MCMC Inference - Boarding School.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.random as random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.ticker as plticker\n",
    "from matplotlib import rc #for latex use on the plots\n",
    "from matplotlib import ticker  # for labels formattings\n",
    "from scipy.stats import poisson\n",
    "import datetime\n",
    "from os import getcwd,path\n",
    "import pandas as pd\n",
    "from numpy import pi as pi\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plot Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".output_png {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "perso_linewidth = 0.6\n",
    "# This will change your default rcParams\n",
    "def init_plotting():\n",
    "    plt.rcParams['figure.figsize'] = (40,20)\n",
    "    plt.rcParams['font.size'] = 30\n",
    "    plt.rcParams['font.family'] = 'serif'\n",
    "    plt.rcParams['axes.labelsize'] = plt.rcParams['font.size']\n",
    "    plt.rcParams['axes.titlesize'] = 1.5*plt.rcParams['font.size']\n",
    "    plt.rcParams['legend.fontsize'] = plt.rcParams['font.size']\n",
    "    plt.rcParams['xtick.labelsize'] = plt.rcParams['font.size']\n",
    "    plt.rcParams['ytick.labelsize'] = plt.rcParams['font.size']\n",
    "    #plt.rcParams['savefig.dpi'] = 2*plt.rcParams['savefig.dpi']\n",
    "    plt.rcParams['axes.linewidth'] = perso_linewidth\n",
    "    plt.rcParams['savefig.dpi'] = '300'\n",
    "    plt.rcParams['savefig.format'] = 'pdf'\n",
    "    plt.rcParams['axes.facecolor'] = '#D3D3D3'\n",
    "    plt.rcParams['axes.edgecolor'] = '0'\n",
    "    plt.rcParams['axes.grid'] = True\n",
    "    plt.rcParams['grid.color']='white'\n",
    "    plt.rcParams['grid.linestyle'] = '-'\n",
    "    plt.rcParams['grid.linewidth'] = '0.4'\n",
    "    plt.rcParams['axes.axisbelow'] = True\n",
    "    plt.rcParams['legend.edgecolor'] = 'black'\n",
    "    plt.rcParams['legend.facecolor'] = 'white'\n",
    "    plt.rcParams['lines.markersize']= 18 \n",
    "    plt.rcParams['lines.markeredgewidth']= '0.1'\n",
    "    plt.rcParams['lines.color']= 'r' \n",
    "    plt.rcParams['lines.marker']= 'o' \n",
    "    plt.rcParams['lines.linestyle']= '-' \n",
    "    plt.rcParams['xtick.color']= '0'\n",
    "    plt.rcParams['ytick.color']= '0'\n",
    "    #plt.rcParams['axes.color_cycle']= ['#3778bf', '#feb308', '#a8a495', '#7bb274', '#825f87']\n",
    "    plt.gca().spines['right'].set_color('none')\n",
    "    plt.gca().spines['right'].set_visible('False')\n",
    "    plt.gca().spines['top'].set_visible('False')\n",
    "    plt.gca().spines['top'].set_color('none')\n",
    "    plt.gca().xaxis.set_ticks_position('bottom')\n",
    "    plt.gca().yaxis.set_ticks_position('left')\n",
    "    plt.rcParams['ytick.minor.size']= 1.5\n",
    "    plt.rcParams['ytick.major.width']= perso_linewidth\n",
    "    plt.rcParams['ytick.minor.width']= perso_linewidth\n",
    "    plt.rcParams['xtick.major.width']= perso_linewidth\n",
    "    plt.rcParams['xtick.minor.width']= perso_linewidth\n",
    "\n",
    "init_plotting()\n",
    "\n",
    "plt.close() # this line to avoid an empty plot showing up\n",
    "\n",
    "### Centering the plots\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "</style>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stochastic Simulation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial state and constants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple SIS Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "##### Simple SIS model\n",
    "\n",
    "\\begin{itemize}\n",
    "\\item $E_1:X \\rightarrow X+1,Y \\rightarrow Y-1$\n",
    "\\item $E_2:X \\rightarrow X-1,Y \\rightarrow Y+1$\n",
