\n",
+        "import random\n",
+        "\n",
+        "from IPython.display import HTML\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.animation as animation\n",
+        "import matplotlib as mpl\n",
+        "import matplotlib.patches as mpatches\n",
+        "import matplotlib.image as mgimg\n",
+        "import seaborn as sns\n",
+        "from tqdm.notebook import tqdm as tqdm_notebook"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+            "  import pandas.util.testing as tm\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OSVHfSGTn9pc",
+        "colab_type": "text"
+      },
+      "source": [
+        "<h1>The SIR model</h1>\n",
+        "\n",
+        "Epidemic models are a really useful tool in the prediction and prevention of infectous diseases. There are several models in the literature, but we will focus on the SIR model.\n",
+        "One of the simplest model for an infectous disease is considering a single pathogen carried by individuals where the people who contract this pathogen are able to infect others. We also make the assumption that we have N people in a isolated system (for example people on the same island or state with closed borders). \n",
+        "\n",
+        "We now divide the population three different categories:\n",
+        "* Susceptible [**S**]: people that have yet to contract the disease and do not have the immunity\n",
+        "* Infected [**I**]: people that have the disease and are able to spread it\n",
+        "* Recovered [**R**]: people that have recovered from the disease and they are immune to it. Note that also people that are dead are included in this category since they cannot spread the virus anymore.\n",
+        "\n",
+        "We define S, I and R as the number of people in each category, in this way\n",
+        "$$\n",
+        "S+I+R=N\n",
+        "$$\n",
+        "and we also define s=S/N, i=I/N and r=R/N as the percentage of the population in each category, in this way we have that:\n",
+        "$$\n",
+        "s+i+r=1\n",
+        "$$\n",
+        "\n",
+        "We also make the assumption of memoryless recovery, in this way we have that the time that an individual spends being sick is exponentially distribuited with parameter $\\gamma$. This means that :\n",
+        "$$\n",
+        "\\mathbb{E}[\\text{Time spent being sick}]= \\frac{1}{\\gamma}\n",
+        "$$\n",
+        "\n",
+        "We can now wrote the following equations for the SIR model:\n",
+        "\\begin{align} \n",
+        "\\frac{\\partial s}{\\partial t} &= -\\lambda i s\\\\\n",
+        "\\frac{\\partial i}{\\partial t} &= \\lambda i s - \\gamma i \\\\\n",
+        "\\frac{\\partial r}{\\partial t} &= \\gamma i\n",
+        "\\end{align}\n",
+        "\n",
+        "Another important parameter is the basic reproductive ratio $R_0$ (which due to covid19 almost every italian has heard about it) which is simply defined as:\n",
+        "$$\n",
+        "R_0 = \\frac{\\lambda}{\\gamma}\n",
+        "$$\n",
+        "And can be viewed as:\n",
+        "$$\n",
+        "R_0 = \\mathbb{E}[\\text{#secondary infections\n",
+        "caused by an infected individual at time 0]}\n",
+        "$$\n",
+        "\n",
+        "Since there are no closed form solution for the differential equation of the SIR model, we will give a numerical solution, using scipy, in order to better comprehend the importance that $R_0$ has."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "E0PezoYIu6Au",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#Define the three differential equation\n",
+        "def partial_derivatives(initial_condition, t, my_lambda, gamma):\n",
+        "    s, i, r = initial_condition\n",
+        "\n",
+        "    # Susceptible Update Equation\n",
+        "    ds = -my_lambda * i * s\n",
+        "\n",
+        "    #  Infected Update Equation\n",
+        "    di = my_lambda * i * s - gamma * i\n",
+        "\n",
+        "    # Recovered Update Equation\n",
+        "    dr = gamma * i\n",
+        "\n",
+        "    return ds, di, dr\n",
+        "\n",
+        "\n",
+        "#Use odeint to get a numerical solution and plot the results\n",
+        "def solve_eq(S0, I0, R0, my_lambda, gamma, days):\n",
+        "\n",
+        "    R_0 = my_lambda/gamma\n",
+        "    N = S0+I0+R0\n",
+        "\n",
+        "    s0 = S0/N\n",
+        "    i0 = I0/N\n",
+        "    r0 = R0/N\n",
+        "    t = np.linspace(0, days, days)\n",
+        "    initial_condition = s0, i0, r0\n",
+        "\n",
+        "\n",
+        "    s_plot, i_plot, r_plot = odeint(partial_derivatives, initial_condition, t, args = (my_lambda, gamma)).T\n",
+        "    print('{0:.2f}% of the total population contracted the virus'.format(s_plot[days-1]*100))\n",
+        "    plt.figure(figsize=(10,7))\n",
+        "\n",
+        "    plt.plot(t, s_plot, label = 'Susceptible',linewidth = 3, c = '#0000FF')\n",
+        "    plt.plot(t, i_plot, label = 'Infected',linewidth = 3, c = '#FF4500')\n",
+        "    plt.plot(t, r_plot, label = 'Recovered',linewidth = 3, c = '#00a14e')\n",
+        "    plt.grid()\n",
+        "    plt.title('N = {}, R0 = {}'.format(N, R_0))\n",
+        "    plt.xlabel('Time [Days]')\n",
+        "    plt.ylabel('Fraction of individuals')\n",
+        "    plt.legend()\n",
+        "    plt.show()"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2F-goFSi8ufa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 933
+        },
+        "outputId": "225db02a-b1c3-4dfc-f8cc-1184bee1a003"
+      },
+      "source": [
+        "solve_eq(9999, 1, 0, 1.1, 0.5, 100)\n",
+        "solve_eq(9999, 1, 0, 1.25, 1, 100)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "15.62% of the total population contracted the virus\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x504 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "62.83% of the total population contracted the virus\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x504 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b2o2azWRy6kh",
+        "colab_type": "text"
+      },
+      "source": [
+        "As we can see when we $R_0$ of 1.25 in the long run only 15% of the population got infected, while with an $R_0$ of 2.2 over 60% of the population contracted the virus. Note that the the estimation of $R_0$ for covid19 is in the range 1,4÷3,8 (see https://www.iss.it/primo-piano/-/asset_publisher/o4oGR9qmvUz9/content/id/5268851), this is why it's important to try to lower this paramater by applying different measure. In this case we will use the notation $R_t$, which simply $R_0$ where some form of prevention is applied.\n",
+        "\n",
+        "One thing that is missing from the model is the birth of new individuals and the death unrelated to the virus which can happen in any of the three different categories. We make the assumption that the birth rate and death rate are the same, so that our system is at a steady-state and the \"natural\" lifespan is exponetially distributed.\n",
+        "\n",
+        "We call this birth/death rate $\\mu$. Therefore:\n",
+        "$$\n",
+        "\\mathbb{E}[\\text{Natural lifespan}] = \\frac{1}{\\mu}\n",
+        "$$ \n",
+        "\n",
+        "We can now update the three previous differential equations: \n",
+        "\\begin{align} \n",
+        "\\frac{\\partial s}{\\partial t} &= \\mu-\\lambda i s -\\mu s\\\\\n",
+        "\\frac{\\partial i}{\\partial t} &= \\lambda i s - \\gamma i -\\mu i \\\\\n",
+        "\\frac{\\partial r}{\\partial t} &= \\gamma i - \\mu r\n",
+        "\\end{align}\n",
+        "And $R_0$ becomes:\n",
+        "$$\n",
+        "R_0 = \\frac{\\lambda}{\\gamma+\\mu}\n",
+        "$$\n",
+        "\n",
+        "As we can see the the birth rate is only present in the susceptible class, meaning that no new born has the virus or the immunity innately. The natural death is instead proportional to the percentage of the population for each class (recall that $s+i+r=1$)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H95bQ0WI8jAd",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Cellular automata</h1>\n",
+        "\n",
+        "In our implentation we cosidered a slighly different approach. We take a square or hexagonal grid where each cell state is determined by two different parameters: $\\pi_{i,j}$ and $u_{i,j}$.\n",
+        "The first one is simply in which state (recovered, infected or susceptible) the cell currently is, while the latter takes in account the neighbors state which will determine the future states.\n",
+        "\n",
+        "Another important concept that we introduce is the fact that an infected individual goes through three different sub-phases: incubation, infection and latency.\n",
+        "We define the times that an individual spends in each sub-phase as\n",
+        "\n",
+        "\n",
+        "*   $t_i$ for the incubation time\n",
+        "*   $t_p$ for the proper infection time\n",
+        "*   $t_l$ for the latency time\n",
+        "\n",
+        "Each state $\\pi_{i,j}$ is a value an integer value between -1 and $t_i+t_p+t_l$, where\n",
+        "\n",
+        "*   -1 is assigned to the recovered individual\n",
+        "*    0 is the susceptible\n",
+        "*    greater or equal to 1 is instead reserved for the infected\n",
+        "\n",
+        "\n",
+        "In this way we can define the future state of a cell based on the immediatly previous state and on the states of the neighbors $u_{i,j}$, defined in the following equation:\n",
+        "\n",
+        "$$\n",
+        "\\pi_{i j}(t+1)=\\left\\{\\begin{array}{ll}\n",
+        "\\pi_{i j}(t)+1 & \\text { if } 0<\\pi_{i j}(t)<t_{i}+t_{p}+t_{l} \\\\\n",
+        "-1 & \\text { if } \\pi_{i j}(t) \\geqslant t_{i}+t_{p}+t_{l} \\\\\n",
+        "0 & \\text { if } \\pi_{i j}(t)=0 \\text { and } u_{i j}(t+1)<h \\\\\n",
+        "1 & \\text { if } \\pi_{i j}(t)=0 \\text { and } u_{i j}(t+1) \\geqslant h\n",
+        "\\end{array}\\right.\n",
+        "$$\n",
+        "\n",
+        "\n",
+        "Where h is a random number between [0,1] with probability density function p(h).\n",
+        "\n",
+        "The update equation for $u_{i,j}$ is instead:\n",
+        "$$\n",
+        "u_{i j}(t+1)=\\frac{1}{N}\\left(\\sum_{f n} I_{i j}(t) e^{-1}+\\sum_{s n} I_{i j}(t) e^{-2}+\\sum_{t c} I_{i j}(t) e^{-3}+\\cdots\\right)\n",
+        "$$\n",
+        "The first sum in over the first neighbors (as in the closest), the second sum over the second neighbors and so on. The number of sums that is considered is determined by the interaction radius, so if we choose an interaction radius of 1 only the immediate 4 neighbors (in the case of a square lattice) will be affected by the current state of the cell.\n",
+        "\n",
+        "$N$ is a normalization constant which also is affected by the interaction radius and is defined as:\n",
+        "$$\n",
+        "N = \\frac{1}{4*(e^{-1}+e^{-2}+e^{-3}+\\cdots)}\n",
+        "$$\n",
+        "\n",
+        "And the field $I_{i,j}$ is defined as\n",
+        "\n",
+        "$$\n",
+        "I_{i j}(t+1)=\\left\\{\\begin{array}{ll}\n",
+        "F\\left(\\pi_{i j}(t)\\right) & \\text { if } \\pi_{i j}(t) \\geqslant 1 \\\\\n",
+        "0 & \\text { if } \\pi_{i j}(t) \\leqslant 0\n",
+        "\\end{array}\\right.\n",
+        "$$\n",
+        "\n",
+        "\n",
+        "\n",
+        "Where $F(x)$ is a positive real function:\n",
+        "$$\n",
+        "F(x): (0,t_{i}+t_{p}+t_{l}) \\rightarrow \\mathbb{R}^+\n",
+        "$$\n",
+        "In other words F(x) assign to an individual the infectiouness as a function of its state $\\pi_{i,j}$.\n",
+        "An example of F (which is the default implementation in our code with $f$ = 0.5) could be simply:\n",
+        "\n",
+        "$$\n",
+        "F\\left(\\pi_{i j}(t)\\right) =\\left\\{\\begin{array}{ll}\n",
+        "f & \\text { if } \\pi_{i j}(t) > 0 \\\\\n",
+        "0 & \\text { if } \\pi_{i j}(t) =\\ 0\n",
+        "\\end{array}\\right.\n",
+        "$$\n",
+        "\n",
+        "Or if we want to take in consideration the fact that an indivual has a different infectiouness during the incubation, infection and latency time we could use something like:\n",
+        "\n",
+        "$$\n",
+        "F\\left(\\pi_{i j}(t)\\right) =\\left\\{\\begin{array}{ll}\n",
+        "f_i & \\text { if } 0 < \\pi_{i j}(t) \\leqslant t_i \\\\\n",
+        "f_p & \\text { if } \\pi_{i j}(t) \\leqslant t_i+t_p \\\\\n",
+        "f_l & \\text { if } \\pi_{i j}(t) \\leqslant t_i+t_p+t_l \n",
+        "\\end{array}\\right.\n",
+        "$$\n",
+        "\n",
+        "All of the above equations are implemented in the following class, which will be used for all the simulations."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bow6jTJPhfWo",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Implementation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9T02BLCs_kaG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "class gridV1:\n",
+        "\n",
+        "    def __init__(self, shape = (5,5), grid_type = \"hex\", ti = 1, tp = 1, tl = 1, f = 0.5, h = None, print_var = False, track_update = True):\n",
+        "        \"\"\"\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        shape : Tuple, optional\n",
+        "            Shape of the grid The default is (5,5).\n",
+        "        grid_type : String, optional\n",
+        "            If hex we will use a grid with hexagon. Otherwise we will use a square grid. The default is \"hex\".\n",
+        "        ti : int, optional\n",
+        "            Incubation time. The default is 1.\n",
+        "        tp : int, optional\n",
+        "            Proper infection time. The default is 1.\n",
+        "        tl : int, optional\n",
+        "            Latecy time. The default is 1.\n",
+        "        f : float, optional\n",
+        "            Infectiouness of an individual. It's the value of F(t) function. The default is 0.5.\n",
+        "        h : float, optional\n",
+        "            The threshold for infection. If it's set to None or is minor of 0 or major of 1 it will randomly choesen between 0 and 1- The default is None.\n",
+        "        print_var : bool, optional\n",
+        "            Debug variable used during the creation of the class. If set to True the code will print the various step of execution. The default is False.\n",
+        "        track_update : bool, optional\n",
+        "            If set to True the class will save all the evolution of the population. The default is True.\n",
+        "        \"\"\"\n",
+        "\n",
+        "        self.print_var = print_var\n",
+        "        if(self.print_var): print(\"CREATION - START\")\n",
+        "        \n",
+        "        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+        "        # \"Network\" creation\n",
+        "        if(self.print_var): print(\"    GRID CREATION - START\")\n",
+        "        \n",
+        "\n",
+        "        if(isinstance(shape, tuple)): self.shape = shape\n",
+        "        else: self.shape = (20, 20)\n",
+        "        \n",
+        "        if(grid_type == \"hex\"): #Hexagonal lattice\n",
+        "            self.G = nx.generators.lattice.triangular_lattice_graph(shape[0], shape[1])\n",
+        "            self.l_type = 0\n",
+        "        else: #Square lattice\n",
+        "            self.G = nx.generators.lattice.grid_graph([shape[0], shape[1]])\n",
+        "            self.l_type = 1\n",
+        "        if(self.print_var): print(\"    GRID CREATION - END\")\n",
+        "        \n",
+        "        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+        "        # Set times\n",
+        "        self.ti = ti\n",
+        "        self.tp = tp\n",
+        "        self.tl = tl\n",
+        "        \n",
+        "        # Other Variables\n",
+        "        self.f = f # Used to evaluate F(t)\n",
+        "        \n",
+        "        if (h == None or h <= 0 or h > 1): self.h = np.random.random\n",
+        "        else: self.h = h \n",
+        "        \n",
+        "        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+        "        # Nodes attributes\n",
+        "        if(self.print_var): print(\"    SET ATTRIBUTES - START\")\n",
+        "        \n",
+        "        self.piInizialization(shape[0] / 2, shape[1] / 2)\n",
+        "        nx.set_node_attributes(self.G, 0, \"u\")\n",
+        "        \n",
+        "        if(self.print_var): print(\"    SET ATTRIBUTES - END\")\n",
+        "        \n",
+        "        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+        "        # Tracking variable\n",
+        "        self.tot_time = 0\n",
+        "        self.grid_state = [self.G.copy()]\n",
+        "        self.track_update = track_update\n",
+        "        \n",
+        "        if(self.print_var): print(\"CREATION - END\")\n",
+        "    \n",
+        "    def piInizialization(self, i_costant = 50, j_costant = 50, init_function = None):\n",
+        "        \"\"\"\n",
+        "        Initialize the pi value of the nodes according the formula in the paper.\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        i_costant : int, optional\n",
+        "            The default is 50.\n",
+        "        j_costant : int, optional\n",
+        "            The default is 50.\n",
+        "        init_function: lambda function, option\n",
+        "            If provided use this formula instead of the formula in the paper. The lambda function musthave 4 input values\n",
+        "        \"\"\"\n",
+        "        node_list = self.G.nodes\n",
+        "        tmp_dict = {}\n",
+        "        \n",
+        "        for node in node_list:\n",
+        "            if(init_function == None):\n",
+        "                tmp_value = math.sqrt((i_costant - node[0]) ** 2 + (j_costant - node[1]) ** 2)\n",
+        "            else:\n",
+        "                tmp_value = init_function(i_costant, j_costant, node[0], node[1])\n",
+        "            # print(tmp_value) \n",
+        "            if(tmp_value <= 5): tmp_dict[node] = self.ti\n",
+        "            else: tmp_dict[node] = 0\n",
+        "        \n",
+        "        # print(tmp_dict)\n",
+        "        nx.set_node_attributes(self.G, tmp_dict, \"pi\")\n",
+        "        \n",
+        "    def drawGridNetx(self, fig_size = (10, 10)):\n",
+        "        \"\"\"\n",
+        "        Draw the grid using the prebuilt function provided by networkx. Can be used for both square and hexagon grid.\n",
+        "        Can draw grid of any dimension but it become very slow with bigger grids (dimension > 40 x 40)\n",
+        "        \n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        fig_size : Tuple, optional\n",
+        "            The default is (10, 10).\n",
+        "        \"\"\"\n",
+        "        color_dict = nx.get_node_attributes(self.G, \"pi\")\n",
+        "        if(self.print_var): print((color_dict.values()))\n",
+        "        \n",
+        "        plt.figure(figsize = fig_size)\n",
+        "        nx.draw_networkx(self.G, node_color = list(color_dict.values()), with_labels = False)\n",
+        "    \n",
+        "    def drawGridNetxHexRegular(self, fig_size = (10, 10)):\n",
+        "        \"\"\"\n",
+        "        Draw the grid using the prebuilt function provided by networkx. \n",
+        "        Use a fixed layout, useful for regular represetation of Hexagonal grids\n",
+        "        \n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        fig_size : Tuple, optional\n",
+        "            The default is (10, 10).\n",
+        "        \"\"\"\n",
+        "        color_dict = nx.get_node_attributes(self.G, \"pi\")\n",
+        "        \n",
+        "        fig =  plt.figure(figsize = (15,15))\n",
+        "        ax = fig.add_subplot(1,1,1)\n",
+        "        layout_regular={}\n",
+        "\n",
+        "        col_arr = []\n",
+        "        list_colors = list(color_dict.values())\n",
+        "        for color in list_colors:\n",
+        "            if color == -1: # recovered\n",
+        "                col_arr.append(\"#7CFC00\")\n",
+        "            elif color == 0: # susceptible\n",
+        "                col_arr.append(\"#0000FF\")\n",
+        "            elif color == 1: # just being infected\n",
+        "                col_arr.append(\"#FFA500\")\n",
+        "            else: # infected\n",
+        "                col_arr.append(\"#FF4500\")\n",
+        "\n",
+        "        #if(self.print_var): print(type(list(color_dict.values())[0]),color_dict.values(), col_arr)\n",
+        "        \n",
+        "        for i in self.G.nodes(): \n",
+        "          if i[1]%2 == 0: layout_regular[i]=i\n",
+        "          else: layout_regular[i]=(i[0]+0.5,i[1])\n",
+        "        ColorLegend = {\"Recovered\":\"#7CFC00\",\"Susceptible\":\"#0000FF\",\"1-Day_Infected\":\"#FFA500\",\">1-day_Infected\":\"#FF4500\"}\n",
+        "        for l in list(ColorLegend.keys()):\n",
+        "            ax.scatter([0],[0],color=ColorLegend[l],label=l)\n",
