Hood.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import complexism as cx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.random as rd\n",
    "from complexism.element import StepTicker, Event\n",
    "from complexism.agentbased import ActiveBehaviour, PassiveBehaviour\n",
    "import complexism.agentbased.statespace as ss\n",
    "from complexism.agentbased.statespace.be import StateEnterTrigger, ActiveModBehaviour\n",
    "import epidag.data as dat\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex-specific demography, [1990, 2050], Birth, Death, Migration"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = pd.read_csv('data/SimFM.csv')\n",
    "\n",
    "ds = cx.DemographySex()\n",
    "ds.load_birth_data(mat, i_year='Year', i_f='BirthF', i_m='BirthM')\n",
    "ds.load_death_data(mat, i_year='Year', i_f='DeathF', i_m='DeathM')\n",
    "ds.load_migration_data(mat, i_year='Year', i_f='MigrationF', i_m='MigrationM')\n",
    "ds.load_population_data(mat, i_year='Year', i_f='PopF', i_m='PopM')\n",
    "ds.complete_loading()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "psc = \"\"\"\n",
    "PCore pSLIR {\n",
    "    beta = 0.4\n",
    "    lambda = 0.2\n",
    "    gamma = 0.05\n",
    "    Recov ~ exp(gamma)\n",
    "    Die ~ exp(0.02)\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "dsc = \"\"\"\n",
    "CTBN dIR {\n",
    "    life[Alive | Dead]\n",
    "    ir[I | R]\n",
    "\n",
    "    Alive{life:Alive}\n",
    "    Dead{life:Dead}\n",
    "    Inf{life:Alive, ir:I}\n",
    "    Rec{life:Alive, ir:R}\n",
    "\n",
    "    Die -> Dead # from transition Die to state Dead by distribution Die\n",
    "    Inf -- Recov -> Rec\n",
    "\n",
    "    Alive -- Die # from state Alive to transition Die\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SL_ODE(y, t, p, x):\n",
    "    life = x['life']\n",
    "    fs, ms, fl, ml = y\n",
    "\n",
    "    brs = life.get_birth_rate(t)\n",
    "    mrs = life.get_migration_rate(t)\n",
    "    drs = life.get_death_rate(t)\n",
    "\n",
    "    return [\n",
    "        fs * (brs['Female'] - drs['Female'] + mrs['Female'] - p['lambda']),\n",
    "        ms * (brs['Male'] - drs['Male'] + mrs['Male'] - p['lambda']),\n",
    "        fl * (brs['Female'] - drs['Female'] + mrs['Female']) + fs * p['lambda'],\n",
    "        ml * (brs['Male'] - drs['Male'] + mrs['Male']) + ms * p['lambda']\n",
    "\n",
    "    ]\n",
    "\n",
    "\n",
    "def measure(y, t, p, x):\n",
    "    return {\n",
    "        'Sus': y[0] + y[1],\n",
    "        'Lat': y[2] + y[3]\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl = cx.Director()\n",
    "\n",
    "ctrl.read_bayes_net(psc)\n",
    "ctrl.read_state_space_model(dsc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bp = ctrl.new_sim_model('IR', 'StSpABM')\n",
    "bp.set_agent('dIR')\n",
    "bp.set_observations(states=['Inf', 'Rec', 'Dead'])\n",
    "bp.add_behaviour('Life', 'Reincarnation', s_death='Dead', s_birth='Inf')\n",
    "\n",
    "y0 = bp.get_y0_proto()\n",
    "y0.define(n=1000, st='Inf')\n",
    "\n",
    "lyo = ctrl.new_sim_model('MIR', 'MultiModel')\n",
    "lyo.add_entry('M', 'IR', y0, fr=1, to=3)\n",
    "lyo.add_summary('#M', 'Inf', 'Inf')\n",
    "lyo.add_summary('#M', 'Rec', 'Rec')\n",
    "\n",
    "bp = ctrl.new_sim_model('SL', 'ODEEBM')\n",
    "bp.set_fn_ode(SL_ODE, ['FS', 'MS', 'FL', 'ML'])\n",
    "bp.set_external_variables({'life': ds})\n",
    "\n",
    "bp.set_dt(dt=0.1, odt=1)\n",
    "bp.add_observing_function(measure)\n",
    "\n",
    "\n",
    "lyo = ctrl.new_sim_model('SLIR', 'MultiModel')\n",
    "\n",
    "y0 = bp.get_y0_proto()\n",
    "ns = {k: v.sum()/100 for k, v in ds.get_population(1990).items()}\n",
    "y0.define(st='FS', n=ns['Female'])\n",
    "y0.define(st='MS', n=ns['Male'])\n",
    "\n",
    "\n",
    "lyo.add_entry('mir', 'MIR')\n",
    "lyo.add_entry('sl', 'SL', y0)\n",
    "lyo.set_observations()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ctrl.generate_model('M1', 'SLIR', bn='pSLIR')\n",
    "\n",
    "y0 = ctrl.get_y0s('SLIR')\n",
    "cx.start_counting()\n",
    "out = cx.simulate(model, y0, 1990, 2035, 1, log=False)\n",
    "cx.stop_counting()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12.857321\n",
       "Name: dT, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cx.get_counting_results().dT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import epidag as dag\n",
    "import complexism as cx\n",
    "import complexism.fn as cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "psc = \"\"\"\n",
    "PCore pSIR {\n",
    "    beta = 0.4\n",
    "    gamma = 0.5\n",
    "    Infect ~ exp(beta)\n",
    "    Recov ~ exp(0.5)\n",
    "    Die ~ exp(0.02)\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "dsc = \"\"\"\n",
    "CTBN SIR {\n",
    "    life[Alive | Dead]\n",
    "    sir[S | I | R]\n",
    "\n",
    "    Alive{life:Alive}\n",
    "    Dead{life:Dead}\n",
    "    Inf{life:Alive, sir:I}\n",
    "    Rec{life:Alive, sir:R}\n",
    "    Sus{life:Alive, sir:S}\n",
    "\n",
    "    Die -> Dead # from transition Die to state Dead by distribution Die\n",
    "    Sus -- Infect -> Inf\n",
    "    Inf -- Recov -> Rec\n",
    "\n",
    "    Alive -- Die # from state Alive to transition Die\n",
    "}\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = cx.Director()\n",
    "da.read_bayes_net(psc)\n",
    "da.read_state_space_model(dsc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SIR': ['agent'], 'agent': ['Die', 'Infect', 'Recov']}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mbp = da.new_sim_model('SIR', 'StSpABM')\n",
    "mbp.set_agent(prefix='Ag', group='agent', dynamics='SIR')\n",
    "mbp.add_behaviour('Dead', 'Reincarnation', s_death='Dead', s_birth='Sus')\n",
    "mbp.get_parameter_hierarchy(dc=da.get_state_space_model('SIR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<complexism.agentbased.statespace.abmstsp.StSpAgentBasedModel at 0x1a60cfcd1d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mbp.generate('sir', bn='pSIR', da=da)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'ModelType': 'CTBN', 'ModelName': 'SIR', 'Microstates': {'life': ['Alive', 'Dead'], 'sir': ['S', 'I', 'R']}, 'States': {'Alive': {'life': 'Alive'}, 'Dead': {'life': 'Dead'}, 'Inf': {'life': 'Alive', 'sir': 'I'}, 'Rec': {'life': 'Alive', 'sir': 'R'}, 'Sus': {'life': 'Alive', 'sir': 'S'}}, 'Transitions': {'Die': {'To': 'Dead', 'Dist': 'Die'}, 'Infect': {'To': 'Inf', 'Dist': 'Infect'}, 'Recov': {'To': 'Rec', 'Dist': 'Recov'}}, 'Targets': {'Alive': ['Die'], 'Dead': [], 'Inf': ['Recov'], 'Rec': [], 'Sus': ['Infect']}, 'Order': ['life', 'sir']},\n",
       " gamma: 0.5, beta: 0.4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc.get_samplers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name:\tpSIR\n",
       "Nodes:\n",
       "\tDie ~ exp(0.02)\n",
       "\tRecov ~ exp(0.5)\n",
       "\tgamma = 0.5\n",
       "\tbeta = 0.4\n",
       "\tInfect ~ exp(beta)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three types of agents are supported by PyComplexism\n",
    "\n",
    "- StSpAgent (State space agent): agents with states from state-space models\n",
    "- DiffEqAgent (Differential equation agent): agents with inner dynamics from differential equations\n",
    "- GenericAgent: agents allow fully customisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building an agent-based model\n",
    "1. Design a parameter model\n",
    "2. Design a dynamic model: ctmc, ctbn, or none\n",
    "3. Design agents: state-space, diffeq, generic\n",
    "4. Design population structure\n",
    "5. Add mods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_blueprint = {\n",
    "    'agent': {\n",
    "        'prefix': 'agent',\n",
    "        'group': 'agent',\n",
    "        'exo': {},\n",
    "        'type': 'stsp',\n",
    "        'dynamics': 'SIR',\n",
    "        'default': {\n",
    "            'st': 'Sus'\n",
    "        }\n",
    "    },\n",
    "    'population': {\n",
    "        'networks': {}\n",
    "    },\n",
    "    'mods': {\n",
    "        \n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>State</th>\n",
       "      <th>Infect</th>\n",
       "      <th>Recov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Sus</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Inf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>Helen</td>\n",
       "      <td>Rec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name State  Infect  Recov\n",
       "Time                            \n",
       "0.0   Helen   Sus     0.0    0.0\n",
       "1.0   Helen   Inf     1.0    0.0\n",
       "2.0   Helen   Inf     0.0    0.0\n",
       "3.0   Helen   Inf     0.0    0.0\n",
       "4.0   Helen   Rec     0.0    1.0\n",
       "5.0   Helen   Rec     0.0    0.0\n",
       "6.0   Helen   Rec     0.0    0.0\n",
       "7.0   Helen   Rec     0.0    0.0\n",
       "8.0   Helen   Rec     0.0    0.0\n",
       "9.0   Helen   Rec     0.0    0.0\n",
       "10.0  Helen   Rec     0.0    0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_blueprint = {\n",
    "    'agent': {\n",
    "        'prefix': 'Helen',\n",
    "        'group': 'agent',\n",
    "        'exo': {},\n",
    "        'type': 'stsp',\n",
    "        'dynamics': 'SIR',\n",
    "        'default': {\n",
    "            'st': 'Sus'\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "Name = 'M1'\n",
    "Gene = sm.generate()\n",
    "proto = Gene.get_prototype('agent')\n",
    "stsp = dc.generate_model('agent', **proto.get_samplers())\n",
    "ag = cx.StSpAgent('Helen', stsp['Sus'], proto.get_sibling('Helen'))\n",
    "model = cx.SingleIndividualABM(Name, ag)\n",
    "cx.simulate(model, None, 0, 10, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sus</th>\n",
       "      <th>Inf</th>\n",
       "      <th>Rec</th>\n",
       "      <th>Alive</th>\n",
       "      <th>Dead</th>\n",
       "      <th>Tr(Name: Infect, To: Inf, By: exp(beta))</th>\n",
       "      <th>Tr(Name: Recov, To: Rec, By: exp(0.5))</th>\n",
       "      <th>Tr(Name: Die, To: Dead, By: exp(0.02))</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>63</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>94</td>\n",
       "      <td>6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>53</td>\n",
       "      <td>91</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>66</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>89</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>79</td>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>81</td>\n",
       "      <td>86</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sus  Inf  Rec  Alive  Dead  Tr(Name: Infect, To: Inf, By: exp(beta))  \\\n",
       "Time                                                                         \n",
       "0.0    95    5    0    100     0                                       0.0   \n",
       "1.0    63   26   11    100     0                                      32.0   \n",
       "2.0    36   34   26     96     4                                      25.0   \n",
       "3.0    22   30   42     94     6                                      14.0   \n",
       "4.0    14   24   53     91     9                                       8.0   \n",
       "5.0    10   14   66     90    10                                       4.0   \n",
       "6.0     6   13   70     89    11                                       4.0   \n",
       "7.0     3   10   75     88    12                                       2.0   \n",
       "8.0     3    6   79     88    12                                       0.0   \n",
       "9.0     2    6   79     87    13                                       1.0   \n",
       "10.0    1    4   81     86    14                                       1.0   \n",
       "\n",
       "      Tr(Name: Recov, To: Rec, By: exp(0.5))  \\\n",
       "Time                                           \n",
       "0.0                                      0.0   \n",
       "1.0                                     11.0   \n",
       "2.0                                     15.0   \n",
       "3.0                                     17.0   \n",
       "4.0                                     13.0   \n",
       "5.0                                     14.0   \n",
       "6.0                                      5.0   \n",
       "7.0                                      5.0   \n",
       "8.0                                      4.0   \n",
       "9.0                                      1.0   \n",
       "10.0                                     3.0   \n",
       "\n",
       "      Tr(Name: Die, To: Dead, By: exp(0.02))  \n",
       "Time                                          \n",
       "0.0                                      0.0  \n",
       "1.0                                      0.0  \n",
       "2.0                                      4.0  \n",
       "3.0                                      2.0  \n",
       "4.0                                      3.0  \n",
       "5.0                                      1.0  \n",
       "6.0                                      1.0  \n",
       "7.0                                      1.0  \n",
       "8.0                                      0.0  \n",
       "9.0                                      1.0  \n",
       "10.0                                     1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_blueprint = {\n",
    "    'agent': {\n",
    "        'prefix': 'agent',\n",
    "        'group': 'agent',\n",
    "        'exo': {},\n",
    "        'type': 'stsp',\n",
    "        'dynamics': 'SIR',\n",
    "        'default': {\n",
    "            'st': 'Sus'\n",
    "        }\n",
    "    },\n",
    "    'population': {\n",
    "        'networks': {}\n",
    "    },\n",
    "    'mods': {\n",
    "        \n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "y0 = [\n",
    "    {'n': 95, 'attributes': {'st': 'Sus'}},\n",
    "    {'n': 5, 'attributes': {'st': 'Inf'}},\n",
    "]\n",
    "\n",
    "Name = 'M1'\n",
    "Gene = sm.generate()\n",
    "eve = cx.StSpBreeder('Ag ', 'agent', Gene, dc)\n",
    "pop = cx.Population(eve)\n",
    "# model = cx.SingleIndividualABM(Name, ag)\n",
    "# cx.simulate(model, None, 0, 10, 1)\n",
    "model = cx.AgentBasedModel(Name, Gene, pop)\n",
    "model.add_observing_event(pop.Eve.DCore.Transitions['Infect'])\n",
    "model.add_observing_event(pop.Eve.DCore.Transitions['Recov'])\n",
    "model.add_observing_event(pop.Eve.DCore.Transitions['Die'])\n",
    "\n",
    "def f(model, tab, ti):\n",
    "    tab['Sus'] = model.Population.count(st='Sus')\n",
    "    tab['Inf'] = model.Population.count(st='Inf')\n",
    "    tab['Rec'] = model.Population.count(st='Rec')\n",
    "    tab['Alive'] = model.Population.count(st='Alive')\n",
    "    tab['Dead'] = model.Population.count(st='Dead')\n",
    "    \n",
    "\n",
    "model.add_observing_function(f)\n",
    "\n",
    "cx.simulate(model, y0, 0, 10, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sus</th>\n",
       "      <th>Inf</th>\n",
       "      <th>Rec</th>\n",
       "      <th>Alive</th>\n",
       "      <th>Dead</th>\n",
       "      <th>Infect</th>\n",
       "      <th>Die</th>\n",
       "      <th>Recov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>995</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>654</td>\n",
       "      <td>249</td>\n",
       "      <td>78</td>\n",
       "      <td>981</td>\n",
       "      <td>19</td>\n",
       "      <td>324.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>434</td>\n",
       "      <td>311</td>\n",
       "      <td>214</td>\n",
       "      <td>959</td>\n",
       "      <td>41</td>\n",
       "      <td>210.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>276</td>\n",
       "      <td>286</td>\n",
       "      <td>376</td>\n",
       "      <td>938</td>\n",
       "      <td>62</td>\n",
       "      <td>149.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>185</td>\n",
       "      <td>220</td>\n",
       "      <td>514</td>\n",
       "      <td>919</td>\n",
       "      <td>81</td>\n",
       "      <td>86.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>115</td>\n",
       "      <td>165</td>\n",
       "      <td>616</td>\n",
       "      <td>896</td>\n",
       "      <td>104</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>72</td>\n",
       "      <td>134</td>\n",
       "      <td>677</td>\n",
       "      <td>883</td>\n",
       "      <td>117</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>51</td>\n",
       "      <td>101</td>\n",
       "      <td>704</td>\n",
       "      <td>856</td>\n",
       "      <td>144</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>30</td>\n",
       "      <td>66</td>\n",
       "      <td>733</td>\n",
       "      <td>829</td>\n",
       "      <td>171</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>19</td>\n",
       "      <td>52</td>\n",
       "      <td>744</td>\n",
       "      <td>815</td>\n",
       "      <td>185</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>754</td>\n",
       "      <td>800</td>\n",
       "      <td>200</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sus  Inf  Rec  Alive  Dead  Infect   Die  Recov\n",
       "Time                                                 \n",
       "0.0   995    5    0   1000     0     0.0   0.0    0.0\n",
       "1.0   654  249   78    981    19   324.0  19.0   78.0\n",
       "2.0   434  311  214    959    41   210.0  22.0  139.0\n",
       "3.0   276  286  376    938    62   149.0  21.0  167.0\n",
       "4.0   185  220  514    919    81    86.0  19.0  145.0\n",
       "5.0   115  165  616    896   104    66.0  23.0  115.0\n",
       "6.0    72  134  677    883   117    43.0  13.0   71.0\n",
       "7.0    51  101  704    856   144    19.0  27.0   46.0\n",
       "8.0    30   66  733    829   171    18.0  27.0   50.0\n",
       "9.0    19   52  744    815   185    10.0  14.0   24.0\n",
       "10.0   13   33  754    800   200     6.0  15.0   23.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0 = [\n",
    "    {'n': 995, 'attributes': {'st': 'Sus'}},\n",
    "    {'n': 5, 'attributes': {'st': 'Inf'}},\n",
    "]\n",
    "\n",
    "Name = 'M1'\n",
    "Gene = sm.generate()\n",
    "eve = cx.StSpBreeder('Ag ', 'agent', Gene, dc)\n",
    "pop = cx.Population(eve)\n",
    "# model = cx.SingleIndividualABM(Name, ag)\n",
    "# cx.simulate(model, None, 0, 10, 1)\n",
    "model = cx.StSpAgentBasedModel(Name, Gene, pop)\n",
    "\n",
    "for tr in ['Infect', 'Recov', 'Die']:\n",
    "    model.add_observing_transition(tr)\n",
