Bengaluru.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BlorePaperFinal.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AD6tNxzyq-X4"
      },
      "source": [
        "# Unlock Phase 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iw4NW7UErL0A",
        "outputId": "6ca3f484-d735-4bdd-c338-736c48e7beda"
      },
      "source": [
        "%reset -f\n",
        "!pip install faiss-gpu\n",
        "!pip install celluloid\n",
        "import faiss\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import time\n",
        "import torch\n",
        "from google.colab import files\n",
        "import random\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "device = 'cuda'\n",
        "res = faiss.StandardGpuResources()\n",
        "select = 0\n",
        "MonteCarlo = 2\n",
        "\n",
        "# Total Population\n",
        "Population = 108720\n",
        "# Number of Children (0), Adults (1), Seniors (2) and Infections (3), Confirmed Infections (4)\n",
        "Demography = torch.tensor([round(0.2278*Population),round(0.7239*Population),0,round(0.001*Population),5],dtype=torch.long,device='cuda')\n",
        "Demography[2] = Population-torch.sum(Demography[:2]) \n",
        "Demography[3] = 50\n",
        "# Layout Size: XMinimum (0), XMaximum (1), YMinimum (2), YMaximum (3), Grid Resolution (4)\n",
        "Layout = torch.tensor([0,5000,0,2000,1000],dtype=torch.double,device='cuda')\n",
        "# Minimum (0), Maximum (1), Inner (2), Innermost (3) Allowed Speeds\n",
        "VelocityParameters = torch.tensor([-500,500,200,10,1],dtype=torch.long,device='cuda')\n",
        "# Collision Radius (0), Infection Radius (1), OuterCircle (2), InnerCircle (3), Reached Goal (4)\n",
        "Radii = torch.tensor([5,2,570,120,5],dtype=torch.long,device='cuda') # Infection Radius(20) > Collision Radius(5/50) --> no social distancing\n",
        "# Total time (days), Resolution(minutes)\n",
        "Time = torch.tensor([31,15],dtype=torch.long, device='cuda') \n",
        "# TimeSteps in a day (1 step = 15mins) and total\n",
        "TimeStepsDay = torch.div(24*60,Time[1].item())\n",
        "# Total time steps in 31 days\n",
        "TimeStepsTotal = TimeStepsDay*Time[0].long()\n",
        "\n",
        "# Household positions of each agent\n",
        "Residence = torch.tensor(np.loadtxt('blore_agent_location.dat'),dtype=torch.double,device='cuda')\n",
        "# Age of the individual\n",
        "Age = torch.tensor(np.loadtxt('blore_Age.dat'),dtype=torch.long,device='cuda')\n",
        "# Household size\n",
        "H_Size = torch.tensor(np.loadtxt('blore_Age_House.dat'),dtype=torch.long,device='cuda')\n",
        "# 1-member households\n",
        "H_Size[H_Size==0] = 1\n",
        "# ID of the households of each agent\n",
        "H_Number = torch.tensor(np.loadtxt('blore_House_number.dat'),dtype=torch.long,device='cuda')\n",
        "# Location of each household\n",
        "H_location = torch.tensor(np.loadtxt('blore_household_location.dat'),dtype=torch.long,device='cuda')\n",
        "\n",
        "# Re-assign x and y initial positions\n",
        "# Assign positions within unit radius\n",
        "adjustment = 0.5\n",
        "Residence = H_location[H_Number] + torch.DoubleTensor(Population,2).uniform_(-adjustment,adjustment).cuda()\n",
        "# Check if the newly assigned x co-ordinate is within the boundary\n",
        "Residence[Residence[:,0]>Layout[1],0] = (torch.DoubleTensor(Population).uniform_(Layout[1]-adjustment,Layout[1]).cuda())[Residence[:,0]>Layout[1]]\n",
        "Residence[Residence[:,0]<Layout[0],0] = (torch.DoubleTensor(Population).uniform_(Layout[0],Layout[0]+adjustment).cuda())[Residence[:,0]<Layout[0]]\n",
        "# Check if the newly assigned y co-ordinate is within the boundary\n",
        "Residence[Residence[:,1]>Layout[3],1] = (torch.DoubleTensor(Population).uniform_(Layout[3]-adjustment,Layout[3]).cuda())[Residence[:,1]>Layout[3]]\n",
        "Residence[Residence[:,1]<Layout[2],1] = (torch.DoubleTensor(Population).uniform_(Layout[2],Layout[2]+adjustment).cuda())[Residence[:,1]<Layout[2]]\n",
        "\n",
        "Activities = np.zeros((20,96),dtype=np.float32)\n",
        "# Children\n",
        "# Errands \n",
        "Activities[0,24:36]=0.005*norm.pdf(np.arange(6,9,step=0.25), 7.5, 1) # 6-9\n",
        "Activities[0,36:56]=0.01*norm.pdf(np.arange(9,14,step=0.25), 11.5, 1) # 9-14\n",
        "Activities[0,56:72]=0.05*norm.pdf(np.arange(14,18,step=0.25), 17, 1) # 14-21\n",
        "# Adults\n",
        "# Errands \n",
        "Activities[8,24:36]=0.0025*norm.pdf(np.arange(6,9,step=0.25), 7.5, 1) # 6-9\n",
        "Activities[8,36:56]=0.005*norm.pdf(np.arange(9,14,step=0.25), 11.5, 1) # 9-14\n",
        "Activities[8,56:72]=0.0075*norm.pdf(np.arange(14,18,step=0.25), 17, 1) # 14-21\n",
        "# Nomadic\n",
        "Activities[12,24:48] = 0.1*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[12,48:72] = 0.075*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Nomads go home (18) # Weekday\n",
        "Activities[18,0:24] = np.ones(24)\n",
        "Activities[18,84:96] = np.ones(12)\n",
        "# Nomads go home (19) # Weekend\n",
        "Activities[19,0:96] = np.ones(96)\n",
        "# Children\n",
        "# Market\n",
        "Activities[1,24:48]=0.005*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[1,48:72]=0.01*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Adults\n",
        "# Market\n",
        "Activities[9,24:48]=0.05*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[9,48:72]=0.1*norm.pdf(np.arange(12,18,step=0.25), 17, 1) # 12-21\n",
        "# Office \n",
        "Activities[10,24:48]= 2*norm.pdf(np.arange(6,12,step=0.25), 9, 1) # 6-12\n",
        "Activities[11,48:72] = 2*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Weekday Activities\n",
        "Limits = torch.tensor([0,0,0,0,0,0,1,100,1,100,0,0,\n",
        "                       0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                       0,0,0,0,5,100,4,100,3,100,0,0,\n",
        "                       5,100,0,0,4,100,1,100,5,100,0,0,\n",
        "                       10,100,0,0,2,100,1,100,1,100,2,100,\n",
        "                       0,0,0,0,0,0,0,0,1,100,1,100],dtype=torch.long,device='cuda')\n",
        "# Weekend Activities                    \n",
        "Limits_Weekend = torch.tensor([0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0],dtype=torch.long,device='cuda')\n",
        "Location_one = torch.tensor([12,13,14,15,16,17],dtype=torch.long,device='cuda') # Location of i\n",
        "Location_two = torch.tensor([[12,0,0,15,16,0],\n",
        "                             [0,0,0,0,0,0],\n",
        "                             [0,0,14,15,16,0],\n",
        "                             [12,0,14,15,16,0],\n",
        "                             [12,0,14,15,16,17],\n",
        "                             [0,0,0,0,16,17]],dtype=torch.long,device='cuda') # Corresponding locations of j  \n",
        "\n",
        "def goal_assign(x,y,minbias,maxbias,xhalfmark,yhalfmark):\n",
        "  '''\n",
        "  Generate Random Goals\n",
        "  '''\n",
        "  # ordinate\n",
        "  l = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  # abscissa\n",
        "  m = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  temp = x -  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  l[x>xhalfmark] = temp[x>xhalfmark] \n",
        "  temp = y -  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  m[y>yhalfmark] = temp[y>yhalfmark] \n",
        "  temp = x +  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  l[x<xhalfmark] = temp[x<xhalfmark] \n",
        "  temp = y +  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  m[y<yhalfmark] = temp[y<yhalfmark] \n",
        "  return l,m   \n",
        "\n",
        "def generateMatrix(agents,matrixone,limits,Population, H_Number, index,con_one, con_two,Time_Daily,matrixthree,distance,radius):\n",
        "  '''\n",
        "  Generate a conditional contact matrix\n",
        "  '''\n",
        "  # Store end result\n",
        "  matrixtwo = torch.zeros((2,Population,20),dtype=torch.double,device='cuda')\n",
        "  count=0\n",
        "  for i in range(6):\n",
        "    vec_con1 = torch.zeros((Population),device='cuda',dtype=torch.bool)\n",
        "    vec_con1 += (agents==con_one[i])\n",
        "    Time_Daily[vec_con1==True,i]+=1\n",
        "    for j in range(6):\n",
        "      temp = torch.zeros((Population,20),device='cuda',dtype=torch.int32)\n",
        "      vec_con2 = torch.zeros((Population,20),device='cuda',dtype=torch.bool)      \n",
        "      if (con_two[i][j]!=0):\n",
        "        if (count==0):          \n",
        "          vec_con2 = torch.logical_and(H_Number[index]==H_Number[:,None],(agents[index]==con_two[i][j]))\n",
        "        else:\n",
        "          vec_con2 = (agents[index]==con_two[i][j])\n",
        "        temp = vec_con1[:,None]*vec_con2\n",
        "        matrixone[:,count] += torch.sum(temp,1)\n",
        "        #matrixone[:,count] += torch.sum(torch.logical_and(temp,distance<radius),1)\n",
        "        if (count!=0):\n",
        "          matrixone[matrixone[:,count]>limits[:,count],count] = limits[matrixone[:,count]>limits[:,count],count]\n",
        "        #tempone = temp*(1-(torch.cumsum(torch.logical_and(temp,distance<radius),axis=1)>matrixone[:,count][:,None]).long())\n",
        "        tempone = temp*(1-(torch.cumsum(temp,axis=1)>matrixone[:,count][:,None]).long())\n",
        "        matrixtwo[0] += tempone\n",
        "        matrixtwo[1] = temp\n",
        "        if (torch.where(vec_con1==True)[0].size(0)!=0):\n",
        "          matrixthree[i,j] += torch.where(tempone==1)[0].size(0)/torch.where(vec_con1==True)[0].size(0)\n",
        "      count+=1\n",
        "  return matrixtwo[0],matrixtwo[1], matrixone, matrixthree, Time_Daily\n",
        "\n",
        "def nearest_destination(Population,centroids,Residence): \n",
        "  '''\n",
        "  Find the nearest destination: Office/School/Market\n",
        "  '''\n",
        "  distance = torch.zeros((Population,len(centroids)),device='cuda',dtype=torch.double)\n",
        "  for i in range(len(centroids)):\n",
        "    # Check the distance from the centroid of the zone to each household location\n",
        "    distance[:,i] = torch.linalg.norm(Residence-Centroids[i],axis=1)\n",
        "  return torch.argmin(distance,axis=1)\n",
        "\n",
        "# Schools\n",
        "School=torch.tensor([[1000,1500,500,1000],\n",
        "                     [2000,2500,300,500],\n",
        "                     [3500,4000,800,1000],\n",
        "                     [3500,4000,1500,1700],\n",
        "                     [600,900,1600,1800]],dtype=torch.long,device='cuda')\n",
        "School_Positions = torch.zeros((Population,10),dtype=torch.double,device='cuda')\n",
        "School_Positions[:,0] = torch.DoubleTensor(Population).uniform_(School[0,0],School[0,1])\n",
        "School_Positions[:,2] = torch.DoubleTensor(Population).uniform_(School[1,0],School[1,1])\n",
        "School_Positions[:,4] = torch.DoubleTensor(Population).uniform_(School[2,0],School[2,1])\n",
        "School_Positions[:,6] = torch.DoubleTensor(Population).uniform_(School[3,0],School[3,1])\n",
        "School_Positions[:,8] = torch.DoubleTensor(Population).uniform_(School[4,0],School[4,1])\n",
        "School_Positions[:,1] = torch.DoubleTensor(Population).uniform_(School[0,2],School[0,3])\n",
        "School_Positions[:,3] = torch.DoubleTensor(Population).uniform_(School[1,2],School[1,3])\n",
        "School_Positions[:,5] = torch.DoubleTensor(Population).uniform_(School[2,2],School[2,3])\n",
        "School_Positions[:,7] = torch.DoubleTensor(Population).uniform_(School[3,2],School[3,3])\n",
        "School_Positions[:,9] = torch.DoubleTensor(Population).uniform_(School[4,2],School[4,3])\n",
        "\n",
        "# Offices\n",
        "Office = torch.tensor([[1000,1500,1000,1500],\n",
        "                      [2000,2500,0,300],\n",
        "                      [3500,4000,1000,1300],\n",
        "                      [3500,4000,500,800],\n",
        "                      [1500,2000,500,1000],\n",
        "                      [3500,4000,1700,2000],\n",
        "                      [2500,3000,500,1000]],dtype=torch.long,device='cuda')\n",
        "Office_Positions = torch.zeros((Population,14),dtype=torch.double,device='cuda')\n",
        "Office_Positions[:,0] = torch.DoubleTensor(Population).uniform_(Office[0,0],Office[0,1])\n",
        "Office_Positions[:,2] = torch.DoubleTensor(Population).uniform_(Office[1,0],Office[1,1])\n",
        "Office_Positions[:,4] = torch.DoubleTensor(Population).uniform_(Office[2,0],Office[2,1])\n",
        "Office_Positions[:,6] = torch.DoubleTensor(Population).uniform_(Office[3,0],Office[3,1])\n",
        "Office_Positions[:,8] = torch.DoubleTensor(Population).uniform_(Office[4,0],Office[4,1])\n",
        "Office_Positions[:,10] = torch.DoubleTensor(Population).uniform_(Office[5,0],Office[5,1])\n",
        "Office_Positions[:,12] = torch.DoubleTensor(Population).uniform_(Office[6,0],Office[6,1])\n",
        "Office_Positions[:,1] = torch.DoubleTensor(Population).uniform_(Office[0,2],Office[0,3])\n",
        "Office_Positions[:,3] = torch.DoubleTensor(Population).uniform_(Office[1,2],Office[1,3])\n",
        "Office_Positions[:,5] = torch.DoubleTensor(Population).uniform_(Office[2,2],Office[2,3])\n",
        "Office_Positions[:,7] = torch.DoubleTensor(Population).uniform_(Office[3,2],Office[3,3])\n",
        "Office_Positions[:,9] = torch.DoubleTensor(Population).uniform_(Office[4,2],Office[4,3])\n",
        "Office_Positions[:,11] = torch.DoubleTensor(Population).uniform_(Office[5,2],Office[5,3])\n",
        "Office_Positions[:,13] = torch.DoubleTensor(Population).uniform_(Office[6,2],Office[6,3])\n",
        "\n",
        "# Markets\n",
        "Market = torch.tensor([[2500,3500,1000,1500],\n",
        "                       [1000,1500,0,500],\n",
        "                       [1500,2000,1500,2000],\n",
        "                       [0,500,1000,1300],\n",
        "                       [4500,5000,1100,1300],\n",
        "                       [4000,4500,0,500]],dtype=torch.long,device='cuda')\n",
        "Market_Positions = torch.zeros((Population,12),dtype=torch.double,device='cuda')\n",
        "Market_Positions[:,0] = torch.DoubleTensor(Population).uniform_(Market[0,0],Market[0,1])\n",
        "Market_Positions[:,2] = torch.DoubleTensor(Population).uniform_(Market[1,0],Market[1,1])\n",
        "Market_Positions[:,4] = torch.DoubleTensor(Population).uniform_(Market[2,0],Market[2,1])\n",
        "Market_Positions[:,6] = torch.DoubleTensor(Population).uniform_(Market[3,0],Market[3,1])\n",
        "Market_Positions[:,8] = torch.DoubleTensor(Population).uniform_(Market[4,0],Market[4,1])\n",
        "Market_Positions[:,10] = torch.DoubleTensor(Population).uniform_(Market[5,0],Market[5,1])\n",
        "Market_Positions[:,1] = torch.DoubleTensor(Population).uniform_(Market[0,2],Market[0,3])\n",
        "Market_Positions[:,3] = torch.DoubleTensor(Population).uniform_(Market[1,2],Market[1,3])\n",
        "Market_Positions[:,5] = torch.DoubleTensor(Population).uniform_(Market[2,2],Market[2,3])\n",
        "Market_Positions[:,7] = torch.DoubleTensor(Population).uniform_(Market[3,2],Market[3,3])\n",
        "Market_Positions[:,9] = torch.DoubleTensor(Population).uniform_(Market[4,2],Market[4,3])\n",
        "Market_Positions[:,11] = torch.DoubleTensor(Population).uniform_(Market[5,2],Market[5,3])\n",
        "\n",
        "# Centroids\n",
        "Centroids = torch.zeros((18,2),dtype=torch.double,device='cuda')\n",
        "Centroids[:5,0] = School[:,0] + 0.5*School[:,1]\n",
        "Centroids[:5,1] = School[:,2] + 0.5*School[:,3]\n",
        "Centroids[5:12,0] = Office[:,0] + 0.5*Office[:,1]\n",
        "Centroids[5:12,1] = Office[:,2] + 0.5*Office[:,3]\n",
        "Centroids[12:18,0] = Market[:,0] + 0.5*Market[:,1]\n",
        "Centroids[12:18,1] = Market[:,2] + 0.5*Market[:,3]\n",
        "\n",
        "# Find nearest destination\n",
        "Nearest = torch.zeros((Population,3),device='cuda',dtype=torch.long)\n",
        "Nearest[:,0] = nearest_destination(Population,Centroids[:5],Residence)\n",
        "Nearest[:,1] = nearest_destination(Population,Centroids[5:12],Residence)\n",
        "Nearest[:,2] = nearest_destination(Population,Centroids[12:18],Residence)\n",
        "\n",
        "# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "# Disease Transmission Parameters\n",
        "# -------------------------------------\n",
        "\n",
        "# States: SIT/SIN/AIT/AIN/SUT/SUN\n",
        "States = torch.tensor(([0,0,0,0,0,1,2,3],\n",
        "                       [0,0,0,1,0,0,0,0],\n",
        "                       [6,7,6,7,0,0,0,0],\n",
        "                       [9,9,9,9,0,0,0,0],\n",
        "                       [0,0,0,0,0,0,0,0],\n",
        "                       [0,1,2,3,4,5,6,7]),dtype=torch.int,device='cuda')\n",
        "# Disease Transition Probabilities\n",
        "Transition = torch.zeros((3,6,8),dtype=torch.double,device='cuda')\n",
        "undercount = 0\n",
        "# Children \n",
        "Transition[0] = torch.transpose(torch.tensor(([0,0,0.12495,0.00005,0,0.875],\n",
        "                                              [0,0,0.09994,0.00006,undercount,0.9-undercount],\n",
        "                                              [0,0.00998,0.19,0.00002,0,0.8],\n",
        "                                              [0,0.02996,0.12,0.00004,undercount,0.85-undercount],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463]),dtype=torch.double,device='cuda'),0,1) \n",
        "# Adults\n",
        "Transition[1] = torch.transpose(torch.tensor(([0,0,0.124,0.001,0,0.875],\n",
        "                                              [0,0,0.06,0.04,undercount,0.9-undercount],\n",
        "                                              [0,0.03993,0.16,0.00007,0,0.8],\n",
        "                                              [0,0.0493,0.1,0.0007,undercount,0.85-undercount],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5]),dtype=torch.double,device='cuda'),0,1) \n",
        "# Seniors\n",
        "Transition[2] = torch.transpose(torch.tensor(([0,0,0.065,0.06,0,0.875],\n",
        "                                              [0,0,0.02,0.08,undercount,0.9-undercount],\n",
        "                                              [0,0.08,0.1,0.02,0,0.8],\n",
        "                                              [0,0.1,0.01,0.04,undercount,0.85-undercount],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049]),dtype=torch.double,device='cuda'),0,1)\n",
        "# Storage Parameters\n",
        "# Outermost Loop (Scenario/Final Parameters)\n",
        "# Disease Transmission: S/IT/INT/R/D/Total (7+7, Mean and Variance states) \n",
        "FinalTimeSeries = torch.zeros((65,(TimeStepsTotal.long().item())+1), dtype=torch.float,device='cuda')\n",
        "# Time to infection, death, recovery (Mean - 0,1,2, Variance - 3,4,5)\n",
        "FinalConstants = torch.zeros((20), dtype=torch.float,device='cuda')\n",
        "InteractionsFinal = torch.zeros((2,6,6),dtype=torch.float,device='cuda')\n",
        "# Outermost loop\n",
        "# Second Loop (MonteCarlo Run Parameters)\n",
        "# ResultsTimeSeries (Time Series for MC runs)(0-6)\n",
        "ResultsTMC = torch.zeros((MonteCarlo,29,(TimeStepsTotal.long().item())+1), dtype=torch.float,device='cuda')\n",
        "# Results Constants: Time to Infection: Overall(0), Recovery(1), Death(2)\n",
        "ResultsCMC = torch.zeros((MonteCarlo,10,1), dtype=torch.float,device='cuda')\n",
        "InteractionsMC = torch.zeros((MonteCarlo,2,6,6), dtype=torch.float,device='cuda')\n",
        "\n",
        "for mc in range(MonteCarlo):\n",
        "  # Create N agents;\n",
        "  # Age = 0,     \n",
        "  # x_position = 1, \n",
        "  # y_position = 2, \n",
        "  # x_velocity = 3, \n",
        "  # y_velocity = 4, \n",
        "  # occupation = 5,\n",
        "  # current_task = 7, \n",
        "  # status_of_task = 8, \n",
        "  # goal_x = 9, \n",
        "  # goal_y = 10,\n",
        "  # infection_status = 14, \n",
        "  # infection_state = 15, \n",
        "  # time_of_infectiousness = 16\n",
        "  Agents = torch.zeros((Population,20),dtype=torch.double,device='cuda')\n",
        "  # Assign Age \n",
        "  Agents[:,0] = Age\n",
        "  # Indices of Children\n",
        "  Children = torch.where(Agents[:,0]==0)[0]\n",
        "  # Indices of Adults\n",
        "  Adults = torch.where(Agents[:,0]==1)[0]\n",
        "  # Indices of Seniors\n",
        "  Seniors = torch.where(Agents[:,0]==2)[0]\n",
        "  # Assign x and y Positions\n",
        "  # PositionX (1)\n",
        "  Agents[:,1] = Residence[:,0].detach().clone()\n",
        "  # PositionY (2)\n",
        "  Agents[:,2] = Residence[:,1].detach().clone()\n",
        "  # Assign x and y Velocity Components\n",
        "  # VelocityX (3)\n",
        "  Agents[:,3] = torch.DoubleTensor(Population).uniform_(VelocityParameters[0],VelocityParameters[1])\n",
        "  # VelocityY (4)\n",
        "  Agents[:,4] = torch.DoubleTensor(Population).uniform_(VelocityParameters[0],VelocityParameters[1])\n",
        "  # Social Distance:  \n",
        "  SD = Radii[0]*torch.ones((Population),dtype=torch.long,device='cuda')\n",
        "  # Fraction not following (0: Everyone, 1: No one)\n",
        "  SD[torch.tensor([True,False],device='cuda')[torch.multinomial(torch.tensor([0.9,0.1]),Population,replacement=True)]] = 0\n",
        "  # Occupation (5): \n",
        "  # School-going Children (1)\n",
        "  Agents[Children[:int(0.75*Demography[0])],5] = 1\n",
        "  # School-going Adults (1)\n",
        "  Agents[Adults[:int(0.05*Demography[1])],5] = 1\n",
        "  # School-going Seniors (1)\n",
        "  Agents[Seniors[:int(0.02*Demography[2])],5] = 1\n",
        "  # Stay-at-home adults (2)\n",
        "  Agents[Adults[int(0.05*Demography[1]):int(0.25*Demography[1])],5] = 2\n",
        "  # Office Going Adults (3)\n",
        "  Agents[Adults[int(0.25*Demography[1]):int(0.85*Demography[1])],5] = 3\n",
        "  # Nomadic Adults (4)\n",
        "  Agents[Adults[int(0.85*Demography[1]):Demography[1]],5] = 4\n",
        "  # Stay-at-home Seniors (5)\n",
        "  Agents[Seniors[int(0.02*Demography[2]):int(0.62*Demography[2])],5] = 5 \n",
        "  # Office Going Seniors (6)\n",
        "  Agents[Seniors[int(0.62*Demography[2]):int(0.95*Demography[2])],5] = 6\n",
        "  # Reduce office-goers by fraction\n",
        "  stay = torch.where(torch.logical_or(Agents[:,5]==3,Agents[:,5]==6))[0]\n",
        "  staynumber = stay[:int(0.9*stay.size(0))]\n",
        "  Agents[staynumber[Agents[staynumber,0]==1],5] = 2\n",
        "  Agents[staynumber[Agents[staynumber,0]==2],5] = 5\n",
        "  # Nomadic Seniors (7)\n",
        "  Agents[Seniors[int(0.95*Demography[2]):Demography[2]],5] = 7\n",
        "  # Assign Infection Status: Infected and Tested (1), Uninfect(0), Infected and Not Tested (3)\n",
        "  # 0.5% infections among children, 49% infections among adults, 50.5% among seniors\n",
        "  Agents[Children[:int(0.005*Demography[3])],14] = 3 # Infected \n",
        "  Agents[Adults[int(0.005*Demography[3]):int(0.49*Demography[3])],14] = 3 # Infected \n",
        "  Agents[Seniors[int(0.49*Demography[3]):Demography[3]],14] = 3 # Infected \n",
        "  Agents[torch.where(Agents[:,14]==3)[0][:int(Demography[4])],14] = 1 # Confirmed Infections\n",
        "  # Assign Infection State: SIT=0, SIN=1, AIT=2, AIN=3, SUT=4, SUN=5, AUT=6, AUN=7\n",
        "  # Infected\n",
        "  # 80% of the infections are asymptomatic\n",
        "  # 20% of the infections are symptomatic\n",
        "  Agents[torch.where(Agents[:,14]==3)[0][:int(0.8*Demography[3])],15] = 3 # AIN\n",
        "  Agents[torch.where(Agents[:,14]==3)[0][int(0.8*Demography[3]):Demography[3]],15] = 1 # SIN\n",
        "  # Uninfected\n",
        "  # 85% of the uninfected are asymptomatic\n",
        "  # 15% of the uninfected are symptomatic\n",
        "  Agents[torch.where(Agents[:,14]==0)[0][:int(0.85*(Population-Demography[3]))],15] = 7 # AUN\n",
        "  Agents[torch.where(Agents[:,14]==0)[0][int(0.85*(Population-Demography[3])):Population-Demography[3]],15] = 5 # SUN\n",
        "  Agents[torch.where(Agents[:,14]==1)[0],15] = 2\n",
        "  # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "  # Placeholder Variables\n",
        "  # -------------------------------------\n",
        "\n",
        "  # Vectors \n",
        "  Vectors = torch.zeros((12,Population),device='cuda',dtype=torch.double)\n",
        "  # Distance Matrix(0), Index Matrix(1), Condition Matrix(2)\n",
        "  Matrixs = torch.zeros((5,Population,20),dtype=torch.double,device='cuda')\n",
        "  # Initial Position \n",
        "  CurrentPosition = torch.dstack((Agents[:,1],Agents[:,2]))\n",
        "  # Initial Velocity\n",
        "  Velocity = torch.dstack((Agents[:,3],Agents[:,4]))\n",
        "  # All agents are initially at home (12)\n",
        "  Agents[:,7] = 12\n",
        "  # Status of the task is complete and initially at home (12)\n",
        "  Agents[:,8] = Agents[:,7].detach().clone()\n",
        "  # Ordinate of goal position\n",
        "  Agents[:,9] = Agents[:,1].detach().clone()\n",
        "  # Abscissa of goal position\n",
        "  Agents[:,10] = Agents[:,2].detach().clone()\n",
        "  # Initial Goals are to remain at home\n",
        "  Destination = torch.dstack((Agents[:,1],Agents[:,2]))\n",
        "  # Days \n",
        "  Days = torch.tensor([0],device='cuda')       \n",
        "  # Distance\n",
        "  Distance = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  # Infection Status over Time\n",
        "  # Overall\n",
        "  Results = torch.tensor([torch.where(Agents[:,14]==0)[0].size()[0], #0:Uninfected\n",
        "                          torch.where(Agents[:,14]==1)[0].size()[0], #1:Infected and Tested\n",
        "                          torch.where(Agents[:,14]==2)[0].size()[0], #2:Unreported Recoveries\n",
        "                          torch.where(Agents[:,14]==3)[0].size()[0], #3:Infected and Not Tested\n",
        "                          torch.where(Agents[:,14]==4)[0].size()[0], #4:Unreported Deaths\n",
        "                          torch.where(Agents[:,14]==5)[0].size()[0], #5:Reported Recoveries\n",
        "                          torch.where(Agents[:,14]==6)[0].size()[0]],dtype=torch.long,device='cuda') #6:Reported Deaths  \n",
        "  # Children\n",
        "  ResultsC = torch.tensor([torch.where(Agents[Children,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "  # Adults  \n",
        "  ResultsA = torch.tensor([torch.where(Agents[Adults,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "  # Seniors\n",
        "  ResultsS = torch.tensor([torch.where(Agents[Seniors,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "\n",
        "  # Interactions: Weekday=0, Weekend=1, Contact_Matrix=2\n",
        "  Interactions = torch.zeros((3,6,6),dtype=torch.double,device='cuda')\n",
        "  # Weekday = 0, Weekend = 1\n",
        "  Time_avg = torch.zeros((2,6),dtype=torch.double,device='cuda')\n",
        "  Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "  # Number of contacts on a day to day basis: Daily_Contact=0, Contact_Limit=1\n",
        "  Contacts = torch.zeros((2,Population,36),dtype=torch.long,device='cuda')\n",
        "  StatusTime = Agents[:,8].detach().clone()\n",
        "  for t in range(TimeStepsTotal.long().item()):\n",
        "\n",
        "    Days = torch.floor(torch.div(t,TimeStepsDay).cuda())\n",
        "    # Time of the day\n",
        "    time_of_day = t%96 \n",
        "    # Day of the week\n",
        "    day_of_week = Days%7 \n",
        "\n",
        "    # ------------------------------------------------\n",
        "    # Store parameters from the previous run\n",
        "    # ------------------------------------------------\n",
        "\n",
        "    # Infection Status from previous iteration\n",
        "    Vectors[4] = Agents[:,14].detach().clone()\n",
        "    # Infection State from previous iteration\n",
        "    Vectors[5] = Agents[:,15].detach().clone()\n",
        "    # Rtime\n",
        "    Vectors[6] = Vectors[1].detach().clone()\n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # Find Limits for the day\n",
        "    # ---------------------------------------------\n",
        "\n",
        "    if time_of_day==0:\n",
        "      # Assign contact limits for the weekend\n",
        "      if torch.logical_or(day_of_week==5,day_of_week==6):\n",
        "        for i in range(1,36):\n",
        "          Contacts[1][:,i] = Limits_Weekend[2*i+1]\n",
        "      else:\n",
        "        # Assign contact limits for the weekday\n",
        "        for i in range(1,36):\n",
        "          Contacts[1][:,i] = Limits[2*i+1]\n",
        "\n",
        "    #Agents[:,15]: SIT=0, SIN=1, AIT=2, AIN=3, SUT=4, SUN=5, AUT=6, AUN=7\n",
        "    #Unconfirmed to Confirmed (30 times underreported)\n",
        "    Vectors[0] = torch.rand(Population,device='cuda').double()< 0.00001920627#0.000001057605 #0.0000450422\n",
        "    Vectors[7] = torch.logical_and(Agents[:,15]==1,Vectors[0]==True)\n",
        "    Agents[Vectors[7].bool(),15] = 0 # SIN --> SIT\n",
        "    Vectors[7] = torch.logical_and(Agents[:,15]==3,Vectors[0]==True)\n",
        "    Agents[Vectors[7].bool(),15] = 2 # AIN --> AIT\n",
        "    # Confirmed Cases\n",
        "    # Infected and Tested\n",
        "    Agents[:,14][torch.logical_or(Agents[:,15]==0,Agents[:,15]==2)] = 1\n",
        "    # Unconfirmed\n",
        "    Agents[:,14][torch.logical_or(Agents[:,15]==1,Agents[:,15]==3)] = 3  \n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # State Transition of Infected Agents\n",
        "    # ---------------------------------------------\n",
        "\n",
        "    # Find all agents who have been infected for more than a day\n",
        "    Vectors[8] = torch.logical_and(torch.logical_and(torch.logical_or(Vectors[4]==1,Vectors[4]==3),Agents[:,16]%TimeStepsDay==0),Agents[:,16]>0)\n",
        "    #Vectors[8] = torch.logical_or(Vectors[4]==1,Vectors[4]==3)\n",
        "    # SIT=0: Is the person in state 0 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[0][:,0],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIT=0: Is the person in state 0 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[1][:,0],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIT=0: Is the person in state 0 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[2][:,0],Population,replacement=True)][Vectors[7].bool()].double()  \n",
        "    # SIN=1: Is the person in state 1 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[0][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIN=1: Is the person in state 1 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[1][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIN=1: Is the person in state 1 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[2][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[0][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[1][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[2][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[0][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[1][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[2][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # Recovery\n",
        "    Agents[:,14][(Vectors[4]==1) & (Agents[:,15]==6)] = 5 # Reported recoveries\n",
        "    Agents[:,14][(Vectors[4]==3) & (Agents[:,15]==7)] = 2 # Unreported recoveries\n",
        "    # Deaths\n",
        "    Agents[:,14][(Vectors[4]==1) & (Agents[:,15]==9)] = 6 # Reported recoveries\n",
        "    Agents[:,14][(Vectors[4]==3) & (Agents[:,15]==9)] = 4 # Unreported recoveries  \n",
        "    # Time Since Infection\n",
        "    Vectors[8] = torch.logical_or(Agents[:,14]==1,Agents[:,14]==3)\n",
        "    Agents[:,16][Vectors[8].bool()] = Agents[:,16][Vectors[8].bool()]+1\n",
        "    Agents[:,17][Vectors[8].bool()] = torch.round(torch.div(Agents[:,16][Vectors[8].bool()],TimeStepsDay-1)) \n",
        "\n",
        "    # -----------------------------------------\n",
        "    # State Transition of Uninfected Agents\n",
        "    # -----------------------------------------  \n",
        "\n",
        "    # Find agents in the vicinity\n",
        "    CurrentPosition_numpy = np.float32(CurrentPosition[0].cpu().detach().numpy())\n",
        "    # Create a faiss array\n",
        "    index_flat = faiss.IndexFlatL2(CurrentPosition_numpy.shape[1])\n",
        "    gpu_index_flat = faiss.index_cpu_to_gpu(res, 0, index_flat)\n",
        "    gpu_index_flat.add(CurrentPosition_numpy)                  \n",
        "    m, n = gpu_index_flat.search(CurrentPosition_numpy, 20)\n",
        "    Matrixs[1] = torch.from_numpy(n).long()\n",
        "    #Matrixs[1] = torch.reshape(q[q!=torch.arange(0,Population,step=1)[:,None]],(Population,19))\n",
        "    # Distances \n",
        "    Matrixs[0] = torch.squeeze(torch.cdist(CurrentPosition[0][:,None],CurrentPosition[0][Matrixs[1].long()],p=2))\n",
        "    Matrixs[0][Matrixs[1]==torch.arange(0,Population,step=1).cuda()[:,None]] = float(\"Inf\")\n",
        "    # Location Matching and Contact Number Check\n",
        "    Matrixs[2],Matrixs[4],Contacts[0],Interactions[2],Time_Daily = generateMatrix(Agents[:,8],Contacts[0],Contacts[1],Population,H_Number,Matrixs[1].long(),Location_one, Location_two,Time_Daily,Interactions[2],Matrixs[0],Radii[1])\n",
        "\n",
        "    if torch.logical_or(day_of_week==5,day_of_week==6):\n",
        "      Time_avg[1] += torch.mean(Time_Daily.double(),0)\n",
        "      Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "      Interactions[1] += Interactions[2]/48#torch.reshape(torch.sum(Contacts[0],0),(6,6))\n",
        "      Interactions[2] = torch.zeros((6,6),dtype=torch.double,device='cuda')\n",
        "      Contacts[0] = torch.zeros((Population,36),dtype=torch.long,device='cuda')\n",
        "    else:\n",
        "    # Over weekdays\n",
        "      Time_avg[0] += torch.mean(Time_Daily.double(),0)\n",
        "      Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "      Interactions[0] += Interactions[2]/48#torch.reshape(torch.sum(Contacts[0],0),(6,6))\n",
        "      Interactions[2] = torch.zeros((6,6),dtype=torch.double,device='cuda')\n",
        "      Contacts[0] = torch.zeros((Population,36),dtype=torch.long,device='cuda')\n",
        "  \n",
        "    # Find agents with infections in the vicinity\n",
        "    Vectors[3] = torch.sum(torch.logical_and(torch.logical_and(Matrixs[2].bool()==True,Matrixs[0]<Radii[1]),torch.logical_or(Vectors[4][Matrixs[1].long()]==1,Vectors[4][Matrixs[1].long()]==3)),1)\n",
        "    # Find all uninfected agents with infections in their vicinity\n",
        "    Vectors[8] = torch.logical_and(Vectors[4]==0,Vectors[3].long()>0)\n",
        "    # SUT=4: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[0][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUT=4: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[1][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUT=4: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[2][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[0][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[1][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[2][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[0][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[1][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[2][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[0][:,7],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[1][:,7],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[2][:,7],Population,replacement=True)][Vectors[7].bool()].double() \n",
        "\n",
        "\n",
        "    # -----------------------------------------\n",
        "    # Task Allocation\n",
        "    # ----------------------------------------- \n",
        "\n",
        "    # Weekday\n",
        "    if day_of_week<5:\n",
        "      \n",
        "      # Children\n",
        "      # School going children at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==0,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going children: School (1), Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[0,time_of_day],Activities[1,time_of_day],1-Activities[0,time_of_day]-Activities[1,time_of_day]-Activities[2,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home kids\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==0, Agents[:,8]==12)\n",
        "      # Destination for children: Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[0,time_of_day],Activities[1,time_of_day],1-Activities[0,time_of_day]-Activities[1,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at school\n",
        "      Vectors[8] = Agents[:,8]==13\n",
        "      # Remain in School: Stay at school (13), Go home (0) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,13],device='cuda')[torch.multinomial(torch.tensor([Activities[3,time_of_day],1-Activities[3,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==15)\n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==17)\n",
        "      # Stay there (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Adults\n",
        "      # School going adults at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==1,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (5), Market (8), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[2,time_of_day]-Activities[8,time_of_day]-Activities[9,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==3, Agents[:,8]==12)\n",
        "      # Destination for office going adults: Office (2), Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([2,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[10,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]-Activities[10,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay at home adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==2,Agents[:,8]==12)  \n",
        "      # Destination for adults: Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==12))\n",
        "      # Adults who are nomads: Go elsewhere (10), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([10,12],device='cuda')[torch.multinomial(torch.tensor([Activities[12,time_of_day],1-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==16)) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,10,16],device='cuda')[torch.multinomial(torch.tensor([Activities[18,time_of_day],Activities[12,time_of_day],1-Activities[18,time_of_day]-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults at the office\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==14)  \n",
        "      # Stay there (14), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[11,time_of_day],1-Activities[11,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==15)  \n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==17)  \n",
        "      # Stay there (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Seniors\n",
        "      # School going seniors at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==2,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going seniors: School (1), Random (6), Market (9), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[2,time_of_day]-Activities[8,time_of_day]-Activities[9,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==6, Agents[:,8]==12)  \n",
        "      # Destination for office going seniors: Office (3), Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([3,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[10,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]-Activities[10,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==5,Agents[:,8]==12)  \n",
        "      # Destination for seniors: Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==12))  \n",
        "      # Seniors who are nomads: Go elsewhere (11), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([11,12],device='cuda')[torch.multinomial(torch.tensor([Activities[12,time_of_day],1-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==16))  \n",
        "      # Seniors who are nomads: Go home (0), Go elsewhere (11), Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,11,16],device='cuda')[torch.multinomial(torch.tensor([Activities[18,time_of_day],Activities[12,time_of_day],1-Activities[12,time_of_day]-Activities[18,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at office \n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[11,time_of_day],1-Activities[11,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()  \n",
        "      # Seniors running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==15)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "\n",
        "    else:\n",
        "\n",
        "      # Children\n",
        "      # School going children\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==0,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going children: School (1), Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[4,time_of_day],Activities[5,time_of_day],1-Activities[4,time_of_day]-Activities[5,time_of_day]-Activities[6,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home kids\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==0, Agents[:,8]==12)\n",
        "      # Destination for children: Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[4,time_of_day],Activities[5,time_of_day],1-Activities[4,time_of_day]-Activities[5,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at school\n",
        "      Vectors[8] = Agents[:,8]==13\n",
        "      # Remain in School: Stay at school (13), Go home (0) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,13],device='cuda')[torch.multinomial(torch.tensor([Activities[7,time_of_day],1-Activities[7,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==15)\n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==17)\n",
        "      # Stay there Market (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Adults\n",
        "      # School going adults at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==1,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (5), Market (8), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[6,time_of_day]-Activities[13,time_of_day]-Activities[14,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going adults \n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==3, Agents[:,8]==12)  \n",
        "      # Destination for office going adults: Office (2), Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([2,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[15,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]-Activities[15,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==2,Agents[:,8]==12)  \n",
        "      # Destination for adults: Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==12))\n",
        "      # Adults who are nomads: Go elsewhere (10), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([10,12],device='cuda')[torch.multinomial(torch.tensor([Activities[17,time_of_day],1-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==16))\n",
        "      # Adults who are nomads: Go home (0), Go elsehwere (10),Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,10,16],device='cuda')[torch.multinomial(torch.tensor([Activities[19,time_of_day],Activities[17,time_of_day],1-Activities[17,time_of_day]-Activities[19,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==15)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults returning from market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults returning from office\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[16,time_of_day],1-Activities[16,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Seniors\n",
        "      # School going seniors at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==2,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (6), Market (9), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[6,time_of_day]-Activities[13,time_of_day]-Activities[14,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==6, Agents[:,8]==12)  \n",
        "      # Destination for office going seniors: Office (3), Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([3,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[15,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]-Activities[15,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==5,Agents[:,8]==12)  \n",
        "      # Destination for seniors: Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==12))  \n",
        "      # Seniors who are nomads: Go elsewhere (11), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([11,12],device='cuda')[torch.multinomial(torch.tensor([Activities[17,time_of_day],1-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==16))  \n",
        "      # Seniors who are nomads: Go home(0), Go elsewhere (11), Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,11,16],device='cuda')[torch.multinomial(torch.tensor([Activities[19,time_of_day],Activities[17,time_of_day],1-Activities[19,time_of_day]-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at office \n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[16,time_of_day],1-Activities[16,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()  \n",
        "      # Seniors running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==15)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "    \n",
        "    # --------------------------------\n",
        "    # Assign Task-based Goal Positions\n",
        "    # --------------------------------\n",
        "\n",
        "    for i in range(len(School)):\n",
        "      # Goal of School Going Children\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,0]==i,torch.logical_and(Agents[:,8]==12,Agents[:,7]==1))# Stay at home (12) --> Go to School (1)\n",
        "      Agents[:,9][Vectors[8]==1] = School_Positions[Vectors[8]==1,2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = School_Positions[Vectors[8]==1,2*i+1]\n",
        "    for i in range(len(Office)):\n",
        "      # Goal of Office Going Adults and Seniors\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,0]==i,torch.logical_and(Agents[:,8]==12,torch.logical_or(Agents[:,7]==2,Agents[:,7]==3)))# Stay at home --> Office (Adults/Seniors)\n",
        "      Agents[:,9][Vectors[8]==1] = Office_Positions[Vectors[8]==1,2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = Office_Positions[Vectors[8]==1,2*i+1]\n",
        "    for i in range(len(Market)):\n",
        "      # Goal of Market Going Adults/Seniors/Children\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,1]==i,torch.logical_and(Agents[:,8]==12,torch.logical_or(torch.logical_or(Agents[:,7]==7,Agents[:,7]==8),Agents[:,7]==9))) # Home --> Market\n",
        "      temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "      Agents[:,9][Vectors[8]==1] = Market_Positions[torch.randperm(Population)[:int(temp1)],2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = Market_Positions[torch.randperm(Population)[:int(temp1)],2*i+1]\n",
        "    # Goal of Adults running mobile jobs\n",
        "    mobile = goal_assign(CurrentPosition[0][:,0],CurrentPosition[0][:,1],300,800,0.5*Layout[1],0.5*Layout[3]) #300,800\n",
        "    Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(torch.logical_or(Agents[:,8]==16,Agents[:,8]==12),Agents[:,7]==10))\n",
        "    temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of Seniors running mobile jobs\n",
        "    mobile = goal_assign(CurrentPosition[0][:,0],CurrentPosition[0][:,1],300,800,0.5*Layout[1],0.5*Layout[3]) #300,800\n",
        "    Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(torch.logical_or(Agents[:,8]==16,Agents[:,8]==12),Agents[:,7]==11))\n",
        "    temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of kids running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==4)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,500,0.5*Layout[1],0.5*Layout[3])#300,500\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of adults running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==5)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,800,0.5*Layout[1],0.5*Layout[3])#300,800\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of seniors running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==6)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,800,0.5*Layout[1],0.5*Layout[3])#300,800\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1] \n",
        "    # Goal of Agents Going Home\n",
        "    Vectors[8] = torch.logical_or(torch.logical_and(Agents[:,8]==12,Agents[:,7]==12),Agents[:,7]==0)\n",
        "    Agents[:,9][Vectors[8]==1] = Agents[Vectors[8]==1,1].clone().detach()\n",
        "    Agents[:,10][Vectors[8]==1] = Agents[Vectors[8]==1,2].clone().detach()\n",
        "    \n",
        "    # -----------------------------\n",
        "    # Move Towards a Goal\n",
        "    # -----------------------------\n",
        "\n",
        "    # New goal position corresponding to the assigned task\n",
        "    Destination = torch.dstack((Agents[:,9],Agents[:,10]))\n",
        "    # Distance between the current position and assigned goal\n",
        "    Distance = torch.linalg.norm(CurrentPosition[0]-Destination[0],dim=1)\n",
        "    # Goal following velocity vector\n",
        "    Velocity[0] += Destination[0]-CurrentPosition[0]\n",
        "\n",
        "    # ------------------------------\n",
        "    # Collision Avoidance \n",
        "    # ------------------------------\n",
        "\n",
        "    # Reset avoidance vector in every iteration\n",
        "    VRule = torch.zeros((Population,2),dtype=torch.double,device='cuda') \n",
        "    # Find agents inside critical radius \n",
        "    Matrixs[3] = torch.logical_and(torch.logical_and(Matrixs[0]<SD[:,None],Matrixs[0]!=0),Matrixs[4]==True)\n",
        "    # Collision Avoidance Vector\n",
        "    Difference = CurrentPosition[0][:,None]-CurrentPosition[0][Matrixs[1].long()]\n",
        "    Difference[~Matrixs[3].bool()] = 0\n",
        "    # Number of agents inside the critical radius\n",
        "    Vectors[9] = torch.sum(Matrixs[3],1)\n",
        "    # Normalizing the displacement vector\n",
        "    Difference[Matrixs[3].bool()]/=torch.square(Matrixs[0][Matrixs[3].bool()])[:,None]\n",
        "    VRule[Vectors[9]>0] = torch.div(torch.sum(Difference,1)[Vectors[9]>0],torch.sum(Matrixs[3],1)[Vectors[9]>0][:,None]).double()\n",
        "    # Total Collision avoidance vector component\n",
        "    VRule[Vectors[9]>0] -= Velocity[0][Vectors[9]>0].clone().detach()\n",
        "\n",
        "    # --------------------\n",
        "    # Update Velocity\n",
        "    # --------------------\n",
        "\n",
        "    # Update the velocity of agents\n",
        "    Vectors[8] = torch.logical_and(torch.sum(Matrixs[3],1)>0,Agents[:,8]>=12)\n",
        "    Velocity[0][Vectors[8]==1] += VRule[Vectors[8]==1].clone().detach()\n",
        "\n",
        "    # ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "    # Limit Speed  \n",
        "    # --------------------  \n",
        "\n",
        "    # Speed is MaximumVelocity when outside the outer circle\n",
        "    Velocity[0][Distance>Radii[2]] = torch.div(Velocity[0][Distance>Radii[2]],torch.norm(Velocity[0],dim=1)[Distance>Radii[2]][:,None])*VelocityParameters[1]   \n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[2],torch.linalg.norm(Velocity[0],dim=1)!=0)\n",
        "    # Adjustment of velocity according to an exponential function in the intermediate region\n",
        "    Velocity[0][Vectors[8]==1] = torch.div(Velocity[0][Vectors[8]==1],torch.norm(Velocity[0],dim=1)[Vectors[8]==1][:,None])*(1000*torch.exp(-0.009*(Radii[2]-Distance[Vectors[8]==1][:,None])))\n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[4],torch.linalg.norm(Velocity[0],dim=1)!=0)\n",
        "    # Adjustment of velocity in the innermost circle\n",
        "    Velocity[0][Vectors[8]==1] = torch.div(Velocity[0][Vectors[8]==1],torch.norm(Velocity[0],dim=1)[Vectors[8]==1][:,None])*VelocityParameters[4]\n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[4],torch.linalg.norm(Velocity[0],dim=1)==0)\n",
        "    # Minimum velocity in the innermost circle\n",
        "    Velocity[0][Vectors[8]==1] = torch.sign(Velocity[0][Vectors[8]==1])*torch.tensor([0.4,0.4],dtype=torch.double,device='cuda')\n",
        "\n",
        "    # --------------------\n",
        "    # Update Position\n",
        "    # --------------------\n",
        "\n",
        "    # Update the position of agents\n",
        "    CurrentPosition[0] += Velocity[0]\n",
        "\n",
        "    # ---------------\n",
        "    # Retention\n",
        "    # ---------------\n",
        "    \n",
        "    # Change position if an agent goes out of the box (Wrap-around)\n",
        "    CurrentPosition[0][CurrentPosition[0][:,0] > Layout[1],0] = torch.DoubleTensor(1).uniform_(Layout[1]-10,Layout[1]).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,0] < Layout[0],0] = torch.DoubleTensor(1).uniform_(Layout[0],Layout[0]+10).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,1] > Layout[3],1] = torch.DoubleTensor(1).uniform_(Layout[3]-10,Layout[3]).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,1] < Layout[2],1] = torch.DoubleTensor(1).uniform_(Layout[2],Layout[2]+10).cuda()\n",
        "\n",
        "    # -------------------\n",
        "    # Current Task Status\n",
        "    # -------------------\n",
        "\n",
        "    # Set status of those within 10 units of their destination as reached, rest in process (9)\n",
        "    # Find distance between current location and goal\n",
        "    Vectors[10] = torch.linalg.norm(CurrentPosition[0]-Destination[0],dim=1)\n",
        "    # If distance is greater than 10, agent is moving towards the goal\n",
        "    Agents[torch.logical_and(Vectors[10]>=Radii[4],Agents[:,7]<=11),8] = Agents[torch.logical_and(Vectors[10]>=Radii[4],Agents[:,7]<=11),7]\n",
        "    # Reached goal\n",
        "    # At home\n",
        "    Agents[torch.logical_or(Agents[:,7]==12,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==0)),8] = 12\n",
        "    # At school\n",
        "    Agents[torch.logical_or(Agents[:,7]==13,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==1)),8] = 13\n",
        "    # Adults in office\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==14,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==2))),8] = 14\n",
        "    # Seniors in office\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==14,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==3))),8] = 14\n",
        "    # Kids running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==0,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==4))),8] = 15\n",
        "    # Adults running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==5))),8] = 15\n",
        "    # Seniors running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==6))),8] = 15\n",
        "    # Nomadic adults\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==16,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==10))),8] = 16\n",
        "    # Nomadic seniors\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==16,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==11))),8] = 16\n",
        "    # Kids at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==0,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==7))),8] = 17\n",
        "    # Adults at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==8))),8] = 17\n",
        "    # Seniors at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==9))),8] = 17\n",
        "\n",
        "    # ---------------\n",
        "    # Compute Time \n",
        "    # --------------\n",
        "\n",
        "    # Time to infection\n",
        "    Vectors[1][Vectors[4]==0] += 1\n",
        "\n",
        "    # ----------------------------\n",
        "    # Compute Reproduction Number\n",
        "    # ----------------------------\n",
        "    # With confirmed infections in vicinity\n",
        "    Vectors[8] = torch.logical_and(Vectors[4]==0,Agents[:,14]==3)\n",
        "    Matrixs[0][torch.logical_and(torch.logical_and(Matrixs[2].bool()==True,Matrixs[0]<Radii[1]),torch.logical_or(Vectors[4][Matrixs[1].long()]==1,Vectors[4][Matrixs[1].long()]==3))==False] = float('inf')\n",
        "    if torch.where(Vectors[8]==True)[0].size()[0]>0:\n",
        "      # Find the nearest individual who infected me \n",
        "      temp = torch.min(Matrixs[0][Vectors[8]==True],1)[1]\n",
        "      for l in range(temp.size()[0]):\n",
        "        Vectors[2][(Matrixs[1].long())[temp[l]]]+=1\n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # Store Values of the Current Iteration\n",
        "    # ---------------------------------------------\n",
        "    StatusTime = torch.dstack((StatusTime,Agents[:,8]))\n",
        "    #PositionTime = torch.dstack((PositionTime,CurrentPosition))\n",
        "\n",
        "    # Store Infection status for the next iteration\n",
        "    Results = torch.dstack((Results,torch.tensor([torch.where(Agents[:,14]==0)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==1)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==2)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==3)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==4)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==5)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "\n",
        "    ResultsC = torch.dstack((ResultsC,torch.tensor([torch.where(Agents[Children,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "    \n",
        "    ResultsA = torch.dstack((ResultsA,torch.tensor([torch.where(Agents[Adults,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "    \n",
        "    ResultsS = torch.dstack((ResultsS,torch.tensor([torch.where(Agents[Seniors,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "  ResultsTMC[mc][0:7][:] = Results[0] # Disease\n",
        "  ResultsTMC[mc][7:14][:] = ResultsC[0] # Children\n",
        "  ResultsTMC[mc][14:21][:] = ResultsA[0] # Adults\n",
        "  ResultsTMC[mc][21:28][:] = ResultsS[0] # Seniors\n",
        "  # Overall: Time to Infection, Time to Recover, Time to Death\n",
        "  ResultsCMC[mc][0:3] = torch.tensor([[torch.mean(Vectors[1][Vectors[1]>0])/TimeStepsDay.item()],\n",
        "                                    [torch.mean(Agents[:,17][torch.logical_or(Vectors[4]==2,Vectors[4]==5)].double())],\n",
        "                                    [torch.mean(Agents[:,17][torch.logical_or(Vectors[4]==4,Vectors[4]==6)].double())]],device='cuda',dtype=torch.double)\n",
        "  # Overall: Effective Reproduction Number\n",
        "  ResultsCMC[mc][3] = torch.mean(Vectors[2][Vectors[4]!=0])\n",
        "  # Overall: Average time of infectiousness\n",
        "  ResultsCMC[mc][4] = torch.mean(Agents[Vectors[4]!=0,16]/96)\n",
        "\n",
        "  # Overall: Time to Infection, Time to Recover, Time to Death\n",
        "  ResultsCMC[mc][5:8] = torch.tensor([[torch.mean(Vectors[1][Vectors[1]>0])/TimeStepsDay.item()],\n",
        "                                    [torch.mean(Agents[:,17][Vectors[4]==5].double())],\n",
        "                                    [torch.mean(Agents[:,17][Vectors[4]==6].double())]],device='cuda',dtype=torch.double)\n",
        "  # Overall: Effective Reproduction Number\n",
        "  ResultsCMC[mc][8] = torch.mean(Vectors[2][torch.logical_and(Vectors[4]!=0,torch.logical_or(Vectors[4]==1,((Vectors[4]==5)|(Vectors[4]==6))))])\n",
        "  # Overall: Average time of infectiousness\n",
        "  ResultsCMC[mc][9] = torch.mean(Agents[torch.logical_and(Vectors[4]!=0,torch.logical_or(Vectors[4]==1,((Vectors[4]==5)|(Vectors[4]==6)))),16]/96)\n",
        "\n",
        "  # Contact Matrix\n",
        "  InteractionsMC[mc][0][0] = Interactions[0][0]/23\n",
        "  InteractionsMC[mc][0][1] = Interactions[0][1]/23\n",
        "  InteractionsMC[mc][0][2] = Interactions[0][2]/23\n",
        "  InteractionsMC[mc][0][3] = Interactions[0][3]/23\n",
        "  InteractionsMC[mc][0][4] = Interactions[0][4]/23\n",
        "  InteractionsMC[mc][0][5] = Interactions[0][5]/23\n",
        "  InteractionsMC[mc][1][0] = Interactions[1][0]/8\n",
        "  InteractionsMC[mc][1][1] = Interactions[1][1]/8\n",
        "  InteractionsMC[mc][1][2] = Interactions[1][2]/8\n",
        "  InteractionsMC[mc][1][3] = Interactions[1][3]/8\n",
        "  InteractionsMC[mc][1][4] = Interactions[1][4]/8\n",
        "  InteractionsMC[mc][1][5] = Interactions[1][5]/8\n",
        "\n",
        "  # InteractionsMC[mc][0][0] = Interactions[0][0]\n",
        "  # InteractionsMC[mc][0][1] = Interactions[0][1]\n",
        "  # InteractionsMC[mc][0][2] = Interactions[0][2]\n",
        "  # InteractionsMC[mc][0][3] = Interactions[0][3]\n",
        "  # InteractionsMC[mc][0][4] = Interactions[0][4]\n",
        "  # InteractionsMC[mc][0][5] = Interactions[0][5]\n",
        "  # InteractionsMC[mc][1][0] = Interactions[1][0]\n",
        "  # InteractionsMC[mc][1][1] = Interactions[1][1]\n",
        "  # InteractionsMC[mc][1][2] = Interactions[1][2]\n",
        "  # InteractionsMC[mc][1][3] = Interactions[1][3]\n",
        "  # InteractionsMC[mc][1][4] = Interactions[1][4]\n",
        "  # InteractionsMC[mc][1][5] = Interactions[1][5]\n",
        "\n",
        "# Overall: Confirmed and Cumulative=0, Total Infections (T/N)=1,\n",
        "# Confirmed Infections=2, Confirmed Recoveries=3, Confirmed Deaths=4,\n",
        "# Total Recoveries=5, Total Deaths=6, Cumulative (T/N) = 7\n",
        "FinalTimeSeries[0][:] = torch.mean((ResultsTMC[:][:,1]+ResultsTMC[:][:,6]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[1][:] = torch.mean((ResultsTMC[:][:,3]+ResultsTMC[:][:,1]).double(),0)\n",
        "FinalTimeSeries[2][:] = torch.mean(ResultsTMC[:][:,1].double(),0)\n",
        "FinalTimeSeries[3][:] = torch.mean(ResultsTMC[:][:,5].double(),0)\n",
        "FinalTimeSeries[4][:] = torch.mean(ResultsTMC[:][:,6].double(),0)\n",
        "FinalTimeSeries[5][:] = torch.mean((ResultsTMC[:][:,5]+ResultsTMC[:][:,2]).double(),0)\n",
        "FinalTimeSeries[6][:] = torch.mean((ResultsTMC[:][:,6]+ResultsTMC[:][:,4]).double(),0)\n",
        "FinalTimeSeries[7][:] = torch.mean((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]+ResultsTMC[:][:,5]+ResultsTMC[:][:,2]+ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "FinalTimeSeries[8][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,6]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[9][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]).double(),0)\n",
