Computations

Gladly provides two types of computations for transforming data:

Computed attributes — transform a single column into a new column of the same length
Transforms — transform an entire dataset into a new dataset (possibly with different length and columns)

Computed Attributes

A computed attribute transforms a single column into a new column with the same length but different values. It runs on the GPU and can be used wherever an expression is accepted in layer parameters.

Use case: Applying a mathematical function to every value in a column, e.g., computing a histogram, smoothing with a kernel, normalizing, etc.

Usage

{ points: { xData: { histogram: { input: "raw_data", bins: 50 } } } }

Available Computed Attributes

histogram

Computes a histogram texture from input data.

Parameter	Type	Required	Description
`input`	expression	yes	Input data column
`bins`	number	no	Number of bins (default: auto)

filteredHistogram

Computes a histogram that automatically updates when a filter axis changes. Used internally by the histogram layer.

Parameter	Type	Required	Description
`input`	expression	yes	Input data column (normalized to [0,1])
`filterValues`	expression	yes	Raw filter column values
`filterAxisId`	string	yes	Quantity kind of the filter axis
`bins`	number	no	Number of bins

kde

Kernel density estimation — smooths a histogram into a continuous density estimate.

Parameter	Type	Required	Description
`input`	expression	yes	Input data column
`bins`	number	no	Number of output bins
`bandwidth`	number	no	Gaussian sigma in bins (default: 5)

filter1D

Generic 1D convolution filter.

Parameter	Type	Required	Description
`input`	expression	yes	Input data column
`kernel`	expression	yes	1D kernel weights

lowPass

Low-pass filter (Gaussian smoothing).

Parameter	Type	Required	Description
`input`	expression	yes	Input data column
`sigma`	number	no	Gaussian sigma (default: 3)
`kernelSize`	number	no	Kernel size (auto if omitted)

highPass

High-pass filter (original minus low-pass).

Parameter	Type	Required	Description
`input`	expression	yes	Input data column
`sigma`	number	no	Gaussian sigma (default: 3)
`kernelSize`	number	no	Kernel size (auto if omitted)

bandPass

Band-pass filter (difference of two low-pass filters).

Parameter	Type	Required	Description
`input`	expression	yes	Input data column
`sigmaLow`	number	yes	Lower frequency cutoff (sigma)
`sigmaHigh`	number	yes	Upper frequency cutoff (sigma)

convolution

Adaptive convolution with automatic kernel sizing based on data.

Parameter	Type	Required	Description
`signal`	expression	yes	Input signal column
`kernel`	expression	yes	Convolution kernel column

fftConvolution

FFT-based convolution for large signals.

Parameter	Type	Required	Description
`signal`	expression	yes	Input signal column
`kernel`	expression	yes	Convolution kernel column

linspace

Generates linearly spaced values.

Parameter	Type	Required	Description
`start`	number	yes	Start value
`stop`	number	yes	Stop value
`num`	number	yes	Number of samples

range

Generates a range of integer values.

Parameter	Type	Required	Description
`start`	number	yes	Start value
`stop`	number	yes	Stop value (exclusive)
`step`	number	no	Step size (default: 1)

random

Generates random values.

Parameter	Type	Required	Description
`num`	number	yes	Number of samples
`min`	number	no	Minimum value (default: 0)
`max`	number	no	Maximum value (default: 1)

Transforms

A transform transforms an entire dataset into a new dataset. The output can have a different length and different columns than the input. Transforms are specified in config.transforms and produce new data columns accessible via data.transformName.columnName.

Use case: Computing aggregations (histograms, FFTs), creating derived datasets, pre-processing before visualization.

How Transforms Differ from Computed Attributes

Aspect	Computed Attribute	Transform
Output	Single column, same length as input	Entire dataset, possibly different length and columns
Specification	Inline in layer parameters	In `config.transforms` array
Access	Used directly in layer params	Access via `data.transformName.columnName`
Example	`vData: { histogram: { input: "x", bins: 50 } }`	`transforms: [{ name: "hist", HistogramData: {...} }]`

A transform can consist of multiple computed attributes if the transform type is elementwise — see ElementwiseData below.

Usage

config: {
  transforms: [
    {
      name: "histogram",
      HistogramData: {
        input: "temperature",
        bins: 50,
        filter: "depth"
      }
    }
  ]
}

Then access via data.histogram.binCenters and data.histogram.counts.

Available Transforms

HistogramData

Computes a histogram with bin centers and counts. Supports reactive filtering.

Parameter	Type	Required	Description
`input`	string	yes	Input data column name
`bins`	integer	no	Number of bins (0 for auto)
`filter`	expression	no	Filter column — registers a filter axis

Output columns:

binCenters — bin center positions in data space
counts — count per bin

FftData

Computes Fast Fourier Transform with real and imaginary outputs.

Parameter	Type	Required	Description
`input`	string	yes	Input signal column name
`inverse`	boolean	no	Perform inverse FFT (default: false)

Output columns:

real — real component
imag — imaginary component
magnitude — magnitude (computed)
phase — phase (computed)

ElementwiseData

Performs element-wise operations on multiple columns. A transform that consists of multiple computed attributes — each output column is computed independently from input columns using the same length.

Parameter	Type	Required	Description
`dataLength`	integer	no	Override output length (auto-detected)
`columns`	array	yes	Array of column mappings

Output columns:

Defined by columns parameter

Expression Syntax

Expressions can be:

Plain column name (string):
```
xData: "temperature"
```

Computed attribute (object with single key):

xData: { histogram: { input: "raw", bins: 50 } }

Computed data (transform output):

xData: "histogram.binCenters"  // after transform named "histogram"

Transform specification (object in config.transforms):

config: {
  transforms: [
    { name: "myHist", HistogramData: { input: "data", bins: 20 } }
  ]
}
// Results in data.myHist.binCenters and data.myHist.counts

For writing custom computations see Computations.