+        "        nx.draw_networkx(self.G, pos = layout_regular, node_color = col_arr, with_labels = False, ax=ax, node_shape='H', node_size= 5000)\n",
+        "        plt.legend(scatterpoints = 1)\n",
+        "        plt.tight_layout()            \n",
+        "\n",
+        "    def getNodesEdges(self):\n",
+        "        color_dict = nx.get_node_attributes(self.G, \"pi\")\n",
+        "        layout_regular={}\n",
+        "        col_arr = []\n",
+        "\n",
+        "        list_colors = list(color_dict.values())\n",
+        "        for color in list_colors:\n",
+        "            if color == -1: # recovered\n",
+        "                col_arr.append(\"#7CFC00\")\n",
+        "            elif color == 0: # susceptible\n",
+        "                col_arr.append(\"#0000FF\")\n",
+        "            elif color == 1: # just being infected\n",
+        "                col_arr.append(\"#FFA500\")\n",
+        "            else: # infected\n",
+        "                col_arr.append(\"#FF4500\")\n",
+        "\n",
+        "        for i in self.G.nodes(): \n",
+        "          if i[1]%2 == 0: layout_regular[i]=i\n",
+        "          else: layout_regular[i]=(i[0]+0.5,i[1])\n",
+        "        \n",
+        "        return col_arr, layout_regular\n",
+        "\n",
+        "\n",
+        "    def drawGridSquare(self, fig_size = (10, 10), hexGrid = False):\n",
+        "        \"\"\"\n",
+        "        Convert the graph in a matrix and plot it through matplotlib. Works only with the square grid.\n",
+        "        Provide a better and quicker visualization of drawGridNetx for square grid.\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        fig_size : Tuple, optional\n",
+        "            The default is (10, 10).\n",
+        "        \"\"\"\n",
+        "        \n",
+        "        # Get matrix of pi attribute\n",
+        "        color_mat = self.getMatrixAttribute('pi')\n",
+        "        \n",
+        "        max_value = self.ti + self.tl + self.tp\n",
+        "        possible_value = np.arange(-1, max_value + 1, 1)\n",
+        "        color_mat[0, 0:max_value + 2] = possible_value\n",
+        "\n",
+        "        # Creating list of colors\n",
+        "        col_arr = []\n",
+        "        flatten_color_mat = list(color_mat.flatten())\n",
+        "        for color in flatten_color_mat:\n",
+        "            if color == -1: # recovered\n",
+        "                col_arr.append(\"#7CFC00\")\n",
+        "            elif color == 0: # susceptible\n",
+        "                col_arr.append(\"#0000FF\")\n",
+        "            elif color == 1: # just being infected\n",
+        "                col_arr.append(\"#FFA500\")\n",
+        "            else: # infected\n",
+        "                col_arr.append(\"#FF4500\")\n",
+        "        \n",
+        "        # To print the hexgrid or the square grid\n",
+        "        if hexGrid:\n",
+        "            x_size = len(color_mat) # rows\n",
+        "            y_size = len(color_mat[0]) # cols\n",
+        "            Y = X = list(color_mat.flatten())\n",
+        "            print(X,'\\n',Y)\n",
+        "            plt.figure(figsize = fig_size) \n",
+        "            plt.hexbin(X,Y, gridsize=(6,11), edgecolor='k')\n",
+        "            plt.margins(0)\n",
+        "            plt.colorbar()\n",
+        "            \n",
+        "        else:\n",
+        "            # Print matrix\n",
+        "            plt.figure(figsize = fig_size) \n",
+        "            #plt.imshow(color_mat, interpolation='none', cmap=cmap, animated = True)\n",
+        "            plt.imshow(color_mat, interpolation='none', animated = True)\n",
+        "            plt.legend(color_mat)\n",
+        "            \n",
+        "            # plt.matshow(color_mat)\n",
+        "    \n",
+        "        plt.title(\"Grid at the time step: \" + str(self.tot_time))\n",
+        "        plt.show()\n",
+        "\n",
+        "        \n",
+        "    def pi_evaluation(self, previous_pi, next_u):\n",
+        "        if (previous_pi > 0 and previous_pi < self.ti + self.tp + self.tl): return previous_pi + 1\n",
+        "        elif (previous_pi >=  self.ti + self.tp + self.tl): return - 1\n",
+        "        elif (previous_pi == 0 and next_u < self.h()): return 0\n",
+        "        elif (previous_pi == 0 and next_u >= self.h()): return 1\n",
+        "        else: return previous_pi\n",
+        "        \n",
+        "    def u_evaluation(self, actual_node, interaction_radius = 1):\n",
+        "        \"\"\"\n",
+        "        Evaluate the next value of the u(t) function if the node passed\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        actual_node : networkx node\n",
+        "            Node where evaluate u(t).\n",
+        "        interaction_radius : int, optional\n",
+        "            Neighbour radious. 1 means only adjenct node and so on. The default is 1.\n",
+        "\n",
+        "        Returns\n",
+        "        -------\n",
+        "        tmp_u : double\n",
+        "            next value of the u for actual_node.\n",
+        "\n",
+        "        \"\"\"\n",
+        "        # Normalization constant\n",
+        "        N = 0\n",
+        "        for i in range(1, interaction_radius + 1): N = N + math.exp(-i)\n",
+        "        N = 1 / (4 * N)\n",
+        "        \n",
+        "        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+        "        # Neigbour evaluation\n",
+        "        layer_list = []\n",
+        "        all_node_list = [actual_node]\n",
+        "        for i in range(interaction_radius):\n",
+        "            if (i == 0): \n",
+        "                tmp_list = list(self.G.neighbors(actual_node))\n",
+        "                layer_list.append(tmp_list)\n",
+        "                \n",
+        "                all_node_list = all_node_list + tmp_list\n",
+        "            else:\n",
+        "                # Temporary variable\n",
+        "                tmp_list = layer_list[-1]\n",
+        "                tmp_neighbours = []\n",
+        "                \n",
+        "                # Obtain all neighbours of nodes of the previous layer\n",
+        "                for node in tmp_list: tmp_neighbours = tmp_neighbours + list(self.G.neighbors(node))\n",
+        "                \n",
+        "                # Delete duplicate\n",
+        "                tmp_neighbours = list(set(tmp_neighbours))\n",
+        "                \n",
+        "                # Insert in the final list only new node\n",
+        "                tmp_list = [node for node in tmp_neighbours if node not in all_node_list] # (Not tested)\n",
+        "                # tmp_list = np.setdiff1d(tmp_neighbours, all_node_list).tolist()\n",
+        "                \n",
+        "                # Save neighbour and update all node list\n",
+        "                layer_list.append(tmp_list)\n",
+        "                all_node_list = all_node_list + tmp_list\n",
+        "        \n",
