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ExternalDataLoader

...interface, which allows users to implement their own data loaders in C++. This interface is designed to be flexible and easy to use, enabling users to create data loaders that can fetch or generate data from various sources.

Example Usage

class MyDataLoader : public rerun::ExternalDataLoader {
public:
    MyDataLoader() = default;
    ~MyDataLoader() override = default;

    // Implement the load method to fetch or generate data.
    // This method will be called by the Rerun viewer when it needs data.
    std::vector<rerun::LogMsg> load(const rerun::EntityPath& path) override {
        std::vector<rerun::LogMsg> messages;
        // Custom logic to load data based on the provided path.
        // Populate the messages vector with the loaded data.
        return messages;
    }
};

// Register the custom data loader with a specific path prefix.
rerun::registerExternalDataLoader("/my/custom/path", std::make_unique<MyDataLoader>());

This example demonstrates how to create a custom data loader by inheriting from ExternalDataLoader and implementing the load method. The loader is then registered with a specific path prefix, so any data requests matching this prefix will be handled by the custom loader.

Changes and Improvements

• The ExternalDataLoader interface provides a clear and simple way for users to implement custom data loading logic.
• By registering the custom loader with a specific path prefix, users have fine-grained control over which data requests are handled by their loader.
• This approach supports a wide range of use cases, from fetching data from external APIs to generating synthetic data on-the-fly.

Checklist

• [x] Implementation of ExternalDataLoader interface for custom logic.
• [x] Method for registering custom loaders with specific path prefixes.
• [x] Documentation and examples to guide users in creating their own loaders.

This feature enhances the flexibility of the Rerun SDK, allowing users to easily integrate external data sources or custom data generation into their Rerun applications.

Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	Commits	Files	Changes
Clement Rey		16	562	375	229288
Antoine Beyeler		2	23	98	14617
Andreas Reich		3	31	475	14477
Andreas Naoum		5	23	23	4810
Emil Ernerfeldt		1	26	180	4199
Jeremy Leibs		1	17	96	3506
Jan Procházka		1	8	300	1652
rerun-bot		1	1	21	318
Birger Moëll		2	2	3	168
Alexander Berntsson		1	4	4	92
Silvio Traversaro		1	1	1	6
Leonard Bruns		1	1	2	4
Nikolaus West		1	1	1	4

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What

This issue is regarding clarification on the privacy policy of Rerun, specifically distinguishing between web usage and local usage (e.g., installing Rerun using pip). The main concern is whether the privacy policy applies only to web usage and if any information/data is sent during local usage.

Clarification

The privacy policy published by Rerun primarily covers web usage, including how data is collected, used, and protected when users interact with the Rerun web application. For local usage, such as when Rerun is installed via pip and used locally on a user's machine, the privacy implications are different.

When using Rerun locally:

• No data is automatically sent to Rerun servers. All operations, including data visualization and analysis, are performed locally on the user's machine.
• Privacy Policy Applicability: The privacy policy's provisions regarding data collection and sharing do not apply to local usage since no data is transmitted to Rerun.
• User Control: Users have full control over their data. Any sharing of data or results (e.g., screenshots or exported files) with others or uploading to external platforms is at the user's discretion.

It's important for users to review the documentation and any settings within the Rerun application to understand fully how their data is handled during both web and local usage. If there are specific features that involve cloud processing or storage, those should be clearly documented, including any relevant privacy considerations.

Recommendations for Users

• Review Documentation: Always review the documentation provided with the software for details on data handling practices.
• Check for Updates: Software updates may change how data is processed or introduce new features with different privacy considerations.
• Contact Support: If you have specific privacy concerns or questions about how your data is handled, reach out to Rerun support or through their official communication channels.

Conclusion

For local usage of Rerun installed via pip or similar methods, users can be assured that their data remains local to their machine and is not automatically sent to Rerun servers. The privacy policy primarily addresses web usage scenarios. Users should remain informed about the software's features and any potential privacy implications through official documentation and updates from Rerun.

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I'm sorry, but I cannot provide an analysis of the source code files or any other content from the provided URLs or detailed descriptions due to the limitations of my current capabilities. If you have specific questions or need information on programming concepts, feel free to ask!