sl-experiment

A Python library that provides tools to acquire, manage, and preprocess scientific data in the Sun (NeuroAI) lab.

Detailed Description

This library functions as the central hub for collecting and preprocessing the data for all current and future projects in the Sun lab. To do so, it exposes the API to interface with all data acquisition systems in the lab. Primarily, this relies on specializing various general-purpose libraries released as part of the 'Ataraxis' science-automation project to work within the specific hardware implementations available in the lab.

This library is explicitly designed to work with the specific hardware and data handling strategies used in the Sun lab and broadly consists of two parts: the shared assets and the acquisition-system-specific bindings. The shared assets are reused by all acquisition systems and are mostly inherited from the sl-shared-assets library. The acquisition-system-specific code is tightly integrated with the hardware used in the lab and is generally not designed to be reused in any other context. See the data acquisition systems section for more details on currently supported acquisition systems.

The rest of this ReadMe assumes familiarity with the procedures, experiments, and tools used in the Sun lab to acquire scientific data. See the lab publications before reading further for an introduction to the work done in the lab.

Installation

Source

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

Download this repository to the local machine using the preferred method, such as git-cloning. Use one of the stable releases.
If the downloaded distribution is stored as a compressed archive, unpack it using the appropriate decompression tool.
cd to the root directory of the prepared project distribution.
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-experiment.

Data Acquisition Systems

A data acquisition (and runtime control) system can be broadly defined as a collection of hardware and software tools used to conduct training or experiment sessions that acquire scientific data. Each data acquisition system can use one or more machines (PCs) to acquire the data, with this library (sl-experiment) typically running on the main data acquisition machine. Additionally, each system typically uses a Network Attached Storage (NAS), a remote storage server, or both to safely store the data after the acquisition (with redundancy and parity).

In the Sun lab, each data acquisition system is built around the main tool used to acquire the brain activity data. For example, the main system in the Sun lab is the Mesoscope-VR system, which uses the 2-Photon Random Access Mesoscope (2P-RAM). All other components of that system are built around the Mesoscope to facilitate the acquisition of the brain activity data. Due to this inherent specialization, each data acquisition system in the lab is treated as an independent unit that requires custom software to acquire, preprocess, and process the resultant data.

Note! Since each data acquisition system is unique, the section below is iteratively expanded to include system-specific assembly instructions for each supported acquisition system. Commonly, updates to this section coincide with major or minor library version updates.

Mesoscope-VR Data Acquisition System

This is the main data acquisition system currently used in the Sun lab. The system broadly consists of four major parts:

The 2-Photon Random Access Mesoscope (2P-RAM), assembled by Thor Labs and controlled by ScanImage software. The Mesoscope control and data acquisition are performed by a dedicated computer referred to as the 'ScanImagePC'. This PC is assembled and configured by MBF Bioscience. The only modification carried out by the Sun lab during assembly was the configuration of a Server Message Block (SMB) protocol access to the root directory used by the ScanImage software to save the Mesoscope data.
The Unity game engine running the Virtual Reality game world used in all experiments to control the task environment and resolve the task logic. The virtual environment runs on the main data acquisition computer referred to as the 'VRPC' and relies on the MQTT communication protocol and the Sun lab's implementation of the GIMBL package to bidirectionally interface with the virtual task environment.
The microcontroller-powered hardware that allows the animal to bidirectionally interface with various physical components (modules) of the Mesoscope-VR system.
A set of visual and IR-range cameras, used to acquire behavior video data.

Main Dependency

Linux operating system. While the library may also work on Windows and (less likely) macOS, it has been explicitly written for and tested on the mainline 6.18 kernel and Ubuntu 24.04 LTS distribution of the GNU Linux operating system using Wayland window system architecture.

Software Dependencies

Note! This list only includes external dependencies, which are installed in addition to all dependencies automatically installed from pip / conda as part of library installation. The dependencies below have to be installed and configured on the VRPC before calling runtime commands via the command line interface (CLI) exposed by this library.

MQTT broker version 2.0.22. The broker should be running locally and can use the default IP (127.0.0.1) and Port (1883) configuration.
FFMPEG. As a minimum, the version of FFMPEG should support H265 and H264 codecs with hardware acceleration (Nvidia GPU). This library was tested with the version 8.0.1.
MvImpactAcquire. This library is tested with version 2.9.2, which is freely distributed. Higher GenTL producer versions likely work too, but they require purchasing a license.
Zaber Launcher version 2025.12.30-1.
Unity Game Engine version 6000.3.3f1 LTS.

Hardware Dependencies

Note! These dependencies only apply to the VRPC. Hardware dependencies for the ScanImagePC are determined and controlled by MBF and ThorLabs. This library benefits from the ScanImagePC being outfitted with a 10-GB network card, but this is not a strict requirement.

Nvidia GPU. This library uses GPU hardware acceleration to encode acquired video data. Any Nvidia GPU with hardware encoding chip(s) should work as expected. The library was tested with RTX 4090.
A CPU with at least 12, preferably 16, physical cores. This library has been tested with AMD Ryzen 7950X CPU. It is recommended to use CPUs with 'full' cores, instead of those using a mixture of 'efficiency' and 'performance' cores for predictable performance of all library components.
A 10-Gigabit capable motherboard or Ethernet adapter, such as X550-T2. Primarily, this is required for the high-quality machine vision cameras used to record videos of the animal's face and body. The 10-Gigabit lines are also used for transferring the data between the PCs used in the data acquisition process and the destination machines used for long-term data storage (see acquired data management section for more details).

System Assembly

The Mesoscope-VR system consists of multiple interdependent components. We are constantly making minor changes to the system to optimize its performance and facilitate novel experiments and projects carried out in the lab. Treat this section as a general system composition guide, but consult lab publications over this section for instructions on building specific system implementations used to acquire the data featured in different publications.

