ChronIO: A toolbox for analysis of physiological time series data

Overview

ChronIO is a powerful Python toolbox designed to simplify the analysis of time series data across various scientific disciplines. While it excels in neuroscience and behavioral studies, ChronIO's flexibility makes it valuable for a wide range of applications, including:

• Neuroscience: Analyze neural activity patterns and correlate them with behavior
• Physiology: Process and interpret physiological signals from wearables and sensors (i.e. ECG, EMG, or respiratory data)
• Sports Science: Track athletic performance metrics and identify patterns

ChronIO empowers researchers and data scientists to efficiently manage, process, and analyze complex time series datasets, regardless of their programming expertise.

Key Advantages

ChronIO addresses several common pain points in time series analysis:

• Unified Data Structure: Seamlessly handle diverse data types within a consistent framework.
• Intuitive Interface: Simplify complex operations with an easy-to-use API, accessible to beginners and experts alike.
• Flexible Processing: Apply various transformations and analyses to your data with built-in functions.
• Efficient Data Management: Organize and access your data effortlessly, reducing the cognitive load of data handling.
• Reproducibility: Promote reproducible research with standardized data formats and processing pipelines.

FAIR Data Principles

ChronIO is designed with FAIR (Findable, Accessible, Interoperable, Reusable) data principles in mind:

• Findable: Standardized naming conventions and metadata make it easy to locate specific datasets.
• Accessible: Open-source nature ensures that the tools for accessing data are freely available.
• Interoperable: Common data structures facilitate integration with other tools and datasets.
• Reusable: Detailed metadata and reproducible processing steps enhance data reusability.

Quick Start Example

Here's a simple example demonstrating how to use ChronIO to read, process, and export data:

from chronio import BehavioralTimeSeries, Convention
import chronio.analyses as analyses

# Load data
data = BehavioralTimeSeries(fpath="subject0_behavior.csv", time_col="Time")

# Downsample and normalize
data.downsample_by_time(interval=0.1, method='mean', inplace=True)
data.normalize(columns=['Speed', 'Orientation'], inplace=True)

# Get events and export
events = data.get_events(columns=['ButtonPress'])
convention = Convention(directory="output", suffix="csv", metadata_fields=["subject_id", "session"])
events['LeverPress'].export(convention)

Installation

To install ChronIO via Anaconda, follow these steps:

1. Clone the repository:

git clone https://github.com/alimoges42/chronio.git
cd chronio

2. Create and activate the conda environment:

conda env create -f environment.yml
conda activate chronio

3. Install ChronIO locally:

pip install .    # (or "pip install -e." for development mode)

This will install ChronIO and all its dependencies, allowing you to easily update the package as new changes are made.

Contributing

We welcome contributions! If there is a functionality you are interested in, please open an issue or send a pull request to get started.

License

ChronIO is released under the GPL-3 License. See the LICENSE file for details.

Developer Information

Aaron Limoges (Github: @alimoges42)