multiple_simulations_noschools.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import ndlib.models.epidemics as ep\n",
    "import ndlib.models.ModelConfig as mc\n",
    "import ndlib.models.CompositeModel as gc\n",
    "from ndlib.viz.mpl.DiffusionTrend import DiffusionTrend\n",
    "from ndlib.viz.mpl.DiffusionPrevalence import DiffusionPrevalence\n",
    "from pymongo import MongoClient\n",
    "import numpy as np\n",
    "from ndlibCustom.SEIR_ASModel import SEIR_ASModel\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1230056\n",
      "7189509\n"
     ]
    }
   ],
   "source": [
    "G = nx.read_gpickle('Datasets/dataset_noschools.gpickle')\n",
    "relabeled = nx.convert_node_labels_to_integers(G)\n",
    "del(G)\n",
    "print(len(relabeled.nodes()))\n",
    "print(len(relabeled.edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "density = nx.density(relabeled)\n",
    "degree = list(dict(relabeled.degree()).values())\n",
    "#ECCDF in loglog scale\n",
    "cdf = ECDF(degree)\n",
    "x = np.unique(degree)\n",
    "y = cdf(x)\n",
    "fig_cdf = plt.figure(figsize=(16,8))\n",
    "axes = fig_cdf.gca()\n",
    "axes.loglog(x,1-y,marker='o',ms=8, linestyle='--')\n",
    "axes.set_xlabel('Degree',size=20)\n",
    "axes.set_ylabel('ECCDF', size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6128\n",
      "3672\n"
     ]
    }
   ],
   "source": [
    "Gcc = sorted(nx.connected_components(relabeled), key=len, reverse=True)\n",
    "G0 = relabeled.subgraph(Gcc[0])\n",
    "G1 = relabeled.subgraph(Gcc[1])\n",
    "print(len(G0))\n",
    "print(len(G1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disribuzione di età su grafo totale\n",
      "Giovani: 204096 Attivi: 720817 Anziani: 305143\n",
      "\n",
      "Disribuzione di età fuori dalla Giant Component (in percentuale sul totale)\n",
      "Giovani: 202365 ( 99.152 ) Attivi: 716477 ( 99.398 ) Anziani: 305086 ( 99.981 )\n",
      "\n",
      "Nodi appartenenti alla Giant Component\n",
      "Giovani: 1731 Attivi: 4340 Anziani: 57\n"
     ]
    }
   ],
   "source": [
    "agedistrib_g1 = []\n",
    "a1 = 0\n",
    "a2 = 0\n",
    "a3 = 0\n",
    "for i in relabeled.nodes():\n",
    "    if relabeled.nodes[i]['age'] == '65+':\n",
    "        a3 += 1\n",
    "    elif relabeled.nodes[i]['age'] == '20-64':\n",
    "        a2 += 1\n",
    "    else: \n",
    "        a1 +=1\n",
    "print(\"Disribuzione di età su grafo totale\")\n",
    "print(\"Giovani:\",a1,\"Attivi:\",a2,\"Anziani:\",a3)\n",
    "print()\n",
    "g1 = a1\n",
    "g2 = a2\n",
    "g3 = a3\n",
    "for i in G0.nodes():\n",
    "    if G0.nodes[i]['age'] == '65+':\n",
    "        a3 -= 1\n",
    "    elif G0.nodes[i]['age'] == '20-64':\n",
    "        a2 -= 1\n",
    "    else: \n",
    "        a1 -=1\n",
    "print(\"Disribuzione di età fuori dalla Giant Component (in percentuale sul totale)\")\n",
    "print(\"Giovani:\",a1,\"(\",round((a1/g1)*100,3),\")\",\"Attivi:\",a2,\"(\",round((a2/g2)*100,3),\")\",\"Anziani:\",a3,\"(\",round((a3/g3)*100,3),\")\")\n",
    "print()\n",
    "print(\"Nodi appartenenti alla Giant Component\")\n",
    "print(\"Giovani:\",g1-a1,\"Attivi:\",g2-a2,\"Anziani:\",g3-a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean percentual of workers for sez: 49.5462388676737\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client = MongoClient('localhost', 27017)\n",
    "db = client['tesi']\n",