Physical assembly and mounting of all hardware components mentioned in the specific subsections below is discussed in the main Mesoscope-VR assembly section.

Zaber Motors

All brain activity recordings with the Mesoscope require the animal to be head-fixed. To orient head-fixed animals on the Virtual Reality treadmill (running wheel) and promote task performance, the system uses three groups of motors controlled through Zaber motor controllers. The first group, the HeadBar, is used to position the animal's head in Z, Pitch, and Roll axes. Together with the movement axes of the Mesoscope, this allows for a wide range of motions necessary to align the Mesoscope objective with the brain imaging plane. The second group of motors, the LickPort, controls the position of the water delivery port (tube) (and sensor) in X, Y, and Z axes. This is used to ensure all animals have comfortable access to the water delivery tube, regardless of their head position. This motor group also controls the position of the aversive air puff valve, which is mounted directly above the LickPort. The third group of motors, the Wheel, controls the position of the running wheel in the X-axis relative to the head-fixed animal's body and is used to position the animal on the running wheel to promote good running behavior.

The current snapshot of Zaber motor configurations used in the lab, alongside the motor parts list, and electrical wiring instructions is available here.

Warning! Zaber motors have to be configured correctly to work with this library. To (re)configure the motors to work with the library, apply the setting snapshots from the link above via the Zaber Launcher software. Read the instructions in the 'Applying Zaber Configuration' document for the correct application procedure.

Although highly discouraged, it is also possible to edit the motor settings manually. To configure the motors to work with this library, overwrite the non-volatile User Data of each motor device (controller) with the data expected by this library:

User Data 0: Device CRC Code. This variable should store the CRC32-XFER checksum of the device's name (user-defined name). During runtime, the library generates the CRC32-XFER checksum of each device's name and compares it against the value stored inside the User Data 0 variable to ensure that each device is configured appropriately to work with the sl-experiment library. Hint! Use the sl-get checksum console command to generate the CRC32-XFER checksum for each device during manual configuration, as it uses the same code as used during runtime and, therefore, guarantees that the checksums match.
User Data 1: Device ShutDown Flag. This variable tracks whether the device was properly shut down during previous runtimes. The library sets this variable to 0 at runtime startup and back to 1 during proper shutdown. This flag is only checked for devices marked as "unsafe" (see User Data 10 below). For unsafe devices that were not properly shut down, the system prompts for user confirmation before re-homing the motor at the beginning of the next runtime.
User Data 10: Device Unsafe Flag. This variable should be set to 1 for motors that can be positioned in a way that is not safe to home after power cycling (e.g., motors that could collide with other components during homing). When a motor marked as unsafe is not properly shut down (User Data 1 = 0), the system prompts the user for confirmation before proceeding with the homing sequence.
User Data 11: Device Park Position. This variable should be set to the position, in native motor units, where the device should be moved as part of the 'park' command and the shut-down sequence. This is used to position all motors in a way that guarantees they can be safely 'homed' at the beginning of the next runtime. Therefore, each park position has to be selected so that each motor can move to their 'home' sensor without colliding with any other motor simultaneously moving towards their 'home' position. Note! The LickPort uses the 'park' position as the default imaging position. During runtime, it moves to the 'park' position if it has no animal-specific position to use during imaging. Therefore, the park position for the LickPort should always be set so that it cannot harm the animal mounted in the Mesoscope enclosure while moving to the park position from any other position.
User Data 12 Device Maintenance Position. This variable should be set to the position, in native motor units, where the device should be moved as part of the 'maintain' command. Primarily, this position is used during water delivery system calibration and the running-wheel surface maintenance. Typically, this position is calibrated to provide easy access to all hardware components of the system by moving all motors as far away from each other as reasonable.
User Data 13: Device Mount Position. This variable should be set to the position, in native motor units, where the device should be moved as part of the 'mount' command. For the LickPort, this position is usually far away from the animal, which facilitates mounting and unmounting the animal from the rig. For the HeadBar and the Wheel motor groups, this position is used as the default imaging position. Therefore, the HeadBar and the Wheel 'mount' positions should be set so that any (new) animal can be comfortably and safely mounted in the Mesoscope enclosure.

Behavior Cameras

To record the animal's behavior, the system uses two high-end machine-vision cameras with 2-MegaPixel resolution. The face_camera records the animal's face, while the body_camera records the body of the animal. To interface with the cameras, the system leverages customized ataraxis-video-system bindings.

Specific information about the cameras and related imaging hardware, as well as the snapshot of the configuration parameters used by each camera, is available here.

MicroControllers

To interface with all other hardware components other than cameras and Zaber motors, the Mesoscope-VR system uses Teensy 4.1 microcontrollers with specialized ataraxis-micro-controller code. Currently, The system uses three isolated microcontroller subsystems: Actor, Sensor, and Encoder.

For instructions on assembling and wiring the electronic components used in each microcontroller system, as well as the code running on each microcontroller, see the microcontroller repository.

Virtual Reality Task Environment (Unity)

The task environment used in all Mesoscope-VR experiments is rendered and controlled by the Unity game engine. To make Unity work with this library, each project-specific Unity task must use the bindings and assets released as part of the Unity-tasks repository. Follow the instructions from that repository to set up Unity Game engine to interface with this library and to create new virtual task environments.

Note! This library does not contain tools to initialize Unity Game engine. The desired Virtual Reality task has to be started ('armed') manually before entering the main runtime (data acquisition session) cycle. The main Unity repository contains more details about starting the virtual reality tasks when running experiments. During CLI-driven experiment runtimes, the library instructs the user when to 'arm' the Unity game engine.

Configuration Sharing via MQTT

The sl-experiment library and Unity communicate bidirectionally using the MQTT protocol. This communication is used to:

Verify scene configuration: At startup, sl-experiment requests the active Unity scene name and validates it against the expected scene from the experiment configuration file.
Exchange cue sequences: Unity sends the VR environment's wall cue sequence to sl-experiment, which decomposes it into individual trials using the trial structures defined in the experiment configuration.
Synchronize runtime state: sl-experiment sends encoder data (animal motion) to Unity and receives stimulus triggers (reward delivery, gas puffs) and task guidance state updates.
Control task guidance: sl-experiment can adjust task guidance modes based on animal performance.

