multiple_simulations_full.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\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import src.dataset_generators.sezioni_generator\n",
    "import src.dataset_generators.schools_generator\n",
    "import src.layer_generators.household_layer\n",
    "import src.layer_generators.school_layer\n",
    "import src.layer_generators.work_layer\n",
    "import src.graph_generators.graph_generator_full\n",
    "\n",
    "client = MongoClient('localhost', 27017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating...\n",
      "Running simulation number 1\n",
      "Database cleared.\n",
      "Generating sezioni...\n",
      "Sezioni generated. Dumping to db...\n",
      "Dumping to db done.\n",
      "Indexing schools...\n",
      "Indexing schools done. Appending closest schools to sezioni...\n",
      "Dumping to db done.\n",
      "TBR has been triggered. Failed to generate sez n 23502331\n",
      "TBR has been triggered. Failed to generate sez n 50724746\n",
      "Sezioni saltate per via di malformazioni tra famiglie e fasce: 59\n",
      "Sezioni simulate:  1095\n",
      "1.4085301300218342\n",
      "Indexing schools...\n",
      "Done. Calculating mean school size...\n",
      "Scolari all'infanzia 177\n",
      "Scolari alle elementari 241\n",
      "Scolari alle medie 339\n",
      "Scolari alle superiori 271\n",
      "Done. Allocating school to under-19...\n",
      "Current section: 61077 582 3429 3459 3837 3933 4613 4617 4636 4865 4950 4967 5110 51115388 5600 5892 5909 5913 5915 5917 5921 59285934 5964 59675998 6079\n",
      "Kids allocated to schools. Generating classes...\n",
      "Dumping to db done.\n",
      "Indexing workplaces...\n",
      "Loading database into memory...38 4331 4539 5820 5864\n",
      "Creating nodes and family layers edges...\n",
      "Creating scuole_infanzia layer edges...168 / 6107 198 / 6107 215 / 6107 218 / 6107 222 / 6107 244 / 6107 271 / 6107 279 / 6107 290 / 6107 305 / 6107 322 / 6107 346 / 6107 363 / 61076107 372 / 6107 374 / 6107 377 / 6107 380 / 6107 405 / 6107 414 / 61076107 416 / 6107 / 6107 461 / 6107 462 / 61076107 494 / 6107 520 / 6107 522 / 6107 543 / 6107 544 / 6107 546 / 6107 547 / 6107 556 / 6107 558 / 6107 562 / 6107567 / 6107 6107 596 / 6107/ 6107 669 / 61076107 701 / 6107 728 / 6107 729 / 6107 731 / 6107 739 / 6107 745 / 6107 748 / 61076107 6107 802 / 6107 816 / 6107 6107 6107 858 / 6107 928 / 6107 942 / 6107 951 / 6107 958 / 6107 6107 1008 / 6107/ 6107 / 6107 / 6107 1044 / 6107 1053 / 6107 1061 / 6107 1079 / 61071102 / 6107 1108 / 61071113 / 61076107 1160 / 6107 1189 / 6107 1212 / 6107 1216 / 6107 1241 / 6107 1246 / 6107/ 6107 1304 / 6107 1317 / 6107 1318 / 6107 1327 / 6107 1349 / 6107 1376 / 6107 1378 / 6107 1383 / 6107 1393 / 61071403 / 6107 1406 / 6107 1412 / 6107 1417 / 6107/ 6107 1427 / 6107 1464 / 6107 1472 / 6107 1500 / 6107 1511 / 6107 1520 / 6107 1521 / 61076107 61076107 1606 / 6107 1610 / 6107 1613 / 6107 1618 / 6107 1626 / 6107 1641 / 6107 / 6107/ 6107 1692 / 6107 1703 / 6107 1709 / 6107 1718 / 6107 1743 / 6107 6107 1753 / 6107 1756 / 6107 1773 / 6107 1780 / 6107 6107 1799 / 6107 / 6107 1822 / 6107 1825 / 6107 1843 / 6107 1847 / 6107 1851 / 61071874 / 6107 1895 / 6107 1910 / 6107 1930 / 6107 1954 / 61076107 1969 / 61076107 1980 / 61071997 / 6107 2001 / 6107 2009 / 6107 2028 / 6107 2070 / 6107 2073 / 6107 2079 / 6107 2080 / 6107 2081 / 6107 2096 / 6107 6107 2125 / 6107 2126 / 6107 2131 / 6107 2138 / 6107 6107 2154 / 6107 2164 / 6107 2187 / 6107 2188 / 6107 2189 / 6107 2197 / 6107 2198 / 6107 2206 / 6107 2219 / 6107 2231 / 6107 2232 / 6107 2234 / 6107 2239 / 6107 2247 / 6107 2287 / 6107/ 6107 2308 / 6107 2330 / 6107 2340 / 6107 2352 / 6107 2353 / 6107 2367 / 6107 2376 / 6107 2383 / 6107 2388 / 6107 2412 / 6107 2414 / 6107 2442 / 6107 2444 / 6107 2451 / 6107 2455 / 6107 2460 / 6107 2461 / 6107 2466 / 6107 2481 / 6107 2492 / 6107 2503 / 6107 2509 / 6107 2533 / 6107 2534 / 6107 2542 / 6107 2556 / 6107 2576 / 6107 2597 / 6107 2601 / 6107 2616 / 61076107 2650 / 6107 2691 / 6107 2718 / 6107 6107 2731 / 6107 2740 / 6107 2751 / 6107 2802 / 6107 2803 / 6107 2807 / 6107 2820 / 6107 2829 / 6107 2850 / 6107 2873 / 61076107 6107 6107 2896 / 6107 2898 / 6107 2934 / 6107 2941 / 6107 2942 / 6107 2968 / 61072978 / 6107 2979 / 6107 2984 / 6107 2988 / 6107 3054 / 6107 3058 / 6107 3073 / 6107 / 6107 3077 / 6107 6107 3095 / 6107 3099 / 6107 3103 / 6107 3105 / 6107 3107 / 6107 3111 / 6107 3113 / 6107 6107 3156 / 6107 3157 / 6107 3180 / 6107 3182 / 6107 3199 / 6107 3246 / 6107 3247 / 6107 3261 / 61073302 / 6107 3303 / 6107 3318 / 6107 3319 / 6107 3347 / 6107 3357 / 6107 3408 / 6107 3415 / 6107 6107 3436 / 6107 3439 / 6107 3446 / 6107 3457 / 6107 3470 / 6107 3471 / 6107 3479 / 6107 3491 / 6107 3504 / 6107 3540 / 6107 3541 / 6107 3565 / 6107 3582 / 6107 3597 / 6107 3619 / 6107 3634 / 6107 3647 / 6107 3651 / 6107 3657 / 6107 3712 / 6107 3734 / 6107 3739 / 6107 3744 / 6107 3746 / 6107 3759 / 6107 3771 / 61076107 3842 / 6107 3878 / 6107 6107 3903 / 6107 3923 / 6107 61076107 3945 / 6107 3950 / 6107 3951 / 61073954 / 6107 3961 / 6107 3973 / 6107 3982 / 610761074011 / 6107 4027 / 6107 4030 / 6107 4058 / 6107 4080 / 6107 4091 / 6107 4115 / 6107 4141 / 6107 4144 / 61076107 4170 / 61074184 / 6107 4192 / 6107 4199 / 6107 4201 / 61076107/ 61074222 / 6107 61074271 / 6107 4282 / 61076107 4360 / 6107 4363 / 6107 4369 / 6107 6107 4402 / 6107 4439 / 6107 4445 / 6107 4471 / 6107 4546 / 61076107/ 6107 4591 / 6107/ 6107 4616 / 6107 4617 / 6107 4624 / 6107 4625 / 6107 4644 / 6107 / 6107 4666 / 6107/ 6107 4679 / 6107 4691 / 6107 4780 / 6107 4781 / 6107 4789 / 61074800 / 6107 4807 / 6107 4836 / 6107 4870 / 6107 4873 / 6107 4911 / 6107 4922 / 6107 4924 / 6107 4935 / 6107 4940 / 6107 4948 / 6107 4966 / 6107 4987 / 6107 4988 / 6107 4994 / 6107 5036 / 6107610761076107 5096 / 61076107 5109 / 6107 / 6107 5151 / 6107 5156 / 6107 5202 / 61076107 5215 / 6107/ 6107 5242 / 6107 5262 / 6107 5267 / 61076107 / 6107 5365 / 6107 5370 / 6107 6107 5418 / 6107 5436 / 6107 5444 / 61076107 5480 / 6107 5481 / 6107 5492 / 6107 5493 / 6107 5509 / 6107 5510 / 6107 5513 / 61076107 5533 / 6107 5556 / 6107 6107 5567 / 61075663 / 6107 5676 / 6107 5684 / 6107 5687 / 61076107 5712 / 6107 5720 / 6107 5746 / 6107 5751 / 6107 5753 / 6107 5756 / 6107 5778 / 6107 5785 / 6107 5804 / 6107 5820 / 6107 5885 / 6107 5923 / 6107 5928 / 6107 5929 / 6107 5933 / 6107 5958 / 6107 5976 / 6107 5983 / 6107 5985 / 6107 6000 / 6107 6009 / 6107 6019 / 6107 6039 / 6107 6107 6056 / 6107 6076 / 6107 6089 / 6107\n",
      "Creating scuole_elementari layer edges...\n",
      "Creating scuole_media layer edges...\n",
      "Creating scuole_superiori layer edges...4\n",
