def test_make_settings_fail_3(self):
#Mismatched parameter length
__settings__ = {
'system_type': ['pyTestModel, Dummy'],
'settings_name': '__default__',
'units': {
'syn_mRNA': 'M/min',
'deg_mRNA': '1/min',
'syn_Pep': '1/min',
'deg_Pep': '1/min',
'Ind': 'M'
},
'parameters': {
'syn_mRNA': np.array([0.02, 1]),
'deg_mRNA': np.array([0.15]),
'syn_Pep': np.array([0.02]),
'deg_Pep': np.array([0.012]),
'Ind': np.array([1.0])
},
'init': {
1: [0, 0]
},
'parameter_bounds': {
'deg_mRNA': [0.001, 0.03],
'deg_Pep': [0.01, 0.5],
},
'solver_args': {
'rtol': 1.49012e-8,
'atol': 1.49012e-8,
'tcrit': [],
'h0': 0.0,
'hmax': 0.0,
'hmin': 0.0,
'mxstep': 0
},
}
settings = sh.make_settings(**__settings__)
new_settings = config_data[best_model_num]
new_settings['settings_name'] = 'Example_3_pLac'
new_settings['parameters'] = cf.get_params_for_model(
models=sampler_args['models'],
trace=accepted,
model_num=best_model_num,
row_index=best_row_index)
'''
We now call the constructor for the settings data structure. Once the new settings
have been created, we can add it to the database if we wish.
Note that information such as sa_args cannot be stored as settings and will
be ignored.
'''
new_settings = sh.make_settings(
**new_settings, user_core_model=user_core_models[best_system_type])
inducer_conc_str = {key: str(inducer_conc[key]) for key in inducer_conc}
plot_index = {
1: ['OD', 'Fluor/OD'],
2: ['OD', 'Fluor/OD', 'm'],
3: ['OD', 'Fluor/OD', 'm', 'ind'],
4: ['OD', 'Fluor/OD', 'm', 'indi']
}
titles = {
1: {
'OD': 'OD',
'Fluor/OD': 'Pep'
},
2: {
#1. Add the best model to the database (if it is not already inside)
if best_system_type in user_core_models:
best_core_model = user_core_models[best_system_type]
mh.add_to_database(best_core_model)
#2. Create a settings data structure based on the best settings
new_settings = config_data[best_model_num]
new_settings['settings_name'] = 'Example_5_pTet'
new_settings['parameters'] = cf.get_params_for_model(
models=sampler_args['models'],
trace=accepted,
model_num=best_model_num,
row_index=best_row_index)
new_settings = sh.make_settings(**new_settings)
#3. Add the settings to the database
sh.add_to_database(new_settings)
#4. View the settings database
'''
8. A Posteriori Identifiability Analysis
'''
'''
BMSS's trace plotting functions are built for multi-trace operations. This
allows you to easily check if your simulations converge to same region.
'''
seeder = seed(sampler_args['guess'], sampler_args['fixed_parameters'],
sampler_args['bounds'])
def test_make_settings(self):
global settings_1
global core_model_1
core_model = core_model_1
mh.add_to_database(core_model, dialog=False)
__settings__ = {
'system_type': ['pyTestModel', 'Dummy'],
'settings_name': '__default__',
'units': {
'syn_mRNA': 'M/min',
'deg_mRNA': '1/min',
'syn_Pep': '1/min',
'deg_Pep': '1/min',
'Ki': 'M',
'Ind': 'M'
},
'parameters': {
'syn_mRNA': np.array([0.02]),
'deg_mRNA': np.array([0.15]),
'syn_Pep': np.array([0.02]),
'deg_Pep': np.array([0.012]),
'Ki': np.array([0.5]),
'Ind': np.array([1.0])
},
'init': {
1: [0, 0]
},
'parameter_bounds': {
'deg_mRNA': [0.001, 0.03],
'deg_Pep': [0.01, 0.5],
},
'solver_args': {
'rtol': 1.49012e-8,
'atol': 1.49012e-8,
'tcrit': [],
'h0': 0.0,
'hmax': 0.0,
'hmin': 0.0,
'mxstep': 0
},
}
settings = sh.make_settings(**__settings__)
###############################################################################
#Testing Parameter Formats
###############################################################################
__settings__['parameters'] = {
key + '_1': __settings__['parameters'][key]
for key in __settings__['parameters']
}
settings = sh.make_settings(**__settings__, user_core_model=core_model)
__settings__['parameters'] = pd.Series(__settings__['parameters'],
name=500)
settings = sh.make_settings(**__settings__, user_core_model=core_model)
##############################################################################
#Testing Initial Value Formats
##############################################################################
__settings__['init'] = {
'mRNA': [0],
'Pep': [0],
}
settings = sh.make_settings(**__settings__,
init_orient='states',
user_core_model=core_model)
__settings__['init'] = [[0, 0], [0, 0]]
settings = sh.make_settings(**__settings__,
init_orient='states',
user_core_model=core_model)
__settings__['init'] = pd.Series([0, 0], index=['mRNA', 'Pep'])
settings = sh.make_settings(**__settings__,
init_orient='states',
user_core_model=core_model)
__settings__['init'] = pd.DataFrame([[0, 0]],
columns=['mRNA', 'Pep'],
index=[1])
settings = sh.make_settings(**__settings__,
init_orient='states',
user_core_model=core_model)
#Assert here
for key in __settings__:
try:
assert settings[key] is not None
except ValueError as e:
pass
except Exception as e:
raise e
#Save for reuse
settings_1 = settings