def test_kernik_py_vs_mat(self):
protocol = protocols.SingleActionPotentialProtocol(1800)
baseline_model = kernik.KernikModel()
baseline_model.generate_response(protocol)
mat_baseline = np.loadtxt('model_data/original_baseline_3000ms.csv')
compare_voltage_plots(baseline_model, mat_baseline)
def test_run_comparing_experiment_raises_value_error_double_protocol(self):
parameters = [
ga_configs.Parameter(name='g_na', default_value=3671.2302),
ga_configs.Parameter(name='g_f_s', default_value=30.10312),
]
sap_protocol = protocols.SingleActionPotentialProtocol()
sap_config = ga_configs.ParameterTuningConfig(
population_size=2,
max_generations=2,
protocol=sap_protocol,
tunable_parameters=parameters,
params_lower_bound=0.5,
params_upper_bound=1.5,
mutate_probability=1.,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=2)
configs = generate_config_list()
configs.append(sap_config)
with self.assertRaises(ValueError) as ve:
parameter_tuning_experiments.run_comparison_experiment(
configs=configs, iterations=2)
self.assertEqual('Configs do not have unique protocols.',
str(ve.exception))
def test_evaluate_performance_with_combined_protocol(self):
config = ga_configs.ParameterTuningConfig(
population_size=2,
max_generations=2,
protocol=protocols.SingleActionPotentialProtocol(),
tunable_parameters=[
ga_configs.Parameter(name='g_b_na', default_value=0.95),
ga_configs.Parameter(name='g_na', default_value=3671.2302)
],
params_lower_bound=0.9,
params_upper_bound=1.1,
mutate_probability=1.,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.15,
tournament_size=2)
single_ap_ga = parameter_tuning_genetic_algorithm.\
ParameterTuningGeneticAlgorithm(config=config)
single_ap_error = single_ap_ga._evaluate_performance(
new_parameters=[0.94, 3000.])
config.secondary_protocol = protocols.IrregularPacingProtocol(
duration=5, stimulation_offsets=[0.1, 0.2])
combined_protocol_ga = parameter_tuning_genetic_algorithm.\
ParameterTuningGeneticAlgorithm(config=config)
combined_protocol_error = combined_protocol_ga._evaluate_performance(
new_parameters=[0.94, 3000.])
# Combined protocol error should be larger than a single protocol error.
self.assertGreater(combined_protocol_error, single_ap_error)
def setUp(self):
config = ga_configs.ParameterTuningConfig(
population_size=2,
max_generations=2,
protocol=protocols.SingleActionPotentialProtocol(),
tunable_parameters=[
ga_configs.Parameter(name='g_b_na', default_value=1.),
ga_configs.Parameter(name='g_na', default_value=3600.)
],
params_lower_bound=0.9,
params_upper_bound=1.1,
mate_probability=0.9,
mutate_probability=1.,
gene_swap_probability=0.5,
gene_mutation_probability=0.15,
tournament_size=2)
self.ga_result = genetic_algorithm_results.GAResultParameterTuning(
config=config)
generation_one = [
genetic_algorithm_results.ParameterTuningIndividual(
parameters=[0.95, 3671.2302], fitness=0),
genetic_algorithm_results.ParameterTuningIndividual(
parameters=[0.1, 3400.0], fitness=2),
]
generation_two = [
genetic_algorithm_results.ParameterTuningIndividual(
parameters=[0.6, 3500.0], fitness=5),
genetic_algorithm_results.ParameterTuningIndividual(
parameters=[0.9, 3600.0], fitness=9),
]
self.ga_result.generations.append(generation_one)
self.ga_result.generations.append(generation_two)
def plot_all_in_system_of_equations(duration=10):
model = paci_2018.PaciModel()
model.generate_response(
protocol=protocols.SingleActionPotentialProtocol(duration=duration))
for i in range(len(model.y_names)):
plt.figure()
plt.plot([timestamp[i] for timestamp in model.full_y])
plt.savefig('figures/before_hold_i_na_480/{}.png'.format(
model.y_names[i]))
def test_generate_trace_SAP(self):
protocol = protocols.SingleActionPotentialProtocol()
baseline_model = paci_2018.PaciModel()
baseline_model.generate_response(protocol)
self.assertTrue(
len(baseline_model.t) > 100, 'Paci errored in less than .4s')
self.assertTrue(
min(baseline_model.y_voltage) < -.01,
'baseline Paci min is greater than .01')
self.assertTrue(
max(baseline_model.y_voltage) < .06,
'baseline Paci max is greater than .06')
def test_no_ion_selective_setter(self):
protocol = protocols.SingleActionPotentialProtocol()
paci_baseline = paci_2018.PaciModel()
ion_selective_scales = {'I_CaL': .4, 'I_NaCa': .4}
paci_no_ion = paci_2018.PaciModel()
baseline_trace = paci_baseline.generate_response(protocol)
paci_baseline.no_ion_selective = ion_selective_scales
baseline_no_ion_trace = paci_baseline.generate_response(
protocol, is_no_ion_selective=True)
tst.assert_raises(AssertionError, tst.assert_array_equal,
baseline_trace.y, baseline_no_ion_trace.y,
'generate_no_ion_trace() does not update trace')
def test_no_ion_selective_IK1_no_effect(self):
protocol = protocols.SingleActionPotentialProtocol()
paci_k1_increase = paci_2018.PaciModel(
updated_parameters={'G_K1': 1.4})
ion_selective_scales = {'I_K1': .4}
paci_k1_no_ion = paci_2018.PaciModel(
no_ion_selective_dict=ion_selective_scales)
trace_ion_selective = paci_k1_increase.generate_response(protocol)
trace_no_ion_selective = paci_k1_no_ion.generate_response(
protocol, is_no_ion_selective=True)
tst.assert_raises(AssertionError, tst.assert_array_equal,
trace_ion_selective.y, trace_no_ion_selective.y,
'generate_no_ion_trace() does not update trace')
def test_write_data(self):
path_to_output = './data/paci_sap_baseline.csv'
protocol = protocols.SingleActionPotentialProtocol()
baseline_trace = paci_2018.generate_trace(protocol)
write_voltage_data.save_data_to_file(baseline_trace)
with open(path_to_output) as csv_file:
csv_reader = csv.reader(csv_file)
loaded_data = np.array(list(csv_reader))
print(len(loaded_data))
tst.assert_raises(AssertionError, tst.assert_array_equal,
baseline_trace.y, loaded_data[:, 1],
'loaded data is equal to Paci')
def test_generate_trace_SAP(self):
protocol = protocols.SingleActionPotentialProtocol(1800)
baseline_model = kernik.KernikModel()
baseline_model.generate_response(protocol)
import seaborn as sns
plt.plot(baseline_model.t, baseline_model.y_voltage)
pdb.set_trace()
self.assertTrue(
len(baseline_model.t) > 100, 'Kernik errored in less than .4s')
self.assertTrue(
min(baseline_model.y_voltage) < -10,
'baseline Kernik min is greater than -.01')
self.assertTrue(
max(baseline_model.y_voltage) < 60,
'baseline Kernik max is greater than .06')
def plot_voltage_currents():
kernik_protocol = protocols.SingleActionPotentialProtocol(983)
baseline_kernik = KernikModel()
baseline_kernik.generate_response(kernik_protocol)
coi = ['I_K1', 'I_Kr', 'I_Ks', 'I_CaL', 'I_Na', 'I_To']
currents_df = get_current_data(baseline_kernik.current_response_info.currents, coi, baseline_kernik.t)
fig = plt.figure(figsize=(10,8))
plt.plot(baseline_kernik.t, baseline_kernik.y_voltage, label='Kernik')
plt.axis([0, 983, -80, 40])
ax = plt.gca()
ax.tick_params(labelsize=18)
ax.tick_params(axis='x', labelcolor='none')
plt.ylabel('Voltage (mV)', fontsize=20)
#plt.savefig('figures/voltage.svg')
plt.show()
def setUp(self):
config = ga_configs.ParameterTuningConfig(
population_size=2,
max_generations=2,
protocol=protocols.SingleActionPotentialProtocol(),
tunable_parameters=[
ga_configs.Parameter(name='g_b_na', default_value=0.95),
ga_configs.Parameter(name='g_na', default_value=3671.2302)
],
params_lower_bound=0.9,
params_upper_bound=1.1,
mutate_probability=1.0,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.15,
tournament_size=2)
self.ga = parameter_tuning_genetic_algorithm.\
ParameterTuningGeneticAlgorithm(config=config)
def test_update_parameters(self):
protocol = protocols.SingleActionPotentialProtocol()
tunable_parameters = [
ga_configs.Parameter(name='G_Na', default_value=1),
ga_configs.Parameter(name='G_F', default_value=1),
ga_configs.Parameter(name='G_Ks', default_value=1),
ga_configs.Parameter(name='G_Kr', default_value=1),
ga_configs.Parameter(name='G_K1', default_value=1),
ga_configs.Parameter(name='G_bNa', default_value=1),
ga_configs.Parameter(name='G_NaL', default_value=1),
ga_configs.Parameter(name='G_CaL', default_value=1),
ga_configs.Parameter(name='G_pCa', default_value=1),
ga_configs.Parameter(name='G_bCa', default_value=1)
]
new_params = [.7, .7, .7, .7, .7, .7, .7, .7, .7, .7]
baseline_trace = paci_2018.generate_trace(protocol)
new_trace = paci_2018.generate_trace(protocol, tunable_parameters,
new_params)
tst.assert_raises(AssertionError, tst.assert_array_equal,
baseline_trace.y, new_trace.y,
'updating parameters does not change trace')
def main():
protocol = protocols.SingleActionPotentialProtocol()
save_data_to_file(paci_2018.generate_trace(protocol))
load_and_plot_data('./data/paci_sap_baseline.csv')
def plot_baseline_single_action_potential_trace():
trace = paci_2018.generate_trace(
protocol=protocols.SingleActionPotentialProtocol(duration=2))
trace.plot()
plt.savefig(
'figures/Single AP Figure/baseline_single_action_potential_trace.svg')
ga_configs.Parameter(name='G_F', default_value=1),
ga_configs.Parameter(name='G_Ks', default_value=1),
ga_configs.Parameter(name='G_Kr', default_value=1),
ga_configs.Parameter(name='G_K1', default_value=1),
ga_configs.Parameter(name='G_bNa', default_value=1),
ga_configs.Parameter(name='G_NaL', default_value=1),
ga_configs.Parameter(name='G_CaL', default_value=1),
ga_configs.Parameter(name='G_pCa', default_value=1),
ga_configs.Parameter(name='G_bCa', default_value=1),
ga_configs.Parameter(name='K_NaCa', default_value=1)
]
# Parameters are sorted alphabetically to maintain order during each
# generation of the genetic algorithm.
PARAMETERS.sort(key=lambda x: x.name)
SAP_PROTOCOL_KERNIK = protocols.SingleActionPotentialProtocol(1800)
VC_PROTOCOL = protocols.VoltageClampProtocol(steps=[
protocols.VoltageClampStep(duration=0.050, voltage=-0.08),
protocols.VoltageClampStep(duration=0.050, voltage=-0.12),
protocols.VoltageClampStep(duration=0.500, voltage=-0.057),
protocols.VoltageClampStep(duration=0.025, voltage=-0.04),
protocols.VoltageClampStep(duration=0.075, voltage=0.02),
protocols.VoltageClampStep(duration=0.025, voltage=-0.08),
protocols.VoltageClampStep(duration=0.250, voltage=0.04),
protocols.VoltageClampStep(duration=1.900, voltage=-0.03),
protocols.VoltageClampStep(duration=0.750, voltage=0.04),
protocols.VoltageClampStep(duration=1.725, voltage=-0.03),
protocols.VoltageClampStep(duration=0.650, voltage=-0.08),
])
def generate_config_list():
parameters = [
ga_configs.Parameter(name='g_na', default_value=3671.2302),
ga_configs.Parameter(name='g_f_s', default_value=30.10312),
]
sap_protocol = protocols.SingleActionPotentialProtocol()
ip_protocol = protocols.IrregularPacingProtocol(duration=3,
stimulation_offsets=[0.1])
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)
])
sap_config = ga_configs.ParameterTuningConfig(
population_size=2,
max_generations=2,
protocol=sap_protocol,
tunable_parameters=parameters,
params_lower_bound=0.5,
params_upper_bound=1.5,
mutate_probability=1.,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=2)
ip_config = ga_configs.ParameterTuningConfig(population_size=2,
max_generations=2,
protocol=ip_protocol,
tunable_parameters=parameters,
params_lower_bound=0.5,
params_upper_bound=1.5,
mutate_probability=1.,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=2)
vc_config = ga_configs.ParameterTuningConfig(population_size=2,
max_generations=2,
protocol=vc_protocol,
tunable_parameters=parameters,
params_lower_bound=0.5,
params_upper_bound=1.5,
mutate_probability=1.,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=2)
combined_config = ga_configs.ParameterTuningConfig(
population_size=2,
max_generations=2,
protocol=ip_protocol,
tunable_parameters=parameters,
params_lower_bound=0.5,
params_upper_bound=1.5,
mutate_probability=1.,
mate_probability=0.9,
gene_swap_probability=0.5,
gene_mutation_probability=0.1,
tournament_size=2,
secondary_protocol=vc_protocol)
return [sap_config, ip_config, vc_config, combined_config]
"""
##Single Action Potential
"""
The SingleActionPotentialProtocol() class name is a misnomer. It should
be called SpontaneousProtocol(). You initialize the protocol with:
- duration: float – the amount of time you want the model to run
spontaneously
"""
# 1. Imports
from kernik import KernikModel
import protocols
import matplotlib.pyplot as plt
DURATION = 983 # Length of Kernik AP, in milliseconds
SAP_PROTOCOL_KERNIK = protocols.SingleActionPotentialProtocol(DURATION)
# STOP AND OPEN ./protocols.py. Look at the SingleActionPotentialProtocol
# so you understand what the kernik_protocol variable holds.
baseline_kernik = KernikModel() # Initialize the baseline kernik individual
baseline_kernik.generate_response(SAP_PROTOCOL_KERNIK) # Run model
plt.plot(baseline_kernik.t, baseline_kernik.y_voltage)
plt.show()
## ----------------------------------------------------------------------
##VC Protocol
VC_PROTOCOL = protocols.VoltageClampProtocol(steps=[
protocols.VoltageClampStep(duration=0.050, voltage=-0.08),