    "db_sezioni = db['sezioni']\n",
    "db_lavoro = db['lavoro']\n",
    "\n",
    "work_dist = []\n",
    "for i in list(db_sezioni.find()):\n",
    "    #print(\"Indexing sez:\",i[\"properties\"][\"SEZ\"], end=\"\\r\", flush=True)\n",
    "    num_attivi = 0\n",
    "    num_workers = 0\n",
    "    if \"famiglie\" in i.keys():\n",
    "        for fam in i[\"famiglie\"]:\n",
    "            for mem in fam[\"members\"]:\n",
    "                if mem[\"age\"] == \"20-64\": \n",
    "                    num_attivi += 1\n",
    "                    if \"sez_lavoro\" in mem.keys(): num_workers += 1\n",
    "        if (num_workers != 0 and num_attivi != 0):\n",
    "            work_dist.append({\"sez\":i[\"properties\"][\"SEZ\"],\"num\":(num_workers/num_attivi)*100})\n",
    "\n",
    "list_dist =  [i[\"num\"] for i in work_dist]\n",
    "print(\"Mean percentual of workers for sez:\",np.mean(list_dist))\n",
    "\n",
    "# ECDF linear scale\n",
    "plt.style.use('default')\n",
    "cdf = ECDF(list_dist)\n",
    "x = np.unique(list_dist)\n",
    "y = cdf(x)\n",
    "fig_cdf = plt.figure(figsize=(15,10))\n",
    "axes = fig_cdf.gca()\n",
    "axes.plot(x,y,marker='o',ms=8,  linestyle='--')\n",
    "# general title\n",
    "plt.suptitle('Percentuale di lavoratori sul totale della popolazione 20-64 (per sezione)', fontsize=13, fontweight=0, color='black', style='italic', y=0.92)\n",
    "axes.set_xlabel('Percentuale',size=20)\n",
    "axes.set_ylabel('ECDF', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:49<00:00,  4.83s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [03:57<00:00,  3.96s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [03:57<00:00,  3.96s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:01<00:00,  4.02s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:08<00:00,  4.14s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:05<00:00,  4.10s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:02<00:00,  4.05s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:02<00:00,  4.04s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:07<00:00,  4.12s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:02<00:00,  4.04s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:02<00:00,  4.03s/it]\n",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:05<00:00,  4.10s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 60/60 [04:04<00:00,  4.07s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:03<00:00,  4.06s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:03<00:00,  4.06s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [03:53<00:00,  3.90s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 60/60 [03:57<00:00,  3.96s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [03:57<00:00,  3.96s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [04:31<00:00,  4.53s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation number 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|█████▌    | 33/60 [02:56<01:44,  3.88s/it]"
     ]
    }
   ],
   "source": [
    "#CUSTOM SEIR_AS MODEL SIMULATION\n",
    "print(\"Simulating...\")\n",
    "# Model selection\n",
    "model = SEIR_ASModel(relabeled)\n",
    "\n",
    "# Model Configuration\n",
    "cfg = mc.Configuration()\n",
    "cfg.add_model_parameter('beta', 0.57) #Infection rate from I_A/S Neighbors\n",
    "cfg.add_model_parameter('gamma', 0.15) #Recovery/Removal rate\n",
    "cfg.add_model_parameter('alpha', 0.2) #Latent period\n",
    "cfg.add_model_parameter('kappa', 0.25) #Symptomatic/Asymptomatic Ratio\n",
    "\n",
    "#This parameter \"smoothens\" out the infection rate in the first iterations.\n",
    "#1: 1-(1-beta)^(num_of_infected_neighbors)[0.99 on > 5 infected neighbors]\n",
    "#0: beta*(average_infected_weight)\n",
    "cfg.add_model_parameter('tp_rate', 0)\n",
    "\n",
    "cfg.add_model_parameter(\"fraction_infected\", 0.005) #Starting infected nodes\n",
    "model.set_initial_status(cfg)\n",
    "\n",
    "#Run 50 simulations, results will be a mean of all data generated\n",
    "simulations = []\n",
    "for i in range (0,5):\n",
    "    print(\"Running simulation number\",i+1)\n",
    "    simulations.append(model.iteration_bunch(60,progress_bar=True))\n",
    "    model.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create complete status map by dymanically rebuilding infection iteration results\n",
    "#Keys are nodes, values are final statuses\n",
    "#Look for keys in graph to get attributes\n",
    "s_giovani = []\n",
    "r_giovani = []\n",
    "s_attivi = []\n",
    "r_attivi = []\n",
    "s_anziani = []\n",
    "r_anziani = []\n",
    "\n",
    "for iteration in simulations:\n",
    "    graph_stat = {}\n",
    "    for x in iteration[0]['status']:\n",
    "        graph_stat[x] = iteration[0]['status'][x]\n",
    "    for iter in iteration:\n",
    "        for s in iter['status']:\n",
    "            graph_stat[s] = iter['status'][s]    \n",
    "\n",
    "    stats_giovani = {0:[],1:[],2:[],3:[],4:[],5:[]}\n",
    "    stats_attivi = {0:[],1:[],2:[],3:[],4:[],5:[]}\n",
    "    stats_anziani = {0:[],1:[],2:[],3:[],4:[],5:[]}\n",
    "    for i in graph_stat:\n",
    "        if relabeled.nodes[i]['age'] == '65+':\n",
    "            stats_anziani[graph_stat[i]].append(i)\n",
    "        elif relabeled.nodes[i]['age'] == '20-64':\n",
    "            stats_attivi[graph_stat[i]].append(i)\n",
    "        else:\n",
    "            stats_giovani[graph_stat[i]].append(i)\n",
    "            \n",
    "    giovani_p = []\n",
    "    attivi_p = []\n",
    "    anziani_p = []\n",
    "    for i in stats_giovani:\n",
    "        giovani_p.append(len(stats_giovani[i]))\n",
    "        attivi_p.append(len(stats_attivi[i]))\n",
    "        anziani_p.append(len(stats_anziani[i]))\n",
    "    \n",
    "    #Build mean for CI(95%)\n",
    "    s_giovani.append(giovani_p[0])\n",
    "    r_giovani.append(giovani_p[5])\n",
    "    s_attivi.append(attivi_p[0])\n",
    "    r_attivi.append(attivi_p[5])\n",
    "    s_anziani.append(anziani_p[0])\n",
    "    r_anziani.append(anziani_p[5])\n",
    "\n",
    "giovani_p = [np.mean(s_giovani),np.mean(r_giovani)]\n",
    "attivi_p = [np.mean(s_attivi),np.mean(r_attivi)]\n",
    "anziani_p = [np.mean(s_anziani),np.mean(r_anziani)]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15, 10]\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "\n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(giovani_p))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, giovani_p, color='#7f6d5f', width=barWidth, edgecolor='white', label='0-19')\n",
    "plt.bar(r2, attivi_p, color='#557f2d', width=barWidth, edgecolor='white', label='20-64')\n",
    "plt.bar(r3, anziani_p, color='#2d7f5e', width=barWidth, edgecolor='white', label='65+')\n",
    "\n",
    "# Add xticks on the middle of the group bars\n",
