def test_construct_optimal_protocol_returns_successfully_fixed_steps(self):
vc_ga_config = ga_configs.VoltageOptimizationConfig(
window=100,
step_size=1,
steps_in_protocol=-1, # Will be set later
step_duration_bounds=(0.05, 0.6),
step_voltage_bounds=(-.12, .06),
target_currents=None, # Will be set later
population_size=2,
max_generations=2,
mate_probability=0.9,
mutate_probability=0.9,
gene_swap_probability=0.2,
gene_mutation_probability=0.2,
tournament_size=2)
vc_optimization_config = ga_configs.CombinedVCConfig(
currents=['i_na', 'i_k1', 'i_kr'],
step_range=range(5, 6, 1),
adequate_fitness_threshold=0.4,
ga_config=vc_ga_config)
random.seed(2)
optimized_vc_protocol = voltage_clamp_optimization_experiments.\
construct_optimal_protocol(
vc_protocol_optimization_config=vc_optimization_config)
self.assertEqual(len(optimized_vc_protocol.steps), 18)
self.assertEqual(optimized_vc_protocol.steps[0],
protocols.VoltageClampProtocol.HOLDING_STEP)
self.assertEqual(optimized_vc_protocol.steps[6],
protocols.VoltageClampProtocol.HOLDING_STEP)
self.assertEqual(optimized_vc_protocol.steps[12],
protocols.VoltageClampProtocol.HOLDING_STEP)
def test_find_single_current_optimal_protocol_exceeds_threshold(self):
vc_ga_config = ga_configs.VoltageOptimizationConfig(
window=100,
step_size=1,
steps_in_protocol=-1, # Will be set later
step_duration_bounds=(0.05, 0.6),
step_voltage_bounds=(-.12, .06),
target_currents=None, # Will be set later
population_size=2,
max_generations=2,
mate_probability=0.9,
mutate_probability=0.9,
gene_swap_probability=0.2,
gene_mutation_probability=0.2,
tournament_size=2)
vc_optimization_config = ga_configs.CombinedVCConfig(
currents=['i_na'],
step_range=range(5, 6, 1),
adequate_fitness_threshold=0.4,
ga_config=vc_ga_config)
random.seed(2)
optimized_vc_protocol = voltage_clamp_optimization_experiments.\
find_single_current_optimal_protocol(
current=vc_optimization_config.currents[0],
vc_opt_config=vc_optimization_config)
self.assertEqual(len(optimized_vc_protocol.steps), 6)
def setUp(self):
vc_ga_config = ga_configs.VoltageOptimizationConfig(
window=100,
step_size=1,
steps_in_protocol=6,
step_duration_bounds=(0.1, 2.0),
step_voltage_bounds=(-1.2, 0.6),
population_size=2,
max_generations=2,
mate_probability=0.9,
mutate_probability=1.0,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=3)
self.vc_ga = voltage_clamp_optimization.VCOGeneticAlgorithm(
config=vc_ga_config)
def setUp(self):
vc_ga_config = ga_configs.VoltageOptimizationConfig(
window=100,
step_size=1,
steps_in_protocol=6,
step_duration_bounds=(0.1, 2.0),
step_voltage_bounds=(-1.2, 0.6),
population_size=2,
max_generations=2,
mate_probability=0.9,
mutate_probability=1.0,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=3)
self.ga_result = genetic_algorithm_results. \
GAResultVoltageClampOptimization(config=vc_ga_config)
sample_vc_protocol = protocols.VoltageClampProtocol(steps=[
protocols.VoltageClampStep(duration=0.1, voltage=-0.08),
protocols.VoltageClampStep(duration=0.1, voltage=-0.12),
protocols.VoltageClampStep(duration=0.5, voltage=-0.06),
protocols.VoltageClampStep(duration=0.05, voltage=-0.04),
protocols.VoltageClampStep(duration=0.15, voltage=0.02),
protocols.VoltageClampStep(duration=0.025, voltage=-0.08),
protocols.VoltageClampStep(duration=0.3, voltage=0.04),
])
generation_one = [
genetic_algorithm_results.VCOptimizationIndividual(
protocol=sample_vc_protocol, fitness=0),
genetic_algorithm_results.VCOptimizationIndividual(
protocol=sample_vc_protocol, fitness=2),
]
generation_two = [
genetic_algorithm_results.VCOptimizationIndividual(
protocol=sample_vc_protocol, fitness=5),
genetic_algorithm_results.VCOptimizationIndividual(
protocol=sample_vc_protocol, fitness=9),
]
self.ga_result.generations.append(generation_one)
self.ga_result.generations.append(generation_two)
tunable_parameters=PARAMETERS,
params_lower_bound=0.1,
params_upper_bound=3,
mate_probability=0.9,
mutate_probability=0.9,
gene_swap_probability=0.2,
gene_mutation_probability=0.2,
tournament_size=4)
VCO_CONFIG_KERNIK = ga_configs.VoltageOptimizationConfig(
window=100,
step_size=50,
steps_in_protocol=8,
step_duration_bounds=(25, 800),
step_voltage_bounds=(-120, 60),
target_currents=['I_Na', 'I_K1', 'I_To', 'I_CaL', 'I_Kr', 'I_Ks'],
population_size=10,
max_generations=2,
mate_probability=0.9,
mutate_probability=0.9,
gene_swap_probability=0.2,
gene_mutation_probability=0.2,
tournament_size=2)
COMBINED_VC_CONFIG = ga_configs.CombinedVCConfig(
currents=[
'I_Na',
'I_K1',
'I_To',
'I_CaL',
'I_Kr',
'I_Ks',