config
knowledge.config
TVB-O Model Configuration Module
This module provides utility functions to configure and manage TVB neural mass models using ontology-based configurations. These configurations can be applied to model objects to change their default behavior or parameterize them as per published studies or lab-tested configurations.
Functions
get_default(NMM: str) -> dict: Fetch default values for neural mass model parameters.get_param_config(config_key: str, return_NMM: bool = False, use_classes: bool = False) -> Union[dict, Tuple[dict, str]]: Fetch specific parameter configurations based on a reference key.update_default(model_name: str, config_key: str) -> dict: Update the default parameter configuration of a given model.configure_model(tvb_model: tvb.simulator.models, config_key: str) -> tvb.simulator.models: Apply a specific configuration to a TVB model object, altering its state variables and parameters.
Usage
To fetch and apply configurations, use the respective functions:
model_defaults = get_default("ExampleModel")
updated_defaults = update_default("ExampleModel", "custom_config")
configured_model = configure_model(model_instance, "custom_config")Dependencies
This module relies on the ontology module for fetching configurations. Ensure it’s properly initialized and available.
See Also
tvbo.ontology: For handling and fetching ontology-based configurations.
- Configurations are either sourced from peer-reviewed publications or have been extensively tested in labs.
- Ensure you’ve set up the environment and dependencies correctly for smooth operation of these utilities.
Classes
| Name | Description |
|---|---|
| ModelConfiguration | A class that handles parameter settings for a given Neural Mass Model (NMM). |
ModelConfiguration
knowledge.config.ModelConfiguration(NMM)A class that handles parameter settings for a given Neural Mass Model (NMM).
Attributes
model : ontology.NMM Neural Mass Model instance or identifier. config : dict Configuration settings for the NMM. config_key : str Key identifying the current configuration. report : str Report generated by the get_report method.
Parameters
NMM : ontology.NMM or str An instance or identifier of the Neural Mass Model.
Methods
apply_config(config_key) Apply a new configuration based on the provided key. get_report(format=“latex”, decimals=3, kwargs) Generate a report for the current configuration. save_report(path=“.”, format=“latex”, kwargs) Save the generated report to a file.
Methods
| Name | Description |
|---|---|
| apply_config | Apply a new configuration based on the provided key. |
| get_report | Generate a report for the current configuration. |
| save_report | Save the generated report to a file. |
apply_config
knowledge.config.ModelConfiguration.apply_config(config_key)Apply a new configuration based on the provided key.
Parameters
config_key : str Key identifying the new configuration to apply.
get_report
knowledge.config.ModelConfiguration.get_report(
format='latex',
decimals=3,
**kwargs,
)Generate a report for the current configuration.
Parameters
format : str, optional Format of the report, by default “latex”. decimals : int, optional Number of decimal places to use in the report, by default 3. **kwargs : Additional keyword arguments.
Returns
str Generated report.
save_report
knowledge.config.ModelConfiguration.save_report(
path='.',
format='latex',
**kwargs,
)Save the generated report to a file.
Parameters
path : str, optional Path to save the report, by default “.”. format : str, optional Format of the report, by default “latex”. **kwargs : Additional keyword arguments.
Functions
| Name | Description |
|---|---|
| configure_model | Apply specific parameter configuration stored in TVB-O. All configurations have been either published in a |
| get_default | Retrieve the default values for a given Neural Mass Model (NMM). |
| get_param_config | Retrieve the parameter configuration for a given configuration key. |
| getattr_case_insensitive | Get the value of an attribute from an object, ignoring case sensitivity. |
| update_default | Update the default configuration for a specific model using a given configuration key. |
configure_model
knowledge.config.configure_model(tvb_model, config_key)Apply specific parameter configuration stored in TVB-O. All configurations have been either published in a peer-reviewed journal or tested extensively in our lab. Each configuration has a specific reference with further information.
Parameters
tvb_model : tvb.simulator.models TVB model object config_key : str Reference-key for Parameter configuration.
Returns
tvb.simulator.models Updated model instance with parameter values according to specific configuration.
get_default
knowledge.config.get_default(NMM)Retrieve the default values for a given Neural Mass Model (NMM).
If the provided NMM is a string identifier, it attempts to fetch the actual NMM instance. It then collects the default values for each descendant class of the NMM.
Parameters
NMM : ontology.NMM or str An instance or identifier of the Neural Mass Model.
Returns
dict A dictionary with descendant classes of the NMM as keys and their corresponding default values as values.
Examples
get_default(“some_NMM_identifier”) {
: 0.5, : 1.2, …}
get_param_config
knowledge.config.get_param_config(
config_key='default',
model=None,
return_NMM=False,
use_classes=False,
)Retrieve the parameter configuration for a given configuration key.
This function searches the ontology for the provided configuration key and returns a dictionary with the relevant parameter details. Additionally, it checks for ancestors related to Neural Mass Model (NMM) and fetches the relevant variables and instances associated with the configuration.
Parameters
config_key : str The key for which the configuration details are to be fetched. return_NMM : bool, optional If True, returns the Neural Mass Model associated with the configuration. Default is False. use_classes : bool, optional If True, the labels in the returned dictionary will use class objects instead of string labels. Default is False.
Returns
dict or tuple If return_NMM is False: A dictionary with the configuration details. If return_NMM is True: A tuple where the first element is the configuration details dictionary and the second is the NMM.
Examples
get_param_config(“some_config_key”) {‘some_variable’: {‘category’: ‘State Variable’, ‘range’: [0.5, 1.2]}}
get_param_config(“some_config_key”, return_NMM=True) ({‘some_variable’: {‘category’: ‘State Variable’, ‘range’: [0.5, 1.2]}}, ‘NMM_instance’)
getattr_case_insensitive
knowledge.config.getattr_case_insensitive(obj, attr_name, default=None)Get the value of an attribute from an object, ignoring case sensitivity.
This function searches for an attribute with a case-insensitive match to the given attr_name within the obj object. If a match is found, the corresponding value is returned. If no match is found, the default value is returned.
:param obj: The object to search for the attribute. :param attr_name: The name of the attribute to search for. :param default: The default value to return if no matching attribute is found. :return: The value of the matching attribute or the default value.
update_default
knowledge.config.update_default(model_name, config_key=None, config_dict=None)Update the default configuration for a specific model using a given configuration key.
This function first fetches the default values for the provided model using the ontology. If the config_key is “default”, it returns the fetched default values. Otherwise, it retrieves the parameter configuration associated with the config_key and updates the default values accordingly.
Parameters
model_name : str The name of the model for which the default values need to be updated. config_key : str The reference key for the parameter configuration that will be used to update the default values.
Returns
dict The updated default configuration values for the model. If config_key is “default”, the original default values are returned.
Examples
update_default(“example_model”, “default”) {‘param1’: 1.2, ‘param2’: 2.3}
update_default(“example_model”, “custom_config”) {‘param1’: 1.8, ‘param2’: 2.3}
Notes
The function makes use of ontology for its operations. Ensure that ontology is properly initialized and contains the required data.