      "Creating workplace layer edges...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing self edges.../ 6107126 / 6107 160 / 6107 166 / 6107 186 / 6107 194 / 6107 228 / 6107 229 / 6107 248 / 6107 251 / 6107 308 / 6107 309 / 61076107 320 / 6107 366 / 6107 397 / 6107 437 / 6107 438 / 6107 475 / 6107 500 / 6107 502 / 6107 529 / 6107 539 / 6107 618 / 6107 633 / 6107 645 / 6107 666 / 6107 668 / 6107 687 / 6107 694 / 6107 696 / 6107 701 / 6107 702 / 6107 717 / 6107 729 / 6107 730 / 6107 753 / 6107 772 / 6107 802 / 6107 811 / 6107 6107 834 / 6107 850 / 6107 857 / 6107 864 / 6107 872 / 6107 886 / 6107 895 / 6107 897 / 6107 907 / 6107 985 / 6107 995 / 6107 1009 / 6107 1031 / 6107 1064 / 6107 1067 / 6107 1081 / 6107 1155 / 6107 1168 / 6107 1169 / 6107 1174 / 61076107 1183 / 6107 1218 / 6107 1224 / 6107 1233 / 6107 1238 / 6107 1239 / 6107 1240 / 6107 1247 / 6107 1248 / 6107 1252 / 6107 1253 / 6107 1297 / 6107 1303 / 6107 1305 / 6107 1326 / 6107 1328 / 6107 1330 / 6107 1355 / 6107 1365 / 6107 1368 / 6107 1373 / 6107 1391 / 6107 1407 / 6107 1453 / 6107 1475 / 6107 1482 / 6107 1483 / 6107 1484 / 6107 1491 / 6107 1492 / 6107 1508 / 6107 1510 / 6107 1526 / 6107 1540 / 6107 1541 / 6107 1551 / 6107 1553 / 6107 1556 / 6107 1557 / 6107 1612 / 6107 1626 / 6107 1632 / 6107 1639 / 6107 1642 / 6107 1699 / 6107 1700 / 6107 1703 / 6107 1786 / 6107 1806 / 6107 1807 / 6107 1808 / 6107 1809 / 6107 1929 / 6107 1954 / 61071967 / 6107 2015 / 6107 2017 / 6107 2020 / 6107 2096 / 6107 2106 / 6107 2107 / 6107 2108 / 6107 2111 / 6107 2114 / 61076107 2123 / 6107 2157 / 6107 2164 / 6107 2179 / 6107 2188 / 6107 2189 / 6107 2190 / 6107 2191 / 6107 2195 / 6107 2222 / 6107 2229 / 6107 2231 / 6107 2240 / 6107 2241 / 6107 2250 / 6107 2275 / 6107 2282 / 6107 2289 / 6107 2306 / 6107 2376 / 6107 2395 / 6107 2409 / 6107 2478 / 6107 2480 / 6107 2482 / 6107/ 6107 2499 / 6107 2500 / 6107 2501 / 6107 2511 / 6107 2517 / 6107 2522 / 6107 2535 / 6107 2537 / 6107 2575 / 6107 2633 / 6107 2639 / 6107 2640 / 6107 2665 / 6107 2694 / 6107 2705 / 61076107 2729 / 6107 2735 / 6107 2757 / 6107 2765 / 6107 2766 / 6107 2776 / 6107 2780 / 6107 2781 / 6107 2784 / 6107 2785 / 6107 2791 / 6107 2847 / 6107 2885 / 6107 2966 / 6107 2969 / 6107 2974 / 6107 2976 / 6107 2977 / 6107 2979 / 6107 2980 / 6107 2984 / 6107 2991 / 6107 3014 / 6107 3016 / 6107 3019 / 6107 3025 / 6107 3153 / 6107 3161 / 6107 3166 / 6107 3178 / 6107 3180 / 6107 3181 / 6107 3194 / 6107 3197 / 6107 3198 / 6107 3203 / 6107 3328 / 6107 3335 / 6107 3338 / 6107 3339 / 6107 3344 / 6107 3345 / 6107 3367 / 6107 3378 / 6107 3379 / 6107 3380 / 6107 3391 / 6107 3515 / 6107 3516 / 6107 3517 / 6107 3539 / 6107 3542 / 6107 3545 / 6107 3561 / 6107 3563 / 61076107 3575 / 6107 3588 / 6107 3592 / 6107 3599 / 6107 3603 / 6107 3604 / 6107 3666 / 6107 3721 / 6107 3730 / 6107 3735 / 6107 3746 / 6107 3846 / 6107 3847 / 6107 3853 / 6107 3858 / 6107 3865 / 6107 3869 / 6107 3882 / 6107 3886 / 6107 3914 / 6107 3924 / 6107 3932 / 6107 3937 / 6107 3990 / 6107 3997 / 6107 3998 / 6107 3999 / 61076107 4028 / 6107 4034 / 6107 4065 / 6107 4066 / 6107 4144 / 6107 4145 / 6107 4147 / 6107 4161 / 6107 4166 / 6107 4257 / 6107 4277 / 6107 4285 / 6107 4286 / 6107 4288 / 6107 4290 / 6107 4291 / 6107 4296 / 6107 4299 / 6107 4308 / 6107 4322 / 6107 4392 / 6107 4393 / 6107 4396 / 6107 4398 / 6107 4399 / 6107 4400 / 6107610761076107 4506 / 6107 4507 / 6107 4510 / 6107 4519 / 6107 4523 / 6107 4524 / 6107 4596 / 6107 4655 / 6107 4665 / 6107 4669 / 6107 4677 / 6107 4680 / 6107 4686 / 6107 4727 / 6107 4750 / 610761074789 / 61076107 4797 / 6107 4810 / 6107 4811 / 6107 4875 / 6107 4890 / 6107 4896 / 6107 4902 / 6107 4903 / 6107 4904 / 6107 4942 / 6107 4948 / 6107 4961 / 6107 4964 / 6107 4972 / 6107 / 6107 5009 / 6107 5013 / 6107 5027 / 6107 5144 / 6107 5146 / 6107 5149 / 6107 5150 / 6107 5179 / 6107 5315 / 6107 5323 / 6107 5324 / 6107 5326 / 6107 5387 / 6107 5395 / 6107 5397 / 6107 5430 / 6107 6107 5493 / 6107 5550 / 6107 5558 / 6107 5559 / 6107 5564 / 6107 5566 / 6107 5616 / 6107 5691 / 6107 5712 / 6107 5722 / 6107 5731 / 6107 5738 / 6107 5745 / 6107 5746 / 6107 5747 / 6107 5749 / 6107 5802 / 6107 5810 / 6107 5817 / 6107 5818 / 6107 5819 / 6107 5982 / 6107 5989 / 6107 6000 / 6107 6007 / 6107\n",
      "total nodes: 1230105\n",
      "total edges: 9050256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 3/60 [00:17<05:35,  5.89s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-8c7f62dc93bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_initial_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0mlastsim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteration_bunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprogress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlastsim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'node_count'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/dev/Tesi/ndlibCustom/CustomDiffusionModel.py\u001b[0m in \u001b[0;36miteration_bunch\u001b[0;34m(self, bunch_size, node_status, progress_bar)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0msystem_status\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbunch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0mits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_status\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m             \u001b[0msystem_status\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msystem_status\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/dev/Tesi/ndlibCustom/SEIR_ASModel.py\u001b[0m in \u001b[0;36miteration\u001b[0;34m(self, node_status)\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mu_status\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Susceptible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m                 \u001b[0minfected_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighbors\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m                 \u001b[0mtriggered\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfected_neighbors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/dev/Tesi/ndlibCustom/SEIR_ASModel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mu_status\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Susceptible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m                 \u001b[0minfected_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighbors\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m                 \u001b[0mtriggered\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfected_neighbors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#CUSTOM SEIR_AS MODEL SIMULATION\n",
    "print(\"Simulating...\")\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",
    "    \n",
    "    #Clear last iteration's graph DB\n",
    "    try:\n",
    "        client.drop_database('tesi')\n",
    "        print(\"Database cleared.\")\n",
    "    except:\n",
