Welcome to the Braingeneers Python Utilities repository! This package collects and provides various Python code and utilities developed as part of the Braingeneers project. The package adheres to the Python Package Authority (PyPA) standards for package structure and organization.

We welcome contributions from collaborators and researchers interested in our work. If you have improvements, suggestions, or new findings to share, please submit a pull request. Your contributions help advance our research and analysis efforts.

To get started with your development (or fork), click the "Open with GitHub Codespaces" button below to launch a fully configured development environment with all the necessary tools and extensions.

Instruction on how to contribute to this project can be found in the CONTRIBUTION.md.

You can install braingeneers using pip with the following commands:

pip install braingeneers

pip install --force-reinstall git+https://github.com/braingeneers/braingeneerspy.git

You can install braingeneers with specific optional dependencies based on your needs. Use the following command examples:

• Install with machine-learning dependencies:

pip install "braingeneers[ml]"

• Install with Hengen lab dependencies:

pip install "braingeneers[hengenlab]"

• Install with developer dependencies (running tests and building sphinx docs):

pip install "braingeneers[dev]"

• Install with all optional dependencies:

pip install "braingeneers[all]"

To make changes and publish them on GitHub, please refer to the CONTRIBUTING.md file for up-to-date guidelines.

braingeneers includes several subpackages and modules, each serving a specific purpose within the Braingeneers project:

• braingeneers.analysis: Contains code for data analysis.
• braingeneers.data: Provides code for basic data access, including subpackages for handling electrophysiology, fluidics, and imaging data.
• braingeneers.iot: Offers code for Internet of Things (IoT) communication, including a messaging interface.
• braingeneers.ml: Contains code related to machine learning, such as a high-performance PyTorch data loader for electrophysiology data.
• braingeneers.utils: Provides utility functions, including S3 access and smart file opening.

This module extends the awswrangler.s3 package for Braingeneers/PRP access. For API documentation and usage examples, please visit the official documentation.

Here's a basic usage example:

import braingeneers.utils.s3wrangler as wr

# Get all UUIDs from s3://braingeneers/ephys/
uuids = wr.list_directories('s3://braingeneers/ephys/')
print(uuids)

This module configures smart_open for Braingeneers use on PRP/S3. When importing this version of smart_open, Braingeneers defaults will be autoconfigured. Note that smart_open supports both local and S3 files, so it can be used for all files, not just S3 file access.

Here's a basic usage example:

import braingeneers.utils.smart_open_braingeneers as smart_open

with smart_open.open('s3://braingeneersdev/test_file.txt', 'r') as f:
    print(f.read())

You can also safely replace Python's default open function with smart_open.open:

import braingeneers.utils.smart_open_braingeneers as smart_open

open = smart_open.open

By default, smart_open and s3wrangler are pre-configured for the standard Braingeneers S3 endpoint. However, you can specify a custom ENDPOINT if you'd like to use a different S3 service. This can be a local path or an endpoint URL for another S3 service (note that s3wrangler only supports S3 services, not local paths, while smart_open supports local paths).

To set a custom endpoint, follow these steps:

1. Set an environment variable ENDPOINT with the new endpoint. For example, on Unix-based systems:

   export ENDPOINT="https://s3-west.nrp-nautilus.io"

2. Call braingeneers.set_default_endpoint(endpoint: str) and braingeneers.get_default_endpoint(). These functions will update both smart_open and s3wrangler (if it's an S3 endpoint, local path endpoints are ignored by s3wrangler).

When running a job on the PRP, you can use the PRP internal S3 endpoint, which is faster than the default external endpoint. To do this, add the following environment variable to your job YAML file:

spec:
  template:
    spec:
      containers:
      - name: ...
        command: ...
        args: ...
        env:
          - name: "ENDPOINT"
            value: "http://rook-ceph-rgw-nautiluss3.rook"

Please note that this will only work on jobs run in the PRP environment. Setting the ENDPOINT environment variable can also be used to specify an endpoint other than the PRP/S3.

The docs directory has been set up using sphinx-build -M html docs/source/ docs/build/ to create a base project Documentation structure. You can add inline documentation (NumPy style) to further enrich our project's documentation. To render the documentation locally, navigate to the docs/build/html folder in the terminal and run python3 -m http.server.

• src/: This folder contains scripts and notebooks representing completed work by the team.

• pyproject.toml: This file follows the guidelines from PyPA for documenting project setup information.

The devcontainer.json file allows you to customize your Codespace container and VS Code environment using extensions. You can add more extensions to tailor the environment to your specific needs. Explore the VS Code extensions marketplace for additional tools that may enhance your workflow.

For more information about Braingeneers, visit our website.