graph LR
geomstats_datasets__base["geomstats.datasets._base"]
geomstats_datasets_prepare_emg_data["geomstats.datasets.prepare_emg_data"]
geomstats_datasets_prepare_graph_data["geomstats.datasets.prepare_graph_data"]
geomstats_datasets_prepare_emg_data -- "relies on" --> geomstats_datasets__base
geomstats_datasets_prepare_graph_data -- "relies on" --> geomstats_datasets__base
The Data Management subsystem is encapsulated within the geomstats.datasets package. These three components are chosen as central to the Data Management subsystem because they collectively cover the essential aspects of data handling in a Scientific Computing/Machine Learning Toolkit: _base provides the fundamental, abstract layer for data access and storage, crucial for modularity and backend flexibility. prepare_emg_data and prepare_graph_data represent specialized data preparation modules for distinct data types (EMG and graph data, respectively). Their inclusion highlights the toolkit's ability to process diverse and complex datasets, which is a hallmark of a robust scientific computing library. They demonstrate the "Library/Toolkit Pattern" by offering reusable, domain-specific utilities.
This component serves as the foundational layer for data acquisition and local storage management. It embodies the "Backend Abstraction (Strategy/Adapter Pattern)" by providing a generic interface for fetching data, abstracting away the specifics of data sources (e.g., remote vs. local). It ensures consistent data access and storage mechanisms across the library.
Related Classes/Methods:
This specialized component focuses on the transformation and preparation of Electromyography (EMG) data. It exemplifies the "Library/Toolkit Pattern" by offering specific utilities tailored for a particular data type, making it a reusable module for EMG-related tasks, including formatting labels and creating data batches.
Related Classes/Methods:
This component is dedicated to the preparation and embedding of graph-structured data. Similar to prepare_emg_data, it follows the "Library/Toolkit Pattern" by providing specialized algorithms and utilities for handling graph data, including techniques for graph embeddings, random walks, and loss calculations.
Related Classes/Methods: