DeepLynx Time Series Loader

The Challenge

Accessing structured data from digital twin platforms often requires custom scripts or specialized interfaces, which can increase development complexity and limit accessibility for non-expert users. Many existing tools are not optimized for efficient, direct retrieval of timeseries or tabular data, creating a barrier for teams looking to incorporate DeepLynx-stored data into analysis, visualization, or simulation environments.

How It Works

• The DeepLynx Loader provides a direct interface for retrieving timeseries and tabular data from DeepLynx instances.

• The software is implemented as a Python module and a C-based library compatible with Unity.

• Users import the library into their application or project, then call methods to query and access data without needing to write custom API requests.

• While initially focused on Python and Unity, the architecture allows adaptation to other environments or programming languages as needed.

Key Advantages

• Simplifies data access from DeepLynx for both technical and non-technical users.

• Reduces the need for custom scripting or direct API manipulation.

• Supports both Python environments and Unity-based applications via a C library.

• Designed for flexibility and can be extended to support additional platforms.

• Accelerates data integration into analysis pipelines or interactive digital twin environments.

Market Applications

• Simulation Developers: Integrate real-world digital twin data into Unity-based models.

• Data Scientists and Analysts: Pull tabular and timeseries datasets into Python environments for analysis and visualization.

• Research Projects: Streamline access to structured data from DeepLynx for academic or industrial studies.

• Digital Twin Developers: Incorporate real-time or historical DeepLynx data into simulation and monitoring systems.

This software is open source and available at no cost. Download now by visiting the product's GitHub page.