def Neutral_Evolution_with_incompatible_gene_outcrossing001(p):
np.random.seed(p)
population_simulator = population(growth_speed_array=[1, 1, 1],
incompatible=1,
outcrossing_rate=0.01,
max_generation=2000)
return population_simulator.evolve_until_fix()
def positive_selection_simulation(p):
np.random.seed(p)
population_simulator = population(growth_speed_array=[1, 1, 1],
max_generation=50,
outcrossing_rate=0.01,
incompatible=1)
return population_simulator.evolve_until_fix()
def Neutral_Evolution_with_incompatible_gene_outcrossing0001_large_population_fitness_balancing(
p):
np.random.seed(p)
population_simulator = population(population_size=10000,
growth_speed_array=[1, 1.0035, 1.007],
incompatible=1,
outcrossing_rate=0.001)
return population_simulator.evolve_until_fix()
import insert
import find
from dbconnect import config, connect
from models import species, population, line, chromosome, variant, genotype, trait, phenotype, growout_type, growout, location, gwas_algorithm, genotype_version, imputation_method, kinship_algorithm, kinship, population_structure_algorithm, population_structure, gwas_run, gwas_result
if __name__ == '__main__':
conn = connect()
# ADD A HARD-CODED SPECIES TO DB USING insert_species()
mySpecies = species('maize', 'Zea mays', None, None)
insertedSpeciesID = insert.insert_species(conn, mySpecies)
print(insertedSpeciesID)
# ADD A HARD-CODED POPULATION TO DB USING insert_population()
myPopulation = population('Maize282',maizeSpeciesID)
insertedPopulationID = insert.insert_population(conn, myPopulation)
print(insertedPopulationID)
# LOOK UP ID OF A HARD-CODED SPECIES USING find_species()
maizeSpeciesID = find.find_species(conn, 'maize')
print("SpeciesID of maize:")
print(maizeSpeciesID)
# LOOK UP ID OF A HARD-CODED POPULATION USING find_population()
maize282popID = find.find_population(conn, 'Maize282')
print("PopulationID of Maize282:")
print(maize282popID)
# LOOK UP ID OF A HARD-CODED CHROMOSOME USING find_chromosome()
maizeChr10ID = find.find_chromosome(conn, 'chr10', maizeSpeciesID)
def neutral_simulation(p):
np.random.seed(p)
population_simulator = population(growth_speed_array=[1, 1, 1])
return population_simulator.evolve_until_fix()
def Neutral_Evolution_with_incompatible_gene_outcrossing1(p):
np.random.seed(p)
population_simulator = population(population_size=10000,
growth_speed_array=[1, 1, 1],
incompatible=1)
return population_simulator.evolve_until_fix()
def heterozygous_advantage_simulation(p):
np.random.seed(p)
population_simulator = population(growth_speed_array=[1, 1.02, 1])
return population_simulator.evolve_until_fix()