    "\\end{itemize}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "initial_state=[30,70]\n",
    "\n",
    "### Define events increments for Gillepsie's Direct and FR: +1/-1 increment added to each compartment when Ei fires\n",
    "E1=[-1,1]\n",
    "E2=[1,-1]\n",
    "E=np.vstack((E1,E2))\n",
    "\n",
    "\n",
    "### Define state-change vector for tau-Leap\n",
    "v1=[-1,1]\n",
    "v2=[1,-1]\n",
    "v=np.vstack((v1,v2))\n",
    "\n",
    "\n",
    "simulation_period=10  ### Years\n",
    "\n",
    "dt=1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "#### Boarding School SIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Boarding School\n",
    "\n",
    "##### Actual case count\n",
    "csv_file_path=path.join(getcwd(), 'data','Boarding.csv')\n",
    "df=pd.read_csv(csv_file_path,index_col=0,header=0)\n",
    "\n",
    "t_reported=df.day\n",
    "Y_reported=df.flu\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "N=764\n",
    "X0=763\n",
    "Y0=1\n",
    "Z0=0\n",
    "initial_state=[X0,Y0,Z0]\n",
    "# ### Define events increments for Gillepsie's Direct and FR: +1/-1 increment added to each compartment when Ei fires\n",
    "#E1=[1,0,0]  ## New birth\n",
    "E1=[-1,1,0] ## Infection\n",
    "#E3=[-1,0,0] ## Death in S\n",
    "E2=[0,-1,1] ## Recovery\n",
    "#E5=[0,-1,0] ## Death in I\n",
    "#E6=[0,0,-1] ## Death in R\n",
    "\n",
    "\n",
    "E=np.vstack((E1,E2))\n",
    "\n",
    "\n",
    "### Define state-change vector for tau-Leap: Signs before each rate\n",
    "v1=[-1,0]\n",
    "v2=[1,-1]\n",
    "v3=[1,0]\n",
    "v=np.vstack((v1,v2,v3))\n",
    "\n",
    "simulation_period=1/16  ### Years\n",
    "\n",
    "dt=0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Ratefunc(state,Beta,Gamma):\n",
    "#     ##### Define rates here:\n",
    "#     X=state[0]\n",
    "#     Y=state[1]\n",
    "#     Beta=0.03\n",
    "#     Gamma=1/100\n",
    "#     N=X+Y\n",
    "#     #######\n",
    "    \n",
    "#     R=np.zeros(0)\n",
    "#     R=np.append(R,[(Beta*X*Y)/N])\n",
    "#     R=np.append(R,[Gamma*Y])\n",
    "\n",
    "    X=state[0]\n",
    "    Y=state[1]\n",
    "    Z=state[2]\n",
    "    \n",
    "    #Beta=1.66\n",
    "    #Gamma=1/2.2\n",
    "    \n",
    "    N=X+Y+Z\n",
    "    #######\n",
    "    \n",
    "    R=np.zeros(0)\n",
    "    R=np.append(R,[(Beta*X*Y)/N])\n",
    "    R=np.append(R,[Gamma*Y])\n",
    "\n",
    "\n",
    "    \n",
    "    return R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Gillepsie's Direct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def GillepsieDirect(state,R,E): ## State: current state, R:Rates, E: Event increment, Y: observed values\n",
    "    \n",
    "    r_t=sum(R)\n",
    "    rnd1=random.random()\n",
    "    dt=(-1/r_t)*np.log(rnd1)\n",
    "    rnd2=random.random()\n",
    "    P=rnd2*r_t\n",
    "\n",
    "      \n",
    "    l1=np.append([0],R)\n",
    "    l2=np.cumsum(l1)\n",
    "    event_to_shoot=np.where(l2>=P)[0][0]-1\n",
    "    \n",
    "    state=state+E[event_to_shoot]\n",
    "    \n",
    "    \n",
    "    return state,dt        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Gillespie's First Reaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def GillepsieFR(state,R,E):\n",
    "    \n",
    "    n_events=len(R)\n",
    "\n",
    "    rnd=random.random(n_events)\n",
    "    dts=(-1/R)*np.log(rnd)\n",
    "    \n",
    "    \n",
    "    dt=min(dts)\n",
    "    event_to_shoot=np.where(dts==dt)[0][0]\n",
    "    state=state+E[event_to_shoot]\n",
    "    \n",
    "    return state,dt    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\tau$-Leap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TauLeap(state,R,v,dt):\n",