    "# general title\n",
    "plt.suptitle(\"Stato complessivo degli agenti all'ultima iterazione per fasce di età\", fontsize=13, fontweight=0, color='black', style='italic', y=0.92)\n",
    "plt.xlabel('Stato', fontweight='bold')\n",
    "labels = ['S',\"R\"]\n",
    "plt.xticks([r + barWidth for r in range(len(giovani_p))], labels)\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print()\n",
    "print(\"Mean values\")\n",
    "print(giovani_p,attivi_p,anziani_p)\n",
    "print()\n",
    "print(\"CI 95%\")\n",
    "print(\"Giovani (S):\",st.t.interval(alpha=0.95, df=len(s_giovani)-1, loc=np.mean(s_giovani), scale=st.sem(s_giovani)))\n",
    "print(\"Giovani (R):\",st.t.interval(alpha=0.95, df=len(r_giovani)-1, loc=np.mean(r_giovani), scale=st.sem(r_giovani)))\n",
    "print(\"Attivi (S):\",st.t.interval(alpha=0.95, df=len(s_attivi)-1, loc=np.mean(s_attivi), scale=st.sem(s_attivi)))\n",
    "print(\"Attivi (R):\",st.t.interval(alpha=0.95, df=len(r_attivi)-1, loc=np.mean(r_attivi), scale=st.sem(r_attivi)))\n",
    "print(\"Anziani (S):\",st.t.interval(alpha=0.95, df=len(s_anziani)-1, loc=np.mean(s_anziani), scale=st.sem(s_anziani)))\n",
    "print(\"Anziani (R):\",st.t.interval(alpha=0.95, df=len(r_anziani)-1, loc=np.mean(r_anziani), scale=st.sem(r_anziani)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check if herd immunity has ben achieved inside the giant component G0\n",
    "G0_susceptible = []\n",
    "G0_neighbors = []\n",
    "G0_neighbors_s = []\n",
    "G0_neighbors_r = []\n",
    "\n",
    "for iteration in simulations:\n",
    "    nodelist = []\n",
    "    neighbors = {}\n",
    "    graph_stat = {}\n",
    "    neighbors_s = {}\n",
    "    neighbors_r = {}\n",
    "    #Rebuild current iteration status tree\n",
    "    for x in iteration[0]['status']:\n",
    "        graph_stat[x] = iteration[0]['status'][x]\n",
    "    for iter in iteration:\n",
    "        for s in iter['status']:\n",
    "            graph_stat[s] = iter['status'][s]   \n",
    "    #Cycle through nodes\n",
    "    for i in G0.nodes():\n",
    "        if i in graph_stat.keys():\n",
    "            #Process selected S_Nodes\n",
    "            if graph_stat[i] == 0:\n",
    "                nodelist.append(i)\n",
    "                neighborhood = [n for n in relabeled.neighbors(i)]\n",
    "                neighbors_s[i] = []\n",
    "                neighbors_r[i] = []\n",
    "                sum = 0\n",
    "                sus = 0\n",
    "                rem = 0\n",
    "                for n in neighborhood:\n",
    "                    neighbors[n] = graph_stat[n]\n",
    "                    sum += 1\n",
    "                    if graph_stat[n] == 0: sus += 1\n",
    "                    if graph_stat[n] == 5: rem += 1\n",
    "                neighbors_s[i].append(int((sus/sum)*100))\n",
    "                neighbors_r[i].append(int((rem/sum)*100))\n",
    "    \n",
    "    G0_susceptible.append(len(nodelist))\n",
    "    G0_neighbors.append(neighbors)\n",
    "    G0_neighbors_s.append(neighbors_s)\n",
    "    G0_neighbors_r.append(neighbors_r)\n",
    "\n",
    "    \n",
    "neighbors_s = [n[0] for n in neighbors_s.values()]\n",
    "neighbors_r = [n[0] for n in neighbors_r.values()]\n",
    "print(\"Nodi in stato S all'ultima iterazione:\",np.mean(G0_susceptible))\n",
    "print(\"CI 95%\",st.t.interval(alpha=0.95, df=len(G0_susceptible)-1, loc=np.mean(G0_susceptible), scale=st.sem(G0_susceptible)))\n",
    "print()\n",
    "\n",
    "print(\"Numero di neighbors dei nodi con stato S all'ultima iterazione:\",np.mean([len(n) for n in G0_neighbors]))\n",
    "print(\"CI 95%\",st.t.interval(alpha=0.95, df=len([len(n) for n in G0_neighbors])-1, loc=np.mean([len(n) for n in G0_neighbors]), scale=st.sem([len(n) for n in G0_neighbors])))\n",
    "print()\n",
    "\n",
    "G0_NS = []\n",
    "G0_NIE = []\n",
    "G0_NR = []\n",
    "for iteration_neighbors in G0_neighbors:\n",
    "    NS = 0\n",
    "    NIE = 0\n",
    "    NR = 0\n",
    "    for i in iteration_neighbors:\n",
    "        if iteration_neighbors[i] == 0: NS += 1\n",
    "        elif iteration_neighbors[i] == 5: NR += 1\n",
    "        else: NIE +=1\n",
    "    G0_NS.append(NS)\n",
    "    G0_NIE.append(NIE)\n",
    "    G0_NR.append(NR)\n",
    "        \n",
    "print(\"S:\",np.mean(G0_NS),\"R:\",np.mean(G0_NR),\"EAS/IAS:\",np.mean(G0_NIE))\n",
    "print(\"CI 95%\")\n",
    "print(\"S\",st.t.interval(alpha=0.95, df=len(G0_NS)-1, loc=np.mean(G0_NS), scale=st.sem(G0_NS)))\n",
    "print(\"R\",st.t.interval(alpha=0.95, df=len(G0_NR)-1, loc=np.mean(G0_NR), scale=st.sem(G0_NR)))\n",
    "print(\"EAS/IAS\",st.t.interval(alpha=0.95, df=len(G0_NIE)-1, loc=np.mean(G0_NIE), scale=st.sem(G0_NIE)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#TODO: Find out how the fuck this is plotted\n",
    "\n",
    "# ECDF linear scale\n",
    "plt.style.use('default')\n",
    "cdf = ECDF(neighbors_s)\n",
    "x = np.unique(neighbors_s)\n",
    "y = cdf(x)\n",
    "fig_cdf = plt.figure(figsize=(15,10))\n",
    "axes = fig_cdf.gca()\n",
    "axes.plot(x,y,marker='o',ms=6, linestyle='--', label='Percentuale di S')\n",
    "cdf = ECDF(neighbors_r)\n",
    "x = np.unique(neighbors_r)\n",
    "y = cdf(x)\n",
    "axes.plot(x,y,marker='o',ms=6, linestyle='--', label='Percentuale di R')\n",
    "# general title\n",
    "plt.suptitle('Composizione del neighbourhood dei nodi con stato S all ultima iterazione (in percentuale)', fontsize=13, fontweight=0, color='black', style='italic', y=0.92)\n",
    "axes.set_xlabel('Percentuale',size=20)\n",
    "axes.set_ylabel('ECDF', size = 20)\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'S':neighbors_s,'R':neighbors_r})\n",
    "\n",
    "#Style\n",
    "palette = plt.get_cmap('Set1')\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "#Hist only\n",
    "plt.figure(figsize=(15,10))\n",
    "s = sns.histplot(df[\"R\"], kde=False, color=palette(0), label=\"Susceptible\")\n",
    "r = sns.histplot(df[\"S\"], kde=False, color=palette(1), label=\"Removed\")\n",
    "s.set(xlabel='Percentuale', ylabel='Individui')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.hist([df[\"S\"], df[\"R\"]], color=[palette(0),palette(1)], label=[\"Susceptible\",\"Removed\"])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Percentuale\")\n",
    "plt.ylabel(\"Individui\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "f, axes = plt.subplots(1, 2, figsize=(15, 10), sharex=True)\n",
    "s = sns.histplot(df[\"R\"], ax=axes[0], color=palette(0))\n",
    "r = sns.histplot(df[\"S\"], ax=axes[1], color=palette(1))\n",
    "s.set(xlabel='Percentuale', ylabel='Individui')\n",
    "r.set(xlabel='Percentual', ylabel='Individui')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_giovani_a = []\n",
    "sim_giovani_s = []\n",
    "sim_attivi_a = []\n",
    "sim_attivi_s = []\n",
