scicom.knowledgespread.model
============================

.. py:module:: scicom.knowledgespread.model


Classes
-------

.. autoapisummary::

   scicom.knowledgespread.model.KnowledgeSpread


Functions
---------

.. autoapisummary::

   scicom.knowledgespread.model.getActiveAgents
   scicom.knowledgespread.model.getNetworkStructure


Module Contents
---------------

.. py:function:: getActiveAgents(model)

   Get all agents active at time t.


.. py:function:: getNetworkStructure(model)

.. py:class:: KnowledgeSpread(num_scientists = 100, num_timesteps = 20, epiDim = 1.0001, epiRange = 0.01, oppositionPercent = 0.05, loadInitialConditions = False, epiInit = 'complex', timeInit = 'saturate', beta = 8, slope = 5, base = 2)

   Bases: :py:obj:`mesa.Model`


   A model for knowledge spread. 

   Agents have an initial topic vector and are positioned in epistemic space. 
   The number of agents can grow linearly, as a s-curve, or exponentially.
   Agents initial positions on epistemic space can be diverse (checker board-like),
   around a central position or in opossing camps. 

   Agents activation probability is age-dependent. After reaching a personal productivity end,
   agents are removed from the scheduler.



   .. py:attribute:: numScientists
      :value: 100



   .. py:attribute:: numTimesteps
      :value: 20



   .. py:attribute:: epiRange
      :value: 0.01



   .. py:attribute:: opposPercent
      :value: 0.05



   .. py:attribute:: loadInitialConditions
      :value: False



   .. py:attribute:: epiInit
      :value: 'complex'



   .. py:attribute:: timeInit
      :value: 'saturate'



   .. py:attribute:: beta
      :value: 8



   .. py:attribute:: slope
      :value: 5



   .. py:attribute:: base
      :value: 2



   .. py:attribute:: schedule


   .. py:attribute:: space


   .. py:attribute:: socialNetwork


   .. py:attribute:: grid


   .. py:attribute:: datacollector


   .. py:attribute:: running
      :value: True



   .. py:method:: _setupAgents()

      Create initial setup of agents.



   .. py:method:: _setupSocialSpace(nEdges=4, density=0.2, densityGrowth=0)

      Setup initial social connections.



   .. py:method:: step()

      Run one simulation step.



   .. py:method:: run(n)

      Run model for n steps.