sl-shared-assets

A Python library that provides data acquisition and processing assets shared between Sun (NeuroAI) lab libraries.

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Detailed Description

This library makes the two main Sun lab libraries used for data acquisition (sl-experiment) and processing (sl-forgery) independent of each other.

The library broadly stores two types of assets. First, it stores dataclasses used to save the data acquired in the lab and configure data acquisition and processing runtimes. Second, it provides the low-level tools and methods used to manage the data at all stages of Sun lab data workflow: acquisition, processing, and analysis.

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Table of Contents

• Dependencies
• Installation
• Usage
  • MCP Server
• API Documentation
• Development
  • Adding New Acquisition Systems
• Versioning
• Authors
• License
• Acknowledgments

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Dependencies

All library dependencies are installed automatically by all supported installation methods (see the Installation section).

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Installation

Source

Note, installation from source is highly discouraged for anyone who is not an active project developer.

1. Download this repository to the local machine using the preferred method, such as git-cloning. Use one of the stable releases that include precompiled binary and source code distribution (sdist) wheels.
2. If the downloaded distribution is stored as a compressed archive, unpack it using the appropriate decompression tool.
3. cd to the root directory of the prepared project distribution.
4. Run python -m pip install . to install the project. Alternatively, if using a distribution with precompiled binaries, use python -m pip install WHEEL_PATH, replacing 'WHEEL_PATH' with the path to the wheel file.

pip

Use the following command to install the library using pip: pip install sl-shared-assets.

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Usage

Most library components are intended to be used via other Sun lab libraries. For details on using shared assets for data acquisition and preprocessing, see the sl-experiment library. For details on using shared assets for data processing and dataset formation, see the sl-forgery library.

Warning! End users should not use any component of this library directly or install this library into any Python environment. All assets from this library are intended to be used exclusively by developers working on other Sun lab libraries.

MCP Server

This library provides an MCP server that exposes configuration management tools for AI agent integration. The server enables agents to query and configure shared Sun lab workflow components.

Starting the Server

Start the MCP server using the CLI:

sl-configure mcp

Available Tools

Tool	Description
get_working_directory_tool	Returns the current Sun lab working directory path
set_working_directory_tool	Sets the Sun lab working directory
get_server_configuration_tool	Returns the compute server configuration (password masked)
create_server_configuration_template_tool	Creates a server configuration template for manual password entry
get_google_credentials_tool	Returns the path to the Google service account credentials file
set_google_credentials_tool	Sets the path to the Google credentials file
get_task_templates_directory_tool	Returns the path to the sl-unity-tasks templates directory
set_task_templates_directory_tool	Sets the path to the task templates directory
list_available_templates_tool	Lists all available task templates
get_template_info_tool	Returns detailed information about a specific task template

Claude Desktop Configuration

Add the following to the Claude Desktop configuration file:

{
  "mcpServers": {
    "sl-shared-assets": {
      "command": "sl-configure",
      "args": ["mcp"]
    }
  }
}

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API Documentation

Developers working on integrating sl-shared-assets into other libraries should see the API documentation for the detailed description of the methods and classes exposed by components of this library.

Note! The API documentation includes important information about the 'configuration' Command-Line Interface (CLI) exposed by this library.

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Development

This section provides guidance for developers extending this library.

Adding New Acquisition Systems

The library uses registry patterns to support multiple data acquisition systems. Each system requires configuration dataclasses. The following steps outline how to add support for a new acquisition system.

Step 1: Add the system to the AcquisitionSystems enum

In configuration_utilities.py, add a new entry to the AcquisitionSystems enum:

from enum import StrEnum
class AcquisitionSystems(StrEnum):
    MESOSCOPE_VR = "mesoscope"
    NEW_SYSTEM = "new_system"  # Add new system here

Step 2: Create the system configuration module

Create a new file (e.g., new_system_configuration.py) containing:

• A system configuration dataclass inheriting from YamlConfig with hardware and software settings
• An experiment configuration dataclass for runtime experiment parameters
• A save() method if custom serialization logic is needed

Step 3: Update type aliases and registries

In configuration_utilities.py:

1. Extend the SystemConfiguration type alias to include the new configuration class
2. Extend the ExperimentConfiguration type alias to include the new experiment configuration class
3. Add an entry to _SYSTEM_CONFIG_CLASSES mapping the system name to its configuration class
4. Create an experiment factory function and register it in _EXPERIMENT_CONFIG_FACTORIES

Step 4: Update downstream libraries

Coordinate changes with sl-experiment (data acquisition) and sl-forgery (data processing) as needed.

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Versioning

This project uses semantic versioning. See the tags on this repository for the available project releases.

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Authors

• Ivan Kondratyev (Inkaros)
• Kushaan Gupta (kushaangupta)
• Natalie Yeung

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License

This project is licensed under the GPL3 License: see the LICENSE file for details.

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Acknowledgments

• All Sun lab members for providing the inspiration and comments during the development of this library.
• The creators of all other dependencies and projects listed in the pyproject.toml file.

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