    "    \n",
    "    Lambda=dt*R\n",
    "    dMs=np.random.poisson(Lambda)\n",
    "\n",
    "    dstate=v@dMs.T\n",
    "    state=state+dstate\n",
    "    \n",
    "    return state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "X=np.array([X0])\n",
    "Y=np.array([Y0])\n",
    "\n",
    "t=np.array([0])\n",
    "\n",
    "\n",
    "E=np.zeros(0)\n",
    "\n",
    "### Define Events\n",
    "E=np.append(E,[-1,1])\n",
    "E=np.vstack((E,[1,-1]))\n",
    "\n",
    "\n",
    "for i in range(5000):\n",
    "    R=np.zeros(0)\n",
    "    \n",
    "    \n",
    "    ### Define Variables\n",
    "    Params=[X[i],Y[i]]\n",
    "    \n",
    "    ### Define Rates\n",
    "    R=np.append(R,[(Beta*X[i]*Y[i])/N])\n",
    "    R=np.append(R,[Gamma*Y[i]])\n",
    "    \n",
    "    Params=[X[i],Y[i]]\n",
    "    \n",
    "    \n",
    "    [x,y],dt=GillepsieDirect(Params,R,E)\n",
    "        \n",
    "    X=np.append(X,x)\n",
    "    Y=np.append(Y,y)\n",
    "    t=np.append(t,t[i]+dt)\n",
    "    \n",
    "Y_direct=Y\n",
    "t_direct=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "X=np.array([X0])\n",
    "Y=np.array([Y0])\n",
    "\n",
    "t=np.array([0])\n",
    "\n",
    "\n",
    "E=np.zeros(0)\n",
    "\n",
    "### Define Events\n",
    "E=np.append(E,[-1,1])\n",
    "E=np.vstack((E,[1,-1]))\n",
    "\n",
    "for i in range(5000):\n",
    "    R=np.zeros(0)\n",
    "    \n",
    "    \n",
    "    ### Define Variables\n",
    "    Params=[X[i],Y[i]]\n",
    "    \n",
    "    ### Define Rates\n",
    "    R=np.append(R,[(Beta*X[i]*Y[i])/N])\n",
    "    R=np.append(R,[Gamma*Y[i]])\n",
    "\n",
    "    \n",
    "    Params=[X[i],Y[i]]\n",
    "    \n",
    "    [x,y],dt=GillepsieFR(Params,R,E)\n",
    "    \n",
    "    \n",
    "    X=np.append(X,x)\n",
    "    Y=np.append(Y,y)\n",
    "    t=np.append(t,t[i]+dt)\n",
    "    \n",
    "Y_FR=Y\n",
    "t_FR=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "X=np.array([X0])\n",
    "Y=np.array([Y0])\n",
    "\n",
    "t=np.array([0])\n",
    "\n",
    "E=np.zeros(0)\n",
    "\n",
    "### Define Events\n",
    "E=np.append(E,[-1,1])\n",
    "E=np.vstack((E,[1,-1]))\n",
    "\n",
    "### Define dt\n",
    "dt=1.0\n",
    "\n",
    "for i in range(5000):\n",
    "    R=np.zeros(0)\n",
    "   \n",
    "    ### Define Variables\n",
    "    Params=[X[i],Y[i]]\n",
    "    \n",
    "    ### Define Rates\n",
    "    R=np.append(R,[(Beta*X[i]*Y[i])/N])\n",
    "    R=np.append(R,[Gamma*Y[i]])\n",
    "    \n",
    "    \n",
    "    Params=np.array([X[i],Y[i]])\n",
    "    \n",
    "    if any(Params<=0):\n",
    "        break\n",
    "    \n",
    "    \n",
    "    [x,y]=TauLeap(Params,R,E,dt)\n",
    "    \n",
    "    \n",
    "    X=np.append(X,x)\n",
    "    Y=np.append(Y,y)\n",
    "    t=np.append(t,t[i]+dt)\n",
    "    \n",
    "Y_TauLeap=Y\n",
    "t_TauLeap=t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Gillepsie Incrementer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def GillepsieSim(initial_state,E,simulation_period,method,Beta,Gamma):\n",
    "    state=np.array(initial_state)\n",
    "    n_vars=len(initial_state)\n",
    "    state.shape=(1,n_vars)\n",
    "    t=np.array([0])\n",
    "     \n",
    "    while (t[-1]/365)<simulation_period:\n",
    "        R=Ratefunc(state[-1],Beta,Gamma)\n",
    "        if method=='FR':\n",
    "            new_state,dt=GillepsieFR(state[-1],R,E)\n",
    "        if method=='Direct':\n",
    "            new_state,dt=GillepsieDirect(state[-1],R,E)\n",
    "        \n",
    "        state=np.vstack((state,new_state))\n",
    "        t=np.append(t,t[-1]+dt)\n",
    "       \n",
