DFT Leakage Window Detail Simulator All tools
Interactive simulator

DFT Leakage Window Detail Simulator

Inspect DFT bin offset and window effects through scalloping loss, leakage floor, and equivalent noise bandwidth.

Parameters
Sample count N
count

Input Sample count N.

Bin offset
bin

Input Bin offset.

Window sidelobe
dB

Input Window sidelobe.

Coherent gain
-

Input Coherent gain.

Results
Scalloping loss
Equivalent noise bandwidth
Leakage floor
Frequency resolution
Windowed spectrum
Scalloping and leakage
Offset-window attenuation map
Model and equations

$$|X[k]|=\left|\sum_n x[n]w[n]e^{-j2\pi kn/N}\right|$$

This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks.

How to read it

Use the main plot to read the controlling trend, including break points that a single result card can hide.

Use the sensitivity view to find input combinations where margin collapses quickly.

For early design, focus on which input controls margin before trusting the absolute value.

Learn DFT Leakage Window Detail by dialogue

🙋
When reading DFT Leakage Window Detail, where should I look first? Moving Sample count N changes both the plots and the result cards.
🎓
Start with Scalloping loss, but do not treat the number as the whole answer. Use Windowed spectrum to confirm the assumed state, then read Scalloping and leakage for the distribution or trend. Use the main plot to read the controlling trend, including break points that a single result card can hide.
🙋
I can see why Sample count N changes Scalloping loss. How should I judge the influence of Bin offset?
🎓
Move Bin offset in small steps and watch Equivalent noise bandwidth. That reveals which term is controlling the result. This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. A single operating point is not enough; sweep the realistic scatter range.
🙋
What is Offset-window attenuation map for? It feels like the ordinary curve already tells the story.
🎓
Offset-window attenuation map is for finding boundaries where the condition becomes risky or margin collapses quickly. Use the sensitivity view to find input combinations where margin collapses quickly. In First-pass comparison of design options before review, the important question is often what happens after a small change, not only the nominal value.
🙋
So if Scalloping loss is within the target, can I accept the condition?
🎓
Treat this as a first-pass review. It helps with Narrowing controlling factors and worst-side conditions before detailed analysis and Teaching or explaining the equation, numbers, and visualization under the same inputs, but final decisions still need standards, measured data, detailed analysis, and vendor limits. For early design, focus on which input controls margin before trusting the absolute value.

Practical use

First-pass comparison of design options before review.

Narrowing controlling factors and worst-side conditions before detailed analysis.

Teaching or explaining the equation, numbers, and visualization under the same inputs.

FAQ

Start with Scalloping loss and Equivalent noise bandwidth. Then use Windowed spectrum to confirm the assumed state and Scalloping and leakage to read distribution or bias. Use the main plot to read the controlling trend, including break points that a single result card can hide
Move Sample count N alone, then move Bin offset by a comparable amount and compare the change in Scalloping loss. Offset-window attenuation map shows combinations where margin or performance changes quickly.
Use it for First-pass comparison of design options before review. Instead of trusting a single point, widen the input range and check whether Scalloping loss keeps enough margin before moving to detailed analysis.
This simplified model captures the main relationship only. Boundary conditions, losses, nonlinear effects, and code-specific corrections still need separate checks. Final decisions still require standards, measured data, detailed analysis, and vendor limits.