        "FinalTimeSeries[10][:] = torch.std(ResultsTMC[:][:,1].double(),0)\n",
        "FinalTimeSeries[11][:] = torch.std(ResultsTMC[:][:,5].double(),0)\n",
        "FinalTimeSeries[12][:] = torch.std(ResultsTMC[:][:,6].double(),0)\n",
        "FinalTimeSeries[13][:] = torch.std((ResultsTMC[:][:,2]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[14][:] = torch.std((ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "FinalTimeSeries[15][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]+ResultsTMC[:][:,2]+ResultsTMC[:][:,5]+ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "\n",
        "# Children: Confirmed and Cumulative=16, Total Infections (T/N)=17,\n",
        "# Confirmed Infections=18, Confirmed Recoveries=19, Confirmed Deaths=20,\n",
        "# Total Recoveries=21, Total Deaths=22, Cumulative (T/N) = 23\n",
        "FinalTimeSeries[16][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "FinalTimeSeries[17][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,10]).double(),0)\n",
        "FinalTimeSeries[18][:] = torch.mean(ResultsTMC[:][:,8].double(),0)\n",
        "FinalTimeSeries[19][:] = torch.mean(ResultsTMC[:][:,12].double(),0)\n",
        "FinalTimeSeries[20][:] = torch.mean(ResultsTMC[:][:,13].double(),0)\n",
        "FinalTimeSeries[21][:] = torch.mean((ResultsTMC[:][:,12]+ResultsTMC[:][:,9]).double(),0)\n",
        "FinalTimeSeries[22][:] = torch.mean((ResultsTMC[:][:,13]+ResultsTMC[:][:,11]).double(),0)\n",
        "FinalTimeSeries[23][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,9]+ResultsTMC[:][:,10]+ResultsTMC[:][:,11]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)                                                                                                                              \n",
        "FinalTimeSeries[24][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "FinalTimeSeries[25][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,10]).double(),0)\n",
        "FinalTimeSeries[26][:] = torch.std(ResultsTMC[:][:,8].double(),0)\n",
        "FinalTimeSeries[27][:] = torch.std(ResultsTMC[:][:,12].double(),0)\n",
        "FinalTimeSeries[28][:] = torch.std(ResultsTMC[:][:,13].double(),0)\n",
        "FinalTimeSeries[29][:] = torch.std((ResultsTMC[:][:,12]+ResultsTMC[:][:,9]).double(),0)\n",
        "FinalTimeSeries[30][:] = torch.std((ResultsTMC[:][:,13]+ResultsTMC[:][:,11]).double(),0)\n",
        "FinalTimeSeries[31][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,9]+ResultsTMC[:][:,10]+ResultsTMC[:][:,11]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "\n",
        "# Adults: Confirmed and Cumulative=32, Total Infections (T/N)=33,\n",
        "# Confirmed Infections=34, Confirmed Recoveries=35, Confirmed Deaths=36,\n",
        "# Total Recoveries=37, Total Deaths=38, Cumulative (T/N) = 39\n",
        "FinalTimeSeries[32][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[33][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,17]).double(),0)\n",
        "FinalTimeSeries[34][:] = torch.mean(ResultsTMC[:][:,15].double(),0)\n",
        "FinalTimeSeries[35][:] = torch.mean(ResultsTMC[:][:,19].double(),0)\n",
        "FinalTimeSeries[36][:] = torch.mean(ResultsTMC[:][:,20].double(),0)\n",
        "FinalTimeSeries[37][:] = torch.mean((ResultsTMC[:][:,19]+ResultsTMC[:][:,16]).double(),0)\n",
        "FinalTimeSeries[38][:] = torch.mean((ResultsTMC[:][:,20]+ResultsTMC[:][:,18]).double(),0)\n",
        "FinalTimeSeries[39][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,16]+ResultsTMC[:][:,17]+ResultsTMC[:][:,18]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[40][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[41][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,17]).double(),0)\n",
        "FinalTimeSeries[42][:] = torch.std(ResultsTMC[:][:,15].double(),0)\n",
        "FinalTimeSeries[43][:] = torch.std(ResultsTMC[:][:,19].double(),0)\n",
        "FinalTimeSeries[44][:] = torch.std(ResultsTMC[:][:,20].double(),0)\n",
        "FinalTimeSeries[45][:] = torch.std((ResultsTMC[:][:,19]+ResultsTMC[:][:,16]).double(),0)\n",
        "FinalTimeSeries[46][:] = torch.std((ResultsTMC[:][:,20]+ResultsTMC[:][:,18]).double(),0)\n",
        "FinalTimeSeries[47][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,16]+ResultsTMC[:][:,17]+ResultsTMC[:][:,18]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "\n",
        "# Seniors: Confirmed and Cumulative=48, Total Infections (T/N)=49,\n",
        "# Confirmed Infections=50, Confirmed Recoveries=51, Confirmed Deaths=52,\n",
        "# Total Recoveries=53, Total Deaths=54, Cumulative (T/N) = 55\n",
        "FinalTimeSeries[48][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[49][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,24]).double(),0)\n",
        "FinalTimeSeries[50][:] = torch.mean(ResultsTMC[:][:,22].double(),0)\n",
        "FinalTimeSeries[51][:] = torch.mean(ResultsTMC[:][:,26].double(),0)\n",
        "FinalTimeSeries[52][:] = torch.mean(ResultsTMC[:][:,27].double(),0)\n",
        "FinalTimeSeries[53][:] = torch.mean((ResultsTMC[:][:,23]+ResultsTMC[:][:,26]).double(),0)\n",
        "FinalTimeSeries[54][:] = torch.mean((ResultsTMC[:][:,25]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[55][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,23]+ResultsTMC[:][:,24]+ResultsTMC[:][:,25]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[56][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[57][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,24]).double(),0)\n",
        "FinalTimeSeries[58][:] = torch.std(ResultsTMC[:][:,22].double(),0)\n",
        "FinalTimeSeries[59][:] = torch.std(ResultsTMC[:][:,26].double(),0)\n",
        "FinalTimeSeries[60][:] = torch.std(ResultsTMC[:][:,27].double(),0)\n",
        "FinalTimeSeries[61][:] = torch.std((ResultsTMC[:][:,23]+ResultsTMC[:][:,26]).double(),0)\n",
        "FinalTimeSeries[62][:] = torch.std((ResultsTMC[:][:,25]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[63][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,23]+ResultsTMC[:][:,24]+ResultsTMC[:][:,25]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "\n",
        "# Overall: Time to Infection=0, Time to Recover=1, Time to Death=2, Rep Number=3, Time of Infectiousness=4\n",
        "# Confirmed: Time to Infection=5, Time to Recover=6, Time to Death=7, Rep Number=8, Time of Infectiousness=9\n",
        "FinalConstants[0] = torch.mean(ResultsCMC[:][:,0][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,0]),ResultsCMC[:][:,0]==0)==False)].double(),0)\n",
        "FinalConstants[1] = torch.mean(ResultsCMC[:][:,1][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,1]),ResultsCMC[:][:,1]==0)==False)].double(),0)\n",
        "FinalConstants[2] = torch.mean(ResultsCMC[:][:,2][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,2]),ResultsCMC[:][:,2]==0)==False)].double(),0)\n",
        "FinalConstants[3] = torch.std(ResultsCMC[:][:,0][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,0]),ResultsCMC[:][:,0]==0)==False)].double(),0)\n",
        "FinalConstants[4] = torch.std(ResultsCMC[:][:,1][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,1]),ResultsCMC[:][:,1]==0)==False)].double(),0)\n",
        "FinalConstants[5] = torch.std(ResultsCMC[:][:,2][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,2]),ResultsCMC[:][:,2]==0)==False)].double(),0) \n",
        "FinalConstants[6] = torch.mean(ResultsCMC[:][:,3].double())\n",
        "FinalConstants[7] = torch.std(ResultsCMC[:][:,3].double())\n",
        "FinalConstants[8] = torch.mean(ResultsCMC[:][:,4].double())\n",
        "FinalConstants[9] = torch.std(ResultsCMC[:][:,4].double())\n",
        "FinalConstants[10] = torch.mean(ResultsCMC[:][:,5][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,5]),ResultsCMC[:][:,5]==0)==False)].double(),0)\n",
        "FinalConstants[11] = torch.mean(ResultsCMC[:][:,6][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,6]),ResultsCMC[:][:,6]==0)==False)].double(),0)\n",
        "FinalConstants[12] = torch.mean(ResultsCMC[:][:,7][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,7]),ResultsCMC[:][:,7]==0)==False)].double(),0)\n",
        "FinalConstants[13] = torch.std(ResultsCMC[:][:,5][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,5]),ResultsCMC[:][:,5]==0)==False)].double(),0)\n",
        "FinalConstants[14] = torch.std(ResultsCMC[:][:,6][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,6]),ResultsCMC[:][:,6]==0)==False)].double(),0)\n",
        "FinalConstants[15] = torch.std(ResultsCMC[:][:,7][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,7]),ResultsCMC[:][:,7]==0)==False)].double(),0) \n",
        "FinalConstants[16] = torch.mean(ResultsCMC[:][:,8].double())\n",
        "FinalConstants[17] = torch.std(ResultsCMC[:][:,8].double())\n",
        "FinalConstants[18] = torch.mean(ResultsCMC[:][:,9].double())\n",
        "FinalConstants[19] = torch.std(ResultsCMC[:][:,9].double())\n",
        "\n",
        "InteractionsFinal[0] = torch.mean(InteractionsMC[:][:,0],0)\n",
        "InteractionsFinal[1] = torch.mean(InteractionsMC[:][:,1],0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: faiss-gpu in /usr/local/lib/python3.7/dist-packages (1.7.1.post3)\n",
            "Requirement already satisfied: celluloid in /usr/local/lib/python3.7/dist-packages (0.2.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from celluloid) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (1.19.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (3.0.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->celluloid) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_o-86_QrE0J"
      },
      "source": [
        "## Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0Q1Gw48r7F1"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from IPython.display import HTML\n",
        "import seaborn as sns\n",
        "from matplotlib.ticker import AutoMinorLocator, MultipleLocator\n",
        "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
        "from matplotlib import collections as matcoll\n",
        "from matplotlib import cm\n",
        "import numpy as np\n",
        "from scipy.stats import norm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "U-ycsFRRr9Tb",
        "outputId": "91641a01-71ea-4d5c-ec57-dd2e862fe3d3"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "s = 96\n",
        "\n",
        "t=96*31\n",
        "Population = 108720\n",
        "x = (np.arange(0,t,step=s)/96)+1\n",
        "\n",
        "cinf_mean = ((FinalTimeSeries[0]/Population)*100).cpu().detach().numpy()\n",
        "cinf_std = ((FinalTimeSeries[8]/Population)*100).cpu().detach().numpy()\n",
        "\n",
        "ainf_mean = ((FinalTimeSeries[2]/Population)*100).cpu().detach().numpy()\n",
        "ainf_std = ((FinalTimeSeries[10]/Population)*100).cpu().detach().numpy()\n",
        "\n",
        "recovered_mean = ((FinalTimeSeries[3]/Population)*100).cpu().detach().numpy()\n",
        "recovered_std = ((FinalTimeSeries[11]/Population)*100).cpu().detach().numpy()\n",
        "\n",
        "deceased_mean = ((FinalTimeSeries[4]/Population)*100).cpu().detach().numpy()\n",
        "deceased_std = ((FinalTimeSeries[12]/Population)*100).cpu().detach().numpy() \n",
        "\n",
        "rinf_mean = recovered_mean\n",
        "rinf_std = recovered_std\n",
        "\n",
        "dinf_mean = deceased_mean\n",
        "dinf_std = deceased_std\n",
        "data = [0.004,0.005,0.005,0.005,0.005,0.005,0.005,0.006,0.006,0.006,0.007,0.007,\n",
        "        0.007,0.008,0.008,0.009,0.010,0.010,0.011,0.012,0.015,0.016,0.017,0.019,\n",
        "        0.021,0.022,0.029,0.038,0.047,0.052,0.061]\n",
        "\n",
        "plt.plot(x,cinf_mean[np.arange(0,t,step=s)],'x-',color='blue',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(cinf_mean[np.arange(0,t,step=s)]-cinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(cinf_mean[np.arange(0,t,step=s)]+cinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='blue')\n",
        "\n",
        "plt.plot(x,ainf_mean[np.arange(0,t,step=s)],'x-',color='r',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(ainf_mean[np.arange(0,t,step=s)]-ainf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(ainf_mean[np.arange(0,t,step=s)]+ainf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='r')\n",
        "\n",
        "plt.plot(x,rinf_mean[np.arange(0,t,step=s)],'x-',color='g',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(rinf_mean[np.arange(0,t,step=s)]-rinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(rinf_mean[np.arange(0,t,step=s)]+rinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='g')\n",
        "\n",
        "plt.plot(x,dinf_mean[np.arange(0,t,step=s)],'x-',color='dimgray',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(dinf_mean[np.arange(0,t,step=s)]-dinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(dinf_mean[np.arange(0,t,step=s)]+dinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='dimgray')\n",
        "\n",
        "plt.plot(np.arange(1,32,step=1),data,'o-',color='black',linewidth=2)\n",
        "\n",
        "plt.rcParams['figure.facecolor'] = 'ivory'\n",
        "\n",
        "plt.rcParams[\"font.weight\"] = \"bold\"\n",
        "\n",
        "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
        "\n",
        "ax.grid()\n",
        "\n",
        "ax.set_axisbelow(True)\n",
        "ax.xaxis.set_major_locator(plt.MaxNLocator(16))\n",
        "ax.tick_params(which='major', length=7, color='black')\n",
        "ax.tick_params(which='minor', length=4, color='black' )\n",
        "ax.xaxis.set_minor_locator(plt.MaxNLocator())\n",
        "\n",
        "ax.tick_params(which='both', # Options for both major and minor ticks\n",
        "               top=False, # turn off top ticks\n",
        "               left=False, # turn off left ticks\n",
        "               right=False,  # turn off right ticks\n",
        "               bottom=True) # turn off bottom ticks\n",
        "ax.set_xticks(np.arange(1,32,step=2))\n",
        "ax.set_xticklabels([\"1\",\"3\",\"5\",\"7\",\"9\",\"11\",\"13\",\"15\",\"17\",\"19\",\"21\",\"23\",\"25\",\n",
        "                    \"28\",\"29\",\"31\"])\n",
        "\n",
        "plt.xticks(fontsize=16,fontweight='bold')\n",
        "plt.yticks(np.arange(0,0.11,step=0.01),fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.xlabel('Time (Days)',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.ylabel('Fraction of the total population (%)',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.legend(['Total Infections','Active Infections','Recoveries','Mortalities','Data'],\n",
        "           fontsize=16,loc=\"upper left\",fancybox=True, \n",
        "           framealpha=0.0, shadow=False, borderpad=1)\n",
        "\n",
        "plt.title('Disease evolution in Bengaluru',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.savefig('FB_1.pdf',format='pdf', bbox_inches='tight',Transparent=True)\n",
        "\n",
        "#files.download('FB_1.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "PZi5NkZqsXpi",
        "outputId": "3437e083-9de2-4e4e-a7af-bcb12edb3afd"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "data1 = InteractionsFinal[0].cpu().detach().numpy()\n",
        "\n",
        "data1 = np.ma.masked_where(data1 == 0, data1)\n",
        "\n",
        "data2 = InteractionsFinal[0].cpu().detach().numpy()\n",
        "\n",
        "data1[0,0] = 4.0\n",
        "\n",
        "data2[0,0] = 4.0\n",
        "\n",
        "vmin = np.min(np.array([data1,data2]))\n",
        "\n",
        "vmax = np.max(np.array([data1,data2]))\n",
        "\n",
        "alpha = ['$Q_{1}$','$Q_{2}$','$Q_{3}$','$Q_{4}$','$Q_{5}$','$Q_{6}$']\n",
        "\n",
        "cmap = ListedColormap([\"lemonchiffon\",\"goldenrod\",\"cyan\",\"orange\",\"gainsboro\", \"black\",\"yellow\"])\n",
        "\n",
        "cax = ax.matshow(data1,interpolation='nearest',cmap=cmap, rasterized=True, vmin=vmin, vmax=vmax)\n",
        "\n",
        "cbar=fig.colorbar(cax,extend='min', label='Average Contacts')\n",
        "\n",
        "plt.gca().set_xticks([x - 0.5 for x in plt.gca().get_xticks()][1:], minor='true')\n",
        "\n",
        "plt.gca().set_yticks([y - 0.5 for y in plt.gca().get_yticks()][1:], minor='true')\n",
        "\n",
        "plt.grid(which='minor')\n",
        "\n",
        "cmap.set_bad(color='white')\n",
        "\n",
        "ax.set_xticklabels(['']+alpha,y=-0.15,fontsize=12)\n",
        "\n",
        "ax.set_yticklabels(['']+alpha,fontsize=12)\n",
        "\n",
        "cbar.ax.tick_params(labelsize=12)\n",
        "\n",
        "plt.title('Weekday daily-contacts matrix',fontweight='bold',fontsize=14)\n",
        "\n",
        "plt.savefig('IFB_1.pdf',format='pdf', bbox_inches='tight',Transparent=True)\n",
        "\n",
        "#files.download('IFB_1.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJXkBRxPRr0F"
      },
      "source": [
        "!pip install hickle\n",
        "import hickle as hk\n",
        "from google.colab import files\n",
        "import numpy as np\n",
        "FinalTimeSeries = hk.load('TS_FB_1.hkl')\n",
        "c = hk.load('C_FB_1.hkl')\n",
        "InteractionsFinal = hk.load('I_FB_1.hkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGHFU8cOrFvJ"
      },
      "source": [
        "## Save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "VXGORuTyq9-U",
        "outputId": "506e6192-18e0-4a0e-b563-830743370b2e"
      },
      "source": [
        "!pip install hickle\n",
        "import hickle as hk\n",
        "from google.colab import files\n",
        "hk.dump(FinalTimeSeries.cpu().detach().numpy(),'TS_FB_1.hkl', mode='w')\n",
        "hk.dump(FinalConstants.cpu().detach().numpy(),'C_FB_1.hkl', mode='w')\n",
        "hk.dump(InteractionsFinal.cpu().detach().numpy(),'I_FB_1.hkl', mode='w')\n",
        "files.download('TS_FB_1.hkl')\n",
        "files.download('C_FB_1.hkl')\n",
        "files.download('I_FB_1.hkl')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: hickle in /usr/local/lib/python3.7/dist-packages (4.0.4)\n",
            "Requirement already satisfied: dill>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (0.3.4)\n",
            "Requirement already satisfied: six>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (1.15.0)\n",
            "Requirement already satisfied: h5py<3.0.0,>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (2.10.0)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.7/dist-packages (from hickle) (1.19.5)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_3bab9a1f-fd07-4b4f-9a22-2f3dd2501509\", \"TS_FB_1.hkl\", 780164)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_ea833d34-5487-4343-acc5-624879a85285\", \"C_FB_1.hkl\", 6224)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_2cfb2ec8-be90-4843-808a-56f077bbcfe7\", \"I_FB_1.hkl\", 6432)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oj60_SLrAX3"
      },
      "source": [
        "# Unlock Phase 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jw6RzsuPrBR1",
        "outputId": "ca004ca6-2a5d-4261-daeb-bbd83870d62b"
      },
      "source": [
        "%reset -f\n",
        "!pip install faiss-gpu\n",
        "!pip install celluloid\n",
        "import faiss\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import time\n",
        "import torch\n",
        "from google.colab import files\n",
        "import random\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "device = 'cuda'\n",
        "res = faiss.StandardGpuResources()\n",
        "select = 0\n",
        "MonteCarlo = 2\n",
        "\n",
        "# Total Population\n",
        "Population = 108720\n",
        "# Number of Children (0), Adults (1), Seniors (2) and Infections (3), Confirmed Infections (4)\n",
        "Demography = torch.tensor([round(0.2278*Population),round(0.7239*Population),0,round(0.001*Population),5],dtype=torch.long,device='cuda')\n",
        "Demography[2] = Population-torch.sum(Demography[:2]) \n",
        "Demography[3] = 50\n",
        "# Layout Size: XMinimum (0), XMaximum (1), YMinimum (2), YMaximum (3), Grid Resolution (4)\n",
        "Layout = torch.tensor([0,5000,0,2000,1000],dtype=torch.double,device='cuda')\n",
        "# Minimum (0), Maximum (1), Inner (2), Innermost (3) Allowed Speeds\n",
        "VelocityParameters = torch.tensor([-500,500,200,10,1],dtype=torch.long,device='cuda')\n",
        "# Collision Radius (0), Infection Radius (1), OuterCircle (2), InnerCircle (3), Reached Goal (4)\n",
        "Radii = torch.tensor([5,2,570,120,5],dtype=torch.long,device='cuda') # Infection Radius(20) > Collision Radius(5/50) --> no social distancing\n",
        "# Total time (days), Resolution(minutes)\n",
        "Time = torch.tensor([31,15],dtype=torch.long, device='cuda') \n",
        "# TimeSteps in a day (1 step = 15mins) and total\n",
        "TimeStepsDay = torch.div(24*60,Time[1].item())\n",
        "# Total time steps in 31 days\n",
        "TimeStepsTotal = TimeStepsDay*Time[0].long()\n",
        "\n",
        "# Household positions of each agent\n",
        "Residence = torch.tensor(np.loadtxt('blore_agent_location.dat'),dtype=torch.double,device='cuda')\n",
        "# Age of the individual\n",
        "Age = torch.tensor(np.loadtxt('blore_Age.dat'),dtype=torch.long,device='cuda')\n",
        "# Household size\n",
        "H_Size = torch.tensor(np.loadtxt('blore_Age_House.dat'),dtype=torch.long,device='cuda')\n",
        "# 1-member households\n",
        "H_Size[H_Size==0] = 1\n",
        "# ID of the households of each agent\n",
        "H_Number = torch.tensor(np.loadtxt('blore_House_number.dat'),dtype=torch.long,device='cuda')\n",
        "# Location of each household\n",
        "H_location = torch.tensor(np.loadtxt('blore_household_location.dat'),dtype=torch.long,device='cuda')\n",
        "\n",
        "# Re-assign x and y initial positions\n",
        "# Assign positions within unit radius\n",
        "adjustment = 0.5\n",
        "Residence = H_location[H_Number] + torch.DoubleTensor(Population,2).uniform_(-adjustment,adjustment).cuda()\n",
        "# Check if the newly assigned x co-ordinate is within the boundary\n",
        "Residence[Residence[:,0]>Layout[1],0] = (torch.DoubleTensor(Population).uniform_(Layout[1]-adjustment,Layout[1]).cuda())[Residence[:,0]>Layout[1]]\n",
        "Residence[Residence[:,0]<Layout[0],0] = (torch.DoubleTensor(Population).uniform_(Layout[0],Layout[0]+adjustment).cuda())[Residence[:,0]<Layout[0]]\n",
        "# Check if the newly assigned y co-ordinate is within the boundary\n",
        "Residence[Residence[:,1]>Layout[3],1] = (torch.DoubleTensor(Population).uniform_(Layout[3]-adjustment,Layout[3]).cuda())[Residence[:,1]>Layout[3]]\n",
        "Residence[Residence[:,1]<Layout[2],1] = (torch.DoubleTensor(Population).uniform_(Layout[2],Layout[2]+adjustment).cuda())[Residence[:,1]<Layout[2]]\n",
        "\n",
        "Activities = np.zeros((20,96),dtype=np.float32)\n",
        "# Children\n",
        "# Errands \n",
        "Activities[0,24:36]=0.005*norm.pdf(np.arange(6,9,step=0.25), 7.5, 1) # 6-9\n",
        "Activities[0,36:56]=0.01*norm.pdf(np.arange(9,14,step=0.25), 11.5, 1) # 9-14\n",
        "Activities[0,56:72]=0.05*norm.pdf(np.arange(14,18,step=0.25), 17, 1) # 14-21\n",
        "# Adults\n",
        "# Errands \n",
        "Activities[8,24:36]=0.0025*norm.pdf(np.arange(6,9,step=0.25), 7.5, 1) # 6-9\n",
        "Activities[8,36:56]=0.005*norm.pdf(np.arange(9,14,step=0.25), 11.5, 1) # 9-14\n",
        "Activities[8,56:72]=0.0075*norm.pdf(np.arange(14,18,step=0.25), 17, 1) # 14-21\n",
        "# Nomadic\n",
        "Activities[12,24:48] = 0.1*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[12,48:72] = 0.075*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Nomads go home (18) # Weekday\n",
        "Activities[18,0:24] = np.ones(24)\n",
        "Activities[18,84:96] = np.ones(12)\n",
        "# Nomads go home (19) # Weekend\n",
        "Activities[19,0:96] = np.ones(96)\n",
        "# Children\n",
        "# Market\n",
        "Activities[1,24:48]=0.005*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[1,48:72]=0.01*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Adults\n",
        "# Market\n",
        "Activities[9,24:48]=0.05*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[9,48:72]=0.1*norm.pdf(np.arange(12,18,step=0.25), 17, 1) # 12-21\n",
        "# Office \n",
        "Activities[10,24:48]= 2*norm.pdf(np.arange(6,12,step=0.25), 9, 1) # 6-12\n",
        "Activities[11,48:72] = 2*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Weekday Activities\n",
        "Limits = torch.tensor([0,0,0,0,0,0,1,100,1,100,0,0,\n",
        "                       0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                       0,0,0,0,5,100,4,100,3,100,0,0,\n",
        "                       5,100,0,0,4,100,1,100,5,100,0,0,\n",
        "                       10,100,0,0,2,100,1,100,1,100,2,100,\n",
        "                       0,0,0,0,0,0,0,0,1,100,1,100],dtype=torch.long,device='cuda')\n",
        "# Weekend Activities                    \n",
        "Limits_Weekend = torch.tensor([0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0],dtype=torch.long,device='cuda')\n",
        "Location_one = torch.tensor([12,13,14,15,16,17],dtype=torch.long,device='cuda') # Location of i\n",
        "Location_two = torch.tensor([[12,0,0,15,16,0],\n",
        "                             [0,0,0,0,0,0],\n",
        "                             [0,0,14,15,16,0],\n",
        "                             [12,0,14,15,16,0],\n",
        "                             [12,0,14,15,16,17],\n",
        "                             [0,0,0,0,16,17]],dtype=torch.long,device='cuda') # Corresponding locations of j \n",
        "\n",
        "def goal_assign(x,y,minbias,maxbias,xhalfmark,yhalfmark):\n",
        "  '''\n",
        "  Generate Random Goals\n",
        "  '''\n",
        "  # ordinate\n",
        "  l = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  # abscissa\n",
        "  m = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  temp = x -  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  l[x>xhalfmark] = temp[x>xhalfmark] \n",
        "  temp = y -  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  m[y>yhalfmark] = temp[y>yhalfmark] \n",
        "  temp = x +  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  l[x<xhalfmark] = temp[x<xhalfmark] \n",
        "  temp = y +  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  m[y<yhalfmark] = temp[y<yhalfmark] \n",
        "  return l,m   \n",
        "\n",
        "def generateMatrix(agents,matrixone,limits,Population, H_Number, index,con_one, con_two,Time_Daily,matrixthree,distance,radius):\n",
        "  '''\n",
        "  Generate a conditional contact matrix\n",
        "  '''\n",
        "  # Store end result\n",
        "  matrixtwo = torch.zeros((2,Population,20),dtype=torch.double,device='cuda')\n",
        "  count=0\n",
        "  for i in range(6):\n",
        "    vec_con1 = torch.zeros((Population),device='cuda',dtype=torch.bool)\n",
        "    vec_con1 += (agents==con_one[i])\n",
        "    Time_Daily[vec_con1==True,i]+=1\n",
        "    for j in range(6):\n",
        "      temp = torch.zeros((Population,20),device='cuda',dtype=torch.int32)\n",
        "      vec_con2 = torch.zeros((Population,20),device='cuda',dtype=torch.bool)      \n",
        "      if (con_two[i][j]!=0):\n",
        "        if (count==0):          \n",
        "          vec_con2 = torch.logical_and(H_Number[index]==H_Number[:,None],(agents[index]==con_two[i][j]))\n",
        "        else:\n",
        "          vec_con2 = (agents[index]==con_two[i][j])\n",
        "        temp = vec_con1[:,None]*vec_con2\n",
        "        matrixone[:,count] += torch.sum(temp,1)\n",
        "        #matrixone[:,count] += torch.sum(torch.logical_and(temp,distance<radius),1)\n",
        "        if (count!=0):\n",
        "          matrixone[matrixone[:,count]>limits[:,count],count] = limits[matrixone[:,count]>limits[:,count],count]\n",
        "        #tempone = temp*(1-(torch.cumsum(torch.logical_and(temp,distance<radius),axis=1)>matrixone[:,count][:,None]).long())\n",
        "        tempone = temp*(1-(torch.cumsum(temp,axis=1)>matrixone[:,count][:,None]).long())\n",
        "        matrixtwo[0] += tempone\n",
        "        matrixtwo[1] = temp\n",
        "        if (torch.where(vec_con1==True)[0].size(0)!=0):\n",
        "          matrixthree[i,j] += torch.where(tempone==1)[0].size(0)/torch.where(vec_con1==True)[0].size(0)\n",
        "      count+=1\n",
        "  return matrixtwo[0],matrixtwo[1], matrixone, matrixthree, Time_Daily\n",
        "\n",
        "def nearest_destination(Population,centroids,Residence): \n",
        "  '''\n",
        "  Find the nearest destination: Office/School/Market\n",
        "  '''\n",
        "  distance = torch.zeros((Population,len(centroids)),device='cuda',dtype=torch.double)\n",
        "  for i in range(len(centroids)):\n",
        "    # Check the distance from the centroid of the zone to each household location\n",
        "    distance[:,i] = torch.linalg.norm(Residence-Centroids[i],axis=1)\n",
        "  return torch.argmin(distance,axis=1)\n",
        "\n",
        "# Schools\n",
        "School=torch.tensor([[1000,1500,500,1000],\n",
        "                     [2000,2500,300,500],\n",
        "                     [3500,4000,800,1000],\n",
        "                     [3500,4000,1500,1700],\n",
        "                     [600,900,1600,1800]],dtype=torch.long,device='cuda')\n",
        "School_Positions = torch.zeros((Population,10),dtype=torch.double,device='cuda')\n",
        "School_Positions[:,0] = torch.DoubleTensor(Population).uniform_(School[0,0],School[0,1])\n",
        "School_Positions[:,2] = torch.DoubleTensor(Population).uniform_(School[1,0],School[1,1])\n",
        "School_Positions[:,4] = torch.DoubleTensor(Population).uniform_(School[2,0],School[2,1])\n",
        "School_Positions[:,6] = torch.DoubleTensor(Population).uniform_(School[3,0],School[3,1])\n",
        "School_Positions[:,8] = torch.DoubleTensor(Population).uniform_(School[4,0],School[4,1])\n",
        "School_Positions[:,1] = torch.DoubleTensor(Population).uniform_(School[0,2],School[0,3])\n",
        "School_Positions[:,3] = torch.DoubleTensor(Population).uniform_(School[1,2],School[1,3])\n",
        "School_Positions[:,5] = torch.DoubleTensor(Population).uniform_(School[2,2],School[2,3])\n",
        "School_Positions[:,7] = torch.DoubleTensor(Population).uniform_(School[3,2],School[3,3])\n",
        "School_Positions[:,9] = torch.DoubleTensor(Population).uniform_(School[4,2],School[4,3])\n",
        "\n",
        "# Offices\n",
        "Office = torch.tensor([[1000,1500,1000,1500],\n",
        "                      [2000,2500,0,300],\n",
        "                      [3500,4000,1000,1300],\n",
        "                      [3500,4000,500,800],\n",
        "                      [1500,2000,500,1000],\n",
        "                      [3500,4000,1700,2000],\n",
        "                      [2500,3000,500,1000]],dtype=torch.long,device='cuda')\n",
        "Office_Positions = torch.zeros((Population,14),dtype=torch.double,device='cuda')\n",
        "Office_Positions[:,0] = torch.DoubleTensor(Population).uniform_(Office[0,0],Office[0,1])\n",
        "Office_Positions[:,2] = torch.DoubleTensor(Population).uniform_(Office[1,0],Office[1,1])\n",
        "Office_Positions[:,4] = torch.DoubleTensor(Population).uniform_(Office[2,0],Office[2,1])\n",
        "Office_Positions[:,6] = torch.DoubleTensor(Population).uniform_(Office[3,0],Office[3,1])\n",
        "Office_Positions[:,8] = torch.DoubleTensor(Population).uniform_(Office[4,0],Office[4,1])\n",
        "Office_Positions[:,10] = torch.DoubleTensor(Population).uniform_(Office[5,0],Office[5,1])\n",
        "Office_Positions[:,12] = torch.DoubleTensor(Population).uniform_(Office[6,0],Office[6,1])\n",
        "Office_Positions[:,1] = torch.DoubleTensor(Population).uniform_(Office[0,2],Office[0,3])\n",
        "Office_Positions[:,3] = torch.DoubleTensor(Population).uniform_(Office[1,2],Office[1,3])\n",
        "Office_Positions[:,5] = torch.DoubleTensor(Population).uniform_(Office[2,2],Office[2,3])\n",
        "Office_Positions[:,7] = torch.DoubleTensor(Population).uniform_(Office[3,2],Office[3,3])\n",
        "Office_Positions[:,9] = torch.DoubleTensor(Population).uniform_(Office[4,2],Office[4,3])\n",
        "Office_Positions[:,11] = torch.DoubleTensor(Population).uniform_(Office[5,2],Office[5,3])\n",
        "Office_Positions[:,13] = torch.DoubleTensor(Population).uniform_(Office[6,2],Office[6,3])\n",
        "\n",
        "# Markets\n",
        "Market = torch.tensor([[2500,3500,1000,1500],\n",
        "                       [1000,1500,0,500],\n",
        "                       [1500,2000,1500,2000],\n",
        "                       [0,500,1000,1300],\n",
        "                       [4500,5000,1100,1300],\n",
        "                       [4000,4500,0,500]],dtype=torch.long,device='cuda')\n",
        "Market_Positions = torch.zeros((Population,12),dtype=torch.double,device='cuda')\n",
        "Market_Positions[:,0] = torch.DoubleTensor(Population).uniform_(Market[0,0],Market[0,1])\n",
        "Market_Positions[:,2] = torch.DoubleTensor(Population).uniform_(Market[1,0],Market[1,1])\n",
        "Market_Positions[:,4] = torch.DoubleTensor(Population).uniform_(Market[2,0],Market[2,1])\n",
        "Market_Positions[:,6] = torch.DoubleTensor(Population).uniform_(Market[3,0],Market[3,1])\n",
        "Market_Positions[:,8] = torch.DoubleTensor(Population).uniform_(Market[4,0],Market[4,1])\n",
        "Market_Positions[:,10] = torch.DoubleTensor(Population).uniform_(Market[5,0],Market[5,1])\n",
        "Market_Positions[:,1] = torch.DoubleTensor(Population).uniform_(Market[0,2],Market[0,3])\n",
        "Market_Positions[:,3] = torch.DoubleTensor(Population).uniform_(Market[1,2],Market[1,3])\n",
        "Market_Positions[:,5] = torch.DoubleTensor(Population).uniform_(Market[2,2],Market[2,3])\n",
        "Market_Positions[:,7] = torch.DoubleTensor(Population).uniform_(Market[3,2],Market[3,3])\n",
        "Market_Positions[:,9] = torch.DoubleTensor(Population).uniform_(Market[4,2],Market[4,3])\n",
        "Market_Positions[:,11] = torch.DoubleTensor(Population).uniform_(Market[5,2],Market[5,3])\n",
        "\n",
        "# Centroids\n",
        "Centroids = torch.zeros((18,2),dtype=torch.double,device='cuda')\n",
        "Centroids[:5,0] = School[:,0] + 0.5*School[:,1]\n",
        "Centroids[:5,1] = School[:,2] + 0.5*School[:,3]\n",
        "Centroids[5:12,0] = Office[:,0] + 0.5*Office[:,1]\n",
        "Centroids[5:12,1] = Office[:,2] + 0.5*Office[:,3]\n",
        "Centroids[12:18,0] = Market[:,0] + 0.5*Market[:,1]\n",
        "Centroids[12:18,1] = Market[:,2] + 0.5*Market[:,3]\n",
        "\n",
        "# Find nearest destination\n",
        "Nearest = torch.zeros((Population,3),device='cuda',dtype=torch.long)\n",
        "Nearest[:,0] = nearest_destination(Population,Centroids[:5],Residence)\n",
        "Nearest[:,1] = nearest_destination(Population,Centroids[5:12],Residence)\n",
        "Nearest[:,2] = nearest_destination(Population,Centroids[12:18],Residence)\n",
        "\n",
        "# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "# Disease Transmission Parameters\n",
        "# -------------------------------------\n",
        "\n",
        "# States: SIT/SIN/AIT/AIN/SUT/SUN\n",
        "States = torch.tensor(([0,0,0,0,0,1,2,3],\n",
        "                       [0,0,0,1,0,0,0,0],\n",
        "                       [6,7,6,7,0,0,0,0],\n",
        "                       [9,9,9,9,0,0,0,0],\n",
        "                       [0,0,0,0,0,0,0,0],\n",
        "                       [0,1,2,3,4,5,6,7]),dtype=torch.int,device='cuda')\n",
        "# Disease Transition Probabilities\n",
        "Transition = torch.zeros((3,6,8),dtype=torch.double,device='cuda')\n",
        "undercount = 0\n",
        "# Children \n",
        "Transition[0] = torch.transpose(torch.tensor(([0,0,0.12495,0.00005,0,0.875],\n",
        "                                              [0,0,0.09994,0.00006,undercount,0.9-undercount],\n",
        "                                              [0,0.00998,0.19,0.00002,0,0.8],\n",
        "                                              [0,0.02996,0.12,0.00004,undercount,0.85-undercount],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463]),dtype=torch.double,device='cuda'),0,1) \n",
        "# Adults\n",
        "Transition[1] = torch.transpose(torch.tensor(([0,0,0.124,0.001,0,0.875],\n",
        "                                              [0,0,0.06,0.04,undercount,0.9-undercount],\n",
        "                                              [0,0.03993,0.16,0.00007,0,0.8],\n",
        "                                              [0,0.0493,0.1,0.0007,undercount,0.85-undercount],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5]),dtype=torch.double,device='cuda'),0,1) \n",
        "# Seniors\n",
        "Transition[2] = torch.transpose(torch.tensor(([0,0,0.065,0.06,0,0.875],\n",
        "                                              [0,0,0.02,0.08,undercount,0.9-undercount],\n",
        "                                              [0,0.08,0.1,0.02,0,0.8],\n",
        "                                              [0,0.1,0.01,0.04,undercount,0.85-undercount],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049]),dtype=torch.double,device='cuda'),0,1)\n",
        "# Storage Parameters\n",
        "# Outermost Loop (Scenario/Final Parameters)\n",
        "# Disease Transmission: S/IT/INT/R/D/Total (7+7, Mean and Variance states) \n",
        "FinalTimeSeries = torch.zeros((65,(TimeStepsTotal.long().item())+1), dtype=torch.float,device='cuda')\n",
        "# Time to infection, death, recovery (Mean - 0,1,2, Variance - 3,4,5)\n",
        "FinalConstants = torch.zeros((20), dtype=torch.float,device='cuda')\n",
        "InteractionsFinal = torch.zeros((2,6,6),dtype=torch.float,device='cuda')\n",
        "# Outermost loop\n",
        "# Second Loop (MonteCarlo Run Parameters)\n",
        "# ResultsTimeSeries (Time Series for MC runs)(0-6)\n",
        "ResultsTMC = torch.zeros((MonteCarlo,29,(TimeStepsTotal.long().item())+1), dtype=torch.float,device='cuda')\n",
        "# Results Constants: Time to Infection: Overall(0), Recovery(1), Death(2)\n",
        "ResultsCMC = torch.zeros((MonteCarlo,10,1), dtype=torch.float,device='cuda')\n",
        "InteractionsMC = torch.zeros((MonteCarlo,2,6,6), dtype=torch.float,device='cuda')\n",
        "\n",
        "for mc in range(MonteCarlo):\n",
        "  # Create N agents;\n",
        "  # Age = 0,     \n",
        "  # x_position = 1, \n",
        "  # y_position = 2, \n",
        "  # x_velocity = 3, \n",
        "  # y_velocity = 4, \n",
        "  # occupation = 5,\n",
        "  # current_task = 7, \n",
        "  # status_of_task = 8, \n",
        "  # goal_x = 9, \n",
        "  # goal_y = 10,\n",
        "  # infection_status = 14, \n",
        "  # infection_state = 15, \n",
        "  # time_of_infectiousness = 16\n",
        "  Agents = torch.zeros((Population,20),dtype=torch.double,device='cuda')\n",
        "  # Assign Age \n",
        "  Agents[:,0] = Age\n",
        "  # Indices of Children\n",
        "  Children = torch.where(Agents[:,0]==0)[0]\n",
        "  # Indices of Adults\n",
        "  Adults = torch.where(Agents[:,0]==1)[0]\n",
        "  # Indices of Seniors\n",
        "  Seniors = torch.where(Agents[:,0]==2)[0]\n",
        "  # Assign x and y Positions\n",
        "  # PositionX (1)\n",
        "  Agents[:,1] = Residence[:,0].detach().clone()\n",
        "  # PositionY (2)\n",
        "  Agents[:,2] = Residence[:,1].detach().clone()\n",
        "  # Assign x and y Velocity Components\n",
        "  # VelocityX (3)\n",
        "  Agents[:,3] = torch.DoubleTensor(Population).uniform_(VelocityParameters[0],VelocityParameters[1])\n",
        "  # VelocityY (4)\n",
        "  Agents[:,4] = torch.DoubleTensor(Population).uniform_(VelocityParameters[0],VelocityParameters[1])\n",
        "  # Social Distance:  \n",
        "  SD = Radii[0]*torch.ones((Population),dtype=torch.long,device='cuda')\n",
        "  # Fraction not following (0: Everyone, 1: No one)\n",
        "  SD[torch.tensor([True,False],device='cuda')[torch.multinomial(torch.tensor([0.9,0.1]),Population,replacement=True)]] = 0\n",
        "  # Occupation (5): \n",
        "  # School-going Children (1)\n",
        "  Agents[Children[:int(0.75*Demography[0])],5] = 1\n",
        "  # School-going Adults (1)\n",
        "  Agents[Adults[:int(0.05*Demography[1])],5] = 1\n",
        "  # School-going Seniors (1)\n",
        "  Agents[Seniors[:int(0.02*Demography[2])],5] = 1\n",
        "  # Stay-at-home adults (2)\n",
        "  Agents[Adults[int(0.05*Demography[1]):int(0.25*Demography[1])],5] = 2\n",
        "  # Office Going Adults (3)\n",
        "  Agents[Adults[int(0.25*Demography[1]):int(0.85*Demography[1])],5] = 3\n",
        "  # Nomadic Adults (4)\n",
        "  Agents[Adults[int(0.85*Demography[1]):Demography[1]],5] = 4\n",
        "  # Stay-at-home Seniors (5)\n",
        "  Agents[Seniors[int(0.02*Demography[2]):int(0.62*Demography[2])],5] = 5 \n",
        "  # Office Going Seniors (6)\n",
        "  Agents[Seniors[int(0.62*Demography[2]):int(0.95*Demography[2])],5] = 6\n",
        "  # Reduce office-goers by fraction\n",
        "  #stay = torch.where(torch.logical_or(Agents[:,5]==3,Agents[:,5]==6))[0]\n",
        "  #staynumber = stay[:int(0*stay.size(0))]\n",
        "  #Agents[staynumber[Agents[staynumber,0]==1],5] = 2\n",
        "  #Agents[staynumber[Agents[staynumber,0]==2],5] = 5\n",
        "  # Nomadic Seniors (7)\n",
        "  Agents[Seniors[int(0.95*Demography[2]):Demography[2]],5] = 7\n",
        "  # Assign Infection Status: Infected and Tested (1), Uninfect(0), Infected and Not Tested (3)\n",
        "  # 0.5% infections among children, 49% infections among adults, 50.5% among seniors\n",
        "  Agents[Children[:int(0.005*Demography[3])],14] = 3 # Infected \n",
        "  Agents[Adults[int(0.005*Demography[3]):int(0.49*Demography[3])],14] = 3 # Infected \n",
        "  Agents[Seniors[int(0.49*Demography[3]):Demography[3]],14] = 3 # Infected \n",
        "  Agents[torch.where(Agents[:,14]==3)[0][:int(Demography[4])],14] = 1 # Confirmed Infections\n",
        "  # Assign Infection State: SIT=0, SIN=1, AIT=2, AIN=3, SUT=4, SUN=5, AUT=6, AUN=7\n",
        "  # Infected\n",
        "  # 80% of the infections are asymptomatic\n",
        "  # 20% of the infections are symptomatic\n",
        "  Agents[torch.where(Agents[:,14]==3)[0][:int(0.8*Demography[3])],15] = 3 # AIN\n",
        "  Agents[torch.where(Agents[:,14]==3)[0][int(0.8*Demography[3]):Demography[3]],15] = 1 # SIN\n",
        "  # Uninfected\n",
        "  # 85% of the uninfected are asymptomatic\n",
        "  # 15% of the uninfected are symptomatic\n",
        "  Agents[torch.where(Agents[:,14]==0)[0][:int(0.85*(Population-Demography[3]))],15] = 7 # AUN\n",
        "  Agents[torch.where(Agents[:,14]==0)[0][int(0.85*(Population-Demography[3])):Population-Demography[3]],15] = 5 # SUN\n",
        "  Agents[torch.where(Agents[:,14]==1)[0],15] = 2\n",
        "  # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "  # Placeholder Variables\n",
        "  # -------------------------------------\n",
        "\n",
        "  # Vectors \n",
        "  Vectors = torch.zeros((12,Population),device='cuda',dtype=torch.double)\n",
        "  # Distance Matrix(0), Index Matrix(1), Condition Matrix(2)\n",
        "  Matrixs = torch.zeros((5,Population,20),dtype=torch.double,device='cuda')\n",
        "  # Initial Position \n",
        "  CurrentPosition = torch.dstack((Agents[:,1],Agents[:,2]))\n",
        "  # Initial Velocity\n",
        "  Velocity = torch.dstack((Agents[:,3],Agents[:,4]))\n",
        "  # All agents are initially at home (12)\n",
        "  Agents[:,7] = 12\n",
        "  # Status of the task is complete and initially at home (12)\n",
        "  Agents[:,8] = Agents[:,7].detach().clone()\n",
        "  # Ordinate of goal position\n",
        "  Agents[:,9] = Agents[:,1].detach().clone()\n",
        "  # Abscissa of goal position\n",
        "  Agents[:,10] = Agents[:,2].detach().clone()\n",
        "  # Initial Goals are to remain at home\n",
        "  Destination = torch.dstack((Agents[:,1],Agents[:,2]))\n",
        "  # Days \n",
        "  Days = torch.tensor([0],device='cuda')       \n",
        "  # Distance\n",
        "  Distance = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  # Infection Status over Time\n",
        "  # Overall\n",
        "  Results = torch.tensor([torch.where(Agents[:,14]==0)[0].size()[0], #0:Uninfected\n",
        "                          torch.where(Agents[:,14]==1)[0].size()[0], #1:Infected and Tested\n",
        "                          torch.where(Agents[:,14]==2)[0].size()[0], #2:Unreported Recoveries\n",
        "                          torch.where(Agents[:,14]==3)[0].size()[0], #3:Infected and Not Tested\n",
        "                          torch.where(Agents[:,14]==4)[0].size()[0], #4:Unreported Deaths\n",
        "                          torch.where(Agents[:,14]==5)[0].size()[0], #5:Reported Recoveries\n",
        "                          torch.where(Agents[:,14]==6)[0].size()[0]],dtype=torch.long,device='cuda') #6:Reported Deaths  \n",
        "  # Children\n",
        "  ResultsC = torch.tensor([torch.where(Agents[Children,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "  # Adults  \n",
        "  ResultsA = torch.tensor([torch.where(Agents[Adults,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "  # Seniors\n",
        "  ResultsS = torch.tensor([torch.where(Agents[Seniors,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "\n",
        "  # Interactions: Weekday=0, Weekend=1, Contact_Matrix=2\n",
        "  Interactions = torch.zeros((3,6,6),dtype=torch.double,device='cuda')\n",
        "  # Weekday = 0, Weekend = 1\n",
        "  Time_avg = torch.zeros((2,6),dtype=torch.double,device='cuda')\n",
        "  Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "  # Number of contacts on a day to day basis: Daily_Contact=0, Contact_Limit=1\n",
        "  Contacts = torch.zeros((2,Population,36),dtype=torch.long,device='cuda')\n",
        "  StatusTime = Agents[:,8].detach().clone()\n",
        "  for t in range(TimeStepsTotal.long().item()):\n",
        "\n",
        "    Days = torch.floor(torch.div(t,TimeStepsDay).cuda())\n",
        "    # Time of the day\n",
        "    time_of_day = t%96 \n",
        "    # Day of the week\n",
        "    day_of_week = Days%7 \n",
        "\n",
        "    # ------------------------------------------------\n",
        "    # Store parameters from the previous run\n",
        "    # ------------------------------------------------\n",
        "\n",
        "    # Infection Status from previous iteration\n",
        "    Vectors[4] = Agents[:,14].detach().clone()\n",
        "    # Infection State from previous iteration\n",
        "    Vectors[5] = Agents[:,15].detach().clone()\n",
        "    # Rtime\n",
        "    Vectors[6] = Vectors[1].detach().clone()\n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # Find Limits for the day\n",
        "    # ---------------------------------------------\n",
        "\n",
        "    if time_of_day==0:\n",
        "      # Assign contact limits for the weekend\n",
        "      if torch.logical_or(day_of_week==5,day_of_week==6):\n",
        "        for i in range(1,36):\n",
        "          Contacts[1][:,i] = Limits_Weekend[2*i+1]\n",
        "      else:\n",
        "        # Assign contact limits for the weekday\n",
        "        for i in range(1,36):\n",
        "          Contacts[1][:,i] = Limits[2*i+1]\n",
        "\n",
        "    #Agents[:,15]: SIT=0, SIN=1, AIT=2, AIN=3, SUT=4, SUN=5, AUT=6, AUN=7\n",
        "    #Unconfirmed to Confirmed (30 times underreported)\n",
        "    Vectors[0] = torch.rand(Population,device='cuda').double()< 0.00001920627#0.000006057605 #0.0000450422\n",
        "    Vectors[7] = torch.logical_and(Agents[:,15]==1,Vectors[0]==True)\n",
        "    Agents[Vectors[7].bool(),15] = 0 # SIN --> SIT\n",
        "    Vectors[7] = torch.logical_and(Agents[:,15]==3,Vectors[0]==True)\n",
        "    Agents[Vectors[7].bool(),15] = 2 # AIN --> AIT\n",
        "    # Confirmed Cases\n",
        "    # Infected and Tested\n",
        "    Agents[:,14][torch.logical_or(Agents[:,15]==0,Agents[:,15]==2)] = 1\n",
        "    # Unconfirmed\n",
        "    Agents[:,14][torch.logical_or(Agents[:,15]==1,Agents[:,15]==3)] = 3  \n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # State Transition of Infected Agents\n",
        "    # ---------------------------------------------\n",
        "\n",
        "    # Find all agents who have been infected for more than a day\n",
        "    Vectors[8] = torch.logical_and(torch.logical_and(torch.logical_or(Vectors[4]==1,Vectors[4]==3),Agents[:,16]%TimeStepsDay==0),Agents[:,16]>0)\n",
        "    #Vectors[8] = torch.logical_or(Vectors[4]==1,Vectors[4]==3)\n",
        "    # SIT=0: Is the person in state 0 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[0][:,0],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIT=0: Is the person in state 0 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[1][:,0],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIT=0: Is the person in state 0 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[2][:,0],Population,replacement=True)][Vectors[7].bool()].double()  \n",
        "    # SIN=1: Is the person in state 1 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[0][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIN=1: Is the person in state 1 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[1][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIN=1: Is the person in state 1 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[2][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[0][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[1][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[2][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[0][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[1][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[2][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # Recovery\n",
        "    Agents[:,14][(Vectors[4]==1) & (Agents[:,15]==6)] = 5 # Reported recoveries\n",
        "    Agents[:,14][(Vectors[4]==3) & (Agents[:,15]==7)] = 2 # Unreported recoveries\n",
        "    # Deaths\n",
        "    Agents[:,14][(Vectors[4]==1) & (Agents[:,15]==9)] = 6 # Reported recoveries\n",
        "    Agents[:,14][(Vectors[4]==3) & (Agents[:,15]==9)] = 4 # Unreported recoveries  \n",
        "    # Time Since Infection\n",
        "    Vectors[8] = torch.logical_or(Agents[:,14]==1,Agents[:,14]==3)\n",
        "    Agents[:,16][Vectors[8].bool()] = Agents[:,16][Vectors[8].bool()]+1\n",
        "    Agents[:,17][Vectors[8].bool()] = torch.round(torch.div(Agents[:,16][Vectors[8].bool()],TimeStepsDay-1)) \n",
        "\n",
        "    # -----------------------------------------\n",
        "    # State Transition of Uninfected Agents\n",
        "    # -----------------------------------------  \n",
        "\n",
        "    # Find agents in the vicinity\n",
        "    CurrentPosition_numpy = np.float32(CurrentPosition[0].cpu().detach().numpy())\n",
        "    # Create a faiss array\n",
        "    index_flat = faiss.IndexFlatL2(CurrentPosition_numpy.shape[1])\n",
        "    gpu_index_flat = faiss.index_cpu_to_gpu(res, 0, index_flat)\n",
        "    gpu_index_flat.add(CurrentPosition_numpy)                  \n",
        "    m, n = gpu_index_flat.search(CurrentPosition_numpy, 20)\n",
        "    Matrixs[1] = torch.from_numpy(n).long()\n",
        "    #Matrixs[1] = torch.reshape(q[q!=torch.arange(0,Population,step=1)[:,None]],(Population,19))\n",
        "    # Distances \n",
        "    Matrixs[0] = torch.squeeze(torch.cdist(CurrentPosition[0][:,None],CurrentPosition[0][Matrixs[1].long()],p=2))\n",
        "    Matrixs[0][Matrixs[1]==torch.arange(0,Population,step=1).cuda()[:,None]] = float(\"Inf\")\n",
        "    # Location Matching and Contact Number Check\n",
        "    Matrixs[2],Matrixs[4],Contacts[0],Interactions[2],Time_Daily = generateMatrix(Agents[:,8],Contacts[0],Contacts[1],Population,H_Number,Matrixs[1].long(),Location_one, Location_two,Time_Daily,Interactions[2],Matrixs[0],Radii[1])\n",
        "\n",
        "    if torch.logical_or(day_of_week==5,day_of_week==6):\n",
        "      Time_avg[1] += torch.mean(Time_Daily.double(),0)\n",
        "      Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "      Interactions[1] += Interactions[2]/48#torch.reshape(torch.sum(Contacts[0],0),(6,6))\n",
        "      Interactions[2] = torch.zeros((6,6),dtype=torch.double,device='cuda')\n",
        "      Contacts[0] = torch.zeros((Population,36),dtype=torch.long,device='cuda')\n",
        "    else:\n",
        "    # Over weekdays\n",
        "      Time_avg[0] += torch.mean(Time_Daily.double(),0)\n",
        "      Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "      Interactions[0] += Interactions[2]/48#torch.reshape(torch.sum(Contacts[0],0),(6,6))\n",
        "      Interactions[2] = torch.zeros((6,6),dtype=torch.double,device='cuda')\n",
        "      Contacts[0] = torch.zeros((Population,36),dtype=torch.long,device='cuda')\n",
        "  \n",
        "    # Find agents with infections in the vicinity\n",
        "    Vectors[3] = torch.sum(torch.logical_and(torch.logical_and(Matrixs[2].bool()==True,Matrixs[0]<Radii[1]),torch.logical_or(Vectors[4][Matrixs[1].long()]==1,Vectors[4][Matrixs[1].long()]==3)),1)\n",
        "    # Find all uninfected agents with infections in their vicinity\n",
        "    Vectors[8] = torch.logical_and(Vectors[4]==0,Vectors[3].long()>0)\n",
        "    # SUT=4: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[0][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUT=4: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[1][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUT=4: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[2][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[0][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[1][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[2][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[0][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[1][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[2][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[0][:,7],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[1][:,7],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[2][:,7],Population,replacement=True)][Vectors[7].bool()].double() \n",
        "\n",
        "\n",
        "    # -----------------------------------------\n",
        "    # Task Allocation\n",
        "    # ----------------------------------------- \n",
        "\n",
        "    # Weekday\n",
        "    if day_of_week<5:\n",
        "      \n",
        "      # Children\n",
        "      # School going children at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==0,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going children: School (1), Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[0,time_of_day],Activities[1,time_of_day],1-Activities[0,time_of_day]-Activities[1,time_of_day]-Activities[2,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home kids\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==0, Agents[:,8]==12)\n",
        "      # Destination for children: Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[0,time_of_day],Activities[1,time_of_day],1-Activities[0,time_of_day]-Activities[1,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at school\n",
        "      Vectors[8] = Agents[:,8]==13\n",
        "      # Remain in School: Stay at school (13), Go home (0) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,13],device='cuda')[torch.multinomial(torch.tensor([Activities[3,time_of_day],1-Activities[3,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==15)\n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==17)\n",
        "      # Stay there (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Adults\n",
        "      # School going adults at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==1,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (5), Market (8), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[2,time_of_day]-Activities[8,time_of_day]-Activities[9,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==3, Agents[:,8]==12)\n",
        "      # Destination for office going adults: Office (2), Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([2,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[10,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]-Activities[10,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay at home adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==2,Agents[:,8]==12)  \n",
        "      # Destination for adults: Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==12))\n",
        "      # Adults who are nomads: Go elsewhere (10), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([10,12],device='cuda')[torch.multinomial(torch.tensor([Activities[12,time_of_day],1-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==16)) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,10,16],device='cuda')[torch.multinomial(torch.tensor([Activities[18,time_of_day],Activities[12,time_of_day],1-Activities[18,time_of_day]-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults at the office\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==14)  \n",
        "      # Stay there (14), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[11,time_of_day],1-Activities[11,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==15)  \n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==17)  \n",
        "      # Stay there (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Seniors\n",
        "      # School going seniors at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==2,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going seniors: School (1), Random (6), Market (9), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[2,time_of_day]-Activities[8,time_of_day]-Activities[9,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==6, Agents[:,8]==12)  \n",
        "      # Destination for office going seniors: Office (3), Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([3,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[10,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]-Activities[10,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==5,Agents[:,8]==12)  \n",
        "      # Destination for seniors: Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==12))  \n",
        "      # Seniors who are nomads: Go elsewhere (11), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([11,12],device='cuda')[torch.multinomial(torch.tensor([Activities[12,time_of_day],1-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==16))  \n",
        "      # Seniors who are nomads: Go home (0), Go elsewhere (11), Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,11,16],device='cuda')[torch.multinomial(torch.tensor([Activities[18,time_of_day],Activities[12,time_of_day],1-Activities[12,time_of_day]-Activities[18,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at office \n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[11,time_of_day],1-Activities[11,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()  \n",
        "      # Seniors running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==15)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "\n",
        "    else:\n",
        "\n",
        "      # Children\n",
        "      # School going children\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==0,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going children: School (1), Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[4,time_of_day],Activities[5,time_of_day],1-Activities[4,time_of_day]-Activities[5,time_of_day]-Activities[6,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home kids\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==0, Agents[:,8]==12)\n",
        "      # Destination for children: Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[4,time_of_day],Activities[5,time_of_day],1-Activities[4,time_of_day]-Activities[5,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at school\n",
        "      Vectors[8] = Agents[:,8]==13\n",
        "      # Remain in School: Stay at school (13), Go home (0) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,13],device='cuda')[torch.multinomial(torch.tensor([Activities[7,time_of_day],1-Activities[7,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==15)\n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==17)\n",
        "      # Stay there Market (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Adults\n",
        "      # School going adults at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==1,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (5), Market (8), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[6,time_of_day]-Activities[13,time_of_day]-Activities[14,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going adults \n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==3, Agents[:,8]==12)  \n",
        "      # Destination for office going adults: Office (2), Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([2,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[15,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]-Activities[15,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==2,Agents[:,8]==12)  \n",
        "      # Destination for adults: Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==12))\n",
        "      # Adults who are nomads: Go elsewhere (10), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([10,12],device='cuda')[torch.multinomial(torch.tensor([Activities[17,time_of_day],1-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==16))\n",
        "      # Adults who are nomads: Go home (0), Go elsehwere (10),Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,10,16],device='cuda')[torch.multinomial(torch.tensor([Activities[19,time_of_day],Activities[17,time_of_day],1-Activities[17,time_of_day]-Activities[19,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==15)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults returning from market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults returning from office\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[16,time_of_day],1-Activities[16,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Seniors\n",