+        "        # self.a = layer_list # Check variable to control if detect correctly the neighbour\n",
+        "        \n",
+        "        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n",
+        "        # u(t) evaluation\n",
+        "        tmp_u = 0\n",
+        "        pi_dict = nx.get_node_attributes(self.G, 'pi')\n",
+        "        for neighbour_list, i in zip(layer_list, range(1, len(layer_list) + 1)):\n",
+        "            tmp_sum = 0\n",
+        "            for node in neighbour_list:\n",
+        "                tmp_sum = tmp_sum + self.I_function(pi_dict[node]) * math.exp(-i)\n",
+        "            \n",
+        "            tmp_u = tmp_u + tmp_sum\n",
+        "            \n",
+        "        tmp_u =  (1 / N) * tmp_u\n",
+        "        \n",
+        "        return tmp_u\n",
+        "            \n",
+        "           \n",
+        "    def F_function(self, pi_value, custom_function = None):\n",
+        "        \"\"\"\n",
+        "        F(t) function as defined in the paper\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        pi_value : int\n",
+        "            pi value of the node considered.\n",
+        "        custom_function: lambda function or any other function\n",
+        "            Used to evalaute I(t) as I(t) = custom_function(pi_value)\n",
+        "        \"\"\"\n",
+        "        \n",
+        "        if(custom_function != None):\n",
+        "            return custom_function(pi_value)\n",
+        "        else:\n",
+        "            if(pi_value <= 0): return 0\n",
+        "            else: return self.f\n",
+        "        \n",
+        "    def I_function(self, pi_value):\n",
+        "        \"\"\"\n",
+        "        I(t) function as defined in the paper\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        pi_value : int\n",
+        "            pi value of the node considered.\n",
+        "        \"\"\"\n",
+        "        if(pi_value <= 0): return 0\n",
+        "        else: return self.F_function(pi_value)\n",
+        "    \n",
+        "    def update(self, interaction_radius = 1):\n",
+        "        \"\"\"\n",
+        "        Compute a single update of the grid (t ---> t + 1)\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        interaction_radius : int, optional\n",
+        "            Radious to be consider for the evaluation of the u function. The default is 1.\n",
+        "\n",
+        "        \"\"\"\n",
+        "        # Temporary variables creation\n",
+        "        tmp_dict_u = {}\n",
+        "        tmp_dict_pi = {}\n",
+        "        prev_pi = nx.get_node_attributes(self.G, 'pi')\n",
+        "        \n",
+        "        # New values evaluations for pi and u for all nodes\n",
+        "        for node in self.G:\n",
+        "            next_u = self.u_evaluation(node, interaction_radius)\n",
+        "            next_pi = self.pi_evaluation(prev_pi[node], next_u)\n",
+        "            \n",
+        "            tmp_dict_u[node] = next_u\n",
+        "            tmp_dict_pi[node] = next_pi\n",
+        "        \n",
+        "        # Save the new pi and u in all nodes\n",
+        "        nx.set_node_attributes(self.G, tmp_dict_u, 'u')\n",
+        "        nx.set_node_attributes(self.G, tmp_dict_pi, 'pi')\n",
+        "        \n",
+        "        # Advance time and save the new state of the grid\n",
+        "        self.tot_time += 1\n",
+        "        if(self.track_update): self.grid_state.append(self.G.copy())\n",
+        "        \n",
+        "        \n",
+        "    def computeNTimeSteps(self, n):\n",
+        "        \"\"\"\n",
+        "        Compute n time steps (t ---> t + n)\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        n : int\n",
+        "            Number of time steps to be computed.\n",
+        "\n",
+        "        \"\"\"\n",
+        "        for i in range(n):\n",
+        "            if self.print_var: print(\"Steps number \", i,  \"   -  (\", round(i/n * 100, 2), \"%)\")\n",
+        "            \n",
+        "            self.update()\n",
+        "            \n",
+        "    def historyList(self, attribute = 'pi'):\n",
+        "        \"\"\"\n",
+        "        Return a list of numpy matrix. Each element of the is the grid at one step in time.\n",
+        "        The length of the list is equal to the total number of updates until that moment.\n",
+        "        N.B. This method works only with the square grid.\n",
+        "        \n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        attribute : str, optional\n",
+        "            Attribute of the grid to save. The default is 'pi'.\n",
+        "\n",
+        "        Returns\n",
+        "        -------\n",
+        "        history_list : list\n",
+        "\n",
+        "        \"\"\"\n",
+        "        history_list = []\n",
+        "        \n",
+        "        for grid_istance in self.grid_state:\n",
+        "            history_list.append(self.getMatrixAttribute(attribute, grid_istance))\n",
+        "            \n",
+        "        return history_list\n",
+        "    \n",
+        "            \n",
+        "    def getMatrixAttribute(self, attribute = 'pi', grid_istance = None):\n",
+        "        # Extract the attributes and convert into a list\n",
+        "        if(grid_istance == None): tmp_dict = nx.get_node_attributes(self.G, attribute)\n",
+        "        else: tmp_dict = nx.get_node_attributes(grid_istance, attribute)\n",
+        "        tmp_list = list(tmp_dict.values())\n",
+        "        \n",
+        "        # Convert list in a numpy array and reshape in a matrix\n",
+        "        mat = np.asarray(tmp_list)\n",
+        "        if self.l_type==1:  mat = mat.reshape(self.shape)\n",
+        "        else: \n",
+        "          temp_x=int(math.floor(self.shape[0]/2))+1\n",
+        "          temp_y=int(self.shape[1]+1)\n",
+        "          mat = mat.reshape((temp_x,temp_y))\n",
+        "        \n",
+        "        return mat"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bkYGRdA1h7ld",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VyjXYYqyXxAG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def multiple_circles_inf(a,b,x,y):\n",
+        "    r = 3\n",
+        "    tmp_value_1 = math.sqrt((20 - x) ** 2 + (20 - y) ** 2)\n",
+        "    tmp_value_2 = math.sqrt((40 - x) ** 2 + (56 - y) ** 2)\n",
+        "    tmp_value_3 = math.sqrt((12 - x) ** 2 + (30 - y) ** 2)\n",
+        "    tmp_value_4 = math.sqrt((65 - x) ** 2 + (10 - y) ** 2)\n",
+        "    tmp_value_5 = math.sqrt((40 - x) ** 2 + (40 - y) ** 2)\n",
+        "    tmp_value_6 = math.sqrt((65 - x) ** 2 + (67 - y) ** 2)\n",
+        "    tmp_value_7 = math.sqrt((10 - x) ** 2 + (70 - y) ** 2)\n",
+        "    if tmp_value_1 < (r+2) or tmp_value_2 < (r-1) or tmp_value_3 < r or tmp_value_4 < r or tmp_value_5 < r or tmp_value_6 < r or tmp_value_7 < (r+3): return 4\n",
+        "    else: return 100"
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zrTgwYc0vAg2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 934,