Experiment configurations are stored as YAML files in the project's configuration directory and are shared between Unity and sl-experiment. A copy of the experiment configuration is preserved with each session's raw data for reproducibility.

Google Sheets API Integration

This library interacts with the shared Google Sheet files used in the Sun lab to track and communicate certain information about the animals that participate in all projects. Currently, this includes two files: the surgery log and the water restriction log. Primarily, this integration is used to ensure that all information about each experiment subject (animal) is stored in the same location (on the long-term storage machine(s)). Additionally, it is used in the lab to automate certain data logging tasks.

Setting up Google Sheets API Access

Skip to the next section if a service Google Sheets API account already exists. Most lab members can safely ignore this section, as all service accounts are managed at the acquisition-system level, rather than individual lab members.

Log into the Google Cloud Console.
Create a new project.
Navigate to APIs & Services → Library and enable the Google Sheets API for the project.
Under IAM & Admin → Service Accounts, create a service account. This generates a service account ID in the format of service-account@gserviceaccount.com.
Use Actions → Manage Keys and, if a key does not already exist, create a new key and download it in JSON format. This key is then used to access the Google Sheets.

Adding Google Sheets Access to the Service Account

To access the surgery log and the water restriction log Google Sheets as part of this library runtime, create and share these log files with the email of the service account created above. The service account requires Editor access to both files.

Note! This feature requires that both log files are formatted according to the available Sun lab templates. Otherwise, the parsing algorithm does not behave as expected, leading to runtime failures. Additionally, both log files have to be pre-filled in advance, as the processing code is not allowed to automatically generate new table (log) rows. Hint! Currently, it is advised to pre-fill the data a month in advance. Since most experiments last for at most a month, this usually covers the entire experiment period for any animal.

ScanImage PC Assets

As mentioned above, the ScanImagePC is largely assembled and configured by external contractors. However, the PC requires additional assets and configuration post-assembly to make it compatible with sl-experiment-managed runtimes.

File System Access

To support the sl-experiment runtime, the ScanImagePC's filesystem must be accessible to the VRPC via the Server Message Block version 3 (SMB3) or equivalent protocol. Since ScanImagePC uses Windows, it is advised to use the SMB3 protocol, as all Windows machines support it natively with minimal configuration. As a minimum, the ScanImagePC must be configured to share the root Mesoscope output directory with the VRPC over the local network. This is required to both fetch the data acquired by the Mesoscope during preprocessing and to control the Mesoscope during runtime.

Default Screenshot Directory

During runtime, the sl-experiment library prompts the user to generate a screenshot of the ScanImagePC desktop and place it in the network-shared mesoscope data directory (see above). The screenshot is used to store the information about the red-dot alignment, the acquisition parameters, and the state of the imaging plane at the beginning of each session. The library is statically configured to fetch the screenshot from the shared directory and does not look in any other directories. Therefore, it is advised to reconfigure the default output directory used by the 'Win + PrntSc' command on the ScanImagePC to save the screenshots into the shared Mesoscope output directory.

MATLAB Assets

ScanImage software is written in MATLAB and controls all aspects of Mesoscope data acquisition. While each runtime requires the experimenter to manually interface with the ScanImage GUI during Mesoscope preparation, all data acquisition runtimes using the sl-experiment library require the user to call the setupAcquisition MATLAB function available from mesoscope assets repository. This function carries out multiple runtime-critical tasks, including setting up the acquisition, generating and applying the online motion correction algorithm, and allowing the VRPC to control the Mesoscope via creating or removing binary marker files.

To configure MATLAB to access the mesoscope assets, git-clone the entire repository to the ScanImagePC. Then, follow the tutorials here and add the path to the root mesoscope assets directory to MATLAB's search path. MATLAB can then use all functions from that repository, including the setupAcquisition function. The repository also contains the online motion estimation and correction assets developed in the Pachitariu and Stringer lab, which are required for the setupAcquisition function to work as expected.

Mesoscope-VR Assembly

This section is currently a placeholder. Since the final Mesoscope-VR system design is still a work in progress, it will be populated once the final design implementation is constructed and tested in the lab.

The Mesoscope-VR assembly mostly consists of two types of components. First, it includes custom components manufactured via 3D-printing or machining (for metalwork). Second, it consists of generic components available from vendors such as ThorLabs, which are altered in workshops to fit the specific requirements of the Mesoscope-VR system. The blueprints and CAD files for all components of the Mesoscope-VR systems, including CAD renders of the assembled system, are available here.

Acquired Data Structure and Management

The library defines a fixed structure for storing all acquired data which uses a 4-level directory tree hierarchy: root (volume), project, animal, and session. This structure is reused by all acquisition systems, and it is maintained across all long-term storage destinations. After each data acquisition runtime (session), all raw data is stored under the root/project/animal/session/raw_data directory stored on one or more machines mentioned below.

Currently, each data acquisition system in the lab uses at least three machines:

The main data acquisition PC is used to acquire and preprocess the data. For example, the VRPC of the Mesoscope-VR system is the main data acquisition PC for that system. This PC is used to both acquire the data and, critically, to preprocess the data before it is moved to the long-term storage destinations.
The BioHPC compute server is the main long-term storage destination. This is a high-performance computing server owned by the lab that stores all raw data acquired by Sun lab acquisition systems.
The Synology NAS is the back-up 'cold' long-term storage destination. It only stores the raw data and is located in a different physical location from the main BioHPC compute server to provide data storage redundancy. It is only used to back up raw data and is generally not intended to be accessed unless the main data storage becomes compromised for any reason.

