hyperspy/hyperspy: Release v1.6.2

Peña, Francisco de la; Prestat, Éric; Fauske, Vidar Tonaas; Burdet, Pierre; Furnival, Tom; Jokubauskas, Petras; Nord, Magnus; Ostasevicius, Tomas; Lähnemann, Jonas; MacArthur, Katherine E.; Johnstone, Duncan N.; Sarahan, Mike; Taillon, Joshua; Aarholt, Thomas; pquinn-dls,; Migunov, Vadim; Eljarrat, Alberto; Caron, Jan; Mazzucco, Stefano; Martineau, Baudouin; Somnath, Suhas; Poon, Timothy; Slater, Tom; Francis, Carter; actions-user,; Walls, John M.; Cautaerts, Niels; Tappy, Nicolas; Winkler, Florian; Donval, Gaël

doi:10.5281/zenodo.4683076

Cited by 6 publications

(5 citation statements)

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“…EDS data processing was conducted with Hyperspy. [45] Diffraction data was analyzed with the support of CrysTBox. [46] In Situ Scattering Measurements: Combined SAXS and WAXS measurements during flash-annealing of samples (i)-(iv) were carried out at beamline P21.2, Petra III, Desy, Hamburg [47] with a photon energy of 52.7keV.…”

Section: Methodsmentioning

confidence: 99%

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In Situ Mapping of Phase Evolutions in Rapidly Heated Zr‐Based Bulk Metallic Glass with Oxygen Impurities

Tidefelt,

Löfstrand,

Goetz

et al. 2024

Advanced Science

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Metallic glasses exhibit unique mechanical properties. For metallic glass composites (MGC), composed of dispersed nanocrystalline phases in an amorphous matrix, these properties can be enhanced or deteriorated depending on the volume fraction and size distribution of the crystalline phases. Understanding the evolution of crystalline phases during devitrification of bulk metallic glasses upon heating is key to realizing the production of these composites. Here, results are presented from a combination of in situ small‐ and wide‐angle X‐ray scattering (SAXS and WAXS) measurements during heating of Zr‐based metallic glass samples at rates ranging from 102 to 104 Ks−1 with a time resolution of 4ms. By combining a detailed analysis of scattering experiments with numerical simulations, for the first time, it is shown how the amount of oxygen impurities in the samples influences the early stages of devitrification and changes the dominant nucleation mechanism from homogeneous to heterogeneous. During melting, the oxygen rich phase becomes the dominant crystalline phase whereas the main phases dissolve. The approach used in this study is well suited for investigation of rapid phase evolution during devitrification, which is important for the development of MGC.

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Section: Methodsmentioning

confidence: 99%

“…EDS data processing was conducted with Hyperspy. [ 45 ] Diffraction data was analyzed with the support of CrysTBox. [ 46 ]…”

Section: Methodsmentioning

confidence: 99%

In Situ Mapping of Phase Evolutions in Rapidly Heated Zr‐Based Bulk Metallic Glass with Oxygen Impurities

Tidefelt,

Löfstrand,

Goetz

et al. 2024

Advanced Science

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show abstract

“…The total of 93 s of acquisition was sliced into 232 hyperspectral images with intervals of 400 ms, which corresponds to, roughly, 100 complete ADF frames and an exposition time, per pixel, of 400 µs. As we shall see, this temporal sampling is more than enough to unveil the calcite decomposition dynamics but at the same time allowing us to have sufficient electrons per slice to perform meaningful principal component analysis (PCA) on each one of them using the Hyperspy package [34].…”

Section: Study Of Calcite Decompositionmentioning

confidence: 99%

Event-based hyperspectral EELS: towards nanosecond temporal resolution

Auad,

Walls,

Blazit

et al. 2021

Preprint

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The acquisition of a hyperspectral image is nowadays a standard technique used in the Scanning Transmission Electron Microscope (STEM). It relates a spatial position of the electron probe with the spectral data associated to it. In the case of Electron Energy Loss Spectroscopy (EELS), frame-based hyperspectral acquisition is much slower than the achievable rastering time of the scan unit (SU), which sometimes leads to undesirable effects in the sample, such as electron irradiation damage, that goes unperceived during frame acquisition. In this work, we have developed an event-based hyperspectral EELS by using a Timepix3 application-specific integrated circuit (ASIC) detector with two supplementary time-to-digital (TDC) lines embedded. In such a system, electron events are characterized by their positional and temporal coordinates, while TDC events only by temporal ones. By sending reference signals from the SU to the TDC line, it is possible to reconstruct the entire spectral image with arbitrary scanning pixel dwell time and thus perform, with no additional cost, a hyperspectral image at ADF rate. To demonstrate our event-based hyperspectral EELS, we have studied the decomposition of calcite (CaCO 3 ) into calcium oxide (CaO) under the electron beam irradiation.

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“…As ParticleSpy is written in python, it can be easily expanded upon and integrated into workflows, particularly those using the Hyper-Spy ecosystem. 27 Previous studies have successfully used trainable segmentation to segment inorganic nanoparticles from electron microscopy data, 28,29 but these studies have not investigated the parameters used in trainable segmentation. To produce accurate and effective segmentation results, the mechanisms behind trainable segmentation, and the filter kernels and classifiers used, need to be understood.…”

Section: Introductionmentioning

confidence: 99%

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles

Bell

Treder

Kim

et al. 2022

Journal of Microscopy

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We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

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hyperspy/hyperspy: Release v1.6.2

Cited by 6 publications

References 0 publications

In Situ Mapping of Phase Evolutions in Rapidly Heated Zr‐Based Bulk Metallic Glass with Oxygen Impurities

In Situ Mapping of Phase Evolutions in Rapidly Heated Zr‐Based Bulk Metallic Glass with Oxygen Impurities

Event-based hyperspectral EELS: towards nanosecond temporal resolution

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles

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