dspFFT

classFFT of sine waves

This visualization demonstrates the Fast Fourier Transform by combining three sine waves (at 5, 8, and 15 Hz) into a single signal and computing its frequency spectrum. The code generates the composite waveform using `sineGenerator`, then applies the `FFT` class from `@drbagwell/dft_fft_classes` to transform 1024 sampled points, logging the resulting spectrum to the console. Built with D3.js and `observeResize` for responsive axes, the example focuses on the algorithmic FFT computation rather than rendering a visual plot.

87% match

DFT and FFT Implementation

This visualization compares the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) algorithms for spectral analysis. It plots frequency peaks of sine wave data, showing signal amplitude against frequency to reveal the dominant frequencies in a combined test signal. The implementation includes custom JavaScript classes for both transforms, using typed arrays for performance, and renders the results as a line chart with D3.js scales and axes. The visualization also includes a basic Fourier transform implementation for calculating the power spectrum.

82% match

FFT of sine waves

This example visualizes the discrete Fourier transform (FFT) of combined sine waves, showing the magnitude spectrum of three superimposed signals at frequencies 5, 8, and 15 Hz sampled at 120 Hz. Three sine waves are generated with a custom `sineGenerator` function, summed, and processed through a radix-4 FFT implementation. The resulting magnitude values are plotted as a line chart using D3.js, with scales and axes handled via `scaleLinear` and the `@drbagwell/responsive-viz-axes` library, alongside a custom `viz` function that renders the plot within an SVG container.

82% match

sine wave for FFT

This example demonstrates how to combine multiple sine waves into a single signal and visualize the result. It generates three distinct sine waves at frequencies of 8, 15, and 5 Hz using a custom `sineGenerator` function, then sums them into a combined amplitude signal that is plotted as a line chart using D3's `scaleLinear` and `line` methods. The visualization includes labeled axes for data points and signal amplitude, with the chart rendered inside a gray rectangular region. While the code includes FFT utilities from the included `fft.js` and `fftutil.js` modules, the current view only displays the raw combined waveform rather than the frequency-domain analysis.

80% match

Fast Fourier Transform Class

This example visualizes the frequency spectrum of a time-series signal, showing the amplitude of frequency peaks after a Fast Fourier Transform (FFT) is applied to a dataset of 16,384 samples. A line chart displays the frequency peaks of sine waves, with the x-axis representing frequency and the y-axis representing signal amplitude. The implementation uses D3.js for rendering and employs a custom `FourierTransform` function to compute frequency, amplitude, and phase from the data, which is sourced from a pre-loaded dataset. The visualization also includes axes and labels rendered via the `@drbagwell/responsive-viz-axes` library.

Fast Fourier Transform

This visualization demonstrates the process of applying a Fast Fourier Transform (FFT) to a signal. It plots a line chart of time-series temperature data, followed by the computation of the FFT to decompose the signal into its frequency components. The code uses `d3` for scalable vector graphics (SVG) rendering and `FourierTransform` from `fourier.js` to calculate the amplitude and phase for each frequency. The results are displayed alongside the original signal, showing the dominant frequencies present in the data.

FFT products

This example demonstrates the Fourier Transform and Fast Fourier Transform algorithms applied to digital signal processing. The visualization displays the frequency spectrum output generated by these transforms, which are implemented through custom JavaScript classes including DFT, FFT, and supporting utilities for windowing and filter operations. The rendering uses WebGL and is part of a comprehensive DSP library that includes ADSR envelopes, biquad filters, and oscillators. The code calculates spectral magnitudes from time-domain buffers using precomputed sine and cosine lookup tables.

dataFFT

This visualization demonstrates the Fast Fourier Transform (FFT) by plotting the frequency spectrum of a signal, showing a scatter plot of frequency peaks against their amplitudes. A CSV file containing signal data is fetched and processed through a naive Fourier transform algorithm, which calculates the real and imaginary components, amplitude, and phase for each frequency bin. The resulting data is rendered as a line chart of amplitude versus frequency using D3's `scaleLinear`, `line`, and `extent` functions, with axes and labels configured via the `@drbagwell/responsive-viz-axes` library. The visualization includes a gray inner plotting area and a black line trace, illustrating the spectral content of the input signal.