    "    #         if any(state[-1]<=0):\n",
    "    #             break\n",
    "    return state,t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### $\\tau$-Leap Incrementer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def TauLeapSim(initial_state,v,dt,simulation_period):   ### dt:days, simulation_period:years\n",
    "    state=np.array(initial_state)\n",
    "    n_vars=len(initial_state)\n",
    "    state.shape=(1,n_vars)\n",
    "    \n",
    "    simulation_period_days=simulation_period*365\n",
    "    \n",
    "    n=int(simulation_period_days/dt)\n",
    "    \n",
    "    \n",
    "    t=np.array([0])\n",
    "    \n",
    "    \n",
    "    for i in range(n):\n",
    "        \n",
    "        R=Ratefunc(state[-1])\n",
    "        new_state=TauLeap(state[-1],R,v,dt)\n",
    "        state=np.vstack((state,new_state))\n",
    "        t=np.append(t,t[-1]+dt)\n",
    "#         if any(state[-1]<=0):\n",
    "#             break\n",
    "        \n",
    "        \n",
    "    return state,t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Direct Method Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta= 1.5\n",
      "Gamma= 0.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arashs\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:5: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.51\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.52\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.53\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.54\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.55\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.56\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.57\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.58\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.59\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.61\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.62\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6300000000000001\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6400000000000001\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6500000000000001\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6600000000000001\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6700000000000002\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6800000000000002\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n",
      "Beta= 1.6900000000000002\n",
      "Gamma= 0.3\n",
      "Gamma= 0.31\n",
      "Gamma= 0.32\n",
      "Gamma= 0.33\n",
      "Gamma= 0.34\n",
      "Gamma= 0.35000000000000003\n",
      "Gamma= 0.36000000000000004\n",
      "Gamma= 0.37000000000000005\n",
      "Gamma= 0.38000000000000006\n",
      "Gamma= 0.39000000000000007\n",
      "Gamma= 0.4000000000000001\n",
      "Gamma= 0.4100000000000001\n",
      "Gamma= 0.4200000000000001\n",
      "Gamma= 0.4300000000000001\n",
      "Gamma= 0.4400000000000001\n",
      "Gamma= 0.4500000000000001\n",
      "Gamma= 0.46000000000000013\n",
      "Gamma= 0.47000000000000014\n",
      "Gamma= 0.48000000000000015\n",
      "Gamma= 0.49000000000000016\n"
     ]
    }
   ],
   "source": [
    "method='Direct'\n",
    "\n",
    "#fig2=plt.figure(figsize=(40,24))\n",
    "#ax21=plt.subplot(311)\n",
    "#ax22=plt.subplot(312)\n",
    "#ax23=plt.subplot(313)\n",
    "\n",
    "n_sims=50\n",
    "Betas=np.arange(1.5,1.7,0.01)\n",
    "Gammas=np.arange(0.3,0.5,0.01)\n",
    "\n",
    "\n",
    "start_time=datetime.datetime.now()\n",
    "\n",
    "LL=np.zeros(n_sims)\n",
    "meanLL=np.zeros((len(Betas)*len(Gammas),3))\n",
    "n=len(Y_reported)\n",
    "\n",
    "counter=0\n",
    "\n",
    "for Beta in Betas:\n",
    "    print(\"Beta=\",Beta)\n",
