def main():
"""Block main."""
iteration = 9000
# Create a test environment to visualize
env = ViewerModel(50, width, height, 10, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = '/home/aadeshnpn/Documents/BYU/hcmi/hri/cooperative_transport/1538447335350COT/'
jname = pfileloc + '1538447335350.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
# print (len(phenotypes))
env.create_agents(phenotypes=phenotypes[30:90])
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents, [env.site],
food=env.foods)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
print('foraging percent', env.foraging_percent())
# print('Execution done')
# Find if food has been deposited in the hub
grid = env.grid
neighbours = grid.get_neighborhood(env.hub.location, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
def main():
"""Block main."""
iteration = 5000
# Create a test environment to visualize
env = ViewerModel(100, width, height, 10, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = ('/home/aadeshnpn/Documents/BYU/hcmi/swarm/' +
'results/1550156973273193-5000ValidateSForge/')
jname = pfileloc + '1550156973273193-4999.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
# print (len(phenotypes))
env.create_agents(phenotypes=phenotypes)
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents, [env.site],
food=env.foods)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
# print('Execution done')
# Find if food has been deposited in the hub
print('foraging percent', env.foraging_percent())
def main(iter):
"""Block for the main function."""
print('=======Start=========')
# env = evolve(iter)
pname = (
'/home/aadeshnpn/Documents/BYU/hcmi/hri/nest_maint/10000NestMAgents')
jfilename = pname + '/1539014820252.json'
jdata = JsonPhenotypeData.load_json_file(jfilename)
phenotypes = jdata['phenotypes']
# if len(env.phenotypes) > 1:
for N in range(425, 550, 25):
if len(phenotypes) > 1:
steps = [5000 for i in range(16)]
aname = pname + '/' + str(N)
os.mkdir(aname)
env = (phenotypes, aname)
Parallel(n_jobs=16)(delayed(simulate)(env, i, N) for i in steps)
# jname = '/home/aadeshnpn/Documents/BYU/hcmi/swarm/results/
# 1538612308183NestM/1538612308183.json'
# phenotype = JsonPhenotypeData.load_json_file(jname)
# pname = '/home/aadeshnpn/Documents/BYU/hcmi/swarm/results
# /1538612308183NestM/'
# env = (phenotype['phenotypes'], pname)
# Parallel(n_jobs=8)(delayed(simulate)(env, i) for i in steps)
# for step in steps:
# simulate(env, step)
# Parallel(n_jobs=4)(delayed(simulate_res1)(env, i) for i in steps)
# Parallel(n_jobs=4)(delayed(simulate_res2)(env, i) for i in steps)
# simulate(env, 10000)
print('=======End=========')
def test_json_phenotype(json):
jname = '/home/aadeshnpn/Documents/BYU/hcmi/swarm/results/1550083569946511-12000EvoSForge/' + json # noqa : E501
# jname = '/tmp/1543367322976111-8000EvoSForge/' + json
phenotype = JsonPhenotypeData.load_json_file(jname)['phenotypes']
print(len(phenotype))
# phenotype = ' '
test_loop(phenotype, 5000)
def main():
"""Block main."""
iteration = 5000
# Create a test environment to visualize
env = ViewerModel(100, width, height, 20, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = '/home/aadeshnpn/Documents/BYU/hcmi/hri/thesis/sf/'
jname = pfileloc + '1538473090382007.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
env.create_agents(phenotypes=phenotypes)
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents,
env.sites,
food=env.foods,
debris=[])
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
# print('Execution done')
# Find if food has been deposited in the hub
grid = env.grid
neighbours = grid.get_neighborhood(env.hub.location, 10)
food_objects = grid.get_objects_from_list_of_grid('Food', neighbours)
for food in food_objects:
print('food', food.id, food.location)
"""
def main():
"""Block main."""
