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| Main Authors: | , , , , |
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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18175147 |
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Table of Contents:
- <p>This is a python code that calculates p-atic defects for p 2,3,4,5 or 6 for experimental data given as greyscale images (every cell has one value, the background has the value 0).</p> <p> </p> <p>This code belongs to the following publication:</p> <p>Lea Happel, Griseldis Oberschelp, Anneli Richter, Gwenda Roselene Rode,Valeriia Grudtsyna, Amin Doostmohammadi, Axel Voigt: A spectrum of p-atic symmetries and defects in confluent epithelia, published in SoftMatter in 2026 (doi: 10.1039/D5SM01010A)</p> <p> </p> <p>The program was written with Python 3.11, it requires the following packages:</p> <p>copy</p> <p>csv</p> <p>cv2</p> <p>gc</p> <p>lic</p> <p>matplotlib</p> <p>numpy</p> <p>os</p> <p>pandas</p> <p>scipy</p> <p>seaborn</p> <p>shapely</p> <p>skimage</p> <p>vtk</p> <p> </p> <p>The program expects a folder in the base_directory which is called "image_data" and which contains *.tif files for the single frames.</p> <p>This base_directory is then needed as command line argument for detect_contours and Orientation_field_Linear.</p> <p> </p> <p>First, execute detect_contours to calculate the p-atic orientations of the cells.</p> <p> </p> <p>Then, execute Orientation_field_Linear to calculate the defects in this p-atic fields.</p> <p> </p> <p>For a more detailed description of the used algorithms, please refer to the Methods section of the correspond publication.</p> <p>Please note that the plotted images are mirrored on the y-axis with respect to your input greyscale images.</p>