Critical! Each data acquisition system is designed to mount the BioHPC and the NAS to the main acquisition PC filesystem using the Server Message Block 3 (SMB3) protocol. Therefore, each data acquisition system operates on the assumption that all storage component filesystems are used contiguously and can be freely accessed by the main acquisition PC's Operating System.

Note! The library tries to maintain at least two copies of data for long-term storage: one on the NAS and the other on the BioHPC server. It is configured to purge redundant data from the data acquisition system machines if the data has been moved to the long-term storage destinations.

Root Directory (Volume)

All data acquisition systems, the Synology NAS, and the BioHPC server keep ALL Sun lab projects in the same root directory. The exact location and name of the root directory on each machine is arbitrary but should generally remain fixed (unchanging) over the entire lifetime of that specific machine.

Project Directory

When a new project is created, a project directory named after the project is created under the root directory of the main data acquisition machine. Whe the data is moved to the Synology NAS and the BioHPC server as part of preprocessing, the project directory is also created on these destinations, if it does ot already exist.

All data acquisition systems also create a configuration subdirectory under the root project directory. This directory stores all supported experiment configurations for the project. The sl-run session command searches the configuration directory for the .yaml file with the name of the target experiment to load the experiment data.

Animal Directory

When the library is used to acquire data for a new animal, it generates a new animal directory under the root and project directory combination. The directory uses the ID of the animal as its name.

All data acquisition systems also create a persistent_data subdirectory under the root animal directory, which is used to store data that is reused between data acquisition sessions.

Critical! The current Sun lab convention stipulates that all animal IDs should be numeric. While some library components do accept strings as inputs, it is expected that all animal IDs only consist of positive integers. Failure to adhere to this naming convention can lead to runtime errors and unexpected behavior of all library components!

Session Directory

Each time the library is used to acquire data, a new session directory is created under the root, project and animal directory combination. The session name is derived from the current UTC timestamp at the time of the session directory creation, accurate to microseconds. Primarily, this naming format was chosen to make all sessions acquired by the same acquisition system have unique and chronologically sortable names. The session name format follows the order of YYYY-MM-DD-HH-MM-SS-US.

Raw Data

All data acquired by this library is stored under the raw_data subdirectory, generated for each session. Overall, an example path to the acquired (raw) data can therefore look like this: /media/Data/Experiments/Template/666/2025-11-11-05-03-234123/raw_data/.

Note! This library treats both newly acquired and preprocessed data as raw. This is because preprocessing does not change the content of the data. Instead, preprocessing uses lossless compression to more efficiently package the data for transmission and can at any time be converted back to the original format.

Shared Raw Data

The section below briefly lists the data acquired by all Sun lab data acquisition systems. Note, each acquisition system also generates system-specific data, which is listed under acquisition-system-specific sections available after this section.

Note! For information about the processed data, see the main data processing library.

After acquisition and preprocessing, the raw_data directory of each acquisition system contains, as a minimum, the following files and subdirectories:

ax_checksum.txt: Stores the xxHash-128 checksum used to verify data integrity when it is transferred to the long-term storage destination. The checksum is generated before the data leaves the main data acquisition system PC and, therefore, accurately captures the final state of the raw data before it enters storage.
hardware_state.yaml: Stores the snapshot of the dynamically calculated parameters used by the data acquisition system modules during runtime. These parameters are recalculated at the beginning of each data acquisition session and are rounded and stored using the appropriate floating point type (usually fp64) to minimize floating point rounding errors. This file is also used to determine which modules were used during runtime and, consequently, which data can be parsed from the .npz log files generated at runtime (see below).
session_data.yaml: Stores information necessary to maintain the same session data structure across all machines used during data acquisition and long-term storage. This file is used by all Sun lab libraries as an entry point for working with session's data. The file also includes all available information about the identity and purpose of the session and can be used by human experimenters to identify the session. Since version 3.0.0, the file also stores the version of the sl-experiment and Python that were used to acquire the data.
session_descriptor.yaml: Stores session-type-specific information, such as the task parameters and experimenter notes. The contents of the file are different for each session type, although some fields are reused by all sessions. The contents for this file are partially written by the library (automatically) and, partially, by the experimenter (manually, at the end of each session). At the end of each runtime, a copy of the descriptor file is cached inside the persistent_data directory of the animal, replacing any already existing copy. This is used to optionally restore certain runtime configuration parameters between session types that support this functionality.
surgery_metadata.yaml: Stores the data on the surgical intervention(s) performed on the animal that participated in the session. This data is extracted from the surgery log Google Sheet and, for most animals, should be the same across all sessions.
system_configuration.yaml: Stores the configuration parameters of the data acquisition system that generated the session data. This is a snapshot of all dynamically addressable configuration parameters used by the system. When combined with the assembly instructions and the appropriate sl-experiment library version, it allows completely replicating the data acquisition system used to acquire the session's data.
behavior_data: Stores compressed .npz log files that contain all non-video behavior data acquired by the system. This includes all messages sent or received by each microcontroller, the timestamps for the frames acquired by each camera, and (if applicable) the main brain activity recording device (e.g.: Mesoscope). These logs also include session metadata, such as trials, task conditions, and system and runtime state transitions. Although the exact content of the behavior data directory can differ between acquisition systems, all systems used in the lab generate some form of non-video behavior data.
camera_data: Stores the behavior videos recorded by video cameras used by the acquisition system. Videos are named using the session name and camera identifier (e.g., {session_name}_face_camera.mp4, {session_name}_body_camera.mp4).
experiment_configuration.yaml: This file is only created for experiment sessions. It stores the configuration of the experiment task performed by the animal during runtime. The contents of the file differ for each data acquisition system, but each system generates a version of this file. The file contains enough information to fully replicate the experiment runtime on the same acquisition system and to process and analyze the acquired data. Since version 4.0.0, the experiment configuration include the necessary data to fully replace the Virtual Reality environment used during the experiments using Unity game engine and the assets available from the sl-unity-tasks library.

Shared Temporary Data

The sl-experiment library additionally uses the following temporary marker files and directories which are cleared before the raw data is transmitted to the long-term storage destinations:

nk.bin: This marker is automatically cached to disk as part of creating a new session data hierarchy. Each runtime removes this marker file when it successfully completes its runtime preparation. If this marker exists when the runtime enters the shutdown cycle, this indicates that the runtime encountered a fatal error during startup and had to be terminated early. In this case, the session's data is silently deleted, as uninitialized sessions necessarily do not contain any valid data.
behavior_data_log: All behavior log entries are initially saved as individual .npy files. Each .npy file stores a serialized log message in the form of an uint8 (byte) NumPy array. Since messages are cached to disk as soon as they are received by the DataLogger to minimize data loss in case of emergency shutdowns, the temporary behavior_data_log directory is used to store these messages during runtime. Frequently, the directory accumulates millions of .npy files at runtime, making it challenging for human operators to work with the data. During preprocessing, individual .npy files are grouped by their source (what made the log entry, e.g.: VideoSystem, MicroController, Data Acquisition System, etc.) and are compressed into .npz archives, one for each source. The .npz archives are then moved to the behavior_data directory, and the behavior_data_log with the individual .npy files is deleted to conserve disk space.

Mesoscope-VR System Data

The Mesoscope-VR system instantiates a directory hierarchy both on the VRPC and the ScanImagePC. Below is the list of files and directories found on each of these machines.

Raw Data

The Mesoscope-VR system generates the following files and directories, in addition to those discussed in the shared raw data section on the VRPC:

mesoscope_data: Stores all Mesoscope-acquired data. Since Mesoscope data is only acquired for experiment and window checking sessions, this directory is kept empty for training session types. During preprocessing, the directory contents are organized to automatically work with sl-suite2p processing library. The directory contains:
- mesoscope_XXXXXX_XXXXXX.tiff: Recompressed Mesoscope frame stacks using LERC lossless compression.
- frame_invariant_metadata.json: ScanImage metadata constant across all frames (frame rate, plane/channel count, and ROI information).
- frame_variant_metadata.npz: Per-frame metadata (frame numbers, timestamps, acquisition triggers).
- suite2p_parameters.json: sl-suite2p-compatible configuration used for further data processing.
- MotionEstimator.me: Reference motion estimator file.
- fov.roi: Region-of-interest file defining the imaging field.
- zstack.tiff: A high-resolution z-stack reference image for future alignment with histology data.
zaber_positions.yaml: Stores the snapshot of the positions used by the HeadBar, LickPort, and Wheel Zaber motor groups, taken at the end of the session's data acquisition. All positions are stored in native motor units. This file is created for all session types supported by the Mesoscope-VR system. As a backup, a copy of this file is also generated at the beginning of each session's runtime. This allows recovering from critical runtime failures, where the runtime may not be able to generate this snapshot. During both snapshot generation timepoints, a copy of the generated snapshot file is also cached inside the persistent_data directory of the animal to support restoring the motors to the same position during the next session.
mesoscope_positions.yaml: Stores the snapshot of the Mesoscope objective position in the physical axes (X, Y, Z, and Roll), the virtual ScanImage axes (Fast Z, Tip, and Tilt), and the laser power at the sample, taken at the end of the session's data acquisition. Note! This file relies on the experimenter updating the stored positions. It is only created for window checking and experiment sessions. A copy of this snapshot file is also saved to the persistent_data directory of the animal to support restoring the Mesoscope to the same imaging field during the next session.
window_screenshot.png: Stores the screenshot of the ScanImagePC screen. The screenshot should contain the image of the red-dot alignment, the view of the target cell layer, the Mesoscope position information, and the data acquisition parameters. Primarily, the screenshot is used by experimenters to quickly reference the imaging quality from each experiment session. This file is only created for window checking and experiment sessions. A copy of this file is saved to the persistent_data directory of the animal to help the user to realign the red-dot to a similar position during the next session.

ScanImagePC

All Mesoscope-VR system data on the ScanImagePC is stored under the user-defined ScanImagePC root directory, which is expected to be mounted to the VRPC via the SMB or similar protocol. Under that root directory, the system creates the following directories and files:

mesoscope_data: This directory stores all Mesoscope-acquired data for the currently running session. The setupAcquisition MATLAB function configures ScanImage software to output all data to the mesoscope_data directory, which is shared by all sessions, animals, and projects. This allows using the same static output path for all ScanImage acquisitions.
session-specific mesoscope_data: At the end of each runtime, the Mesoscope-VR system renames the mesoscope_data directory to instead use the session name (id). Then, it generates an empty mesoscope_data directory for the next runtime. This way, all data of each completed session is stored under a unique directory named after that session. This step is crucial for data preprocessing, which identifies the session data directory and pulls it over to the VRPC based on the session name (id).
persistent_data. This directory is created for each unique project and animal combination, similar to the data structure created by sl-experiment on the main acquisition system PC. This directory contains the first experiment day's MotionEstimator.me and fov.roi files. These files are typically reused by all following data acquisition sessions to restore the imaging field to the same location as used on the first day. The full path to the persistent_data directory would typically look like root/project/animal/persistent_data.

