TY  - JOUR
A1  - Hake, Tim
A1  - Bodenberger, Bernhard
A1  - Groth, Detlef
T1  - In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks
JF  - Human biology and public health
N2  - The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.
KW  - Python
KW  - correlation
KW  - network reconstruction
KW  - bootstrap
KW  - St. Nicolas House Algorithm
Y1  - 2023
U6  - https://doi.org/10.52905/hbph2023.1.63
SN  - 2748-9957
VL  - 1
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - JOUR
A1  - Mani, Deepak
A1  - Kupsch, Andreas
A1  - Müller, Bernd R.
A1  - Bruno, Giovanni
T1  - Diffraction Enhanced Imaging Analysis with Pseudo-Voigt Fit Function
JF  - Journal of imaging : open access journal
N2  - Diffraction enhanced imaging (DEI) is an advanced digital radiographic imaging technique employing the refraction of X-rays to contrast internal interfaces. This study aims to qualitatively and quantitatively evaluate images acquired using this technique and to assess how different fitting functions to the typical rocking curves (RCs) influence the quality of the images. RCs are obtained for every image pixel. This allows the separate determination of the absorption and the refraction properties of the material in a position-sensitive manner. Comparison of various types of fitting functions reveals that the Pseudo-Voigt (PsdV) function is best suited to fit typical RCs. A robust algorithm was developed in the Python programming language, which reliably extracts the physically meaningful information from each pixel of the image. We demonstrate the potential of the algorithm with two specimens: a silicone gel specimen that has well-defined interfaces, and an additively manufactured polycarbonate specimen.
KW  - diffraction enhanced imaging
KW  - analyzer-based imaging
KW  - X-ray refraction;
KW  - non-destructive evaluation
KW  - Pseudo-Voigt fit function
KW  - Python
Y1  - 2022
U6  - https://doi.org/10.3390/jimaging8080206
SN  - 2313-433X
VL  - 8
IS  - 8
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Krause, Florian
A1  - Lindemann, Oliver
T1  - Expyriment: A Python library for cognitive and neuroscientific experiments
JF  - Behavior research methods : a journal of the Psychonomic Society
N2  - Expyriment is an open-source and platform-independent lightweight Python library for designing and conducting timing-critical behavioral and neuroimaging experiments. The major goal is to provide a well-structured Python library for script-based experiment development, with a high priority being the readability of the resulting program code. Expyriment has been tested extensively under Linux and Windows and is an all-in-one solution, as it handles stimulus presentation, the recording of input/output events, communication with other devices, and the collection and preprocessing of data. Furthermore, it offers a hierarchical design structure, which allows for an intuitive transition from the experimental design to a running program. It is therefore also suited for students, as well as for experimental psychologists and neuro-scientists with little programming experience.
KW  - Software
KW  - Programming library
KW  - Python
KW  - Experimental design
KW  - Stimulus presentation
Y1  - 2014
U6  - https://doi.org/10.3758/s13428-013-0390-6
SN  - 1554-351X
SN  - 1554-3528
VL  - 46
IS  - 2
SP  - 416
EP  - 428
PB  - Springer
CY  - New York
ER  -