    "sim_anziani_a = []\n",
    "sim_anziani_s = []\n",
    "\n",
    "CI_giovani_a = {}\n",
    "CI_giovani_s = {}\n",
    "CI_attivi_a = {}\n",
    "CI_attivi_s = {}\n",
    "CI_anziani_a = {}\n",
    "CI_anziani_s = {}\n",
    "\n",
    "for iteration in simulations:\n",
    "    graph_stat = {}\n",
    "    giovani_a = {}\n",
    "    giovani_s = {}\n",
    "    attivi_a = {}\n",
    "    attivi_s = {}\n",
    "    anziani_a = {}\n",
    "    anziani_s = {}\n",
    "    for i in iteration[0]['status']:\n",
    "        graph_stat[i] = iteration[0]['status'][i]\n",
    "        \n",
    "    #Rebuild simulations by age and status evolution\n",
    "    for iter in iteration:\n",
    "        inc_1a = 0\n",
    "        inc_1s = 0\n",
    "        inc_2a = 0\n",
    "        inc_2s = 0\n",
    "        inc_3a = 0\n",
    "        inc_3s = 0\n",
    "        for s in iter['status']:\n",
    "            if graph_stat[s] == 0 or graph_stat[s] == 1 or graph_stat[s] == 2:\n",
    "                if iter['status'][s] == 3:\n",
    "                    if relabeled.nodes[s]['age'] == '20-64':\n",
    "                        inc_2a += 1\n",
    "                    elif relabeled.nodes[s]['age'] == '65+':\n",
    "                        inc_3a += 1\n",
    "                    else: \n",
    "                        inc_1a += 1\n",
    "                elif iter['status'][s] == 4:\n",
    "                    if relabeled.nodes[s]['age'] == '20-64':\n",
    "                        inc_2s += 1\n",
    "                    elif relabeled.nodes[s]['age'] == '65+':\n",
    "                        inc_3s += 1\n",
    "                    else: \n",
    "                        inc_1s += 1\n",
    "        giovani_a[iter['iteration']] = inc_1a\n",
    "        giovani_s[iter['iteration']] = inc_1s\n",
    "        attivi_a[iter['iteration']] = inc_2a\n",
    "        attivi_s[iter['iteration']] = inc_2s\n",
    "        anziani_a[iter['iteration']] = inc_3a\n",
    "        anziani_s[iter['iteration']] = inc_3s\n",
    "    \n",
    "    #Store values of each simulation\n",
    "    sim_giovani_a.append(giovani_a)\n",
    "    sim_giovani_s.append(giovani_s)\n",
    "    sim_attivi_a.append(attivi_a)\n",
    "    sim_attivi_s.append(attivi_s)\n",
    "    sim_anziani_a.append(anziani_a)\n",
    "    sim_anziani_s.append(anziani_s)\n",
    "    \n",
    "#Calculate mean values of each class\n",
    "for i in range(0,len(sim_giovani_a[0])):\n",
    "    values = []\n",
    "    for index in range (0,len(sim_giovani_a)):\n",
    "        values.append(sim_giovani_a[index][i])\n",
    "    giovani_a[i] = np.mean(values)\n",
    "    CI_giovani_a[i] = st.t.interval(alpha=0.95, df=len(values)-1, loc=np.mean(values), scale=st.sem(values))\n",
    "    if np.isnan(CI_giovani_a[i][0]) == True and np.isnan(CI_giovani_a[i][1]) == True: CI_giovani_a[i] = (0,0)\n",
    "    \n",
    "for i in range(0,len(sim_giovani_s[0])):\n",
    "    values = []\n",
    "    for index in range (0,len(sim_giovani_s)):\n",
    "        values.append(sim_giovani_s[index][i])\n",
    "    giovani_s[i] = np.mean(values)\n",
    "    CI_giovani_s[i] = st.t.interval(alpha=0.95, df=len(values)-1, loc=np.mean(values), scale=st.sem(values))\n",
    "    if np.isnan(CI_giovani_s[i][0]) == True and np.isnan(CI_giovani_s[i][1]) == True: CI_giovani_s[i] = (0,0)\n",
    "        \n",
    "for i in range(0,len(sim_attivi_a[0])):\n",
    "    values = []\n",
    "    for index in range (0,len(sim_attivi_a)):\n",
    "        values.append(sim_attivi_a[index][i])\n",
    "    attivi_a[i] = np.mean(values)\n",
    "    CI_attivi_a[i] = st.t.interval(alpha=0.95, df=len(values)-1, loc=np.mean(values), scale=st.sem(values))\n",
    "    if np.isnan(CI_attivi_a[i][0]) == True and np.isnan(CI_attivi_a[i][1]) == True: CI_attivi_a[i] = (0,0)\n",
    "\n",
    "for i in range(0,len(sim_attivi_s[0])):\n",
    "    values = []\n",
    "    for index in range (0,len(sim_attivi_s)):\n",
    "        values.append(sim_attivi_s[index][i])\n",
    "    attivi_s[i] = np.mean(values)\n",
    "    CI_attivi_s[i] = st.t.interval(alpha=0.95, df=len(values)-1, loc=np.mean(values), scale=st.sem(values))\n",
    "    if np.isnan(CI_attivi_s[i][0]) == True and np.isnan(CI_attivi_s[i][1]) == True: CI_attivi_s[i] = (0,0)\n",
    "\n",
    "for i in range(0,len(sim_anziani_a[0])):\n",
    "    values = []\n",
    "    for index in range (0,len(sim_anziani_a)):\n",
    "        values.append(sim_anziani_a[index][i])\n",
    "    anziani_a[i] = np.mean(values)\n",
    "    CI_anziani_a[i] = st.t.interval(alpha=0.95, df=len(values)-1, loc=np.mean(values), scale=st.sem(values))\n",
    "    if np.isnan(CI_anziani_a[i][0]) == True and np.isnan(CI_anziani_a[i][1]) == True: CI_anziani_a[i] = (0,0)\n",
    "\n",
    "for i in range(0,len(sim_anziani_s[0])):\n",
    "    values = []\n",
    "    for index in range (0,len(sim_anziani_s)):\n",
    "        values.append(sim_anziani_s[index][i])\n",
    "    anziani_s[i] = np.mean(values)\n",
    "    CI_anziani_s[i] = st.t.interval(alpha=0.95, df=len(values)-1, loc=np.mean(values), scale=st.sem(values))\n",
    "    if np.isnan(CI_anziani_s[i][0]) == True and np.isnan(CI_anziani_s[i][1]) == True: CI_anziani_s[i] = (0,0)\n",
    "        \n",
    "# Make a data frame\n",
    "dataframes = []\n",
    "dataframes.append(pd.DataFrame({'x': range(1,len(simulations[0])+1),'Giovani Asintomatici':list(giovani_a.values()),\n",
    "                 'CI bottom':[CI_giovani_a[i][0] for i in CI_giovani_a],\n",
    "                 'CI top':[CI_giovani_a[i][1] for i in CI_giovani_a]}))\n",
    "# Make a data frame\n",
    "dataframes.append(pd.DataFrame({'x': range(1,len(simulations[0])+1),'Giovani Sintomatici':list(giovani_s.values()),\n",
    "                 'CI bottom':[CI_giovani_s[i][0] for i in CI_giovani_s],\n",
    "                 'CI top':[CI_giovani_s[i][1] for i in CI_giovani_s]}))\n",
    "# Make a data frame\n",
    "dataframes.append(pd.DataFrame({'x': range(1,len(simulations[0])+1),'Attivi Asintomatici':list(attivi_a.values()),\n",
    "                 'CI bottom':[CI_attivi_a[i][0] for i in CI_attivi_a],\n",
    "                 'CI top':[CI_attivi_a[i][1] for i in CI_attivi_a]}))\n",
    "# Make a data frame\n",
    "dataframes.append(pd.DataFrame({'x': range(1,len(simulations[0])+1),'Attivi Sintomatici':list(attivi_s.values()),\n",
    "                 'CI bottom':[CI_attivi_s[i][0] for i in CI_attivi_s],\n",
    "                 'CI top':[CI_attivi_s[i][1] for i in CI_attivi_s]}))\n",
    "# Make a data frame\n",
    "dataframes.append(pd.DataFrame({'x': range(1,len(simulations[0])+1),'Anziani Asintomatici':list(anziani_a.values()),\n",
    "                 'CI bottom':[CI_anziani_a[i][0] for i in CI_anziani_a],\n",
    "                 'CI top':[CI_anziani_a[i][1] for i in CI_anziani_a]}))\n",