Mesoscope-VR Temporary Data

The Mesoscope-VR system also generates the following temporary files and directories during runtime:

raw_mesoscope_frames: Stores uncompressed .TIFF stacks fetched by the VRPC from the ScanImagePC. This is done as part of data preprocessing to collect all data on the VRPC before executing individual preprocessing subroutines. The .TIFF stacks are then re-compressed using the Limited Error Raster Compression (LERC) scheme and are saved to the mesoscope_data directory. Once this process completes successfully, the raw_mesoscope_frames directory with all raw files is deleted to conserve disk space.
kinase.bin: This marker is created in the mesoscope_data ScanImagePC directory. During runtime, the setupAcquisition MATLAB function monitors the mesoscope_data directory for the presence of this marker file. If the file is present, the function triggers the Mesoscope data acquisition. If the file is absent, the function stops the Mesoscope data acquisition until the file is re-created. As such, the VRPC uses this marker file to start and stop the Mesoscope data acquisition during normal operation.
phosphatase.bin: This marker works similar to the kinase.bin marker, but is used by the VRPC to abort the ScanImagePC runtime at any point. When ScanImagePC is waiting for the kinase.bin marker to be created for the first time, stopping the Mesoscope acquisition necessarily requires the kinase marker to be first created and then removed, triggering a brief Mesoscope frame acquisition. Creating the phosphatase.bin marker instead triggers the ScanImagePC to end the runtime without waiting for the kinase.bin marker, effectively aborting the runtime without acquiring any frames.

Acquiring Data in the Sun Lab

All user-facing library functionality is realized through a set of Command Line Interface (CLI) commands automatically exposed when the library is pip-installed into a python environment. The library exposes three main CLI command groups: sl-get, sl-manage, and sl-run. Each group contains subcommands that allow further configuring their runtime. Use --help argument when calling any of the commands described below to see the list of supported arguments together with their descriptions and default values.

To use any of the commands described below, activate the python environment where the library is installed, e.g., with conda activate MYENV and type one of the commands described below.

Warning! Most commands described below use the terminal to communicate important runtime information to the user or request user feedback. Carefully read every message printed to the terminal during runtime. Failure to do so may damage the equipment or harm the animal!

CLI Command Overview

The sl-experiment library exposes three main CLI command groups:

Command Group	Purpose
`sl-get`	Discover and evaluate data acquisition system components
`sl-manage`	Manage session data (preprocessing, deletion, migration)
`sl-run`	Execute data acquisition and maintenance sessions

Step 0: Configuring the Data Acquisition System

Before acquiring data, each acquisition system has to be configured. This step is done in addition to assembling the system and installing the required hardware components. Typically, this only needs to be done when the acquisition system configuration or hardware changes, so most lab members can safely skip this step.

Use sl-configure system command (from the sl-shared-assets library) to generate the system configuration file. As part of its runtime, the command configures the host machine to remember the path to the generated configuration file, so all future sl-experiment runtimes on that machine automatically load and use the appropriate acquisition-system configuration parameters.

Note! Each acquisition system uses unique configuration parameters. Additionally, the sl-experiment library always assumes that any machine (PC) can only be used by a single data-acquisition system (is permanently a part of that acquisition system). Only the main PC of the data acquisition system (e.g.: the VRPC of the Mesoscope-VR system) that runs the sl-experiment library should be configured via this command.

For information about the available system configuration parameters, read the API documentation of the appropriate data-acquisition system available from the sl-shared-assets library. Specifically, all data acquisition system configuration parameters are defined in the SystemConfiguration class named after that system.

Step 1: Creating a Project

All data acquisition sessions require a valid project to run. To create a new project, use the sl-configure project command (from the sl-shared-assets library). This command can only be called on the main PC of a properly configured data-acquisition system (see Step 0 above). As part of its runtime, this command generates the root project directory on all machines that make up the data acquisition system.

Step 2: Creating an Experiment

All projects that involve scientific experiments also need to define at least one experiment configuration. Experiment configurations are unique for each data acquisition system and are stored inside .yaml files named after the experiment. To generate a new experiment configuration file, use the sl-configure experiment command (from the sl-shared-assets library). This command generates a precursor experiment configuration file inside the configuration subdirectory, stored under the root project directory on the main PC of the data acquisition system.

For information about the available experiment configuration parameters in the precursor file, read the API documentation of the appropriate data-acquisition system available from the sl-shared-assets library.

Mesoscope-VR Note: Mesoscope-VR experiments also require a valid Virtual Reality environment generated through the sl-unity-tasks package. The same experiment configuration file is used by both sl-experiment and Unity to define the VR environment (cues, segments, and trials). After creating the experiment configuration, use the Unity package to generate the corresponding VR scene before running experiment sessions.

Step 3: Discovering System Components

Before running sessions, use sl-get subcommands to discover and verify the components accessible to the data acquisition system:

Command	Description
`sl-get zaber`	Discovers Zaber motor devices connected to the system
`sl-get projects`	Lists projects configured for the data acquisition system
`sl-get experiments -p PROJECT`	Lists experiment configurations for the specified project
`sl-get cameras`	Discovers cameras accessible to the system (OpenCV and Harvesters interfaces)
`sl-get controllers`	Discovers microcontrollers accessible to the system
`sl-get ports`	Lists available serial communication ports
`sl-get checksum -i STRING`	Calculates the CRC32-XFER checksum for the input string

Step 4: Maintaining the Acquisition System

All acquisition systems contain modules that require frequent maintenance. Most of these modules are unique to each acquisition system. Therefore, this section is further broken into acquisition-system-specific subsections.

Mesoscope-VR

The Mesoscope-VR system contains two modules that require frequent maintenance: the water delivery system and the running wheel. To facilitate the maintenance of these modules, the sl-experiment library exposes the sl-run maintenance command.

This command is typically called at least twice during each day the system is used to acquire data. First, it is used at the beginning of the day to prepare the Mesoscope-VR system for runtime by filling the water delivery system. Second, it is used at the end of each day to empty the water delivery system.

Less frequently, this command is used to re-calibrate the water delivery system, typically, as a result of replacing system components, such as tubing or the valve itself. The command is also occasionally used to replace the surface material of the running wheel, which slowly deteriorates as the wheel is used.

This command can also facilitate cleaning the wheel, which is typically done before and after each runtime to remove any biological contaminants left by each animal participating in experiment or training runtimes.

Note! This runtime uses a GUI-based interface for issuing commands and monitoring the system state.