    "    for Gamma in Gammas:\n",
    "        print(\"Gamma=\",Gamma)\n",
    "        for i in range(n_sims):\n",
    "            state,t=GillepsieSim(initial_state,E,simulation_period,method,Beta,Gamma)\n",
    "            X=state[:,0]\n",
    "            Y=state[:,1]\n",
    "            Z=state[:,2]\n",
    "\n",
    "            diff=Y_reported-np.interp(t_reported,t,Y)\n",
    "            sse=sum(diff**2)\n",
    "            variance=sse/n\n",
    "            LL[i]=-(n/2)*np.log(2*pi*variance)-n/2\n",
    "        \n",
    "        meanLL[counter]=[Beta,Gamma,np.mean(LL)]\n",
    "        LL=np.zeros(n_sims)\n",
    "        counter=counter+1\n",
    "           \n",
    "\n",
    "\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            #     ax21.plot(t,X,'-')\n",
    "        #     ax22.plot(t,Y,'-')\n",
    "        #     ax23.plot(t,Z,'-')\n",
    "\n",
    "#        end_time=datetime.datetime.now()\n",
    "#        sim_time=str(end_time-start_time)\n",
    "\n",
    "#        print(\"Simulation Time: \",sim_time)\n",
    "#        print(\"Mean Log-Likelihood: \",np.mean(LL))\n",
    "#        print(\"Var Log-Likelihood: \",np.var(LL))\n",
    "\n",
    "### Actual case count   \n",
    "# ax22.plot(t_reported,Y_reported,'-ok',linewidth=3,label='Actual reported cases')\n",
    "\n",
    "# ax21.set_ylabel('Number of Susceptibles')\n",
    "# ax22.set_ylabel('Number of Infectious')\n",
    "# ax23.set_ylabel('Number of Recovereds')\n",
    "\n",
    "# ax21.set_xlim((0,15))\n",
    "# ax22.set_xlim((0,15))\n",
    "# ax23.set_xlim((0,15))\n",
    "\n",
    "# fig2.suptitle(\"Gillepsie's Direc Method - \"+str(n_sims)+' Trajectories (Simulation time: '+sim_time+')',fontsize=24)\n",
    "# ax22.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'meanLL' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-9f87b583e245>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig33\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0max331\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mAxes3D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig33\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0max331\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeanLL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmeanLL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmeanLL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'meanLL' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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knGOapuj7XkwNAMC7ek10XVXV5cWHt6LrZdO16HpdT5/HpJQuP76+7pbLia4B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\nSp1SirZtI6UUwzAIqYFdm+f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RJMnU0HW7IJzQdX/ME7qu67oLXwMAsEkIVQMAAABbqC1M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sorXvEKueuuu+Ts2bNrOZbz58/Lu9/9bvnmN78pp06dkl/4hV+Qq6++Wr7xjW/IF7/4Rcnz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\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q0t7eHoaqR1aRHqlYLIZfp1KpcStMT8RxnKZ/7ujomPD6M844I/x6//79U74fAAAApoZQ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\n+Tkf+MAHlMlkJEmXXHKJHnnkkVnXQqdqAAAAIFhOdrIkoWo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x141d1c91208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig33=plt.figure()\n",
    "ax331=Axes3D(fig33)\n",
    "ax331.scatter(meanLL[:,0],meanLL[:,1],meanLL[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "a=max(meanLL[:,2])\n",
    "b=meanLL[meanLL[:,2]==a]\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# FR Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "method='FR'\n",
    "\n",
    "fig3=plt.figure(figsize=(40,24))\n",
    "ax31=plt.subplot(311)\n",
    "ax32=plt.subplot(312)\n",
    "ax33=plt.subplot(313)\n",
    "\n",
    "n_sims=100\n",
    "\n",
    "start_time=datetime.datetime.now()\n",
    "\n",
    "for i in range(n_sims):\n",
    "    state,t=GillepsieSim(initial_state,E,simulation_period,method)\n",
    "    X=state[:,0]\n",
    "    Y=state[:,1]\n",
    "    Z=state[:,2]\n",
    "    ax31.plot(t,X,'-')\n",
    "    ax32.plot(t,Y,'-')\n",
    "    ax33.plot(t,Z,'-')    \n",
    "    \n",
    "end_time=datetime.datetime.now()\n",
    "sim_time=str(end_time-start_time)\n",
    "\n",
    "\n",
    "### Actual case count   \n",
    "ax32.plot(t_reported,Y_reported,'-ok',linewidth=3,label='Actual reported cases')\n",
    "\n",
    "ax31.set_ylabel('Number of Susceptibles')\n",
    "ax32.set_ylabel('Number of Infectious')\n",
    "ax33.set_ylabel('Number of Recovereds')\n",
    "\n",
    "\n",
    "ax31.set_xlim((0,15))\n",
    "ax32.set_xlim((0,15))\n",
    "ax33.set_xlim((0,15))\n",
    "\n",
    "\n",
    "\n",
    "fig3.suptitle(\"Gillepsie's First Reaction Method - \"+str(n_sims)+' Trajectories (Simulation time: '+sim_time+')',fontsize=24)\n",
    "ax32.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\tau$-Leap Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Ratefunc() missing 2 required positional arguments: 'Beta' and 'Gamma'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-91b00e582d54>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_sims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTauLeapSim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minitial_state\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msimulation_period\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-16-ef787402aa6b>\u001b[0m in \u001b[0;36mTauLeapSim\u001b[1;34m(initial_state, v, dt, simulation_period)\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m         \u001b[0mR\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mRatefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m         \u001b[0mnew_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTauLeap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mstate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnew_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Ratefunc() missing 2 required positional arguments: 'Beta' and 'Gamma'"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x141d1c6af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4=plt.figure(figsize=(40,24))\n",
    "ax41=plt.subplot(311)\n",
    "ax42=plt.subplot(312)\n",
    "ax43=plt.subplot(313)\n",
    "\n",
    "n_sims=100\n",
    "\n",
    "start_time=datetime.datetime.now()\n",
    "\n",
    "for i in range(n_sims):\n",
    "    state,t=TauLeapSim(initial_state,v,dt,simulation_period)\n",
    "\n",
    "    X=state[:,0]\n",
    "    Y=state[:,1]\n",
    "    Z=state[:,2]\n",
    "    \n",
    "    ax41.plot(t,X,'-')\n",
    "    ax42.plot(t,Y,'-')\n",
    "    ax43.plot(t,Z,'-')\n",
    "    \n",
    "end_time=datetime.datetime.now()\n",
    "sim_time=str(end_time-start_time)\n",
    "\n",
    "### Actual case count   \n",
    "ax42.plot(t_reported,Y_reported,'-ok',linewidth=3,label='Actual reported cases')\n",
    "\n",
    "ax41.set_ylabel('Number of Susceptibles')\n",
    "ax42.set_ylabel('Number of Infectious')\n",
    "ax43.set_ylabel('Number of Recovereds')\n",
    "\n",
    "\n",
    "ax41.set_xlim((0,15))\n",
    "ax42.set_xlim((0,15))\n",
    "ax43.set_xlim((0,15))\n",
    "\n",
    "\n",
    "\n",
    "fig4.suptitle(r\"$\\tau$-Leap Method (dt: \"+str(dt) + \" days) - \"+str(n_sims)+' Trajectories (Simulation time: '+sim_time+')',fontsize=24)\n",
    "ax42.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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