scicom.knowledgespread.utils
============================

.. py:module:: scicom.knowledgespread.utils

.. autoapi-nested-parse::

   Utility functions for initial condition generation.



Classes
-------

.. autoapisummary::

   scicom.knowledgespread.utils.GenerateInitalPopulation
   scicom.knowledgespread.utils.GenerateSocNet


Functions
---------

.. autoapisummary::

   scicom.knowledgespread.utils.epistemicRange
   scicom.knowledgespread.utils.ageFunction


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

.. py:function:: epistemicRange(baseRange, age)

   Return the age dependent search radius for epistemic discovery.


.. py:function:: ageFunction(agent, a, b, c, radius)

   Return an age dependent radius.

   Bell-shaped function with center at c, slop and width
   of plateau defined by a, b.

   Can be used to show age-dependent activity of agents. 


.. py:class:: GenerateInitalPopulation(numScientists, timesteps)

   Generate sets of initial conditions.


   .. py:attribute:: N


   .. py:attribute:: Tstep


   .. py:method:: epistemicFunc(fc, x, y, beta=8)

      Epistemic space initial sampling.



   .. py:method:: timeFunc(dataframe, step, fcT='saturate', slope=5, base=5)

      Population growth function.



   .. py:method:: sample(fcE='complex', fcT='saturate', beta=8, slope=5, base=5)

      Generate the sample population and add activation time.



   .. py:method:: _fullDist(fcE, beta)

      Full distribution to sample from.



.. py:class:: GenerateSocNet(dataframe, minDist = 0.0001)

   .. py:attribute:: population


   .. py:attribute:: density
      :value: ''



   .. py:attribute:: allEdges
      :value: ''



   .. py:attribute:: socialNet
      :value: ''



   .. py:attribute:: minDist
      :value: 0.0001



   .. py:method:: _getWeighted(row, degree)


   .. py:method:: initSocNet()

      Generates initial social network sample from population.

      The dataframe input should contain the colum names: id, x, y, z, t 
      Returns social network sample with from_id, to_id, dist, time



   .. py:method:: growNodes(time, nEdges)

      Add nodes with weighted preferential attachment.

      For a time step select all new agents. For each
      agent query all potential edges to previously active
      agents. Weight these edges with the degree of the 
      previous social network and the distances. From this
      select N edges for each new agent.
      Return the concatenated new edges



   .. py:method:: growEdges(time, density, densityGrowth)

      Add edges with weighted preferential attachement.

      For a given time, select the current social network,
      including newly added nodes. Add weights by current 
      degree and distances. 
      Sample a suffiecient number of edges to keep density
      at a given level. 



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