        "      # School going seniors at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==2,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (6), Market (9), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[6,time_of_day]-Activities[13,time_of_day]-Activities[14,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==6, Agents[:,8]==12)  \n",
        "      # Destination for office going seniors: Office (3), Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([3,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[15,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]-Activities[15,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==5,Agents[:,8]==12)  \n",
        "      # Destination for seniors: Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==12))  \n",
        "      # Seniors who are nomads: Go elsewhere (11), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([11,12],device='cuda')[torch.multinomial(torch.tensor([Activities[17,time_of_day],1-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==16))  \n",
        "      # Seniors who are nomads: Go home(0), Go elsewhere (11), Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,11,16],device='cuda')[torch.multinomial(torch.tensor([Activities[19,time_of_day],Activities[17,time_of_day],1-Activities[19,time_of_day]-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at office \n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[16,time_of_day],1-Activities[16,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()  \n",
        "      # Seniors running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==15)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "    \n",
        "    # --------------------------------\n",
        "    # Assign Task-based Goal Positions\n",
        "    # --------------------------------\n",
        "\n",
        "    for i in range(len(School)):\n",
        "      # Goal of School Going Children\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,0]==i,torch.logical_and(Agents[:,8]==12,Agents[:,7]==1))# Stay at home (12) --> Go to School (1)\n",
        "      Agents[:,9][Vectors[8]==1] = School_Positions[Vectors[8]==1,2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = School_Positions[Vectors[8]==1,2*i+1]\n",
        "    for i in range(len(Office)):\n",
        "      # Goal of Office Going Adults and Seniors\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,0]==i,torch.logical_and(Agents[:,8]==12,torch.logical_or(Agents[:,7]==2,Agents[:,7]==3)))# Stay at home --> Office (Adults/Seniors)\n",
        "      Agents[:,9][Vectors[8]==1] = Office_Positions[Vectors[8]==1,2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = Office_Positions[Vectors[8]==1,2*i+1]\n",
        "    for i in range(len(Market)):\n",
        "      # Goal of Market Going Adults/Seniors/Children\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,1]==i,torch.logical_and(Agents[:,8]==12,torch.logical_or(torch.logical_or(Agents[:,7]==7,Agents[:,7]==8),Agents[:,7]==9))) # Home --> Market\n",
        "      temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "      Agents[:,9][Vectors[8]==1] = Market_Positions[torch.randperm(Population)[:int(temp1)],2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = Market_Positions[torch.randperm(Population)[:int(temp1)],2*i+1]\n",
        "    # Goal of Adults running mobile jobs\n",
        "    mobile = goal_assign(CurrentPosition[0][:,0],CurrentPosition[0][:,1],300,800,0.5*Layout[1],0.5*Layout[3]) #300,800\n",
        "    Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(torch.logical_or(Agents[:,8]==16,Agents[:,8]==12),Agents[:,7]==10))\n",
        "    temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of Seniors running mobile jobs\n",
        "    mobile = goal_assign(CurrentPosition[0][:,0],CurrentPosition[0][:,1],300,800,0.5*Layout[1],0.5*Layout[3]) #300,800\n",
        "    Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(torch.logical_or(Agents[:,8]==16,Agents[:,8]==12),Agents[:,7]==11))\n",
        "    temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of kids running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==4)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,500,0.5*Layout[1],0.5*Layout[3])#300,500\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of adults running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==5)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,800,0.5*Layout[1],0.5*Layout[3])#300,800\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of seniors running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==6)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,800,0.5*Layout[1],0.5*Layout[3])#300,800\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1] \n",
        "    # Goal of Agents Going Home\n",
        "    Vectors[8] = torch.logical_or(torch.logical_and(Agents[:,8]==12,Agents[:,7]==12),Agents[:,7]==0)\n",
        "    Agents[:,9][Vectors[8]==1] = Agents[Vectors[8]==1,1].clone().detach()\n",
        "    Agents[:,10][Vectors[8]==1] = Agents[Vectors[8]==1,2].clone().detach()\n",
        "    \n",
        "    # -----------------------------\n",
        "    # Move Towards a Goal\n",
        "    # -----------------------------\n",
        "\n",
        "    # New goal position corresponding to the assigned task\n",
        "    Destination = torch.dstack((Agents[:,9],Agents[:,10]))\n",
        "    # Distance between the current position and assigned goal\n",
        "    Distance = torch.linalg.norm(CurrentPosition[0]-Destination[0],dim=1)\n",
        "    # Goal following velocity vector\n",
        "    Velocity[0] += Destination[0]-CurrentPosition[0]\n",
        "\n",
        "    # ------------------------------\n",
        "    # Collision Avoidance \n",
        "    # ------------------------------\n",
        "\n",
        "    # Reset avoidance vector in every iteration\n",
        "    VRule = torch.zeros((Population,2),dtype=torch.double,device='cuda') \n",
        "    # Find agents inside critical radius \n",
        "    Matrixs[3] = torch.logical_and(torch.logical_and(Matrixs[0]<SD[:,None],Matrixs[0]!=0),Matrixs[4]==True)\n",
        "    # Collision Avoidance Vector\n",
        "    Difference = CurrentPosition[0][:,None]-CurrentPosition[0][Matrixs[1].long()]\n",
        "    Difference[~Matrixs[3].bool()] = 0\n",
        "    # Number of agents inside the critical radius\n",
        "    Vectors[9] = torch.sum(Matrixs[3],1)\n",
        "    # Normalizing the displacement vector\n",
        "    Difference[Matrixs[3].bool()]/=torch.square(Matrixs[0][Matrixs[3].bool()])[:,None]\n",
        "    VRule[Vectors[9]>0] = torch.div(torch.sum(Difference,1)[Vectors[9]>0],torch.sum(Matrixs[3],1)[Vectors[9]>0][:,None]).double()\n",
        "    # Total Collision avoidance vector component\n",
        "    VRule[Vectors[9]>0] -= Velocity[0][Vectors[9]>0].clone().detach()\n",
        "\n",
        "    # --------------------\n",
        "    # Update Velocity\n",
        "    # --------------------\n",
        "\n",
        "    # Update the velocity of agents\n",
        "    Vectors[8] = torch.logical_and(torch.sum(Matrixs[3],1)>0,Agents[:,8]>=12)\n",
        "    Velocity[0][Vectors[8]==1] += VRule[Vectors[8]==1].clone().detach()\n",
        "\n",
        "    # ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "    # Limit Speed  \n",
        "    # --------------------  \n",
        "\n",
        "    # Speed is MaximumVelocity when outside the outer circle\n",
        "    Velocity[0][Distance>Radii[2]] = torch.div(Velocity[0][Distance>Radii[2]],torch.norm(Velocity[0],dim=1)[Distance>Radii[2]][:,None])*VelocityParameters[1]   \n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[2],torch.linalg.norm(Velocity[0],dim=1)!=0)\n",
        "    # Adjustment of velocity according to an exponential function in the intermediate region\n",
        "    Velocity[0][Vectors[8]==1] = torch.div(Velocity[0][Vectors[8]==1],torch.norm(Velocity[0],dim=1)[Vectors[8]==1][:,None])*(1000*torch.exp(-0.009*(Radii[2]-Distance[Vectors[8]==1][:,None])))\n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[4],torch.linalg.norm(Velocity[0],dim=1)!=0)\n",
        "    # Adjustment of velocity in the innermost circle\n",
        "    Velocity[0][Vectors[8]==1] = torch.div(Velocity[0][Vectors[8]==1],torch.norm(Velocity[0],dim=1)[Vectors[8]==1][:,None])*VelocityParameters[4]\n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[4],torch.linalg.norm(Velocity[0],dim=1)==0)\n",
        "    # Minimum velocity in the innermost circle\n",
        "    Velocity[0][Vectors[8]==1] = torch.sign(Velocity[0][Vectors[8]==1])*torch.tensor([0.4,0.4],dtype=torch.double,device='cuda')\n",
        "\n",
        "    # --------------------\n",
        "    # Update Position\n",
        "    # --------------------\n",
        "\n",
        "    # Update the position of agents\n",
        "    CurrentPosition[0] += Velocity[0]\n",
        "\n",
        "    # ---------------\n",
        "    # Retention\n",
        "    # ---------------\n",
        "    \n",
        "    # Change position if an agent goes out of the box (Wrap-around)\n",
        "    CurrentPosition[0][CurrentPosition[0][:,0] > Layout[1],0] = torch.DoubleTensor(1).uniform_(Layout[1]-10,Layout[1]).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,0] < Layout[0],0] = torch.DoubleTensor(1).uniform_(Layout[0],Layout[0]+10).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,1] > Layout[3],1] = torch.DoubleTensor(1).uniform_(Layout[3]-10,Layout[3]).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,1] < Layout[2],1] = torch.DoubleTensor(1).uniform_(Layout[2],Layout[2]+10).cuda()\n",
        "\n",
        "    # -------------------\n",
        "    # Current Task Status\n",
        "    # -------------------\n",
        "\n",
        "    # Set status of those within 10 units of their destination as reached, rest in process (9)\n",
        "    # Find distance between current location and goal\n",
        "    Vectors[10] = torch.linalg.norm(CurrentPosition[0]-Destination[0],dim=1)\n",
        "    # If distance is greater than 10, agent is moving towards the goal\n",
        "    Agents[torch.logical_and(Vectors[10]>=Radii[4],Agents[:,7]<=11),8] = Agents[torch.logical_and(Vectors[10]>=Radii[4],Agents[:,7]<=11),7]\n",
        "    # Reached goal\n",
        "    # At home\n",
        "    Agents[torch.logical_or(Agents[:,7]==12,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==0)),8] = 12\n",
        "    # At school\n",
        "    Agents[torch.logical_or(Agents[:,7]==13,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==1)),8] = 13\n",
        "    # Adults in office\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==14,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==2))),8] = 14\n",
        "    # Seniors in office\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==14,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==3))),8] = 14\n",
        "    # Kids running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==0,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==4))),8] = 15\n",
        "    # Adults running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==5))),8] = 15\n",
        "    # Seniors running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==6))),8] = 15\n",
        "    # Nomadic adults\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==16,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==10))),8] = 16\n",
        "    # Nomadic seniors\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==16,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==11))),8] = 16\n",
        "    # Kids at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==0,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==7))),8] = 17\n",
        "    # Adults at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==8))),8] = 17\n",
        "    # Seniors at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==9))),8] = 17\n",
        "\n",
        "    # ---------------\n",
        "    # Compute Time \n",
        "    # --------------\n",
        "\n",
        "    # Time to infection\n",
        "    Vectors[1][Vectors[4]==0] += 1\n",
        "\n",
        "    # ----------------------------\n",
        "    # Compute Reproduction Number\n",
        "    # ----------------------------\n",
        "    # With confirmed infections in vicinity\n",
        "    Vectors[8] = torch.logical_and(Vectors[4]==0,Agents[:,14]==3)\n",
        "    Matrixs[0][torch.logical_and(torch.logical_and(Matrixs[2].bool()==True,Matrixs[0]<Radii[1]),torch.logical_or(Vectors[4][Matrixs[1].long()]==1,Vectors[4][Matrixs[1].long()]==3))==False] = float('inf')\n",
        "    if torch.where(Vectors[8]==True)[0].size()[0]>0:\n",
        "      # Find the nearest individual who infected me \n",
        "      temp = torch.min(Matrixs[0][Vectors[8]==True],1)[1]\n",
        "      for l in range(temp.size()[0]):\n",
        "        Vectors[2][(Matrixs[1].long())[temp[l]]]+=1\n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # Store Values of the Current Iteration\n",
        "    # ---------------------------------------------\n",
        "    StatusTime = torch.dstack((StatusTime,Agents[:,8]))\n",
        "    #PositionTime = torch.dstack((PositionTime,CurrentPosition))\n",
        "\n",
        "    # Store Infection status for the next iteration\n",
        "    Results = torch.dstack((Results,torch.tensor([torch.where(Agents[:,14]==0)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==1)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==2)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==3)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==4)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==5)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "\n",
        "    ResultsC = torch.dstack((ResultsC,torch.tensor([torch.where(Agents[Children,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "    \n",
        "    ResultsA = torch.dstack((ResultsA,torch.tensor([torch.where(Agents[Adults,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "    \n",
        "    ResultsS = torch.dstack((ResultsS,torch.tensor([torch.where(Agents[Seniors,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "  ResultsTMC[mc][0:7][:] = Results[0] # Disease\n",
        "  ResultsTMC[mc][7:14][:] = ResultsC[0] # Children\n",
        "  ResultsTMC[mc][14:21][:] = ResultsA[0] # Adults\n",
        "  ResultsTMC[mc][21:28][:] = ResultsS[0] # Seniors\n",
        "  # Overall: Time to Infection, Time to Recover, Time to Death\n",
        "  ResultsCMC[mc][0:3] = torch.tensor([[torch.mean(Vectors[1][Vectors[1]>0])/TimeStepsDay.item()],\n",
        "                                    [torch.mean(Agents[:,17][torch.logical_or(Vectors[4]==2,Vectors[4]==5)].double())],\n",
        "                                    [torch.mean(Agents[:,17][torch.logical_or(Vectors[4]==4,Vectors[4]==6)].double())]],device='cuda',dtype=torch.double)\n",
        "  # Overall: Effective Reproduction Number\n",
        "  ResultsCMC[mc][3] = torch.mean(Vectors[2][Vectors[4]!=0])\n",
        "  # Overall: Average time of infectiousness\n",
        "  ResultsCMC[mc][4] = torch.mean(Agents[Vectors[4]!=0,16]/96)\n",
        "\n",
        "  # Overall: Time to Infection, Time to Recover, Time to Death\n",
        "  ResultsCMC[mc][5:8] = torch.tensor([[torch.mean(Vectors[1][Vectors[1]>0])/TimeStepsDay.item()],\n",
        "                                    [torch.mean(Agents[:,17][Vectors[4]==5].double())],\n",
        "                                    [torch.mean(Agents[:,17][Vectors[4]==6].double())]],device='cuda',dtype=torch.double)\n",
        "  # Overall: Effective Reproduction Number\n",
        "  ResultsCMC[mc][8] = torch.mean(Vectors[2][torch.logical_and(Vectors[4]!=0,torch.logical_or(Vectors[4]==1,((Vectors[4]==5)|(Vectors[4]==6))))])\n",
        "  # Overall: Average time of infectiousness\n",
        "  ResultsCMC[mc][9] = torch.mean(Agents[torch.logical_and(Vectors[4]!=0,torch.logical_or(Vectors[4]==1,((Vectors[4]==5)|(Vectors[4]==6)))),16]/96)\n",
        "\n",
        "  # Contact Matrix\n",
        "  InteractionsMC[mc][0][0] = Interactions[0][0]/23\n",
        "  InteractionsMC[mc][0][1] = Interactions[0][1]/23\n",
        "  InteractionsMC[mc][0][2] = Interactions[0][2]/23\n",
        "  InteractionsMC[mc][0][3] = Interactions[0][3]/23\n",
        "  InteractionsMC[mc][0][4] = Interactions[0][4]/23\n",
        "  InteractionsMC[mc][0][5] = Interactions[0][5]/23\n",
        "  InteractionsMC[mc][1][0] = Interactions[1][0]/8\n",
        "  InteractionsMC[mc][1][1] = Interactions[1][1]/8\n",
        "  InteractionsMC[mc][1][2] = Interactions[1][2]/8\n",
        "  InteractionsMC[mc][1][3] = Interactions[1][3]/8\n",
        "  InteractionsMC[mc][1][4] = Interactions[1][4]/8\n",
        "  InteractionsMC[mc][1][5] = Interactions[1][5]/8\n",
        "\n",
        "# Overall: Confirmed and Cumulative=0, Total Infections (T/N)=1,\n",
        "# Confirmed Infections=2, Confirmed Recoveries=3, Confirmed Deaths=4,\n",
        "# Total Recoveries=5, Total Deaths=6, Cumulative (T/N) = 7\n",
        "FinalTimeSeries[0][:] = torch.mean((ResultsTMC[:][:,1]+ResultsTMC[:][:,6]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[1][:] = torch.mean((ResultsTMC[:][:,3]+ResultsTMC[:][:,1]).double(),0)\n",
        "FinalTimeSeries[2][:] = torch.mean(ResultsTMC[:][:,1].double(),0)\n",
        "FinalTimeSeries[3][:] = torch.mean(ResultsTMC[:][:,5].double(),0)\n",
        "FinalTimeSeries[4][:] = torch.mean(ResultsTMC[:][:,6].double(),0)\n",
        "FinalTimeSeries[5][:] = torch.mean((ResultsTMC[:][:,5]+ResultsTMC[:][:,2]).double(),0)\n",
        "FinalTimeSeries[6][:] = torch.mean((ResultsTMC[:][:,6]+ResultsTMC[:][:,4]).double(),0)\n",
        "FinalTimeSeries[7][:] = torch.mean((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]+ResultsTMC[:][:,5]+ResultsTMC[:][:,2]+ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "FinalTimeSeries[8][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,6]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[9][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]).double(),0)\n",
        "FinalTimeSeries[10][:] = torch.std(ResultsTMC[:][:,1].double(),0)\n",
        "FinalTimeSeries[11][:] = torch.std(ResultsTMC[:][:,5].double(),0)\n",
        "FinalTimeSeries[12][:] = torch.std(ResultsTMC[:][:,6].double(),0)\n",
        "FinalTimeSeries[13][:] = torch.std((ResultsTMC[:][:,2]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[14][:] = torch.std((ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "FinalTimeSeries[15][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]+ResultsTMC[:][:,2]+ResultsTMC[:][:,5]+ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "\n",
        "# Children: Confirmed and Cumulative=16, Total Infections (T/N)=17,\n",
        "# Confirmed Infections=18, Confirmed Recoveries=19, Confirmed Deaths=20,\n",
        "# Total Recoveries=21, Total Deaths=22, Cumulative (T/N) = 23\n",
        "FinalTimeSeries[16][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "FinalTimeSeries[17][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,10]).double(),0)\n",
        "FinalTimeSeries[18][:] = torch.mean(ResultsTMC[:][:,8].double(),0)\n",
        "FinalTimeSeries[19][:] = torch.mean(ResultsTMC[:][:,12].double(),0)\n",
        "FinalTimeSeries[20][:] = torch.mean(ResultsTMC[:][:,13].double(),0)\n",
        "FinalTimeSeries[21][:] = torch.mean((ResultsTMC[:][:,12]+ResultsTMC[:][:,9]).double(),0)\n",
        "FinalTimeSeries[22][:] = torch.mean((ResultsTMC[:][:,13]+ResultsTMC[:][:,11]).double(),0)\n",
        "FinalTimeSeries[23][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,9]+ResultsTMC[:][:,10]+ResultsTMC[:][:,11]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)                                                                                                                              \n",
        "FinalTimeSeries[24][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "FinalTimeSeries[25][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,10]).double(),0)\n",
        "FinalTimeSeries[26][:] = torch.std(ResultsTMC[:][:,8].double(),0)\n",
        "FinalTimeSeries[27][:] = torch.std(ResultsTMC[:][:,12].double(),0)\n",
        "FinalTimeSeries[28][:] = torch.std(ResultsTMC[:][:,13].double(),0)\n",
        "FinalTimeSeries[29][:] = torch.std((ResultsTMC[:][:,12]+ResultsTMC[:][:,9]).double(),0)\n",
        "FinalTimeSeries[30][:] = torch.std((ResultsTMC[:][:,13]+ResultsTMC[:][:,11]).double(),0)\n",
        "FinalTimeSeries[31][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,9]+ResultsTMC[:][:,10]+ResultsTMC[:][:,11]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "\n",
        "# Adults: Confirmed and Cumulative=32, Total Infections (T/N)=33,\n",
        "# Confirmed Infections=34, Confirmed Recoveries=35, Confirmed Deaths=36,\n",
        "# Total Recoveries=37, Total Deaths=38, Cumulative (T/N) = 39\n",
        "FinalTimeSeries[32][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[33][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,17]).double(),0)\n",
        "FinalTimeSeries[34][:] = torch.mean(ResultsTMC[:][:,15].double(),0)\n",
        "FinalTimeSeries[35][:] = torch.mean(ResultsTMC[:][:,19].double(),0)\n",
        "FinalTimeSeries[36][:] = torch.mean(ResultsTMC[:][:,20].double(),0)\n",
        "FinalTimeSeries[37][:] = torch.mean((ResultsTMC[:][:,19]+ResultsTMC[:][:,16]).double(),0)\n",
        "FinalTimeSeries[38][:] = torch.mean((ResultsTMC[:][:,20]+ResultsTMC[:][:,18]).double(),0)\n",
        "FinalTimeSeries[39][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,16]+ResultsTMC[:][:,17]+ResultsTMC[:][:,18]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[40][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[41][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,17]).double(),0)\n",
        "FinalTimeSeries[42][:] = torch.std(ResultsTMC[:][:,15].double(),0)\n",
        "FinalTimeSeries[43][:] = torch.std(ResultsTMC[:][:,19].double(),0)\n",
        "FinalTimeSeries[44][:] = torch.std(ResultsTMC[:][:,20].double(),0)\n",
        "FinalTimeSeries[45][:] = torch.std((ResultsTMC[:][:,19]+ResultsTMC[:][:,16]).double(),0)\n",
        "FinalTimeSeries[46][:] = torch.std((ResultsTMC[:][:,20]+ResultsTMC[:][:,18]).double(),0)\n",
        "FinalTimeSeries[47][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,16]+ResultsTMC[:][:,17]+ResultsTMC[:][:,18]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "\n",
        "# Seniors: Confirmed and Cumulative=48, Total Infections (T/N)=49,\n",
        "# Confirmed Infections=50, Confirmed Recoveries=51, Confirmed Deaths=52,\n",
        "# Total Recoveries=53, Total Deaths=54, Cumulative (T/N) = 55\n",
        "FinalTimeSeries[48][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[49][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,24]).double(),0)\n",
        "FinalTimeSeries[50][:] = torch.mean(ResultsTMC[:][:,22].double(),0)\n",
        "FinalTimeSeries[51][:] = torch.mean(ResultsTMC[:][:,26].double(),0)\n",
        "FinalTimeSeries[52][:] = torch.mean(ResultsTMC[:][:,27].double(),0)\n",
        "FinalTimeSeries[53][:] = torch.mean((ResultsTMC[:][:,23]+ResultsTMC[:][:,26]).double(),0)\n",
        "FinalTimeSeries[54][:] = torch.mean((ResultsTMC[:][:,25]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[55][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,23]+ResultsTMC[:][:,24]+ResultsTMC[:][:,25]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[56][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[57][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,24]).double(),0)\n",
        "FinalTimeSeries[58][:] = torch.std(ResultsTMC[:][:,22].double(),0)\n",
        "FinalTimeSeries[59][:] = torch.std(ResultsTMC[:][:,26].double(),0)\n",
        "FinalTimeSeries[60][:] = torch.std(ResultsTMC[:][:,27].double(),0)\n",
        "FinalTimeSeries[61][:] = torch.std((ResultsTMC[:][:,23]+ResultsTMC[:][:,26]).double(),0)\n",
        "FinalTimeSeries[62][:] = torch.std((ResultsTMC[:][:,25]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[63][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,23]+ResultsTMC[:][:,24]+ResultsTMC[:][:,25]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "\n",
        "# Overall: Time to Infection=0, Time to Recover=1, Time to Death=2, Rep Number=3, Time of Infectiousness=4\n",
        "# Confirmed: Time to Infection=5, Time to Recover=6, Time to Death=7, Rep Number=8, Time of Infectiousness=9\n",
        "FinalConstants[0] = torch.mean(ResultsCMC[:][:,0][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,0]),ResultsCMC[:][:,0]==0)==False)].double(),0)\n",
        "FinalConstants[1] = torch.mean(ResultsCMC[:][:,1][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,1]),ResultsCMC[:][:,1]==0)==False)].double(),0)\n",
        "FinalConstants[2] = torch.mean(ResultsCMC[:][:,2][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,2]),ResultsCMC[:][:,2]==0)==False)].double(),0)\n",
        "FinalConstants[3] = torch.std(ResultsCMC[:][:,0][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,0]),ResultsCMC[:][:,0]==0)==False)].double(),0)\n",
        "FinalConstants[4] = torch.std(ResultsCMC[:][:,1][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,1]),ResultsCMC[:][:,1]==0)==False)].double(),0)\n",
        "FinalConstants[5] = torch.std(ResultsCMC[:][:,2][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,2]),ResultsCMC[:][:,2]==0)==False)].double(),0) \n",
        "FinalConstants[6] = torch.mean(ResultsCMC[:][:,3].double())\n",
        "FinalConstants[7] = torch.std(ResultsCMC[:][:,3].double())\n",
        "FinalConstants[8] = torch.mean(ResultsCMC[:][:,4].double())\n",
        "FinalConstants[9] = torch.std(ResultsCMC[:][:,4].double())\n",
        "FinalConstants[10] = torch.mean(ResultsCMC[:][:,5][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,5]),ResultsCMC[:][:,5]==0)==False)].double(),0)\n",
        "FinalConstants[11] = torch.mean(ResultsCMC[:][:,6][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,6]),ResultsCMC[:][:,6]==0)==False)].double(),0)\n",
        "FinalConstants[12] = torch.mean(ResultsCMC[:][:,7][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,7]),ResultsCMC[:][:,7]==0)==False)].double(),0)\n",
        "FinalConstants[13] = torch.std(ResultsCMC[:][:,5][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,5]),ResultsCMC[:][:,5]==0)==False)].double(),0)\n",
        "FinalConstants[14] = torch.std(ResultsCMC[:][:,6][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,6]),ResultsCMC[:][:,6]==0)==False)].double(),0)\n",
        "FinalConstants[15] = torch.std(ResultsCMC[:][:,7][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,7]),ResultsCMC[:][:,7]==0)==False)].double(),0) \n",
        "FinalConstants[16] = torch.mean(ResultsCMC[:][:,8].double())\n",
        "FinalConstants[17] = torch.std(ResultsCMC[:][:,8].double())\n",
        "FinalConstants[18] = torch.mean(ResultsCMC[:][:,9].double())\n",
        "FinalConstants[19] = torch.std(ResultsCMC[:][:,9].double())\n",
        "\n",
        "InteractionsFinal[0] = torch.mean(InteractionsMC[:][:,0],0)\n",
        "InteractionsFinal[1] = torch.mean(InteractionsMC[:][:,1],0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: faiss-gpu in /usr/local/lib/python3.7/dist-packages (1.7.1.post3)\n",
            "Requirement already satisfied: celluloid in /usr/local/lib/python3.7/dist-packages (0.2.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from celluloid) (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (1.3.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (1.19.5)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (3.0.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->celluloid) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtM-KoEDrG1u"
      },
      "source": [
        "## Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC71CekpsSsV"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from IPython.display import HTML\n",
        "import seaborn as sns\n",
        "from matplotlib.ticker import AutoMinorLocator, MultipleLocator\n",
        "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
        "from matplotlib import collections as matcoll\n",
        "from matplotlib import cm\n",
        "import numpy as np\n",
        "from scipy.stats import norm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "mzgtH5oGsIIP",
        "outputId": "40c36a29-9a2a-45d1-abd8-9f223557da42"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "s = 96\n",
        "\n",
        "t=96*31\n",
        "\n",
        "x = (np.arange(0,t,step=s)/96)+1\n",
        "\n",
        "cinf_mean = ((FinalTimeSeries[0]/Population)*100)#.cpu().detach().numpy()\n",
        "cinf_std = ((FinalTimeSeries[8]/Population)*100)#.cpu().detach().numpy()\n",
        "\n",
        "ainf_mean = ((FinalTimeSeries[2]/Population)*100)#.cpu().detach().numpy()\n",
        "ainf_std = ((FinalTimeSeries[10]/Population)*100)#.cpu().detach().numpy()\n",
        "\n",
        "recovered_mean = ((FinalTimeSeries[3]/Population)*100)#.cpu().detach().numpy()\n",
        "recovered_std = ((FinalTimeSeries[11]/Population)*100)#.cpu().detach().numpy()\n",
        "\n",
        "deceased_mean = ((FinalTimeSeries[4]/Population)*100)#.cpu().detach().numpy()\n",
        "deceased_std = ((FinalTimeSeries[12]/Population)*100)#.cpu().detach().numpy() \n",
        "\n",
        "rinf_mean = recovered_mean\n",
        "rinf_std = recovered_std\n",
        "\n",
        "dinf_mean = deceased_mean\n",
        "dinf_std = deceased_std\n",
        "data = [0.004,0.004,0.005,0.005,0.005,0.005,0.005,0.005,0.006,0.006,0.006,0.007,0.007,\n",
        "        0.007,0.008,0.008,0.009,0.010,0.010,0.011,0.012,0.015,0.016,0.017,0.019,\n",
        "        0.021,0.022,0.029,0.038,0.047,0.052,0.061]\n",
        "\n",
        "plt.plot(x,cinf_mean[np.arange(0,t,step=s)],'x-',color='blue',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(cinf_mean[np.arange(0,t,step=s)]-cinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(cinf_mean[np.arange(0,t,step=s)]+cinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='blue')\n",
        "\n",
        "plt.plot(x,ainf_mean[np.arange(0,t,step=s)],'x-',color='r',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(ainf_mean[np.arange(0,t,step=s)]-ainf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(ainf_mean[np.arange(0,t,step=s)]+ainf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='r')\n",
        "\n",
        "plt.plot(x,rinf_mean[np.arange(0,t,step=s)],'x-',color='g',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(rinf_mean[np.arange(0,t,step=s)]-rinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(rinf_mean[np.arange(0,t,step=s)]+rinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='g')\n",
        "\n",
        "plt.plot(x,dinf_mean[np.arange(0,t,step=s)],'x-',color='dimgray',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(dinf_mean[np.arange(0,t,step=s)]-dinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(dinf_mean[np.arange(0,t,step=s)]+dinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='dimgray')\n",
        "\n",
        "#plt.plot(np.arange(1,32,step=1),data,'o-',color='black',linewidth=2)\n",
        "\n",
        "plt.rcParams['figure.facecolor'] = 'ivory'\n",
        "\n",
        "plt.rcParams[\"font.weight\"] = \"bold\"\n",
        "\n",
        "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
