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).
Spectrum Imager
Stand-alone tool for treatment of EELS and EDX spectrum-images.
Main Features:
>> Read most common formats of spectrum-imaging and allows for data conversion. Currently supported formats:
| Application | Extension | Read | Write |
| DigitalMicrograph (Ametek) | dm3, dm4 | 1D, 2D, 3D | 1D, 2D, 3D |
| Velox (ThermoFisher) | emd | 1D, 2D, 3D | No |
| PantaRhei (CEOS) | prz | 1D, 2D, 3D | 1D, 2D, 3D |
| ESPRIT (Bruker) | bcf | 3D | No |
| EDAX APEX (Ametek) | pts | 2D, 3D | No |
| Hyperspy 3D STEM | hspy | 1D, 2D, 3D | 1D, 2D, 3D |
| numpy | npy | 1D, 2D, 3D | 1D, 2D, 3D |
| binary | raw | 1D, 2D, 3D | No |
| Jeol STEM | pts | 2D, 3D | 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.
Spectrum Imager Download
version 1.6.6 (23.09.2025)
Download Learning Examples
MSA plugin for DigitalMicrograph
Caution: debugging for new GMS versions is not supported anymore
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: