ECL`

AnalyzeSmoothing

this function takes either a list of data objects or a list of xy coordinates and applies different types of smoothing functions to the datasets in order to reduce the noise and make clear the broader trends in your data.

Details

Smoothen input data based on a particular approach provided by the user.

Input

Output

Data Processing Options

Examples

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Example Setup

These expressions are used in the examples to follow:

Basic Examples (3)

A nonequally spaced set of data points with units:

A simple set of data points with units:

A unitless set of data points:

Additional Examples (9)

A protocol object:

Dataset with distribution entries:

Different types of data objects:

List of quantity datasets:

Multiple datasets:

Multiple MassSpectrometry objects:

Spiral dataset:

Unordered data that folds onto itself:

Vertical lines:

Options (7)

AscendingOrder (1)

If we want to check the ascending order of the dataset:

BaselineAdjust (1)

Baseline adjustment can be used for cases where there is a systematic drift:

CutoffFrequency (1)

The option CutoffFrequency is used for smoothing with LowpassFilter or HighpassFilter. So all frequencies higher/lower than this value will be removed, respectively:

EqualSpacing (1)

Making the dataset equally spaced:

Method (1)

Bilateral method is successful to denoise the data and retain its trend:

Radius (1)

The option Radius is used for smoothing with all options except LowpassFilter or HighpassFilter. This radius is used for smoothing around each datapoint with the neighborhood equal to radius in either directions: