morpheus.utils.module_utils

Module utilities for Morpheus.

Functions

get_config_with_overrides(config, module_id)	This function returns the module configuration with the overrides.
get_module_config(module_id, builder)	Returns the module configuration for the specified module id.
load_module(config, builder)	Loads a module that exists in the module registry.
make_nested_module(module_id, namespace, ...)	This function creates a nested module and registers it in the module registry.
merge_dictionaries(primary_dict, secondary_dict)	Recursively merge two dictionaries, using primary_dict as a tie-breaker.
register_module(module_id, namespace)	Registers a module if not exists in the module registry.
to_period_approximation(data_df, period)	This function converts a dataframe to a period approximation.
verify_module_meta_fields(config)	This function make sure the module configuration contains meta fields.
verify_module_registration(func)	Module availability in the module registry is verified by this function.

Classes

ModuleLoader(module_interface, name, config)	Class to hold the definition of a module.
ModuleLoaderFactory(module_id, module_namespace)	Class that acts as a simple wrapper to load a SegmentModule.

get_config_with_overrides(

config,

module_id,

module_name=None,

module_namespace='morpheus',

)

This function returns the module configuration with the overrides.

get_module_config(module_id, builder)

Returns the module configuration for the specified module id.

Parameters:

module_idstr

Unique identifier for a module in the module registry.

buildermrc.Builder

MRC Builder object.

Returns:

configtyping.Dict

Module configuration.

load_module(config, builder)

Loads a module that exists in the module registry.

Parameters:

configtyping.Dict

Module configuration.

buildermrc.Builder

MRC Builder object.

Returns:

module

Module object.

make_nested_module(module_id, namespace, ordered_modules_meta)

This function creates a nested module and registers it in the module registry. This module unifies a chain of two or more modules into a single module.

Parameters:

module_idstr

Unique identifier for a module in the module registry.

namespacestr

Namespace to virtually cluster the modules.

ordered_modules_metatyping.List[typing.Dict[str, str]]

The sequence in which the edges between the nodes are made will be determined by ordered modules meta.

merge_dictionaries(primary_dict, secondary_dict)

Recursively merge two dictionaries, using primary_dict as a tie-breaker.

Lists are treated as a special case, and all unique elements from both dictionaries are included in the final list.

Args:

primary_dict (dict): The primary dictionary. secondary_dict (dict): The secondary dictionary.

Returns:

dict: The merged dictionary.

register_module(module_id, namespace)

Registers a module if not exists in the module registry.

Parameters:

module_idstr

Unique identifier for a module in the module registry.

namespacestr

Namespace to virtually cluster the modules.

Returns:

inner_func

Encapsulated function.

to_period_approximation(data_df, period)

This function converts a dataframe to a period approximation.

Parameters:

data_dfDataFrameType

Input cudf/pandas dataframe.

periodstr

Period.

Returns:

DataFrame

Period approximation of the input cudf/pandas dataframe.

verify_module_meta_fields(config)

This function make sure the module configuration contains meta fields.

Parameters:

configtyping.Dict

Module configuration.

verify_module_registration(func)

Module availability in the module registry is verified by this function.

Parameters:

funcFunction that requires wrapping.

Returns:

inner_func

Encapsulated function.