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A python module for scientific analysis and visualization of эd objects
To visualize a file from a web URL (or from your Dropbox!),
simply type in your terminal:
pip install vedo
To visualize a full 3D interactive scene, type:
Let's create an interactive 3D scene with a simple cone,
in 3 lines of python code:
from vedo import Cone
# Create a simple cone
c = Cone()
# Show it (with axes)
# Now you can interact with the 3D scene,
# Press "h" and explore the possibilities!
Let's write a simple python script that loads a polygonal Mesh and generates
a cool rendering by adding some custom light sources to the scene:
from vedo import *
# Load a polygonal mesh, make it white and glossy:
man = Mesh('https://vedo.embl.es/example/data/man.vtk')
# Create two points:
p1 = Point([ 1,0,1], c='yellow')
p2 = Point([-1,0,2], c='red')
# Add colored light sources at the point positions:
l1 = Light(p1, c='yellow')
l2 = Light(p2, c='red')
# Show everything in one go:
show(man, l1, l2, p1, p2, "Hello World", axes=True)
Let's create a Volume - a volumetric dataset - from a
import numpy as np
from vedo import *
# Create a scalar field: the distance from point (15,15,15)
X, Y, Z = np.mgrid[:30, :30, :30]
scalar_field = ((X-15)**2 + (Y-15)**2 + (Z-15)**2)/225
# Create the Volume from the numpy object
vol = Volume(scalar_field)
# Generate the surface that contains all voxels in range [1,2]
lego = vol.legosurface(1,2).add_scalarbar()
Scientific publications leveraging
X. Diego et al.,
"Key features of Turing systems are determined purely by network topology",
Phys. Rev. X 8, 021071, 20 June 2018,
M. Musy, K. Flaherty et al.,
"A Quantitative Method for Staging Mouse Limb Embryos based on Limb Morphometry",
Development, (2018) 145 (7): dev154856,
F. Claudi, A. L. Tyson, T. Branco,
"Brainrender. A python based software for visualisation of neuroanatomical and morphological data.",
- J. S. Bennett, D. Sijacki,
"Resolving shocks and filaments in galaxy formation simulations: effects on gas properties and star formation in the circumgalactic medium",
Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 1, November 2020,
A. Pollack et al.,
"Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study"
Geothermics Volume 95, September 2021,
- X. Lu et al.,
"3D electromagnetic modeling of graphitic faults in the Athabasca Basin using a finite-volume time-domain approach with unstructured grids",
M. Deepa Maheshvare et al.,
"A Graph-Based Framework for Multiscale Modeling of Physiological Transport",
Front. Netw. Physiol. 1:802881,
F. Claudi, T. Branco,
"Differential geometry methods for constructing manifold-targeted recurrent neural networks",
G. Dalmasso et al.,
"4D reconstruction of developmental trajectories using spherical harmonics",
J. Klatzow, G. Dalmasso, N. Martínez-Abadías, J. Sharpe, V. Uhlmann,
"µMatch: 3D shape correspondence for microscopy data",
Front. Comput. Sci., 15 February 2022,
Presentations at Conferences:
M. Musy, G. Dalmasso, J. Sharpe and N. Sime,
"Plotting in FEniCS with python",
SDTM, Washington DC, June 2019.
"Evolution in space and time of 3D volumetric images".
Talk at Image-based Modeling and Simulation of Morphogenesis.
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany, March 2019.
"A four-dimensional growing mouse limb bud reconstruction".
Talk at SEBD, Spain, November 2020.
"vedo. A python module for scientific analysis and visualization of 3D data".
Seminar at MOIA
(Microscopy Optics and Image Analysis), Heidelberg, November 2021.
M. Musy et al.,
vedo, a python module for scientific analysis and visualization of 3D objects and point clouds",