        "\n",
        "ax.grid()\n",
        "\n",
        "ax.set_axisbelow(True)\n",
        "ax.xaxis.set_major_locator(plt.MaxNLocator(16))\n",
        "ax.tick_params(which='major', length=7, color='black')\n",
        "ax.tick_params(which='minor', length=4, color='black' )\n",
        "ax.xaxis.set_minor_locator(plt.MaxNLocator())\n",
        "\n",
        "ax.tick_params(which='both', # Options for both major and minor ticks\n",
        "               top=False, # turn off top ticks\n",
        "               left=False, # turn off left ticks\n",
        "               right=False,  # turn off right ticks\n",
        "               bottom=True) # turn off bottom ticks\n",
        "ax.set_xticks(np.arange(1,32,step=2))\n",
        "ax.set_xticklabels([\"1\",\"3\",\"5\",\"7\",\"9\",\"11\",\"13\",\"15\",\"17\",\"19\",\"21\",\"23\",\"25\",\n",
        "                    \"28\",\"29\",\"31\"])\n",
        "\n",
        "plt.xticks(fontsize=16,fontweight='bold')\n",
        "plt.yticks(np.arange(0,2.2,step=0.2),fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.xlabel('Time (Days)',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.ylabel('Fraction of the total population (%)',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.legend(['Total Infections','Active Infections','Recoveries','Mortalities'],\n",
        "           fontsize=16,loc=\"upper left\",fancybox=True, \n",
        "           framealpha=0.0, shadow=False, borderpad=1)\n",
        "\n",
        "plt.title('Disease evolution in Bengaluru',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.savefig('FB_2.pdf',format='pdf', bbox_inches='tight',Transparent=True)\n",
        "\n",
        "files.download('FB_2.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_697288e2-b9f9-4251-9e3b-19601b35df60\", \"FB_2.pdf\", 20854)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "1RMhLXqIsjkO",
        "outputId": "3c15f4fb-23bf-4b5a-bc5e-5d5a5b4036ef"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "data1 = InteractionsFinal[0]#.cpu().detach().numpy()\n",
        "\n",
        "data1 = np.ma.masked_where(data1 == 0, data1)\n",
        "\n",
        "data2 = InteractionsFinal[0]#.cpu().detach().numpy()\n",
        "\n",
        "data1[0,0] = 4.0\n",
        "data2[0,0] = 4.0\n",
        "\n",
        "\n",
        "vmin = np.min(np.array([data1,data2]))\n",
        "\n",
        "vmax = np.max(np.array([data1,data2]))\n",
        "\n",
        "alpha = ['$Q_{1}$','$Q_{2}$','$Q_{3}$','$Q_{4}$','$Q_{5}$','$Q_{6}$']\n",
        "\n",
        "cmap = ListedColormap([\"lemonchiffon\",\"goldenrod\",\"cyan\",\"orange\",\"gainsboro\", \"black\",\"yellow\"])\n",
        "\n",
        "cax = ax.matshow(data1,interpolation='nearest',cmap=cmap, rasterized=True, vmin=vmin, vmax=vmax)\n",
        "\n",
        "cbar=fig.colorbar(cax,extend='min', label='Average Contacts')\n",
        "\n",
        "plt.gca().set_xticks([x - 0.5 for x in plt.gca().get_xticks()][1:], minor='true')\n",
        "\n",
        "plt.gca().set_yticks([y - 0.5 for y in plt.gca().get_yticks()][1:], minor='true')\n",
        "\n",
        "plt.grid(which='minor')\n",
        "\n",
        "cmap.set_bad(color='white')\n",
        "\n",
        "ax.set_xticklabels(['']+alpha,y=-0.15,fontsize=12)\n",
        "\n",
        "ax.set_yticklabels(['']+alpha,fontsize=12)\n",
        "\n",
        "cbar.ax.tick_params(labelsize=12)\n",
        "\n",
        "plt.title('Weekday daily-contacts matrix',fontweight='bold',fontsize=14)\n",
        "\n",
        "plt.savefig('IB_2.pdf',format='pdf', bbox_inches='tight',Transparent=True)\n",
        "\n",
        "files.download('IB_2.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_ecbb684a-747c-4643-a2a9-e2a5c63a42dd\", \"IB_2.pdf\", 16454)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UO0rWU1ZelY0"
      },
      "source": [
        "from google.colab import files\n",
        "FinalTimeSeries = hk.load('TS_FB_1.hkl')\n",
        "c = hk.load('C_FB_2.hkl')\n",
        "InteractionsFinal = hk.load('I_FB_2.hkl')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZShM0cSrIOY"
      },
      "source": [
        "## Save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qz7qdgS4s0uV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "023688b7-21ee-485f-f74e-115a49b3c3e8"
      },
      "source": [
        "!pip install hickle\n",
        "import hickle as hk\n",
        "from google.colab import files\n",
        "hk.dump(FinalTimeSeries.cpu().detach().numpy(),'TS_FB_2.hkl', mode='w')\n",
        "hk.dump(FinalConstants.cpu().detach().numpy(),'C_FB_2.hkl', mode='w')\n",
        "hk.dump(InteractionsFinal.cpu().detach().numpy(),'I_FB_2.hkl', mode='w')\n",
        "files.download('TS_FB_2.hkl')\n",
        "files.download('C_FB_2.hkl')\n",
        "files.download('I_FB_2.hkl')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: hickle in /usr/local/lib/python3.7/dist-packages (4.0.4)\n",
            "Requirement already satisfied: h5py<3.0.0,>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (2.10.0)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.7/dist-packages (from hickle) (1.19.5)\n",
            "Requirement already satisfied: six>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (1.15.0)\n",
            "Requirement already satisfied: dill>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (0.3.4)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6cedfa3a-5667-4fc6-9e0b-8c47bf691b89\", \"TS_FB_2.hkl\", 780164)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6182ddee-d73d-4ebf-89e4-40b90a4609db\", \"C_FB_2.hkl\", 6224)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_92ac1cd9-152f-4cee-a8cd-2b17ea5ba957\", \"I_FB_2.hkl\", 6432)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HE-9016rBj3"
      },
      "source": [
        "# Unlock Phase 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMrD8O8lrCvU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4689db6e-0933-41e0-971e-a130d5e28993"
      },
      "source": [
        "%reset -f\n",
        "!pip install faiss-gpu\n",
        "!pip install celluloid\n",
        "import faiss\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import time\n",
        "import torch\n",
        "from google.colab import files\n",
        "import random\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "device = 'cuda'\n",
        "res = faiss.StandardGpuResources()\n",
        "select = 0\n",
        "MonteCarlo = 2\n",
        "\n",
        "# Total Population\n",
        "Population = 108720\n",
        "# Number of Children (0), Adults (1), Seniors (2) and Infections (3), Confirmed Infections (4)\n",
        "Demography = torch.tensor([round(0.2278*Population),round(0.7239*Population),0,round(0.001*Population),5],dtype=torch.long,device='cuda')\n",
        "Demography[2] = Population-torch.sum(Demography[:2]) \n",
        "Demography[3] = 50\n",
        "# Layout Size: XMinimum (0), XMaximum (1), YMinimum (2), YMaximum (3), Grid Resolution (4)\n",
        "Layout = torch.tensor([0,5000,0,2000,1000],dtype=torch.double,device='cuda')\n",
        "# Minimum (0), Maximum (1), Inner (2), Innermost (3) Allowed Speeds\n",
        "VelocityParameters = torch.tensor([-500,500,200,10,1],dtype=torch.long,device='cuda')\n",
        "# Collision Radius (0), Infection Radius (1), OuterCircle (2), InnerCircle (3), Reached Goal (4)\n",
        "Radii = torch.tensor([5,2,570,120,5],dtype=torch.long,device='cuda') # Infection Radius(20) > Collision Radius(5/50) --> no social distancing\n",
        "# Total time (days), Resolution(minutes)\n",
        "Time = torch.tensor([31,15],dtype=torch.long, device='cuda') \n",
        "# TimeSteps in a day (1 step = 15mins) and total\n",
        "TimeStepsDay = torch.div(24*60,Time[1].item())\n",
        "# Total time steps in 31 days\n",
        "TimeStepsTotal = TimeStepsDay*Time[0].long()\n",
        "\n",
        "# Household positions of each agent\n",
        "Residence = torch.tensor(np.loadtxt('blore_agent_location.dat'),dtype=torch.double,device='cuda')\n",
        "# Age of the individual\n",
        "Age = torch.tensor(np.loadtxt('blore_Age.dat'),dtype=torch.long,device='cuda')\n",
        "# Household size\n",
        "H_Size = torch.tensor(np.loadtxt('blore_Age_House.dat'),dtype=torch.long,device='cuda')\n",
        "# 1-member households\n",
        "H_Size[H_Size==0] = 1\n",
        "# ID of the households of each agent\n",
        "H_Number = torch.tensor(np.loadtxt('blore_House_number.dat'),dtype=torch.long,device='cuda')\n",
        "# Location of each household\n",
        "H_location = torch.tensor(np.loadtxt('blore_household_location.dat'),dtype=torch.long,device='cuda')\n",
        "\n",
        "# Re-assign x and y initial positions\n",
        "# Assign positions within unit radius\n",
        "adjustment = 0.5\n",
        "Residence = H_location[H_Number] + torch.DoubleTensor(Population,2).uniform_(-adjustment,adjustment).cuda()\n",
        "# Check if the newly assigned x co-ordinate is within the boundary\n",
        "Residence[Residence[:,0]>Layout[1],0] = (torch.DoubleTensor(Population).uniform_(Layout[1]-adjustment,Layout[1]).cuda())[Residence[:,0]>Layout[1]]\n",
        "Residence[Residence[:,0]<Layout[0],0] = (torch.DoubleTensor(Population).uniform_(Layout[0],Layout[0]+adjustment).cuda())[Residence[:,0]<Layout[0]]\n",
        "# Check if the newly assigned y co-ordinate is within the boundary\n",
        "Residence[Residence[:,1]>Layout[3],1] = (torch.DoubleTensor(Population).uniform_(Layout[3]-adjustment,Layout[3]).cuda())[Residence[:,1]>Layout[3]]\n",
        "Residence[Residence[:,1]<Layout[2],1] = (torch.DoubleTensor(Population).uniform_(Layout[2],Layout[2]+adjustment).cuda())[Residence[:,1]<Layout[2]]\n",
        "\n",
        "Activities = np.zeros((20,96),dtype=np.float32)\n",
        "# Children\n",
        "# Errands \n",
        "Activities[0,24:36]=0.005*norm.pdf(np.arange(6,9,step=0.25), 7.5, 1) # 6-9\n",
        "Activities[0,36:56]=0.01*norm.pdf(np.arange(9,14,step=0.25), 11.5, 1) # 9-14\n",
        "Activities[0,56:72]=0.05*norm.pdf(np.arange(14,18,step=0.25), 17, 1) # 14-21\n",
        "# Adults\n",
        "# Errands \n",
        "Activities[8,24:36]=0.0025*norm.pdf(np.arange(6,9,step=0.25), 7.5, 1) # 6-9\n",
        "Activities[8,36:56]=0.005*norm.pdf(np.arange(9,14,step=0.25), 11.5, 1) # 9-14\n",
        "Activities[8,56:72]=0.0075*norm.pdf(np.arange(14,18,step=0.25), 17, 1) # 14-21\n",
        "# Nomadic\n",
        "Activities[12,24:48] = 0.1*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[12,48:72] = 0.075*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Nomads go home (18) # Weekday\n",
        "Activities[18,0:24] = np.ones(24)\n",
        "Activities[18,84:96] = np.ones(12)\n",
        "# Nomads go home (19) # Weekend\n",
        "Activities[19,0:96] = np.ones(96)\n",
        "# Children\n",
        "# Market\n",
        "Activities[1,24:48]=0.005*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[1,48:72]=0.01*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "# Adults\n",
        "# Market\n",
        "Activities[9,24:48]=0.05*norm.pdf(np.arange(6,12,step=0.25), 10, 1) # 6-12\n",
        "Activities[9,48:72]=0.1*norm.pdf(np.arange(12,18,step=0.25), 17, 1) # 12-21\n",
        "# Office \n",
        "Activities[10,24:48]= 2*norm.pdf(np.arange(6,12,step=0.25), 9, 1) # 6-12\n",
        "Activities[11,48:72] = 2*norm.pdf(np.arange(12,18,step=0.25), 16, 1) # 12-21\n",
        "\n",
        "# School\n",
        "Activities[2,24:40]=2*norm.pdf(np.arange(6,10,step=0.25), 8, 1) # 6-10\n",
        "# Go home from school:\n",
        "Activities[3,60:72]=2*norm.pdf(np.arange(15,18,step=0.25), 16.5, 1) # 3-6\n",
        "\n",
        "# Weekday Activities\n",
        "Limits = torch.tensor([0,0,0,0,0,0,1,100,1,100,0,0,\n",
        "                       0,0,7,100,0,0,2,100,2,100,0,0,\n",
        "                       0,0,0,0,15,100,4,100,3,100,0,0,\n",
        "                       5,100,4,100,6,100,1,100,100,100,0,0,\n",
        "                       10,100,4,100,5,100,1,100,1,100,2,100,\n",
        "                       0,0,0,0,0,0,0,0,1,100,1,100],dtype=torch.long,device='cuda')\n",
        "# Weekend Activities                    \n",
        "Limits_Weekend = torch.tensor([0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0,\n",
        "                               0,0,0,0,0,0,0,0,0,0,0,0],dtype=torch.long,device='cuda')\n",
        "Location_one = torch.tensor([12,13,14,15,16,17],dtype=torch.long,device='cuda') # Location of i\n",
        "Location_two = torch.tensor([[12,0,0,15,16,0],\n",
        "                                 [0,13,0,15,16,0],                                \n",
        "                                 [0,0,14,15,16,0],\n",
        "                                 [12,13,14,15,16,0],\n",
        "                                 [12,13,14,15,16,17],\n",
        "                                 [0,0,0,0,16,17]],dtype=torch.long,device='cuda') # Corresponding locations of j \n",
        "\n",
        "def goal_assign(x,y,minbias,maxbias,xhalfmark,yhalfmark):\n",
        "  '''\n",
        "  Generate Random Goals\n",
        "  '''\n",
        "  # ordinate\n",
        "  l = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  # abscissa\n",
        "  m = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  temp = x -  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  l[x>xhalfmark] = temp[x>xhalfmark] \n",
        "  temp = y -  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  m[y>yhalfmark] = temp[y>yhalfmark] \n",
        "  temp = x +  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  l[x<xhalfmark] = temp[x<xhalfmark] \n",
        "  temp = y +  torch.FloatTensor(Population).uniform_(minbias,maxbias).cuda()\n",
        "  m[y<yhalfmark] = temp[y<yhalfmark] \n",
        "  return l,m   \n",
        "\n",
        "def generateMatrix(agents,matrixone,limits,Population, H_Number, index,con_one, con_two,Time_Daily,matrixthree,distance,radius):\n",
        "  '''\n",
        "  Generate a conditional contact matrix\n",
        "  '''\n",
        "  # Store end result\n",
        "  matrixtwo = torch.zeros((2,Population,20),dtype=torch.double,device='cuda')\n",
        "  count=0\n",
        "  for i in range(6):\n",
        "    vec_con1 = torch.zeros((Population),device='cuda',dtype=torch.bool)\n",
        "    vec_con1 += (agents==con_one[i])\n",
        "    Time_Daily[vec_con1==True,i]+=1\n",
        "    for j in range(6):\n",
        "      temp = torch.zeros((Population,20),device='cuda',dtype=torch.int32)\n",
        "      vec_con2 = torch.zeros((Population,20),device='cuda',dtype=torch.bool)      \n",
        "      if (con_two[i][j]!=0):\n",
        "        if (count==0):          \n",
        "          vec_con2 = torch.logical_and(H_Number[index]==H_Number[:,None],(agents[index]==con_two[i][j]))\n",
        "        else:\n",
        "          vec_con2 = (agents[index]==con_two[i][j])\n",
        "        temp = vec_con1[:,None]*vec_con2\n",
        "        matrixone[:,count] += torch.sum(temp,1)\n",
        "        #matrixone[:,count] += torch.sum(torch.logical_and(temp,distance<radius),1)\n",
        "        if (count!=0):\n",
        "          matrixone[matrixone[:,count]>limits[:,count],count] = limits[matrixone[:,count]>limits[:,count],count]\n",
        "        #tempone = temp*(1-(torch.cumsum(torch.logical_and(temp,distance<radius),axis=1)>matrixone[:,count][:,None]).long())\n",
        "        tempone = temp*(1-(torch.cumsum(temp,axis=1)>matrixone[:,count][:,None]).long())\n",
        "        matrixtwo[0] += tempone\n",
        "        matrixtwo[1] = temp\n",
        "        if (torch.where(vec_con1==True)[0].size(0)!=0):\n",
        "          matrixthree[i,j] += torch.where(tempone==1)[0].size(0)/torch.where(vec_con1==True)[0].size(0)\n",
        "      count+=1\n",
        "  return matrixtwo[0],matrixtwo[1], matrixone, matrixthree, Time_Daily\n",
        "\n",
        "def nearest_destination(Population,centroids,Residence): \n",
        "  '''\n",
        "  Find the nearest destination: Office/School/Market\n",
        "  '''\n",
        "  distance = torch.zeros((Population,len(centroids)),device='cuda',dtype=torch.double)\n",
        "  for i in range(len(centroids)):\n",
        "    # Check the distance from the centroid of the zone to each household location\n",
        "    distance[:,i] = torch.linalg.norm(Residence-Centroids[i],axis=1)\n",
        "  return torch.argmin(distance,axis=1)\n",
        "\n",
        "# Schools\n",
        "School=torch.tensor([[1000,1500,500,1000],\n",
        "                     [2000,2500,300,500],\n",
        "                     [3500,4000,800,1000],\n",
        "                     [3500,4000,1500,1700],\n",
        "                     [600,900,1600,1800]],dtype=torch.long,device='cuda')\n",
        "School_Positions = torch.zeros((Population,10),dtype=torch.double,device='cuda')\n",
        "School_Positions[:,0] = torch.DoubleTensor(Population).uniform_(School[0,0],School[0,1])\n",
        "School_Positions[:,2] = torch.DoubleTensor(Population).uniform_(School[1,0],School[1,1])\n",
        "School_Positions[:,4] = torch.DoubleTensor(Population).uniform_(School[2,0],School[2,1])\n",
        "School_Positions[:,6] = torch.DoubleTensor(Population).uniform_(School[3,0],School[3,1])\n",
        "School_Positions[:,8] = torch.DoubleTensor(Population).uniform_(School[4,0],School[4,1])\n",
        "School_Positions[:,1] = torch.DoubleTensor(Population).uniform_(School[0,2],School[0,3])\n",
        "School_Positions[:,3] = torch.DoubleTensor(Population).uniform_(School[1,2],School[1,3])\n",
        "School_Positions[:,5] = torch.DoubleTensor(Population).uniform_(School[2,2],School[2,3])\n",
        "School_Positions[:,7] = torch.DoubleTensor(Population).uniform_(School[3,2],School[3,3])\n",
        "School_Positions[:,9] = torch.DoubleTensor(Population).uniform_(School[4,2],School[4,3])\n",
        "\n",
        "# Offices\n",
        "Office = torch.tensor([[1000,1500,1000,1500],\n",
        "                      [2000,2500,0,300],\n",
        "                      [3500,4000,1000,1300],\n",
        "                      [3500,4000,500,800],\n",
        "                      [1500,2000,500,1000],\n",
        "                      [3500,4000,1700,2000],\n",
        "                      [2500,3000,500,1000]],dtype=torch.long,device='cuda')\n",
        "Office_Positions = torch.zeros((Population,14),dtype=torch.double,device='cuda')\n",
        "Office_Positions[:,0] = torch.DoubleTensor(Population).uniform_(Office[0,0],Office[0,1])\n",
        "Office_Positions[:,2] = torch.DoubleTensor(Population).uniform_(Office[1,0],Office[1,1])\n",
        "Office_Positions[:,4] = torch.DoubleTensor(Population).uniform_(Office[2,0],Office[2,1])\n",
        "Office_Positions[:,6] = torch.DoubleTensor(Population).uniform_(Office[3,0],Office[3,1])\n",
        "Office_Positions[:,8] = torch.DoubleTensor(Population).uniform_(Office[4,0],Office[4,1])\n",
        "Office_Positions[:,10] = torch.DoubleTensor(Population).uniform_(Office[5,0],Office[5,1])\n",
        "Office_Positions[:,12] = torch.DoubleTensor(Population).uniform_(Office[6,0],Office[6,1])\n",
        "Office_Positions[:,1] = torch.DoubleTensor(Population).uniform_(Office[0,2],Office[0,3])\n",
        "Office_Positions[:,3] = torch.DoubleTensor(Population).uniform_(Office[1,2],Office[1,3])\n",
        "Office_Positions[:,5] = torch.DoubleTensor(Population).uniform_(Office[2,2],Office[2,3])\n",
        "Office_Positions[:,7] = torch.DoubleTensor(Population).uniform_(Office[3,2],Office[3,3])\n",
        "Office_Positions[:,9] = torch.DoubleTensor(Population).uniform_(Office[4,2],Office[4,3])\n",
        "Office_Positions[:,11] = torch.DoubleTensor(Population).uniform_(Office[5,2],Office[5,3])\n",
        "Office_Positions[:,13] = torch.DoubleTensor(Population).uniform_(Office[6,2],Office[6,3])\n",
        "\n",
        "# Markets\n",
        "Market = torch.tensor([[2500,3500,1000,1500],\n",
        "                       [1000,1500,0,500],\n",
        "                       [1500,2000,1500,2000],\n",
        "                       [0,500,1000,1300],\n",
        "                       [4500,5000,1100,1300],\n",
        "                       [4000,4500,0,500]],dtype=torch.long,device='cuda')\n",
        "Market_Positions = torch.zeros((Population,12),dtype=torch.double,device='cuda')\n",
        "Market_Positions[:,0] = torch.DoubleTensor(Population).uniform_(Market[0,0],Market[0,1])\n",
        "Market_Positions[:,2] = torch.DoubleTensor(Population).uniform_(Market[1,0],Market[1,1])\n",
        "Market_Positions[:,4] = torch.DoubleTensor(Population).uniform_(Market[2,0],Market[2,1])\n",
        "Market_Positions[:,6] = torch.DoubleTensor(Population).uniform_(Market[3,0],Market[3,1])\n",
        "Market_Positions[:,8] = torch.DoubleTensor(Population).uniform_(Market[4,0],Market[4,1])\n",
        "Market_Positions[:,10] = torch.DoubleTensor(Population).uniform_(Market[5,0],Market[5,1])\n",
        "Market_Positions[:,1] = torch.DoubleTensor(Population).uniform_(Market[0,2],Market[0,3])\n",
        "Market_Positions[:,3] = torch.DoubleTensor(Population).uniform_(Market[1,2],Market[1,3])\n",
        "Market_Positions[:,5] = torch.DoubleTensor(Population).uniform_(Market[2,2],Market[2,3])\n",
        "Market_Positions[:,7] = torch.DoubleTensor(Population).uniform_(Market[3,2],Market[3,3])\n",
        "Market_Positions[:,9] = torch.DoubleTensor(Population).uniform_(Market[4,2],Market[4,3])\n",
        "Market_Positions[:,11] = torch.DoubleTensor(Population).uniform_(Market[5,2],Market[5,3])\n",
        "\n",
        "# Centroids\n",
        "Centroids = torch.zeros((18,2),dtype=torch.double,device='cuda')\n",
        "Centroids[:5,0] = School[:,0] + 0.5*School[:,1]\n",
        "Centroids[:5,1] = School[:,2] + 0.5*School[:,3]\n",
        "Centroids[5:12,0] = Office[:,0] + 0.5*Office[:,1]\n",
        "Centroids[5:12,1] = Office[:,2] + 0.5*Office[:,3]\n",
        "Centroids[12:18,0] = Market[:,0] + 0.5*Market[:,1]\n",
        "Centroids[12:18,1] = Market[:,2] + 0.5*Market[:,3]\n",
        "\n",
        "# Find nearest destination\n",
        "Nearest = torch.zeros((Population,3),device='cuda',dtype=torch.long)\n",
        "Nearest[:,0] = nearest_destination(Population,Centroids[:5],Residence)\n",
        "Nearest[:,1] = nearest_destination(Population,Centroids[5:12],Residence)\n",
        "Nearest[:,2] = nearest_destination(Population,Centroids[12:18],Residence)\n",
        "\n",
        "# ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "# Disease Transmission Parameters\n",
        "# -------------------------------------\n",
        "\n",
        "# States: SIT/SIN/AIT/AIN/SUT/SUN\n",
        "States = torch.tensor(([0,0,0,0,0,1,2,3],\n",
        "                       [0,0,0,1,0,0,0,0],\n",
        "                       [6,7,6,7,0,0,0,0],\n",
        "                       [9,9,9,9,0,0,0,0],\n",
        "                       [0,0,0,0,0,0,0,0],\n",
        "                       [0,1,2,3,4,5,6,7]),dtype=torch.int,device='cuda')\n",
        "# Disease Transition Probabilities\n",
        "Transition = torch.zeros((3,6,8),dtype=torch.double,device='cuda')\n",
        "undercount = 0\n",
        "# Children \n",
        "Transition[0] = torch.transpose(torch.tensor(([0,0,0.12495,0.00005,0,0.875],\n",
        "                                              [0,0,0.09994,0.00006,undercount,0.9-undercount],\n",
        "                                              [0,0.00998,0.19,0.00002,0,0.8],\n",
        "                                              [0,0.02996,0.12,0.00004,undercount,0.85-undercount],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463],\n",
        "                                              [0.2537,0,0,0,0,0.7463]),dtype=torch.double,device='cuda'),0,1) \n",
        "# Adults\n",
        "Transition[1] = torch.transpose(torch.tensor(([0,0,0.124,0.001,0,0.875],\n",
        "                                              [0,0,0.06,0.04,undercount,0.9-undercount],\n",
        "                                              [0,0.03993,0.16,0.00007,0,0.8],\n",
        "                                              [0,0.0493,0.1,0.0007,undercount,0.85-undercount],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5],\n",
        "                                              [0.5,0,0,0,0,0.5]),dtype=torch.double,device='cuda'),0,1) \n",
        "# Seniors\n",
        "Transition[2] = torch.transpose(torch.tensor(([0,0,0.065,0.06,0,0.875],\n",
        "                                              [0,0,0.02,0.08,undercount,0.9-undercount],\n",
        "                                              [0,0.08,0.1,0.02,0,0.8],\n",
        "                                              [0,0.1,0.01,0.04,undercount,0.85-undercount],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049],\n",
        "                                              [0.5951,0,0,0,0,0.4049]),dtype=torch.double,device='cuda'),0,1)\n",
        "# Storage Parameters\n",
        "# Outermost Loop (Scenario/Final Parameters)\n",
        "# Disease Transmission: S/IT/INT/R/D/Total (7+7, Mean and Variance states) \n",
        "FinalTimeSeries = torch.zeros((65,(TimeStepsTotal.long().item())+1), dtype=torch.float,device='cuda')\n",
        "# Time to infection, death, recovery (Mean - 0,1,2, Variance - 3,4,5)\n",
        "FinalConstants = torch.zeros((20), dtype=torch.float,device='cuda')\n",
        "InteractionsFinal = torch.zeros((2,6,6),dtype=torch.float,device='cuda')\n",
        "# Outermost loop\n",
        "# Second Loop (MonteCarlo Run Parameters)\n",
        "# ResultsTimeSeries (Time Series for MC runs)(0-6)\n",
        "ResultsTMC = torch.zeros((MonteCarlo,29,(TimeStepsTotal.long().item())+1), dtype=torch.float,device='cuda')\n",
        "# Results Constants: Time to Infection: Overall(0), Recovery(1), Death(2)\n",
        "ResultsCMC = torch.zeros((MonteCarlo,10,1), dtype=torch.float,device='cuda')\n",
        "InteractionsMC = torch.zeros((MonteCarlo,2,6,6), dtype=torch.float,device='cuda')\n",
        "\n",
        "for mc in range(MonteCarlo):\n",
        "  # Create N agents;\n",
        "  # Age = 0,     \n",
        "  # x_position = 1, \n",
        "  # y_position = 2, \n",
        "  # x_velocity = 3, \n",
        "  # y_velocity = 4, \n",
        "  # occupation = 5,\n",
        "  # current_task = 7, \n",
        "  # status_of_task = 8, \n",
        "  # goal_x = 9, \n",
        "  # goal_y = 10,\n",
        "  # infection_status = 14, \n",
        "  # infection_state = 15, \n",
        "  # time_of_infectiousness = 16\n",
        "  Agents = torch.zeros((Population,20),dtype=torch.double,device='cuda')\n",
        "  # Assign Age \n",
        "  Agents[:,0] = Age\n",
        "  # Indices of Children\n",
        "  Children = torch.where(Agents[:,0]==0)[0]\n",
        "  # Indices of Adults\n",
        "  Adults = torch.where(Agents[:,0]==1)[0]\n",
        "  # Indices of Seniors\n",
        "  Seniors = torch.where(Agents[:,0]==2)[0]\n",
        "  # Assign x and y Positions\n",
        "  # PositionX (1)\n",
        "  Agents[:,1] = Residence[:,0].detach().clone()\n",
        "  # PositionY (2)\n",
        "  Agents[:,2] = Residence[:,1].detach().clone()\n",
        "  # Assign x and y Velocity Components\n",
        "  # VelocityX (3)\n",
        "  Agents[:,3] = torch.DoubleTensor(Population).uniform_(VelocityParameters[0],VelocityParameters[1])\n",
        "  # VelocityY (4)\n",
        "  Agents[:,4] = torch.DoubleTensor(Population).uniform_(VelocityParameters[0],VelocityParameters[1])\n",
        "  # Social Distance:  \n",
        "  SD = Radii[0]*torch.ones((Population),dtype=torch.long,device='cuda')\n",
        "  # Fraction not following (0: Everyone, 1: No one)\n",
        "  SD[torch.tensor([True,False],device='cuda')[torch.multinomial(torch.tensor([0.9,0.1]),Population,replacement=True)]] = 0\n",
        "  # Occupation (5): \n",
        "  # School-going Children (1)\n",
        "  Agents[Children[:int(0.75*Demography[0])],5] = 1\n",
        "  # School-going Adults (1)\n",
        "  Agents[Adults[:int(0.05*Demography[1])],5] = 1\n",
        "  # School-going Seniors (1)\n",
        "  Agents[Seniors[:int(0.02*Demography[2])],5] = 1\n",
        "  # Stay-at-home adults (2)\n",
        "  Agents[Adults[int(0.05*Demography[1]):int(0.25*Demography[1])],5] = 2\n",
        "  # Office Going Adults (3)\n",
        "  Agents[Adults[int(0.25*Demography[1]):int(0.85*Demography[1])],5] = 3\n",
        "  # Nomadic Adults (4)\n",
        "  Agents[Adults[int(0.85*Demography[1]):Demography[1]],5] = 4\n",
        "  # Stay-at-home Seniors (5)\n",
        "  Agents[Seniors[int(0.02*Demography[2]):int(0.62*Demography[2])],5] = 5 \n",
        "  # Office Going Seniors (6)\n",
        "  Agents[Seniors[int(0.62*Demography[2]):int(0.95*Demography[2])],5] = 6\n",
        "  # Reduce office-goers by fraction\n",
        "  #stay = torch.where(torch.logical_or(Agents[:,5]==3,Agents[:,5]==6))[0]\n",
        "  #staynumber = stay[:int(0*stay.size(0))]\n",
        "  #Agents[staynumber[Agents[staynumber,0]==1],5] = 2\n",
        "  #Agents[staynumber[Agents[staynumber,0]==2],5] = 5\n",
        "  # Nomadic Seniors (7)\n",
        "  Agents[Seniors[int(0.95*Demography[2]):Demography[2]],5] = 7\n",
        "  # Assign Infection Status: Infected and Tested (1), Uninfect(0), Infected and Not Tested (3)\n",
        "  # 0.5% infections among children, 49% infections among adults, 50.5% among seniors\n",
        "  Agents[Children[:int(0.005*Demography[3])],14] = 3 # Infected \n",
        "  Agents[Adults[int(0.005*Demography[3]):int(0.49*Demography[3])],14] = 3 # Infected \n",
        "  Agents[Seniors[int(0.49*Demography[3]):Demography[3]],14] = 3 # Infected \n",
        "  Agents[torch.where(Agents[:,14]==3)[0][:int(Demography[4])],14] = 1 # Confirmed Infections\n",
        "  # Assign Infection State: SIT=0, SIN=1, AIT=2, AIN=3, SUT=4, SUN=5, AUT=6, AUN=7\n",
        "  # Infected\n",
        "  # 80% of the infections are asymptomatic\n",
        "  # 20% of the infections are symptomatic\n",
        "  Agents[torch.where(Agents[:,14]==3)[0][:int(0.8*Demography[3])],15] = 3 # AIN\n",
        "  Agents[torch.where(Agents[:,14]==3)[0][int(0.8*Demography[3]):Demography[3]],15] = 1 # SIN\n",
        "  # Uninfected\n",
        "  # 85% of the uninfected are asymptomatic\n",
        "  # 15% of the uninfected are symptomatic\n",
        "  Agents[torch.where(Agents[:,14]==0)[0][:int(0.85*(Population-Demography[3]))],15] = 7 # AUN\n",
        "  Agents[torch.where(Agents[:,14]==0)[0][int(0.85*(Population-Demography[3])):Population-Demography[3]],15] = 5 # SUN\n",
        "  Agents[torch.where(Agents[:,14]==1)[0],15] = 2\n",
        "  # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "  # Placeholder Variables\n",
        "  # -------------------------------------\n",
        "\n",
        "  # Vectors \n",
        "  Vectors = torch.zeros((12,Population),device='cuda',dtype=torch.double)\n",
        "  # Distance Matrix(0), Index Matrix(1), Condition Matrix(2)\n",
        "  Matrixs = torch.zeros((5,Population,20),dtype=torch.double,device='cuda')\n",
        "  # Initial Position \n",
        "  CurrentPosition = torch.dstack((Agents[:,1],Agents[:,2]))\n",
        "  # Initial Velocity\n",
        "  Velocity = torch.dstack((Agents[:,3],Agents[:,4]))\n",