    "# Make a data frame\n",
    "dataframes.append(pd.DataFrame({'x': range(1,len(simulations[0])+1),'Anziani Sintomatici':list(anziani_s.values()),\n",
    "                 'CI bottom':[CI_anziani_s[i][0] for i in CI_anziani_s],\n",
    "                 'CI top':[CI_anziani_s[i][1] for i in CI_anziani_s]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# style\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.figure(figsize=(15,10))\n",
    " \n",
    "# create a color palette\n",
    "palette = plt.get_cmap('Set1')\n",
    " \n",
    "# multiple line plot\n",
    "num = 0\n",
    "for df in dataframes:\n",
    "    num += 1\n",
    "    for column in df.drop('x', axis=1):\n",
    "        if column != 'CI bottom' and column != 'CI top':\n",
    "            plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1, alpha=0.9, label=column)\n",
    "            plt.plot(df['x'], df['CI bottom'], marker='', color=palette(num), linewidth=0, alpha=0.9)\n",
    "            plt.plot(df['x'], df['CI top'], marker='', color=palette(num), linewidth=0, alpha=0.9)\n",
    "            plt.fill_between(df['x'], df['CI bottom'], df['CI top'], color=palette(num), alpha=0.3)\n",
    "                                                             \n",
    "\n",
    "#Add legend\n",
    "plt.legend()\n",
    "\n",
    "# general title\n",
    "plt.suptitle(\"Andamento dell'infezione A/S per fascia di età\", fontsize=13, fontweight=0, color='black', style='italic', y=0.92)\n",
    "    \n",
    "#Add titles\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the figure\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "# create a color palette\n",
    "palette = plt.get_cmap('Set1')\n",
    " \n",
    "# multiple line plot\n",
    "\n",
    "num = 0\n",
    "for df in dataframes:\n",
    "    num += 1\n",
    "    for column in df.drop('x', axis=1):\n",
    "        if column != 'CI bottom' and column != 'CI top':\n",
    "            plt.subplot(2,3, num)\n",
    "            plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1, alpha=0.9, label=column)\n",
    "            plt.plot(df['x'], df['CI bottom'], marker='', color=palette(num), linewidth=0, alpha=0.9)\n",
    "            plt.plot(df['x'], df['CI top'], marker='', color=palette(num), linewidth=0, alpha=0.9)\n",
    "            plt.fill_between(df['x'], df['CI bottom'], df['CI top'], color=palette(num), alpha=0.3)\n",
    "            \n",
    "            # Same limits for everybody!\n",
    "            plt.xlim(0,61)\n",
    "            plt.ylim(0,25000)\n",
    "        \n",
    "            # Not ticks everywhere\n",
    "            if num in range(7) :\n",
    "                plt.tick_params(labelbottom='off')\n",
    "            if num not in [1,4,7] :\n",
    "                plt.tick_params(labelleft='off')\n",
    "\n",
    "            # Add title\n",
    "            plt.title(column, loc='left', fontsize=12, fontweight=0, color=palette(num))\n",
    "            \n",
    "# general title\n",
    "plt.suptitle(\"Andamento dell'infezione A/S per fascia di età\", fontsize=13, fontweight=0, color='black', style='italic', y=0.98)\n",
    " \n",
    "# Axis title\n",
    "plt.text(0.5, 0.02, 'Time', ha='center', va='center')\n",
    "plt.text(0.06, 0.5, 'Note', ha='center', va='center', rotation='vertical')\n",
    "plt.show()"
   ]
  }
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