WaveSpace ¶

Python tools for the simulation and analysis of cortical traveling waves

Installation ¶

Download latest version from [here](https://github.com/kpetras/WaveSpace/tree/main/package/dist)

Open a terminal, navigate to the directory you downloaded to and install with

pip install WaveSpace-x.x.x-py3-none-any.whl

replace ‘x.x.x’ with relevant version number

Modules ¶

Decomposition ¶

Implements various frequency decomposition techniques, such as Fourier and wavelet transforms, Empirical Mode Decomposition (EMD).

Preprocessing ¶

Provides functions for cleaning, normalizing, and filtering time series data.

Plotting Helpers ¶

Contains utilities for visualizing cortical traveling waves using matplotlib & pyvista, including time-frequency plots, phase maps, and spatial-temporal representations.

Simulation ¶

Tools for generating synthetic cortical traveling waves, aiding in model validation and hypothesis testing.

Spatial Arrangement ¶

Handles spatial organization of sensor positions. Includes interpolation options.

Statistics ¶

Offers methods for computing null distributions.

Utils ¶

A collection of general-purpose helper functions used throughout the toolbox, including data manipulation and file I/O.

Wave Analysis ¶

Core module for detecting, characterizing, and quantifying cortical traveling waves using advanced signal processing techniques.

The WaveData Class ¶

The WaveData class serves as a container for time-series data related to cortical traveling waves. It provides functionalities for data storage, manipulation, and analysis, ensuring a structured workflow for handling multi-channel neural recordings.

Key Features ¶

Initialization (`__init__`): Stores channel positions, time vectors, sample rates, and maintains a structured dataset with multiple DataBuckets for flexible data handling.
Data Management: - Supports multiple datasets through DataBuckets, enabling users to store, retrieve, and manipulate data flexibly. - Provides methods to add, delete, and check the existence of specific DataBuckets. - Allows appending datasets and setting an active dataset for streamlined analysis.
Data Processing: - Cropping: Enables temporal cropping of data using specific time intervals. - Trial Pruning: Removes unwanted trials from datasets while maintaining metadata consistency.
Metadata Handling: - Stores and retrieves spatial arrangements of recording channels (set_channel_positions, get_channel_positions). - Maintains a history of operations for reproducibility (log_history). - Supports storage and retrieval of simulation and trial metadata (set_simInfo, get_trialInfo).
I/O and Persistence: - Saves objects to files for later retrieval (save_to_file). - Provides a structured string representation (__repr__) for quick dataset summaries.

This class is essential for organizing and processing large-scale neural recordings, offering flexibility in data structuring, preprocessing, and visualization. Let me know if you’d like any refinements!