tempor.core.plugins package¶

Submodules¶

Module contents¶

This module contains the core plugin functionality of TemporAI.

tempor.core.plugins.parse_plugin_type(plugin_type: None | Literal[all] | str, must_be_one_of: list[str] | None = None) → None | Literal[all] | str[source]¶

Get the default plugin type if plugin_type is None. If plugin_type is "all", raise error, as that is a reserved value.

Parameters:¶

plugin_type : plugin_typing.PluginTypeArg¶

Plugin type.

must_be_one_of : Optional[List[str]]¶

List of plugin types that plugin_type must be one of.

Returns:¶

Default plugin type if plugin_type is None, otherwise plugin_type.

Return type:¶

plugin_typing.PluginTypeArg

tempor.core.plugins.create_fqn(suffix: str, plugin_type: None | Literal[all] | str) → str[source]¶

Create a fully-qualified name for a plugin or category, like [plugin_type].category.name or [plugin_type].category respectively.

Parameters:¶

suffix : Union[plugin_typing.PluginCategory, plugin_typing.PluginFullName]¶

Plugin category or plugin full name.

plugin_type : plugin_typing.PluginTypeArg¶

Plugin type.

Returns:¶

Fully-qualified name.

Return type:¶

str

tempor.core.plugins.filter_list_by_plugin_type(lst: list[str], plugin_type: None | Literal[all] | str) → list[str][source]¶

Filter a list of plugin FQNs by plugin type.

Parameters:¶

lst : List[plugin_typing._PluginFqn]¶

List of plugin FQNs.

plugin_type : plugin_typing.PluginTypeArg¶

Plugin type.

Returns:¶

Filtered list which will only include FQNs with the specified plugin_type.

Return type:¶

List[plugin_typing.PluginFullName]

tempor.core.plugins.filter_dict_by_plugin_type(d: dict[str, Any], plugin_type: None | Literal[all] | str) → dict[str, Any][source]¶

Filter a dictionary with plugin FQN keys by plugin type.

Parameters:¶

d : Dict[plugin_typing._PluginFqn, Any]¶

Dictionary to filter.

plugin_type : plugin_typing.PluginTypeArg¶

Plugin type.

Returns:¶

Filtered dictionary which will only include items where FQN keys match the specified plugin_type.

Return type:¶

Dict[plugin_typing.PluginFullName, Any]

tempor.core.plugins.remove_plugin_type_from_fqn(fqn: str) → str[source]¶

Remove the plugin type part of a plugin FQN or category FQN.

Parameters:¶

fqn : Union[plugin_typing._PluginCategoryFqn, plugin_typing._PluginFqn]¶

Plugin FQN of plugin category FQN.

Returns:¶

The FQN with the plugin type part removed.

Return type:¶

Union[plugin_typing.PluginCategory, plugin_typing.PluginFullName]

tempor.core.plugins.get_plugin_type_from_fqn(fqn: str) → None | Literal[all] | str[source]¶

Get the plugin type part of a plugin FQN or category FQN.

Parameters:¶

fqn : Union[plugin_typing._PluginCategoryFqn, plugin_typing._PluginFqn]¶

Plugin FQN of plugin category FQN.

Returns:¶

The plugin type.

Return type:¶

plugin_typing.PluginTypeArg

class tempor.core.plugins.Plugin[source]¶

Bases: object

The base class that all plugins must inherit from.

name : ClassVar[str] = 'NOT_SET'¶

Plugin name, such as 'my_nn_classifier'. Must be set by the plugin class using @register_plugin.

category : ClassVar[str] = 'NOT_SET'¶

Plugin category, such as 'prediction.one_off.classification'. Must be set by the plugin class using @register_plugin.

plugin_type : ClassVar[None | Literal[all] | str] = 'NOT_SET'¶

Plugin type, such as 'method'. May be optionally set by the plugin class using @register_plugin, else will set the default plugin type.

classmethod full_name() → str[source]¶

The full name of the plugin with its category: category.subcategory.name. There may be 0 or more subcategories.

Returns:¶

Plugin full name.

Return type:¶

plugin_typing.PluginFullName

tempor.core.plugins.PLUGIN_CATEGORY_REGISTRY : dict[str, type[Plugin]] = {'[dataformat].event_samples': <class 'tempor.data.samples.EventSamplesBase'>, '[dataformat].static_samples': <class 'tempor.data.samples.StaticSamplesBase'>, '[dataformat].time_series_samples': <class 'tempor.data.samples.TimeSeriesSamplesBase'>, '[datasource].prediction.one_off': <class 'tempor.datasources.datasource.OneOffPredictionDataSource'>, '[datasource].prediction.temporal': <class 'tempor.datasources.datasource.TemporalPredictionDataSource'>, '[datasource].time_to_event': <class 'tempor.datasources.datasource.TimeToEventAnalysisDataSource'>, '[datasource].treatments.one_off': <class 'tempor.datasources.datasource.OneOffTreatmentEffectsDataSource'>, '[datasource].treatments.temporal': <class 'tempor.datasources.datasource.TemporalTreatmentEffectsDataSource'>, '[method].prediction.one_off.classification': <class 'tempor.methods.prediction.one_off.classification.BaseOneOffClassifier'>, '[method].prediction.one_off.regression': <class 'tempor.methods.prediction.one_off.regression.BaseOneOffRegressor'>, '[method].prediction.temporal.classification': <class 'tempor.methods.prediction.temporal.classification.BaseTemporalClassifier'>, '[method].prediction.temporal.regression': <class 'tempor.methods.prediction.temporal.regression.BaseTemporalRegressor'>, '[method].preprocessing.encoding.static': <class 'tempor.methods.preprocessing.encoding._base.BaseEncoder'>, '[method].preprocessing.encoding.temporal': <class 'tempor.methods.preprocessing.encoding._base.BaseEncoder'>, '[method].preprocessing.imputation.static': <class 'tempor.methods.preprocessing.imputation._base.BaseImputer'>, '[method].preprocessing.imputation.temporal': <class 'tempor.methods.preprocessing.imputation._base.BaseImputer'>, '[method].preprocessing.nop': <class 'tempor.methods.core._base_transformer.BaseTransformer'>, '[method].preprocessing.scaling.static': <class 'tempor.methods.preprocessing.scaling._base.BaseScaler'>, '[method].preprocessing.scaling.temporal': <class 'tempor.methods.preprocessing.scaling._base.BaseScaler'>, '[method].time_to_event': <class 'tempor.methods.time_to_event.BaseTimeToEventAnalysis'>, '[method].treatments.one_off.regression': <class 'tempor.methods.treatments.one_off._base.BaseOneOffTreatmentEffects'>, '[method].treatments.temporal.classification': <class 'tempor.methods.treatments.temporal._base.BaseTemporalTreatmentEffects'>, '[method].treatments.temporal.regression': <class 'tempor.methods.treatments.temporal._base.BaseTemporalTreatmentEffects'>, '[metric].prediction.one_off.classification': <class 'tempor.metrics.metric.OneOffClassificationMetric'>, '[metric].prediction.one_off.regression': <class 'tempor.metrics.metric.OneOffRegressionMetric'>, '[metric].time_to_event': <class 'tempor.metrics.metric.TimeToEventMetric'>}¶

Important dictionary for plugin functionality. Records all plugin categories ('[plugin_type].category.<0 or more subcategories if applicable>') and their corresponding plugin classes.

tempor.core.plugins.PLUGIN_REGISTRY : dict[str, type[Plugin]] = {'[dataformat].event_samples.event_samples_dask': <class 'tempor.data.samples_experimental.EventSamplesDask'>, '[dataformat].event_samples.event_samples_df': <class 'tempor.data.samples.EventSamples'>, '[dataformat].static_samples.static_samples_dask': <class 'tempor.data.samples_experimental.StaticSamplesDask'>, '[dataformat].static_samples.static_samples_df': <class 'tempor.data.samples.StaticSamples'>, '[dataformat].time_series_samples.time_series_samples_dask': <class 'tempor.data.samples_experimental.TimeSeriesSamplesDask'>, '[dataformat].time_series_samples.time_series_samples_df': <class 'tempor.data.samples.TimeSeriesSamples'>, '[datasource].prediction.one_off.google_stocks': <class 'tempor.datasources.prediction.one_off.plugin_google_stocks.GoogleStocksDataSource'>, '[datasource].prediction.one_off.sine': <class 'tempor.datasources.prediction.one_off.plugin_sine.SineDataSource'>, '[datasource].prediction.temporal.dummy_prediction': <class 'tempor.datasources.prediction.temporal.plugin_dummy_prediction.DummyTemporalPredictionDataSource'>, '[datasource].prediction.temporal.uci_diabetes': <class 'tempor.datasources.prediction.temporal.plugin_uci_diabetes.UCIDiabetesDataSource'>, '[datasource].time_to_event.pbc': <class 'tempor.datasources.time_to_event.plugin_pbc.PBCDataSource'>, '[datasource].treatments.one_off.pkpd': <class 'tempor.datasources.treatments.one_off.plugin_pkpd.PKPDDataSource'>, '[datasource].treatments.temporal.dummy_treatments': <class 'tempor.datasources.treatments.temporal.plugin_dummy_treatments.DummyTemporalTreatmentEffectsDataSource'>, '[method].prediction.one_off.classification.cde_classifier': <class 'tempor.methods.prediction.one_off.classification.plugin_cde_classifier.CDEClassifier'>, '[method].prediction.one_off.classification.laplace_ode_classifier': <class 'tempor.methods.prediction.one_off.classification.plugin_laplace_classifier.LaplaceODEClassifier'>, '[method].prediction.one_off.classification.nn_classifier': <class 'tempor.methods.prediction.one_off.classification.plugin_nn_classifier.NeuralNetClassifier'>, '[method].prediction.one_off.classification.ode_classifier': <class 'tempor.methods.prediction.one_off.classification.plugin_ode_classifier.ODEClassifier'>, '[method].prediction.one_off.regression.cde_regressor': <class 'tempor.methods.prediction.one_off.regression.plugin_cde_regressor.CDERegressor'>, '[method].prediction.one_off.regression.laplace_ode_regressor': <class 'tempor.methods.prediction.one_off.regression.plugin_laplace_regressor.LaplaceODERegressor'>, '[method].prediction.one_off.regression.nn_regressor': <class 'tempor.methods.prediction.one_off.regression.plugin_nn_regressor.NeuralNetRegressor'>, '[method].prediction.one_off.regression.ode_regressor': <class 'tempor.methods.prediction.one_off.regression.plugin_ode_regressor.ODERegressor'>, '[method].prediction.temporal.classification.seq2seq_classifier': <class 'tempor.methods.prediction.temporal.classification.plugin_seq2seq_classifier.Seq2seqClassifier'>, '[method].prediction.temporal.regression.seq2seq_regressor': <class 'tempor.methods.prediction.temporal.regression.plugin_seq2seq_regressor.Seq2seqRegressor'>, '[method].preprocessing.encoding.static.static_onehot_encoder': <class 'tempor.methods.preprocessing.encoding.static.plugin_static_onehot_encoder.StaticOneHotEncoder'>, '[method].preprocessing.encoding.temporal.ts_onehot_encoder': <class 'tempor.methods.preprocessing.encoding.temporal.plugin_ts_onehot_encoder.TimeSeriesOneHotEncoder'>, '[method].preprocessing.imputation.static.static_tabular_imputer': <class 'tempor.methods.preprocessing.imputation.static.plugin_static_tabular_imputer.StaticTabularImputer'>, '[method].preprocessing.imputation.temporal.bfill': <class 'tempor.methods.preprocessing.imputation.temporal.plugin_bfill.BFillImputer'>, '[method].preprocessing.imputation.temporal.ffill': <class 'tempor.methods.preprocessing.imputation.temporal.plugin_ffill.FFillImputer'>, '[method].preprocessing.imputation.temporal.ts_tabular_imputer': <class 'tempor.methods.preprocessing.imputation.temporal.plugin_ts_tabular_imputer.TemporalTabularImputer'>, '[method].preprocessing.nop.nop_transformer': <class 'tempor.methods.preprocessing.nop.NopTransformer'>, '[method].preprocessing.scaling.static.static_minmax_scaler': <class 'tempor.methods.preprocessing.scaling.static.plugin_static_minmax_scaler.StaticMinMaxScaler'>, '[method].preprocessing.scaling.static.static_standard_scaler': <class 'tempor.methods.preprocessing.scaling.static.plugin_static_standard_scaler.StaticStandardScaler'>, '[method].preprocessing.scaling.temporal.ts_minmax_scaler': <class 'tempor.methods.preprocessing.scaling.temporal.plugin_ts_minmax_scaler.TimeSeriesMinMaxScaler'>, '[method].preprocessing.scaling.temporal.ts_standard_scaler': <class 'tempor.methods.preprocessing.scaling.temporal.plugin_ts_standard_scaler.TimeSeriesStandardScaler'>, '[method].time_to_event.dynamic_deephit': <class 'tempor.methods.time_to_event.plugin_ddh.DynamicDeepHitTimeToEventAnalysis'>, '[method].time_to_event.ts_coxph': <class 'tempor.methods.time_to_event.plugin_ts_coxph.CoxPHTimeToEventAnalysis'>, '[method].time_to_event.ts_xgb': <class 'tempor.methods.time_to_event.plugin_ts_xgb.XGBTimeToEventAnalysis'>, '[method].treatments.one_off.regression.synctwin_regressor': <class 'tempor.methods.treatments.one_off.regression.plugin_synctwin_regressor.SyncTwinTreatmentsRegressor'>, '[method].treatments.temporal.classification.crn_classifier': <class 'tempor.methods.treatments.temporal.classification.plugin_crn_classifier.CRNTreatmentsClassifier'>, '[method].treatments.temporal.regression.crn_regressor': <class 'tempor.methods.treatments.temporal.regression.plugin_crn_regressor.CRNTreatmentsRegressor'>, '[metric].prediction.one_off.classification.accuracy': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.AccuracyOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.aucprc': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.AucPrcOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.aucroc': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.AucRocOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.f1_score_macro': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.F1ScoreMacroOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.f1_score_micro': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.F1ScoreMicroOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.f1_score_weighted': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.F1ScoreWeightedOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.kappa': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.KappaOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.kappa_quadratic': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.KappaQuadraticOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.mcc': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.MccOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.precision_macro': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.PrecisionMacroOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.precision_micro': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.PrecisionMicroOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.precision_weighted': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.PrecisionWeightedOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.recall_macro': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.RecallMacroOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.recall_micro': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.RecallMicroOneOffClassificationMetric'>, '[metric].prediction.one_off.classification.recall_weighted': <class 'tempor.metrics.prediction.one_off.plugin_builtin_classification.RecallWeightedOneOffClassificationMetric'>, '[metric].prediction.one_off.regression.mae': <class 'tempor.metrics.prediction.one_off.plugin_builtin_regression.MaeOneOffRegressionMetric'>, '[metric].prediction.one_off.regression.mse': <class 'tempor.metrics.prediction.one_off.plugin_builtin_regression.MseOneOffRegressionMetric'>, '[metric].prediction.one_off.regression.r2': <class 'tempor.metrics.prediction.one_off.plugin_builtin_regression.R2OneOffRegressionMetric'>, '[metric].time_to_event.brier_score': <class 'tempor.metrics.time_to_event.plugin_builtin_time_to_event.BrierScoreTimeToEventMetric'>, '[metric].time_to_event.c_index': <class 'tempor.metrics.time_to_event.plugin_builtin_time_to_event.CIndexTimeToEventMetric'>}¶

Important dictionary for plugin functionality. Records all plugins by their fully-qualified name '[plugin_type].category.<0 or more subcategories if applicable>.plugin_name'.

tempor.core.plugins.register_plugin_category(category: str, expected_class: type, plugin_type: None | Literal[all] | str = None) → None[source]¶

A decorator to register a plugin category (with optional subcategories). If plugin_type is provided, this will also be assigned (or created, if such plugin type doesn’t yet exist), otherwise the default plugin type will be used.

Parameters:¶

category : plugin_typing.PluginCategory¶

Plugin category, dot-separated, with optional subcategories, such as 'prediction.one_off.classification'.

expected_class : Type¶

The expected plugin class for this category. The plugin class must be a subclass of this class. Note that this class must itself be a subclass of Plugin.

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type to register the category under. Different plugin types should be used to indicate different domains of your code (e.g. methods vs metrics vs datasets). Defaults to None.

Raises:¶

TypeError – If the expected_class is not correctly defined.

tempor.core.plugins.register_plugin(name: str, category: str, plugin_type: None | Literal[all] | str = None) → Callable[[Callable[[P], T]], Callable[[P], T]][source]¶

A decorator to register a plugin class. If plugin_type is provided, this will also be assigned, otherwise the default plugin type will be used. The category must have already been registered with @register_plugin_category before this can be used to register a plugin.

Parameters:¶

name : str¶

Plugin name, such as 'my_nn_classifier'.

category : plugin_typing.PluginCategory¶

Plugin category, dot-separated, with optional subcategories, such as 'prediction.one_off.classification'.

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type of the category. If left as None, default plugin type is assumed. plugin_type must correctly correspond to the category being specified. Defaults to None.

Returns:¶

Decorated class.

Return type:¶

Callable[[Callable[P, T]], Callable[P, T]]

tempor.core.plugins.ListCategoriesReturn¶

Type alias for the return type of list_categories.

alias of Union[Dict[str, Type[Plugin]], Dict[str, Dict[str, Type[Plugin]]]]

class tempor.core.plugins.PluginLoader[source]¶

Bases: object

A class to load plugins. Provides functionality to list and get plugins.

list(plugin_type: None | Literal[all] | str = None) → dict[source]¶

List all plugins of plugin_type as a nested dictionary, where the keys are the plugin categories and optional subcategories. The values of the dictionary are the plugin names.

If plugin_type is "all", will list for all plugin types, outputting inside a nested dictionary with plugin type keys.

Parameters:¶

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type for which to list. Use default category if None. Defaults to None.

Returns:¶

A dictionary as described above.

Return type:¶

Dict

list_full_names(plugin_type: None | Literal[all] | str = None) → list[str] | dict[str, list[str]][source]¶

List all plugins of plugin_type as a list of plugin full names (including categories).

If plugin_type is "all", will list for all plugin types, outputting inside a nested dictionary with plugin type keys.

Parameters:¶

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type for which to list. Use default category if None. Defaults to None.

Returns:¶

A list as described above (List[PluginFullName]) if plugin_type is not "all". Otherwise a nested dictionary with plugin type keys and such lists as values (Dict[str, List[PluginFullName]]]).

Return type:¶

Union[List[plugin_typing.PluginFullName], Dict[str, List[plugin_typing.PluginFullName]]]

list_classes(plugin_type: None | Literal[all] | str = None) → dict[source]¶

List all plugin classes of plugin_type as a nested dictionary, where the keys are the plugin categories and optional subcategories. The values of the dictionary are the plugin classes.

If plugin_type is "all", will list for all plugin types, outputting inside a nested dictionary with plugin type keys.

Parameters:¶

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type for which to list. Use default category if None. Defaults to None.

Returns:¶

A dictionary as described above.

Return type:¶

Dict

list_categories(plugin_type: None | Literal[all] | str = None) → dict[str, type[Plugin]] | dict[str, dict[str, type[Plugin]]][source]¶

List all plugin categories of plugin_type as a dictionary, where the keys are the plugin category names (including optional subcategories) and the values are the expected plugin classes for that category.

If plugin_type is "all", will list for all plugin types, outputting inside a nested dictionary with plugin type keys.

Parameters:¶

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type for which to list. Use default category if None. Defaults to None.

