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WaveSpace

Python tools for the simulation and analysis of cortical traveling waves

Installation

Download latest version from here

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

pip install WaveSpace-1.1.0-py3-none-any.whl

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.

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!

Wave Analysis:

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

overview

Example:

from WaveSpace.Simulation import SimulationFuns
from WaveSpace.PlottingHelpers import Plotting
from WaveSpace.Utils import HelperFuns as hf

import numpy as np
import matplotlib.pyplot as plt

Type =  "PlaneWave" 
nTrials = 1
matrixSize = 20
SampleRate= 128
SimDuration= 1.0

waveData = SimulationFuns.simulate_signal(
    Type, 
    nTrials, 
    matrixSize, 
    SampleRate, 
    SimDuration, 
    #SimOptions from here on
    TemporalFrequency = 10,
    SpatialFrequency = 0.7,
    WaveDirection = 45,
    )

hf.squareSpatialPositions(waveData)
ani = Plotting.animate_grid_data(waveData, DataBucketName="SimulatedData", dataInd=0, 
                                 probepositions=[(4,7), (8,7), (12,7), (16,7)])
ani.save('PlaneWave.mp4', writer='ffmpeg', fps=6)