def get_sampler_args_sa(filename, user_core_models={}):
'''Shortcut for setting up simulated annealing for one model
:meta: private
'''
config_data, core_models = setup_helper(filename, from_config, user_core_models)
models, params = compile_models(core_models, config_data)
guess = {}
bounds = {}
priors = {}
step_size = {}
blocks = []
fixed_parameters = []
trials = []
for key in config_data:
models[key]['init'] = config_data[key]['init']
models[key]['tspan'] = config_data[key]['tspan']
models[key]['int_args']['solver_args'] = config_data[key]['solver_args']
temp = {param + '_' + str(key): float(config_data[key]['guess'][param]) for param in config_data[key]['guess']}
guess = {**guess, **temp}
temp = {param + '_' + str(key): config_data[key]['parameter_bounds'][param] for param in config_data[key]['parameter_bounds']}
bounds = {**bounds, **temp}
temp = {param + '_' + str(key): config_data[key]['priors'][param] for param in config_data[key]['priors']}
priors = {**priors, **temp}
temp = {param + '_' + str(key): config_data[key]['step_size'][param] for param in config_data[key]['step_size']}
step_size = {**step_size, **temp}
blocks += [[param + '_' + str(key) for param in block] for block in config_data[key]['sa_args'].get('blocks', [])]
fixed_parameters += [param + '_' + str(key) for param in config_data[key]['fixed_parameters']]
fixed_parameters += [i + '_' + str(key) for i in core_models[key-1]['inputs']]
if 'trials' in config_data[key]['sa_args']:
trials.append(config_data[key]['sa_args']['trials'])
sampler_args = {'data' : {},
'guess' : guess,
'models' : models,
'fixed_parameters' : fixed_parameters,
'priors' : priors,
'bounds' : bounds,
'blocks' : blocks,
'step_size': step_size,
'trials' : max(trials) if trials else 3000,
}
return sampler_args, config_data
def get_sampler_args_bh(filename, user_core_models={}):
'''Shortcut for setting up differential evolution for one model
:meta: private
'''
config_data = from_config(filename, 'bh') if type(filename) == str else filename
core_models = [mh.quick_search(config_data[key]['system_type']) for key in config_data]
models, params = compile_models(core_models, config_data)
guess = {}
bounds = {}
priors = {}
fixed_parameters = []
step_size = {}
scipy_args = {}
for key in config_data:
models[key]['init'] = config_data[key]['init']
models[key]['tspan'] = config_data[key]['tspan']
models[key]['int_args']['solver_args'] = config_data[key]['solver_args']
temp = {param + '_' + str(key): float(config_data[key]['guess'][param]) for param in config_data[key]['guess']}
guess = {**guess, **temp}
temp = {param + '_' + str(key): config_data[key]['parameter_bounds'][param] for param in config_data[key]['parameter_bounds']}
bounds = {**bounds, **temp}
temp = {param + '_' + str(key): config_data[key]['priors'][param] for param in config_data[key]['priors']}
priors = {**priors, **temp}
temp = {param + '_' + str(key): config_data[key]['step_size'][param] for param in config_data[key]['step_size']}
step_size = {**step_size, **temp}
fixed_parameters += [param + '_' + str(key) for param in config_data[key]['fixed_parameters']]
fixed_parameters += [i + '_' + str(key) for i in core_models[key-1]['inputs']]
scipy_args = config_data[key]['bh_args'] if 'bh_args' in config_data[key] else scipy_args
sampler_args = {'data' : {},
'guess' : guess,
'models' : models,
'fixed_parameters' : fixed_parameters,
'priors' : priors,
'bounds' : bounds,
'step_size' : step_size,
'scipy_args' : scipy_args,
'step_size_is_ratio' : False
}
return sampler_args, config_data
def get_sensitivity_args(filename, user_core_models={}):
'''Reads the config file and adds combines it with core_model data. If you are
using a core model that is not in the database, you must provide the core_model
using the core_models argument where the key is the system_type. Returns a
dictionary of keyword arguments and the config_data.
:param filename: The name of the file to read
:type filename: str
:param user_core_model: A dictionary of core_models indexed by their system_type.
core_models already in the database do not need to be specified here.
:type user_core_model: dict, optional
'''
config_data, core_models = setup_helper(filename, from_config,
user_core_models)
models, params = compile_models(core_models, config_data)
bounds = {}
fixed_parameters = []
for key in config_data:
models[key]['init'] = config_data[key]['init']
models[key]['tspan'] = config_data[key]['tspan']
models[key]['int_args']['solver_args'] = config_data[key][
'solver_args']
temp = {
param + '_' + str(key): config_data[key]['parameter_bounds'][param]
for param in config_data[key]['parameter_bounds']
}
bounds = {**bounds, **temp}
fixed_parameters += [
param + '_' + str(key)
for param in config_data[key]['fixed_parameters']
]
sensitivity_args = {
'models': models,
'params': params,
'parameter_bounds': bounds,
'fixed_parameters': fixed_parameters,
'analysis_type': 'sobol',
'N': 1000
}
return sensitivity_args, config_data