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README.md

Violin Plots for Matlab

A violin plot is an easy to read substitute for a box plot that replaces the box shape with a kernel density estimate of the data, and optionally overlays the data points itself. The original boxplot shape is still included as a grey box/line in the center of the violin.

Violin plots are a superset of box plots, and give a much richer understanding of the data distribution, while not taking more space. You will be able to instantly spot too-sparse data, or multi-modal distributions, which could go unnoticed in boxplots.

violinplot is meant as a direct substitute for boxplot (excluding named arguments). Additional constructor parameters include the width of the plot, the bandwidth of the kernel density estimation, and the X-axis position of the violin plot.

For more information about violin plots, read "Violin plots: a box plot-density trace synergism" by J. L. Hintze and R. D. Nelson in The American Statistician, vol. 52, no. 2, pp. 181-184, 1998 (DOI: 10.2307/2685478).

load carbig MPG Origin
Origin = cellstr(Origin);
figure
vs = violinplot(MPG, Origin);
ylabel('Fuel Economy in MPG');
xlim([0.5, 7.5]);

example image

Citation

DOI

If you want to cite this repository, use

Bechtold, Bastian, 2016. Violin Plots for Matlab, Github Project
https://github.com/bastibe/Violinplot-Matlab, DOI: 10.5281/zenodo.4559847

datacite.yml
Title Code for: Network state changes in sensory thalamus represent learned outcomes
Authors Hasegawa,Masashi;German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany and University of Basel, Department of Biomedicine, Basel, Switzerland
Huang,Ziyan;German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
Paricio-Montesinos,Ricardo;German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
Gründemann,Jan;German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany and University of Basel, Department of Biomedicine, Basel, Switzerland
Description Code for generation of figures of Hasegawa, Huang, Paricio-Montesinos, Gründemann, 2024. Network state changes in sensory thalamus represent learned outcome. Code can be used to perform single cell and poplulation level analyses of in vivo calcium imaging data from mice.
License BSD 3-Clause License (https://opensource.org/license/bsd-3-clause)
References Hasegawa M, Huang Z, Paricio-Montesinos R, Gründemann J (2024) Network state changes in sensory thalamus represent learned outcomes. tba [doi:tba] (IsSupplementTo)
Hasegawa M, Huang Z, Paricio-Montesinos R, Gründemann J (2024) Data for: Network state changes in sensory thalamus represent learned outcomes. G-Node. https://doi.org/10.12751/g-node.7xxnmw [doi:10.12751/g-node.7xxnmw] (IsReferencedBy)
Funding DFG; SFB1089, SPP2411, Walter Benjamin Programme 528405672
EU; ERC Starting Grant 803870
Swiss National Science Foundation; PP00P3_170672
Schweizerische Hirnliga; Forschungspreis
The Forschungsfonds Nachwuchsforschende of the University of Basel
The Department of Biomedicine at the University of Basel
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)
DZNE Innovative Minds Program
Keywords Neuroscience
Thalamus
Imaging
Resource Type Software