text: 
  # parameters.py
  """
  Exp 215 - {'Initial_genes': '500', 'Host_mutation_rate': '0.03', 'TE_progeny': '0.00, 0, 0.55, 1, 0.30, 2, 0.15, 3', 'TE_Insertion_Distribution': 'Flat()', 'Carrying_capacity': '300', 'TE_excision_rate': '0.5', 'Junk_BP': '14', 'Gene_Insertion_Distribution': 'Flat()', 'mutation_effect': '0.01', 'TE_death_rate': '0.0005'}
  """
  from TEUtil import *;
  
  # note that "#" indicates a comment
  
  # set the following to True if you want messages printed to the screen
  # while the program runs - search for these keywords in TESim.py to see
  # what each one prints out
  
  output = {
  	"SPLAT":		False,
  	"SPLAT FITNESS":	False,
  	"INITIALIZATION":	False,
  	"GENERATION":		True,
  	"HOST EXTINCTION":	True,
  	"TE EXTINCTION":	True,
          "TRIAL NO":		True,
          "GENE INIT":		False,
  	"TE INIT":		False,
  	};
  
  TE_Insertion_Distribution = Flat();
  Gene_Insertion_Distribution = Flat();
  # Triangle( pmax, pzero ) generates values between pmax and pzero with 
  #   a triangular probability distribution, where pmax is the point of highest
  #   probability, and pzero is the point of lowest probability
  #   - you can change the orientation of the triangle by reversing the values
  #   of pmax and pzero
  # Flat() generates values between 0 and 1 with uniform probability
  
  Gene_length = 1000;		        # use 1000?
  TE_length = 1000;			# use 1000?
  
  TE_death_rate = 0.0005;
  TE_excision_rate = 0.5;	# set this to zero for retro transposons
  
  # for retro transposons this is the probability of the given number of progeny
  # for dna transposons this is the probability of the given number of progeny
  #   ___PLUS___ the original re-inserting
  TE_progeny = ProbabilityTable( 0.00, 0, 0.55, 1, 0.30, 2, 0.15, 3 );
  
  
  						 
  Initial_genes = 500;
  Append_gene = True;	# True: when the intialization routine tries to place 
  			# a gene inside another gene, it instead appends it
  			# at the end of the original gene (use this with small
  			# amounts of Junk_BP).
  			# False:  when the intialization routine tries to place
  			# a gene inside another gene, try to place it somewhere
  			# else again (don't use theis option with samll amounts
  			# of Junk_BP).
  
  Initial_TEs = 1;
  
  MILLION = 1000000;
  
  Junk_BP = 14 * MILLION;
  
  Host_start_fitness = 1.0;
  Host_mutation_rate = 0.03;
  Host_mutation = ProbabilityTable( 0.40, lambda fit: 0.0,
                                    0.30, lambda fit: fit - random.random()*0.01,
                                    0.15, lambda fit: fit,
                                    0.15, lambda fit: fit + random.random()*0.01
                                    );
  	
  # what happens when a TA hits a gene
  Insertion_effect = ProbabilityTable(0.30, lambda fit: 0.0,
                                      0.20, lambda fit: fit - random.random()*0.01,
                                      0.30, lambda fit: fit,
                                      0.20, lambda fit: fit + random.random()*0.01
                                      );
  		
  Carrying_capacity = 300;
  Host_reproduction_rate = 1;  # how many offspring each host has
  
  Host_survival_rate = lambda propfit: min( Carrying_capacity * propfit, 0.95 );
      # propfit = proportion of fitness owned by this individual
  
  Maximum_generations = 1500;
  Terminate_no_TEs = True;	# end simulation if there are no TEs left
  
  # seed = 0;
  seed = None;   # if seed = None, the random number generator's initial state is
                 # set "randomly"
  
  save_frequency = 50;    # Frequency with with which to save state of experiment
  
  saved = None;   # if saved = None then we start a new simulation from scratch
                  # if saves = string, then we open that file and resume a simulation
  