    "\n",
    "for st in ['Sus', 'Inf', 'Rec', 'Alive', 'Dead']:\n",
    "    model.add_observing_state(st)\n",
    "\n",
    "cx.simulate(model, y0, 0, 10, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sm1 = dag.as_simulation_core(bn)\n",
    "sm1.deep_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mc_bp = BlueprintABM('AB_abm', 'pAB', 'AB_mc')\n",
    "mc_bp.set_observations(states=['N', 'A', 'B', 'AB'])\n",
    "pc = pc_bp.get_simulation_model().sample_core()\n",
    "dc = dc_bp.generate_model(pc=pc)\n",
    "\n",
    "model = mc_bp.generate('A', pc=pc, dc=dc)\n",
    "out = simulate(model, y0={'N': 500}, fr=0, to=10)\n",
    "model.output(mid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "par_script = \"\"\" \n",
    "PCore pSIR{\n",
    "    transmission_rate = 1.5/200\n",
    "    rec_rate ~ triangle(0.1, 0.2, 0.3)\n",
    "    beta ~ exp(transmission_rate)\n",
    "    gamma ~ exp(rec_rate)\n",
    "    Die ~ exp(0.2)\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "dc_ctbn_script = '''\n",
    "CTBN SIR_BN {\n",
    "    life[Alive | Dead]\n",
    "    sir[S | I | R]\n",
    "    \n",
    "    Alive{life:Alive}\n",
    "    Dead{life:Dead}\n",
    "    Inf{life:Alive, sir:I}\n",
    "    Rec{life:Alive, sir:R}\n",
    "    Sus{life:Alive, sir:S}\n",
    "\n",
    "    Die -> Dead # from transition Die to state Dead by distribution Die\n",
    "    Sus -- Infect_Net(beta) -> Inf\n",
    "    Sus -- Infect(beta) -> Inf \n",
    "    Inf -- Recov(gamma) -> Rec\n",
    "    \n",
    "    Alive -- Die # from state Alive to transition Die\n",
    "}\n",
    "'''\n",
    "\n",
    "da = Director()\n",
    "da.read_pc(par_script)\n",
    "da.read_dc(dc_ctbn_script)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = da.get_pc('pSIR')\n",
    "pc = pc.sample_core()\n",
    "pc.Locus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfd = da.new_mc('ABM_M2', 'ABM', tar_pc='pSIR', tar_dc='SIR_BN')\n",
    "cfd.add_behaviour('cycle', be_type='Reincarnation', s_birth = 'Sus', s_death = 'Dead')\n",
    "cfd.add_behaviour('foi', be_type='ComFDShock', s_src='Inf', t_tar = 'Infect', dt=0.5)\n",
    "cfd.add_behaviour('step', be_type='TimeStep', ts=[0.5, 4], ys=[1, 0.1, 5], t_tar = 'Infect')\n",
    "\n",
    "cfd.set_observations(states=['Sus', 'Inf', 'Rec'])\n",
    "\n",
    "mod, out = da.simulate('ABM_M2', y0={'Sus': 95, 'Inf': 5}, fr=0 ,to=10, dt=.01)\n",
    "out.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfd = da.new_mc('ABM_M2', 'ABM', tar_pc='pSIR', tar_dc='SIR_BN')\n",
    "cfd.add_network('IDU', 'BA', m=2)\n",
    "cfd.add_behaviour('cycle', be_type='Reincarnation', s_birth = 'Sus', s_death = 'Dead')\n",
    "cfd.add_behaviour('transmission', be_type='NetShock', s_src = 'Inf', t_tar = 'Infect_Net', net='IDU')\n",
    "cfd.add_behaviour('foi', be_type='ForeignShock', t_tar = 'Infect')\n",
    "\n",
    "cfd.set_observations(states=['Inf'], behaviours=['foi'])\n",
    "\n",
    "cfd = da.new_mc('ABM_M5', 'ABM', tar_pc='pSIR', tar_dc='SIR_BN')\n",
    "cfd.add_network('IDU', 'BA', m=5)\n",
    "cfd.add_behaviour('cycle', be_type='Reincarnation', s_birth = 'Sus', s_death = 'Dead')\n",
    "cfd.add_behaviour('transmission', be_type='NetShock', s_src = 'Inf', t_tar = 'Infect_Net', net='IDU')\n",
    "cfd.add_behaviour('foi', be_type='ForeignShock', t_tar = 'Infect')\n",
    "\n",
    "cfd.set_observations(states=['Inf'], behaviours=['foi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lyo = da.new_layout('Lyo1')\n",
    "\n",
    "lyo.add_entry('A', 'ABM_M2', {'Sus': 20, 'Inf': 10})\n",
    "lyo.add_entry('B', 'ABM_M2', {'Sus': 20}, size=3)\n",
    "lyo.add_entry('C', 'ABM_M5', {'Sus': 20, 'Inf': 10}, size=2)\n",
