Key features

This page presents briefly DataLab key features.

DataLab supports dark and light mode depending on your platform settings (this is handled by the guidata package, and may be overridden by setting the QT_COLOR_MODE environment variable to dark or light).

Data visualization

Signal	Image	Feature
✓	✓	Screenshots (save, copy)
✓	Z-axis	Lin/log scales
✓	✓	Data table editing
✓	✓	Statistics on user-defined ROI
✓	✓	Markers
	✓	Aspect ratio (1:1, custom)
	✓	50+ available colormaps (customizable)
	✓	Intensity profiles (line, average, radial)
✓	✓	Annotations
✓	✓	Persistance of settings in workspace
	✓	Distribute images on a grid
✓	✓	Single or superimposed views

Data processing

Signal	Image	Feature
✓	✓	Process isolation for running computations
✓	✓	Remote control from Jupyter, Spyder or any IDE
✓	✓	Remote control from a third-party application
✓	✓	Sum, average, difference, product, …
✓	✓	Operations with a constant
✓	✓	ROI extraction, Swap X/Y axes
✓		Semi-automatic multi-peak detection
✓		Convolution
	✓	Flat-field correction
	✓	Rotation (flip, rotate), resize, …
	✓	Intensity profiles (line, average, radial)
	✓	Pixel binning
✓	✓	Square root, power, logarithm, exponential, …
✓		Derivative, integral
✓	✓	Linear calibration
✓	✓	Normalization, Clipping, Offset correction
✓		Reverse X-axis
	✓	Thresholding (manual, Otsu, …)
✓	✓	Gaussian filter, Wiener filter
✓	✓	Moving average, moving median
✓	✓	FFT, inverse FFT, Power/Phase/Magnitude spectrum, Power Spectral Density
✓		Interpolation, resampling
✓		Detrending
✓		Interactive fit: Gauss, Lorentz, Voigt, polynomial, CDF, …
✓		Interactive multigaussian fit
✓		Frequency filters (low-pass, high-pass, band-pass, band-stop)
✓		Windowing (Hamming, Hanning, …)
	✓	Butterworth filter
	✓	Exposure correction (gamma, log, …)
	✓	Restauration (Total Variation, Bilateral, …)
	✓	Morphology (erosion, dilation, …)
	✓	Edges detection (Roberts, Sobel, …)
✓	✓	Computing on custom ROI
✓		FWHM, FW @ 1/e²
✓		Dynamic parameters (ENOB, SNR, …), Sampling period/Rate
	✓	Centroid (robust method w/r noise)
	✓	Minimum enclosing circle center
	✓	2D peak detection
	✓	Contour detection
	✓	Circle Hough transform
	✓	Blob detection (OpenCV, Laplacian of Gaussian, …)