Interface IWindowFunction<T>

Namespace

AiDotNet.Interfaces

Assembly

AiDotNet.dll

Defines functionality for creating window functions used in signal processing and data analysis.

public interface IWindowFunction<T>

Type Parameters

T

The numeric data type used for calculations (e.g., float, double).

Remarks

Window functions are mathematical functions that are zero-valued outside of a chosen interval. They are applied to signals to reduce artifacts in spectral analysis and filter design.

For Beginners: Window functions are like special "lenses" that help us focus on specific parts of data while smoothly fading out the rest.

Imagine you're taking a photo through a window:

• A rectangular window gives you a clear view of everything inside the frame, but creates a sharp cutoff at the edges (which can cause problems in signal analysis)
• Other window shapes (like Hamming or Gaussian) are like windows that gradually get darker toward the edges, creating a smooth transition between what's in focus and what's not

Window functions are commonly used for:

• Analyzing audio signals (like in music apps that show frequency visualizations)
• Processing images or video
• Filtering out unwanted noise or frequencies
• Smoothly connecting segments of data

Different window functions have different shapes and properties that make them suitable for different applications.

Methods

Create(int)

Creates a window function with the specified size.

Vector<T> Create(int windowSize)

Parameters

windowSize int

The number of points in the window function.

Returns

Vector<T>

A vector containing the window function values.

Remarks

This method generates a vector containing the window function values.

For Beginners: This method creates an array of numbers that represent the "shape" of the window.

For example, if you request a window of size 5:

• A rectangular window might return [1, 1, 1, 1, 1]
• A triangular window might return [0, 0.5, 1, 0.5, 0]
• A Hamming window might return [0.08, 0.54, 1, 0.54, 0.08]

These values are then typically multiplied with your data to apply the windowing effect. The larger the window size, the more data points will be included in your analysis.

GetWindowFunctionType()

Gets the type of window function being implemented.

WindowFunctionType GetWindowFunctionType()

Returns

WindowFunctionType

The type of window function.

Remarks

This method returns an enumeration value that identifies which specific window function is being used (e.g., Hamming, Hanning, Blackman, etc.).

For Beginners: This tells you which "shape" of window is being used. Common window types include:

• Rectangular: The simplest window, like looking through a clear glass window with sharp edges
• Hamming/Hanning: Bell-shaped windows that taper to near-zero at the edges
• Gaussian: A window shaped like a bell curve (normal distribution)
• Blackman: A window that provides good frequency resolution with low leakage
• Kaiser: A flexible window where you can adjust the trade-off between main-lobe width and side-lobe level

Different window types are better for different applications:

• Rectangular windows preserve the original signal amplitude but can introduce artifacts
• Hamming/Hanning windows reduce artifacts but slightly modify the signal amplitude
• More specialized windows offer different trade-offs between frequency resolution and spectral leakage