    "        print(\"There is no database currently saved.\")\n",
    "    \n",
    "    #Generate new multilayer graph\n",
    "    src.dataset_generators.sezioni_generator.main()\n",
    "    src.dataset_generators.schools_generator.main()\n",
    "    src.layer_generators.household_layer.main()\n",
    "    src.layer_generators.school_layer.main()\n",
    "    src.layer_generators.work_layer.main()\n",
    "    graph = src.graph_generators.graph_generator_full.main() \n",
    "    \n",
    "    #TODO: Save graph data!\n",
    "    \n",
    "    # Model selection\n",
    "    model = SEIR_ASModel(graph)\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",
    "    lastsim = model.iteration_bunch(60,progress_bar=True)\n",
    "    for i in lastsim:\n",
    "        i.pop('node_count')\n",
    "        i.pop('status_delta')\n",
    "    simulations.append(lastsim)\n",
    "    del(graph) #Remove graph and reclaim memory for next iteration\n",
    "    clear_output() #Clear output to avoid cluttering: simulation number will be printed at each iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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()\n",
    "print(np.mean(degree))\n",
    "print(density)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "#CUSTOM SEIR_AS MODEL SIMULATION\n",
    "#Run 50 simulations, results will be a mean of all data generated\n",
    "print(\"Simulating...\")\n",
    "simulations = []\n",
    "\n",
    "for i in range (0,25):\n",
    "    #Drop last iteration database\n",
    "    try:\n",
    "        client.drop_database['tesi']\n",
    "    except:\n",
    "        print(\"There is no database currently stored.\")\n",
    "    \n",
    "    #Create a completely new multilayer graph \n",
    "    src.dataset_generators.sezioni_generator.main()\n",
    "    src.dataset_generators.schools_generator.main()\n",
    "    src.layer_generators.household_layer.main()\n",
    "    src.layer_generators.school_layer.main()\n",
    "    src.layer_generators.work_layer.main()\n",
    "    graph = src.graph_generators.graph_generator_full.main()\n",
    "    \n",
    "    # Model selection\n",
    "    model = SEIR_ASModel(graph)\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",
    "    cfg.add_model_parameter(\"fraction_infected\", 0.005) #Starting infected nodes\n",
    "    model.set_initial_status(cfg)\n",
    "    \n",
    "    #Run a simulation of 60 iterations\n",
    "    print(\"Running simulation number\",i+1)\n",
    "    lastsim = model.iteration_bunch(60,progress_bar=True)\n",
    "    for i in lastsim:\n",
    "        i.pop('node_count')\n",
    "        i.pop('status_delta')\n",
    "    simulations.append(lastsim)"
   ]
  },
  {
   "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",
    "    #Build graph_stat with starting statuses\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)\n",
    "            plt.plot(df['x'], df['CI top'], marker='', color=palette(num), linewidth=0, alpha=0)\n",
    "            plt.fill_between(df['x'], df['CI bottom'], df['CI top'], color=palette(num), alpha=0.3, linewidth=0)\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)\n",
    "            plt.plot(df['x'], df['CI top'], marker='', color=palette(num), linewidth=0, alpha=0)\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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