+          "referenced_widgets": [
+            "3705066797044d2c8d792f908f4c7dd4",
+            "782a8bc30081414eabc4daecc9bc6a91",
+            "b7a3718b18684efbafab25c8007df011",
+            "d05b1de484b74eefb1800301a85b17f8",
+            "3e75a8ce472e4553a91743824876d33a",
+            "17d23456e2e84d64bdad572ef0837a32",
+            "abbbe885fc124670b5c198a79ab35289",
+            "32bc86d363114927b29f166ca950856f"
+          ]
+        },
+        "outputId": "03ccb20a-01f8-4276-b972-90b7bbfd8a5f"
+      },
+      "source": [
+        "shape = (80, 80)\n",
+        "figsize = (14,12)\n",
+        "prova_grid = gridV1(shape, \"square\", print_var = False, ti=2, tp=4, tl=2)\n",
+        "\n",
+        "n_epoch = 100\n",
+        "\n",
+        "custom_init = True\n",
+        "if(custom_init):\n",
+        "  prova_grid.piInizialization(10, 10, multiple_circles_inf)\n",
+        "\n",
+        "for i in tqdm_notebook(range(n_epoch)):\n",
+        "    prova_grid.update()\n",
+        "\n",
+        "fig, (ax1, ax2) = plt.subplots(2,1, figsize = figsize, gridspec_kw={'height_ratios': [2, 1]})\n",
+        "\n",
+        "ax2.set_xlim(0, n_epoch)\n",
+        "ax2.set_ylim(0, 1)\n",
+        "ax2.grid()\n",
+        "ax2.set_xlabel('Time [Days]')\n",
+        "ax2.set_ylabel('Fraction of individuals')\n",
+        "\n",
+        "# Colors: -1 for recovered, 0 for susceptible, 1 for just being infected, >1 infected\n",
+        "colors = ['#7CFC00','#0000FF', '#FFA500', '#FF4500']\n",
+        "labels = [\"Recovered\", \"Susceptible\", \"1-day infected\", \">1-day infected\"]\n",
+        "history_list = prova_grid.historyList()\n",
+        "\n",
+        "if(custom_init):\n",
+        "  history_list.pop(0)\n",
+        "\n",
+        "\n",
+        "# Compute the percentage of each class at each epoch\n",
+        "s_plot = []\n",
+        "r_plot = []\n",
+        "i_plot = []\n",
+        "ig_plot = []\n",
+        "N = shape[0]*shape[1]\n",
+        "t = np.linspace(0, len(history_list), len(history_list))\n",
+        "\n",
+        "\n",
+        "\n",
+        "for matrix in history_list:\n",
+        "  r_plot.append(np.count_nonzero(matrix == -1)/N)\n",
+        "  s_plot.append(np.count_nonzero(matrix == 0)/N)\n",
+        "  i_plot.append(np.count_nonzero(matrix > 0)/N)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Dynamically pick of the max value of the history list. It may vary according to\n",
+        "# the number of steps chosen\n",
+        "max_hist = np.max([np.max(x) for x in history_list])\n",
+        "range_arr = np.arange(-1, max_hist+1)\n",
+        "\n",
+        "# Creating a custom cmap\n",
+        "cmap = mpl.colors.ListedColormap(colors)\n",
+        "ims = []\n",
+        "ims2 = []\n",
+        "for i in range(len(history_list)):\n",
+        "    # Insert Title\n",
+        "    title = plt.text(-0.25,0.5,\"Grid at step: \"+str(i),bbox={'facecolor':'w', 'alpha':0.5, 'pad':5},\n",
+        "                     ha=\"center\",va=\"bottom\", transform=ax1.transAxes, fontsize=\"xx-large\")\n",
+        "    \n",
+        "    # Plot infection matrix\n",
+        "    #im = plt.imshow(history_list[i], cmap = cmap, animated = True, vmin = -1, vmax = max_hist)\n",
+        "    im1 = ax1.imshow(history_list[i], cmap = cmap, animated = True, norm = mpl.colors.Normalize(vmin=-1, vmax=2))\n",
+        "\n",
+        "    # Plot Susceptible/Infected/Recovered curves\n",
+        "    im2, = ax2.plot(t[0:i+1], s_plot[0:i+1], animated = True, c = '#0000FF',linewidth = 3)\n",
+        "    im3, = ax2.plot(t[0:i+1], i_plot[0:i+1], animated = True, c = '#FF4500',linewidth = 3)\n",
+        "    im4, = ax2.plot(t[0:i+1], r_plot[0:i+1], animated = True, c = '#7CFC00',linewidth = 3)\n",
+        "\n",
+        "    # Add figures to list to create the animation\n",
+        "    ims.append([im1,im2,im3, im4, title])\n",
+        "\n",
+        "\n",
+        "\n",
+        "patches = [mpatches.Patch(color = colors[i], label = \"{l}\".format(l = labels[i])) for i in range(len(labels))]\n",
+        "# put those patched as legend-handles into the legend\n",
+        "ax1.legend(handles = patches, bbox_to_anchor = (1.01, 1), loc = 2, borderaxespad = 0., fontsize='large')\n",
+        "ani = animation.ArtistAnimation(fig, ims, blit=True, interval = 700 ,repeat_delay=1000)\n",
+        "fig.tight_layout()\n",
+        "plt.close()\n",
+        "ani.save(\"animation.mp4\")\n",
+        "HTML(ani.to_html5_video())"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3705066797044d2c8d792f908f4c7dd4",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "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>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yeGGYl7mrrCv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 525
+        },
+        "outputId": "21375829-ec11-4293-c469-450756bdedb9"
+      },
+      "source": [
+        "#testing different initial distributions\n",
+        "shape = (40,40)\n",
+        "figsize = (17,7)\n",
+        "prova_grid1 = gridV1(shape, \"square\", print_var = False, ti=2, tp=4, tl=2)\n",
+        "prova_grid2 = gridV1(shape, \"square\", print_var = False, ti=2, tp=4, tl=2)\n",
+        "prova_grid3 = gridV1(shape, \"square\", print_var = False, ti=2, tp=4, tl=2)\n",
+        "n_epoch = 40\n",
+        "\n",
+        "custom_init = True\n",
+        "\n",
+        "def init_f1(a,b,x,y): #edge experiment\n",
+        "  if y == 0 or y == 4: return 0\n",
+        "  return 100\n",
+        "\n",
+        "def init_f2(a,b,x,y): #cross\n",
+        "  if  x == a/2: return 0\n",
+        "  elif y == b/2: return 0\n",
+        "  else: return 100\n",
+        "\n",
+        "def init_f3(a,b,x,y): #random iid distribution with given prob\n",
+        "  th = 0.1\n",
+        "  if random.random() > th: return 100\n",
+        "  else: return 0\n",
+        "\n",
+        "\n",
+        "if(custom_init):\n",
+        "  prova_grid1.piInizialization(40, 40, init_f1)\n",
+        "  prova_grid2.piInizialization(40, 40, init_f2)\n",
+        "  prova_grid3.piInizialization(40, 40, init_f3)\n",
+        "\n",
+        "for i in range(n_epoch):\n",
+        "    prova_grid1.update()\n",
+        "    prova_grid2.update()\n",
+        "    prova_grid3.update()\n",
+        "\n",
+        "fig, axes = plt.subplots(2,3, figsize = figsize)\n",
+        "\n",
+        "for j in range(0,3):\n",
+        "  axes[1,j].set_xlim(0, n_epoch)\n",
+        "  axes[1,j].set_ylim(0, 1)\n",
+        "  axes[1,j].grid()\n",
+        "  axes[1,j].set_xlabel('Time [Days]')\n",
+        "  axes[1,j].set_ylabel('Fraction of individuals')\n",
+        "\n",
+        "# Colors: -1 for recovered, 0 for susceptible, 1 for just being infected, >1 infected\n",
+        "colors = ['#7CFC00','#0000FF', '#FFA500', '#FF4500']\n",
+        "labels = [\"Recovered\", \"Susceptible\", \"1-day infected\", \">1-day infected\"]\n",
+        "history_list1 = prova_grid1.historyList()\n",
+        "history_list2 = prova_grid2.historyList()\n",
