#---------------------------------------------------------------------------
#
# Quasispecies.py: demonstration of quasispecies evolutionary dynamics
# using binary strings, book chapter 7
#
# usage: python quasispecies.py [ <mp> <histfile> ]
# where:
# mp is the proportion of mutations with respect to the error threshold
# histfile is the name of an output file that will contain the histogram
# of species at the end of the simulation, grouped by their distance to
# the optimum (number of bits that differ from the optimum sequence
# (here '1111111111')
#
# by Lidia Yamamoto, Kraainem, Belgium, July 2013
#
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# Copyright (C) 2015 Lidia A. R. Yamamoto
# Contact: http://www.artificial-chemistries.org/
#
# This file is part of PyCellChemistry.
#
# PyCellChemistry is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# version 3, as published by the Free Software Foundation.
#
# PyCellChemistry is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with PyCellChemistry, see file COPYING. If not, see
# http://www.gnu.org/licenses/
#

Modules
		artchem.BinaryStrings numpy sys

Classes
		Evolution.Evolution Quasispecies class Quasispecies(Evolution.Evolution) Methods defined here: __init__(self, mp) create a random initial population of molecules with intentionally bad fitness; set the mutation probability per bit as a proportion 'mp' of the error threshold dist_histogram(self) histogram of number of species at hamming distance d=[0;nbits] from master sequence plot(self, vtime) plot fitness information plot_dist_histogram(self, fname) plot distance histogram to file 'fname' propensity(self, mol) propensity of a given reaction react(self) one iteration of Gillespie's SSA on the quasi-species equation run(self, histfile) stochastic implementation of quasi-species equation using Gillespie's SSA set_propensities(self) initial calculation of all propensities update_propensity(self, mol, dm) update propensity of molecule, given that dm units of it were added (dm > 0) or removed (dm < 0) Methods inherited from Evolution.Evolution: avgfitness(self) compute the average fitness of the population bestworstfit(self, mset) find the best and worst individuals in a given multiset fitness(self, binstr) calculate the fitness of an individual (normalized to one) optimum(self) produce an optimum individual for the desired fitness function randmol(self) generate a random molecule in the form of an N-bit integer

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