        "  # All agents are initially at home (12)\n",
        "  Agents[:,7] = 12\n",
        "  # Status of the task is complete and initially at home (12)\n",
        "  Agents[:,8] = Agents[:,7].detach().clone()\n",
        "  # Ordinate of goal position\n",
        "  Agents[:,9] = Agents[:,1].detach().clone()\n",
        "  # Abscissa of goal position\n",
        "  Agents[:,10] = Agents[:,2].detach().clone()\n",
        "  # Initial Goals are to remain at home\n",
        "  Destination = torch.dstack((Agents[:,1],Agents[:,2]))\n",
        "  # Days \n",
        "  Days = torch.tensor([0],device='cuda')       \n",
        "  # Distance\n",
        "  Distance = torch.zeros((Population),dtype=torch.double,device='cuda')\n",
        "  # Infection Status over Time\n",
        "  # Overall\n",
        "  Results = torch.tensor([torch.where(Agents[:,14]==0)[0].size()[0], #0:Uninfected\n",
        "                          torch.where(Agents[:,14]==1)[0].size()[0], #1:Infected and Tested\n",
        "                          torch.where(Agents[:,14]==2)[0].size()[0], #2:Unreported Recoveries\n",
        "                          torch.where(Agents[:,14]==3)[0].size()[0], #3:Infected and Not Tested\n",
        "                          torch.where(Agents[:,14]==4)[0].size()[0], #4:Unreported Deaths\n",
        "                          torch.where(Agents[:,14]==5)[0].size()[0], #5:Reported Recoveries\n",
        "                          torch.where(Agents[:,14]==6)[0].size()[0]],dtype=torch.long,device='cuda') #6:Reported Deaths  \n",
        "  # Children\n",
        "  ResultsC = torch.tensor([torch.where(Agents[Children,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Children,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "  # Adults  \n",
        "  ResultsA = torch.tensor([torch.where(Agents[Adults,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Adults,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "  # Seniors\n",
        "  ResultsS = torch.tensor([torch.where(Agents[Seniors,14]==0)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==1)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==2)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==3)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==4)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==5)[0].size()[0],\n",
        "                          torch.where(Agents[Seniors,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')   \n",
        "\n",
        "  # Interactions: Weekday=0, Weekend=1, Contact_Matrix=2\n",
        "  Interactions = torch.zeros((3,6,6),dtype=torch.double,device='cuda')\n",
        "  # Weekday = 0, Weekend = 1\n",
        "  Time_avg = torch.zeros((2,6),dtype=torch.double,device='cuda')\n",
        "  Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "  # Number of contacts on a day to day basis: Daily_Contact=0, Contact_Limit=1\n",
        "  Contacts = torch.zeros((2,Population,36),dtype=torch.long,device='cuda')\n",
        "  StatusTime = Agents[:,8].detach().clone()\n",
        "  for t in range(TimeStepsTotal.long().item()):\n",
        "\n",
        "    Days = torch.floor(torch.div(t,TimeStepsDay).cuda())\n",
        "    # Time of the day\n",
        "    time_of_day = t%96 \n",
        "    # Day of the week\n",
        "    day_of_week = Days%7 \n",
        "\n",
        "    # ------------------------------------------------\n",
        "    # Store parameters from the previous run\n",
        "    # ------------------------------------------------\n",
        "\n",
        "    # Infection Status from previous iteration\n",
        "    Vectors[4] = Agents[:,14].detach().clone()\n",
        "    # Infection State from previous iteration\n",
        "    Vectors[5] = Agents[:,15].detach().clone()\n",
        "    # Rtime\n",
        "    Vectors[6] = Vectors[1].detach().clone()\n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # Find Limits for the day\n",
        "    # ---------------------------------------------\n",
        "\n",
        "    if time_of_day==0:\n",
        "      # Assign contact limits for the weekend\n",
        "      if torch.logical_or(day_of_week==5,day_of_week==6):\n",
        "        for i in range(1,36):\n",
        "          Contacts[1][:,i] = Limits_Weekend[2*i+1]\n",
        "      else:\n",
        "        # Assign contact limits for the weekday\n",
        "        for i in range(1,36):\n",
        "          Contacts[1][:,i] = Limits[2*i+1]\n",
        "\n",
        "    #Agents[:,15]: SIT=0, SIN=1, AIT=2, AIN=3, SUT=4, SUN=5, AUT=6, AUN=7\n",
        "    #Unconfirmed to Confirmed (30 times underreported)\n",
        "    Vectors[0] = torch.rand(Population,device='cuda').double()< 0.00001920627#0.000006057605 #0.0000450422\n",
        "    Vectors[7] = torch.logical_and(Agents[:,15]==1,Vectors[0]==True)\n",
        "    Agents[Vectors[7].bool(),15] = 0 # SIN --> SIT\n",
        "    Vectors[7] = torch.logical_and(Agents[:,15]==3,Vectors[0]==True)\n",
        "    Agents[Vectors[7].bool(),15] = 2 # AIN --> AIT\n",
        "    # Confirmed Cases\n",
        "    # Infected and Tested\n",
        "    Agents[:,14][torch.logical_or(Agents[:,15]==0,Agents[:,15]==2)] = 1\n",
        "    # Unconfirmed\n",
        "    Agents[:,14][torch.logical_or(Agents[:,15]==1,Agents[:,15]==3)] = 3  \n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # State Transition of Infected Agents\n",
        "    # ---------------------------------------------\n",
        "\n",
        "    # Find all agents who have been infected for more than a day\n",
        "    Vectors[8] = torch.logical_and(torch.logical_and(torch.logical_or(Vectors[4]==1,Vectors[4]==3),Agents[:,16]%TimeStepsDay==0),Agents[:,16]>0)\n",
        "    #Vectors[8] = torch.logical_or(Vectors[4]==1,Vectors[4]==3)\n",
        "    # SIT=0: Is the person in state 0 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[0][:,0],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIT=0: Is the person in state 0 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[1][:,0],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIT=0: Is the person in state 0 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==0),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,0][torch.multinomial(Transition[2][:,0],Population,replacement=True)][Vectors[7].bool()].double()  \n",
        "    # SIN=1: Is the person in state 1 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[0][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIN=1: Is the person in state 1 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[1][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SIN=1: Is the person in state 1 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==1),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,1][torch.multinomial(Transition[2][:,1],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[0][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[1][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIT=2: Is the person in state 2 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==2),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,2][torch.multinomial(Transition[2][:,2],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[0][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[1][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AIN=3: Is the person in state 3 a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==3),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,3][torch.multinomial(Transition[2][:,3],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # Recovery\n",
        "    Agents[:,14][(Vectors[4]==1) & (Agents[:,15]==6)] = 5 # Reported recoveries\n",
        "    Agents[:,14][(Vectors[4]==3) & (Agents[:,15]==7)] = 2 # Unreported recoveries\n",
        "    # Deaths\n",
        "    Agents[:,14][(Vectors[4]==1) & (Agents[:,15]==9)] = 6 # Reported recoveries\n",
        "    Agents[:,14][(Vectors[4]==3) & (Agents[:,15]==9)] = 4 # Unreported recoveries  \n",
        "    # Time Since Infection\n",
        "    Vectors[8] = torch.logical_or(Agents[:,14]==1,Agents[:,14]==3)\n",
        "    Agents[:,16][Vectors[8].bool()] = Agents[:,16][Vectors[8].bool()]+1\n",
        "    Agents[:,17][Vectors[8].bool()] = torch.round(torch.div(Agents[:,16][Vectors[8].bool()],TimeStepsDay-1)) \n",
        "\n",
        "    # -----------------------------------------\n",
        "    # State Transition of Uninfected Agents\n",
        "    # -----------------------------------------  \n",
        "\n",
        "    # Find agents in the vicinity\n",
        "    CurrentPosition_numpy = np.float32(CurrentPosition[0].cpu().detach().numpy())\n",
        "    # Create a faiss array\n",
        "    index_flat = faiss.IndexFlatL2(CurrentPosition_numpy.shape[1])\n",
        "    gpu_index_flat = faiss.index_cpu_to_gpu(res, 0, index_flat)\n",
        "    gpu_index_flat.add(CurrentPosition_numpy)                  \n",
        "    m, n = gpu_index_flat.search(CurrentPosition_numpy, 20)\n",
        "    Matrixs[1] = torch.from_numpy(n).long()\n",
        "    #Matrixs[1] = torch.reshape(q[q!=torch.arange(0,Population,step=1)[:,None]],(Population,19))\n",
        "    # Distances \n",
        "    Matrixs[0] = torch.squeeze(torch.cdist(CurrentPosition[0][:,None],CurrentPosition[0][Matrixs[1].long()],p=2))\n",
        "    Matrixs[0][Matrixs[1]==torch.arange(0,Population,step=1).cuda()[:,None]] = float(\"Inf\")\n",
        "    # Location Matching and Contact Number Check\n",
        "    Matrixs[2],Matrixs[4],Contacts[0],Interactions[2],Time_Daily = generateMatrix(Agents[:,8],Contacts[0],Contacts[1],Population,H_Number,Matrixs[1].long(),Location_one, Location_two,Time_Daily,Interactions[2],Matrixs[0],Radii[1])\n",
        "\n",
        "    if time_of_day==0:\n",
        "      # Over weekends\n",
        "      if torch.logical_or(day_of_week==5,day_of_week==6):\n",
        "        Time_avg[1] += torch.mean(Time_Daily.double(),0)\n",
        "        Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "        Interactions[1] += Interactions[2]/48#torch.reshape(torch.sum(Contacts[0],0),(6,6))\n",
        "        Interactions[2] = torch.zeros((6,6),dtype=torch.double,device='cuda')\n",
        "        Contacts[0] = torch.zeros((Population,36),dtype=torch.long,device='cuda')\n",
        "      else:\n",
        "      # Over weekdays\n",
        "        Time_avg[0] += torch.mean(Time_Daily.double(),0)\n",
        "        Time_Daily = torch.zeros((Population,6),dtype=torch.long,device='cuda')\n",
        "        Interactions[0] += Interactions[2]/48#torch.reshape(torch.sum(Contacts[0],0),(6,6))\n",
        "        Interactions[2] = torch.zeros((6,6),dtype=torch.double,device='cuda')\n",
        "        Contacts[0] = torch.zeros((Population,36),dtype=torch.long,device='cuda')\n",
        "  \n",
        "    # Find agents with infections in the vicinity\n",
        "    Vectors[3] = torch.sum(torch.logical_and(torch.logical_and(Matrixs[2].bool()==True,Matrixs[0]<Radii[1]),torch.logical_or(Vectors[4][Matrixs[1].long()]==1,Vectors[4][Matrixs[1].long()]==3)),1)\n",
        "    # Find all uninfected agents with infections in their vicinity\n",
        "    Vectors[8] = torch.logical_and(Vectors[4]==0,Vectors[3].long()>0)\n",
        "    # SUT=4: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[0][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUT=4: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[1][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUT=4: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==4),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,4][torch.multinomial(Transition[2][:,4],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[0][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[1][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # SUN=5: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==5),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,5][torch.multinomial(Transition[2][:,5],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[0][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[1][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUT=6: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==6),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,6][torch.multinomial(Transition[2][:,6],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person a child\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==0)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[0][:,7],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person an adult\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==1)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[1][:,7],Population,replacement=True)][Vectors[7].bool()].double()\n",
        "    # AUN=7: Is the susceptible person a senior\n",
        "    Vectors[7] = torch.logical_and(torch.logical_and(Vectors[8]==1,Vectors[5]==7),Agents[:,0]==2)\n",
        "    Agents[:,15][Vectors[7].bool()] = States[:,7][torch.multinomial(Transition[2][:,7],Population,replacement=True)][Vectors[7].bool()].double() \n",
        "\n",
        "\n",
        "    # -----------------------------------------\n",
        "    # Task Allocation\n",
        "    # ----------------------------------------- \n",
        "\n",
        "    # Weekday\n",
        "    if day_of_week<5:\n",
        "      \n",
        "      # Children\n",
        "      # School going children at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==0,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going children: School (1), Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[0,time_of_day],Activities[1,time_of_day],1-Activities[0,time_of_day]-Activities[1,time_of_day]-Activities[2,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home kids\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==0, Agents[:,8]==12)\n",
        "      # Destination for children: Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[0,time_of_day],Activities[1,time_of_day],1-Activities[0,time_of_day]-Activities[1,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at school\n",
        "      Vectors[8] = Agents[:,8]==13\n",
        "      # Remain in School: Stay at school (13), Go home (0) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,13],device='cuda')[torch.multinomial(torch.tensor([Activities[3,time_of_day],1-Activities[3,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==15)\n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==17)\n",
        "      # Stay there (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Adults\n",
        "      # School going adults at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==1,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (5), Market (8), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[2,time_of_day]-Activities[8,time_of_day]-Activities[9,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==3, Agents[:,8]==12)\n",
        "      # Destination for office going adults: Office (2), Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([2,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[10,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]-Activities[10,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay at home adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==2,Agents[:,8]==12)  \n",
        "      # Destination for adults: Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==12))\n",
        "      # Adults who are nomads: Go elsewhere (10), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([10,12],device='cuda')[torch.multinomial(torch.tensor([Activities[12,time_of_day],1-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==16)) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,10,16],device='cuda')[torch.multinomial(torch.tensor([Activities[18,time_of_day],Activities[12,time_of_day],1-Activities[18,time_of_day]-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults at the office\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==14)  \n",
        "      # Stay there (14), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[11,time_of_day],1-Activities[11,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==15)  \n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==17)  \n",
        "      # Stay there (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Seniors\n",
        "      # School going seniors at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==2,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going seniors: School (1), Random (6), Market (9), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[2,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[2,time_of_day]-Activities[8,time_of_day]-Activities[9,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==6, Agents[:,8]==12)  \n",
        "      # Destination for office going seniors: Office (3), Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([3,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[10,time_of_day],Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]-Activities[10,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==5,Agents[:,8]==12)  \n",
        "      # Destination for seniors: Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[8,time_of_day],Activities[9,time_of_day],1-Activities[8,time_of_day]-Activities[9,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==12))  \n",
        "      # Seniors who are nomads: Go elsewhere (11), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([11,12],device='cuda')[torch.multinomial(torch.tensor([Activities[12,time_of_day],1-Activities[12,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==16))  \n",
        "      # Seniors who are nomads: Go home (0), Go elsewhere (11), Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,11,16],device='cuda')[torch.multinomial(torch.tensor([Activities[18,time_of_day],Activities[12,time_of_day],1-Activities[12,time_of_day]-Activities[18,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at office \n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[11,time_of_day],1-Activities[11,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()  \n",
        "      # Seniors running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==15)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "\n",
        "    else:\n",
        "\n",
        "      # Children\n",
        "      # School going children\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==0,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going children: School (1), Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[4,time_of_day],Activities[5,time_of_day],1-Activities[4,time_of_day]-Activities[5,time_of_day]-Activities[6,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home kids\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==0, Agents[:,8]==12)\n",
        "      # Destination for children: Random (4), Market (7), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([4,7,12],device='cuda')[torch.multinomial(torch.tensor([Activities[4,time_of_day],Activities[5,time_of_day],1-Activities[4,time_of_day]-Activities[5,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at school\n",
        "      Vectors[8] = Agents[:,8]==13\n",
        "      # Remain in School: Stay at school (13), Go home (0) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,13],device='cuda')[torch.multinomial(torch.tensor([Activities[7,time_of_day],1-Activities[7,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==15)\n",
        "      # Stay there (15), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Kids at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==0,Agents[:,8]==17)\n",
        "      # Stay there Market (17), Go home (0)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Adults\n",
        "      # School going adults at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==1,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (5), Market (8), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[6,time_of_day]-Activities[13,time_of_day]-Activities[14,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going adults \n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==3, Agents[:,8]==12)  \n",
        "      # Destination for office going adults: Office (2), Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([2,5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[15,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]-Activities[15,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home adults\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==2,Agents[:,8]==12)  \n",
        "      # Destination for adults: Random (5), Market (8), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([5,8,12],device='cuda')[torch.multinomial(torch.tensor([Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==12))\n",
        "      # Adults who are nomads: Go elsewhere (10), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([10,12],device='cuda')[torch.multinomial(torch.tensor([Activities[17,time_of_day],1-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic adults on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(Agents[:,5]==4,Agents[:,8]==16))\n",
        "      # Adults who are nomads: Go home (0), Go elsehwere (10),Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,10,16],device='cuda')[torch.multinomial(torch.tensor([Activities[19,time_of_day],Activities[17,time_of_day],1-Activities[17,time_of_day]-Activities[19,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==15)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults returning from market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Adults returning from office\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==1,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[16,time_of_day],1-Activities[16,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      \n",
        "      # Seniors\n",
        "      # School going seniors at home\n",
        "      Vectors[8] = torch.logical_and(torch.logical_and(Agents[:,0]==2,Agents[:,5]==1), Agents[:,8]==12)  \n",
        "      # Destination for school going adults: School (1), Random (6), Market (9), Stay at home (12) \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([1,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[6,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[6,time_of_day]-Activities[13,time_of_day]-Activities[14,time_of_day]]),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Office going seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==6, Agents[:,8]==12)  \n",
        "      # Destination for office going seniors: Office (3), Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([3,6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[15,time_of_day],Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]-Activities[15,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Stay-at-home seniors\n",
        "      Vectors[8] = torch.logical_and(Agents[:,5]==5,Agents[:,8]==12)  \n",
        "      # Destination for seniors: Random (6), Market (9), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([6,9,12],device='cuda')[torch.multinomial(torch.tensor([Activities[13,time_of_day],Activities[14,time_of_day],1-Activities[13,time_of_day]-Activities[14,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors at home\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==12))  \n",
        "      # Seniors who are nomads: Go elsewhere (11), Stay home (12)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([11,12],device='cuda')[torch.multinomial(torch.tensor([Activities[17,time_of_day],1-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Nomadic Seniors on the move\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(Agents[:,5]==7,Agents[:,8]==16))  \n",
        "      # Seniors who are nomads: Go home(0), Go elsewhere (11), Stay there (16)\n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,11,16],device='cuda')[torch.multinomial(torch.tensor([Activities[19,time_of_day],Activities[17,time_of_day],1-Activities[19,time_of_day]-Activities[17,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at office \n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==14)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,14],device='cuda')[torch.multinomial(torch.tensor([Activities[16,time_of_day],1-Activities[16,time_of_day]],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()  \n",
        "      # Seniors running errands\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==15)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,15],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "      # Seniors at the market\n",
        "      Vectors[8] = torch.logical_and(Agents[:,0]==2,Agents[:,8]==17)  \n",
        "      Agents[:,7][Vectors[8]==1] = torch.tensor([0,17],device='cuda')[torch.multinomial(torch.tensor([0.7,0.3],device='cuda'),Population,replacement=True)][Vectors[8]==1].double()\n",
        "    \n",
        "    # --------------------------------\n",
        "    # Assign Task-based Goal Positions\n",
        "    # --------------------------------\n",
        "\n",
        "    for i in range(len(School)):\n",
        "      # Goal of School Going Children\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,0]==i,torch.logical_and(Agents[:,8]==12,Agents[:,7]==1))# Stay at home (12) --> Go to School (1)\n",
        "      Agents[:,9][Vectors[8]==1] = School_Positions[Vectors[8]==1,2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = School_Positions[Vectors[8]==1,2*i+1]\n",
        "    for i in range(len(Office)):\n",
        "      # Goal of Office Going Adults and Seniors\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,0]==i,torch.logical_and(Agents[:,8]==12,torch.logical_or(Agents[:,7]==2,Agents[:,7]==3)))# Stay at home --> Office (Adults/Seniors)\n",
        "      Agents[:,9][Vectors[8]==1] = Office_Positions[Vectors[8]==1,2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = Office_Positions[Vectors[8]==1,2*i+1]\n",
        "    for i in range(len(Market)):\n",
        "      # Goal of Market Going Adults/Seniors/Children\n",
        "      Vectors[8] = torch.logical_and(Nearest[:,1]==i,torch.logical_and(Agents[:,8]==12,torch.logical_or(torch.logical_or(Agents[:,7]==7,Agents[:,7]==8),Agents[:,7]==9))) # Home --> Market\n",
        "      temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "      Agents[:,9][Vectors[8]==1] = Market_Positions[torch.randperm(Population)[:int(temp1)],2*i]\n",
        "      Agents[:,10][Vectors[8]==1] = Market_Positions[torch.randperm(Population)[:int(temp1)],2*i+1]\n",
        "    # Goal of Adults running mobile jobs\n",
        "    mobile = goal_assign(CurrentPosition[0][:,0],CurrentPosition[0][:,1],300,800,0.5*Layout[1],0.5*Layout[3]) #300,800\n",
        "    Vectors[8] = torch.logical_and(Agents[:,0]==1,torch.logical_and(torch.logical_or(Agents[:,8]==16,Agents[:,8]==12),Agents[:,7]==10))\n",
        "    temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of Seniors running mobile jobs\n",
        "    mobile = goal_assign(CurrentPosition[0][:,0],CurrentPosition[0][:,1],300,800,0.5*Layout[1],0.5*Layout[3]) #300,800\n",
        "    Vectors[8] = torch.logical_and(Agents[:,0]==2,torch.logical_and(torch.logical_or(Agents[:,8]==16,Agents[:,8]==12),Agents[:,7]==11))\n",
        "    temp1 = torch.where(Vectors[8]==1)[0].size(0)\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of kids running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==4)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,500,0.5*Layout[1],0.5*Layout[3])#300,500\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of adults running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==5)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,800,0.5*Layout[1],0.5*Layout[3])#300,800\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1]\n",
        "    # Goal of seniors running errands\n",
        "    Vectors[8] = torch.logical_and(Agents[:,8]==12,Agents[:,7]==6)\n",
        "    mobile = goal_assign(Agents[:,1].clone(),Agents[:,2].clone(),300,800,0.5*Layout[1],0.5*Layout[3])#300,800\n",
        "    Agents[:,9][Vectors[8]==1] = mobile[0][Vectors[8]==1]\n",
        "    Agents[:,10][Vectors[8]==1] = mobile[1][Vectors[8]==1] \n",
        "    # Goal of Agents Going Home\n",
        "    Vectors[8] = torch.logical_or(torch.logical_and(Agents[:,8]==12,Agents[:,7]==12),Agents[:,7]==0)\n",
        "    Agents[:,9][Vectors[8]==1] = Agents[Vectors[8]==1,1].clone().detach()\n",
        "    Agents[:,10][Vectors[8]==1] = Agents[Vectors[8]==1,2].clone().detach()\n",
        "    \n",
        "    # -----------------------------\n",
        "    # Move Towards a Goal\n",
        "    # -----------------------------\n",
        "\n",
        "    # New goal position corresponding to the assigned task\n",
        "    Destination = torch.dstack((Agents[:,9],Agents[:,10]))\n",
        "    # Distance between the current position and assigned goal\n",
        "    Distance = torch.linalg.norm(CurrentPosition[0]-Destination[0],dim=1)\n",
        "    # Goal following velocity vector\n",
        "    Velocity[0] += Destination[0]-CurrentPosition[0]\n",
        "\n",
        "    # ------------------------------\n",
        "    # Collision Avoidance \n",
        "    # ------------------------------\n",
        "\n",
        "    # Reset avoidance vector in every iteration\n",
        "    VRule = torch.zeros((Population,2),dtype=torch.double,device='cuda') \n",
        "    # Find agents inside critical radius \n",
        "    Matrixs[3] = torch.logical_and(torch.logical_and(Matrixs[0]<SD[:,None],Matrixs[0]!=0),Matrixs[4]==True)\n",
        "    # Collision Avoidance Vector\n",
        "    Difference = CurrentPosition[0][:,None]-CurrentPosition[0][Matrixs[1].long()]\n",
        "    Difference[~Matrixs[3].bool()] = 0\n",
        "    # Number of agents inside the critical radius\n",
        "    Vectors[9] = torch.sum(Matrixs[3],1)\n",
        "    # Normalizing the displacement vector\n",
        "    Difference[Matrixs[3].bool()]/=torch.square(Matrixs[0][Matrixs[3].bool()])[:,None]\n",
        "    VRule[Vectors[9]>0] = torch.div(torch.sum(Difference,1)[Vectors[9]>0],torch.sum(Matrixs[3],1)[Vectors[9]>0][:,None]).double()\n",
        "    # Total Collision avoidance vector component\n",
        "    VRule[Vectors[9]>0] -= Velocity[0][Vectors[9]>0].clone().detach()\n",
        "\n",
        "    # --------------------\n",
        "    # Update Velocity\n",
        "    # --------------------\n",
        "\n",
        "    # Update the velocity of agents\n",
        "    Vectors[8] = torch.logical_and(torch.sum(Matrixs[3],1)>0,Agents[:,8]>=12)\n",
        "    Velocity[0][Vectors[8]==1] += VRule[Vectors[8]==1].clone().detach()\n",
        "\n",
        "    # ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "    # Limit Speed  \n",
        "    # --------------------  \n",
        "\n",
        "    # Speed is MaximumVelocity when outside the outer circle\n",
        "    Velocity[0][Distance>Radii[2]] = torch.div(Velocity[0][Distance>Radii[2]],torch.norm(Velocity[0],dim=1)[Distance>Radii[2]][:,None])*VelocityParameters[1]   \n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[2],torch.linalg.norm(Velocity[0],dim=1)!=0)\n",
        "    # Adjustment of velocity according to an exponential function in the intermediate region\n",
        "    Velocity[0][Vectors[8]==1] = torch.div(Velocity[0][Vectors[8]==1],torch.norm(Velocity[0],dim=1)[Vectors[8]==1][:,None])*(1000*torch.exp(-0.009*(Radii[2]-Distance[Vectors[8]==1][:,None])))\n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[4],torch.linalg.norm(Velocity[0],dim=1)!=0)\n",
        "    # Adjustment of velocity in the innermost circle\n",
        "    Velocity[0][Vectors[8]==1] = torch.div(Velocity[0][Vectors[8]==1],torch.norm(Velocity[0],dim=1)[Vectors[8]==1][:,None])*VelocityParameters[4]\n",
        "    Vectors[8] = torch.logical_and(Distance<=Radii[4],torch.linalg.norm(Velocity[0],dim=1)==0)\n",
        "    # Minimum velocity in the innermost circle\n",
        "    Velocity[0][Vectors[8]==1] = torch.sign(Velocity[0][Vectors[8]==1])*torch.tensor([0.4,0.4],dtype=torch.double,device='cuda')\n",
        "\n",
        "    # --------------------\n",
        "    # Update Position\n",
        "    # --------------------\n",
        "\n",
        "    # Update the position of agents\n",
        "    CurrentPosition[0] += Velocity[0]\n",
        "\n",
        "    # ---------------\n",
        "    # Retention\n",
        "    # ---------------\n",
        "    \n",
        "    # Change position if an agent goes out of the box (Wrap-around)\n",
        "    CurrentPosition[0][CurrentPosition[0][:,0] > Layout[1],0] = torch.DoubleTensor(1).uniform_(Layout[1]-10,Layout[1]).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,0] < Layout[0],0] = torch.DoubleTensor(1).uniform_(Layout[0],Layout[0]+10).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,1] > Layout[3],1] = torch.DoubleTensor(1).uniform_(Layout[3]-10,Layout[3]).cuda()\n",
        "    CurrentPosition[0][CurrentPosition[0][:,1] < Layout[2],1] = torch.DoubleTensor(1).uniform_(Layout[2],Layout[2]+10).cuda()\n",
        "\n",
        "    # -------------------\n",
        "    # Current Task Status\n",
        "    # -------------------\n",
        "\n",
        "    # Set status of those within 10 units of their destination as reached, rest in process (9)\n",
        "    # Find distance between current location and goal\n",
        "    Vectors[10] = torch.linalg.norm(CurrentPosition[0]-Destination[0],dim=1)\n",
        "    # If distance is greater than 10, agent is moving towards the goal\n",
        "    Agents[torch.logical_and(Vectors[10]>=Radii[4],Agents[:,7]<=11),8] = Agents[torch.logical_and(Vectors[10]>=Radii[4],Agents[:,7]<=11),7]\n",
        "    # Reached goal\n",
        "    # At home\n",
        "    Agents[torch.logical_or(Agents[:,7]==12,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==0)),8] = 12\n",
        "    # At school\n",
        "    Agents[torch.logical_or(Agents[:,7]==13,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==1)),8] = 13\n",
        "    # Adults in office\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==14,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==2))),8] = 14\n",
        "    # Seniors in office\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==14,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==3))),8] = 14\n",
        "    # Kids running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==0,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==4))),8] = 15\n",
        "    # Adults running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==5))),8] = 15\n",
        "    # Seniors running errands\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==15,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==6))),8] = 15\n",
        "    # Nomadic adults\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==16,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==10))),8] = 16\n",
        "    # Nomadic seniors\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==16,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==11))),8] = 16\n",
        "    # Kids at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==0,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==7))),8] = 17\n",
        "    # Adults at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==1,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==8))),8] = 17\n",
        "    # Seniors at market\n",
        "    Agents[torch.logical_and(Agents[:,0]==2,torch.logical_or(Agents[:,7]==17,torch.logical_and(Vectors[10]<Radii[4],Agents[:,7]==9))),8] = 17\n",
        "\n",
        "    # ---------------\n",
        "    # Compute Time \n",
        "    # --------------\n",
        "\n",
        "    # Time to infection\n",
        "    Vectors[1][Vectors[4]==0] += 1\n",
        "\n",
        "    # ----------------------------\n",
        "    # Compute Reproduction Number\n",
        "    # ----------------------------\n",
        "    # With confirmed infections in vicinity\n",
        "    Vectors[8] = torch.logical_and(Vectors[4]==0,Agents[:,14]==3)\n",
        "    Matrixs[0][torch.logical_and(torch.logical_and(Matrixs[2].bool()==True,Matrixs[0]<Radii[1]),torch.logical_or(Vectors[4][Matrixs[1].long()]==1,Vectors[4][Matrixs[1].long()]==3))==False] = float('inf')\n",
        "    if torch.where(Vectors[8]==True)[0].size()[0]>0:\n",
        "      # Find the nearest individual who infected me \n",
        "      temp = torch.min(Matrixs[0][Vectors[8]==True],1)[1]\n",
        "      for l in range(temp.size()[0]):\n",
        "        Vectors[2][(Matrixs[1].long())[temp[l]]]+=1\n",
        "\n",
        "    # ---------------------------------------------\n",
        "    # Store Values of the Current Iteration\n",
        "    # ---------------------------------------------\n",
        "    StatusTime = torch.dstack((StatusTime,Agents[:,8]))\n",
        "    #PositionTime = torch.dstack((PositionTime,CurrentPosition))\n",
        "\n",
        "    # Store Infection status for the next iteration\n",
        "    Results = torch.dstack((Results,torch.tensor([torch.where(Agents[:,14]==0)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==1)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==2)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==3)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==4)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==5)[0].size()[0],\n",
        "                                                  torch.where(Agents[:,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "\n",
        "    ResultsC = torch.dstack((ResultsC,torch.tensor([torch.where(Agents[Children,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Children,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "    \n",
        "    ResultsA = torch.dstack((ResultsA,torch.tensor([torch.where(Agents[Adults,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Adults,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "    \n",
        "    ResultsS = torch.dstack((ResultsS,torch.tensor([torch.where(Agents[Seniors,14]==0)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==1)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==2)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==3)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==4)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==5)[0].size()[0],\n",
        "                                                    torch.where(Agents[Seniors,14]==6)[0].size()[0]],dtype=torch.long,device='cuda')))\n",
        "  ResultsTMC[mc][0:7][:] = Results[0] # Disease\n",
        "  ResultsTMC[mc][7:14][:] = ResultsC[0] # Children\n",
        "  ResultsTMC[mc][14:21][:] = ResultsA[0] # Adults\n",
        "  ResultsTMC[mc][21:28][:] = ResultsS[0] # Seniors\n",
        "  # Overall: Time to Infection, Time to Recover, Time to Death\n",
        "  ResultsCMC[mc][0:3] = torch.tensor([[torch.mean(Vectors[1][Vectors[1]>0])/TimeStepsDay.item()],\n",
        "                                    [torch.mean(Agents[:,17][torch.logical_or(Vectors[4]==2,Vectors[4]==5)].double())],\n",
        "                                    [torch.mean(Agents[:,17][torch.logical_or(Vectors[4]==4,Vectors[4]==6)].double())]],device='cuda',dtype=torch.double)\n",
        "  # Overall: Effective Reproduction Number\n",
        "  ResultsCMC[mc][3] = torch.mean(Vectors[2][Vectors[4]!=0])\n",
        "  # Overall: Average time of infectiousness\n",
        "  ResultsCMC[mc][4] = torch.mean(Agents[Vectors[4]!=0,16]/96)\n",
        "\n",
        "  # Overall: Time to Infection, Time to Recover, Time to Death\n",
        "  ResultsCMC[mc][5:8] = torch.tensor([[torch.mean(Vectors[1][Vectors[1]>0])/TimeStepsDay.item()],\n",
        "                                    [torch.mean(Agents[:,17][Vectors[4]==5].double())],\n",
        "                                    [torch.mean(Agents[:,17][Vectors[4]==6].double())]],device='cuda',dtype=torch.double)\n",
        "  # Overall: Effective Reproduction Number\n",
        "  ResultsCMC[mc][8] = torch.mean(Vectors[2][torch.logical_and(Vectors[4]!=0,torch.logical_or(Vectors[4]==1,((Vectors[4]==5)|(Vectors[4]==6))))])\n",
        "  # Overall: Average time of infectiousness\n",
        "  ResultsCMC[mc][9] = torch.mean(Agents[torch.logical_and(Vectors[4]!=0,torch.logical_or(Vectors[4]==1,((Vectors[4]==5)|(Vectors[4]==6)))),16]/96)\n",
        "\n",
        "  # Contact Matrix\n",
        "  #InteractionsMC[mc][0][0] = Interactions[0][0]/Population/23\n",
        "  #InteractionsMC[mc][0][1] = Interactions[0][1]/23/torch.where(Agents[:,5]==1)[0].size(0)\n",
        "  #InteractionsMC[mc][0][2] = Interactions[0][2]/23/torch.where(torch.logical_or(Agents[:,5]==3,Agents[:,5]==6)==True)[0].size(0)\n",
        "  #InteractionsMC[mc][0][3] = Interactions[0][3]/(1800*23)\n",
        "  #InteractionsMC[mc][0][4] = Interactions[0][4]/torch.where(torch.logical_or(Agents[:,5]==4,Agents[:,5]==7)==True)[0].size(0)/23\n",
        "  #InteractionsMC[mc][0][5] = Interactions[0][5]/(2800*23)\n",
        "  #InteractionsMC[mc][1][0] = Interactions[1][0]/Population/8\n",
        "  #InteractionsMC[mc][1][1] = Interactions[1][1]/8/torch.where(Agents[:,5]==1)[0].size(0)\n",
        "  #InteractionsMC[mc][1][2] = Interactions[1][2]/8/torch.where(torch.logical_or(Agents[:,5]==3,Agents[:,5]==6)==True)[0].size(0)\n",
        "  #InteractionsMC[mc][1][3] = Interactions[1][3]/Population/8\n",
        "  #InteractionsMC[mc][1][4] = Interactions[1][4]/torch.where(torch.logical_or(Agents[:,5]==4,Agents[:,5]==7)==True)[0].size(0)/8\n",
        "  #InteractionsMC[mc][1][5] = Interactions[1][5]/Population/8\n",
        "\n",
        "  InteractionsMC[mc][0][0] = Interactions[0][0]/23\n",
        "  InteractionsMC[mc][0][1] = Interactions[0][1]/23\n",
        "  InteractionsMC[mc][0][2] = Interactions[0][2]/23\n",
        "  InteractionsMC[mc][0][3] = Interactions[0][3]/23\n",
        "  InteractionsMC[mc][0][4] = Interactions[0][4]/23\n",
        "  InteractionsMC[mc][0][5] = Interactions[0][5]/23\n",
        "  InteractionsMC[mc][1][0] = Interactions[1][0]/8\n",
        "  InteractionsMC[mc][1][1] = Interactions[1][1]/8\n",
        "  InteractionsMC[mc][1][2] = Interactions[1][2]/8\n",
        "  InteractionsMC[mc][1][3] = Interactions[1][3]/8\n",
        "  InteractionsMC[mc][1][4] = Interactions[1][4]/8\n",
        "  InteractionsMC[mc][1][5] = Interactions[1][5]/8\n",
        "\n",
        "# Overall: Confirmed and Cumulative=0, Total Infections (T/N)=1,\n",
        "# Confirmed Infections=2, Confirmed Recoveries=3, Confirmed Deaths=4,\n",
        "# Total Recoveries=5, Total Deaths=6, Cumulative (T/N) = 7\n",
        "FinalTimeSeries[0][:] = torch.mean((ResultsTMC[:][:,1]+ResultsTMC[:][:,6]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[1][:] = torch.mean((ResultsTMC[:][:,3]+ResultsTMC[:][:,1]).double(),0)\n",
        "FinalTimeSeries[2][:] = torch.mean(ResultsTMC[:][:,1].double(),0)\n",
        "FinalTimeSeries[3][:] = torch.mean(ResultsTMC[:][:,5].double(),0)\n",
        "FinalTimeSeries[4][:] = torch.mean(ResultsTMC[:][:,6].double(),0)\n",
        "FinalTimeSeries[5][:] = torch.mean((ResultsTMC[:][:,5]+ResultsTMC[:][:,2]).double(),0)\n",
        "FinalTimeSeries[6][:] = torch.mean((ResultsTMC[:][:,6]+ResultsTMC[:][:,4]).double(),0)\n",
        "FinalTimeSeries[7][:] = torch.mean((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]+ResultsTMC[:][:,5]+ResultsTMC[:][:,2]+ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "FinalTimeSeries[8][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,6]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[9][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]).double(),0)\n",
        "FinalTimeSeries[10][:] = torch.std(ResultsTMC[:][:,1].double(),0)\n",
        "FinalTimeSeries[11][:] = torch.std(ResultsTMC[:][:,5].double(),0)\n",
        "FinalTimeSeries[12][:] = torch.std(ResultsTMC[:][:,6].double(),0)\n",
        "FinalTimeSeries[13][:] = torch.std((ResultsTMC[:][:,2]+ResultsTMC[:][:,5]).double(),0)\n",
        "FinalTimeSeries[14][:] = torch.std((ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "FinalTimeSeries[15][:] = torch.std((ResultsTMC[:][:,1]+ResultsTMC[:][:,3]+ResultsTMC[:][:,2]+ResultsTMC[:][:,5]+ResultsTMC[:][:,4]+ResultsTMC[:][:,6]).double(),0)\n",
        "\n",
        "# Children: Confirmed and Cumulative=16, Total Infections (T/N)=17,\n",
        "# Confirmed Infections=18, Confirmed Recoveries=19, Confirmed Deaths=20,\n",
        "# Total Recoveries=21, Total Deaths=22, Cumulative (T/N) = 23\n",
        "FinalTimeSeries[16][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "FinalTimeSeries[17][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,10]).double(),0)\n",
        "FinalTimeSeries[18][:] = torch.mean(ResultsTMC[:][:,8].double(),0)\n",
        "FinalTimeSeries[19][:] = torch.mean(ResultsTMC[:][:,12].double(),0)\n",
        "FinalTimeSeries[20][:] = torch.mean(ResultsTMC[:][:,13].double(),0)\n",
        "FinalTimeSeries[21][:] = torch.mean((ResultsTMC[:][:,12]+ResultsTMC[:][:,9]).double(),0)\n",
        "FinalTimeSeries[22][:] = torch.mean((ResultsTMC[:][:,13]+ResultsTMC[:][:,11]).double(),0)\n",
        "FinalTimeSeries[23][:] = torch.mean((ResultsTMC[:][:,8]+ResultsTMC[:][:,9]+ResultsTMC[:][:,10]+ResultsTMC[:][:,11]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)                                                                                                                              \n",
        "FinalTimeSeries[24][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "FinalTimeSeries[25][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,10]).double(),0)\n",
        "FinalTimeSeries[26][:] = torch.std(ResultsTMC[:][:,8].double(),0)\n",
        "FinalTimeSeries[27][:] = torch.std(ResultsTMC[:][:,12].double(),0)\n",
        "FinalTimeSeries[28][:] = torch.std(ResultsTMC[:][:,13].double(),0)\n",
        "FinalTimeSeries[29][:] = torch.std((ResultsTMC[:][:,12]+ResultsTMC[:][:,9]).double(),0)\n",
        "FinalTimeSeries[30][:] = torch.std((ResultsTMC[:][:,13]+ResultsTMC[:][:,11]).double(),0)\n",
        "FinalTimeSeries[31][:] = torch.std((ResultsTMC[:][:,8]+ResultsTMC[:][:,9]+ResultsTMC[:][:,10]+ResultsTMC[:][:,11]+ResultsTMC[:][:,12]+ResultsTMC[:][:,13]).double(),0)\n",
        "\n",
        "# Adults: Confirmed and Cumulative=32, Total Infections (T/N)=33,\n",
        "# Confirmed Infections=34, Confirmed Recoveries=35, Confirmed Deaths=36,\n",
        "# Total Recoveries=37, Total Deaths=38, Cumulative (T/N) = 39\n",
        "FinalTimeSeries[32][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[33][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,17]).double(),0)\n",
        "FinalTimeSeries[34][:] = torch.mean(ResultsTMC[:][:,15].double(),0)\n",
        "FinalTimeSeries[35][:] = torch.mean(ResultsTMC[:][:,19].double(),0)\n",
        "FinalTimeSeries[36][:] = torch.mean(ResultsTMC[:][:,20].double(),0)\n",
        "FinalTimeSeries[37][:] = torch.mean((ResultsTMC[:][:,19]+ResultsTMC[:][:,16]).double(),0)\n",
        "FinalTimeSeries[38][:] = torch.mean((ResultsTMC[:][:,20]+ResultsTMC[:][:,18]).double(),0)\n",
        "FinalTimeSeries[39][:] = torch.mean((ResultsTMC[:][:,15]+ResultsTMC[:][:,16]+ResultsTMC[:][:,17]+ResultsTMC[:][:,18]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[40][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "FinalTimeSeries[41][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,17]).double(),0)\n",
        "FinalTimeSeries[42][:] = torch.std(ResultsTMC[:][:,15].double(),0)\n",
        "FinalTimeSeries[43][:] = torch.std(ResultsTMC[:][:,19].double(),0)\n",
        "FinalTimeSeries[44][:] = torch.std(ResultsTMC[:][:,20].double(),0)\n",
        "FinalTimeSeries[45][:] = torch.std((ResultsTMC[:][:,19]+ResultsTMC[:][:,16]).double(),0)\n",
        "FinalTimeSeries[46][:] = torch.std((ResultsTMC[:][:,20]+ResultsTMC[:][:,18]).double(),0)\n",
        "FinalTimeSeries[47][:] = torch.std((ResultsTMC[:][:,15]+ResultsTMC[:][:,16]+ResultsTMC[:][:,17]+ResultsTMC[:][:,18]+ResultsTMC[:][:,19]+ResultsTMC[:][:,20]).double(),0)\n",
        "\n",
        "# Seniors: Confirmed and Cumulative=48, Total Infections (T/N)=49,\n",
        "# Confirmed Infections=50, Confirmed Recoveries=51, Confirmed Deaths=52,\n",
        "# Total Recoveries=53, Total Deaths=54, Cumulative (T/N) = 55\n",
        "FinalTimeSeries[48][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[49][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,24]).double(),0)\n",
        "FinalTimeSeries[50][:] = torch.mean(ResultsTMC[:][:,22].double(),0)\n",
        "FinalTimeSeries[51][:] = torch.mean(ResultsTMC[:][:,26].double(),0)\n",
        "FinalTimeSeries[52][:] = torch.mean(ResultsTMC[:][:,27].double(),0)\n",
        "FinalTimeSeries[53][:] = torch.mean((ResultsTMC[:][:,23]+ResultsTMC[:][:,26]).double(),0)\n",
        "FinalTimeSeries[54][:] = torch.mean((ResultsTMC[:][:,25]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[55][:] = torch.mean((ResultsTMC[:][:,22]+ResultsTMC[:][:,23]+ResultsTMC[:][:,24]+ResultsTMC[:][:,25]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[56][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[57][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,24]).double(),0)\n",
        "FinalTimeSeries[58][:] = torch.std(ResultsTMC[:][:,22].double(),0)\n",
        "FinalTimeSeries[59][:] = torch.std(ResultsTMC[:][:,26].double(),0)\n",
        "FinalTimeSeries[60][:] = torch.std(ResultsTMC[:][:,27].double(),0)\n",
        "FinalTimeSeries[61][:] = torch.std((ResultsTMC[:][:,23]+ResultsTMC[:][:,26]).double(),0)\n",
        "FinalTimeSeries[62][:] = torch.std((ResultsTMC[:][:,25]+ResultsTMC[:][:,27]).double(),0)\n",
        "FinalTimeSeries[63][:] = torch.std((ResultsTMC[:][:,22]+ResultsTMC[:][:,23]+ResultsTMC[:][:,24]+ResultsTMC[:][:,25]+ResultsTMC[:][:,26]+ResultsTMC[:][:,27]).double(),0)\n",
        "\n",
        "# Overall: Time to Infection=0, Time to Recover=1, Time to Death=2, Rep Number=3, Time of Infectiousness=4\n",
        "# Confirmed: Time to Infection=5, Time to Recover=6, Time to Death=7, Rep Number=8, Time of Infectiousness=9\n",
        "FinalConstants[0] = torch.mean(ResultsCMC[:][:,0][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,0]),ResultsCMC[:][:,0]==0)==False)].double(),0)\n",
        "FinalConstants[1] = torch.mean(ResultsCMC[:][:,1][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,1]),ResultsCMC[:][:,1]==0)==False)].double(),0)\n",
        "FinalConstants[2] = torch.mean(ResultsCMC[:][:,2][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,2]),ResultsCMC[:][:,2]==0)==False)].double(),0)\n",
        "FinalConstants[3] = torch.std(ResultsCMC[:][:,0][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,0]),ResultsCMC[:][:,0]==0)==False)].double(),0)\n",
        "FinalConstants[4] = torch.std(ResultsCMC[:][:,1][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,1]),ResultsCMC[:][:,1]==0)==False)].double(),0)\n",
        "FinalConstants[5] = torch.std(ResultsCMC[:][:,2][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,2]),ResultsCMC[:][:,2]==0)==False)].double(),0) \n",
        "FinalConstants[6] = torch.mean(ResultsCMC[:][:,3].double())\n",
        "FinalConstants[7] = torch.std(ResultsCMC[:][:,3].double())\n",
        "FinalConstants[8] = torch.mean(ResultsCMC[:][:,4].double())\n",
        "FinalConstants[9] = torch.std(ResultsCMC[:][:,4].double())\n",
        "FinalConstants[10] = torch.mean(ResultsCMC[:][:,5][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,5]),ResultsCMC[:][:,5]==0)==False)].double(),0)\n",
        "FinalConstants[11] = torch.mean(ResultsCMC[:][:,6][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,6]),ResultsCMC[:][:,6]==0)==False)].double(),0)\n",
        "FinalConstants[12] = torch.mean(ResultsCMC[:][:,7][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,7]),ResultsCMC[:][:,7]==0)==False)].double(),0)\n",
        "FinalConstants[13] = torch.std(ResultsCMC[:][:,5][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,5]),ResultsCMC[:][:,5]==0)==False)].double(),0)\n",
        "FinalConstants[14] = torch.std(ResultsCMC[:][:,6][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,6]),ResultsCMC[:][:,6]==0)==False)].double(),0)\n",
        "FinalConstants[15] = torch.std(ResultsCMC[:][:,7][torch.where(torch.logical_or(torch.isnan(ResultsCMC[:][:,7]),ResultsCMC[:][:,7]==0)==False)].double(),0) \n",
        "FinalConstants[16] = torch.mean(ResultsCMC[:][:,8].double())\n",
        "FinalConstants[17] = torch.std(ResultsCMC[:][:,8].double())\n",
        "FinalConstants[18] = torch.mean(ResultsCMC[:][:,9].double())\n",
        "FinalConstants[19] = torch.std(ResultsCMC[:][:,9].double())\n",
        "\n",
        "InteractionsFinal[0] = torch.mean(InteractionsMC[:][:,0],0)\n",
        "InteractionsFinal[1] = torch.mean(InteractionsMC[:][:,1],0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: faiss-gpu in /usr/local/lib/python3.7/dist-packages (1.7.1.post3)\n",
            "Requirement already satisfied: celluloid in /usr/local/lib/python3.7/dist-packages (0.2.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from celluloid) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (1.3.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (3.0.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->celluloid) (1.19.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->celluloid) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HPJY_CIWrJVJ"
      },
      "source": [
        "## Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EBdNR7wsTgG"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from IPython.display import HTML\n",
        "import seaborn as sns\n",
        "from matplotlib.ticker import AutoMinorLocator, MultipleLocator\n",
        "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
        "from matplotlib import collections as matcoll\n",
        "from matplotlib import cm\n",
        "import numpy as np\n",
        "from scipy.stats import norm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEjHziIRrJ45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "677d0657-c43f-41fe-dd61-e7156fc82ff8"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "s = 96\n",
        "\n",
        "t=96*31\n",
        "\n",
        "x = (np.arange(0,t,step=s)/96)+1\n",
        "\n",
        "cinf_mean = ((FinalTimeSeries[0]/Population)*100).cpu().detach().numpy()\n",
        "cinf_std = ((FinalTimeSeries[8]/Population)*100).cpu().detach().numpy()\n",
        "\n",
        "ainf_mean = ((FinalTimeSeries[2]/Population)*100).cpu().detach().numpy()\n",
        "ainf_std = ((FinalTimeSeries[10]/Population)*100).cpu().detach().numpy()\n",
        "\n",
        "recovered_mean = ((FinalTimeSeries[3]/Population)*100).cpu().detach().numpy()\n",
        "recovered_std = ((FinalTimeSeries[11]/Population)*100).cpu().detach().numpy()\n",
        "\n",
        "deceased_mean = ((FinalTimeSeries[4]/Population)*100).cpu().detach().numpy()\n",
        "deceased_std = ((FinalTimeSeries[12]/Population)*100).cpu().detach().numpy() \n",
        "\n",
        "rinf_mean = recovered_mean\n",
        "rinf_std = recovered_std\n",
        "\n",
        "dinf_mean = deceased_mean\n",
        "dinf_std = deceased_std\n",
        "data = [0.004,0.004,0.005,0.005,0.005,0.005,0.005,0.005,0.006,0.006,0.006,0.007,0.007,\n",
        "        0.007,0.008,0.008,0.009,0.010,0.010,0.011,0.012,0.015,0.016,0.017,0.019,\n",
        "        0.021,0.022,0.029,0.038,0.047,0.052,0.061]\n",
        "\n",
        "plt.plot(x,cinf_mean[np.arange(0,t,step=s)],'x-',color='blue',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(cinf_mean[np.arange(0,t,step=s)]-cinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(cinf_mean[np.arange(0,t,step=s)]+cinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='blue')\n",
        "\n",
        "plt.plot(x,ainf_mean[np.arange(0,t,step=s)],'x-',color='r',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(ainf_mean[np.arange(0,t,step=s)]-ainf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(ainf_mean[np.arange(0,t,step=s)]+ainf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='r')\n",
        "\n",
        "plt.plot(x,rinf_mean[np.arange(0,t,step=s)],'x-',color='g',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(rinf_mean[np.arange(0,t,step=s)]-rinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(rinf_mean[np.arange(0,t,step=s)]+rinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='g')\n",
        "\n",
        "plt.plot(x,dinf_mean[np.arange(0,t,step=s)],'x-',color='dimgray',linewidth=2,linestyle='-')\n",
        "ax.fill_between(x, np.clip(dinf_mean[np.arange(0,t,step=s)]-dinf_std[np.arange(0,t,step=s)],0,np.inf), np.clip(dinf_mean[np.arange(0,t,step=s)]+dinf_std[np.arange(0,t,step=s)],0,np.inf) ,alpha=0.3, facecolor='dimgray')\n",
        "\n",
        "#plt.plot(np.arange(1,32,step=1),data,'o-',color='black',linewidth=2)\n",
        "\n",
        "plt.rcParams['figure.facecolor'] = 'ivory'\n",
        "\n",
        "plt.rcParams[\"font.weight\"] = \"bold\"\n",
        "\n",
        "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
        "\n",
        "ax.grid()\n",
        "\n",
        "ax.set_axisbelow(True)\n",
        "ax.xaxis.set_major_locator(plt.MaxNLocator(16))\n",
        "ax.tick_params(which='major', length=7, color='black')\n",
        "ax.tick_params(which='minor', length=4, color='black' )\n",
        "ax.xaxis.set_minor_locator(plt.MaxNLocator())\n",
        "\n",
        "ax.tick_params(which='both', # Options for both major and minor ticks\n",
        "               top=False, # turn off top ticks\n",
        "               left=False, # turn off left ticks\n",
        "               right=False,  # turn off right ticks\n",
        "               bottom=True) # turn off bottom ticks\n",
        "ax.set_xticks(np.arange(1,32,step=2))\n",
        "ax.set_xticklabels([\"1\",\"3\",\"5\",\"7\",\"9\",\"11\",\"13\",\"15\",\"17\",\"19\",\"21\",\"23\",\"25\",\n",
        "                    \"28\",\"29\",\"31\"])\n",
        "\n",
        "plt.xticks(fontsize=16,fontweight='bold')\n",
        "plt.yticks(np.arange(0,2.2,step=0.2),fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.xlabel('Time (Days)',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.ylabel('Fraction of the total population (%)',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.legend(['Total Infections','Active Infections','Recoveries','Mortalities'],\n",
        "           fontsize=16,loc=\"upper left\",fancybox=True, \n",
        "           framealpha=0.0, shadow=False, borderpad=1)\n",
        "\n",
        "plt.title('Disease evolution in Bengaluru',fontsize=16,fontweight='bold')\n",
        "\n",
        "plt.savefig('FB_3.pdf',format='pdf', bbox_inches='tight',Transparent=True)\n",
        "\n",
        "files.download('FB_3.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6286a075-1843-4a3d-aa1a-32dba23cb8fd\", \"FB_3.pdf\", 21207)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YW65y-r7sla6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "e0be29fa-c5cf-4e0f-e21e-b4358b08d007"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "data1 = InteractionsFinal[0].cpu().detach().numpy()\n",
        "\n",
        "data1 = np.ma.masked_where(data1 == 0, data1)\n",
        "\n",
        "data2 = InteractionsFinal[0].cpu().detach().numpy()\n",
        "\n",
        "data1[0,0] = 4.0\n",
        "data2[0,0] = 4.0\n",
        "vmin = np.min(np.array([data1,data2]))\n",
        "\n",
        "vmax = np.max(np.array([data1,data2]))\n",
        "\n",
        "alpha = ['$Q_{1}$','$Q_{2}$','$Q_{3}$','$Q_{4}$','$Q_{5}$','$Q_{6}$']\n",
        "\n",
        "cmap = ListedColormap([\"lemonchiffon\",\"goldenrod\",\"cyan\",\"orange\",\"gainsboro\", \"black\",\"yellow\"])\n",
        "\n",
        "cax = ax.matshow(data1,interpolation='nearest',cmap=cmap, rasterized=True, vmin=vmin, vmax=vmax)\n",
        "\n",
        "cbar=fig.colorbar(cax,extend='min', label='Average Contacts')\n",
        "\n",
        "plt.gca().set_xticks([x - 0.5 for x in plt.gca().get_xticks()][1:], minor='true')\n",
        "\n",
        "plt.gca().set_yticks([y - 0.5 for y in plt.gca().get_yticks()][1:], minor='true')\n",
        "\n",
        "plt.grid(which='minor')\n",
        "\n",
        "cmap.set_bad(color='white')\n",
        "\n",
        "ax.set_xticklabels(['']+alpha,y=-0.15,fontsize=12)\n",
        "\n",
        "ax.set_yticklabels(['']+alpha,fontsize=12)\n",
        "\n",
        "cbar.ax.tick_params(labelsize=12)\n",
        "\n",
        "plt.title('Weekday daily-contacts matrix',fontweight='bold',fontsize=14)\n",
        "\n",
        "plt.savefig('IFB_3.pdf',format='pdf', bbox_inches='tight',Transparent=True)\n",
        "\n",
        "files.download('IFB_3.pdf')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_518b6167-70ba-4a9d-9d89-1a3993b5e5ea\", \"IFB_3.pdf\", 16420)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkPW_ADDrKTS"
      },
      "source": [
        "## Save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2ofi7V_rK3J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "09f50620-8acd-414f-bee7-526cae619852"
      },
      "source": [
        "!pip install hickle\n",
        "import hickle as hk\n",
        "from google.colab import files\n",
        "hk.dump(FinalTimeSeries.cpu().detach().numpy(),'TS_FB_3.hkl', mode='w')\n",
        "hk.dump(FinalConstants.cpu().detach().numpy(),'C_FB_3.hkl', mode='w')\n",
        "hk.dump(InteractionsFinal.cpu().detach().numpy(),'I_FB_3.hkl', mode='w')\n",
        "files.download('TS_FB_3.hkl')\n",
        "files.download('C_FB_3.hkl')\n",
        "files.download('I_FB_3.hkl')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting hickle\n",
            "  Downloading hickle-4.0.4-py3-none-any.whl (49 kB)\n",
            "\u001b[?25l\r\u001b[K     |██████▊                         | 10 kB 31.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 20 kB 31.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 30 kB 19.6 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 40 kB 16.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 49 kB 3.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (1.15.0)\n",
            "Collecting h5py<3.0.0,>=2.8.0\n",
            "  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 16.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.7/dist-packages (from hickle) (1.19.5)\n",
            "Requirement already satisfied: dill>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from hickle) (0.3.4)\n",
            "Installing collected packages: h5py, hickle\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.1.0\n",
            "    Uninstalling h5py-3.1.0:\n",
            "      Successfully uninstalled h5py-3.1.0\n",
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