GraphWithAsynchronousDynamics.ipynb

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   "source": [
    "from GraphWithDynamics import *"
   ]
  },
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   "execution_count": 10,
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    "class GraphWithAsynchronousDynamics(GraphWithDynamics):\n",
    "    '''A graph with a dynamics that runs asynchronously,\n",
    "    by calculating the minimum time an event could occur\n",
    "    in, then incrementing by that amount if an event did \n",
    "    indeed occur and what it was.'''\n",
    "\n",
    "    # Timestep to increment\n",
    "    DT = 0\n",
    "    \n",
    "    def __init__( self, graph = None, time_limit = 10000, states = [], rates = dict() ):\n",
    "        '''Create a graph, optionally with nodes and edges copied from\n",
    "        the graph given.\n",
    "        \n",
    "        g: graph to copy (optional)'''\n",
    "        GraphWithDynamics.__init__(self, graph, time_limit, states = states, rates = rates)\n",
    "        \n",
    "    def model( self, n ):\n",
    "        '''The dynamics function that's run over the network. This\n",
    "        is a placeholder to be re-defined by sub-classes.\n",
    "        \n",
    "        n: the node being simulated'''\n",
    "        raise NotYetImplementedError('model()')\n",
    "\n",
    "    def set_timestep_rate( self ):\n",
    "        '''To calculate the timestep jump, need to know the minimum time needed for an event to happen. Timestep\n",
    "        will be calculated by 1 over the value returned here.\n",
    "        '''\n",
    "        raise NotYetImplementedError('model()')\n",
    "        \n",
    "\n",
    "    def _dynamics( self ):\n",
    "        '''Asynchronous dynamics. We apply _dynamics_step() at each timestep (calculated based on infection / recovery rates)\n",
    "        and then check for completion using at_equilibrium().\n",
    "        \n",
    "        returns: a dict of simulation properties'''\n",
    "\n",
    "        # Initialise values\n",
    "        rc = dict()\n",
    "        timestepEvents = 0\n",
    "        events = 0\n",
    "        eventDist = dict()\n",
    "        \n",
    "        # Calculate the maximum rate\n",
    "        max_trans_rate = self.set_timestep_rate()\n",
    "        \n",
    "        # Timestep = 1 over number of nodes by maximum transition rate\n",
    "        self.DT = 1.0/(self.order()*max_trans_rate);\n",
    "        \n",
    "        # Run continuously until equilibrium reached\n",
    "        while True:\n",
    "            \n",
    "            #Pick a node at random\n",
    "            n = numpy.random.randint(self.order())\n",
    "            # Random number for probability distribution\n",
    "            r = numpy.random.random()\n",
    "            # Run an action on the node dependent on it's current state\n",
    "            event = self.asyn_node_action(n, self.DT, r)\n",
    "            \n",
    "            # Increment the timestep, if nothing happened, don't update the population distribution history\n",
    "            self.increment_timestep(self.DT, event)\n",
    "            \n",
    "            # test for termination\n",
    "            if self.at_equilibrium():\n",
    "                break\n",
    "            \n",
    "        # return the simulation-level results\n",
    "        rc['timesteps'] = self.CURRENT_TIMESTEP\n",
    "        rc['events'] = events\n",
    "        rc['event_distribution'] = eventDist\n",
    "        \n",
    "        # compute the limits and means\n",
    "        cs = sorted(networkx.connected_components(self.skeletonise()), key = len, reverse = True)\n",
    "        max_outbreak_size = len(cs[0])\n",
    "        max_outbreak_proportion = (max_outbreak_size + 0.0) / self.order()\n",
    "        mean_outbreak_size = numpy.mean([ len(c) for c in cs ])\n",
    "        \n",
    "        # add parameters and metrics for this simulation run\n",
    "        rc['number_of_nodes'] = self.order(),\n",
    "        rc['mean_outbreak_size'] = mean_outbreak_size,\n",
    "        rc['max_outbreak_size'] = max_outbreak_size,\n",
    "        rc['max_outbreak_proportion'] = max_outbreak_proportion\n",
    "        \n",
    "        return rc\n",
    "    \n",
    "    def asyn_node_action(self, node = 0, dt = 0.0, r = 0.0):\n",
    "        '''Internal function defining what to do for the node'''\n",
    "        raise NotYetImplementedError('asyn_node_action()')"
   ]
  }
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