Smooth Data

Smooth noisy data in the Live Editor

Since R2019b

Description

The Smooth Data task lets you interactively smooth noisy data. The task automatically generates MATLAB^® code for your live script.

Using this task, you can:

• Customize the method for smoothing data in a workspace variable.

• Adjust parameters to generate less or more smoothing.

• Visualize the input data compared to the smoothed data.

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Open the Task

To add the Smooth Data task to a live script in the MATLAB Editor:

• On the Live Editor tab, select Task > Smooth Data.

• In a code block in the script, type a relevant keyword, such as smooth or noisy. Select Smooth Data from the suggested command completions.

Examples

• Add Interactive Tasks to a Live Script
• Clean Messy Data and Locate Extrema Using Live Editor Tasks

Parameters

Input data — Valid input data from workspace

vector | table | timetable

This task operates on input data contained in a vector, table, or timetable. The data can be of type single, double, logical, or signed or unsigned integer types such as int64.

For table or timetable input data, to smooth all variables with a supported type, select All supported variables. To smooth all variables of type single or double, or signed or unsigned integer types, select All numeric variables. To choose which supported variables to smooth, select Specified variables.

Smoothing method — Method for smoothing data

Moving mean (default) | Moving median | Gaussian filter | ...

Specify the smoothing method as one of these options, which operate over local windows of data.

Method	Description
Moving mean	Moving average. This method is useful for reducing periodic trends in data.
Moving median	Moving median. This method is useful for reducing periodic trends in data when outliers are present.
Gaussian filter	Gaussian-weighted moving average.
Local linear regression (Lowess)	Lowess linear regression. This method can be computationally expensive, but it results in fewer discontinuities.
Local quadratic regression (Loess)	Loess quadratic regression. This method is slightly more computationally expensive than local linear regression.
Robust Lowess	Robust Lowess linear regression. This method is a more computationally expensive version of local linear regression, but it is more robust to outliers.
Robust Loess	Robust Loess quadratic regression. This method is a more computationally expensive version of local quadratic regression, but it is more robust to outliers.
Savitzky-Golay polynomial filter	Savitzky-Golay polynomial filter, which smooths according to a polynomial of specified degree, and is fitted over each window. This method can be more effective than other methods when the data varies rapidly.

Moving window — Window for smoothing methods

Centered (default) | Asymmetric

Specify the window type and size for the smoothing method instead of specifying a general smoothing factor.

Window	Description
Centered	Specified window length centered about the current point
Asymmetric	Specified window containing the number of elements before the current point and the number of elements after the current point

Window sizes are relative to the X-axis variable units.

Limitations

The Smooth Data task does not support 2-D smoothing windows.

Version History

Introduced in R2019b

R2022b: Plot multiple table variables

Simultaneously plot multiple table variables in the display of this Live Editor task. For table or timetable data, to visualize all selected table variables at once in a tiled chart layout, set the Variable to display field.

R2022b: Append smoothed table variables

Append input table variables with table variables containing smoothed data. For table or timetable input data, to append the smoothed data, set the Output format field.

R2022a: Live Editor task does not run automatically if inputs have more than 1 million elements

This Live Editor task does not run automatically if the inputs have more than 1 million elements. In previous releases, the task always ran automatically for inputs of any size. If the inputs have a large number of elements, then the code generated by this task can take a noticeable amount of time to run (more than a few seconds).

When a task does not run automatically, the Autorun indicator is disabled. You can either run the task manually when needed or choose to enable the task to run automatically.

R2021a: Operate on multiple table variables

This Live Editor task can operate on multiple table variables at the same time. For table or timetable input data, to operate on multiple variables, select All supported variables or Specified variables. Return all of the variables or only the modified variables, and specify which variable to visualize.