2D FFT Analysis

This tutorial demonstrates how to use the WaveSpace toolbox to perform 2D FFT analysis on simulated data, including selecting source points, running the FFT, and visualizing the results.

Setup

Before running the example, ensure the project root is on the Python path:

# Add the project root directory to the Python path when working with source code,
# not necessary when package is installed
import sys
import os
path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, path )
print(path)

from WaveSpace.Utils import ImportHelpers
from WaveSpace.PlottingHelpers import Plotting
from WaveSpace.WaveAnalysis import WaveAnalysis as wa

import numpy as np
import matplotlib.pyplot as plt

Loading Simulated Data

saveFolder = "ExampleData/Output/"
waveData = ImportHelpers.load_wavedata_object(saveFolder + "SimulatedData")

Selecting Source Points

# Create 10 sample points along the diagonal of the channel array
gridSize = waveData.get_data("SimulatedData").shape[1]
nPoints = range(0,gridSize,2)
sourcePointsDiagonal = []
for i in nPoints:
    sourcePointsDiagonal.append([i,i])

# # Create 10 sample points along a horizontal of the channel array
# yLocation = int(np.floor(gridSize/2))
# sourcePointsHorizontal = []
# for i in nPoints:
#     sourcePointsHorizontal.append([i,yLocation])

Running 2D FFT Analysis

# restrict to (temp) frequencies between lower and upper bound:
lowerBound = 2
upperBound = 40

wa.FFT_2D(waveData, sourcePointsDiagonal, lowerBound, upperBound, DataBucketName="SimulatedData")

result = waveData.get_data("Result")

Visualizing Results

# Get the number of trials
n_trials = waveData.get_data("SimulatedData").shape[0]
trialInfo = waveData.get_trialInfo()
conditions = np.unique(trialInfo)

for condition in conditions:
    indices = [i for i, cond in enumerate(trialInfo) if cond == condition]
    logRatios=np.mean(np.log(result["Max Along Power"][indices]/result["Max Reverse Power"][indices]))
    newlineseries = np.zeros((len(sourcePointsDiagonal),waveData.get_data("SimulatedData").shape[3]))

    for ind, position in enumerate(sourcePointsDiagonal):
        newlineseries[ind] = np.mean(waveData.get_data("SimulatedData")[indices, position[0], position[1], :], axis=0)
    plt.figure()
    plt.imshow(newlineseries, aspect=4)
    plt.title(f"Channels over time (Condition {condition})")
    plt.show()

    plot = Plotting.plotfft_zoomed(np.mean(waveData.get_data("FFT_ABS")[indices,:,:],axis=0), waveData.get_sample_rate(), -20, 20, "fft abs", scale='log')
    plot.show()

    x_labels = np.arange(1)
    plt.figure()
    plt.bar(x_labels, [np.mean(result["Max Along Power"][indices])], color='b', width = 0.25 )
    plt.bar(x_labels + 0.25, [np.mean(result["Max Reverse Power"][indices])] , color='r', width = 0.25 )
    plt.legend(labels=["Along", "Reverse"])
    plt.xticks(x_labels + 0.125, ["0 degree"])
    plt.title(f"Max Power (Condition {condition})")
    plt.show()