simulation module

Author:Dominic Hunt

simulation.csv_model_simulation(modelData, simID, file_name_generator)

Saves the fitting data to a CSV file

Parameters:

• modelData (dict) – The data from the model
• simID (string) – The identifier for the simulation
• file_name_generator (function) – Creates a new file with the name <handle> and the extension <extension>. It takes two string parameters: (handle, extension) and returns one fileName string

simulation.log_simulation_parameters(task_parameters, model_parameters, simID)

Writes to the log the description and the label of the task and model

Parameters:

• task_parameters (dict) – The task parameters
• model_parameters (dict) – The model parameters
• simID (string) – The identifier for each simulation.

See also

recordSimParams()

Records these parameters for later use

simulation.record_simulation(file_name_generator, task_data, model_data, simID, pickle=False)

Records the data from an task-model run. Creates a pickled version

Parameters:

• file_name_generator (function) – Creates a new file with the name <handle> and the extension <extension>. It takes two string parameters: (handle, extension) and returns one fileName string
• task_data (dict) – The data from the task
• model_data (dict) – The data from the model
• simID (str) – The label identifying the simulation
• pickle (bool, optional) – If true the data for each model, task and participant is recorded. Default is False

See also

pickleLog()

records the picked data

simulation.run(task_name='Basic', task_changing_properties=None, task_constant_properties=None, model_name='QLearn', model_changing_properties=None, model_constant_properties=None, model_changing_properties_repetition=1, label=None, config_file=None, output_path=None, pickle=False, min_log_level='INFO', numpy_error_level='log')

A framework for letting models interact with tasks and record the data

Parameters:

• task_name (string) – The name of the file where a tasks.taskTemplate.Task class can be found. Default Basic
• task_changing_properties (dictionary of floats or lists of floats) – Parameters are the options that you are or are likely to change across task instances. When a parameter contains a list, an instance of the task will be created for every combination of this parameter with all the others. Default None
• task_constant_properties (dictionary of float, string or binary valued elements) – These contain all the the task options that describe the task being studied but do not vary across task instances. Default None
• model_name (string) – The name of the file where a model.modelTemplate.Model class can be found. Default QLearn
• model_changing_properties (dictionary containing floats or lists of floats, optional) – Parameters are the options that you are or are likely to change across model instances. When a parameter contains a list, an instance of the model will be created for every combination of this parameter with all the others. Default None
• model_constant_properties (dictionary of float, string or binary valued elements, optional) – These contain all the the model options that define the version of the model being studied. Default None
• model_changing_properties_repetition (int, optional) – The number of times each parameter combination is repeated.
• config_file (string, optional) – The file name and path of a .yaml configuration file. Overrides all other parameters if found. Default None
• output_path (string, optional) – The path that will be used for the run output. Default None
• pickle (bool, optional) – If true the data for each model, task and participant is recorded. Default is False
• label (string, optional) – The label for the simulation. Default None, which means nothing will be saved
• min_log_level (str, optional) – Defines the level of the log from (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default INFO
• numpy_error_level ({'log', 'raise'}) – Defines the response to numpy errors. Default log. See numpy.seterr

See also

tasks.taskTemplate(), model.modelTemplate()