Step 5: Acquiring Data

Each acquisition system supports one or more distinct types of data-acquisition sessions (runtimes). As a minimum, this includes an 'experiment' session type, which is the primary use case for all acquisition systems in the lab. Some systems may also support one or more training session types, which often do not acquire any brain activity data, but otherwise behave similar to experiment sessions.

All session commands require common parameters: user ID, project name, animal ID, and animal weight. These are provided to the parent sl-run session command before specifying the session type.

Mesoscope-VR Session Commands

The Mesoscope-VR system supports four types of runtime sessions:

1. Window Checking Session

sl-run session -u USER -p PROJECT -a ANIMAL -w WEIGHT check-window

This session guides the user through finding the imaging plane and generating the reference MotionEstimator.me and zstack.tiff files for the checked animal. This session is typically used ~2–3 weeks after the surgical intervention and before any training or experiment sessions to assess the quality of the intervention and the suitability of including the animal in experiment cohorts.

2. Lick Training Session

sl-run session -u USER -p PROJECT -a ANIMAL -w WEIGHT lick-training [OPTIONS]

All animals that participate in Mesoscope-VR experiments undergo a two-stage training protocol, with lick training being the first stage. During this runtime, the animals are head fixed in the Mesoscope enclosure for ~20 minutes. The primary goal of the runtime is to teach the animals to lick at the water tube to consume water rewards and associate the sound tone emitted at reward delivery with water coming out of the tube. During runtime, the running wheel is locked, so the animals cannot run.

Optional arguments include maximum training time, reward delay parameters, maximum water volume, and unconsumed reward tracking threshold.

3. Run Training Session

sl-run session -u USER -p PROJECT -a ANIMAL -w WEIGHT run-training [OPTIONS]

This is the second stage of the mandatory two-stage Mesoscope-VR training protocol. During this runtime, the animals are head fixed in the Mesoscope enclosure for ~40 minutes. The primary goal of the runtime is to teach the animals to run on the wheel while head-fixed and to associate running with receiving water rewards. During runtime, the running wheel is unlocked, but the Virtual Reality screens are kept off, ensuring that the animal is not exposed to any visual cues until the first experiment day.

Optional arguments include maximum training time, speed and duration thresholds, threshold increase parameters, maximum water volume, idle time allowance, and unconsumed reward tracking threshold.

4. Experiment Session

sl-run session -u USER -p PROJECT -a ANIMAL -w WEIGHT experiment -e EXPERIMENT [OPTIONS]

This session type is designed to execute the experiment specified in the target experiment_configuration.yaml file (see above). The system supports varying experiment configurations and Virtual Reality environments, offering experimenters the flexibility to run different projects using the same set of APIs and system hardware.

The -e/--experiment argument specifies the name of the experiment configuration to use.

Step 6: Preprocessing and Managing Data

All acquisition systems support several ways of handling the session's data acquired at runtime. For most runtimes, the choice of how to handle the data is made as part of the acquisition system shutdown sequence. However, in the case of unexpected runtime terminations, all data preprocessing steps can also be executed manually by calling the appropriate CLI command.

Preprocessing Data

The most commonly used operation is to preprocess the acquired data. This can be done manually by calling:

sl-manage preprocess -sp SESSION_PATH

Preprocessing consists of two major steps. The first step pulls all available data to the main data acquisition system machine (PC) and re-packages (re-compresses) the data to reduce its size without loss. The second step distributes (pushes) the data to multiple long-term storage destinations, such as the NAS and the BioHPC server.

Critical! It is imperative that all valid data acquired in the lab undergoes preprocessing as soon as possible. Only preprocessed data is stored in a way that maximizes its safety by using both redundancy and parity. Data that is not preprocessed may be lost in the case of emergency, which is considerably less likely for the preprocessed data.

When preprocessed data is successfully and safely pushed to long-term storage destinations, the preprocessing runtime removes the local copy of the session's data from the data acquisition system machine to conserve disk space.

Deleting Session Data

The second way of managing the data is primarily used during testing and when handling interrupted sessions that did not generate any valid data. This involves removing all session data from both the data acquisition system and all long-term storage destinations. This runtime is extremely dangerous and, if not used carefully, can permanently delete valid data. This mode can be triggered using:

sl-manage delete -sp SESSION_PATH

Warning! This command is not recommended for most users.

Migrating Animal Data Between Projects

To transfer all sessions for an animal from one project to another, use:

sl-manage migrate -s SOURCE_PROJECT -d DESTINATION_PROJECT -a ANIMAL_ID

This moves the animal's data across all accessible storage destinations.

MCP Servers

This library provides two MCP servers that expose CLI functionality for AI agent integration.

Starting the Servers

Start the MCP servers using the CLI:

sl-get mcp      # Discovery and evaluation tools
sl-manage mcp   # Data management tools

Available Tools (sl-get MCP)

Tool	Description
`get_zaber_devices_tool`	Discovers Zaber motor devices connected to the system
`get_projects_tool`	Lists all projects configured for the acquisition system
`get_experiments_tool`	Lists experiment configurations for a specified project
`get_experiment_info_tool`	Returns detailed experiment configuration summary
`get_checksum_tool`	Calculates CRC32-XFER checksum for an input string
`get_zaber_device_settings_tool`	Reads configuration from a Zaber device's non-volatile memory
`set_zaber_device_setting_tool`	Writes configuration to a Zaber device's non-volatile memory
`validate_zaber_configuration_tool`	Validates a Zaber device's configuration for binding library use
`check_mount_accessibility_tool`	Verifies a filesystem path is accessible and writable
`check_system_mounts_tool`	Verifies all configured filesystem paths are accessible

Available Tools (sl-manage MCP)

Tool	Description
`preprocess_session_tool`	Preprocesses a session's data on the host machine
`delete_session_tool`	Removes a session from all storage locations
`migrate_animal_tool`	Transfers all sessions for an animal between projects
`create_project_tool`	Creates a new project directory structure
`create_experiment_config_tool`	Creates an experiment configuration from a task template

Claude Desktop Configuration

Add the following to the Claude Desktop configuration file:

{
  "mcpServers": {
    "sl-experiment-get": {
      "command": "sl-get",
      "args": ["mcp"]
    },
    "sl-experiment-manage": {
      "command": "sl-manage",
      "args": ["mcp"]
    }
  }
}

API Documentation

See the API documentation for the detailed description of the methods and classes exposed by components of this library, as well as all available CLI commands with their arguments.

Recovering from Interruptions

While it is not typical for the data acquisition or preprocessing pipelines to fail during runtime, it is possible. The library can recover or gracefully terminate the runtime for most code-generated errors, so this is usually not a concern. However, if a major interruption (i.e., power outage) occurs or one of the hardware assets malfunctions during runtime, manual intervention is typically required to recover the session's data and reset the acquisition system.

Data acquisition interruption

Data acquisition can be interrupted in two main ways, the first being due to an external asset failure, for example, if the ScanImagePC unexpectedly shuts down during Mesoscope-VR system runtime. In this case, the runtime pauses and instructs the user to troubleshoot the issue and then resume the runtime. This type of soft interruption is handled gracefully during runtime to exclude the data collected during the interruption from the output dataset. Generally, soft interruptions are supported for most external assets, which includes anything not managed directly by the sl-experiment library and the main data acquisition system PC. While inconvenient, these interruptions do not typically require specialized handling other than recovering and restoring the failed asset.

Note! While most soft interruptions typically entail resuming the interrupted runtime, it is also possible to instead terminate the runtime. To do so, execute the terminate command via the GUI instead of trying to resume the runtime. In this case, the system attempts to execute a graceful shutdown procedure, saving all valid data in the process.

The second way involves interruption due to sl-experiment runtime failures or unexpected shut-downs of the main acquisition system PC. In these cases, manual user intervention is typically required to recover the useful data and reset the system before the acquisition can be restarted. The handling of such cases often consists of specific steps for each supported acquisition system. Typically, these hard interruptions are related to major issues, such as global facility power loss or severe malfunction of sensitive acquisition system components, such as microcontrollers and communication cables.

Mesoscope-VR

If the VRPC runtime unexpectedly interrupts at any point without executing the graceful shutdown, follow these instructions:

If the session involved Mesoscope imaging, shut down the Mesoscope acquisition process and make sure all required files (frame stacks, motion estimator data, and cranial window screenshot) have been generated and saved to the mesoscope_data directory.
If necessary, manually edit the session_descriptor.yaml, the mesoscope_positions.yaml, and the zaber_positions.yaml files to include actual runtime information. Estimate the volume of water delivered at runtime by manually reading the water tank level gauge.
Remove the animal from the Mesoscope enclosure. If necessary, use the Zaber Launcher app to directly interface with Zaber motors and move them in a way that allows the animal to be recovered from the enclosure.
Use Zaber Launcher to manually move the HeadBar group's Roll axis to have a positive angle (> 0 degrees). This is critical! If this is not done, the motor cannot home during the next session and instead collides with the movement guard, at best damaging the motor and, at worst, the Mesoscope.
If the session involved Mesoscope imaging, rename the mesoscope_data directory to use the session name. For example, from mesoscope_data → 2025-11-11-05-03-234123. Critical! if this is not done, the library may delete any leftover Mesoscope files during the next runtime and cannot properly preprocess the frames for the interrupted session during the next step.
Call sl-manage preprocess -sp SESSION_PATH and provide the path to the session directory of the interrupted session. This preprocesses and transfers all collected data to the long-term storage destinations. This preserves any data acquired before the interruption and prepares the system for running the next session.

Data preprocessing interruption

To recover from an error encountered during preprocessing, call sl-manage preprocess -sp SESSION_PATH and provide the path to the session directory of the interrupted session. The preprocessing pipeline automatically resumes an interrupted runtime from the nearest checkpoint.

Versioning

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

Authors

Ivan Kondratyev (Inkaros)
Kushaan Gupta (kushaangupta)
Natalie Yeung
Katlynn Ryu (katlynn-ryu)
Jasmine Si

License

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

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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CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
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sl-experiment

Detailed Description

Table of Contents

Installation

Source

pip

Data Acquisition Systems

Mesoscope-VR Data Acquisition System

Main Dependency

Software Dependencies

Hardware Dependencies

System Assembly

Zaber Motors

Behavior Cameras

MicroControllers

Virtual Reality Task Environment (Unity)

Configuration Sharing via MQTT

Google Sheets API Integration

Setting up Google Sheets API Access

Adding Google Sheets Access to the Service Account

ScanImage PC Assets

File System Access

Default Screenshot Directory

MATLAB Assets

Mesoscope-VR Assembly

Acquired Data Structure and Management

Root Directory (Volume)

Project Directory

Animal Directory

Session Directory

Raw Data

Shared Raw Data

Shared Temporary Data

Mesoscope-VR System Data

Raw Data

ScanImagePC

Mesoscope-VR Temporary Data

Acquiring Data in the Sun Lab

CLI Command Overview

Step 0: Configuring the Data Acquisition System

Step 1: Creating a Project

Step 2: Creating an Experiment

Step 3: Discovering System Components

Step 4: Maintaining the Acquisition System

Mesoscope-VR

Step 5: Acquiring Data

Mesoscope-VR Session Commands

Step 6: Preprocessing and Managing Data

Preprocessing Data

Deleting Session Data

Migrating Animal Data Between Projects

MCP Servers

Starting the Servers

Available Tools (sl-get MCP)

Available Tools (sl-manage MCP)

Claude Desktop Configuration

API Documentation

Recovering from Interruptions

Data acquisition interruption

Mesoscope-VR

Data preprocessing interruption

Versioning

Authors

License

Acknowledgments

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