Initial commit of 03_no_migration_isolated_mating.py

examples/03_no_migration_isolated_mating.py

@@ -0,0 +1,99 @@
+# needed to run this example without prior
+# installation of DOSE into Python site-packages
+import run_examples_without_installation
+
+# Example codes starts from here
+import dose, genetic, random
+
+parameters = {
+              "simulation_name": "03_no_migration_isolated_mating",
+              "population_names": ['pop_01'],
+              "population_locations": [[(x,y,z) for x in xrange(5) for y in xrange(5) for z in xrange(1)]],
+              "deployment_code": 3,
+              "chromosome_bases": ['0','1'],
+              "background_mutation": 0.2,
+              "additional_mutation": 0,
+              "mutation_type": 'point',
+              "chromosome_size": 50,
+              "genome_size": 1,
+              "cells": 50,
+              "max_cell_population": 200,
+              "clean_cell": True,
+              "max_codon": 2000,
+              "population_size": 1250,
+              "eco_cell_capacity": 50,
+              "world_x": 5,
+              "world_y": 5,
+              "world_z": 1,
+              "goal": 0,
+              "maximum_generations": 1000,
+              "fossilized_ratio": 0.01,
+              "fossilized_frequency": 100,
+              "print_frequency": 10,
+              "ragaraja_version": 2,
+              "eco_buried_frequency": 1000,
+             }
+
+class simulation_functions(dose.dose_functions):
+
+    def organism_movement(self, World, x, y, z): pass
+
+    def organism_location(self, World, x, y, z): pass
+
+    def ecoregulate(self, World): pass
+
+    def update_ecology(self, World, x, y, z): pass
+
+    def update_local(self, World, x, y, z): pass
+
+    def report(World): pass
+
+    def fitness(self, Populations, pop_name): pass
+
+    def mutation_scheme(self, organism): 
+        organism.genome[0].rmutate(parameters["mutation_type"],
+                                   parameters["additional_mutation"])
+
+    def prepopulation_control(self, Populations, pop_name): pass
+
+    def mating(self, Populations, pop_name): 
+        for location in parameters["population_locations"][0]:
+            group = dose.filter_location(location, Populations[pop_name].agents)
+            for x in xrange(len(group)/2):
+                parents = []
+                for i in xrange(2):
+                    parents.append(random.choice(Populations[pop_name].agents))
+                    while parents[i] not in group:
+                        parents[i] = random.choice(Populations[pop_name].agents)
+                    Populations[pop_name].agents.remove(parents[i])
+                crossover_pt = random.randint(0, len(parents[0].genome[0].sequence))
+                (new_chromo1, new_chromo2) = genetic.crossover(parents[0].genome[0], 
+                                                               parents[1].genome[0], 
+                                                               crossover_pt)
+                children = [genetic.Organism([new_chromo1],
+                                             parameters["mutation_type"],
+                                             parameters["additional_mutation"]),
+                            genetic.Organism([new_chromo2],
+                                             parameters["mutation_type"],
+                                             parameters["additional_mutation"])]
+                for child in children:
+                    child.status['location'] = location
+                    child.generate_name()
+                    child.status['deme'] = pop_name
+                    Populations[pop_name].agents.append(child)
+
+    def postpopulation_control(self, Populations, pop_name): pass
+
+    def generation_events(self, Populations, pop_name): pass
+
+    def population_report(self, Populations, pop_name):
+        report_list = []
+        for organism in Populations[pop_name].agents:
+            chromosome = ''.join(organism.genome[0].sequence)
+            location = str(organism.status['location'])
+            report_list.append(chromosome + '  ' + location)
+        return '\n'.join(report_list)
+
+    def deployment_scheme(Populations, pop_name, World): pass
+
+dose.simulate(parameters, simulation_functions)