def test_get_sensitivity_args(self):
global user_core_models
global settings_file_1
global sg_args
global variables
global config_data
filename = settings_file_1
#Get arguments for simulation
result = ssg.get_strike_goldd_args(filename, user_core_models=user_core_models)
#Expect models, params, config_data = result
assert len(result) == 3
try:
sg_args, config_data, variables = result
except:
sg_args, config_data, variables = result.values()
'''
#Set up core models and sampler arguments
#Details in Tutorial 7 Part 1
model_files = [
'LogicGate_Not_Single.ini',
'LogicGate_Not_Double.ini',
'LogicGate_Not_Double_MaturationSecond.ini',
]
user_core_models = [mh.from_config(filename) for filename in model_files]
user_core_models = {
core_model['system_type']: core_model
for core_model in user_core_models
}
sg_args, config_data, variables = ssg.get_strike_goldd_args(
model_files, user_core_models=user_core_models)
'''
The optional argument dst allows you to supply your own dictionary to which the
results will be added after each iteration. This allows you to thread and/or
save the results before all the iterations have been completed. Just use an
empty dictionary for dst.
'''
#Run strike-goldd algorithm
#Details in Tutorial 7 Part 2
dst = {}
sg_results = sg.analyze_sg_args(sg_args, dst=dst)
outfile = 'sg_results.yaml'
yaml_dict = ir.export_sg_results(sg_results,
variables,
config_data,
user_core_models=user_core_models,
import setup_bmss as lab
import BMSS.models.setup_sg as ssg
import BMSS.strike_goldd_simplified as sg
import BMSS.models.ia_results as ir
import BMSS.models.model_handler as mh
'''
Tutorial 6 Part 2: A priori Identifiability Analysis with Strike-Goldd
- Call the function required to run the strike_goldd algorithm.
'''
if __name__ == '__main__':
filename = 'settings_sg.ini'
sg_args, config_data, variables = ssg.get_strike_goldd_args(filename)
'''
We can now run the strike-goldd algorithm. This returns a dictionary where
the result for each model is indexed under model_num.
The optional argument dst allows you to supply your own dictionary to which the
results will be added after each iteration. This allows you to thread and/or
save the results before all the iterations have been completed. Just use an
empty dictionary.
'''
dst = {}
sg_results = sg.analyze_sg_args(sg_args, dst=dst)
print('Result from strike_goldd')
print(sg_results[1])
print()
'''
The results can be exported as a human-readable report as a .yaml file
9. A Priori Identifiability Analysis
'''
'''
BMSS allows you perform algebraic identifiability analysi using the STRIKE-GOLDD
algorithm developed by Villaverde et al. which makes use of symbolic algebra
and Lie derivatives to assess parameter identifiability.
'''
ssg.make_settings_template(system_types_settings_names,
filename='settings_template_sg.ini',
user_core_models=user_core_models)
'''
Once again, template files for the settings can be generated.
'''
sg_args, config_data, variables = ssg.get_strike_goldd_args(
'settings_sg.ini', user_core_models=user_core_models, write_file=True)
'''
The optional argument dst allows you to supply your own dictionary to which the
results will be added after each iteration. This allows you to thread and/or
save the results before all the iterations have been completed. Just use an
empty dictionary for dst.
'''
#Run strike-goldd algorithm
#Details in Tutorial 7 Part 2
dst = {}
sg_results = sg.analyze_sg_args(sg_args, dst=dst)
outfile = 'sg_results.yaml'
yaml_dict = ir.export_sg_results(sg_results,
variables,
config_data,
user_core_models=user_core_models,
import importlib
import setup_bmss as lab
import BMSS.models.setup_sg as ssg
'''
Tutorial 6 Part 3: Generating .py Files
- Create standalone .py files for running strike-goldd
'''
if __name__ == '__main__':
'''
Just as .py files for integration can be generated for core models, so can .py
for running strike_goldd. Simply set the write_file argument in
get_strike_goldd_args to True.
'''
filename = 'settings_sg.ini'
sg_args, variables, config_data = ssg.get_strike_goldd_args(
filename, write_file=True)
'''
The newly written .py files are have the prefix 'sg_' in front of them. Open
files and take a look inside!
'''
mod = importlib.import_module('sg_TestModel_Monod_Inducible')
result = mod.run_strike_goldd()
print('Result')
print(result)
1. Reading the Settings File
'''
filename = 'settings_sg.ini'
config_data = ssg.from_config(filename)
'''
Note: In order to speed up calculations, provide parameter vaues for
fixed_parameters where possible.
'''
'''
2. Compiling Arguments
'''
core_model = mh.from_config('Monod_Inducible.ini')
user_core_models = {core_model['system_type']: core_model}
sg_args, config_data, variables = ssg.get_strike_goldd_args(
filename, user_core_models=user_core_models, write_file=False)
print('Keys in sg_args[1]: ')
print(sg_args[1].keys())
print()
'''
sg_args is a dictionary in the form {model_num: model} where a model contains
information on the system of equations, the number of unknown parameters and
other arguments required for running the strike-goldd algorithm.
The keys are as follows:
'h' : A 1 column Matrix of states that measured during the experiment.
'x' : A 1 column Matrix of states in for the model
'p' : A 1 column Matrix of unknown parameters.
'f' : A 1 column Matrix of differential equations for each state in order.
'u' : A dictionary where the keys are the inputs and the values are the number of non-zero derivatives.