TEMDM

The software available on this page specializes in the treatment of STEM (Scanning Transmission Electron Microscopy) data, specifically focusing on spectrum-imaging data using Multivariate Statistical Analysis (MSA), i.e. Principal Component Analysis (PCA).

Stand-alone tool for spectrum-images (Windows only, current version 1.5.1.2):

last updated 24.10.2024

Main Features:

>> Read most common formats of spectrum-imaging and 4DSTEM and allows for data conversion. Currently supported formats:

Developer	Extension	Read	Write
Ametek (Gatan), 3D, 4D STEM	dm3,dm4	Yes	Yes
ThermoFisher 3D STEM (event-based) Velox	emd	Yes	No
ThermoFisher 4D STEM EMPAD	raw	Yes	No
Bruker 3D STEM (event-based)	bcf	Yes	No
Jeol 3D STEM (event-based)	pts	Yes	No
Hyperspy 3D STEM	hspy	Yes	Yes
Panta Rhei	prz	Yes	Yes
numpy	npy	Yes	Yes
binary	raw	Yes	No
EMPAD 4DSTEM	raw	Yes	No

>> Perform “silent” (no user interaction) PCA denoising of spectrum-images. An automatically determined number of principal components can be further changed manually with seeing immediately the result.

>> Allows for clustering data in the latent factor space with visualizing cluster spatial shapes and spectral signatures.

temDM MSA plugin for DigitalMicrograph

This DigitalMicrograph plugin enables Multivariate Statistical Analysis of STEM EELS or EDX data cubes. The plugin primarily focuses on Principal Component Analysis (PCA), which effectively extracts the components with the highest variance in the raw data. This process significantly reduces the data’s dimensionality and eliminates noise. To facilitate better interpretation, the obtained PCA components can be further refined using methods like Varimax or ICA to rotate them in the factor space. Additionally, the PCA results can be transformed into endmember spectra, which are easily interpretable. These spectra are derived by manipulating the data in the factor space. Moreover, for datasets with complex internal data distribution, the plugin provides the capability to cluster the data within the factor space.

last updated 3.08.2022, version 2.37

Application examples of STEM data cubes: