def ident_processing(ident_object, ident_result):
"""call all required functions to process identifiability information"""
# collect, arrange and collate data
ordered_info = ident_object.order_ident_data(ident_result)
ident_object.create_dict_for_df(ordered_info)
# write all ident data to file
ident_object.write_ident_info_file()
# process ident info for further analysis
ident_object.process_ident()
# extract parameter va;ues for model validation
ident_object.get_parameter_value()
default_parameter_values = true_parameter_values()
ident_object.parameter_values_plot(default_parameter_values,
violin=True,
box=False,
bins=1)
ident_object.identifiability_plot()
ident_object.exp_info_plot()
return None
# retrieve identifiability data from file and process information
ident_index_label = ['sample_name', 'data_set_id']
# retrieve identifiability info from file
ident_df = retrieve_experimental_data_from_file(storage_file_name,
ident_index_label)
all_parameter_info = process_ident(ident_df, arranged_data_df)
# run dynamic simulations to obtain ss data based on estimated parameter values
# get info from data sets that identify all 3 parameters
validation_file_name = os.path.join(
os.getcwd(), 'validate/ident_validate_v3_root_1_mwc_data')
from process_ident_data import get_parameter_value
validation_info = get_parameter_value(all_parameter_info, ident_df)
# get default parameter values
default_parameter_values = true_parameter_values()
# initial value used to generate experimental data
# from validate_estimation import validate_model
# import numpy as np
# y0 = np.array([5, 1, 1])
# # integrator options
# cvode_options = {'iter': 'Newton', 'discr': 'Adams', 'atol': 1e-10, 'rtol': 1e-10, 'time_points': 200,
# 'display_progress': False, 'verbosity': 50}
# validate_model(y0, cvode_options, default_parameter_values, validation_info,
# save_file_name=validation_file_name,
# ss=1, dyn=0, noise=0, kinetics=2)
# retrieve validation info from file
validate_index_labels = [
'estimate_id', 'sample_name', 'data_set_id', 'experiment_id'
def v1_kcat_mwc_validate():
name = MPI.Get_processor_name()
rank = MPI.COMM_WORLD.Get_rank()
size = MPI.COMM_WORLD.Get_size()
if rank == 0:
# create ident object first
v1_ident = ModelIdent(
ident_fun=kotte_model.flux_1_kcat_ident,
arranged_data_file_name=os.path.join(
os.getcwd(), 'exp/exp_v1_2_experiments_mwc'),
ident_data_file_name=os.path.join(os.getcwd(),
'ident/ident_v1_kcat_mwc'),
**{
'original_exp_file':
os.path.join(os.getcwd(), 'exp/experiments_mwc'),
'flux_id':
1,
'flux_choice':
2,
'values_figure':
os.path.join(os.getcwd(),
'results/v1_kcat_mwc_parameter_values.eps'),
'ident_figure':
os.path.join(os.getcwd(), 'results/v1_kcat_mwc_ident.eps'),
'exp_figure':
os.path.join(os.getcwd(), 'results/v1_kcat_mwc_exp.eps'),
'figure_format':
'eps',
'ident_index_label': ['sample_name', 'data_set_id']
})
# retrieve identifiability data and process it for validation
v1_ident.validation_info()
user_ode_opts = {
'iter': 'Newton',
'discr': 'Adams',
'atol': 1e-10,
'rtol': 1e-10,
'time_points': 200,
'display_progress': True,
'verbosity': 30
}
# initial ss to begin all simulations from
y0 = np.array([5, 1, 1])
# get and set true parameter values, if available separately
default_parameters = true_parameter_values()
v1_valid_obj = ValidateSim(
kotte_model.kotte_ode, kotte_model.kotte_flux, **{
'kinetics':
1,
'ode_opts':
user_ode_opts,
't_final':
200,
'wt_y0':
y0,
'i_parameter':
default_parameters,
'sample_size':
1,
'noise_std':
0.05,
'validate_index_label':
['estimate_id', 'sample_name', 'data_set_id', 'experiment_id'],
'validate_file_name':
os.path.join(os.getcwd(), 'validate/v1_kcat_mwc_validate'),
'original_exp_file':
v1_ident.original_exp_file,
'c_validate_file':
os.path.join(os.getcwd(),
'results/v1_kcat_mwc_c_validate.eps'),
'f_validate_file':
os.path.join(os.getcwd(),
'results/v1_kcat_mwc_f_validate.eps'),
'c_exp_file':
os.path.join(os.getcwd(), 'results/v1_kcat_mwc_c_exp.eps'),
'v_exp_file':
os.path.join(os.getcwd(), 'results/v1_kcat_mwc_f_exp.eps'),
'format':
v1_ident.figure_format
})
parameter_estimates, estimate_info = v1_valid_obj.create_parameter_list(
v1_ident.select_values)
job = ParallelValidate(slaves=range(1, size))
validate_results = job.run_all(task='initial_sim',
**{
'parameters': parameter_estimates,
'estimate_info': estimate_info,
'sim_obj': v1_valid_obj
})
job.terminate_slaves()
# process validation data for plots
validate_processing(v1_valid_obj, validate_results)
else:
print('I am %s Slave with rank %s of %s' %
(name, str(rank), str(size)))
ValidateSlave().run()
return None
def create_experiment_data(save_file_name,
noise=0,
kinetics=2,
number_samples=1,
noise_std=0.05):
"""run generate_expdata and store resulting experimental data as csv file from data frame"""
# generate no-noise experimental data for testing identifiability
user_ode_opts = {
'iter': 'Newton',
'discr': 'Adams',
'atol': 1e-10,
'rtol': 1e-10,
'time_points': 200,
'display_progress': True,
'verbosity': 30
}
# initial ss to begin all simulations from
y0 = np.array([5, 1, 1])
# get and set true parameter values, if available separately
default_parameters = true_parameter_values()
# create simulation object to simulate model with above parameters and initial conditions
model_1 = ModelSim(kotte_model.kotte_ck_ode,
kotte_model.kotte_ck_flux,
noise=noise,
**{
'kinetics': kinetics,
'ode_opts': user_ode_opts,
't_final': 200,
'wt_y0': y0,
'i_parameter': default_parameters,
'sample_size': number_samples,
'noise_std': noise_std
})
# initial value determination for wt before perturbation
wt_ss, wt_dynamic = model_1.run_initial_sim([default_parameters],
['default_parameters'])
# all parameter perturbations
parameter_perturbation = [{
"wt": 0
}, {
"ac": 1
}, {
"ac": 4
}, {
"ac": 9
}, {
"ac": -.1
}, {
"ac": -.5
}, {
"k1cat": .1
}, {
"k1cat": .5
}, {
"k1cat": 1
}, {
"k1cat": -.1
}, {
"k1cat": -.5
}, {
"V3max": .1
}, {
"V3max": .5
}, {
"V3max": 1
}, {
"V3max": -.1
}, {
"V3max": -.5
}, {
"V2max": .1
}, {
"V2max": .5
}, {
"V2max": 1
}, {
"V2max": -.1
}, {
"V2max": -.5
}]
experiment_id = [
'experiment_{}'.format(parameter_id)
for parameter_id, _ in enumerate(parameter_perturbation)
]
experiment_details = model_1.change_parameter_values(
parameter_perturbation)
# call model.simulate to get initial (WT) steady state for all parameter sets strating from same y0
model_1.sim_model(parameter=experiment_details,
experiment_ids=experiment_id,
initial_value=[wt_ss[0]['y']])
# create dictionary suitable for writing to df
experiment_df, multi_index_labels = model_1.create_df(
parameter_perturbation, experiment_details)
# get experimental system steady state data without noise using Convenience Kinetics for v3 (kinetics = 2)
# experiment_df, multi_index_labels, dyn_df, dyn_labels = generate_expdata(y0, cvode_options, ode_parameter_values,
# noise=noise, kinetics=kinetics,
# dynamic_plot=0, perturbation_plot=0,
# number_of_samples=number_samples, noise_std=noise_std)
# save data frame to csv file
experiment_df.to_csv(save_file_name, index_label=multi_index_labels)
# dyn_df.to_csv(save_dyn_file_name, index_label=dyn_labels)
print(' Experiment Data written to file \n')
return experiment_df