    "lyo.add_relation('*@Inf', 'Lyo1@FOI')\n",
    "lyo.add_relation('Lyo1@FOI', '*@foi')\n",
    "lyo.set_observations(['B_2', 'B_1', '*'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mod, out = da.simulate('Lyo1', fr=0, to=10)\n",
    "out.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = generate_pc_dc(da.get_pc('pSIR'), da.get_dc('SIR_BN'))\n",
    "mc = CoreODE(dc)\n",
    "mc.Mods['FOI'] = InfectionFD('FOI', 'Infect', 'Inf')\n",
    "mc.Mods['Dead'] = Reincarnation('Dead', s_death='Dead', s_birth='Sus')\n",
    "model = ODEModel('SIR', mc, dt=0.01)\n",
    "model.add_obs_transition('Infect')\n",
    "model.add_obs_state('Alive')\n",
    "model.add_obs_state('Inf')\n",
    "model.add_obs_state('Sus')   \n",
    "model.add_obs_behaviour('Dead')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfd = da.new_mc('EBM', 'CoreODE', tar_pc='pSIR', tar_dc='SIR_BN')\n",
    "cfd.add_behaviour('FOI', be_type='InfectionDD', s_src = 'Inf', t_tar = 'Infect')\n",
    "cfd.add_behaviour('Dead', be_type='Reincarnation', s_death='Dead', s_birth='Sus')\n",
    "cfd.set_arguments('dt', 0.5)\n",
    "cfd.set_observations(states=['Sus', 'Inf', 'Rec', 'Alive'], behaviours=['FOI'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod, out = da.simulate('EBM', y0={'Sus': 95, 'Inf': 5}, fr=0 ,to=10, dt=1)\n",
    "#da.copy_model(mod_src=mod)\n",
    "out.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "out = simulate(model, y0={'Sus': 95, 'Inf': 5}, fr=0 ,to=10, dt=0.1)\n",
    "out.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class A:\n",
    "    def __init__(self, ls):\n",
    "        self.List = ls\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return iter(self.List)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "a = A([1,2,3])\n",
    "for i in a:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'StSpAgentBasedModel' object has no attribute 'Pop'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-03cca1a038bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     58\u001b[0m ]\n\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimulate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Model\\PyABMS\\complexism\\fn.py\u001b[0m in \u001b[0;36msimulate\u001b[1;34m(model, y0, fr, to, dt, seed, mid, log)\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[0msim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSimulator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeep_log\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m     \u001b[0msim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimulate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    138\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Model\\PyABMS\\complexism\\mcore\\simulator.py\u001b[0m in \u001b[0;36msimulate\u001b[1;34m(self, y0, fr, to, dt)\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msimulate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeal_with_disclosures\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialise_observations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Model\\PyABMS\\complexism\\mcore\\modelnode.py\u001b[0m in \u001b[0;36minitialise\u001b[1;34m(self, ti, y0)\u001b[0m\n\u001b[0;32m    157\u001b[0m                 \u001b[0my0\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatch_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_y0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Model\\PyABMS\\complexism\\agentbased\\abm.py\u001b[0m in \u001b[0;36mpreset\u001b[1;34m(self, ti)\u001b[0m\n\u001b[0;32m     81\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mbe\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBehaviours\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m             \u001b[0mbe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mag\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPopulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAgents\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m             \u001b[0mag\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Model\\PyABMS\\complexism\\agentbased\\statespace\\be\\modbe.py\u001b[0m in \u001b[0;36minitialise\u001b[1;34m(self, ti, model, *args, **kwargs)\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minitialise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    266\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mag\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magents\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 267\u001b[1;33m             \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_neighbours\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mS_src\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNet\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    268\u001b[0m             \u001b[0mag\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mti\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'StSpAgentBasedModel' object has no attribute 'Pop'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import complexism as cx\n",
    "import complexism.agentbased.statespace as ss\n",
    "import epidag as dag\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import complexism as cx\n",
    "import complexism.agentbased.statespace as ss\n",
    "import epidag as dag\n",
    "\n",
    "\n",
    "model_name = 'M_SIR'\n",
    "\n",
    "# Step 1 set a parameter core\n",
    "bn = cx.read_bn_script(cx.load_txt('example/scripts/pSIR.txt'))\n",
    "sm = dag.as_simulation_core(bn,\n",
    "                            hie={'city': ['agent'],\n",
    "                                 'agent': ['Infect', 'Recov', 'Die']})\n",
    "pc = sm.generate(model_name)\n",
    "\n",
    "\n",
    "# Step 1- set dynamic cores as agents need\n",
    "dbp = cx.read_dbp_script(cx.load_txt('example/scripts/SIR_BN.txt'))\n",
    "\n",
    "\n",
    "# Step 2 define at least one type of agent\n",
    "eve = ss.StSpBreeder('Ag', 'agent', pc, dbp)\n",
    "pop = cx.Population(eve)\n",
    "\n",
    "\n",
    "# Step 3 generate a model\n",
    "model = cx.StSpAgentBasedModel(model_name, pc, pop)\n",
    "\n",
    "\n",
    "# Step 4 add behaviours to the model\n",
    "# ss.FDShock.decorate('FOI', model, s_src='Inf', t_tar='Infect')\n",
    "# ss.FDShockFast.decorate('FOI', model=model, s_src='Inf', t_tar='Infect', dt=0.1)\n",
    "pc.impulse({'beta': pc['beta']/300})\n",
    "# ss.DDShock.decorate('FOI', model=model, s_src='Inf', t_tar='Infect')\n",
    "ss.NetShock.decorate('FOI', model=model, s_src='Inf', t_tar='Infect', net='net')\n",
    "\n",
    "\n",
    "# Step 5 decide outputs\n",
    "for tr in ['Infect', 'Recov', 'Die']:\n",
    "    model.add_observing_transition(tr)\n",
    "\n",
    "for st in ['Sus', 'Inf', 'Rec', 'Alive', 'Dead']:\n",
    "    model.add_observing_state(st)\n",
    "\n",
    "model.add_observing_behaviour('FOI')\n",
    "\n",
    "\n",
    "# Step 6 simulate\n",
    "y0 = [\n",
    "    {'n': 290, 'attributes': {'st': 'Sus'}},\n",
    "    {'n': 10, 'attributes': {'st': 'Inf'}}\n",
    "]\n",
    "\n",
    "output = cx.simulate(model, y0, 0, 10, 1)\n",
    "print(output)\n",
    "\n",
    "\n",
    "# Step 7 inference, draw figures, and manage outputs\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1)\n",
    "\n",
    "output[['Sus', 'Inf', 'Rec']].plot(ax=axes[0])\n",
    "output[['Recov', 'Infect']].plot(ax=axes[1])\n",
    "output[['FOI']].plot(ax=axes[2])\n",
    "plt.show()\n"
   ]
  }
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