+        "history_list3 = prova_grid3.historyList()\n",
+        "\n",
+        "if(custom_init):\n",
+        "  history_list1.pop(0)\n",
+        "  history_list2.pop(0)\n",
+        "  history_list3.pop(0)\n",
+        "\n",
+        "# Compute the percentage of each class at each epoch\n",
+        "s_plot1 = []\n",
+        "r_plot1 = []\n",
+        "i_plot1 = []\n",
+        "ig_plot1 = []\n",
+        "\n",
+        "s_plot2 = []\n",
+        "r_plot2 = []\n",
+        "i_plot2 = []\n",
+        "ig_plot2 = []\n",
+        "\n",
+        "s_plot3 = []\n",
+        "r_plot3 = []\n",
+        "i_plot3 = []\n",
+        "ig_plot3 = []\n",
+        "\n",
+        "N = shape[0]*shape[1]\n",
+        "\n",
+        "t1 = np.linspace(0, len(history_list1), len(history_list1))\n",
+        "t2 = np.linspace(0, len(history_list2), len(history_list2))\n",
+        "t3 = np.linspace(0, len(history_list3), len(history_list3))\n",
+        "\n",
+        "for matrix in history_list1:\n",
+        "  r_plot1.append(np.count_nonzero(matrix == -1)/N)\n",
+        "  s_plot1.append(np.count_nonzero(matrix == 0)/N)\n",
+        "  i_plot1.append(np.count_nonzero(matrix > 0)/N)\n",
+        "\n",
+        "for matrix in history_list2:\n",
+        "  r_plot2.append(np.count_nonzero(matrix == -1)/N)\n",
+        "  s_plot2.append(np.count_nonzero(matrix == 0)/N)\n",
+        "  i_plot2.append(np.count_nonzero(matrix > 0)/N)\n",
+        "\n",
+        "for matrix in history_list3:\n",
+        "  r_plot3.append(np.count_nonzero(matrix == -1)/N)\n",
+        "  s_plot3.append(np.count_nonzero(matrix == 0)/N)\n",
+        "  i_plot3.append(np.count_nonzero(matrix > 0)/N)\n",
+        "\n",
+        "# Creating a custom cmap\n",
+        "cmap = mpl.colors.ListedColormap(colors)\n",
+        "ims =[]\n",
+        "for i in range(len(history_list1)):\n",
+        "\n",
+        "  im1 = axes[0,0].imshow(history_list1[i], cmap=cmap, animated=True, norm = mpl.colors.Normalize(vmin=-1, vmax=2))\n",
+        "  im2, = axes[1,0].plot(t1[0:i+1], s_plot1[0:i+1], animated = True, c = '#0000FF',linewidth = 3)\n",
+        "  im3, = axes[1,0].plot(t1[0:i+1], i_plot1[0:i+1], animated = True, c = '#FF4500',linewidth = 3)\n",
+        "  im4, = axes[1,0].plot(t1[0:i+1], r_plot1[0:i+1], animated = True, c = '#7CFC00',linewidth = 3)\n",
+        "\n",
+        "  im5 = axes[0,1].imshow(history_list2[i], cmap=cmap, animated=True, norm = mpl.colors.Normalize(vmin=-1, vmax=2))\n",
+        "  im6, = axes[1,1].plot(t2[0:i+1], s_plot2[0:i+1], animated = True, c = '#0000FF',linewidth = 3)\n",
+        "  im7, = axes[1,1].plot(t2[0:i+1], i_plot2[0:i+1], animated = True, c = '#FF4500',linewidth = 3)\n",
+        "  im8, = axes[1,1].plot(t2[0:i+1], r_plot2[0:i+1], animated = True, c = '#7CFC00',linewidth = 3)\n",
+        "\n",
+        "  im9 = axes[0,2].imshow(history_list3[i], cmap=cmap, animated=True, norm = mpl.colors.Normalize(vmin=-1, vmax=2))\n",
+        "  im10, = axes[1,2].plot(t3[0:i+1], s_plot3[0:i+1], animated = True, c = '#0000FF',linewidth = 3)\n",
+        "  im11, = axes[1,2].plot(t3[0:i+1], i_plot3[0:i+1], animated = True, c = '#FF4500',linewidth = 3)\n",
+        "  im12, = axes[1,2].plot(t3[0:i+1], r_plot3[0:i+1], animated = True, c = '#7CFC00',linewidth = 3)\n",
+        "\n",
+        "  ims.append([im1,im2,im3,im4,im5,im6,im7,im8,im9,im10,im11,im12])\n",
+        "\n",
+        "ani = animation.ArtistAnimation(fig, ims, blit=True, interval = 700 ,repeat_delay=1000)\n",
+        "fig.tight_layout()\n",
+        "plt.close()\n",
+        "ani.save(\"animation_initializations.mp4\")\n",
+        "HTML(ani.to_html5_video())\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
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+              "\">\n",
+              "  Your browser does not support the video tag.\n",
+              "</video>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dQPtUyQA9Y7A",
+        "colab_type": "text"
+      },
+      "source": [
+        "#    Estimate the probability of infection\n",
+        "\n",
+        "Another thing we can try is trying to assign a probability to each node, given some initial conditions, of being infected. We do this by simply running the simulation N times and count how many times the single cell has been infected."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6d2V2rMw312y",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def init_f1(a,b,x,y): #edge experiment\n",
+        "  if y == 0 or y == 4: return 0\n",
+        "  return 100\n",
+        "\n",
+        "def init_f2(a,b,x,y): #cross\n",
+        "  if  x == a: return 0\n",
+        "  elif y == b: return 0\n",
+        "  else: return 100\n",
+        "\n",
+        "def square_animation(shape = (20,20), figsize = (14,12), n_epoch = 40, custom_init = False, init_function = None):\n",
+        " \n",
+        "    \n",
+        "    prova_grid = gridV1(shape, \"square\", print_var = False, ti=2, tp=2, tl=2)\n",
+        "    \n",
+        "    if custom_init and init_function != None:\n",
+        "       prova_grid.piInizialization(int(shape[0]/2), int(shape[1]/2), init_function)\n",
+        "    else:\n",
+        "      prova_grid.piInizialization(int(shape[0]/2), int(shape[1]/2))\n",
+        "\n",
+        "    for i in range(n_epoch):\n",
+        "      prova_grid.update()\n",
+        "\n",
+        "    history_list = prova_grid.historyList()\n",
+        "\n",
+        "    if custom_init:\n",
+        "        history_list.pop(0)\n",
+        "    \n",
+        "    return history_list"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "in-gtcj83YZa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66,
+          "referenced_widgets": [
+            "14abed9550e343019ee2236a5b96ff71",
+            "061431042e72430c8d0f2f6efe012998",
+            "912d8dff9ba642028cb1b67cb9b70663",
+            "4b649d29ec2c4649a002f2881ae23f57",
+            "31e6b9e6e30c4709a83eb14378183fe2",
+            "547e2340ff924d0aa54108b0998cb974",
+            "f6c7877ae7504e5b81c3682487500c30",
+            "61ce94c3191645f28c0de2c3cb3462d4"
+          ]
+        },
+        "outputId": "06e3decd-4b31-4a7a-b982-eabc9f1487e1"
+      },
+      "source": [
+        "# Point 4 assignment\n",
+        "# In this chunk of code we will see what is the probability of each cell performing N iteration\n",
+        "N = 50\n",
+        "shape = (25, 25)\n",
+        "#square_animation(shape=shape, custom_init=True)\n",
+        "occ_matrix_1 = np.zeros(shape)\n",
+        "occ_matrix_2 = np.zeros(shape)\n",
+        "occ_matrix_3 = np.zeros(shape)\n",
+        "n_infection_1 = []\n",
+        "n_infection_2 = []\n",
+        "n_infection_3 = []\n",
+        "\n",
+        "for i in tqdm_notebook(range(N)):\n",
+        "    # This will return all the epochs of a single epoch i.e. the number of steps per epoch\n",
+        "    curr_hist_1 = square_animation(shape = shape)\n",
+        "    last_iter_1 = curr_hist_1[-1]\n",
+        "\n",
+        "    curr_hist_2 = square_animation(shape = shape, custom_init = True, init_function = init_f1)\n",
+        "    last_iter_2 = curr_hist_2[-1]\n",
+        "\n",
+        "    curr_hist_3 = square_animation(shape = shape, custom_init = True, init_function = init_f2)\n",
+        "    last_iter_3 = curr_hist_3[-1]\n",
+        "\n",
+        "    n_infection_1.append((shape[0]*shape[1])-np.count_nonzero(last_iter_1 == 0))\n",
+        "    n_infection_2.append((shape[0]*shape[1])-np.count_nonzero(last_iter_2 == 0))\n",
+        "    n_infection_3.append((shape[0]*shape[1])-np.count_nonzero(last_iter_3 == 0))\n",
+        "\n",
+        "    rows_1, cols_1 = np.where(last_iter_1 != 0)\n",
+        "    rows_2, cols_2 = np.where(last_iter_2 != 0)\n",
+        "    rows_3, cols_3 = np.where(last_iter_3 != 0)\n",
+        "\n",
+        "    #print(rows, cols)\n",
+        "    occ_matrix_1[rows_1,cols_1] += 1\n",
+        "    occ_matrix_2[rows_2,cols_2] += 1\n",
+        "    occ_matrix_3[rows_3,cols_3] += 1\n",
+        "\n",
+        "occ_matrix_1 = occ_matrix_1/N\n",
+        "occ_matrix_2 = occ_matrix_2/N\n",
+        "occ_matrix_3 = occ_matrix_3/N"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "14abed9550e343019ee2236a5b96ff71",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nu8p9Jy4Is8d",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 526
+        },
+        "outputId": "2557b7d7-1054-4519-eea8-ce239d6a24ac"
+      },
+      "source": [
+        "per_infected_1 = (100*np.mean(n_infection_1))/(shape[0]*shape[1])\n",
+        "per_infected_2 = (100*np.mean(n_infection_2))/(shape[0]*shape[1])\n",
+        "per_infected_3 = (100*np.mean(n_infection_3))/(shape[0]*shape[1])\n",
+        "\n",
+        "fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize = (36,12))\n",
+        "sns.heatmap(occ_matrix_1, linewidth=0.5, annot=True, ax=ax1)\n",
+        "sns.heatmap(occ_matrix_2, linewidth=0.5, annot=True, ax=ax2)\n",
+        "sns.heatmap(occ_matrix_3, linewidth=0.5, annot=True, ax=ax3)\n",
+        "ax1.set_title(\"On average {:.2f}% has contracted the virus\".format(per_infected_1), fontsize=22, color='#fc0377')\n",
+        "ax2.set_title(\"On average {:.2f}% has contracted the virus\".format(per_infected_2), fontsize=22, color='#fc0377')\n",
+        "ax3.set_title(\"On average {:.2f}% has contracted the virus\".format(per_infected_3), fontsize=22, color='#fc0377')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 2592x864 with 6 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BlOC67mvdcTz",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Hexagonal lattice"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Su8KxpihFJw2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a0ff38ab-f38a-41e1-d3b5-3ff2f8ebf1e9"
+      },
+      "source": [
+        "shape = (20,20)\n",
+        "prova_grid = gridV1(shape, \"hex\", print_var = True)\n",
+        "\n",
+        "\n",
+        "epochs=30\n",
+        "ColorLegend = {\"Recovered\":\"#7CFC00\",\"Susceptible\":\"#0000FF\",\"1-Day_Infected\":\"#FFA500\",\">1-day_Infected\":\"#FF4500\"}\n",
+        "fig = plt.figure(figsize=(24,40))\n",
+        "ax = fig.add_subplot(1,1,1)\n",
+        "plt.axis('off')\n",
+        "\n",
+        "for i in range(0,epochs):\n",
+        "\n",
+        "  col_arr, layout_regular = prova_grid.getNodesEdges()\n",
+        "\n",
+        "  for l in list(ColorLegend.keys()):\n",
+        "      ax.scatter([0],[0],color=ColorLegend[l],label=l)\n",
+        "\n",
+        "  nodes = nx.draw_networkx_nodes(prova_grid.G, pos=layout_regular, node_color = col_arr, with_labels = False, ax=ax, node_shape='h', node_size= 16000)\n",
+        "  nodes.set_edgecolor('w')\n",
+        "  nodes.set_linewidth(4)\n",
+        "  #edges = nx.draw_networkx_edges(prova_grid.G, pos=layout_regular)\n",
+        "  ax.set_title(\"Grid at step: \"+str(i), fontsize=50)\n",
+        "  legend_aspect=ax.legend(ColorLegend, loc='upper right', bbox_to_anchor=(1.5, 1.04), fontsize=35, framealpha=0.45)\n",
+        "  legend_aspect.legendHandles[0]._sizes=[650]\n",
+        "  legend_aspect.legendHandles[1]._sizes=[650]\n",
+        "  legend_aspect.legendHandles[2]._sizes=[650]\n",
+        "  legend_aspect.legendHandles[3]._sizes=[650]\n",
+        "  prova_grid.update()\n",
+        "  fig.savefig('hex_frame_' + str(i) + '.png', bbox_inches = 'tight')\n",
+        "\n",
+        "ims=[]\n",
+        "fig = plt.figure(figsize=(10,10))\n",
+        "ax = fig.add_subplot(1,1,1)\n",
+        "plt.axis('off')\n",
+        "\n",
+        "for i in range(epochs):\n",
+        "  frame = mgimg.imread('hex_frame_' + str(i) + '.png')\n",
+        "  frameplot = ax.imshow(frame, animated=True)\n",
+        "  ims.append([frameplot, title]) \n",
+        "  plt.close()\n",
+        "\n",
+        "\n",
+        "ani = animation.ArtistAnimation(fig, ims, blit=True, interval = 700 ,repeat_delay=1000)\n",
+        "fig.tight_layout()\n",
+        "ani.save(\"animation_hex.mp4\")\n",
+        "HTML(ani.to_html5_video())\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "CREATION - START\n",
+            "    GRID CREATION - START\n",
+            "    GRID CREATION - END\n",
+            "    SET ATTRIBUTES - START\n",
+            "    SET ATTRIBUTES - END\n",
+            "CREATION - END\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
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+              "  Your browser does not support the video tag.\n",
+              "</video>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 70
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t9s3dmEdrSJq",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Conclusions\n",
+        "\n",
+        "In this project we tried different approaches to simulate the SIR model. The results obtained in some simulation are similar to the numerical solution for the differential equations, which is to be expected.\n",
+        "Even though the SIR model is one of the most used, there are several different model which are used in different scenarios.\n",
+        "For example the SIRS model where the immunity is not permanent and an individual could contract the virus more than once. Or the SICR model, where infected enter a chronic from which the (almost) never recover. \n",
+        "\n",
+        "Another important thing to notice from our results is that the initial spatial distribution of the infected really influence the final percentage of people who contracted the virus. A distribution of small and randomly placed outbreaks gives the \"worst result\" by far, while a single big outbreak is a lot more manageable. For example, in the estimation of the probability in the heatmaps 2 and 3, we can see that, even though we start from the same number of infected cells, we have a variation of ~12% between the final number of the infected people. \n",
+        "\n",
+        "In this project we treated only the SIR model. But this work is a good starting point for future projects on more advanced models (like SIRS, SICS, etc.).\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file