scicom.utilities.statistics
===========================

.. py:module:: scicom.utilities.statistics

.. autoapi-nested-parse::

   Prune a network.



Classes
-------

.. autoapisummary::

   scicom.utilities.statistics.PruneNetwork


Functions
---------

.. autoapisummary::

   scicom.utilities.statistics.prune


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

.. py:class:: PruneNetwork(dataframe)

   Create statistics for communication networks by deletion.

   For a given dataset with sender and receiver information,
   create a weighted network with igraph. For a given number
   of iterations, deletion amounts, and deletion types, the
   algorithm then generates network statistics for randomly
   sampled subnetworks.


   .. py:attribute:: inputDF


   .. py:method:: makeNet(dataframe)

      Create network from dataframe.

      Assumes the existence of sender, receiver and step
      column names.



   .. py:method:: setSurvivalProb(graph, *, method = 'agents', ranked = True)

      Generate probabilities for different survival modes.



   .. py:method:: scaleSurvivalProb(probabilities, *, method = 'agents')

      Scale survival for methods agents and regions.



   .. py:method:: basicNetStats(graph)

      Generate base statistics of network.



   .. py:method:: netStats(G)

      Generate network statistics.



   .. py:method:: deleteFromNetwork(iterations = 10, delAmounts = (0.1, 0.25, 0.5, 0.75, 0.9), delTypes = ('unif', 'log_normal1', 'exp', 'beta', 'log_normal2', 'log_normal3'), delMethod = ('agents', 'regions', 'time'), rankedVals = (True, False))

      Run the deletion by sampling.



.. py:function:: prune(modelparameters, network, columns, iterations = 10, delAmounts = (0.1, 0.25, 0.5, 0.75, 0.9), delTypes = ('unif', 'log_normal1', 'exp', 'beta', 'log_normal2', 'log_normal3'), delMethod = ('agents', 'regions', 'time'), rankedVals = (True, False))

   Generate pruned networks from input.

   Assumes existence of columns "sender", "receiver",
   "sender_location", "receiver_location" and "step".