https://seapipy.readthedocs.io/
Using PIP
pip install seapipy
Using conda
conda install conda-forge::seapipy
To create a simple tissue 10x10 tissue, you can create a lattice object and initialize the vertices, edges and cells of the system. Then, you might create values for the cell volumes directly. You could also create normally distributed tensions for the edges.
import seapipy as sep
lattice = sep.lattice_class.Lattice(10, 10)
vertices, edges, cells = lattice.create_example_lattice()
volume_values = {k: 500 for k, v in cells.items()}
initial_edges_tensions = lattice.get_normally_distributed_densities(edges)
Then, you could create the Surface Evolver object using this variables and then initialize the Surface Evolver slate where all the functions will be written into, before saving to disk
se_object = sep.surface_evolver.SurfaceEvolver(vertices,
edges,
cells,
initial_edges_tensions,
volume_values,
polygonal=False)
se_file = se_object.generate_fe_file()
The polygonal=False allows curved edges to exist in the tissue. Now, various Surface Evolver functions might be added to the file buffer in the se_file variable. For example we could add an initial relaxing for the tissue with
se_object.initial_relaxing()
Afterwards, we could add a saving function to create a checkpoint in the Surface Evolver simulation using
se_object.save_one_step("path/to/saving/checkpoint", "step_")
Which would save the state of the Surface Evolver simulation at "path/to/saving/chekcpoint" with name "step_" followed by the number of times it has been saved. Finally you could save the whole Surface Evolver slate into the disk and run it using
se_object.save_fe_file("SurfaceEvolverFile")
sep.command.run_evolver("path/to/SurfaceEvolverFile", "path/to/SurfaceEvolverExecutable")
This tool is also described in bioRxiv.
To cite that preprint you may use:
ForSys: non-invasive stress inference from time-lapse microscopy Augusto Borges, Jerónimo R. Miranda-Rodríguez, Alberto Sebastián Ceccarelli, Guilherme Ventura, Jakub Sedzinski, Hernán López-Schier, Osvaldo Chara bioRxiv 2024.05.28.595800; doi: https://doi.org/10.1101/2024.05.28.595800