Returns:¶

A dictionary as described above (Dict[PluginFullName, Type[Plugin]]) if plugin_type is not "all". Otherwise a nested dictionary with plugin type keys and such dictionaries as values (Dict[PluginType, Dict[PluginFullName, Type[Plugin]]]).

Return type:¶

ListCategoriesReturn

list_plugin_types() → list[str][source]¶

List all plugin types.

Returns:¶

A list of plugin types.

Return type:¶

List[plugin_typing.PluginType]

get(name: str, *args: Any, **kwargs: Any) → type[source]¶

get(name: str, plugin_type: None | Literal[all] | str, *args: Any, **kwargs: Any) → type

get(name: str, *args: Any, plugin_type: None | Literal[all] | str = None, **kwargs: Any) → type

Get a plugin by its full name (including category, i.e. of form 'my_category.my_subcategory.my_plugin'). Use *args and **kwargs to pass arguments to the plugin initializer. The returned object is an instance of the plugin class. If the plugin is not of the default plugin type, must provide plugin_type also.

The method can be called with plugin_type and plugin initializer arguments, as follows:

• As first positional argument after the plugin name:

plugin_instance = get(
    "my_category.my_subcategory.my_plugin",  # Plugin full name.
    "method",  # Plugin type provided as a positional argument (first).
    0.4,  # First positional argument to plugin initializer.
    123,  # Second positional argument to plugin initializer...
    kwarg=2,  # Keyword argument(s) to plugin initializer from here on.
)

• As keyword argument:

plugin_instance = get(
    "my_category.my_subcategory.my_plugin",  # Plugin full name.
    0.4,  # First positional argument to plugin initializer.
    123,  # Second positional argument to plugin initializer...
    plugin_type="method",  # Plugin type provided as a keyword argument.
    kwarg=2,  # Keyword argument(s) to plugin initializer from here on.
)

Parameters:¶

name : plugin_typing.PluginFullName¶

Plugin full name including all (sub)categories, of form 'my_category.my_subcategory.my_plugin'

*args : Any

Arguments to pass to the plugin initializer.

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type. If left as None, default plugin type is assumed. plugin_type must correctly correspond to the category implied by plugin full name. Defaults to None.

**kwargs : Any

Keyword arguments to pass to the plugin initializer.

Returns:¶

The plugin instance initialised with *args and **kwargs as provided.

Return type:¶

Any

get_class(name: str, plugin_type: None | Literal[all] | str = None) → type[source]¶

Get a plugin class (not instance) by its full name (including category, i.e. of form 'my_category.my_subcategory.my_plugin'). If the plugin is not of the default plugin type, must provide plugin_type.

Parameters:¶

name : plugin_typing.PluginFullName¶

Plugin full name including all (sub)categories, of form 'my_category.my_subcategory.my_plugin'

plugin_type : plugin_typing.PluginTypeArg, optional¶

Plugin type. If left as None, default plugin type is assumed. plugin_type must correctly correspond to the category implied by plugin full name. Defaults to None.

Returns:¶

Plugin class (not instance).

Return type:¶

Type

tempor.core.plugins.PLUGIN_FILENAME_PREFIX = 'plugin_'¶

Prefix expected for plugin filenames of python files that contain plugin code.

class tempor.core.plugins.importing[source]¶

Bases: object

A namespace for plugin importing functionality.

static import_plugins(init_file: str) → None[source]¶

Import all plugin modules inside the package directory associated with init_file (__init__.py). Importing in this context means programmatic import and execution of the plugin modules.

Parameters:¶

init_file : str¶

The init file for the package directory containing the plugin modules (files).

Raises:¶

RuntimeError – Raised if there are import problems with any of the plugin modules.

static gather_modules_names(package_init_file: str) → list[str][source]¶

Gather the names of all plugin modules inside the package directory associated with init_file. Useful for e.g. setting the __all__ variable.

Parameters:¶

package_init_file : str¶

The init file for the package directory containing the plugin modules (files).

Returns:¶

A list of plugin module names.

Return type:¶

List[str]