iteration = 5000
# Create a test environment to visualize
env = ViewerModel(100, width, height, 10, iter=iteration, viewer=viewer)
# Build environment from json
env.build_environment_from_json()
# Load a json file containing the phenotype
pfileloc = '/home/aadeshnpn/Documents/BYU/hcmi/hri/nest_maint/1539014820252NestM/'
jname = pfileloc + '1539014820252.json'
phenotypes = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# Create the agents in the environment from the sampled behaviors
print(len(phenotypes))
env.create_agents(phenotypes=phenotypes)
# Overiding the default viewer properties
env.ui = UI((width, height), [env.hub],
env.agents, [],
food=[],
debris=env.debris)
# Iterate and execute each step in the environment
for i in range(iteration):
env.step()
# print('Execution done')
# Find if food has been deposited in the hub
print('Cleaning Percentage', env.foraging_percent())
print(len(env.debris_cleaned()))
def test_json_phenotype(json):
# jname = '/home/aadeshnpn/Documents/BYU/hcmi/swarm/results/1543189989736115-62000EvoSForge/' + json # noqa : E501
jname = '/tmp/1543367322976111-8000EvoSForge/' + json
phenotype = JsonPhenotypeData.load_json_file(jname)['phenotypes']
# print (phenotype)
# phenotype = ' '
if validation_loop(phenotype, 2000):
print('foraging success')
def main():
"""Parse args and call bt visualize module."""
# dname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/supporting_materials/nest_maintenance' # noqa: E501
# jfname = dname + '/1539014820252.json'
# dname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/supporting_materials/cooperative_transport' # noqa: E501
# jfname = dname + '/1538447335350.json'
dname = '/home/aadeshnpn/Documents/BYU/hcmi/hri/supporting_materials/foraging' # noqa: E501
jfname = dname + '/1538473090382007.json'
jdata = JsonPhenotypeData.load_json_file(jfname)
phenotypes = jdata['phenotypes']
for i in range(len(phenotypes)):
# if i >= 5:
# break
m = Model()
a = Agent('1', m)
bt = BTConstruct(None, a, phenotypes[i])
bt.construct()
bt.visualize(name=dname + '/' + str(i))
def simulate(env, iteration):
"""Test the performane of evolved behavior."""
# phenotype = agent.individual[0].phenotype
# phenotypes = extract_phenotype(agents)
phenotypes = JsonPhenotypeData.load_json_file(env[0])
# print (phenotypes)
if len(phenotypes['phenotypes']) < 1:
return
else:
phenotypes = phenotypes['phenotypes']
threshold = 1.0
sim = SimModel(
100, 100, 100, 10, iter=iteration, xmlstrings=phenotypes, pname=env[1])
sim.build_environment_from_json()
# for all agents store the information about hub
for agent in sim.agents:
agent.shared_content['Hub'] = {sim.hub}
# agent.shared_content['Sites'] = {sim.site}
simresults = SimulationResults(
sim.pname, sim.connect, sim.sn, sim.stepcnt, sim.food_in_hub(),
phenotypes[0]
)
simresults.save_phenotype()
simresults.save_to_file()
# Iterate and execute each step in the environment
for i in range(iteration):
# For every iteration we need to store the results
# Save them into db or a file
sim.step()
simresults = SimulationResults(
sim.pname, sim.connect, sim.sn, sim.stepcnt, sim.food_in_hub(),
phenotypes[0]
)
simresults.save_to_file()
# print ('food at site', len(sim.food_in_loc(sim.site.location)))
# print ('food at hub', len(sim.food_in_loc(sim.hub.location)))
# print("Total food in the hub", len(food_objects))
# food_objects = sim.food_in_loc(sim.hub.location)
# for food in food_objects:
# print('simulate phenotye:', dir(food))
value = sim.food_in_hub()
foraging_percent = (
value * 100.0) / (sim.num_agents * 2.0)
sucess = False
print('Foraging percent', value)
if foraging_percent >= threshold:
print('Foraging success')
sucess = True
sim.experiment.update_experiment_simulation(value, sucess)
# Plot the fitness in the graph
graph = GraphACC(sim.pname, 'simulation.csv')
graph.gen_plot()